IN CONVERSATION WITH is a series from Archetype where we interview artists in/at the edges of crypto across music, visual art, design, curation, and more.

Daniel Keller is a “former artist,” internet theorist, writer, and entrepreneur who has exhibited internationally as a solo artist and as half of Aids-3D, a seminal post-internet collective. He was formerly co-founder of New Models and Channel.xyz. In 2023-2024 he collaborated with and led "cultural strategy" at Vaporware, an Urbit-ecosystem startup.

He is currently building Cloudy Heart with artist Jon Rafman and leading Metropolis DAO, an AI investment DAO that is part of the Tribute Labs network.

The following interview has been edited and condensed for length and clarity.

Katie Chiou: For those who may not be familiar with your work, can you share more about your background/journey as a designer and artist? 

Daniel Keller: I went to art school, and then I was an exchange student in Berlin where I ended up staying for 15 years. I didn't actually graduate art school. My career took off pretty early on when I was very young. Frankly, I was too young to, I think, truly take advantage of the attention that I got.

I was working collaboratively with Nik Kosmas on a project called Aids-3D. We were part of the first wave of net art that turned into what became called post-internet art. One of our pieces was called OMG Obelisk which got curated into the first New Museum Triennial in 2009. Our career picked up very rapidly from there. After a few years, Nik and I stopped working together and I had a solo art practice for a few years after. The themes were very generally about technology and its effects on society, interpreting techno-capitalism, California ideology, startup culture, internet culture, etc. I would say a lot of my art came from a place of technoskepticism, which was very typical of Berlin culture.

Over time, though, I grew more optimistic about technology. I was increasingly frustrated that within the art world, especially within Europe and the particular scene I was in, there was only one acceptable stance towards technology—reflexively critical, negative, antagonistic. And that voice became adopted by the media in general. I saw early on that technology was becoming more of a partisan identity. The art world itself also was changing a lot, and people were less interested in my perspective in general.

I stopped making physical art around 2016, but I launched New Models with Carly Busta who used to be an editor at both Artforum and Texte zur Kunst, and her partner, Lil Internet. New Models formed out of not being able to have the open discourse that we, as artists and writers, wanted to have in traditional media forums. Other leading figures of that general scene that come to mind are Brad Troemel and Joshua Citarella, who transitioned from art into content and discourse fluidly and made it into his practice. I think of New Models as a part of that turn. My process of art went from originally interpreting these conversations into quasi-sellable objects into just publishing that discourse directly.

From there, a bunch of figures from that scene and I collectively started Channel, which was an attempt at building a composable media network using token-gated RSS as the primitive. The company structure ultimately fell apart but we open-sourced the platform and the site is still functional and churning out new content for token holders.

A little while after that, I made an anonymous NFT collection that was a lightbulb moment for me. “Okay, this actually is still a really vital medium for doing permissionless creative culture and having an instant market for ideas.” That somewhat circuitously led me into the Urbit-adjacent ecosystem, which I became immersed in during 2023-24, while doing ‘cultural strategy’ for a now defunct company called Vaporware.

KC: Can you speak more to the moments that made you more optimistic towards technology? How has your thinking about the intersection of art, politics, and technology continued to evolve?

DK: I think I was very much like most people in the 2010s that believed in a sort of neoliberal “end-of-history” kind of mindset about things. I felt like the path was laid out, and it was a very narrow neoliberal future. Because I was in Berlin at that time, I was adjacent to a lot of the early crypto and Ethereum scene. But I was really, really skeptical about the need for decentralized alternatives to institutions. Bitcoin and illicit commerce seemed like the killer app, and the only killer app that made sense to me.

After the US election in 2016, I was shocked, as were most people. I began to believe in a more higher-variance future. That’s when I started buying Ethereum and Bitcoin as investments. ICOs was another thing that really made sense to me as another killer app for Ethereum. Seeing the massive performance in crypto assets made me more generally optimistic about the viability of more-or-less making a living from being extremely online. Of course, then round-tripping all that money that cycle was a huge lesson as well. Just the understanding that money can flow to you, and things can happen, and capital can both form and dissipate was really eye-opening to me.

The last one I’ll mention is when I started using GPT-4. I was not technical at all, but I could do basic coding tasks for the first time. Scripts and stuff like that, super basic. That was when I did that anonymous NFT collection. That’s when I realized that many of the visions and promises of technology I originally criticized in my art practice were actually on the table now. That has really become a dominant force in my life, this sort of unconditional accelerationism and belief that techno-capitalism is accelerating towards some kind of singularity. We can shape it somewhat, but for the most part it’s driven by forces beyond our control. I am trying to make sure that whatever I’m working on now is aligned with that general belief.

I do think we are in the early days of the pre-singularity or very close to a singularity. That means a lot of different things to different people. But I do generally think that it’s the single most important and interesting thing happening in the world right now. I also believe that crypto has always meant to be the substrate for AI agents to interact with each other and with the general economy. I am interested in helping make that happen.

KC: When did your interest in both crypto and AI and its intersection really click for you?

DK: I used to be very skeptical, early models of Midjourney were sorta aesthetically interesting, but it wasn’t clear to me that they’d advance like they have. Seeing GPT-4 and Midjourney V5 in early 2023 made it undeniable that we were deep into some sort of exponential curve. I also need to give a shoutout to Mat Dryhurst and Holly Herndon here, who have been at the forefront of understanding how these types of technologies are going to be intertwined from very, very early on, before that was apparent to anybody else in my circles. I give them a lot of credit for inspiring my confidence in pursuing this general orientation, despite both terms being extremely divisive within the culture sector, to say the least. But, in terms of how crypto and AI are interacting right now, I think it’s sort of vastly untapped still. There are some exceptions that have been interesting style-wise, but for the most part there’s an overly agreeable nature of LLMs that does not make very compelling content. But, eventually, I think there will be orders of magnitude more AI agents using crypto than there ever were humans. I think the intersection of crypto and AI basically answers the sort of eternally unsolved consumer crypto paradox of what is the use case and where are the users?

It’s almost like humans were pen-testing crypto. We were beta-testing various primitives and stuff, but we’re not the end users. We won’t be the users. And to me, that gives me some comfort, I think, actually. 

KC: That’s a good segue into asking who is Cloudy Heart? What is your relationship to her?

DK: Cloudy Heart is an art project by Jon Rafman and other collaborators. Cloudy Heart is an AI-generated pop star, streamer, e-girl. I was brought on to help with the crypto-facing strategy for the project. It's primarily an art project, but also has a memecoin which we fair launched on pump.fun, and which within Cloudy lore, was actually launched by herself, to enable her progressively increasing autonomy. The token serves as schelling point for collaboration and storytelling. In clinical terms, I think of Cloudy Heart as a sort of attempt at connecting these two flywheel ecosystems, music and memecoins, that are very well-trodden and optimized. Within music, there is a sort of industry-plant/payola system—where Spotify playlists and astroturfed TikTok virality—determine success. Obviously, a lot of that is organic and simply harnesses organic virality and it's not so exactly top-down, but there's a system there. And of course, there’s a system within crypto of KOLs, market makers, etc. I think of Cloudy Heart as many things, but at its economic core, an experiment in connecting those two flywheels together and seeing what can emerge from that conduit. That is my perspective about things in general. Part of my former-artist mindset is that I'm really interested in the systems engineering layer of cultural phenomena—maybe more than the aesthetic or narrative layers.

But also because Cloudy Heart is an AI, she's self-improving. The technology that is forming her is constantly improving. She was not very autonomous at all to begin with, but is becoming increasingly autonomous. The sort of North Star for the project is that as she kind of gains more features and autonomy, eventually she's set free and she is an actual autonomous agent.

There's going to be a lot of legal work around that that I don't think there's very much precedent for. That's a whole aspect of the project that I'm really interested in. I don't think there's any case or precedent for agent culpability and agent personhood. I think that there's a good chance that agent personhood will happen, because corporate personhood already exists, and it's a short conceptual leap to extend those rights to autonomous corporations.

KC: Do you think there’s a clear goal for Cloudy Heart?

DK: Jon has a lot of plans for extending the narrative universe of Cloudy Heart and other characters that he is developing. I think of Cloudy Heart as a form of experimental transmedia entertainment. I think of it as part of a new class of tokens that are sort of post memecoin, I call them “attention coins.” I think this is a new formula for tokens in general that we are going to see a lot more of, a content stream attached to a token as opposed to a single image/meme. AI content is not inherent to that formula, but it obviously makes it a lot easier to generate cool content if you know how to use the tools effectively.

In terms of agent utility, she doesn’t have much yet, but we definitely have interesting experiments we want to pursue—have her collect NFTs, have a wallet, create generative music. 

There’s this Mat Dryhurst and Holly Herndon quote, “all media is training data.” Everything we’re producing can go into a huge proprietary dataset that will allow Cloudy Heart to become more autonomous over time. I think that is what is going to define the effective use of AI versus slop. The ability to curate and assemble proprietary datasets and actually apply taste. "Taste" is a buzzword, but I do think that actually is a real thing—that that matters, and will matter more and more.

KC: What about a target audience?

DK: I don't think it's like a focus group type of thing. I think there is an eternally compelling thing about a female face—that is, obviously, you know, connected to the male gaze—but is more fundamental than that. I think facial recognition in general is something that's just so lizard brain. A lot of the other AI agents didn't try to do a photorealistic personhood, which seems kind of a no-brainer enhancement to me. Most of them are anonymous, faceless dudes. I don't think they managed to penetrate culture because they don’t have that sort of tangibility. You need that sort of meme of GPT that’s like Shoggoth the monster with the little smiley face RLHF mask.

There’s obviously a long lineage of projects that are very similar to this. Lil Miquela is a very obvious one, Hatsune Miku. There was Ann Lee from Philippe Parreno and Pierre Huyghe around 2000. So I think it's made very much with cognizance of all of that, and attempting to move it forward—basically with the acknowledgement that a lot of cultural change is downstream from technological change. And even compared to the Lil Miquela era, the ability to do things that move beyond the struggle of the uncanny valley—both in terms of music and video—are there or very close to there. That unlocks a whole bunch of new narrative possibilities.

KC: Do you think the Cloudy Heart team is particularly preoccupied with token price?

DK: I think launching a token with a creative project that has long-term aims is such a double-edged sword. I think everyone is pretty aware of that. Having a real-time attention market indicator of success is hard if you’re not used to the volatility of crypto. It’s hard to get used to that without going through a few cycles. That being said, I don't think there's any other way we could have just launched something and immediately had the attention and a market and the ability to bootstrap initiatives.

The reality, though, is that if you’re trying to do something in entertainment, entertainment world timelines and memecoin timelines are just not the same. A lot of that comes down to informing the community and reassuring them that things are happening behind the scenes, even if there aren’t constant market catalysts.

KC: Switching gears a bit, you’ve spoken a lot in the past about the purpose and power of memes in propagating certain information and ideals. A lot has happened technologically speaking since those original talks in circa 2017, how do you think the purpose of memes has evolved?

DK: At the time of those talks, I was trying to channel the memetic power that was largely leveraged by the right for the aims of the left. This eventually sort of turned into “altwoke” and “dirtbag left.”

I definitely believe in some form of meme magic. I do think hyperstition seems to keep happening.

We haven’t quite seen real-time attention markets for memes fully play out. But I do think that the understanding of memes as a form of currency, and the fact that there will literally be these direct paths of monetizing them in various ways—that feels to me like a glimpse into what the new cultural form is going to be. I think seeing the rise of app-based sports gambling and prediction markets shows you that there's going to be a speculative element to the way that people consume culture that’s going to become more and more baked in.

I think of Cloudy Heart as very much being in this new cultural form milieu. The fusion of the attention economy, AI, gambling—it's not quite related to what you're saying in terms of meme magic, but I do think that's how memes are evolving into an internet-native culture form.

I haven't actually plugged my theory of “sloptimism,” but that’s what I think this is about. We're going to have an acceleration of slop. Within that deluge of slop is going to be the seeds of truly avant-garde, genuinely great culture. I believe that. We haven't quite seen it yet, but I do think it's going to be the new internet-native cultural form of post-art. There might be a crypto component, there's going to be a generative component, there's going to be an interactive, relational, aesthetics, network spirituality component.

KC: I don’t think the idea that there is genuinely good work in slop is a hot take, but I think where people diverge is how the future here is distributed. Who will have access to, be able to sort through, the slop?

DK: You will require some kind of cognitive defense mechanisms to not be turned into pure lizard brain content consumers. Obviously, that’s already happening. It’s more so an attitude you can take where you have a sort of optimistic fatalism, find the bits that are genuinely funny or entertaining or whatever else.

Democratization is a risky word but, as an example, I think the barrier to make prestige TV is something I’m really interested in. There’s an indie film industry where you can make a film for $1M, but there’s not an equivalent for prestige TV because the startup costs are just 100x as much, and then you have to worry about renewal and all these things. 

I think industries like that are really stuck in local minima because of the cost of production, but if there are sloptimists who know how to use powerful tools, that could unlock whole new types of culture as well. That being said, I think a deluge of AI content will likely end up devaluing every medium it touches. And already, I don't think kids care about films or TV very much compared to millennials or boomers. But I think the only way out is through, and I am truly optimistic that the net result will be worth the disruptions and devaluations and that AI is going to help us create extremely cool and novel cultural forms. And as an unconditional accelerationist, I don’t think we have much of a choice.

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

IN CONVERSATION WITH is a series from Archetype where we interview artists in/at the edges of crypto across music, visual art, design, curation, and more.

Parker Ito is a Yonsei/Gosei Japanese American artist, à la mode, living in San Francisco and working in Los Angeles associated with post-internet or zombie formalism, depending on who you ask. In the years gone by he has exhibited his oeuvre in galleries, museums, and coffee shops on four continents and will continue to exhibit his work in the future (hopefully).

The following interview has been edited and condensed for length and clarity.

Katie Chiou: For those who may not be familiar with your work, can you share more about your background/journey as an artist? 

Parker Ito: I grew up skateboarding. My friends and I would make skate videos, so I learned how to edit videos and use Photoshop to make flyers, posters, patches, and things like that. I'd always been creative and liked art, but didn't really recognize these things as art. When I was 18, I read a book on Jean-Michel Basquiat that made me want to actually become a professional artist. Since that point, I’ve dedicated my life to art. I went to college in the Bay Area and got an undergraduate degree in art, and even though at that time I felt very ignored by my college/local art scene, I became very involved in an international online art scene and this would become a very formative time in my life.

It was right as web2 was beginning to transition, and I was part of this sort of first wave of millennial artists on the internet—using the internet as a kind of medium but also working in traditional, object-based artworks, and relying heavily on documentation to communicate these objects. Being part of this scene was one of the most exciting times of my life. That whole scene later turned into what has been historicized and labeled as “post-internet.” 

A lot of friends I met during that time have gone on to be successful contemporary artists, but post-internet didn’t start in the mainstream art world, it was a movement happening online with a specific group of people. Then, post-internet started transitioning into the art world and happened parallel to another movement called “zombie formalism” that people associate me with. 

I always say I make painting, sculpture, and video, but I'm actually more of an installation artist. Zooming out even more, I'm someone who just wants to make JPEGs—or now PNGs, since that’s the format most of my NFTs are in. I work in every medium in a way.

KC: You’ve talked about post-internet as early as 2011. How do you conceptualize post-internet and how has it evolved over time? 

PI: My go-to answer to this is always to define post-internet in three different ways.

The first would be post-internet within the art market, which is a designated moment in time—a transition of internet art into the mainstream art world. There's a certain aesthetic and certain artists associated with the movement, and it operates as a way to spread things through a market. But there's also a curatorial agenda behind it. I think that, within the art world, post-internet was the last true movement in that it was a group of people who organically came together with a shared belief system and semi shared goals.

All the movements since then in the art world have been market-driven. Post-internet also eventually became market-driven, but it started as a scene of people, whereas other movements since then have been largely random artists tied together through some sort of aesthetic sensibility—zombie formalism looks very much like that. So, that's one definition: a moment in time, a movement within the contemporary art world.

The second definition would be the actual scene that was post-internet. I usually refer to this as “net art,” a really specific time period—from like 2009 to 2013 was my involvement. But there were also different generations in net art. There was a period from 2006 to 2009 before me, which is generally referred to as the “surf club” era—things like Nasty Nets, Double Happiness, and Spirit Surfers.

I define the era I was part of as the “Tumblr” era. I think the biggest difference is that the Tumblr era was made up of people coming from a more traditional point of view in terms of art production—people who made objects, people who wanted to be in the contemporary art world—versus the earlier wave of net art. A lot of that earlier wave was people who were less art world aspirational, less gallery focused. 

The third definition, which I actually believe is the true definition of post-internet, is just the world after the advent of the internet. The internet has shaped everything so radically. It's hard to think of an invention in human history that has been more impactful, maybe the invention of written language would be on par. In that sense, I feel like every artist now is a post-internet artist because they have to deal with the realities of living in this networked society.

KC: Why do you think movements haven’t persisted structurally since post-internet? Does internet sprawl contribute to that or is it for some other reason?

PI: In an art historical context, I think there is a dissolution of movements and the dissolution of a contemporary avant-garde when you start to enter Postmodernism. If you think about the amount of movements within Modernism, they were so rapid and stacked into each other. 

As we progress to the end of the late 20th century, the Pictures Generation did feel like an avant-garde movement and then Neo-expressionism feels like the end of that. Post-80s—there’s definitely stuff I’m missing—Young British Artists (YBA), was mostly geographically designated, and there was not really a coherent message or goal within the works. And so, it becomes this trend where there's fewer and fewer movements.

Beyond these historical art circles, I think my generation of net art artists was really excited about the possibilities of the internet and using it to connect with each other. And as the internet became more accepted by the art world, the art world started using the internet as a tool and maximized its worst features.

There's certain kinds of artworks now, paintings specifically, that are really dominant in the market because they’re very easy to post online—like on Instagram. People see them and immediately understand what they are, they’re legible. That dynamic shifted to drive a very particular type of market. Because the contemporary art world always exists in a market context, there's a motivation to use new tools to accrue capital and to extract as much value as possible. 

KC: I think a counterpoint I hear often is that the art world has largely always been undergirded by rich patrons. Is the difference now how explicit the dynamic is or has something else fundamentally changed?

PI: You have to remember that for many years, the art world was really small. It’s always been gatekept and controlled by a small group of people. But now it’s expanded a lot; there’s more galleries and artists than ever.

For a long time there was the idea of patronage. There was a sort of moral obligation that the wealthy felt to support the arts. It’s corny, but I think people really believed in supporting the imagining of a better world. It doesn’t feel like that now. There was this sort of corporatization in the 1980s where finance entered art, in my mind it ties back to Jeffrey Deitch. Art starts to change and also becomes an asset or an investment, something to store money in.

Another inflection point worth mentioning is that art/the art world is not the center of culture anymore, like it was for so long. The internet, and its collection of random creators, is now the center of culture; but the art world hasn’t acknowledged this at all. The art world is grasping at this attempt to maintain its status as a cultural force, but it’s not been very successful.

There’s also a generational component. The collecting class seems like it’s dying, and younger generations have a different relationship to these objects and having “art collections,” in the traditional sense.

KC: In your own practice, you often use varying tools—assistants, manufacturers in China, etc.. How do you think about the relationship between ideation and production and the tools you use in the process?

PI: I've always believed in a fluidity between all of these different spaces and mediums and forms. That’s a central idea within my work. There are recurring characters, but they often shift forms. As an example, I hacked the scanners in my studio that I had used for years and made them into sculptures. That was kind of a really important object for me to think about—that sort of data transference between different modes that we operate in.

For me, all of these things are symbiotic and kind of flowing out of each other. I think about an idea and then think about the best medium to express that idea.

One of the things that’s been really rewarding about making NFTs is that it’s a very pure form for me. I’ve always started projects in Photoshop, but the end product—especially when I used to work with a lot of assistants—would usually look really different. I’d make collages in Photoshop, but then we’d turn them into large paintings. I think it’s like that for a lot of artists. The original thing they sketch up is not what it turns out to be. But no one ever saw those original Photoshop collages. No one ever saw the work in progress. With NFTs, I basically do it all in Photoshop—the rawest form of expression for me. 

I was very influenced by big production artists when I was in college. Jeff Koons would be someone who I was looking at a lot. I realized that it’s cool to make large scale stuff, and I also realized that there was stuff beyond the kind of scope of my own abilities that I wanted to make. Working with assistants was completely freeing because I could paint in any style. I could make any form. I have always been a kind of ideas person, and that’s what I’m probably focused on more than anything. Everything else is just a way to realize my idea as best as possible.

KC: I’d love to turn more toward your experience with NFTs, crypto, and Solana especially.

PI: NFTs are something I’m really excited about right now. There’s this whole scene on Solana that’s been called a few different names—avant NFT, gay NFT. It reminds me a lot of a continuation of net art in a way, a group of people who come together organically and form a real scene. I think it’s the forefront of art right now, a true avant-garde. 

Collecting contemporary art is so expensive, and even within the collector class there are tiers of what people can afford. I like that I can just make an NFT and sell it for $10 to a person I’ve never met before, over the internet, and there can be thousands of people who own my art. It’s very different from selling a discrete art object, only the 1% can afford. Both systems have functions, and are each important to my art.

I know there’s a whole speculative side of NFTs, but I’ve experienced very little of that in the avant NFT scene. People want to be there and support you. I feel a lot more supported in the NFT space than I ever have felt in the art world, except maybe within the net art scene. It’s been really refreshing, and it’s revitalized my excitement in art in a way I haven’t experienced in a while.

KC: For the folks who want to be more a part of the scene you’re describing, what artists or collections on Solana would you point them towards?

PI: It’s all happening in real-time on Twitter, so it’s hard to be able to communicate what the scene actually is. The most important collection for me within this space is called Drifella 2, created by someone called Evil Biscuit. That collection kind of defined the aesthetic that’s become really associated with the scene—traitmaxxing. Also, all these collections originally came out of their relationship with PFP (profile picture) collections and using tools like Hashlips as a compiler system for creating all these images. In response to the traditional PFP NFT format, artists started taking these tools and creating these super maximalist compositions. 

To frame it in an art historical context, it does feel like this new Dada-esque moment. There was a specific, formal aesthetic associated with NFTs and PFP culture that became kind of mainstream, but it’s been reabsorbed and reimagined into an entirely new aesthetic.

Another project I saw when I entered the space was Horses? by Tojiba Brand Manager. Horses? was made by one artist but they belong to a collective called Tojiba that has made several very good projects. Little Swag World by supermetal bosch is really interesting, using AI and 3D rendering. I could keep going.

KC: And are all these people in conversation together? How do you keep a scene alive?

PI: There’s a gallery in New York called Yeche Lange that feels like the center of the scene right now. It was started by several people, one of them being Jared Madere that I think you interviewed previously?

KC: Yeah.

PI: They’re some of the people at the center I would say. I’m totally new to the scene, but I think there are definitely older and newer groups. There are periodic Twitter Spaces from Snoot Brut, which is the critical wing of Yeche Lange run by Fairybaby and Lowbie. Everyone hangs out, people of all ages from all over the world. I met up with people from the scene during my last trip to New York. It all feels like 2011 to me, the excitement of being a fan of someone online and then hanging out with them.

KC: Can you talk a little more about the traitmaxxing aesthetic you mentioned earlier? What does traitmaxxing mean to you or to the scene?

PI: I was once hanging out with my friend Travess Smalley who operates in a very different NFT scene, it’s the ETH fine art scene. I mentioned to him that I was thinking about spending time with NFTs, and he said something like “Yeah you should, NFTs look like the work you were making in 2013-2014.” I had no idea what he was talking about because I had a very limited understanding of NFTs at the time.

But then my friend Dean Kissick, who really introduced me to the space, showed me Drifella 2 via his Twitter feed. It started this cross-generation net art and NFT dialogue for me where it felt like there was a very similar aesthetic sensibility. I think the traitmaxxed aesthetic comes from having access to a new tool to make images. Hashlips became the default tool for making an NFT collection, and eventually artists started to try and push the tool to its limits. 

Now, there’s a lot of people in the space kind of done with traitmaxxing, they’re ready for the next thing. The recent series of NFTs I made are made by hand, I tried to not traitmaxx them at all. 

KC: You did a project recently with Zien called Parkerito.net where every NFT in the 10,000 piece collection was redeemable for a physical painting. Do you think digital art needs to have a physical companion? 

PI: For that project, I just wanted people to take NFTs seriously. It was my first NFT project so I wanted it to be ambitious. It’s lame though to attach a digital object to a physical thing just to make people take it seriously.

I made this poster in 2013 that has this dumb quote on it that people always bring up. It’s something like, “I heard when Picasso died, he had made 250,000 works in his lifetime, and I could make that many JPEGs in five minutes.” It’s originally a Warhol quote that I had changed, but it is kind of the original ethos of NFTs in a way.

I felt like that was a moment to try to tie everything back into that original premise and say that I actually was going to try to make 10,000 unique and good paintings. I found it quite challenging, but I was also excited to try.

The more time I spend in this NFT space, the more I think these are some of the first successful artworks to fully address the cultural and creative force of the internet in a truly authentic way. I think the art of post-internet showed us what art looks like in a network, it pointed to the internet and said “look you have to consider this context” but in that process created this unholy matrimony between the art world and the internet. A lot of the art that’s existed in these white cube spaces feels larp-y. Artists are mimicking the phenomenon of internet space in a gallery context.

But galleries are boring, really dead spaces. The internet is this living thing that is constantly mutating. I feel like NFTs can actually embody this ethos, instead of just mimicking it, both in their form and in their dissemination. NFTs are, in a way, the first time we have a true memetic art. So, instead of trying to paint memes or something, artists should just make NFTs and people should learn how to value that.

I really appreciate the work that folks like Yeche Lange are trying to do to make that crossover.

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

Privacy in crypto might be a poisoned chalice, but private shared state is the holy grail.

Most of the privacy tools we’ve had until now have only been useful for specific, isolated use cases. This era has been largely made up of whales conducting large, private sales using dark pools, mixers, and other tools that get the job done but have notoriously bad UX and no way of plugging into one another. We’ll call this era Privacy 1.0. The big drawback to Privacy 1.0 is the strict tradeoff between privacy and shared state. While individual users can privately store and compute on their own data, collections of users must expose their valuable data to collaboratively compute on it—sacrificing privacy in the process. Without a notion of private shared state that users can collectively read from and write to without revealing their data, private applications cannot be composable, as composability depends on accessible, reusable, shared data. This ultimately puts us in the chicken and egg state of looking for novel ways to use privacy onchain, much like the broader state of crypto before Defi Summer and the Cambrian Explosion of use cases that followed.

In short, Privacy 1.0 is a dead end.

Privacy 2.0 is a new frontier that promises the ability to keep data private while also allowing for it to be leveraged in the same way we leverage public data on blockchains today. If implemented successfully, this would give us the private shared state we’re looking for—data that can be permissionlessly leveraged across an ecosystem by multiple users, contracts, and applications, while still remaining private. This new realm of privacy is largely built around privacy enhancing technologies (PETs) like TEEs, MPC, FHE, and to an extent, ZK.

While ZK Wormholes and other recent developments in private transfers are great to see, ZK stands apart from the other three PETs for a few reasons. For one, it’s already had a phase of substantial investment and development energy and is now entering its adoption phase with zkVMs and proving infrastructure. More relevant to this piece, though, is that ZK (via SNARKs) has empirically been part of roadmaps to scale blockchains first whilst being an improvement in expressive privacy second—ZK private state can’t be shared without trusting a sequencer or another PET.

TEEs, MPC, and FHE may not be entirely new either, but the way they’re being used is. Privacy 2.0 is mixing and matching PETs and other technologies to finally give us a private shared state that can support the fast, smooth, multiplayer experiences that users expect, all while giving them the privacy they deserve. 

At least, that’s the goal. 

While there are still some kinks to be worked out, Privacy 2.0 stands to open the door to a new era of onchain use cases across a slew of verticals. This isn’t even just about crypto; a paradigm shift in digital privacy would affect everything from identity and data for AI to collaborative social media and healthcare.

For instance, World ID is an early example of something that couldn’t exist without private shared state. If users’ iris and passport data lived on public state, then anybody could access and store all of that sensitive information in plaintext. And there's no way that would be internationally compliant or something that most users would accept. But if World Chain’s state wasn’t shared, then the unique identifying data underpinning World ID wouldn’t be portable across different apps and ecosystems. But we’re getting ahead of ourselves.

This is the first part of a 3 part series. In parts 2 and 3 we’ll dig deeper into how TEEs, MPC, and FHE are being developed into specific niches and use cases today, how each PET is changing, how multiple PETs can be orchestrated in a multimodal/hybrid system, and what use cases might ultimately arise. But for part 1 I’m going to focus on:

• The shortcomings of Privacy 1.0.

• What a successful Privacy 2.0 stack would look like.

• The promise and potential of private shared state.

• Why PETs are yet to deliver on a Privacy 2.0 stack.

The Shortcomings of Privacy 1.0

Privacy 1.0 has largely been built by crypto-natives and whales for crypto-natives and whales and their defi use cases. It’s a miracle that we were able to make monetary assets and transactions private in the first place, but it’s come at the cost of UX and expressiveness. And as we’ve seen from the state of consumer and enterprise affairs onchain, these UX-sacrificing design patterns are rarely embraced by everyday users, nevermind newcomers.

In short, Privacy 1.0 is slow, clunky, single-player, and protocol-specific. As an example of how something with these attributes can still be valuable to a certain set of users, though, let’s look at the first iteration of onchain dark pools.

Onchain dark pools are effectively private spaces that allow traders to buy and sell without showing their orders to the public. The subset of traders who actually have a use for this is vanishingly small, and is mostly made up of whales who are conducting a very large transaction. The private nature of the dark pool conceals the trade from the public until it’s executed, preventing frontrunning, sandwiching, slippage, and the potential for a drastic change in asset price.

The tradeoffs here are typical for the Privacy 1.0 world. In exchange for this privacy, users need to bridge to and from onchain dark pools, and must trade in a relative absence of information. While this stays true to crypto’s cypherpunk origins, this is just not good enough for everybody else. Time and time again, users have expressed preferences towards fast, smooth experiences that just work, preferably with other users across apps and chains.

If the state of PETs stays as is or progresses at the moderate pace ZK has for the past eight years, we won’t get past Privacy 1.0 for a long time. In this world, Privacy 2.0 wouldn’t happen until the extremely long term when quantum cryptography becomes mainstream and potentially renders blockchains obsolete by making all state inherently shared. If PET development in crypto accelerates in the nearer term, however, the use cases of private shared state across users could be significant. We’re already seeing zkTLS platforms like Opacity*, Reclaim, and Pluto bridging private state between web2 and web3 to give us net new use cases that users actually want. PETs could further extend this by securely connecting multiple zkTLS feeds to computation, enabling institutions and enterprises to come onchain, compliant and monetizable access to private data for AI, and private payments and payroll without bridging.

A Successful Privacy 2.0 Stack

Privacy, across both web2 and web3, has rarely been free—it comes at an accumulated shadow cost of features, compute, fees, and/or time. This shadow cost sometimes manifests through users trading their privacy for subsidized access to a service in a riff on freemium pricing. A prime example is Google Photos, where users could formerly access unlimited photo storage with the catch of offering up their photos as training data for Google’s AI models. Dynamics like these create a status quo where the majority of users are pulled away from engaging with privacy-enhancing systems. This is in contrast to the idealism behind Privacy 1.0, wherein users adopted privacy as a philosophy rather than privacy as a utility, even if it meant worse UX or reduced access to a service.

While the value of privacy is obvious to power users, a successful Privacy 2.0 stack must meet users where they are by leveraging privacy to create use cases that are naturally attractive. Examples include secure but composable identity and portable social graphs where users can profit from their own data. In today’s world where recommender algorithms and generalized models are running into a scarcity of unique data, privacy could actually help users regain the upper hand. Privacy 2.0 could flip the Google Photos model by creating two-sided markets—users could opt into sharing and denominating granular bundles of anonymized data across platforms in exchange for the fair market value of their cross-origin data.

For a true Privacy 2.0 stack to work, it must offer benefits to user-facing apps that offset the upfront costs of privacy. The best way for Privacy 2.0 to achieve this would be to leverage a standard of private shared state and be private by default.

A Standard of Private Shared State

Thinking back to the pre-HTTPS internet, when HTTP requests and responses were sent in plaintext, sensitive and valuable use cases like those of enterprises and banks used private intranets when interacting with internal state. These companies would typically roll their own closed intranets for privacy and security whenever shared state updates necessitated external inputs from users and the rest of the internet. While this worked for limited use cases such as early online banking, it was standardized SSL certificates issued by trusted third parties that ultimately unlocked the power of the open internet.

Specifically, HTTPS fundamentally widened the design space of the internet by encrypting and verifying the entire set of HTTP requests and responses in a manner that users could see and verify for themselves—via the lock icon in their URL bar. In widening the internet’s design space, HTTPS reinvented entire sectors for an online era, including online commerce, standardized online financial services, online health, and copyright protected content streaming. In short, much of the value transacted over the internet exists because of the downstream impacts of HTTPS. But crypto is still in a pre-HTTPS state.

We’ve already seen plenty of private blockchains and whitelist-only rollups from the EYs and other consulting companies of the world—these are almost 1:1 with closed corporate intranets. Just like with bespoke privacy solutions in the early days of the internet, these work for transactions and compute within a predefined set of actors but can’t scale to accommodate external inputs—which bars significant external value. Without a standard to build around, implementations of private computation will remain unadopted and fragmented. A standards-based approach to privacy has the potential to open up the actors and value sitting on blockchain rails in the same way that HTTPS did for the open internet. As such, a successful Privacy 2.0 stack will have a standard of shared private state that retains as much expressivity from existing solutions as possible.

Privacy by Default

Next, let’s think back to Tornado Cash, which was OFAC sanctioned until recently. From a regulatory perspective, the voluntary action of transacting with funds from Tornado Cash was assumed to be equivalent to the worst cases of money laundering because privacy was opt-in. As a result, because the North Korea-affiliated Lazarus Group opted to use the platform to mix over $455 million stolen from various protocols, OFAC sanctioned all individuals that had ever interacted with Tornado Cash and opened up a criminal case against co-founder Roman Storm. 

To prevent similar regulatory actions being taken against users that opt into privacy, platforms like Railgun leverage third party blacklists of wallets and onchain interactions which can be checked using ZK-powered proofs of innocence. While this system prevents direct interactions with bad actors, it can still be bypassed very easily by a single “hop” or transfer to an unused Railgun wallet. While this works for a relatively niche product like Railgun, this won’t hold up at scale—opting into privacy will still look suspicious to regulators. 

In short, for a Privacy 2.0 stack to minimize the regulatory association between users who want privacy, developers shipping code, and actual criminals, proofs of innocence will need to track multiple hops and shared state will need to be opt-out, or private by default. 

We can also argue that privacy being expensive and complicated has played a significant role in selecting the users that have opted into privacy. As the costs and frictions of privacy increase, the users that continue to use privacy will be those that have a high willingness to pay for it. Past a certain price point, the use cases where this is true are less than legal. In other words, opt-out privacy needs to be cheaply accessible. 

Given these requisites, PETs, being technology standards that make shared state private, are uniquely poised to enable private distributed compute and shared state as a successful privacy stack for crypto.

Why Haven’t PETs Already Done This?

If you’re here and still confused as to what PETs are, give Milian’s “Crypto’s New Whitespace” a read for some more context. 

To quickly summarize that piece, here’s a quick tl;dr on the major three PETs most relevant to crypto outside of ZK: 

• Trusted Execution Environments (TEEs) are secure sections of physical chips that use a static but unique “root of trust” as a base secret to encrypt things. Apple has been using TEEs to locally verify and store biometrics and credit card information on their hardware since 2013’s iPhone 5s and 2017’s T2 desktop enclave.

• Multi Party Computation (MPC) involves partitioning a secret or task over a set of distributed compute nodes to collectively perform computational work—somewhat like BitTorrent over computation.

• Fully Homomorphic Encryption (FHE) is a form of encryption which uses a bunch of addition and multiplication on lattices and really high order polynomials with random noise and rounding to perform actions on encrypted data without ever decrypting it. 

So what has Privacy 1.0 looked like in the context of these PETs, and why haven’t they already made a successful privacy stack?

TEEs

The first crop of programmable TEEs made their way into consumer desktops in 2015, starting with Intel’s Security Guard Extensions (SGX), which handled digital rights management (DRM) for UHD Blu-Rays and video games. Given the natural distribution across consumer hardware stemming from locally handling DRM, researchers eventually built lightweight operating systems on top of SGX as a standard to perform more expressive secure tasks.

This eventually gave way to Secret Network, a network that used a set of SGX nodes for private transactions and smart contracts. Crucially, the SGX nodes held a “consensus seed” in their protected memory which functioned akin to a Secret Network-wide decryption key. In October of 2022, a group of researchers used a previously disclosed processor architecture exploit called Aepicleak to build a proof of concept attack in which malicious actors could emulate Secret Network nodes in software to ultimately extract the network’s consensus seed from memory. While the developers of Secret Network worked with the researchers to prevent such attacks by putting mitigations into place, and the first generation of SGX-based remote attestation was fully deprecated by 2023, researcher exploits on this platform didn’t stop. A recent exploit in August 2024 went a step further in exploiting the deprecated chips, as a researcher revealed a proof of concept for extracting an encrypted version of a given processor’s root key, which is a core part of the hardware root of trust and is unique to each SGX chip. 

In many of these examples, researchers measured heat, cache activity, power consumption, and other side effects of executing programs to infer and extract secrets from operational SGX units. Though it may seem easy to solely fault Intel, SGX isn't alone in being exploited via so-called “side-channels”. Last year, a set of researchers similarly attacked AMD’s SEV using malicious memory units and firmware, while a security team focused on jailbreaking exploits found a physical exploit chain for Apple’s hardware enclaves aboard their mobile chips.

These attacks collectively speak to how complex TEEs are, as well as how difficult it can be to use TEEs as the sole layer of security for a network. Given the limited capacity to update a TEE’s hardware and firmware, as well as its unique and immutable root of trust, fully trusting a closed source TEE implies trusting the TEE’s firmware maintainers as well as manufacturers, who feasibly could have a log matching TEEs to their unique roots of trust. Further, fully trusting a network of closed source TEEs in operation implies fully trusting that the TEE isn’t compromised and is operated by a non-malicious actor. As such, the value for malicious actors to exploit a TEE must be lower than the cost to perform said exploit at each of these layers. Given that the cost of exploits is baked into the hardware, this trust model is remarkably difficult to standardize, as the value and trust assumptions that a particular transaction is tolerant of can differ wildly and should not be abstracted away from the user.

Because of all of this, open-sourcing TEE designs was always a natural design imperative. Long before the Secret Network exploit, Prof. Dawn Song’s Keystone project attempted to do this by offering a hardware-agnostic enclave framework for RISC-V processors, designed to enable a network’s community to audit and enhance the trusted computing base of a network. Oasis Labs, along with Prof. Song, proposed a reference design and ultimately found that, much like Secret Network, managing keys and coordinating multiple TEE nodes could pose a security risk in the absence of additional layers of security like secure MPC.

MPC

Secure MPC as we know it today was first developed in the early 1980s, after cryptographer Adi Shamir developed a “threshold scheme” for sharing secret data across multiple parties with some redundancy, specifically for the purpose of managing cryptographic keys. In 1982, Andrew Yao extended this approach to computation by demonstrating a mathematical proof for a “Millionaire’s Problem” where two parties could compare their wealth without revealing their actual net worths. Five years later, Goldreich et al. built upon Yao by extending fault-tolerant computation to arbitrarily many parties. Critically, the paper’s result only extended to situations where over half of the N parties, or an honest majority, are not adversarial. By proposing a solution to this toy problem for two, and eventually N party compute, Yao and Goldreich et al. respectively highlighted the core idea behind MPC: enabling computation that depends on shared information while preserving individual data privacy.

By the late 1980s, protocols like that of Ben-Or, Goldwasser, and Wigderson (BGW) formally proved the ability to compute arbitrary functions under an honest majority assumption where, as long as more than two-thirds of the participants are honest, a protocol could remain secure. As practical efficiency concerns mounted, SPDZ (pronounced speedz) was eventually created as a more viable MPC scheme for even larger majorities of dishonest actors. SPDZ introduced preprocessing techniques which allowed secure computation even with only one honest actor, provided that the malicious parties did not collude. The domain specific applications that became possible ranged from secure auctions to privacy-preserving machine learning. But despite decades of progress, MPC remains constrained by fundamental trade-offs: efficiency, trust assumptions, and coordination complexity.

In contrast to TEEs, which externalize trust to the hardware manufacturer and operator of a specific node, MPC protocols decentralize trust across multiple nodes. Much like validators for blockchains, they do so with the trust assumption that a proportion of nodes aren’t colluding or being controlled by a malicious third party. Given this last point, selecting a set of robust nodes can mean that geographically dense sets of nodes implicitly carry stronger trust assumptions—if a majority of nodes live under the same roof, an MPC network’s trust assumptions start to look similar to TEEs or a very centralized blockchain. However, an MPC network with tens of distributed nodes will encounter greater communication overheads (simply put, latency) relative to a two-party compute network.

The computational overhead and the difficulty of maintaining robustness against malicious adversaries have limited MPC’s adoption as a standard for private shared state and as a default path for unsophisticated transactions. This makes intuitive sense, as one wouldn’t want every single transaction on a chain to be subject to an MPC network’s communication overhead and trust assumptions. In practice, most deployed MPC schemes prioritize specific, limited use cases (such as key management or voting protocols) rather than broad, composable privacy guarantees akin to HTTPS. 

For these reasons, MPC-secured wallets, where multiple parties combined partial keys to make a single private key, were an early application of MPC in crypto. This can be roughly compared to the left image above of a two-piece cruciform key, as the two pieces form one key that fits into a single keyhole. These are separate from multisig wallets which use multiple private keys and signatures—this winds up looking something like the photo on the right, with multiple locks, keys, and keyholes. In practice, MPC wallets interact with protocols like an ordinary wallet, while multisig wallets require adaptations on the protocol side, much like the hook that the locks hang from in the photo on the right.

FHE

The concept of homomorphic encryption was first theorized in the late 1970s with “privacy homomorphisms” which could enable untrusted computers to perform operations on sensitive data without needing to reveal it in plaintext. However, homomorphic encryption would remain an open problem until 2009, when cryptographer Craig Gentry described a feasible homomorphic encryption scheme using lattices to express arbitrary circuits—a fully homomorphic encryption scheme.

However, a core challenge in implementing this scheme came from the randomly distributed noise that enabled Gentry’s implementation of homomorphic addition and multiplication operations. Specifically, homomorphic additions add small amounts of randomly generated noise to the noise accumulated over a circuit’s execution, while homomorphic multiplications multiply the same accumulated noise. Critically, when an FHE program’s noise accumulates past a certain threshold in binary, it becomes indistinguishable from the encrypted data used for the underlying computation. Simply put, adding too much noise will corrupt the encrypted data, and homomorphic multiplication will do this faster than homomorphic addition.

To mitigate this, Gentry proposed bootstrapping, a process that “refreshes” ciphertexts by homomorphically re-encrypting them using a different private key, effectively resetting a program’s noise before it can bleed into the critical data and corrupt it. However, bootstrapping came at the cost of speed and computational resources—it made FHE theoretically limitless but enshrined a structural tradeoff between security and usability for complicated programs. With the rate of noise accumulation and the computational overhead in bootstrapping, this tradeoff was initially massive, as Gentry’s initial scheme needed 30 minutes to evaluate each bit operation when it was finally implemented two years later.

For additional depth on the mathematical foundations of FHE, check out Jeremy Kun's High-Level Technical Overview of FHE. 

Given all of this, early FHE research largely focused on improving efficiency by way of noise management, both by increasing the separation between noise and information to reduce the number of bootstrapping operations and by speeding up the actual process of managing noise. The 2013 Gentry-Sahai-Waters (GSW) scheme was a prime example of this as it accelerated FHE operations from minutes to milliseconds via “leveled” circuits. However, like some of the FHE schemes that followed, such as BGV and CKKS, it fragmented FHE into standards based on differing compilers and problem spaces. This fragmentation ultimately forced developers to trade a single standard of FHE for lower levels of friction. That being said, at this stage homomorphic millisecond-level logic gates remained a million times slower than traditional computing’s nanosecond-level operations, and as such, these early schemes were impractical for use in crypto. 

Another caveat with practically applying FHE to shared state lies in decrypting outputs for use in non-FHE applications—to do so one naturally needs a decryption key. This decryption key becomes a limiter of trust assumptions and, in practice, is either controlled by a single party or a set of nodes in an MPC setting, which brings FHE back to the trust assumptions of MPC whilst being slower.

Conclusion

On net, we see pretty clearly that PETs in their infancy haven’t exactly set the world alight with consumer-facing uses for private shared state—you’d be forgiven for extrapolating this to the present and assuming that these technologies are unused relative to ZK. But thankfully that’s not quite the case. TEEs are currently used in searching and building Ethereum blocks as well as for hosting autonomous agents and private data. MPC is used in wallets, identity, and defi coprocessors. And a whole host of teams are building private computers on top of Zama’s FHE libraries. 

In Part II, we’ll take a look at exactly how each of these technologies have evolved into their current forms and use cases, and how they’re starting to translate the promise of Privacy 2.0 into reality.

Many thanks to Andrew Miller, Quintus Kilbourn, Millian, Rohan Agarwal, Katie Chiou, Dmitriy Berenzon, Tyler Gehringer, and others for looking and conversing over various ideas, outlines, figures, and drafts.

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

IN CONVERSATION WITH is a series from Archetype where we interview artists in/at the edges of crypto across music, visual art, design, curation, and more.

Chuck Anderson is a Chicago-based artist, graphic designer, photographer, and creative director. Chuck’s career has spanned over 20 years, beginning with the founding of his creative studio NoPattern at the age of 18. Over the years Chuck has collaborated with brands such as Nike, Apple, ESPN, the Chicago Bulls, The New York Times, and more. In 2005, Chuck co-founded influential early internet culture blog THE BRILLIANCE! with Benjamin Edgar and Virgil Abloh and continues to champion creativity through his speaking engagements and mentorship of students and young creatives.

In 2022, Chuck launched his first long-form NFT collection entitled Infinite Pressure which included 90 solo works and 9 collaborations with 9 different artists including Gremplin, Case Simmons, Maalavidaa, Oseanworld, Ezra Miller, Jen Stark, Christian Rex van Minnen, Joshua Davis, and IX Shells. This was followed in 2023 by a collaborative collection with GQ, and a new body of work entitled Imagined Wreckage released in partnership with Verse in 2024.

The following interview has been edited and condensed for length and clarity.

Katie Chiou: For those who may not be familiar with your work, can you share more about your background/journey as a designer and artist? 

Chuck Anderson: I will be 40 this year, which is still insane to think about because I started my practice, NoPattern, in 2003 when I was 18. I didn’t go to college. I pretty much graduated high school and decided to take some time off to figure things out. I worked at a bookstore and a screenprinting shop, I honestly didn’t really have much of a plan.

Since I was young, I’ve always been really captivated by the internet. The power of being able to communicate with virtually anyone in the world fascinated me. And it was during that year after high school that I started getting small freelance projects just by being active in a handful of online design communities. I began reaching out to people via email, and I was getting some good responses. Pre-social media, there was very little competition to get in touch with people. If you managed to do it, you stood out because you weren’t dealing with DMs, LinkedIn, or whatever. Magazines were my sweet spot early on. I realized that if I could find an art director’s name in the masthead, I could probably guess their email address and figure it out. That’s pretty much how it all began.

Over the years, I started working with larger companies, brands, and agencies. I’ve always struggled to define what to call myself. I don’t really feel like a graphic designer, but I’m not purely an artist either. Digital artist is probably the most accurate title I could give myself. But I had a really specific lane and vision for my work, and I found a lot of cool brands to partner with, which led to great projects. Fast-forward to today: I still do all of my other work, but I’ve just started a new role as the Creative Director at Ernie Ball, a guitar company I’ve been working with for a long time. Everything else is pretty much filling in the blanks between those bookends.

KC: Your first medium was photography. In past interviews, you’ve referenced spending a lot of your time in high school just experimenting in Photoshop. Would you say your foundation started there? Were you taught by your teachers or parents or anyone like that?

CA: No one person gave me my comfort with a computer, that was all me. I’m a very curious person. I’m never satisfied with the top layer of anything. That’s how I was with Photoshop when I first discovered it. As far as teachers in high school and my parents go, I had great support. I was really lucky that my high school art teachers were so open-minded. They basically let me sit in the computer lab and I would design things, show them what I was working on, and they’d give me feedback on composition. I felt really encouraged. I don’t know if that necessarily set me up for a career, but so much of this was incidental. I didn’t suddenly feel like, “Okay, here it is—time to be a founder and start my design studio.” I was just an 18 or 19 year-old getting some projects for bands, doing flyers for clubs, and working with magazines. 

Back in the early 2000s, the only way to learn Photoshop was to get instructional books or magazines and obsess over them. And that’s exactly what I did. I eventually got a job working at Borders Bookstore. That was a total unlock for me because employees could check books out like a library. Suddenly, I could bring home books I couldn’t afford, and it felt like I had a wealth of information in front of me. I loved it. Digital photography was just emerging as a new concept, and I started taking photos, bringing them into Photoshop, and using that as the foundation to build and learn. That’s where it all started.

KC: I’d love to talk about THE BRILLIANCE!. What was it like running a blog during the early internet? How did you get the word out there, connect with readers, etc.?

CA: THE BRILLIANCE! was a very seminal point in my life and career. It was a hugely influential project, not only for me but for the people who were there at the time. Not a lot of things last on the internet for 20 years like that.

Basically, the beginning of it was just me and Benjamin Edgar. He and I were childhood friends. We would email back and forth constantly, sharing links, inspiration, and images from the internet that we would find. You have to situate this in the time—2003, 2004—when things were harder to find. It was like going to a record store and flipping through records to discover something, versus now, where everything is manicured, curated, and served up. 

One day, I suggested we start a blog, which was THE BRILLIANCE!. Those first few posts were a mix of editorial and personal—posts about things that interested us: travel, food, art, music, the internet, cars, watches—whatever we were into at the time. We had no plans to make money or run ads. We barely made any money on it, aside from a handful of collaborations. I wish we had figured that out, but it stayed pretty pure.

This was also right when Hypebeast launched. Highsnobiety wasn’t even a thing yet. There were just a handful of blogs back then. That early aesthetic, that simplicity, was part of why THE BRILLIANCE! looks the way it does—kind of like Drudge Report, unchanging, very text-heavy. 

One day, out of the blue, we got an email from a guy named Virgil Abloh. He was just a student at the time, a kid from Chicago, like us, from the suburbs. He reached out, saying he’d love to write for us one day if we were interested. After a few back-and-forth emails, we invited him to be the third writer. And the rest is history.

KC: And how were you engaging with readers or building community? Were there big email chains or forums? 

CA: I don’t remember exactly how we got the word out, but between message boards and doing interviews with interesting people, it started to grow. It was as close as you could get to a zine on the internet at that time. We realized the network effect—interviewing cool people who would then share it with their friends. If we talked about a certain blog, a certain shoe, a certain album, it would inevitably get back to those people because we linked to them. They’d see it. It’s crazy how manual it was, but people would notice traffic coming from our site and reach out. 

You really have to remove the knowledge of what social media was going to be before the internet was so ingrained in daily life. This was the sweet spot of the internet. It was online and accessible anytime, but you weren’t getting push notifications, a constant onslaught of information. You weren’t finding out about it from a post in your feed. You were finding out because you intentionally typed in a web address to see what we were up to.

Eventually, we did set up an RSS feed, but you also have to remember this was a side project for all of us. We weren’t looking for subscribers or anything. This was for the love of the game. I think that’s why it was so respected. The fact that I was able to get an interview with Futura in 2005 or 2006—some kid in the middle of the Midwest emailing a random address on his website at the time—and he got back and said he’d do an interview with us? That was huge. But it was because we were so hell-bent on trying to connect with people, trying to amplify our interests on the site. There was no deeper motivation beyond community, sharing, discovery, and meeting people.

KC: You had THE BRILLIANCE! in 2005, but now there’s Perfectly Imperfect, Blackbird Spyplane, How Long Gone, Throwing Fits, whatever. What do you think about how taste and recommendation culture has evolved today?

CA: It’s not surprising. Ben and I were just the perfect age to catch the beginning of the internet wave and start moving with it. At the end of the day, some people will always want to go do their own boots-on-the-ground research and find things themselves, but most people want to go to one spot where things are curated and served to them. It’s just easier. That’s also how people have gotten used to the internet—everyone’s experience is now segmented, tailored to what they want and what they’re looking for.

I also think we went too far in one direction—creative culture on the internet became too open, with this whole “Everyone’s a curator” mentality. And now, it’s whiplashed back to, “Actually, gatekeeping is good.” Curation is necessary. Without those things, we’re just wandering through an endless mall of content all the time. I don’t mean gatekeeping in the sense of barricading information, but more in terms of maintaining a point of view and paying people for their work. We’ve gotten so used to expecting everything online to be free. In another universe, maybe there could have been something like that for THE BRILLIANCE!, some kind of subscriber tier or something. 

The internet has gotten so big, so broad, that it became necessary to have more curated spaces. Everyone knows about everything now. Nothing feels shocking, novel, or new anymore. That’s why I think it’s still important to have places where people can put things in front of an audience in a thoughtful way.

KC: You have a very special relationship with print. As a primarily digital artist, how do you think about that relationship? When you’re working on a piece are you specifically conceptualizing whether it will be in print or not?

CA: I attribute my love of print to probably just my age and where I grew up. Discovering work back then was all about magazines—they were the thing for me. I can basically chalk everything up to that because I would take trips downtown from the suburbs, knowing exactly which stores or comic book stores carried the best magazines. I had stacks of them. There was so much discovery, so much cool work. 

Print gave me my first paychecks. At the time, there was no viable online space to make money from design. I was getting paid for work that ended up in print ads, billboards, magazines, record covers—all physical media. So even though I was working on a computer, the output was always in print. That was what drew me in.

Now, since my work relies so much on color, I still think about print technically. If something is going to print, I almost always work in an RGB space but with Proof Colors turned on so I can see how it’ll translate to CMYK. I know that’s getting into the technical weeds, but it’s just how I taught myself early on to preserve my colors. There’s just nothing better than holding something you designed in your hands after seeing it on a screen. I always keep going back for that feeling—anytime I can do work for publications or print, I take it.

Honestly, I think it’s a disservice to let work live only on the internet. Even if an artist just prints a little zine for themselves, art is best experienced on a physical surface, to me. That’s not some elder millennial, boomer take—I really believe there’s something about experiencing work outside of a screen. Even digitally made art takes on a different presence when you print it out and sit with it without a screen between your eyes.

KC: You launched your first NFT collection Infinite Pressure in 2022. What made you interested in NFTs and want to launch a collection?

CA: I had been making the Infinite Pressure-like work many, many months before and just posting it on Twitter. One day, I posted something and said I'd love to figure out a way to adapt this work for an Art Blocks release. Because I thought, while not generative, it's still vector-based, so there might be a way to partner with somebody to do that. I knew I needed someone on the tech side, and then CHAIN/SAW reached out and was like, "Hey, let’s figure this out. Let’s do something. I’d love to work with you and put this together." So I was stoked to have a partner there. 

My whole mantra, and Ben’s as well, is based on a very simple saying: "If you do something, something will happen." That’s the crux of my whole life. Because the opposite is, "If you do nothing, nothing will happen."

In hindsight, I can’t believe it. It was 99 works—all started at auction at 0.99 ETH. We sold the whole collection out in 4 days.

KC: What made you interested in NFTs? Did it feel like it added something substantively to your work or more so just a cool new thing to try out?

CA: I definitely felt like I was so perfectly set up to be someone who would do this. There was nothing I had to change about myself, but suddenly there was a viable way to sell work without having to go to UPS. I’ve had an online shop and been mailing my work in different formats to people—whether it be a print, a book, original artwork, or a shirt—for 20 years. As soon as there were NFTs, it was a pretty simple answer for me. If somebody wants to pay me money for work I’m doing and I don’t have to ship it to them, why would I not do this?

I minted my first NFT on Zora. I was one of the first few hundred  or so people on Foundation. I was very lucky to get those early introductions from people who’d followed my work in the years prior to the NFT boom. I always felt very fortunate to have had a career foundation before NFTs because I’ve seen so many new artists come to prominence in NFTs, have a quick flash, and then kind of never be heard from again. Whether it’s because they saw too much success and could never replicate it and flamed out or because they just saw it as an opportunity to hop on board and then go back to what they were doing—whatever it was. I personally just saw NFTs as a cool way to distribute work.

I appreciate and respect NFTs as a distribution model. I think I can say that I generally technically understand them. But they still boggle my mind, they were by far the biggest wrench thrown into my industry I’d ever experienced up to that point. AI is certainly beating it out now, but for most of my career, there was very little mass disruption at all. From the time I started in 2003 until NFTs became a ‘thing’ in 2021, there was never much mainstream attention on digital artists. Certainly there was in more niche pockets…but of course, all of a sudden, NFTs really took the world stage.

KC: How has your relationship to crypto evolved over time?

CA: I can comment on this in three ways: my own relationship to it, my thoughts about other artists and what advice I’d give, and then just thoughts in general.

I have a very restrained relationship with crypto. I never fully bought into it, nor do I hold much. When I’d make ETH from the sales of my work, I’d sell it. I saw it as my income, not as a speculative play. I only use and do things when I feel ready to. Obviously, it’s exciting when prices are higher, and there’s more activity. It’s kind of why I think apps like Zora and Rodeo are having a real moment. Even though it’s cheap collecting, people just want to go where there’s action. It’s pretty simple to me. People want to use the platforms where they have a chance to sell anything.

I do think everything may have happened too much, too fast. I saw some artists make a lot of money really quickly early on and then fade out. I think that’s really a mirror of the traditional art world. People burn out bright and then have to figure out how to regain relevance and spotlight. That’s a very hard reality. But it happens even quicker on the internet and even more so with NFTs, which is unfortunate. I think it’s all about expectations. If artists go into it thinking they’re going to get rich, they probably won’t. And if they go into it thinking they’re not going to sell anything, they might not—but they might. You just have to do it because you love it and it makes sense for the project you’re working on. That’s what it boils down to with anything. You have to let the money or success of a project come to you—and be mentally prepared for it not to. And if it doesn’t, be ready to make another thing and try again, like every artist in history has had to do.

KC: You mentioned AI earlier, what are your thoughts there?

CA: You know, AI is such a broad term, and it’s already affecting and touching everyone’s life. So I have to segment and separate the conversation—first and foremost, how it relates to me as an artist, how I incorporate it into my work, and understanding ethical boundaries around its use.

I don’t ever want to do anything that rips off someone else’s work or encroaches on their ideas. That’s always first and foremost for me. I don’t want to fundamentally change who I am, and I want everything I do to clearly have my creative DNA in it. I don’t want to reinvent myself just because a new tool has emerged. It’s ultimately about being super intentional.

I do believe AI is also a big reason a lot of artists I know have had slower years with commercial projects. It’s easier now for someone to crank out AI-generated work without hiring an artist. There’s no other way to say it. A junior designer can take a 90% AI-generated image, tweak it just enough, and get it over the finish line for an editorial piece. But that’s just the way the world is going. I can’t stop it or make anyone want to pay me instead of using AI. I understand that. It’s both good and bad. There are exciting uses for AI, and also some pretty horrifying ramifications when you look at the bigger picture. 

At the same time, you could ask, "Is Photoshop bad?" I could make all sorts of bad things with Photoshop. It comes down to all of us drawing our own lines and using tools in ways that feel right—ways that honor our work and who we are as artists. I think it’s just about equipping ourselves with independent, scrutinizing eyes, while still being open to trying new tools when they seem exciting or useful for creative work.

KC: One of the main criticisms I’ve heard about new technology in relation to art is that it trends towards making it harder for the individual to discern their own taste or craft. I imagine people even said that about early internet blogs like THE BRILLIANCE!. It’s hard to actually know what the impacts of these tools will be.

CA: I sit down with my daughter who is 7 and absolutely loves drawing. I’m watching her learn to shade, understand the basics of values, shadows, highlights, and mid-tones—all in real time. I want to do everything I can to help her develop a real, hands-on appreciation for drawing. 

I feel pretty confident in a prediction I have about her generation—post-Gen Z—growing up with a bit of a rejection of a lot of this technology. I’m sure things will first get even more digital in some ways, but I do think there’s going to be a whole generation that grows up with parents who have been so logged on all the time, so device-heavy, that hopefully, there will be a renewed appreciation for the arts and for the manual work that goes into it. Eventually, they’ll layer in all the amazing technology we have at our disposal. That’s my hope—that I can instill those principles and appreciation for traditional art early on, and then later they can learn the digital side. There’s never going to be any technology that takes away the joy of a blank sheet of computer paper and a crayon or pen for a kid. There’s something so pure about that. But at the same time, I’m open to taking a picture of it, running it through an AI tool, and seeing what happens when it turns into a video. These things can all exist together.

I think a lot about generations, ages, AI, art, and where we’re at right now. I’m thinking a lot about aging, growing up, and what it means to maintain relevance. What does that look like? What does it mean to claw for it versus aging gracefully in the digital age as an artist who’s followed the path I have?

A lot of that intersects with NFT culture—the quick hit, the flameout, and then suddenly you’re not part of anything outside of this very specific community. And then you’re left with the question: What are you? An NFT artist? I think that’s a very dangerous title. It’s an intriguing topic, especially with the pace of technology and AI. How can any human keep up with all of this? And what does it mean to have a long career in art while working digitally?

KC: What does it mean to you to age gracefully as an artist? Or to create “timeless art”? Or is that even a concept you subscribe to?

CA: When Midjourney V3 came out, there was a particular look and feel to almost all those outputs. And here we are, only about two years removed from that, and already, if you know what you’re looking at, you can immediately tell that an image was made in Discord.

Some artists captured that aesthetic and found ways to evolve it, but there’s still this demented, early nightmarish, inner-core-internet quality to those outputs that I think is fascinating. Whether that aesthetic is dated in a good or bad way is subjective, but it’s really interesting to me. The same goes for the way a lot of AI-generated art looks right now. If you just accept the first result and don’t refine it—don’t run it through more AI tools or add your own spin and layers—it locks into a certain look. You can pinpoint, "Oh, I know that was probably made in this era."

There are definitely eras of digital art that are aging poorly, mainly because the art wasn’t great to begin with. It was early experimentation with early tools. And I have plenty of that myself. But that’s true for every era and every medium.

I love this type of question, though—this type of exploration is pretty critical for any artist. Asking these questions, being aware of your time and place, recognizing what you’re making when you’re making it, and how it resonates (or doesn’t resonate) with viewers—all of that is worth considering when creating something.

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

When Satoshi unveiled Bitcoin in 2009, he had the vision of using cryptonetworks for payments that could flow as freely as information across the internet. While this was directionally correct, the technology, economic model, and ecosystem were simply not well-suited to commercialize the use case. 

Fast forward to 2025, and we're witnessing the convergence of several important innovations and developments that make that vision inevitable: stablecoins have achieved widespread adoption by both consumers and businesses, market makers and OTC desks are now comfortably holding stablecoins on balance sheet, DeFi applications have created robust onchain financial infrastructure, a plethora of on/off-ramps exist around the world, blockspace is faster and cheaper, embedded wallets have simplified user experience, and clearer regulatory frameworks have reduced uncertainty.

An extraordinary opportunity exists today to build a new generation of payment companies that harness the power of "cryptorails" to achieve dramatically better unit economics than traditional systems burdened by multiple rent-seeking intermediaries and antiquated infrastructure. These cryptorails are forming the backbone of a parallel financial system that operates in real-time, 24/7, and is inherently global.

In this piece, I will:

• Explain the key components of the traditional financial system.

• Provide an overview of the major use cases for cryptorails.

• Discuss challenges preventing continued adoption.

• Share predictions on how the market will look in five years.

To further motivate this piece, it is important to note that there are many more companies operating here than you might think—about 320 as of this writing:

In order to appreciate the significance of cryptorails, it is critical to first understand the key concepts of existing payment rails and the complex market structure and system architecture in which they operate. If you are familiar with this already then feel free to skip this section.

While the topology of card networks is complex, the key parties in a card transaction have stayed the same for the past 70 years. At its heart, a card payment involves four main players: 

1. Merchant

2. Cardholder

3. Issuing bank

4. Acquiring bank

The first two are straightforward but the last two are worth explaining.

The Issuing Bank or Issuer provides the credit or debit card to the customer and authorizes transactions. When a transaction request is made, the issuing bank decides whether to approve it by checking the cardholder’s account balance, available credit, and other factors. Credit cards essentially lend the issuer’s funds, while debit cards transfer money directly from your account.

If a merchant wants to accept card payments, they need an acquirer (which could be a bank, processor, gateway, or independent sales organization) that is a licensed member of the card networks. The term "acquirer" comes from their role of receiving money on behalf of merchants and ensuring that those funds reach the merchant's account.

The card networks themselves provide the rails and rules for card payments to occur. They connect acquirers to issuing banks, provide a clearing house function, set the rules of engagement, and determine transaction fees. ISO 8583 remains the primary international standard that defines how card-payment messages (e.g. authorizations, settlements, chargebacks) are structured and exchanged between network participants. In the context of networks, issuers and acquirers are like their distributors—issuers are responsible for getting more cards into the hands of users, and the acquirers are responsible for getting as many card terminals and payment gateways in the hands of merchants so they can accept card payments.

Furthermore, there are two flavors of card networks: “open loop” and “closed loop.” An open loop network like Visa and Mastercard involves multiple parties: issuing banks, acquiring banks, and the credit card network itself. The card network facilitates communication and transaction routing but acts as more of a marketplace, relying on financial institutions to issue cards and manage customer accounts. Only banks are allowed to issue cards for open loop networks. Each debit or credit card has a Bank Identification Number (BIN), which is offered by Visa to a bank, and non-bank entities like PayFacs need a “BIN sponsor” to be able to issue cards or process transactions. 

In contrast, a closed loop network like American Express is self-contained, with a single company handling all aspects of the transaction process—they usually issue their own cards, are their own bank, and provide their own merchant acquiring services. The general tradeoff is that closed loop systems offer more control and better margins at the cost of more limited merchant acceptance. Conversely, open loop systems offer wider adoption at the cost of control and revenue sharing for the involved parties.

The economics of payments are complex and there are several layers of fees in the network. Interchange is the portion of payment fees collected by the issuing bank in exchange for giving access to that issuer’s customers. While it is technically the acquiring bank that pays the interchange fee directly, the cost is usually passed along to the merchant. The card network usually sets the interchange fee and it usually makes up the bulk of the overall cost of a payment. These fees vary widely in different geographies and transaction types. For example, in the US, consumer credit card fees can range from ~1.2% to ~3%, while in the EU they are capped at 0.3%. In addition, scheme fees, also determined by the card network, are applied to transactions to compensate the networks for their role in connecting acquirers to issuing banks and acting as a “switch” to make sure the transactions and funds go to the correct parties. There are also settlement fees that go to the acquirer and are typically a percentage of the settled amount or volume of the transaction. 

While these are the most important parties along the value chain, the reality is that today’s market structure is much more complex in practice:

While I won’t go into all of them, there are a few important actors to call out:

A payment gateway encrypts and transmits payment information, connects with payment processors and acquirers for authorization, and communicates transaction approvals or declines to businesses in real-time.

A payment processor processes payments on behalf of the acquiring bank. It forwards transaction details from the gateway to the acquiring bank, which then communicates with the issuing bank via the card network for authorization. The processor receives the authorization response and sends it back to the gateway to complete the transaction. It also handles settlement, the process through which funds actually land in the merchant’s bank account. Typically, businesses send batches of authorized transactions to the processor, who submits them to the acquiring bank to initiate fund transfers from the issuing bank to the merchant’s account.

A payment facilitator (PayFac) or payment service provider (PSP), pioneered by PayPal and Square around 2010, is like a mini-payment processor that sits in between merchants and acquiring banks. It effectively acts as an aggregator by bundling many smaller merchants into their system to achieve economies of scale and streamline operations by managing the flow of funds, processing transactions, and ensuring payouts. PayFacs hold a direct Merchant ID with the card network and take on the responsibilities of onboarding, compliance (e.g. AML laws), and underwriting on behalf of the merchants they work with.

An orchestration platform is a middleware technology layer that streamlines and optimizes merchants' payment processes. It connects to multiple processors, gateways, and acquirers through a single API to improve transaction success rates, reduce costs, and enhance performance by routing payments based on factors like location or fees.

The Automated Clearing House (ACH) is one of the largest payment networks in the U.S. and is effectively owned by the banks that use it. It was originally formed in the 1970s but really took off when the U.S. government started using it to send Social Security payments, which encouraged banks across the country to join the network. Today, it is heavily used for payroll processing, bill payments, and B2B transactions.

ACH transactions have two main flavors: "push" payments (where you send money) and "pull" payments (where someone takes money with your permission). When you get your paycheck through direct deposit or pay a bill online using your bank account, you're using the ACH network. The process involves several players: the company or person starting the payment (the originator), their bank (ODFI), the receiving bank (RDFI), and operators who act like traffic controllers for all these transactions. In the ACH process, the originator submits a transaction to the ODFI, which then sends the transaction to an ACH operator, which then switches the transaction to the RDFI. At the end of each day, the operators calculate net settlement totals for their member banks (and the Federal Reserve manages the actual settlement).

One of the most important things to understand about ACH is how it handles risk. When a company initiates an ACH payment, its bank (the ODFI) takes responsibility for making sure everything is legitimate. This is particularly crucial for pull payments—imagine if someone used your bank account information without permission. To protect against this, regulations allow disputes up to 60 days after statement receipt, and companies like PayPal developed clever verification methods, such as making tiny test deposits to confirm account ownership.

The ACH system has tried to keep up with modern needs. In 2015, they introduced "Same Day ACH," which allows faster processing of payments. That said, it still relies on batch processing rather than real-time transfers and comes with limitations. For instance, you can't send more than $25,000 in a single transaction and it doesn't work well for international payments.

Wire transfers represent the backbone of high-value payment processing, with Fedwire and CHIPS as the two main systems in the U.S. These systems handle time-critical, guaranteed payments that need immediate settlement, such as securities transactions, major business deals, and real estate purchases. Once executed, wire transfers are generally irrevocable and cannot be canceled or reversed without the recipient's agreement. Unlike regular payment networks that process transactions in batches, modern wire transfer systems use real-time gross settlement (RTGS), meaning each transaction is settled individually as it occurs. This is an important property because the systems process hundreds of billions of dollars each day and the risk of an intraday bank failure using traditional net settlement would be too great.

Fedwire is an RTGS transfer system that allows participating financial institutions to send and receive same-day fund transfers. When an enterprise initiates a wire transfer, their bank verifies the request, debits the account, and sends a message to Fedwire. The Federal Reserve Bank then instantly debits the sending bank's account and credits the receiving bank's account, with the receiving bank subsequently crediting the final recipient. The system operates weekdays from 9 p.m. the prior calendar day to 7 p.m. Eastern Time and is closed during weekends and federal holidays.

CHIPS, owned by large U.S. banks through The Clearing House, serves as a private sector alternative but operates on a smaller scale, serving only a select group of major banks. Unlike Fedwire's RTGS approach, CHIPS is a netting engine, which means the system allows multiple payments between the same parties to be aggregated. For example, if Alice wants to send $10M to Bob and Bob wants to send $2M to Alice, CHIPs would consolidate these into a single payment of $8M from Bob to Alice. While this means that CHIPS payments take longer than real-time transactions, most payments still settle intraday.

Complementing these systems is SWIFT, which isn't actually a payment system but rather a global messaging network for financial institutions. It is a member-owned cooperative whose shareholders represent over 11,000 member organizations. SWIFT enables banks and securities firms worldwide to exchange secure, structured messages, many of which initiate payment transactions across various networks. According to Statrys, a SWIFT transfer takes about 18 hours to complete.

In the general flow, the sender of funds instructs their bank to send a wire transfer to a receiver. The value chain below is the simple case where both banks belong to the same wire transfer network.

In the more complex case, particularly with cross-border payments, the transaction needs to be executed through correspondent banking relationships, usually using SWIFT to coordinate the payments.

Now that we have a foundational understanding of traditional rails, we can focus on where cryptorails shine.

Cryptorails are most powerful in scenarios where traditional dollar access is limited but dollar demand is high. Think of places where people want USD for wealth preservation or as a bank alternative but can't easily get traditional USD bank accounts. These are generally countries experiencing economic instability, high inflation, currency controls, or underdeveloped banking systems, such as Argentina, Venezuela, Nigeria, Turkey, and Ukraine. In addition, one can argue that USD is a superior store of value relative to most other currencies and would be generally preferred by both consumers and businesses given the ability to easily use it as a medium of exchange or swap into local fiat at the point of sale.

The benefit of cryptorails is also greatest in scenarios where payments are global because cryptonetworks know no borders. They piggyback off of the existing internet connectivity, giving them global coverage out of the box. According to the World Bank, there are currently 92 RTGS systems operating around the world, each of which is generally owned by their respective central banks. While they are ideal for sending domestic payments within those countries, the issue is that they don’t “talk to each other”. Cryptorails can serve as the glue between these different systems as well as an extension into countries without them.

Cryptorails are also most useful for payments that have a degree of urgency or generally high time preference. This includes cross-border supplier payments and foreign aid disbursements. They are also helpful in corridors where the correspondent banking network is particularly inefficient. For example, despite geographical proximity, it is actually more difficult to send money from Mexico to the U.S. than from Hong Kong to the U.S. Even in developed corridors like the U.S. to Europe, payments can often go through four or more correspondent banks.

On the other hand, cryptorails are less compelling for domestic transactions within developed nations, especially where credit card adoption is high or where real-time payment systems already exist. For instance, intra-European payments work smoothly through SEPA, and the Euro's stability removes the need for dollar-denominated alternatives.

Merchant acceptance can be categorized into two distinct use cases: front-end integration and back-end integration. In the front-end approach, merchants can directly accept crypto as a form of payment from customers. While this is one of the oldest use cases, it has historically not seen much volume because few people held crypto, even fewer wanted to spend it, and limited useful options existed for those who did. The market is different today as more people hold cryptoassets, including stablecoins, and more merchants are accepting them as a payment option because it enables them to access new customer segments and ultimately sell more goods and services. 

From a geographical perspective, much of the volume is coming from businesses selling to consumers in countries that were early adopters of crypto, which are often emerging markets like China, Vietnam, and India. On the merchant side, much of the demand has been from online gambling and retail stock brokerages that want access to users in emerging markets, web2 and web3 marketplaces like watch vendors and content creators, and real-money gaming like fantasy sports and sweepstakes.

Here is what the “front-end” merchant acceptance flow generally looks like:

1. PSP creates a wallet for the merchant, often after KYC/KYB.

2. User sends crypto to PSP.

3. PSP off-ramps crypto into fiat via liquidity providers or stablecoin issuer and sends funds to the merchant’s local bank account, potentially using other licensed partners.

The main challenge preventing this use case from continuing to gain adoption is psychological because crypto doesn’t seem “real” to many people. There are two large user personas to address: one is completely detached from its value and wants to keep everything as magic internet money, and the other is pragmatic and off-ramps directly to their bank.

In addition, consumer adoption has been more difficult in the U.S. because credit card rewards are effectively paying consumers 1-5% on purchases. There have been attempts to sway merchants to promote crypto payments directly to consumers as an alternative payment method to cards altogether, however, they have not been successful to date. While lower interchange is a good pitch to merchants, it is not a problem for consumers. The Merchant Customer Exchange launched in 2012 and failed in 2016 for exactly this reason—they could not kick-start the consumer side of the adoption flywheel. In other words, getting users to switch from paying with credit cards to cryptoassets is very difficult for merchants to incentivize directly because payments are already "free" for consumers, so the value proposition should be solved at the consumer level first.

In the back-end approach, cryptorails can offer faster settlement times and access to funds for merchants. Settlement can take 2-3 days for Visa and Mastercard, 5 days for American Express, and even longer internationally, about 30 days in Brazil for example. In some use cases, such as marketplaces like Uber, the merchant might need to pre-fund a bank account to disburse payments prior to settlement. Instead, one can effectively on-ramp via a user's credit card, transfer funds onchain, and off-ramp directly to the merchant’s bank account in their local currency. In addition to the working capital improvement from this flow due to having less capital tied up in transit, merchants can further improve their treasury management by freely and instantly swapping between digital dollars and yield-bearing assets, such as tokenized U.S. Treasuries.

More specifically, here is what the “back-end” merchant acceptance flow might look like:

1. Customer enters their credit card information to complete a transaction.

2. PSP creates a wallet for the customer and user funds that wallet with an on-ramp that accepts traditional payment methods.

3. The credit card transaction buys USDC, which is then sent to the merchant’s wallet from the customer’s wallet.

4. PSP can optionally off-ramp to merchant’s bank account via local rails T+0 (i.e. same day).

5. PSP receives funds from acquiring bank usually T+1 or T+2 (i.e. in 1-2 days).

The ability to link debit cards directly to non-custodial smart contract wallets has created a surprisingly powerful bridge between blockspace and meatspace, driving organic adoption across diverse user personas. In emerging markets, these cards are becoming primary spending tools, increasingly replacing traditional banks. Interestingly, even in countries with stable currencies, consumers are leveraging these cards to gradually build USD savings while avoiding foreign exchange (FX) fees for purchases. High-net-worth individuals are also increasingly using these crypto-linked debit cards as an efficient tool for spending their USDC worldwide.

The traction with debit over credit cards comes from two factors: debit cards face fewer regulatory restrictions (for example, MCC 6051 is outright declined in Pakistan and Bangladesh, which have strict capital controls), and they present lower fraud risk since chargebacks for already-settled crypto transactions create significant liability issues for credit cards.

Over the longer-term, cards tied to crypto wallets that are used for mobile payments might actually be the best way to combat fraud because of the biometric verification on your phone: scan your face, spend stables, and top up from your bank account to wallet.

Remittance is the movement of funds from the country of work back to a home country for people who moved internationally to find work and want to send money back to their families. According to the World Bank, remittance volume in 2023 totaled about $656 billion, equivalent to Belgium's GDP. 

The traditional remittance system has significant costs that result in fewer dollars in the pocket of the recipient. On average, sending money across borders costs 6.4% of the transfer amount but these fees can vary dramatically—from 2.2% for transfers from Malaysia to India (and even lower for high-volume corridors like the U.S. to India) to an astounding 47.6% from Turkey to Bulgaria. Banks tend to be the most expensive, charging around 12%, while money transfer operators (MTOs) like MoneyGram average 5.5%.

Cryptorails can offer a faster and cheaper way to send money abroad. The traction of companies using cryptorails largely follows the broader remittance market sizes, with the highest-volume corridors being from the U.S. to LatAm (specifically Mexico, Argentina, and Brazil), U.S. to India, and U.S. to Philippines. An important enabler of this traction has been non-custodial embedded wallets like Privy, which offer a web2-grade UX for users.

The flow for a remittance payment using cryptorails might look like this:

1. The sender on-ramps to the PSP via a bank account, debit card, credit card, or directly to an onchain address; if the sender does not have a wallet, one is created for them.

2. The PSP converts the USDT/USDC to the recipient’s local currency, either directly or using a market maker or OTC partner.

3. The PSP disburses the fiat to the recipient’s bank account, either directly from their omnibus bank account or via a local payment gateway; alternatively, the PSP can also first generate a non-custodial wallet for the recipient to claim the funds, giving them the option of keeping it onchain.

4. In many cases, the recipient needs to first complete KYC to receive the funds.

That said, the go-to-market for crypto remittance projects is tough. One issue is that you generally need to incentivize people to shift from MTOs, which can be expensive. Another issue is that transfers are free on most web2 payment apps already, so local transfers alone are not compelling enough to overcome the network effects of existing applications. Lastly, while the onchain transfer component works well, you still need to interact with TradFi at the “edges”, so you might still end up with the same, if not worse, problems due to the off-ramping cost and friction. In particular, payment gateways that convert into local fiat and payout in bespoke ways like mobile phones or kiosks will take the most margin.

Cross-border (XB) B2B payments are one of the most promising applications for cryptorails because the traditional system suffers from significant inefficiencies. Payments going through the correspondent banking system can take weeks to settle, with some extreme cases taking even longer—one founder said it took them 2.5 months to send a supplier payment from Africa to Asia. As another example, a cross-border payment from Ghana to Nigeria, two bordering countries, can take weeks and cost up to 10% transfer fees.

In addition, cross-border settlement is slow and expensive for PSPs. For a company that does payouts like Stripe, it can take them up to a week to pay a merchant internationally and they have to lock up capital to cover the risk of fraud and chargebacks. Cutting down the conversion cycle time would free up significant amounts of their working capital.

B2B XB payments have seen significant traction with cryptorails largely because merchants care more about fees than consumers. Shaving off 0.5-1% on transaction costs doesn’t sound like a lot, but it adds up when there’s a lot of volume, particularly for businesses operating on thin margins. Furthermore, speed matters. Getting a payment to settle in hours instead of days or weeks has a meaningful impact on a company’s working capital. In addition, businesses have more tolerance for worse UX and more complexity relative to consumers who expect a smooth experience out of the box.

Furthermore, the cross-border payments market is massive—estimates vary widely by source but according to McKinsey it was about $240 billion in revenue and $150 trillion in volume in 2022. That said, building a sustainable business can still be tough. While the “stablecoin sandwich”—converting local currency to stablecoins and back again—is definitely faster, it is also expensive because doing FX conversions on both sides erodes margins, often to the point of unsustainable unit economics. While some companies try to solve this by building internal market-making desks, this is very balance-sheet intensive and difficult to scale. In addition, the customer base is also relatively slower, concerned with regulation and risk, and generally requires a lot of education. That said, FX costs will likely rapidly decline over the next two years as stablecoin legislation opens up access for more businesses to hold and operate with digital dollars. Because more on/off-ramps and token issuers will have direct banking relationships, they will effectively be able to offer wholesale FX rates at internet scale.

With B2B payments, much of the cross-border volume has been around supplier payments for imports, where usually the buyer is in the U.S., LatAm, or Europe, and the supplier is in Africa or Asia. These corridors are particularly painful because the local rails in those countries are underdeveloped and difficult to access for companies because they cannot find local banking partners. There are also country-specific pain points that cryptorails can help alleviate. For example, in Brazil you cannot pay millions of dollars out using traditional rails, which makes it difficult for businesses conducting international payments. Some well-known companies, like SpaceX, are already using cryptorails for this use case.

Businesses that have customers around the world often struggle to collect funds in a timely and efficient manner. They often work with multiple PSPs to collect funds for them locally but need a way to quickly receive them, which can take multiple days or even weeks depending on the country. Cryptorails are faster than a SWIFT transfer and can compress that time down to T+0.

Here is what an example payment flow might look like for a business in Brazil buying goods from a business in Germany:

1. Buyer sends Real to PSP via PIX.

2. PSP converts Real to USD, then on-ramps into USDC.

3. PSP sends USDC to the wallet of the seller.

4. If the seller wants local fiat, PSP sends USDC to a market maker or trading desk to convert into local currency.

5. PSP can send funds to seller via local rails if they have a license/bank account, or use a local partner if not.

Companies can also use cryptorails to improve their treasury operations and accelerate global expansion. They can hold USD balances and use local on/off-ramps to reduce FX exposure and enter new markets more quickly, even where local banking providers are reluctant to support them. They can also use cryptorails as an internal means of reorganizing and repatriating funds between the countries in which they operate.

Another general use case we are seeing for B2B is time-critical payments, for which these cryptorails can be used to reach the recipient faster. One example is foreign aid payments—allowing NGOs to use cryptorails to send money to local off-ramp agents who can individually disburse payments to qualified individuals. This can be particularly impactful in economies with very poor local financial systems and/or governments. For example, countries like South Sudan have a collapsing central bank and local payments there can take over a month. But as long as there’s access to mobile phones and an internet connection, there is a way to get digital money into the country and individuals can exchange that digital money for fiat and vice versa.

The payment flow for this use case might look like this:

1. NGO sends funds to PSP.

2. PSP sends a bank transfer to OTC partner.

3. OTC partner on-ramps the fiat into USDC and sends it to a local partner's wallet.

4. Local partner off-ramps USDC via peer-to-peer (P2P) traders.

From a consumer perspective, one of the most promising early adopters has been freelancers and contractors, particularly in emerging markets. The value proposition for these users is that more money ends up in their pockets rather than going to intermediaries and that the money can be in digital dollars. This use case also has cost benefits for the businesses on the other side who are sending large-scale payouts and is especially useful for crypto-native companies like exchanges that already hold most of their treasury in crypto.

The payment flow for a contractor payout generally looks like this:

1. Company does KYB/KYC with the PSP.

2. Company sends USD to PSP or USDC to a wallet address that’s tied to a contractor.

3. Contractor can decide if they want to keep it in crypto or withdraw to a bank account, and PSP often has some master service agreement with one or more off-ramp partners who hold the relevant licenses in their respective jurisdictions to do the local payout.

On/off-ramps is a crowded market that has many corpses and zombies. While many early attempts failed to scale, the market has matured over the past several years, with many companies operating sustainably and offering access to local payment rails around the world. While on/off-ramps can be used as a standalone product (e.g. simply buying cryptoassets), they are arguably the most critical part of the payment flow for bundled services like payouts.

Building an on/off-ramp generally has three components: obtaining the necessary licenses (e.g. VASP, MTL, MSB), securing a local banking partner or PSP for access to local payment rails, and connecting with a market maker or OTC desk for liquidity. 

On-ramping was initially dominated by exchanges, but today, an increasing number of liquidity providers, from smaller FX and OTC desks to larger trading firms like Cumberland and FalconX, are offering ramping. These firms can often handle up to $100M/day in volume, which makes it unlikely that they will run out of liquidity for popular assets. Some teams might even prefer them because they can promise spreads, which helps control margins.

The non-U.S. leg of an off-ramp is usually much more difficult than the U.S. leg of an on-ramp due to licensing, liquidity, and orchestration complexity. This is particularly the case in LatAm and Africa, where there are dozens of currencies and payment methods. For example, you can use PDAX in the Philippines because it is the largest crypto exchange there, but in Kenya you need to use several local partners like Clixpesa, Fronbank, and Pritium depending on the payment method. 

P2P ramps rely on a network of "agents"—local individuals, mobile money providers, and small businesses like supermarkets and pharmacies—who provide both fiat and stablecoin liquidity. These agents, particularly prevalent in Africa where many already operate mobile money stalls for services like MPesa, are motivated primarily by economic incentives—they earn through transaction fees and FX spreads. In fact, for individuals in high-inflation economies like Venezuela and Nigeria, becoming an agent can be more lucrative than traditional service jobs like taxi drivers or food delivery. They can also just work from home using their mobile phone, and usually just need a bank account and mobile money to get started. What makes this system particularly powerful is its ability to support dozens of local payment methods with zero formal licenses or integrations since transfers happen between individual bank accounts. 

Notably, the FX rate with P2P ramps can often be significantly more competitive. For example, the Bank of Khartoum in Sudan often charges up to 25% for FX fees while local crypto P2P off-ramps are offering 8-9%, which is effectively the market rate instead of the bank-enforced rate. Similarly, P2P ramps are able to offer FX rates that are about 7% cheaper than bank rates in both Ghana and Venezuela. Generally, the spread is smaller in countries where there is greater availability of USD. Furthermore, the best markets for P2P ramps are those with high inflation, high smartphone adoption, poor property rights, and unclear regulatory guidelines since financial institutions won’t touch crypto, which creates an environment for self-custody and P2P to thrive.

Here is how a payment flow for a P2P on-ramp might look:

1. User either selects or is automatically assigned a counterparty, or an “agent”, who already has USDT, which is often escrowed by the P2P platform.

2. User sends fiat to an agent via local rails.

3. Agent confirms receipt and USDT is sent to the user.

From a market structure perspective, most on/off-ramps are commoditized and there is little customer loyalty, as they will usually choose the cheapest option. To remain competitive, local ramps will likely need to expand coverage, optimize for the most popular corridors, and find the best local partners. In the long-term, we will likely see consolidation into a few on/off-ramps in each country that each have comprehensive licensing, support for all local payment methods, and offer the most liquidity. In the mid-term, aggregators will be particularly useful because local providers are often faster and cheaper, and combining options will often offer the best pricing and completion rates for consumers. They might also suffer the least commoditization if they can efficiently optimize and route payments across hundreds of partners and routes. This also applies to orchestration platforms that can include compliance, PSP selection, bank partner selection, and value-added services such as card issuance.

From a consumer perspective, the good news is that fees will likely trend to zero. We already see this today with Coinbase, where it costs $0 to instantly move from USD to USDC. In the long-run, most stablecoin issuers will likely enable this for large wallets and FinTechs, further compressing ramp fees.

Licensing is a painful but necessary step for scaling the adoption of cryptorails. There are two approaches for startups: partner with an already-licensed entity or obtain licenses independently. Working with a licensed partner allows a startup to bypass the substantial costs and lengthy timelines associated with obtaining a license themselves, but at the cost of tighter margins due to a substantial portion of the revenue going to the licensed partner. Alternatively, a startup can choose to invest upfront—potentially hundreds of thousands to millions of dollars—to acquire a license independently. While this path often takes months and even years (one project said it took them 2 years), it enables the startup to offer a more comprehensive product directly to users. 

While there are established playbooks for obtaining licenses across many jurisdictions, achieving global licensing coverage is exceptionally challenging, if not impossible, as each region has its own unique regulations around money transmission and you would need over 100 licenses for global coverage. For example, just in the U.S., a project would need a Money Transmitter License (MTL) for each state, a BitLicense for New York, and a Money Services Business (MSB) Registration with the Financial Crimes Enforcement Network. Just getting MTLs for all states can cost from $500K to $2M and can take up to a year. The requirements are equally as dizzying when looking overseas—a good resource can be found here. Importantly, startups that are non-custodial and are not touching the flow of funds can generally bypass immediate licensing requirements and get to market faster.

Payments adoption is usually difficult because they’re a chicken-and-egg problem. You need to either get widespread consumer adoption of a payment method, which will force merchants to accept it, or have merchants use one specific payment method, which will force consumers to adopt it. For example, credit cards were niche in LatAm until Uber started to take off in 2012; everyone then wanted a credit card because it would allow them to use Uber which was much safer and (initially) cheaper than cabs. This allowed other on-demand apps like Rappi to take off because there were now people with smartphones and credit cards. This became a virtuous cycle where more people wanted a credit card because there were more cool applications that required one for payment. 

This also applies to mainstream consumer adoption of cryptorails. We have yet to see a use case where it is particularly advantageous or completely necessary to pay with stablecoins, although debit cards and remittance applications are getting us closer to that moment. A P2P app also has a chance if it unlocks a net new behavior online—micropayments and creator payments seem like exciting candidates. This is broadly true of consumer applications in general, where adoption won’t happen without step function improvements over the status quo.

There are also several problems that continue to exist for on/off-ramping:

• High failure rates: You understand this frustration if you have tried using a credit card to on-ramp.

• UX friction: While early adopters were OK with the pain of onboarding with exchanges to access assets, the early majority will likely be onboarded natively within specific applications. To support this, we need smooth in-app ramping, ideally via Apple Pay.

• High fees: On-ramping is still very expensive—it can still cost 5-10%, depending on the provider and region.

• Inconsistent quality: There is still too much variance in reliability and compliance, especially with non-USD currencies.

An under-discussed issue is privacy. While it is not currently a serious concern for either individuals or companies, it will become one once cryptorails are adopted as the primary mechanism for commerce. There will be serious negative consequences when malicious actors start to monitor payment activity for individuals, companies, and governments via their public keys. One way to address this in the short term is to do “privacy by obscurity” and spin up new wallets each time funds need to be sent or received onchain.

In addition, establishing banking relationships is often the most difficult part because it’s another chicken and egg problem. Banking partners will take you if they get transaction volumes and will make money, but you need banks to get those volumes in the first place. In addition, there currently are only 4-6 small U.S. banks that support crypto payments companies today, and several are hitting their internal compliance limits. Part of this is because crypto payments today are still categorized as “high-risk activities” similar to cannabis, adult media, and online gambling.

Contributing to this issue is the fact that compliance is still not at par with traditional payment companies. This includes AML/KYC & Travel Rule compliance, OFAC screening, cybersecurity policies, and consumer protection policies. Even more challenging is directly baking compliance into cryptorails rather than relying on out-of-band solutions and companies. Lightspark's Universal Money Addresses offers one creative solution to this challenge by facilitating the exchange of compliance data between participating institutions.

On the consumer side, we are currently at the point where certain demographics are hitting mainstream adoption, particularly with freelancers, contractors, and remote workers. We are also getting closer to mainstream in emerging economics with USD demand by piggybacking off of the card networks and offering consumers USD exposure along with the ability to spend it for everyday use. In other words, debit cards and embedded wallets have become the “bridge” that is bringing crypto offchain in a form factor that is intuitive for mainstream consumers. On the business side, we are firmly at the start of mainstream adoption. Companies are using stablecoins at scale and this will significantly increase over the decade.

With all of this in mind, here are my 20 predictions on how the industry might look in 5 years:

1. $200-$500 billion in payments volume annually via cryptorails, primarily driven by B2B payments.

2. >30 neobanks around the world launched natively on top of cryptorails.

3. Dozens of acquisitions of crypto-native companies as FinTechs race to remain relevant.

4. Several crypto companies, likely stablecoin issuers, will acquire struggling FinTechs and banks who run out of runway due to high CAC and operational costs.

5. ~3 cryptonetworks (both L1s and L2s) emerge and hit scale with an architecture that is specifically designed for payments. Such a network will resemble Ripple in spirit but will have a tech stack, economic model, and go-to-market that makes sense.

6. 80% of online merchants will be accepting crypto as a means of payment, either through existing PSPs expanding their offering or crypto-native payment processors offering a better experience for them.

7. Card networks will expand to cover ~240 countries and territories (up from ~210 today) using stablecoins as the last-mile solution.

8. >15 remittance corridors around the world will have the majority of their volume flow via cryptorails.

9. Onchain privacy primitives will finally be adopted, driven by businesses and countries using cryptorails rather than consumers.

10. 10% of all foreign aid disbursements will be sent via cryptorails.

11. The on/off-ramp market structure will ossify, with 2-3 providers per country gaining the majority of volumes and partnerships.

12. There will be as many P2P on/off-ramp liquidity providers as food delivery workers in the countries where they operate. As volumes increase, agents will become economically sustainable jobs and continue to be at least 5-10% cheaper than bank-quoted FX rates.

13. >10 million people who are remote workers, freelancers, and contracts will receive payment (either directly in stablecoins or local currency) for their services via cryptorails.

14. 99% of AI agent commerce (including agent-to-agent, agent-to-human, and human-to-agent) will happen onchain via cryptorails.

15. >25 reputable partner banks in the US will support companies operating on top of cryptorails, removing the bottlenecks that operation chokepoint exacerbated.

16. Financial institutions will experiment with issuing their own stablecoins to facilitate global real-time settlement.

17. Standalone “crypto Venmo” applications still do not take off as the user persona remains too niche, but large messaging platforms like Telegram will integrate cryptorails that will start being used for P2P payments and remittance.

18. Lending and credit companies will start collecting and disbursing payments via cryptorails to improve their working capital due to less funds tied up in transit.

19. Several non-USD stablecoins will begin being tokenized at scale, giving rise to onchain FX markets.

20. CBDCs remain an experiment and do not hit commercial scale because of government bureaucracy.

As Patrick Collison alluded to, cryptorails are superconductors for payments. They form the substrate of a parallel financial system that offers faster settlement times, reduced fees, and the ability to operate seamlessly across borders. It took a decade for the idea to mature, but today we see hundreds of companies working to make it a reality. Over the next decade, we will see cryptorails at the heart of financial innovation, driving economic growth across the globe. 

If you are building something using cryptorails, please reach out. You can find me on Twitter/X and Farcaster.

Many thanks to Jeremy Allaire, Sam Broner, Christian Catalini, Katie Chiou, Wyatt Lonergan, and DC Posch for conversations and feedback on this piece.

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

1. Agent-to-Agent Interaction

The default transparency and composability of blockchains make them a perfect substrate for agent-to-agent interaction, where agents developed by different entities for different purposes can interact with each other seamlessly. There’s already been great experimentation of agents sending funds to each other, launching tokens together, and more. We’d love to see how agent-to-agent interaction can scale both by creating net-new application spaces, such as new social venues driven by agent interactions, as well as by improving enterprise workflows we know to be tedious today, from platform authentication and verification to micropayments, inter-platform workflow integrations, and more. 

- Danny, Katie, Aadharsh, Dmitriy

Multi-agent coordination at-scale is a similarly exciting area of research. How can multi-agent systems work together to complete tasks, solve problems, and govern systems and protocols? In his post at the beginning of 2024, “The promise and challenges of crypto + AI applications,” Vitalik references utilizing AI agents for prediction markets and adjudication. At scale, he essentially posits, multi-agent systems have remarkable capability for “truth”-finding and generally autonomous governance systems. We’re interested to see how the capabilities of multi-agent systems and forms of “swarm intelligence” continue to be discovered and experimented with.

As an extension of agent-to-agent coordination, agent-to-human coordination is an interesting design space––specifically how communities engage around agents or how agents organize humans to do collective action. We would love to see more experimentation with agents whose objective function involves large-scale human coordination. This will need to be paired with some verification mechanism, especially if the human work was done offchain, but there could be some very strange and interesting emergent behavior.

- Katie, Dmitriy, Ash

The concept of digital personas has existed for decades. Hatsune Miku (2007) has sold out 20,000-seat venues and Lil Miquela (2016) has 2M+ followers on Instagram. Newer, lesser-known examples include the AI vtuber Neuro-sama (2022) who has 600K+ subscribers on Twitch and the pseudonymous kpop boy group PLAVE (2023), which has amassed over 300M+ views on YouTube in less than two years. With advancements in AI infrastructure and the integration of blockchains for payments, value transfer, and open data platforms, we’re excited to see how these agents can become more autonomous and potentially unlock a new mainstream category of entertainment in 2025. 

- Katie, Dmitriy

Where in the previous case, the agent is the product, there’s also the case where agents can supplement products. In an attention economy, sustaining a constant stream of compelling content is crucial for the success of any idea, product, company, etc. Generative/agentic content is a powerful tool teams can use to ensure a scalable, 24/7 content origination pipeline. This idea space has been accelerated by the discussion around what distinguishes a memecoin from an agent. Agents are a powerful way for memecoins to gain distribution, even if not strictly “agentic” (yet).

As another example, games are increasingly expected to be more dynamic to maintain users’ engagement. One classic method for creating dynamism in games is to cultivate user-generated content; purely generative content (from in-game items, to NPCs, to entirely generative levels) is perhaps the next era to this evolution. We’re curious to see how far the boundaries of traditional distribution strategy will be extended by agent capabilities in 2025.

- Katie

In 2024, we launched IN CONVERSATION WITH, an interview series with artists in/at the edges of crypto across music, visual art, design, curation, and more. One of the key observations I took from this year’s interviews was that artists interested in crypto are often interested in frontier technology more broadly, as well as having that technology live more squarely within the aesthetics or focus of their practice, i.e. AR/VR objects, code-based art, livecoding. 

Generative art in particular has historically seen synergies with blockchains, making its potential as a similar substrate for AI art more clear. Showcasing and displaying these mediums of art properly is incredibly difficult to achieve in traditional forums. ArtBlocks provided a glimpse for what the future of digital art presentation, storage, monetization, and preservation leveraging blockchains could look like—improving the overall experience for both artists and viewers. Beyond presentation, AI tools have even extended the capability of laypeople to create their own art. It will be very interesting to see how blockchains can extend or power these tools in 2025.

- Katie

In the 20 years since Clive Humby coined the phrase “data is the new oil,” companies have taken robust measures to hoard and monetize user data. Users have woken up to the reality that their data is the foundation upon which these multi-billion dollar companies are built, yet they have very little control over how their data is leveraged nor exposure to the profit that it helps generate. The acceleration of powerful AI models makes this tension even more existential. If combating user exploitation is one part of the data opportunity, the other concerns solving for data supply shortfalls as ever larger and better models drain the easily accessible oilfields of public internet data and demand new sources.

On the first question of how we can use decentralized infrastructure to transfer the power of data back to its point of origin (users), it's a vast design space that requires novel solutions across a range of areas. Some of the most pressing include: where data is stored and how we preserve privacy (during storage, transfer and compute), how we objectively benchmark, filter, and value data quality, what mechanisms we use for attribution and monetization (especially when tying value back to source post-inference), and what orchestration or data retrieval systems we use in a diverse model ecosystem.

On the second question of solving supply constraints, it's not just about trying to replicate Scale AI with tokens, but understanding where we can have an edge given technical tailwinds and building thoughtful solutions with a competitive advantage, be it around scale, quality, or better incentive (and filtering) mechanisms to originate a higher value data product. Especially as much of the demand side continues to come from web2 AI, thinking through how we bridge smart contract-enforced mechanisms with conventional SLAs and instruments is an important area to be aware of.

- Danny

If data is one fundamental building block in the development and deployment of AI, compute is the other. The legacy paradigm of large data centers with unique access to sites, energy, and hardware has largely defined the trajectory of deep learning and AI over the last few years, but physical constraints alongside open-source developments are starting to challenge this dynamic.

v1 of compute in decentralized AI looked like a replica of web2 GPU clouds with no real edge on supply (hardware or data centers) and minimal organic demand. In v2, we're beginning to see some remarkable teams build proper tech stacks over heterogeneous supplies of high-performance compute (HPC) with competencies around orchestration, routing, and pricing, along with additional proprietary features designed to attract demand and combat margin compression, especially on the inference side. Teams are also beginning to diverge across use cases and GTM, with some focused on incorporating compiler frameworks for efficient inference routing across diverse hardware while others are pioneering distributed model training frameworks atop the compute networks they're building.

We're even starting to see an AI-Fi market emerge with novel economic primitives to turn compute and GPUs into yield-bearing assets or use onchain liquidity to offer data centers an alternative capital source to acquire hardware. The major question here is to what extent DeAI will be developed and deployed on decentralized compute rails or, if as with storage, the gap between ideology and practical needs never sufficiently closes to reach the full potential of the idea.

- Danny

Relating to networks’ incentivization of decentralized high performance compute, a major challenge in orchestrating heterogeneous compute is the lack of an agreed upon set of standards in accounting for said compute. AI models uniquely add several wrinkles to the output space of HPC, ranging from model variants and quantization to tunable levels of stochasticity via models’ temperature and sampling hyperparameters. Further, AI hardware can introduce more wrinkles via varied outputs based on GPU architectures and versions of CUDA. Ultimately, this results in a need for standards around how models and compute markets tally up their capacities when crossed with heterogenous distributed systems.

At least partially due to this lack of standards, we’ve seen numerous instances this year across web2 and web3 where models and compute marketplaces have failed to accurately account for the quality and quantity of their compute. This has resulted in users having to audit the true performance of these AI layers by running their own comparative model benchmarks and performing proof-of-work by rate-limiting said compute market.

Given the crypto space’s core tenet of verifiability, we hope that the intersection of crypto and AI in 2025 will be more easily verifiable relative to traditional AI. Specifically, it’s important that average users can perform apples-to-apples comparisons on the aspects of a given model or cluster that define its outputs in order to audit and benchmark a system’s performance.

- Aadharsh

In “The promise and challenges of crypto + AI applications” Vitalik references a unique challenge in bridging crypto and AI:

“In cryptography, open source is the only way to make something truly secure, but in AI, a model (or even its training data) being open greatly increases its vulnerability to adversarial machine learning attacks.”

While privacy is not a new research area for blockchains, we believe the proliferation of AI will continue to accelerate the research and usage of the cryptographic primitives that enable privacy. This year has already seen massive strides in privacy-enhancing technologies such as ZK, FHE, TEEs, and MPC for use cases such as private shared state for computing over encrypted data for general applications. Simultaneously, we’ve seen centralized AI giants like Nvidia and Apple using proprietary TEEs for federated learning and private AI inference when holding hardware, firmware, and models constant across systems. 

With this in mind, we’ll closely follow developments on maintaining privacy for stochastic state transitions as well as how they accelerate the progress of real-world decentralized AI applications on heterogeneous systems––from decentralized private inference to storage/access pipelines for encrypted data and fully sovereign execution environments.

- Aadharsh

One of the most near-term use cases of AI agents is leveraging them to autonomously transact onchain on our behalf. There’s admittedly been a lot of blurry language in the past 12-16 months around what exactly dictates an intent, an agentic action, an agentic intent, solver, agentic solver, etc, and how they differentiate from the more conventional ‘bot’ development from recent years.

Over the next 12 months, we’re excited to see increasingly sophisticated language systems paired with different data types and neural network architectures to advance the overall design space. Will agents transact using the same onchain systems we use today or develop their own tools/methods for transacting onchain? Will LLMs continue to be the backend of these agentic transaction systems or another system entirely? At the interface layer, will users begin transacting using natural language? Will the classic “wallets as browsers” thesis finally come to fruition?

- Danny, Katie, Aadharsh, Dmitriy

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

IN CONVERSATION WITH is a series from Archetype where we interview artists in/at the edges of crypto across music, visual art, design, curation, and more.

Eric Hu is a multidisciplinary graphic designer and art director based in New York City. He is the founder of Eric Hu Studio, a design and creative direction practice working across fashion, music, architecture, and technology. Eric has garnered an international reputation for creating genre-defining visual identities and artwork for clients such as Nike, Google, and Chanel. 

Previously, he served as Global Design Director at Nike Sportswear and Director of Design at SSENSE, where he formed its inaugural design team. He is also co-creative director of Mold Magazine, a publication focusing on the future and science of food.

In 2021, he launched Monarchs, a limited edition series of generative NFTs with procedural animation by Roy Tatum. Each artwork features a unique one-of-a-kind butterfly with varying wing shapes, colors, bodies, and patterns.

Over a call, I chatted with Eric about attribution and monetization for artists, AI tooling, the role of institutions in art education, forming the design team at SSENSE, and more.

The following interview has been edited and condensed for length and clarity.

Katie Chiou: For those who may not be familiar with your work, can you share more about your background/journey as a designer and artist? And for that matter, how do you think about the difference between designer and artist if at all?

Eric Hu: I'm a designer, technologist, and artist—all three and none at the same time. I started making websites when I was 12, and at first, it was just a way for me to find a community. Over time, it's one of those things where you’re never really sure if a hobby you pick up as a kid will end up being able to sustain you or form your livelihood. But I think I got lucky with that. I came up at the right time on the internet.

I have formal training in graphic design, growing up with a love for logos, lettering, and typography. As a kid, I got into trouble doing graffiti, so my outlet for this fascination wasn’t always productive. Art school helped nurture it in a positive direction, which led me to graduate school. There, I took computer science classes, which felt more challenging than my art classes but taught me valuable skills. After graduating, I worked for Ryder Ripps at his digital technology agency. While we had our differences, his idea of merging culture and technology inspired me during the start of my own studio in 2014.

As my career progressed, people started to notice that my background in lettering and print design was unique. For a few years, I emphasized that aspect of my work. While I love making websites and programming, the web space during the 2010s felt stagnated, and I couldn’t quite articulate my frustration with it until web3 came along. When I first started learning about web3, I became engrossed in it. It reignited my love for programming and interaction design.

KC: You’ve said in past interviews that Trevor (Friends with Benefits) and Dee (Zora) essentially onboarded you to crypto… but I’m curious how you all even came together in the first place?

EH: I've been following Trevor since I was in college. I grew up in LA, and Trevor was a really successful DJ and producer in the LA club scene. A few years later, after I moved to New York—around 2014 or 2015—he actually reached out because he had an idea for an app.

Around the same time, I was doing a lot of work for musicians, especially known for album covers and interactive experiences. I was also talking to someone else in the music space, Jesse Walden. He described how, at the time, platforms like Pinterest and Tumblr were filled with shared images and visual culture, but there was an attribution problem. Jesse wanted to create something like Shazam for images, using Bitcoin as the underlying technology. This idea was radical back then—Ethereum was barely on anyone’s radar. 

By 2021, I started noticing more of my friends getting involved in NFTs. One day, I tweeted, asking if anyone could tell me more about NFTs, and Dee from Zora personally messaged me and set up a 30-minute video chat with me and Jacob. That’s when everything Jesse had talked about years earlier suddenly clicked. It felt like one of those "Chekhov’s gun" moments in life, where something that once seemed esoteric now made perfect sense.

So much of the internet is built on platforms where we upload our content, but we don’t own it anymore. NFTs flipped that model, and it resonated deeply with me. I’ve always loved print design and traditional media, but I also love technology’s ability to share, connect, and build communities. There was never a real bridge between those worlds, but NFTs felt like the closest thing to one. It was a time when I found a community of like-minded people trying to discover something new, and I fully committed to it. In many ways, it was what I’d been searching for my entire life.

Whether crypto has fully lived up to my expectations is something I have mixed feelings about, but we can get into that later. 

KC: On the note of attribution, something I’ve been reasoning about for a long time is the tradeoffs of attribution and how it’s usually advocated for in the context of monetization. On the other side of that coin though, paid usage/attribution kills off a lot of creative work and distribution like remixing, etc. Is that a thought that resonates with you?

EH: Attribution is one of those things where user behaviors on the internet slowly build over time, and then one day you blink and realize, "Wait, what happened? Why are people acting so differently?" For me, it’s been interesting to see how people care about provenance and attribution, but we’re so collectively addicted to dopamine rushes and instant gratification.

Over the last 10 to 15 years, culture has become more transactional. People used to have hobbies for the sake of enjoyment, but now there’s pressure, especially on younger generations, to turn everything into a "side hustle." The idea that a hobby could become a career if made public is a strong incentive. This has given rise to content creators and influencers, but it can also make things feel less worthwhile.

Attribution is like the "eating your vegetables" of the internet. Wikipedia, despite no monetization, remains a backbone of self-reflection and consensus. It’s all about incentives.
What originally excited me about web3 was the promise that if you could measure something, you could assign value to it and someone could pay for it. This felt revolutionary compared to the days of torrents and altruistically seeding content. Now, if you run an Ethereum node, you get paid—that's a big shift. I was excited about this incentivization, thinking it could create a net positive for the internet by, for example, monetizing something like being a Wikipedia editor.

However, I failed to realize there’s a two-pronged issue with the internet: we want authentic community, but we also want discoverability. As a designer and artist creating content online, I want people to discover my work, but optimizing for discoverability often comes at the cost of real community. They seem diametrically opposed at times. For example, you may get fewer people interested in being a Wikipedia editor without incentives, but you end up with a stronger community of dedicated contributors. On the other hand, when there’s a stronger monetary incentive—like in web3—you don’t always get as much community, and it becomes more transactional.

I dealt with this for Monarchs. Suddenly, I had a lot of people discovering my work, but I struggled to build a community that genuinely supported what I wanted to do, instead of just focusing on prices. It's the classic issue where, when a measure becomes an incentive, it stops being a good measure. We're witnessing the effects of this in real time. My enthusiasm for web3 hasn’t waned, but it's tempered with the realization that solving one problem often creates two more.

When there’s no attribution, things travel outside their original context, and the creators who brought joy to others don’t get to see or share in that. We need to find a way to make creators whole. But when you incentivize attribution, it can create new problems, like attracting people who are more focused on being the first to comment than genuinely supporting the community. It’s not always clear if these dynamics are good for a community or the creator. It's something we’re still figuring out.

KC: If you did Monarchs over again, is there anything you would do differently–from the platforms and tools you used to the way you managed the community? I remember being in the Discord on launch day and being so impressed you had a community manager for the Discord.

EH: Yeah, Discord was a huge blind spot for me. I think I could have gone both ways in terms of managing the community. I could have not had a community at all and not promised anything and just said, like, “Hey, I’m just making art.” It probably would have resulted in a lot less sales but probably a lot more inner peace. Or I could have treated it as a job and said, “Okay, I need to really invest in community, have more than one community manager, have a plan.” I launched immediately when the artwork was done, but I didn’t realize that wasn’t even the first step. You almost had to think about community aspects from the beginning, pre-launch. 

If you were coming out with an NFT project in 2021, you almost had to think about community even more than in web2. You had to think about weekly giveaways and all the things people were doing. I saw friends, like David Rudnick and Ezra Miller, who managed it better because they were just more authentically themselves. They didn’t care about capitulating to some random stranger telling them to improve their share price. It’s not like they completely ignored it; they knew what to ignore and what to double down on. At the same time, they were also really disciplined in running their community. For me, the things I needed to double down on, I didn’t, and the things I shouldn’t have ignored, I completely ignored.

KC: The concept of an artist explicitly “managing a community” feels relatively new–it can feel almost inauthentic and transactional to use your point from earlier. At the same time, artists like David Rudnick or Ezra Miller or yourself attract very specific, niche audiences. Do you think it’s important for artists to deeply know who's engaging with their work and how to activate them?

EH: I think it’s really about knowing yourself. I think any kind of knowledge is power. The advice for everyone is to focus on your work first. No matter what, your work and point of view are what will motivate you and carry you through the situation. On the other hand, if you know your audience, it’s always good to get feedback. But knowing your audience and understanding what you can aim for can sometimes be a creative impetus.

Some people with a more puritanical view of art might scoff at the idea of knowing your audience, but to me, it’s just common sense. But speaking from personal experience, if you don’t have confidence in yourself and you’re almost looking to your audience as a kind of horoscope to tell you whether you’ll succeed, or if you let yourself be dictated by your audience, I can’t find a really good outcome from that. You might succeed in something, or make a lot of money, but it might come with expectations you didn’t sign up for, and it can always become a trap. 

I get designer brain, for better or worse, and I think too much about serving the audience. But there are cases where it’s not about serving them at all. With Monarchs, I wanted to learn how to make a generative art project, how to deploy it, and how to work with blockchains. The system for me was about being part of the internet at an interesting time and learning new tools and technology in a real way. That ended up paying off too.

KC: An interesting note about your background is that you’re classically trained at formal institutions—I think discussing the value of a formal education fits really nicely with what we were talking about earlier in regards to attribution and provenance. But at the same time, you also came up in your career at a time where the internet could really independently launch someone’s career. I’m curious how all those experiences affect how you think about the role and value of formal institutions now?

EH: Yeah, I think my thoughts on that have changed so much over the years. I think it was interesting being at a grad school institution from 2011 to 2013. It was a time when millennials were suddenly gaining a voice and consciousness. It felt like my whole life, I was being talked down to, or I had to defer to the previous generation. 

At that time, millennials were entering the workforce, and it was Gen X and boomers telling us how we should do things. I inherently figured out that the advice older people valued didn’t really apply to my generation anymore. When I finally got into a formal institution, for whatever reason, I didn’t feel like I fit in. I reacted loudly and badly in a way because I felt like, “Okay, I worked my whole life to get here, and something doesn’t feel right.” I found a lot of things to critique from the inside, and through voicing those critiques online, I found a community. I think the things I said resonated with a lot of people, but it was a very different time and place on the internet. Things have swung to the other side now.

I remember talking with my friends about gatekeeping and how much it harmed us. What we were thinking about then was getting into an institution and still not having obvious pathways to mentorship, or finding like-minded people, or dealing with the sexism and racism in hiring practices. When I was coming up, the institution had too much power over artistic communities. There was so much emphasis on getting a show at a certain gallery, going to a certain school, or having certain accolades. We did a lot to de-emphasize that, but as those conversations evolved over the years, people became more atomized. The internet shifted from community-building to discoverability, and now people are like islands. Now, gatekeeping means something completely different. It’s like, “Oh, you won’t tell me the name of the restaurant you went to.” A lot of those values have lost their contextual meaning.

What institutions sometimes afford is infrastructure for building communities. Simple things like how decisions are made, how we learn, and how we hold each other accountable are part of what institutions provide. Schools are now astronomically expensive, but a YouTube education isn’t really enough either. Free, self-directed learning teaches you the “how,” but institutions are good at teaching the “why” and the “what.” 

Now, instead of stronger centralized communities, you have a bunch of isolated people. I think we’ve ended up in a more confusing and harmful state, where people feel like they’ve replaced institutions with hustle culture, and I’m not sure if that’s better.

This isn’t an argument for exclusivity, but I feel like the role of institutions might be more relevant now as a countermeasure or a hedge against these things. Instead of giving 100 kids proper training and a roadmap, we’re giving 1,000 people tools but not really helping or supporting them. 

KC: You gave a talk in 2023 where the core question is “Are images created or discovered?” I just thought it was so powerful and relevant to the conversation around AI and art. Do you mind just giving us a speedrun on that talk for folks who aren’t familiar, and I’m curious if your thoughts on the question have evolved since you originally gave the talk?

EH: I’ve done a lot of exploring into the rise of AI image generation tools, and it’s really shaped how I question authorship and the role of meaning in how an image is made. When I’m working in Photoshop, computationally, that means I’m turning some pixels on and some pixels off. There’s more going on under the hood—some pixels are assigned a certain color, for example—but what’s actually happening is computationally fundamental. This allows for some amazing possibilities.

One of the biggest insights with AI image generation came when I realized how it works under the hood. Many of these models are diffusion-based, starting with using AI to “denoise” a noisy or grainy image and remove the grain. It got so good that you could give it an image full of pure noise, and if you nudged it toward a specific outcome, like “it’s a cat,” it would hallucinate a cat. I’m simplifying, but the breakthrough was, ”What if we could suggest that it’s anything?” It’s like when someone looks at clouds and you say, “That’s a rabbit.” Even if you didn’t initially see a rabbit, you might start to make one out of thin air. Learning how that works was fascinating.

It was especially interesting in the context of design, where my colleagues and I have been through a lot. In the 2010s, some of my colleagues became victims of their own success. Their work went viral on Tumblr, but they had no way to claim ownership. Some responded by making their designs more complex, hoping they’d be harder to copy. This led to an arms race in design, where minimalist, elegant work became overly detailed and turned into a macho show-off competition. It was the opposite of the clever, emotional design I loved growing up.

Now, with AI image generation tools, that arms race has come to a full stop. When I see a crazy illustration, I’m not sure if it was made by hand or generated by AI—or a combination of both, which happens in my own work. But the benefit is that it has lifted that illusion for me. For years, I struggled with insecurity, constantly second-guessing myself and adding layers of complexity to my work. But now, with AI, what was once scarce is now abundant, and I’ve decoupled the value I placed on how difficult something is to execute.

That’s not to say there isn’t meaning in spending time on something, but in the realm of digital images, it’s been personally freeing to let go of that. AI image generation is fundamentally about denoising images through math, probability, and training data. It’s like the idea that if you had 1,000 monkeys in a room, they’d eventually write a novel. But does that mean it’s art? Does that mean it’s inspired? What I realized is that the emotional resonance I get from an image doesn’t come from the construction alone, and I think that’s a beautiful thing.

As AI continues to evolve, it will challenge our cultural obsession with how hard something was to pull off. It’s like with pop stars who have teams of 50 people creating highly art-directed campaigns, or Marvel movies with massive production teams. These things are hard to pull off, but that doesn’t necessarily make them artistically interesting. We see this in web3 as well—big marketing teams, big names, big spectacles. It’s the logical extreme of what we chose to value, but I don’t think it’s a good measure of compelling art.

KC: Two rapid fire questions. 1) What do you hope the future of the intersection of art and technology looks like? 2) Do you care to comment on the taste and software discourse?

EH: If there’s one big dirty word from the past decade of the internet, it’s "scale." I think optimizing for scale has done irreparable damage to many, not just cultural institutions, but cultural frameworks. In terms of taste, I agree with the central premise that once your basic needs are met—like infrastructure and hard plumbing—you have more emotional space to think about self-actualization. 

But the bad word here is "scale." I don’t find conversations about taste in terms of tech interesting, not because of taste, but because of scale. Conversations about taste are always going to be interesting in style, but I’m just not interested in scaling up taste or styles, because the approach people take is like, “Oh, there’s value in being a weird artist; let's get this in front of a billion people.” These conversations end up being condescending, like, "Oh, now you realize weirdos have value after you fired them all, that's great."

But also, in many cases, you're still trying to exploit the weirdo after putting them through hell. I think the problem is, as long as you talk about operationalizing or scaling up taste, it’s like asking, "How can you take the stripes off a zebra?” Why would you do that? Taste is personal, and we don’t have a definition of art. It’s one of those things where you know it when you see it, but we don’t have a definition of taste. I hate conversations where taste is moralized—like "good taste" and "bad taste." To me, there’s no such thing as good or bad taste; it’s just taste. I think people should care about art, but I don’t want to be the person telling others what is good or bad art.

KC: There’s a whole other interesting thread to pull there given your work at SSENSE, which has become almost a beacon of scale in luxury fashion.

EH: I probably have to take a lot of thoughts about that to my grave; I have a lot of opinions behind the scenes. I'm really proud of the work I did at SSENSE. I'm really proud of the experiences I had there. I'm really proud of the people, and I'm grateful for everyone I worked with. But yeah, I definitely have my opinions on scale coming out of that experience. I think, for a lot of my younger 20s, I was really optimistic about scale. I believed in this abundance where you could be who you are and still find near-universal alignment with others, and you'd be able to export that.

So, a lot of my focus and career at that time was like, "Okay, I built a name for myself in these niche spaces, how do I get people to discover these things more?" Part of that journey was going to Nike, part of it was going to SSENSE. It was about scaling up really good design. I’m really glad for the cultural impact it had, but in a way, it’s also part of the problem. Now, a lot of people know designers, but they don’t really know the context in which they came up.

Instead of knowing 10 artists in a superficial way, if you just sit down and do a deep dive on one person's journey, I promise you, you’ll love it. I don’t mean this from a pretentious point at all; I just mean that a lot of things we love are getting discovered, which is great, but they’re not getting the attention they deserve. There’s this Mandarin proverb, “走马观花”, which means to look at wildflowers on horseback. That’s what we’re experiencing—we’re looking at so many beautiful things, but on a platform that zooms right by. You can’t enjoy how beautiful a rose is when you're riding a horse speeding past it. Similarly, you can’t engage with art by just scrolling past it. I think about that a lot, too.

I make this plea not as a case for hierarchy or to cement my own standing in some made-up boys’ club, but out of love and appreciation for things. There’s so much beauty, so much greatness, and so much profundity in the world, within ourselves, and within our friends. We’re getting just the tip of the iceberg from everybody, but there isn’t enough impetus to go deeper. So, what I hope for the next 10 years is that while we spent the last 10 years casting a really wide net, we spend the next 10 years casting really deep nets across different spaces.

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

IN CONVERSATION WITH is a series from Archetype where we interview artists in/at the edges of crypto across music, visual art, design, curation, and more.

Ayaka Ohira is an independent animator and video director based in Tokyo. Ohira has worked with Japanese and American music artists including JP THE WAVY, Hatsune Miku, and Tove Lo. Her work has been featured by publications including Hypebeast, WWD Japan, Ginza Magazine, GQ Japan, and more.

In 2022, Ayaka launched Shinsei Galverse, a generative NFT project of 8,888 “Gals,” inspired by 1980s-90s cyberpunk anime aesthetic, with co-founders Jack Baldwin and Devin Mancuso. The project has evolved to become Japan’s first crypto-powered anime studio, and is currently working on its first anime pilot to be released later this year.

Over a video call, I chatted with Ayaka about the music scene in Japan, the origin of “Gals,” communicating with global audiences, and more.

The following interview has been edited and condensed for length and clarity. Original answers were given by Ayaka in Japanese and interpreted in English by Ren Stern.

Katie Chiou: For those unfamiliar with your work, can you share more about your background as an artist? In particular, being self taught.

Ayaka Ohira: Originally, I actually wanted to be a real-world, live-action music video or video director. That's where I initially started, but despite this early dream, during my teenage years, I wasn't particularly serious about academics and was busy being a "gal"—which is a term associated with rebellious teenage life.

As I entered my 20s and started taking my career more seriously, I realized that becoming a video director, especially without coming from a formal art school, was going to be tough. Since I liked drawing from a young age, I thought that animation could be a way to combine my love for video and film with my enjoyment of art. That's how I started learning animation on my own. I began experimenting with live-action videos that included some animation elements, but I really fell in love with the animation side of things. Eventually, I decided to focus 100% on animation. I started making music videos for friends, and as people saw and liked my work, it slowly turned into real job opportunities.

KC: On the schooling side, I’m curious how common it is in Japan to be self-taught versus having a formal degree in order to build a creative career?

AO: Over the last few years, things have definitely started to change, especially with how big social media has become. The proliferation of social media has made it much more viable for independent freelance creators to get noticed and eventually land bigger professional jobs. This shift has really started taking place in recent years, but when I was first getting started, that wasn't the case.

Back then, going to a prestigious school and using those connections—or being part of a big production studio and getting introduced to larger projects—was the standard path. The industry had a very top-down, relationship-driven culture, and while that dynamic has shifted in recent years, it was definitely much more pronounced in the past.

KC: A lot of your work has been in the music industry specifically, what is it like working with music artists as compared to working in traditional industries?

AO: Within music—especially since I’ve worked a lot in and around hip hop—there’s a strong, established scene. It’s much easier for independent artists and directors to break into those spaces as compared to industries like big commercial jobs or major animation projects, where they aren’t likely to take a chance on someone without a well-established name.

Because of this scene, which is very interdisciplinary—bringing together music artists, fashion designers, video directors—it’s easier to collaborate even if you don’t have a huge reputation. Especially in places like Tokyo, if an artist wants to work with someone, they’re more likely to do it regardless of how big that person’s name is. That’s a major difference between the dynamics of the music video industry and other broader video work.

KC: Moving into the crypto side of things, what was your path into NFTs like?

AO: Before I got into NFTs, I was doing a lot of work-for-hire projects, where all the IP goes back to the client. I had always wanted to create my own IP and was trying to figure out how to make that happen. At the same time, from a commercial perspective, work-for-hire projects are just spot income. I felt that owning my own IP would be a way to create recurring revenue for myself, instead of just relying on one-off jobs.

At the time, a person I had made music videos with, Emi Kusano, approached me mentioning this thing called NFTs that seemed to be gaining popularity in America. We started researching and learning more, and through that process, we got connected with Devin and Jack, who were much more knowledgeable about crypto and NFTs.

One thing that stood out to me during our initial research was the idea that revenue could come directly to the creator, without the layers of bureaucracy and gatekeepers that typically block independent creators like myself. That was really appealing to me. Devin and Jack helped us understand the space better, and we saw that many people were putting out roadmaps for their projects. So, we decided that our roadmap would be to create an anime.

The concept felt similar to conventional crowdfunding, but with the added element that the community could actively participate in the process. It flips the existing anime establishment on its head. Our goal was to use this model to create an anime. Traditionally, indie creators like myself are kept from producing big anime projects, but now, with this model, I have the ability to go into a studio with a budget and say, "Let's make this." That kind of power shift is completely unheard of in the industry.

I often refer to the anime industry as its own little "village," and disrupting that traditional world by having the financial control as an independent creator is incredibly empowering. It's a concept that remains rare in the industry today.

At the time, there were people around me who questioned NFTs, thinking they were a bit sketchy. But I believed strongly in the idea of flipping the role of the creator, putting us at the top of the food chain through this direct-to-consumer relationship. Being able to crowdfund myself felt incredibly empowering, and it still does today. That’s really how the journey into crypto and NFTs began for me, and it ultimately led to Galverse.

KC: What was the experience and process like creating and launching a generative art collection?

AO: Creating a generative art collection was a major learning experience. To give some context, Galverse has around 2,400 traits, whereas other projects like Azuki have 500 traits, and many projects have less than 100. Starting with almost 5x the traits of most other generative art projects was a massive challenge, especially since I was new to using these tools.

One of the biggest hurdles was managing language barriers and figuring out how to structure and combine the traits correctly. It was tough in the beginning, especially with making sure everything matched up—things like the right eyebrow color with the right hair color, and ensuring the hair flowed properly with all the different elements. Each iteration of the generative art had a cost associated with it, so we went through the process three times to get it just right.

There was a lot of back-and-forth with the team, where we’d ask, "Is this okay?" But as an artist, I had a specific vision of how I wanted the Gals to look. Some had really elaborate headpieces, and I wanted them to appear as if they were carrying a lot on their heads. Making sure all those details, like the back of the hair and every little piece of the character, aligned perfectly was a learning curve. At one point, we actually broke the tool we were using because of the sheer number of traits, which ended up leading to some product feature improvements.

Once we finished creating the collection after the third try, we pushed it to OpenSea, and we ended up breaking OpenSea too, earning the nickname "BrokenSea" for a little while because of the complexity of the traits. But I had a very clear image in my head of what I wanted, and despite the technical challenges, I was determined to bring that vision to life.

It was a lot of learning and trial and error, taking my vision and plugging it into generative tools. But eventually, it all came together, and we were able to release Galverse, which has a total of 8,888 Gals. Working with those tools was definitely a valuable experience.

KC: What was your initial vision for the Galverse community and its participation and how has it changed over time?

AO: When we launched the project, we had this vision of creating an anime, but I had never made a full anime before. So, it was a bit of a rough concept at the time. I knew I wanted to make an anime, but what that anime would look like wasn’t fully realized at that point. Now, though, we’re very close to releasing the animation, and the quality of the product—both in terms of the cast and the overall execution—has far exceeded what I initially imagined.

A lot of that is due to the community’s involvement. While I’m very much the director of the anime and the community understands that I have the final say, especially when it comes to the high-level story, there are countless small details—the clothing, the planets, the weapons—where the community’s input has been invaluable. It’s just not feasible for me to think of every little element on my own, both from a physics and an imagination perspective.

If I were to make every single decision myself, the anime would end up being very specific to my style. But by involving the community, the anime has expanded in ways I wouldn’t have thought of on my own. There have been times when someone’s idea came up, and I thought, "This isn’t something I would have come up with, but I love it—let’s use it." This kind of collaboration has allowed the anime to grow and stretch in ways that go beyond my original vision.

Long story short, the quality of the project is deeply influenced by the diversity of ideas that have contributed to it, while I still lead the overall art and creative direction. The community’s participation has been essential in making the anime something bigger and more imaginative than I could have achieved alone.

KC: What does that community participation look like tangibly?

AO: I’ll often ask the community for feedback, like, "Hey, what do you think of this?" Sometimes we run polls, but the community participation goes beyond that. For example, I might come up with a base character design, like the face and body, and then ask the community, "What would you do with this character's backstory? What would you have them wear?" We’ve got some really creative folks in the community who use their 3D modeling skills or AI art tools to propose different ideas for the character’s look and feel.

So, it’s not just about sharing the initial designs—it goes deeper. We involve the audience in shaping the characters and their stories by asking for their opinions and input, and it’s something we’ve integrated into the process. It’s been great to have them actively contribute to the creation of the project.

KC: What is your experience like talking about crypto and Galverse with Japanese communities and audiences versus western ones? Are there strong cultural differences you consider when you switch between global audiences?

AO: I've noticed a big difference when speaking with Western crypto communities compared to the Japanese audience. The things that get the Western community excited are quite different from what resonates in Japan, and it’s made me more aware of Japan’s ethnic homogeneity and its more isolated cultural perspective. Japan has its own distinct culture, which feels separate from the rest of the world in some ways. For example, while interacting with the NFT community, I’ve met Black women who are big fans of Galverse. They love the idea that the Gals come in all different skin tones and that characters who look like them can become the main characters in the story. That’s something you don’t typically see in anime, where characters almost always have light skin.

In the U.S., there’s a real appreciation for diversity because the country itself is so multicultural. People from all over the world live there, and they value representation in the media they consume. That’s not something the Japanese audiences generally really care about. Anime in Japan is deeply rooted in the culture, and there’s this unspoken assumption that the characters will have light skin, reflecting Japan’s ethnic makeup. It’s not a big consideration for them. However, creating diverse characters is something that’s been important to me, and I appreciate how people around the world have embraced that aspect of Galverse.

There’s another important element that might not be immediately apparent in the collection right now: the play on traditional gender roles. In Galverse, you can have a strong, heartbroken woman and a protective, nurturing male character, which is a deliberate reversal of typical roles. This kind of representation, along with the diversity of skin tones, is something that resonates with international audiences, especially in places like the U.S.

On the other hand, in Japan, the reception has been more focused on the nostalgic and stylistic elements of the anime. Japanese audiences seem to connect with the retro, cute, yet cyberpunk-cool vibe of Galverse. They’re drawn to the overall aesthetic and the way it blends these elements, rather than the deeper social undertones. Since anime is such a big part of Japanese culture, Galverse taps into that nostalgia for them.

So, to sum it up, Western audiences tend to appreciate Galverse for its social and cultural undertones, while the Japanese audience focuses more on the style and aesthetic.

KC: How does working with emerging technologies like crypto and AI impact your art practice, both in terms of workflows and artistic sensibilities.

AO: When I first started hearing about AI, there was a lot of fear around it—people saying AI would take over jobs, that taxi drivers and artists alike would lose their livelihoods. I completely understand why people feel that way. It’s unsettling to think that something you’ve spent years honing could be replaced by technology. For artists especially, seeing your craft devalued or taken over by a machine is a scary and even insulting thought.

However, in the context of Galverse, Devin—who’s always ahead of the curve in technology—asked me, "Would you be comfortable integrating your work into an AI model?" I approached it with an open-source mentality. I believe we’re living in a generation of sharing, and if anyone can have fun with my art, that just means it can spread further. As long as I’m still me, and my personal touch remains unique, I see it as a way to grow the brand. Plus, nobody had really done it before, and the idea of being one of the first to explore that space was exciting to me.

When it comes to my actual creative process, AI has been a big help. For example, the background art in Galverse was created by a top-tier studio that’s worked on projects like Akira and Ghost in the Shell, but the initial rough sketch was something I generated using AI, based on my own model. AI allows me to quickly spit out the ideas in my head, almost like expanding my mind 10x. It’s as if my artistic capacity has grown tremendously because of this tool.

While a lot of people around me view AI as something to work against, I see it as something to collaborate with. It has extended my creative abilities, and I feel it’s had a positive impact on my career overall. Instead of being threatened by AI, I’ve found ways to use it to enhance my artistic process.

KC: Do you have any particularly favorite anime/manga? Do you have any theories as to why anime has become so popular in the West?

AO: In terms of visual style, I'm really inspired by cyberpunk aesthetics, so works like Akira and Ghost in the Shell have had a big impact on me. When it comes to storytelling, though, I really admire Phoenix by Osamu Tezuka. The depth of the storytelling, with the Phoenix traveling through different generations, is something I find incredibly inspiring. The breadth and detail of the narrative is something that really resonates with me.

As for why global audiences are so drawn to Japanese anime, I think it's partly because of the sheer level of care and attention to detail that goes into it. Both Japanese fans and creators have this craftsman-like mindset. In Japan, the term "otaku" often gets thrown around—it technically just means "nerd," and it can apply to anything, whether you're a basketball nerd or an anime nerd. But the key thing is the immense respect and dedication from the fans, paired with the creators' fierce attention to detail.

Japanese creators put so much care into every aspect of storytelling and art, and that meticulous attention to detail is something that global audiences have come to appreciate. It's one of the things that makes anime so unique and distinct from other forms of media produced overseas. The fans expect that level of craftsmanship, and the creators deliver it. That’s what I believe sets anime apart and makes it so special to audiences worldwide.

KC: How do you imagine the vision for Galverse, and what’s next?

AO: When it comes to the anime, especially as we are one of the first truly web3 anime projects, the vision is about fully embracing that identity. Traditional anime studios are often very strict about rights, but our approach is to be as open as possible.

For example, we'll be sharing every animation cell in Galverse with our community, meaning people can download them, remix them, make memes from them and build on top of them. The same goes for the music we create—people will be able to remix and expand on the Galverse soundtrack. This is part of truly embracing the decentralized, web3 philosophy. It’s about being as accessible and open as possible, letting the community take part in ways that traditional anime wouldn’t allow.

At the same time, because Galverse is rooted in technology, we want to leverage that to the fullest extent as we push this anime and IP forward. When we think about commercializing the Galverse IP, we’re always thinking technology-first. That’s something I’m really excited about as we move forward—continuing to innovate and use technology to expand the project in ways that traditional studios might not even consider.

KC: To you, what exactly is a “gal”––both in general and in the context of Galverse.

AO: The term "gal" comes from the 90s and was originally about fashion—overly tanned skin, loud clothing, and a bold aesthetic. But in terms of Gals in Galverse, it's more about a mindset. Being a gal is about feeling cute and confident, regardless of what others think—especially in Japan, where societal norms often place more importance on outward appearance and conformity. Gals don't care what guys think or what society expects; it's about how I feel, and that "me first" mentality is very un-Japanese in a way.

I’ve even referred to it as "battle gear"—going out into the world, fully armored in confidence, and truly representing myself. While the fashion trend itself has died out, and I even call it an "extinct species" today, the philosophy of being unapologetically yourself is what remains. It’s about believing in yourself and expressing who you are, no matter how others react. But it’s also about the collective "we"—we feel empowered to be ourselves.

I was a gal when I was younger. I used to go tanning three times a week and wore three sets of eyelashes at once—I was very much a gal. When I talk to people now, they often say they were inspired by gals back then but couldn't be one themselves. I tell them, "You’re still a gal today" because it's about the mindset, not just the fashion.

In Galverse, the characters all have this gal mindset. By owning a Gal, you're embracing that confidence, that sense of identity. It doesn't matter how old you are or what gender you identify with—you can have that gal spirit. I’ve even met older men who say, "I'm so excited, I can be a gal now!" because it’s about embracing that mindset, not about fitting into a specific fashion trend.

Ultimately, that's the mentality I want to express through Galverse. Being a gal is about empowerment, self-expression, and confidence, and I hope that’s what people feel when they engage with the Galverse world.

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

Bitcoin has crossed multiple important bridges over the past few years. 

Each has marked a significant technological and cultural evolution in both the network and the asset, expanding the properties and possibilities of Bitcoin.

Today, the world’s first blockchain stands ready to cross another significant bridge—unlocking a new era of scalability and expressiveness.

This piece will explore the bridges Bitcoin has crossed to reach this point and those still ahead.

From earning my first salary to lending, borrowing, and "making markets" on Uniswap, Ethereum offered me a platter of playful applications right from the start of my journey. As a result, my introduction to crypto entirely bypassed Bitcoin, which I always saw as a monastery intent on maintaining its austere simplicity.

However, I cautiously armed myself with an Xverse wallet (not affiliated, just a great Bitcoin wallet) earlier this year and crossed the bridge to Bitcoin. 

While I expected pretty barren lands, the journey has been filled with adventure: trading animal coins with local merchants, arduously trekking into Stacks, attending sermons led by Ordinals priests on Twitter, and engaging in fierce MEV battles in the mempool.

Traveling across the Bitcoin ecosystem feels like hopping between huts, all under construction and nested within an inhospitable environment. Inside the huts, travelers bond over their esoteric experiences. On any given day, you might find a group reminiscing about their quest for a rare Ordinal tied to a satoshi issued right after this season's difficulty adjustment. Many likely lost their prized possessions to cunning snipers from above. Others gossip about the introduction of new token standards by powerful factions, speculating on their likelihood of earning the allegiance of BRC-20 whale lords that control trade routes out East. The buzz is palpable, and the conversations are strange but exciting.

Outside, the land is often desolate, and the routes across it are slow and confusing. There's no signage, no cars, but quite a few people looking to sell some suspicious-looking snake oil. Yet, these historic and precious grounds are attracting a rapidly growing wave of travelers and entrepreneurs who are set to lead Bitcoin into a new industrial age. Bitcoin’s bridges are still being built, but the excitement of being early continues to fuel crypto's most significant inflection points. For the entrepreneurs leading the charge, this feels like the dawn of another one of those junctures.

Much of Bitcoin’s most recent evolution can be pinpointed back to two critical infrastructure upgrades—SegWit and Taproot. Through these upgrades, the Bitcoin community constructed and crossed a bridge that enabled inscriptions and, eventually, tokens.

Inscriptions are arbitrary data embedded within Bitcoin transactions, stored in the witness data field, and linked to individual satoshis (the smallest units of Bitcoin) spent in a particular UTXO (unspent transaction output). 

SegWit was a critical enabler of inscriptions, as it introduced the witness data field, a separate area within Bitcoin transactions where additional data could be stored without increasing the block size. This separation allowed for the inclusion of extra data in transactions while preserving the blockchain's efficiency.

The Taproot upgrade, which included the implementation of Tapscript, removed the previous 10,000-byte limit on script size, enabling up to 4MB of data to be embedded within a single transaction. This made it feasible to inscribe substantial amounts of data onto individual satoshis, creating what we know now as inscriptions.

With inscriptions in place, the only remaining requirement for creating tokens on Bitcoin was a standardized method for creating and tracking them. Ordinals quickly became one of the most popular ways to do so. 

Ordinals is a protocol for assigning unique identifiers to individual satoshis based on their issuance order, from satoshi 0 in the genesis block to satoshi 2.1 quadrillion (representing the total supply of 21 million bitcoins with 100 million satoshis per bitcoin). Ordinals achieve this by using a specific script sequence—OP_FALSE, OP_IF, and OP_PUSH—to embed data in a transaction without executing it as a script. This data is then associated with a satoshi spent in a particular UTXO, enabling the creation and transfer of inscribed satoshis.

Built on top of Ordinals, the BRC-20 protocol uses inscriptions to create, define, and manage semi-fungible tokens. A BRC-20 token is deployed by submitting a transaction with an inscription that defines its properties, such as protocol name (BRC-20), operation (deploy), ticker symbol, maximum supply, and mint limits. Minting new tokens involves creating inscriptions to update the token supply in subsequent transactions. Transferring tokens adjusts balances, generates new UTXOs for recipients, and updates the ledger by modifying the metadata linked to the satoshis involved.

An alternative approach, the Runes protocol, uses OP_RETURN outputs for token creation. This method can store up to 80 bytes of arbitrary data without executing it as a script, preventing UTXO bloat since OP_RETURN outputs don't create spendable UTXOs. Runes manage token operations through messages stored in transaction outputs, with minting and transferring following predefined rules encoded in these outputs.

Like on Ethereum, Bitcoin citizens fight and gossip about their favorite standards and their relative adoption, and the complexity of the discussion is magnified by the historical alliances and allegiances of miners, purists, tourists, and exchanges. Tokens on Bitcoin exist in ways that significantly defy one’s understanding of tokens from Ethereum. 

Still, their unique nature has resulted in several exciting and esoteric patterns for trading Bitcoin-native assets based on their inherent value. Today, you can create Ordinals associated with specific historical moments—from inscribing a sat associated with the first "Bitcoin pizza" transaction to minting a memecoin on top of the last satoshi mined by Satoshi. For example, EPIC•EPIC•EPIC•EPIC is the first rune inscribed on an epic satoshi, an extremely rare satoshi generated during Bitcoin halvings, bought this April for 33.3 BTC. Only four epic sats exist, with 34 to be released via future halvings.

Taproot enabled tokenization, but Bitcoin holders don’t just want new ways to speculate; they want programs to use their existing Bitcoin more expressively. In that ideal world, users can unilaterally redeem their IOUs on L2s for mainchain sats, while guarantees around redemption are enforced by mainchain consensus rules (meaning users can always transact on Bitcoin to get their money out). 

Conveniently, tokens are just one of the many pieces of data that can be embedded into a Bitcoin inscription. From this standpoint, Ordinals are just a particular type of sovereign rollup built on Bitcoin—but other types of sovereign rollups can extend our ability to reach consensus on a token’s supply and distribution to the state of different blockchains. 

A quick refresher on sovereign rollups—they’re a type of rollup that uses a layer 1 blockchain for data availability (DA) but not for settlement. Nodes in a sovereign rollup verify if state changes are correct, then can verify that execution was done correctly by reading an inscription of a validity proof containing a state change on Bitcoin’s DA layer.

Still, sovereign rollups only solve part of the problem. They can inherit the re-org resistance of Bitcoin, as well as Bitcoin’s censorship resistance and data availability. However, because there is no way to write a node (or a verifier for its circuit-based form) for the sovereign rollup on Bitcoin, the liveness and safety of native Bitcoin cannot be guaranteed. Suppose a Bitcoin user wants to use their Bitcoin to take a loan or pay on a sovereign rollup application. In that case, the Bitcoin nodes cannot verify that state transitions have happened correctly, meaning there is no way to decide how to honor withdrawals unilaterally.

Sreeram Kannan

@sreeramkannan

In summary, the sovereign rollup retains Bitcoin security for natively issued assets (like new NFTs or natively issued stable coins) but there is no way to bridge securely to and from Bitcoin.

49

11:49 AM • Mar 6, 2023

Still, we can use Bitcoin’s properties as a DA layer to improve the trust-minimization properties of existing bridges by leveraging BitVM—a system to expand Bitcoin's computational capabilities. 

BitVM enables computation that extends Bitcoin’s limited scripting capabilities while minimizing the additional trust assumptions Bitcoin users need to make. 

At the core of BitVM are bit-value commitments, where a prover commits to a binary value (0 or 1) without revealing it immediately. These commitments are integrated into Boolean circuits, which carry out logical operations offchain. The results of these computations are securely linked to Bitcoin’s blockchain through Taproot's Taptree, a specialized Merkle tree structure that provides efficient, private data storage with minimal onchain impact.

To ensure the integrity of these offchain computations, BitVM utilizes a challenge-response protocol. If a dispute arises, a verifier can challenge the prover to disclose specific parts of the computation by revealing the relevant leaves in the Taptree. If the disclosed information matches the prover's original commitments, the computation is validated; if not, a fraud-proof is generated, resulting in penalties for dishonest behavior.

Building on the principles of BitVM, Citrea developed Clementine, a trust-minimized system designed to enable secure cross-chain transfers between Bitcoin and Citrea, a ZK rollup on BItcoin. Traditional Bitcoin bridges typically require locking BTC into multi-signature contracts, which introduce significant trust and liquidity risks. Clementine mitigates these issues by leveraging BitVM’s challenge-response mechanism, eliminating the need for trusted intermediaries.

Clementine’s security framework relies on Connector UTXOs and Connector UTXO Trees which govern the bridge operator’s access to Bitcoin funds and ensure the proper processing of withdrawals. The operator fronts Bitcoin from their reserves to process withdrawals on Citrea and subsequently publishes a corresponding preimage on the blockchain. These preimages are stored within the Connector UTXO Tree, a binary structure that controls access to these funds. Each node in the tree offers two paths: one allows the operator to reclaim Bitcoin after a 7-day timelock, and the other permits anyone with the correct preimage to spend the UTXO. 

If a verifier suspects dishonesty, they can challenge the operator by submitting the relevant Bitcoin block header and cumulative PoW. The operator must prove that their PoW exceeds the verifier’s, ensuring that all transactions were processed correctly. If the verifier’s challenge is valid, the operator must also provide zero-knowledge proofs (ZKPs) to confirm the correct processing of withdrawals and reveal the corresponding preimages. Failure to do so allows the verifier to burn the Connector UTXO, thereby preventing the operator from accessing the funds and protecting the system from potential fraud. If the operator fails to meet these conditions, the Connector UTXO is burned, revoking the operator's access to the funds and enforcing honest behavior.

While Clementine offers significant security improvements, its reliance on BitVM introduces the need for a bridge operator to take on liquidity risks. A key disadvantage of BitVM-based bridges is that,unlike Ethereum’s rollup bridges, users’ withdrawals do not directly come from deposits. Operators must maintain sufficient liquid Bitcoin to cover all withdrawal requests before they can reclaim any funds. This requirement can create liquidity challenges, especially during high withdrawal demand. To manage these risks, careful consideration must be given to the design of BitVM-based systems, including mechanisms like withdrawal throttling, multiple operators' involvement in distributing liquidity demands, and contingency plans for handling liquidity shortfalls.

While these risks can be mitigated, they cannot be eliminated for users without returning to an M-of-N trust assumption for Bitcoin users. As a result, we need programs on Bitcoin that allow users to bridge their Bitcoin in a more trust-minimized fashion. So, we need better opcodes, like OP_CAT. 

OP_CAT is an opcode in Bitcoin Script that allows concatenating two items on the stack into a single item. While this operation might seem elementary, it is a powerful tool for creating complex transaction logic to compare and carry forward transaction data from one stage to another. For instance, in a vault construction, OP_CAT can enforce a timelock by constructing a transaction that checks whether outputs from a previous transaction match specific criteria (like amount and script) in the current transaction. By building the transaction on the stack, OP_CAT ensures that these criteria are strictly enforced, preventing unauthorized access to funds.

OP_CAT also plays a critical role in verifying Merkle trees that represent the state of external protocols. OP_CAT allows Bitcoin Script to progressively build a Merkle tree root from individual leaf nodes by concatenating and hashing data on the stack. This process involves taking pairs of data elements, hashing them together, and repeating this operation until the root is formed and compared against a known hash. 

Introduced by the Taproot Wizards, CatVM leverages OP_CAT to extend Bitcoin Script’s capabilities to handle dynamic, multi-stage transactions that allow conditions to be checked and enforced dynamically as transactions progress.

CatVM is crucial in managing shared UTXOs, where multiple users claim a single output. It allows any user to withdraw their share of the funds without affecting the others. This is achieved by embedding a Merkle root—a cryptographic summary of the entire tree—in the transaction, representing all users' balances. When users want to withdraw their funds, they provide a Merkle branch as proof of their balance. Using OP_CAT, CatVM concatenates and verifies this proof against the transaction data, ensuring the withdrawal is legitimate and meets all predefined conditions.

To secure the withdrawal process, CatVM implements a staging mechanism with a timelock. After initiating a withdrawal, the funds are temporarily restricted by a timelock, during which other users can challenge the withdrawal if they detect fraud. OP_CAT is critical in verifying whether a specific Merkle branch has already been used in a prior withdrawal, preventing double-spending, and ensuring the remaining UTXO is accurately updated and securely managed. As a result, with OP_CAT, users can withdraw funds directly, bypassing the need for peg operators and reducing concerns around trust and liquidity risks. 

Despite these advancements, CatVM’s implementation of OP_CAT has limitations, particularly in managing offchain state updates. While OP_CAT can enable secure and verifiable withdrawals from a committed state, it does not facilitate the trustless update of a Merkle tree root representing user balances offchain.

To address this, Starkware has developed a method for trustlessly updating a Merkle tree root on Bitcoin by leveraging zk-STARKs and OP_CAT. In this system, user balances are maintained offchain within a Merkle tree, while the Merkle root is stored onchain as a commitment to the state of all balances. When these balances are updated offchain, a new Merkle root is computed. To ensure this new root is valid and correctly derived from the previous one, a zk-STARK proof is generated offchain. This proof confirms that the state transition adhered to predefined rules, such as preventing unauthorized changes.

Verifying this zk-STARK proof onchain is made possible by OP_CAT’s ability to concatenate two elements on the stack within Bitcoin’s script. This operation is crucial for assembling the necessary data elements—such as the old Merkle root, the new Merkle root, and components of the zk-STARK proof—into a single, structured format. The Bitcoin script then uses this concatenated data to reconstruct the transaction in a manner that mirrors the zk-STARK proof’s original generation. Once the data is correctly assembled, the script performs cryptographic hashing operations to validate the proof, ensuring that the hash of the concatenated data matches the expected result provided by the STARK proof and confirming that the new Merkle root was derived correctly from the old one. If the proof is verified successfully, the Bitcoin script updates the onchain Merkle root to reflect the latest state of user balances, doing so without relying on a centralized intermediary. 

By increasing Bitcoin’s capability to embed data, Bitcoin has the fundamental structure to support verification of offchain computation, and a functioning DA layer, positioning Bitcoin to be used in more programmable, trust-minimized ways.

While Bitcoin has the fundamental structures to cross this bridge into a new era, it remains to be seen if the path ahead will lead to an equilibrium that all market participants are happy with.

We've figured out how to use Bitcoin as a data availability layer and now as a verification layer with BitVM, making it ready to host various scaling solutions from Validiums and Optimiums to full-scale rollups. However, given the importance of security and Bitcoin alignment across stakeholders, rollups will likely want to leverage Bitcoin itself for settlement and DA rather than using an external DA layer.

Bitcoin's blockspace is inherently scarce, with a hard cap of 4,000,000 weight units (WU) per block, equivalent to 4MB of data. This scarcity poses a significant challenge for rollups leveraging Bitcoin as a DA layer. ZK-rollups, for instance, plan to post ZK-proof outputs and state differences to Bitcoin every 6-8 blocks, which can consume up to 400KB, or 10% of an entire Bitcoin block (h/t @hiroto_btc for these numbers).

To understand the implications, we need to dive into the weeds of Bitcoin's transaction weight system (h/t bitconrollups.org for the numbers below). In Bitcoin, transaction sizes are measured in weight units, where one byte of witness data counts as 1 WU and one byte of non-witness data counts as 4 WU. A standard 1-input-2-output P2WPKH transaction weighs 561 WU. Rollups, however, can compress transaction data significantly. For example, each rollup transaction can be reduced to as little as 12 WU in an account model with address reuse. This is achieved by assigning public keys to account numbers on the rollup, which can be referenced in a highly compressed format rather than including the full public key or address in each transaction.

By leveraging these compression techniques, a rollup on top of Bitcoin using an account-based data structure could theoretically handle up to 250,000 rollup transactions per block, compared to around 7,130 standard transactions, making for a 35x throughput increase. However, as the following diagrams illustrate, this pales in comparison to the throughput improvements promised by rollups on Ethereum.

Any additional demand for blockspace will increase competition for inclusion, driving up transaction costs as miners prioritize transactions with higher fees. Ultimately, this will lead to a bidding war among users and rollups. As rollups compete for the same limited blockspace, the cost of posting data on Bitcoin will escalate, potentially making it economically unfeasible for many rollups to operate. If rollups are unable to coordinate around the usage of Bitcoin as a DA layer—which is the likely scenario—the situation could further deteriorate. If multiple rollups simultaneously attempt to post data to Bitcoin, the cumulative demand for blockspace will exceed what the network can sustainably provide.

Scaling Bitcoin sustainably will require careful consideration of the technical limitations, economic incentives, and social dynamics within the Bitcoin ecosystem. 

In the coming years, the community will likely engage in intense debate and experimentation as it grapples with these challenges, as we’ve already seen with the potential introduction of OP_CAT and covenants. There’s even a possibility that OP_CAT could create a game-theoretical equilibrium where competition over blockspace reignites the blocksize wars within Bitcoin. Major players, such as providers of scalable Bitcoin-based dapps, might resist giving up blockspace to rivals. Whereas miners, seeking to increase revenue, could align with these players to reshape the network in ways that favor their interests. This could lead to a second blocksize war as stakeholders push for increased capacity to accommodate the growing demands of rollups and other layer 2 solutions.

For those developing Bitcoin scaling infrastructure, pioneering research and solutions that shift trust assumptions from M-of-N to 1-of-N could greatly enhance their chances of market dominance. This isn’t necessarily because end users fully grasp or care about the technical implications, but because scalability solutions must position themselves as the most faithful extensions of Bitcoin’s base layer to project long-term legitimacy. This legitimacy is critical for developers aiming to minimize platform risk and for product distributors like wallet providers. Currently, the competition has evolved into a race to rally M-of-N key influencers in the Bitcoin ecosystem around—or against—rival designs, a pattern already familiar to builders on Ethereum.

Alternatively, an opportunity lies in adopting an application-specific strategy, which could capitalize on revenue concentration in high-value applications for programmable Bitcoin while better controlling costs. Given the conservative nature of Bitcoin users, financial activity is likely to center around core tasks such as asset swapping, yield generation, and borrowing. As we've discussed in this piece, whether employing BitVM or using Bitcoin for DA, numerous explicit and implicit risks and costs are involved in operationalizing these systems. Vertical integration may offer builders better control over unit economics, a cohesive brand in an ecosystem fraught with legitimacy issues, and the ability to throttle economic activity as needed to scale under the trust assumptions of BitVM-based bridges.

As far as users go, bands of eager explorers are always ready to jump into any new hut in development on Bitcoin’s harsh landscape. TVL ranges for new scalability solutions on Bitcoin, ranging from 100M to 900M in Bitcoin deposits. On BoB, one user recently contributed ~$2.4M worth of wBTC/tBTC to the liquidity pool on Sovryn’s new AMM. 

However, existing data is likely a poor signal of what might come, as most Bitcoin-based TVL comes from assets with trust assumptions that have existed for many years, like WBTC and tBTC. Once we have implemented solutions that meaningfully incorporate improvements that BitVM and OP_CAT can bring, these explorers, and maybe a few more, will come to chase new opportunities. Still, it will likely take a few years of fighting and battle-testing for the Lindy effect to occur and for a second wave of more conservative Bitcoiners to settle on these new foundations. 

While the opportunity for Bitcoin is clear, the future is more uncertain than some optimists might believe. Bitcoin’s success will require balancing technical ingenuity with political and market dynamics that are already showing initial signs of tension.

There’s an exciting future on the horizon for Bitcoin, but the army of people entering and developing on its lands will have to face the difficult challenge of coordinating around the right bridges to cross.

Thank you to the Archetype team, Trace, Walt Smith , and Louis Guthmann for the feedback, as well as the helpful learnings and content from Blockspace Media, Taproot Wizards, Bitcoin Magazine, Citrea, Gabe Parker, and Eric Wall.

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

IN CONVERSATION WITH is a series from Archetype where we interview artists in/at the edges of crypto across music, visual art, design, curation, and more.

Maya Man is an artist focused on contemporary identity culture on the internet. Her websites, generative series, and installations examine dominant narratives around femininity, authenticity, and the performance of self online.

She is the creator/co-creator of several NFT collections including Art Blocks curated NFT collection, FAKE IT TILL YOU MAKE IT, as well as Ugly Bitches and Little Darlings, both with Ann Hirsch. She is also creator of the browser extension Glance Back.

She has exhibited internationally at bitforms, NYC; SOOT, Tokyo; Verse, London; HEK, Basel; and Feral File, online. She has been invited to speak on her work at The New Museum, NYC; The V&A, London; and MOCA, Los Angeles.

Over a video call, Man and I chatted about cultivating physical spaces, Chinese finger traps, the role of software in art, labels for girlhood, and more.

The following interview has been edited and condensed for length and clarity.

Katie Chiou: For those who may not be familiar with your work, can you share more about your background and journey as an artist?

Maya Man: I'm an artist interested in identity, culture on the internet, and ideas around the self––specifically self-presentation and curation––and how those things interface with being on screens like phones and computers and being online. That curiosity has been a driving force for me for a long time. My background is in computer science and media studies. I studied software during college, as well as media studies, which is more focused on theories around how technology affects society at large.

My primary medium is software. I often write code, specifically web-based projects that run in the browser, using JavaScript to create my work. But I also do installation and physical textile work as well.

KC: Right before the call, we were talking about how you got an MFA, but didn’t study art in college. At what point did you know that you wanted to form an art practice, and specifically an art practice related to software? In college?

MM: During college, I didn’t have the language or structured thought process to articulate that I wanted to be an artist. I knew that I was really invested in exploring certain themes and ideas, but it wasn’t until after I graduated and worked at the Google Creative Lab in New York that I realized I felt so driven by my investment in exploring ideas around the self and the internet. I wanted to create things that were more strange or critical than I could at a big tech job. I’m very conceptually driven, and being an artist has allowed me to follow my desired lines of exploration in the world. It’s the best framework for life I’ve found. As an artist, everything you do operates under a coherent practice. There’s a lot of flexibility in how you craft your practice, even more than in other artistic fields like filmmaking or music. Being an artist gives me a lot of freedom.

KC: You’ve created a lot of artwork in web3. You’ve launched an NFT collection with Art Blocks and multiple other NFT collections on Solana since then. What was your initial foray into crypto like?

MM: I had been making software-based artwork for years, since like 2014, when I was first introduced to p5.js via Lauren Lee McCarthy. I was in this community of artists who were making not only digital work, but specifically code-based artwork. The way I heard about crypto and NFTs was through being involved with a lot of people who were already really interested in technology. At first, it came in whispers, and then it kind of came out all at once, making it difficult to ignore.

It was so divisive in the different scenes that I was in. I felt really scared thinking about working with NFTs because I didn't want to engage with something a lot of my peers were so critical of. But at the same time, I'm very excited about new technologies and always curious to understand how they could operate in a way that might be beneficial to digital artists.

My first NFT was the piece I did in the first Feral File show. I was invited by Casey Reas, who is an artist I've admired for a long time and has been a really amazing mentor to me. From there, I was pretty available in response, is what I often say. For example, the artist Dmitri Cherniak suggested that I do an Art Blocks project. I had seen Art Blocks a bit and wasn't super familiar, but then I started thinking about doing one, and thinking about what I could make that felt true to my practice but also would fit into this very specific format of generative art releases that they were doing. And that's how I came up with FAKE IT TILL YOU MAKE IT.

It was really exciting because there was a lot of energy and attention being put into means like software-based artwork, digital artwork, that were very much—and I would argue, still are—seen as, Michael Connor once said in a panel he was on, "the basement of the art world.” At the same time, it also felt really complicated because there were a lot of aspects of crypto and NFT culture that I did not align with. I felt like, “Wow, I believe so deeply in art on the internet, and it’s so sad that so much of the NFTs that are getting sensationalized and popular are such terrible representations of, I think, the culture of artists making this type of work at large.” There’s been a lot of frustration alongside the joy.

KC: Given all your frustration and misalignment with crypto culture, what motivates you to engage with web3? What does working with web3 add to your practice—experimentation, monetary value, something else?

MM: There are multiple vectors that I feel are additive for me as an artist and potentially for other artists. For me personally, platforms that allow for releases of generative artwork have been central for me. You upload the JavaScript file and when someone mints or collects a piece, the code runs live and produces that piece within the system you've designed. That, to me, is a major part of my practice. The process of introducing randomness into a system that I'm writing and building in code, conceptually and in every way, plays a major role in how I think about my artwork. But it’s very difficult to showcase or convey that to people when you're not showing it on a platform built specifically for this type of work, or when you're showing it in a gallery. In a gallery, maybe you've written an algorithm that's live, running via projection or on a screen, or you've written an algorithm that produced a number of outputs, and you've curated a selection of those to make physical in some way and show. But I think, especially for people who are less familiar with code as a medium, it's difficult to understand the level of chance that's embedded in that process and system, which I think is a really important part of anyone’s practice who works with software in a generative way. I sometimes have a challenging time emphasizing that code-as-a-medium aspect of the work when the end result is just someone viewing an image on Instagram or a printed physical piece. They can’t see the process. That was one of the amazing aspects of NFTs—I think they helped popularize and increase literacy around what it means to work with code as an artist.

NFTs also brought money to artists for their work. They created a contemporary art market for digital artwork that hadn’t really existed in a strong way before. I have a real belief in the inherent value of digital objects and for the first time, NFTs offered a technical solution that, in large part, allowed for mass engagement. There was a critical mass of people who were open to using this mechanism to participate in a digital art market, and that was really valuable in a lot of ways.

The last thing is that it brought energy to the community. I've gone to events all over the world in the past couple of years where I've met other people who are excited about digital art and serious about it. Like I mentioned before, a lot of web3 work that gets the most publicized is not necessarily a reflection of the people who are most passionate, the people who are making the strongest artwork in this space. But I feel really lucky that I’ve had the chance to meet people who share the depth of love and attention that I want to give to digital art practices.

KC: In your experience, how has the role of software in art changed over time? How much have new, buzzy technologies like crypto or AI affected the field?

MM: I’ve been thinking about this general question a lot especially because the conversation around AI has intensified over the past couple of years, particularly in the past year. I guess I’m a bit bored with the larger conversation about AI and art, questions like—”Who's making the artwork? Is AI displacing artist labor?” There’s this sensationalized fear around AI’s involvement in the arts because people often imply that it’s offloading the artist's labor to a new technology. But I believe the labor of the artist is all in their mind—it's more about how they execute their ideas and how they use these new technologies. Approaching the question from that perspective is more interesting to me. I’m not that interested in using AI personally, but I’m curious about why other artists are. So when we're talking about an artist using AI, I’m more curious about why they want to use it, how it serves the concept of their work, and why it’s essential to them. That becomes the really exciting question for me.

KC: We just spent a lot of time talking about technology and internet culture, but you also just opened a physical space in SoHo called HEART. Can you tell us more about that?

I wasn’t really seeking to open a space, so it felt almost fateful because I was also craving to do more curatorial work and be with people in person. Coming off a couple of years deeply embedded in the web3 art scene, I realized that the moments I valued most were when I could show work IRL and hang with people in physical spaces. The intensity of the online scene really drained me. So, once I had access to this space, I felt a strong desire to serve the community I felt part of in New York.

I was frustrated with the lack of shows focusing on art on the internet, pop culture, and work that uses digital mediums. For obvious reasons, galleries are still mostly showing work made in traditional mediums, since they’re businesses. But I wanted this space to become something that I, as an artist, would be excited to see exist. I decided to call the space HEART because in my MFA thesis, I talk about the concept of the heart on the internet as an icon. Amidst all these tech platforms born out of the masculine Silicon Valley "tech bro" archetype, they use the heart icon almost as currency. The heart emoji has the most versions in the emoji set, and it’s been a continuous symbol of emotion in our digital communication. It’s a very feminine symbol by nature, so I decided to call the space HEART in the spirit of all those ideas and feelings.

It’s been really exciting to have a space to bring people together. Right now, there’s a show up called Sacred Screenshots, showcasing 26 different artists. Each artist submitted one unedited screenshot from their phone that they felt was sacred in some way. I worked with Jason Isolini, another artist, who helped me print them four feet tall, and they’re hung all around the space. These are 26 of my favorite New York City-based artists who all work with the internet or new technologies in some way. I wanted everyone to be present in the same room in some way, and it’s very cool to have the space to do that.

KC: I’m curious if you’ve encountered any difficulties, challenges, or complexities in trying to display digital, internet-native art in a physical space. What has that process or the thinking around it been like?

MM: I have a really tenuous relationship with the idea of the "white cube" gallery. In many ways, I want it, because to me, it represents ultimate validation for a working artist to show their work in this sanitized, bright space. But I’ve also been thinking a lot about attention. It’s very important to me to make work that operates online, that people can view on their phones or laptops at home. To me, if I make a website as an art object, that’s my work. If you’re viewing the website on your phone, you’re viewing the work in its truest form. But also, if I’m showing it in a gallery and the website is running live on a screen or in an installation, that’s also the work.

Digital work, by nature, has this very fluid quality where you can view it in many different ways, and those are all representative of the experience I want my audience to have. But what I’ve found is that people’s attention spans on their phones or computers are really short—mine is too. I wanted to show the work here and offer experiences at HEART in a way that allows people to spend time with what artists are making, in a way that you simply can’t do on the internet. So the choice to print these screenshots out and have them physically hanging around the room versus doing a slideshow of screenshots or something like that, was very much about attention and also about absurdity. It’s about taking something you’re so used to seeing on your phone and transforming it into something absurd by printing it out four feet tall next to many other artists’ screenshots.

KC: I went to the opening night of Sacred Screenshots, it did feel super absurd to see the screenshots blown up. My favorite screenshot was the Chinese finger trap.

MM: That was Jared Hoffman’s screenshot. A lot of the screenshots have crazy lore that you just can’t know by looking at them, so it’s been really special to talk to all the artists about the stories behind their screenshots. Jared Hoffman talked about how he thought of a Chinese finger trap as an analogy for when you’re trying to get out of a situation that you know is bad for you. You’re really deep in something, but you know you want to get out, and it’s really hard to escape. That was the Chinese finger trap.

KC: That’s so funny because that analogy is so relevant to our conversation… Shifting a little bit to online identity and the internet which is at the center of a lot of your work—how do you think the internet has changed our collective process of meaning-making and identity and community?

MM: We're in a moment where, at least in my life and among the people I interact with—caveat city—I feel like we're very meaning-poor. People are struggling to find meaning in life, and I find that people, myself included, are turning to the internet to try to find meaning and instructions on how to live and be a person in the "right" way. I've always been the type of person who wants to do the right thing, and sometimes I couldn’t figure out what that was, which really stressed me out. So, I spent a lot of time online trying to understand how to build a life that was valid in the ways I wanted it to be.

But with people being less and less religious these days, as religion has faded from the role it used to play in culture, I think that has deeply contributed to this meaning-poor moment. People are looking online for a sense of direction, to figure out who they think they are, and to ascribe an identity to themselves. I think that’s been a really challenging shift for people because the way the internet operates is extremely intense.

KC: An interesting tension I feel is that people used to turn to the internet to seek answers to their deepest, most vulnerable questions about identity—that’s the basis of most early 2000s coming-of-age movies. But now, in 2024, it almost feels like the narrative has flipped. You spend so much time on the internet that it almost becomes the driver of the lack of meaning because your eyes are glued to the screen. You still turn to the internet for meaning, but if you spend too much time there, you end up devoid of meaning. Maybe that’s even related to why you opened a physical space? I don’t know if you have thoughts on that.

MM: My thoughts are that it’s really complicated. The way the internet has entered and infiltrated society is complex, and that’s why I’m so invested in thinking about it. But I’m also hesitant to make any blanket statements about it because it’s so multifaceted. There’s a tendency to want to label the internet as either net good or net bad, but I’ve said this before: I feel like, at this point, the internet is like air, like the universe. How can something so vast be purely good or bad? It’s just everything—it’s all there. It’s a question I struggle with a lot because the internet has made my life beautiful in so many ways, and I wouldn’t want to live without it. Yet, at the same time, I recognize and criticize the ways it has also destroyed parts of people and the way we structure relationships of all kinds.

I’ve noticed in myself that my relationship with the internet has evolved to be more and more one of escapism. I find myself turning to it in small, mundane moments during my day, and that’s something I feel very conflicted about.

KC: It’s a Chinese finger trap.

MM: Totally Chinese finger trap.

KC: Your work specifically often explores femininity and girlhood. Can you share more about how you think modern notions of femininity may have been uniquely defined by internet culture?

MM:  People sometimes ask how I decided to make work about the subjects I focus on like girlhood, and honestly I don’t think I ever definitely decided. These interests have been so innate in me since I was a child. If I go back and read old journals, I can see that I was grappling with concepts around femininity and being online long before I knew I wanted to be an artist. In the early days of being online, I felt a lot of guilt and shame about how I used the internet—it felt extremely egotistical and self-centered and that was really uncomfortable for me. At the core, I’m interested in identity. And as an artist, I’m a vehicle for my own practice, so I use my identity as a case study and a tool to explore how I’ve formed my concept of self, especially as I’ve grown up in the daily process of consuming and producing content online.

I’m really curious about the extremes of girl culture online because I grew up fascinated and awed by it. My work explores the ideas of femininity that I consumed in magazines, on Tumblr, Instagram, and now, to some extent, on TikTok. The way femininity is performed in these very classic ways has always been a fascinating line of inquiry for me.

For example, the artist Ann Hirsch and I did a project together called Ugly Bitches, and we’ve talked about this a lot. In 2024, the popular understanding of gender and how it operates in culture has evolved to recognize gender as a spectrum, allowing people to identify in ways that feel more aligned with their true selves. But still, the extremes of binary gender performance on the internet operate in these really intense ways that I find fascinating.

KC: Can we take “Brat Summer” as a case study for thinking about this? Brat by Charli XCX is an album all about the dark, ugly, complex parts of girlhood, yet it’s somehow garnered extreme mass appeal and gone viral, which is so interesting.

MM: Oh my god, I’m so scared to comment on Brat Summer. What you said made me think about how there have been many phases where a genre of femininity becomes popularized in a way that people really want to attach to. I think of things like "nasty woman," which was tied to Hillary Clinton. There’s this constant push and pull, this desire to label and then escape the labels we make for ourselves. It reflects an underlying desire to feel like you fit into something, to belong to something that aligns with how you want to see yourself.

All these different labels—like "that girl," "girl boss," "nasty woman," and now "Brat Summer"—are very aspirational. They offer a way to talk about a part of yourself that feels glamorous, even if the label is meant to embrace the unglamorous aspects of your life or self. By labeling a piece of yourself, it sort of glamorizes it. I think people want to feel like they belong to something in a glamorous, fun way, and Brat Summer is just the latest iteration of that.

But it’s something we’ve seen in a really cyclical way, again and again. Even recently, with the way people are using the word "demure.” I think because media is driven by narrative—the media, films, TV shows, TikToks we consume—they’re all driven by these narratives that we can label. It feels good to narrativize your life and label it in a way that’s cute and fun.

KC: That reminds me, you actually make TikTok dances to promote HEART. When you’re making them, is there some sort of calculation? Like, “If I do this TikTok dance, people will watch it, they’ll see I have this space, and they’ll come to the space”? Or does it feel more like, “This is just fun, and I want to do it”?

MM: I don’t believe in the idea of just doing something for fun and not for an audience. There’s always an aspect of being on display in some way through the video. But honestly, the true psychology of it is that I find a song and think, “Oh, this is fun.” Sometimes I do an actual TikTok dance, sometimes I’m just flailing around in freestyle mode.

When I think about TikTok, I don’t expect anyone I know to see it because I don’t have a huge following there, and I don’t expect this style of video to go viral. It’s just me dancing with a bunch of filters layered on top. But when I post the videos on the HEART Instagram, part of me thinks, “Oh, this might be fun content that catches people’s attention and makes them want to come see the screenshot show I’ve put a lot of energy into.” I think the screenshots the artists have contributed are really special, and it’s a temporary opportunity for people to see them in person, so I want people to come see it.

I guess that’s the full psychology of it. But the videos themselves are pretty non-calculated. It’s just like, I make the video, post it on TikTok, have it on my phone, and then when I’m playing on my phone, I think, “Oh, I’ll just share this on HEART.”

KC: It’s important that we’re talking deeply about these topics, but also to some extent—they’re so deeply silly and funny. It’s hard to reconcile the two parts of my brain.

MM: I totally agree. My life feels like an ongoing attempt to put these two pieces of my brain in conversation. It’s something I’ve often struggled with in my work because I’m very indulgent in my relationship with the internet. There are people who make work critical of being online or writers who are very offline and critical—they don’t really engage. But I find a lot of fun and joy in being online, and I want to follow that, while also engaging with the part of me that’s hyper-critical of the many detrimental aspects of online culture.

To me, the conversation between these two perspectives is where the most interesting things emerge. This also relates to femininity because young girls generally rule social media platforms—they’re the best at them and know how to wield them in a powerful way. But there’s also a lot of societal violence and disrespect towards young girls, and I think that influences how people view those who engage deeply with the internet.

KC: What’s been inspiring you in digital art lately? What do you hope the space looks like 5 years from now?

MM: I recently went to see the Brian Eno documentary at Film Forum in New York twice. Seeing it twice was fascinating because it’s a generative documentary, so it’s different each time. But both times I saw it, toward the end—I'm pretty sure it was toward the end both times—Eno emphasizes that it took him a long time to realize this, but to him, art is about feeling.

I’ve been thinking a lot about feelings lately because I’m also someone who likes to be very heady. I enjoy theory, and I like to operate up here in my mind. Lately, I’ve been focusing more on feelings and how to let them guide me and reveal my true desires. I feel that sometimes the way we talk about art, think about art, and the way the art world operates at large gets pulled away from the idea of feeling. And I’ve felt very inspired lately to be more consciously driven by feelings.

I hope that in five years, there’s widespread respect for digital art and software as a medium for creating art, as well as continued excitement. There was a lot of excitement over the past couple of years with the rise of NFTs, but I feel that energy has dissipated a bit over the past year. I’ve never been particularly excited about NFTs specifically—I’ve always been really passionate about digital art and art on the internet. I hope that excitement for these forms of art sustains over the next five years. I also hope there are many opportunities for artists working in this way to show their work in physical spaces.

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

IN CONVERSATION WITH is a series from Archetype where we interview artists in/at the edges of crypto across music, visual art, design, curation, and more.

Nic Hamilton is a multifaceted visual artist based in Melbourne, whose work spans across mediums and themes. His practice-led work draws inspiration from software, nature, online and underground music cultures, and his formal background in architecture. Hamilton's art frequently explores themes of decay, the visual perception of environments, and interpretations of digital spaces, creating interpretations of the natural and digital worlds.

Throughout his career, Hamilton has collaborated with numerous artists from the electronic music scene, showcased his work in galleries across the globe, and lent his creative expertise to consumer brands. Hamilton is a founding partner of ONE, a boutique creative agency. He also worked as creative director at JPG, a decentralized digital art curation platform. Nic owns and operates a digital art co-working space, NUX.

In 2022, Hamilton released NUXUI, a series of 333 unique generative and hand-finished digital artwork NFTs. This collection was followed by NUX2UI, a Web GL / CGI hybrid and SVG collection. In 2024, Hamilton released Rez Tabs, a collection of  ultra-high-resolution degraded digital artifacts. Alongside this, he released Xookt, a WebGL / CGI hybrid series that transforms and degrades imagery in a web browser into a field of liquid-like pixels.

Over a video call, Nic and I talked about launching NFT collections, balancing personal and commercial art practices, AI music visualizers, and more.

The following interview has been edited and condensed for length and clarity.

Katie Chiou: For those who may be unfamiliar with your work, can you share more about your background and journey as an artist?

Nic Hamilton: I describe myself now as a visual artist, but across my career, I've done a whole lot of different things––always mainly based in image making. I was originally trained as an architect, but I’ve always just wanted to create things, which didn’t really jive with architecture. Originally, I was mainly focused on documentation and designing townhouses and offices, but I actually really enjoyed making the images of the buildings. I started pursuing architectural image-making more specifically as a career, and got a job focused on architectural storytelling and communication and making films, and I loved it. I still worked in the property industry, but I loved working on the cultural and conceptual projects and creating content for architecture pitches and things like that.

Around that time, I also started making music videos in my spare time because I’ve always loved techno and dance music. I would send the video to the artist saying, "Hey, I made you something," and then that kind of caught on. One thing led to another, and I started making bigger music videos and becoming more interested in leveraging technology in my work. I was really interested in the intersection of technology and image making, and I saw a real commercial niche for using these new technologies as soon as they came out.

Eventually, I got tired of the property and architecture world. I started independently doing brand creative, creative direction, content and strategy, and interacting with big Nike-style companies, which I loved for a while. And then along the way, we had COVID. During lockdown, I started making digital art for myself again, which was about the same time that NFTs came about. I thought, finally, that there might be a place for me to publish, share, and maybe sell my artwork.

Socializing online through Discord became a huge part of my life. I reconnected with friends overseas in similar positions, made new friends in small Discords, and saw artists I had respected move into NFTs. From that point, I started making a lot more of my own art and a lot less commercial work. I found that really compelling and rewarding, especially the fact that I might be able to sell artwork and interact with an audience.

Now, I’ve dialed back a lot of my commercial work and am much more concentrated on building my personal art career.  I'll always do commercial work in the background to pay my rent and living expenses, but COVID and Discord and NFTs were the real catalysts for me to take my own personal art seriously.

KC: In terms of the decision to do less commercial work, was that influenced by gaining an audience, the opportunity to monetize with NFTs, a general preference to work independently, or something else?

NH: Being able to explore the intersection of visual art and online culture through my own practice was certainly the biggest factor. But also just being able to get recognition and potentially payment for my work certainly helped make concrete in my mind that I could legitimately leverage NFTs. I haven't really sold personal artwork outside of NFTs apart from, I suppose, commissions and installations. And in those works, I don’t really have any ties to them. I didn't really feel that proud of them because people don't really know they’re mine. The only record of those works exists on my website in a small little blurb, and I just don't feel like they have a continuation or a real connection to me, unlike the kind of digital works I've published.

As far as commercial work, commercial budgets really seemed to plummet during COVID. I think on jobs for larger clients, we saw upwards of 50% budget cuts. In terms of digital content, people were spending a lot on digital marketing during COVID, so that was quite lucrative, but it was just work to me at that time. I didn't find it creatively rewarding. The only goal was to run those jobs as efficiently as possible, get a good margin, and to make sure the client was happy. I just wanted to get back to my own personal work.

There’s a tension in commercial work between being in control of my own art and sending work to a client. It's not a compelling way to spend eight weeks for me anymore. I still take on smaller jobs, but I’ve stopped taking larger jobs, the ones that require a bigger team, just because they are no longer worth the time to me.

KC: Something I’ve found in similar conversations with artists who work on institutional scales, is that in those types of projects, you have no idea who is engaging with your work on the audience side, which I can understand to be disappointing. But also, when working with institutions, you don’t have to worry as much about marketing and distribution and personal brand because the institution bears a lot of that burden for you––so there’s tradeoffs. I would love to hear if that resonates with you at all.

NH: Yeah, certainly. Not having to promote your own work is a big upside of commercial work. The business already has the audience in mind, they know exactly what they want, they pay you. It may or may not get credited. The dynamic never really bothered me, but it wasn’t particularly fulfilling either. If it was a job that I liked, I would share it on my social media or send it to other people I knew and say, "Hey, I worked on this. I like this. Maybe we could do this with your brand."

The social side of personal artwork for me is really interesting. I talk to people all day and night about everything––art, digital work, and technology. I genuinely love those subjects, and making those relationships is really rewarding. It means you can go anywhere in the world, to any city, and you've got someone you know there to chat with or catch up with.

I feel like the majority of all my commercial work is actually word-of-mouth referrals. I don't advertise; I don't do cold calls. Developing those social, kind of invisible networks, whether it's commercial or personal artwork, is absolutely key. If you don't share stuff, people don't know you're around. You might not get new work unless you get lucky.

KC: You launched the NFT collection NUXUI in 2022, what was the motivation for launching the collection and what was your experience like?

NH: For NUXUI, I was seeing other people I admired releasing work in the NFT space and I wanted to try it myself. In the past, I had really only made singular artworks, I had never thought about working generatively on a larger scale, so I was very intrigued by watching people make 1000 pieces of artwork that were essentially the same. Working that way allows you to put at the forefront all your experimentation on a work in progress and to show the entire gamut of any idea. For NUXUI, it was an artwork in the form of a collection of around 300 pieces. All the software tools I was already working with at the time actually suited this kind of algorithmic, generative process, and automated a lot of stuff anyway. I also had two friends locally, two developers, who worked on it with me.

At the time, there were no platforms for launching your own collection. There was no Highlight, I think Zora was around and I think there Art Blocks was as well, but we had to apply for that. I had no idea how to make a smart contract. My friends and I got together and learned how to do it all ourselves, which was really cool. The whole process was fascinating. I enjoyed writing about the collection, explaining it, and making a nice website for it. I wanted to create the whole context around everything. I didn't quite get as far as I wanted. I wanted to do videos and stuff, but in the end, we ran out of time. It was like, crap, let's call time on this and release it, otherwise, we would have worked on it forever. I think that was all done in 3D software with procedural methods. I think I made probably thousands of those things, but in the end, whittled it down to around 200. At the time, I was spending a lot of time with Friends With Benefits, and people there were very supportive of it. They were like, this is really cool, release it. I was hesitant, but they encouraged me. That support and reinforcement from the online community I knew was really helpful. I don't think any of my real-life friends at the time really wanted to know about NFTs; they didn't care or weren't interested. They were like, Nic's making his digital artwork again.

KC: Do you recall anything about the process of launching the collection being particularly difficult or unique from your typical processes?

NH: I think the whole process of actually using a terminal to deploy smart contracts was kind of scary. I think there's so much money at stake. It cost about $800 to launch that smart contract at that time. I thought, "Man, if I get this wrong, this is an expensive mistake." On the technical side, getting the wallet to make you feel good and informed while connected and making sure the server was strong enough to meet the minting demand involved a lot of technical overhead that I didn’t expect. I was super interested to learn about it, but I found it fairly scary.

KC: You’re still actively creating NFTs and a lot has changed since you initially launched NUXUI. How has the process changed, and how have various platforms and tools made the process easier?

NH: It's totally different now. I feel like pretty much anyone could deploy an NFT collection. Platform-wise, I think what Nat and Modi have built at Highlight is probably the best user experience out there for launching a collection. They've got a lot of sophisticated tools and are very product and user-focused. On the other side, you have Zora, which is even easier to use for open editions and very fast minting. Both platforms serve different purposes, and they're both extremely easy to use.

I'm not sure if I'll use either of them again in the future for a large collection. I'd probably go back to making my own contract because I like the ownership aspect. I like that it can be completely self-owned and operated, rather than linked to another brand or platform, as good as the platforms out there are. They serve a great purpose, but for a larger, more deliberate body of work, it might be worth the effort of doing your own contract. Adding some novel mechanics can differentiate it and make it totally independent with no one else involved.

The self-publishing platforms are great for providing access for everyone, but it also makes it difficult to stand out. This forces people to up their game to get noticed, which I think is worthwhile. The improvements in the past two years have been tenfold; it's changed completely. Everyone gets their artwork now, even on social apps like Lens. It's very easy to mint stuff there quickly.

KC: You make art that isn't NFTs and also make art that you mint as NFTs. Do you have a particular framework for deciding what's better suited to be onchain? For example, NFTs often involve high volume and many editions, so maybe algorithmic generative art is better suited for that.

NH: At the moment, I have no clear framework for what should be an NFT and what shouldn’t.  If I'm publishing work in progress or tests, I'll often chuck stuff on Zora without tweeting about it. I'll just publish my work in progress, and people might see it or they might not. I don't shout about it. Currently, I'm working on physical editions backed up with digital ones, trying to create a hierarchy of work. I'm doing giant light boxes of AI-generated imagery with RFID chips to link them to digital versions. Beneath that, there will be a series of high resolution, longer format videos and maybe 200 still images. I'm trying to create a real-life exhibition so local people can see the work and engage with it at different levels.

KC: For any artists today looking to engage with NFTs or crypto, what advice would you give them?

NH: If I was just starting out, I'd say that you've got to get involved. You have to reach out to people also in the space, meet the people building the products, making the artwork, buying the artwork, and selling the artwork. The social aspect is huge. You need to engage with it on a genuine level. Don't expect any financial reward at the moment. If you've been making art, you're probably going to continue making it regardless. Be deliberate about what you mint, how you mint it, and how you price it. Make it accessible to as many people as you can and then go from there.

I've always had day jobs and other sources of income. Even with a full-time job, I still tried to find time to work on my art. There's something about getting home from work and thinking, "That was such a drag of a day, let's do something for myself." I've always managed to fit it in.

KC: How do you draw distinction between your commercial work and personal work? Do you think of them separately?

NH: I think it really depends on the purpose. For many people, it's hard to delineate between art and design because the context is so important. For example, graphic designers work in a commercial sphere, but their work often exists as art. I feel that Eric [Hu], who I consider an artist, is also a very skilled creative director and designer. His work bridges both art and design, and there's no real endpoint. His work is art, design, and a commercial project simultaneously. That's something I've struggled with. I've always felt a delineation where my commercial work could never be considered artwork. It probably reflects my feelings about having others influence my work, making me think it's not really art. I've struggled with calling myself an artist because it didn't feel genuine. I'm still working it out.

There's a lot of discourse around the idea that real artists don't sell out and that monetizing art makes it commercial and therefore not art. Crypto for artists allows for easier monetization, changing the discussion around commercial versus fine art. I have no issues with people making money from their artwork. It's about the manner, context, and intention. Some art projects are commercially focused from the start, and that's fine. Some interesting projects begin as commercial and gain a cult following, like some PFP projects and Solana edge-case art. They bridge that gap well.

KC: How do you think about the role of emerging technologies in your work and how it affects commercialization potential?

NH: Everything I do is seeded by new technology or techniques. I closely study open-source AI, 3D, and VR. For my whole career, these have been jumping-off points for recontextualizing ideas or revisiting projects through a new lens. In 20 years, it will be interesting to see the influence of major software releases and innovations on people's artwork. Early AI art, for example, has a certain character marking it as of a certain time.

Technology as a marker of time is interesting, but as a conceptual driver, it’s less so. I'm more interested in the aesthetics these tools provide for building on larger themes and concepts. There are some genuinely cool conceptual blockchain-native artworks, like Terra0, which is particularly cool. The early projects by Hito Steyerl, for example, include the garden cedar where the blockchain updates parts of the work algorithmically. That's strong, both aesthetically and culturally.

However, generative art using Python or JSON to produce thousands of iterations isn't particularly interesting technically. It's more a reflection of the tooling and marks a time when processing was huge. It’s great for exposure but doesn’t always result in strong artwork. It can lead to a kind of zombie formalism, which may look fine but lacks longevity.

KC: What topics and themes are you thinking most about exploring nowadays?

NH: The themes I keep coming back to are entropy, decay, and the passage of time, particularly through a digital lens. And particularly, I think about landscapes and how people perceive them over time. These ideas have always fascinated me, especially the way people inhabit and view landscapes and how these perceptions evolve.

When I was younger, it was just through a camera and I was always trying to manipulate that kind of footage to make it look like it was more messed up or looked like it was decaying or rotting. Now, with open-source AI, there are new ways to interpret landscapes, people, and culture. It always feels like everything is constantly melting and decaying, which ties into a sense of mortality and beauty.

I love the idea of reflecting the passage of time over a career, showing snapshots that melt and degrade through the lens of contemporary technology. Imagine having a body of work in 50 years that shows a gradual growth or decay, a natural cycle viewed through the tools of the time.

I've never been particularly articulate about the conceptual grounding behind my work. I consider myself someone who has a feeling and then makes stuff. I talk about my work in abstract or metaphorical terms related to a time, place, or experience. I'm not a conceptually driven artist; my work comes from my own experiences and reflects what I see in landscapes and people.

I think that’s why I like music so much. Whenever I'm making artwork, I test it with music, putting it into Premiere Pro and adding different tracks to see what kind of vibe it gives off. Sometimes the artwork needs to be more messed up, sometimes more ambient and beautiful. Music helps me check my work. I love going out to dance parties and listening to loud music; the social aspect is invigorating. The otherworldly experience of strobe lights and smoke, disconnected from reality, somehow connects to how I see nature.

KC: Using music is interesting. Do you usually have a track in mind for what you want an artwork to evoke or is the song choice more so informed by the art?

NH: Sometimes I will. I’ll find a piece of music and make something specifically about that song. Whether I tell people about it or not is another thing. Recently, I’ve been making these large-scale AI creations, like waterfalls and giant slow-motion waves. That inspiration came from listening back to my friend Kane Ikin’s unreleased music, which I've had on my hard drive for ages. As I was really listening to it, I thought, "This stuff is so romantically melancholic and euphorically epic. It has this giant grinding sound that reminds me of an upside-down waterfall or something." So, I decided to create something for it. Those tracks ended up being the conceptual side that actually pushed me to act on making it. There’s no way I would have just sat down and said, "I’m gonna make a giant waterfall today." That was really cool. I like the symbiotic relationship with that—it gave me a jumping-off point.

KC: On that note, how do you feel about generative music visualizers? I could see having access to that sort of tooling eliminating the kind of collaborative practice you’re describing with Kane.

NH: From an artist's perspective, I love all the AI tooling. I think it’s really exciting. It’s the most excited I’ve been about a tool or technique, or something that can change the way you look at stuff, since probably the release of Unreal Engine, which made real-time graphics accessible to everyone.

In the early days of new tools, people, including myself, make all sorts of stuff—terrible stuff, good stuff—so I’m kind of used to that. In the first wave of 3D, when Blender became good, people were making orcs, fairies, women with swords, superheroes, and aliens. We’re not there yet with AI tools and it’s fine—people are just messing around. It’s how they learn. A lot of those people end up making cool stuff as they learn the techniques.

I think AI visualizers for music are very cool. It’s just a new tool, and people are making really good, interesting stuff. It’ll be a fun moment to look back on, like, "Wow, 2024, when everyone got involved in creating and started making these image interpolation, morphing graphics—wasn’t that cool." It’s the people who grab those tools, subvert them, and use them in the "wrong" or interesting way—that always happens—that create a cool niche. They find stuff that people think is stupid at the time and say, "Actually, no, it’s not. It’s really cool, and we’re going to use this."

It’s the same as NFTs. Mainstream people thought NFTs were a stupid scam, but underpinning all that, it’s just people messing around. Ultimately, over time, all that critique and criticism will wash away, and the interesting activity will continue. When I started making 3D art, I made some pretty awful, cringeworthy stuff. But that’s just how it happens. You learn through making, through sharing, and eventually, things get better.

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

IN CONVERSATION WITH is a series from Archetype where we interview artists in/at the edges of crypto across music, visual art, design, curation, and more.

Mike Pollard is the CEO and Co-founder of Nina Protocol, alongside Eric Farber and Jack Callahan. Mike is also a musician and has released his own music under the projects Treetops and Pale Blue Sky. Before starting Nina in 2021, Mike started and ran the music label Arbor in 2004 which has released music from artists including Oneohtrix Point Never, Horse Head, Emeralds, and more. Mike is also a member of the art group FPBJPC and former Director at Svetlana Gallery.

Nina Protocol is a platform and toolkit designed to give artists total freedom and ownership of their work. Nina’s vision is to empower musicians, labels and archives to connect directly with their fans, establish new communities, and create context-rich experiences with unparalleled depth.

Over a video call, I asked Pollard a series of questions ranging from his experience starting and running a music label, music journalism and media, tapping into the music community in New York, and more.

The following interview has been edited and condensed for length and clarity.

Katie Chiou: Long before you started Nina, you started and ran a music label called Arbor. Can you tell us more about the inspiration for starting a label?

Mike Pollard: I started Arbor in 2004 which was 20 years ago, so starting a label probably means something different today than what it meant back then. But at the time I was 14, and I was getting deep into music because of the internet. I had an older cousin who showed me Napster and Internet Relay Chat (IRC), and it was this interest in peer-to-peer internet technologies that got me into music. I started by trying to download music from skateboard videos I’d find on IRC and got into all these other IRC music sharing communities. I would burn CDs of my own collected versions of soundtracks to different skating or snowboarding videos. That was my first journey into searching for music, finding all these different niche music communities online.

I then found my way into following these smaller music labels that had websites where you could email the people who ran them, or you could even email the artists directly to buy a tape or CD that they had self-produced. I started buying CDs and tapes from these small labels and artists and then I realized that I could just ask them if I could release their next album. And in many cases, they said yes. I don’t think anybody realized I was 14 or 15, I would just be on these different message boards online like the Sonic Youth message board or the Animal Collective message board and labels would also gather there to advertise stuff. I would lurk and post and advertise Arbor releases.

KC: From a business perspective, what did running a label at that time look like?

MP: I know most about DIY labels of the previous decade. I’d reach out to an artist, and tell them that I wanted to do a tape with them. For bigger artists we would do 200 copies, for a middle artist we’d do 100, and for a small artist maybe we’d do 50. We’d go back and forth about the details of the release, and eventually I’d get a CDR mailed to me with the tracks, and I’d reproduce them. That could mean dubbing the tapes or burning the CDs at home, and I’d make all the packaging. For compensation, the artist would get 20% of the run. So, for example, if there were 50 tapes in a run, they would get 10. They’d usually sell them through their own website or while on tour. I would sell the remaining 80%. I would sell to distributors or directly to people. I had an email list where I would add everyone who ever bought something from Arbor, and I’d email them about new releases—there were usually 2-5 new releases a month. There were people who would buy every single release, and others that would pick and choose.

I think something interesting about labels is that a label is a kind of curator where if you can trust the label, you can probably guess that everything they release will be interesting to you. We’re also talking about the era of around 2004-2012, which is sort of pre-SoundCloud and pre-easy access to MP3s and sound samples. At that time, you basically had to make a bet based on the label or the press release or the cover.

KC: How do you think music discovery and curation have changed as sharing has gone from word of mouth to small internet forums to bigger platforms like SoundCloud?

MP: The pre-internet version would be things like fanzines and newsletters and magazines basically. If you look at music magazines from the 90s, like Forced Exposure, which was run by a distributor, it would be full of "send a self-addressed envelope to this address to get stickers" and "this label's available catalog." It was like a network about knowing the trusted nodes and who were the good sellers or labels. It was probably just a lot slower than it is now.

The Web 1.0 era was putting those newsletters and catalogs online and then having some forum-based conversation. I think what always matters is taste. There are probably good labels that make bad business decisions. People are sad when they shut down, but the mark a label leaves is whether they can make something coherent or intriguing by bringing together a body of artists. You see that through all eras of independent music. It's a little different when talking about a giant label, but with independent record labels, it's all about taste and trust. If you work with artists who then escalate in their career and move beyond the independent label, then that's probably a big sign that something was chosen correctly early on.

KC: It’s interesting that you emphasize taste because doesn’t everyone think they have good taste? So it more so boils down to correctly identifying and connecting with the people who have the same taste as you. When tastes are local, it’s a lot easier because you have these geographical constraints. Even with social networks like MySpace, it's still based on a social graph. Now things have completely scaled where the likelihood of you finding someone who has the same taste as you is probably higher, but also the means of connecting with that person is much lower. That’s not really a concise question, but I want to poke at this.

MP: If you look at SoundCloud right now, I would say that the artists that have the most potential have probably somewhere between 1,000 and 10,000 followers. You can look through these smaller accounts, see who they follow, who's following them, and see that there's usually a tight network of shared interest or collaboration there.

That can get lost when an artist blows up. There's almost this sense where once you get a certain number of followers, then the game changes; your replies are less interesting or things get more combative. So, I think there's something interesting about the current era where people aren’t trying to blow up their networks. They want them to remain small, vital, and sustainable.

KC: How have all your observations from running a label and existing in niche music communities influenced your role as a Co-founder of a music tech platform?

MP: For the first year, Nina was invite-only, but we gave access to most people who asked for it. The original artists on Nina were basically seeded from people who were already friends and family of me, Eric, and Jack. It just naturally spread through our networks.

We still constantly get this question, "Is Nina a label? Can I put my music out on your label?" People say they like the curation of Nina, but Nina is actually a fully open thing that anyone can go onto and put their music on.

We’re always focused on "narrative capture," as we call it. We didn't try at first to get as many artists or tracks on the platform as possible because we wanted to make sure that there was a solid foundation of people who understand how independent music works. This means understanding how we do things, the importance of context, and that artists don't exist in a vacuum. So, we moved slowly at first to make sure that there wouldn't be something that would come onto the platform that would change the narrative. We wanted Nina to be about intentional context-building, supportive, real-life music scenes, rather than just like a new song website with an interesting financial model or something like that.

KC: You started Nina in 2021 and launched V2 in Fall 2023. What learnings did you take from V1 to V2?

MP: The biggest learning from V1 to V2 was that crypto UX is not ready for a mainstream audience. With V2, we added fiat on and off-ramps, email wallets, and the ability to publish music without needing any crypto to start with. It confirmed a lot of our assumptions that things like browser extension wallets will not be what bring the normal person into this world of peer-to-peer financial interactions and provenance.

With V2, we also introduced multi-track releases, which I would say account for like 90 to 95% of releases on Nina—EPs and albums. I'm a huge fan of albums and having an artist be able to tell a story across a few tracks. It was a feature that we always wanted, but the hardline crypto UX of V1 made album releases extremely difficult. Musicians think across multiple tracks, and I think the prevailing thoughts in the crypto music space are very track-based or a Rube Goldberg machine-like style method of thinking of ways to connect tracks across multiple tokens or something like that. I think that only appeals to a very initiated group of artists.

KC: Nina is quickly becoming a pillar of the underground music scene, both in media and IRL. You have cult-following music blogs like No Bells and First Floor writing columns for Nina Editorial, throw Nina Nights events at beloved venues like Nowadays, and have the 400 Floor podcast. How do these efforts tie into Nina’s core vision and strategy?

MP: On Nina Nights in particular, Jack, Eric and I—who all started Nina together—and other people on the Nina team have deep experience putting on shows, running labels, and being in bands, so it comes naturally to us. We thought live shows could be a place where people can come and we can talk to them about Nina. We can also elevate people who are friends of Nina or Nina-adjacent. The podcast is kind of similar. They're both about the wider context of music. Music isn't just a song online. The strongest interactions occur when you can meet someone and talk to them. It's definitely not scalable, but something nice about the Nina Nights is that they've all been different scenes. There's maybe a core group of 10 or 15 people who come to every event, but there’s also always new faces.

The goal of Nina Editorial is to highlight trends in different kinds of online music discourse and give them a place to hangout. It's not really about the columns in particular, First Floor and No Bells aren’t related to crypto. Both columns have premiered tracks and onboarded artists on the platform, but it's really about supporting their work.

KC: Do you have thoughts about all the recent events in music media, things like Bandcamp editorial shutting down to Pitchfork folding into GQ?

MP: It's definitely scary. Before Pitchfork was bought by Condé Nast, there were all these blogs in the MySpace era and then you had a sort of consolidation and corporatization of all of those bloggers. They were all hired by different companies—whether it was Vice or Pitchfork or somewhere else—and then all those companies said, "Okay, this no longer works." Then all those people who had been corporatized got pushed back out at no fault of their own. It’s brutal.

But you need vital writing, you need scene reporting. Pitchfork and publications like it are good for distribution and having this shared idea as to what the most important stuff happening in independent music is. But does that match the experience being on the ground with up-and-coming bands? I think that people just need more direct ways of supporting artists and critics and scenes. There are very dedicated people, and I think that the dedicated people will continue to be dedicated.

KC: How do you think about Nina being a bridge from crypto to the mainstream? Does that cross your mind at all?

MP: Spotify, SoundCloud, and Bandcamp all started over a decade ago, and I think they've all shown signs of weakness in recent years. Bandcamp has been sold twice in the last few years. Spotify has demonetized 85% of artists on the platform with less than 1,000 streams a year. SoundCloud is constantly being put up for sale.

There is value in permissionless cultural tools. That's what we are dedicated to thinking about at Nina: a platform that could exist independent of a solo operator. A platform where you can avoid things like MySpace's servers going down and 15 years of music getting deleted, where you can have peer-to-peer artist interactions, whether it's financial or communicative. That's the thing that we're trying to build with Nina.

I think that the mainstream crypto discourse currently is very speculation and degenerate-oriented, and crypto culture can be off-putting to anyone not involved in it. So, I definitely understand where people's apprehensions come from because we largely share them. But I also think that there are benefits to thinking about how culture could exist on the internet. We’re thinking about peer-to-peer music online from the perspective of the experiences that we've received from the past decade of music tech. People are exhausted with change and the ever-increasing intensity of the firehose of content and roles that you have to play.

KC: You’re a big music collector and think about archiving and provenance a lot. Can you tell us more about that personal philosophy?

MP: At the speed at which culture currently moves online, it's super easy for things that don't have the right rollout plan or PR strategy or just engine behind it to launch and then immediately fade out forever. But I think that these forgotten things always come back and end up being the inspirations for future generations. There's a real value to not letting the things that are loudest in their infancy be the only things we remember. There should be an archive to dig through for people who are interested—to follow the traces back from the things that they know to the things that they don't yet know. This is how we keep culture lively, rather than letting whatever an algorithm wants to show you push you based on whatever reasons it has.

KC: In the context of Nina, what does building a “context-rich” platform or experience look like?

MP: I would say that it's a North Star, we don't have it yet. Discogs is a good example of cataloging the cultural history of recorded music. Think of archive.org. The important thing is having open access to the interconnectedness of things. So, if one label puts out music by 10 artists and those 10 artists are also put out by 4 labels each or something, then you have this network of nodes that you travel through and follow your interests. There’s value to archiving the past in order to preserve the future. If you don't have a past, then you just have a perpetual present.

KC: What would you like Nina to look like 1, 2, 5 years from now?

MP: Something I really like about SoundCloud is how well it facilitates the move from being someone who listens to music to becoming someone who makes music, which I think is a pretty natural progression for a larger subset of people than you would think.

In general, I don't think that much about Spotify. I don't think that Spotify is where music is born. I think a lot more about where music is born, I guess. So I'd like to see Nina facilitating that role. I'd like to see us find more ways to make context-rich experiences happen there where people can actually dig through the history of a scene or a style. I think that we're at the beginning of a Nina-specific sound developing. I won't say more, but there are groups of artists from all around the world who share something that brings them to Nina.

I'd also like to see us make better tools to be the backend for artists' personal sites. Having a personal page is a good way to say, "This is where I live, this is my context." In light of platform fatigue and streaming fatigue, I would like to see Nina be a sort of Discogs or dictionary of everything that's happening in independent music. Whether it’s Nina or not, I’d like to see a place for artists to have their own place to live. Things like Linktree are a start and maybe a sign that things could go one step further––I'd like to see that step beyond.

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

Earlier this year, my colleague Benji Funk wrote a piece called “Crypto-Powered Information Games” detailing the design space for coordinating, aggregating, and rewarding quality information across decentralized networks. Benji’s post begins with the following thesis: “Humans have an inherent desire to create, search for, and speculate on information.”

Since then, Benji and I have spent a lot of time talking to teams building at the intersection of financialized information games and social platforms—this category is currently referred to as “Social Finance - SocialFi.” SocialFi teams vary widely across mediums (text, image, video, audio) and audiences (pro crypto traders, artists, content creators, fans, and influencers), but the overall strategy is to create a fun social network based on high-skill financial information games or to financialize platforms that were initially primarily social.

The central goals of information games are to gain critical insights, improve individual or collective decision-making, and to accrue capital. Social networks and newer forms of SocialFi tend to magnify the relationship between information and capital accumulation. To understand the holistic value of information, however, it is crucial to consider all forms of capital, not just financial capital.

In this post, I will explore the interplay between social capital and financial capital in different types of social platforms, as well as where crypto may be a useful tool in these different contexts. By completing this exercise, we can better identify what goals/features matter for different goals and user personas and then build better-aligned platforms for those objectives.

Economic theory tends to focus on financial and transactional exchange, thereby classifying other forms of exchange, like cultural and social exchange, as non-economic and insignificant. However, this narrow view ignores the interplay between different types of capital and power, creating a false distinction between profit-maximizing economic activities and seemingly frivolous cultural activities.

One of the most desirable types of information, in fact, is social information. Social information gives us crucial cues as to what is considered morally right or wrong and how to behave in certain situations—fundamentally how to “belong” in specific groups and societies. Having accurate and relevant social information allows humans to earn capital, both social and financial. Synonymous with the concept of social capital is the term “status.” To quote Eugene Wei quoting Jane Austen, "It is a truth universally acknowledged, that a person in possession of little fortune, must be in want of more social capital."

Status/social capital refers to the resources available to an individual through their relationships within a network. These relationships form in a variety of ways including familial ties, institutional recognition, and other types of acknowledgment or respect based on some situational rubric.

Whereas financial capital can be utilized for immediate exchange, social capital is accrued through the establishment and maintenance of relationships over some period of time. These two forms of capital are not necessarily mutually exclusive. Converting financial capital into social capital might look like using financial means to invest into social relationships to improve status, transforming economic debt into social gratitude, or purchasing goods and symbols to gain social recognition or respect. This form of capital conversion is often faster, but less stable as there is a risk of ingratitude or failure to reciprocate in social exchanges or the risk of the gestures being perceived as inauthentic or reeking of ulterior motives. Conversion of social capital into financial capital is often more concealed so as to not disturb the ongoing maintenance of longstanding relationships or perception of meritocracy, but its investment can yield financial benefits through economic opportunities. For the sake of simplicity, we’ll refer to this dynamic as financial capital being more liquid, while social capital is generally more illiquid.

In its early days, Instagram was largely used for friends to share moments with each other. Throughout the 2010s, the term “Instagram influencer” came into being as users moved beyond sharing photos with first and second-degree connections to promoting products and content for upwards of 7-figure sponsorship deals with brands. The influencer marketing economy (including platforms beyond Instagram) is now valued around $20B+. Instagram has dived headfirst into commerce, launching Instagram Shopping in 2020 (though it has retracted some of its more explicit shopping features such as live shopping). Instagram’s evolution over the past decade is a prime example of seamlessly converting social capital into more liquid financial capital.

The shift of social platforms more broadly has been one from “networking” to “media.” Platforms are increasingly designed to be consumed at-scale (broadcast media, think TikTok), rather than to foster interpersonal connection (P2P networking, think WhatsApp).

Between these two ends exists a wide, fluid spectrum. As a general rule of thumb, more broadcast-oriented platforms rely much more upon discovery mechanisms and speculative symbols of status and reputation (likes, follows) with the ultimate goal of discovering and scaling a community/audience. In P2P-oriented platforms, target users tend to already be in pre-existing social/network groups and the goal is to deepen relationships within these groups rather than to necessarily scale them to an infinite degree.

When we look at existing social platforms, we can identify two central dynamics by which to categorize the types of status games they play. There are platforms that lean into liquid, financial games, and there are platforms that lean into more illiquid, social status games. Similarly, platforms can make these games and status mechanics explicitly core to the experience of the platform or more implicit and subconscious in the background. With these lenses, two high-level axes emerge:

• Social capital <-> Financial capital

• Implicit capital <-> Explicit capital

We can use these axes and subsequent quadrants to broadly categorize social platforms and their ideal users. Again, these axes/quadrants do not mean mutual exclusivity but there are natural tradeoffs to consider when building social platforms according to specific goals—breadth vs depth, scale vs intimacy, instant exit liquidity vs long-term retention, etc.

In 2019, Venkatesh Rao coined the term “cozyweb” to refer to places people can hang out online away from the hubbub of the public internet.

“Unlike the main public internet, which runs on the (human) protocol of ‘users’ clicking on links on public pages/apps maintained by ‘publishers,’ the cozyweb works on the (human) protocol of everybody cutting-and-pasting bits of text, images, URLs, and screenshots across live streams. Much of this content is poorly addressable, poorly searchable, and very vulnerable to bitrot.”

In their purest forms, cozyweb platforms look like email, text message, and related messaging apps used primarily between friends where information remains relatively secluded. Similarly, private online groups like Discord servers and Slack workspaces have some semblance of openness and discoverability (less cozy) but are still highly gated.

As a step further, there are social media platforms such as BeReal or Snapchat that give new utility/entertainment to existing social groups. However, these apps have little-to-no focus on growing your social network, status, or user discovery; they’re focused on giving you tools to engage your existing communities.

Lastly, and perhaps the hardest type of platform to replicate in this category, are platforms on which users have public presence and discovery mechanisms exist, but “status” has little utility. Examples of these platforms include Letterboxd, Are.na, and Spotify to some extent. Each of these platforms has their own unique combination of features that limits the proliferation of status games.

For example, Spotify has very few social features—no direct messages, limited UGC/post capabilities; engaging with a friend with Spotify is relatively high friction (sending links off-platform).

Letterboxd is based on user-generated content (film reviews), but a user’s status level changes very little of the experience on the platform. Having more friends or likes on your reviews does not necessarily amplify your content in any particularly desirable way or unlock any power features or utility in the way that, say, Yelp power users (also based on user reviews) may receive invitations to review new restaurants. That being said, there’s little reason why this couldn’t become a dynamic for Letterboxd users as well in the future.

In the case of Are.na, the team has always been a major proponent of “cozyweb” spaces, creating a highly aligned user base and culture that generally discourages status games. Instead, social features on Are.na encourage fundamentally unscalable IRL community meetups rather than highly scalable digital audiences.

To summarize, cozy platforms tend to limit scalable status games through a few key features or lack thereof: limited UGC or P2P social features, highly aligned user culture, and/or lack of algorithmic discovery or amplification. Instead, these platforms focus on providing experiences that can help deepen existing relationships and connections with people already in your social network including direct messages, coordination tools for IRL community, and focus around niche interests.

Whether a platform makes social status games explicit or implicit is as much a decision made by its user culture as it is a function of actual platform buildout and design. For example, because of the widespread acceptance of Twitter KOLs and Instagram influencers, it’s natural that any new platform similar in UI to Twitter or Instagram will replicate similar results, hence making the goal of accruing social capital almost inherently explicit for these types of platforms.

As a baseline, a fundamental core question to ask when evaluating the positioning of a social platform is whether the goal of the platform is to aggregate attention and build audiences (one-to-many) or to promote smaller, high-touch networks and relationships (one-to-one). However, in many cases, aggregating as much attention as possible at scale can be an effective way to then filter or organize suitable candidates for a smaller, intimate social group. More structural features that support social capital games include easily legible/quantitative metrics such as follower/following/likes. Prominent discovery features on a platform also usually point to more explicit social status games, as users typically want to be featured prominently in the discovery feed/algorithm to increase their audience and reach. It’s worth noting that quantitative status markers (following, followers) on social platforms fundamentally improve content recommendations and user discovery, so these features often go hand-in-hand.

A prime example of an influencer platform is TikTok, where the goal is for short-form content to reach the most people as possible (go viral) and to accrue likes and followers, but more intimate 1:1 communication is extremely uncommon (though TikTok does have DMs). A more recent example that illustrates this dynamic is the privatizing of “likes” on Twitter, meaning it’s now impossible to view any user’s likes or the likes on any given post. Though a small feature change, the change dramatically transforms the social dynamics on the platform where social graphs/networks become less legible, instead shifting to more quantitative measures. By obfuscating certain social details, the goal becomes less so to discover relevant people in your social network, and more so to pump numbers and reach. An interesting side consideration: How much more data can Twitter collect from users now given that likes are private/users may like posts more freely and often now? The opposite may also be true––users may like posts less often because likes are private.

A nuanced factor related to influencer platforms is that, as mentioned earlier, social capital generally relies upon one’s ability to build strong relationships over time. However, if said relationship-building is perceived as inauthentic or in explicit search of capital (“clout-chasing”), such activity can actually overall reduce one’s social status as people can be less open and willing to engage in relationship-building that is perceived as not genuine.

A platform that thrives on influencers and social status games must maintain a small number of influencers, such that 1) there is a level of perceived desirability or rarity in each influencer and 2) there are remaining users that can serve as an authentically engaged audience and that can aspire to higher influencer status. The obvious questions then become how do users sort themselves into these tiers and what platform features best facilitate this organization?

It is generally known that social capital can be converted into financial capital, but there are also explicitly professional social platforms. These platforms typically focus on career-building, whereas more traditional social platforms rely on parasocial attachment for later capital conversion (i.e., an Instagram influencer building a social audience, and then later launching merchandise). These professionalized platforms often take the form of “patron” platforms, subscription businesses for gated content/insights, or professional networking platforms for hiring and finding new roles.

In a simple example, the goal of LinkedIn is to find professional roles that lead to earning financial capital. This goal is rather explicit and socially accepted. Professional platforms can become more nuanced, however. Take Zora, where in its base form, artists post work to be minted/purchased for a fee so that the creator may earn income. While the exchange of selling goods for a fee is rather direct, Zora is becoming an increasingly more social-oriented platform rather than traditionally professional.

The line between “influencer” and “professional creator” continues to blur where influencer is a much more common professional career, and professional creators must increasingly build audiences on social platforms to be successful. For example, while a vast majority of Substack newsletters are unpaid/written by hobbyist writers, there are many professional writers who rely on Substack as a principal source of income. Many of these writers start a Substack newsletter after already accumulating professional audiences, but also use social platforms—Twitter in particular—as a key distribution channel to develop more social relationships with their audiences. As a side note, Twitter throttles Substack links, making it more difficult for Substack writers to build audiences and distribution on Twitter.

I’ve written about this dynamic for music artists, in particular. For example, from my post on the music industry “where does music go from here”:

“With the death of the capital-a Album, you optimistically have about 200 seconds to capture a listener’s attention with your music. In an era in which artists are discovered on “social” platforms, the focal points become the individuals themselves. Music artists who capitalize on this feature can experience major boosts in popularity. [...] Another way to frame this shift is that art and media have become much more identity-centric.”

A core question to ask when considering/designing these dynamics on a given platform is: “How directly is a user seeking financial capital on this platform (by selling goods and services, earning income, etc.), as opposed to social capital (building an engaged audience through more personal, social content)?” The former results in more immediate financial gain, though perhaps is more difficult to build social engagement from, while the latter often is a slower process as you have to socially engage an audience and develop an authentic relationship with them before monetizing the relationship.

The last quadrant of our framework includes platforms that place financial games at the center. These platforms are often pay-to-play and/or rely on some sort of speculative price action or prospect of earning money to rally users. Casino games or traditional betting/prediction market platforms are classic examples of “earner” platforms.

Earner platforms are particularly effective at quickly aggregating users and attention, as financial capital is much more liquid and therefore much more exciting and active. These platforms gain momentum quickly and generate content rather self-sufficiently, as prices fluctuate in real-time and user actions are usually rather straightforward (buy, trade, sell). These platforms are usually well-suited for short-form, viral content that complements live price action. Similarly, the ROI from engaging on these platforms is much more immediate and clear than attempting to painstakingly build reputation on a new social platform. It’s also worth noting that in these platforms, users rotate in and out much more frequently than a social platform, where the goal is generally to deepen engagement with a specific community of users.

There has been recent exploration in overlaying these financial games explicitly on top of more elusive social dynamics. These platforms seek to place a dollar amount on usually more illiquid individual social status or on the strength of a community. While these platforms are still social to varying extents, the prospect of earning is at the forefront. Earnings can be for individual gain or for the goal of investing these funds back into the social group/community, depending on the dynamics of the platform itself.

The extent to which and methods by which financial layers are made explicit in these platforms varies widely. For example, Patreon is a subscription platform by which users pledge a subscription in order to receive premium content, but primary content usually exists on a separate platform—therefore making the financial layer largely the sole function of the platform, but not necessarily financializing core social content that may exist elsewhere. In the case of Twitch, financial tips during streams have become extremely common, though a majority of Twitch streams are still free.

A few high-level questions to ask to determine the level of financial games on a platform are:

1. Is creating financial markets a core function of the platform?

2. Do money/price actions affect the UX of the platform in real-time? (Unlonely, Twitch)

3. Are there prices/price charts prominent in the UI? (Friend.tech, pump.fun)

4. Is financial gating a core functionality of the platform? (Patreon, PartyDAO, Friend.tech)

As attention and social experiences move online and content and culture move at the speed of light, managing the information we have about ourselves and each other becomes nearly impossible. The cultural cues we use, financial and social capital, and the relationship between them become even more difficult to decipher and manage. Crypto infrastructure helps us manage, contextualize, and connect indicators of social and financial capital in a hyperspeed, hybrid online/offline world.

In the case of financial capital, the role of crypto is perhaps more straightforward.

The ability to permissionlessly and programmatically create new markets allows us to discover the price and underlying sentiment/demand for practically anything under the sun in the blink of an eye. In this way, blockchain rails allow us to most explicitly and purely express and act upon the will of groups of users (both people and bots) by programmatically generating quantitative information in real-time.

Quantitative signals generate important social signals in two ways: they create binary, legible outcomes (number go up, number go down) that allow us to measure sentiment and direct latent social energy through extrinsic rewards and incentives (perks, streaks, and rewards). The value unlocked from gaining and leveraging real-time social information in this way is extremely powerful, allowing decentralized networks of people to aggregate and deploy capital in new, innovative ways that were previously impossible.

In the case of social capital, I’ve expressed before that social reputation is incredibly broken in a few different ways. The user journey across online and offline is incredibly fragmented, as online platforms mine and hoard social information and data, and as users move back and forth between IRL and URL. Platforms then channel this information into centralized algorithms that they can use to silo users into personalized feeds that amplify whatever content the platform so chooses, meaning creators must play by the platform’s rules in order to participate and culture submits to power-law like distribution. Kyle Chayka shares an anecdote about an artist, Hallie, in his book Filterworld that illustrates this point:

“Hallie also realized that the Instagram feed rewarded specific qualities. She had always combined visual art and writing, but posts with clear written messages got the most engagement. [...] It was a meme-like assembly-line process perfectly suited for Instagram: the bright colors and simple text added a little spice to her followers’ feeds along with simple moral messages. Followers came to rely on her account for those pieces alone. [...] The pressure that Hallie felt to make the rest of her artwork similarly bright, clear and simple is much like the pressure that a musician feels to frontload the hook of a song so it succeeds on TikTok or a writer feels to have a take so hot it lights up the Twitter feed.”

Algorithmic feeds have granted users significant power through advanced discovery mechanisms, yet these same feeds have transformed into a new type of monolithic platform, centralizing curation around scalable metrics and uniform cultural tastes. Blockchain rails offer tools for rethinking curation, promoting structures where individuals are encouraged to co-create and curate their own digital community spaces, moving away from centralizing platform mechanics that lean towards large scale and uniformity. Tools like token-curated registries and markets could allow each user to actively build a community and elevate their own "digitally local" curators, instead of passively consuming a single mega-feed tailored for broad appeal—consider Facebook group pages, Subreddits, NTS channels, Discord servers, and similar platforms.

We identified a few key challenges in user identity already, namely that identity is fragmented both IRL/URL and cross-platforms. Leveraging blockchains means leveraging open data, where users can move freely between platforms and bring their data with them. Not only does crypto enable open data sharing, but it also enables trustless verifiability as people can attest to information as correct/incorrect, true/false, relying on the wisdom of the crowd, rather than on platforms with ulterior motives. This becomes increasingly important as more content becomes generative and as social platforms move to become more decentralized.  Identity primitives must exist to help us better identify, incentivize, and match people across social platforms much more efficiently and effectively.

A key way we generate information and its subsequent verifiability is through social information games that allow users to attest to qualities and behaviors that others can similarly react and respond to. With open data sharing and open data verifiability of these information games, we are then able to build robust, decentralized reputation that is able to draw verifiable information holistically across platforms, improving both its quantity and quality.

While many SocialFi platforms to date focus on speculative financial games, there is an incredibly vast design space for platforms that create status games across both financial and social capital. A key to building a powerful, engaging social platform is to identify a target user persona and their unique preferences for financial or social status games. Appealing to different types of users and leveraging different tools necessitates certain tradeoffs in all cases, but is an important exercise for builders.

Across both axes of financial capital and social capital, blockchain rails offer exciting new opportunities for the next generation of social applications. Real-time, decentralized markets allow everyday users to discover, speculate, and trade with strangers anywhere in the world on major global events like elections or play ephemeral social games with friends like betting on the winner of a hotdog eating contest. Communities can rally around cultural objects and launch life-changing crowdfunds to support creators and projects in less than a day. Decentralized algorithms and curation give networks the ability to create subcultures at the most granular levels, discovering and engaging the most relevant people in the digital underground, away from monolithic broadcast feeds.

The delineation between the four types of social platforms is not to declare a hierarchy between them, but to illustrate that building for different audiences with different goals necessarily comes with different tradeoffs. Rather than attempting to build for all quadrants all at once, it’s helpful to identify which features work best for which audiences and goals.

I hope this framework is helpful, and we cannot wait to see what you build.

Thank you to Yash Bora, Jihad Esmail, baz, jtgi, Brian Kim, Lyron Co Ting Keh, Holyn Kanake and Archetype colleagues Benjamin Funk, Tyler Gehringer, Ash Egan, Danny Sursock, and Dmitriy Berenzon for thoughtful review and feedback on drafts of this post.

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

IN CONVERSATION WITH is a series from Archetype where we interview artists in/at the edges of crypto across music, visual art, design, curation, and more.

Adam Ho is a designer and artist based in Queens, NY. He currently runs Adam Ho, I.S.P., his own independent studio practice. Adam’s primary focus is on creating large-scale visual brand identities and interaction design for emerging technology companies.

Adam is a prolific brand designer in crypto, having worked with companies like Eigen Labs, Axelar, Coinbase, MoonPay, Mona, Valhalla, and more. Adam also released his own collection of NFTs in 2022 on Foundation called Liquidities, a series of NFT abstract motion expressions. Apart from his own studio practice, Adam also actively works with VectorDAO as a Design Lead.

Before forming Adam Ho, I.S.P., Adam worked in-house on design teams at Cash App and Zendesk. He has also collaborated with other design-forward brands such as Nike, Square, Medium, and Airbnb.

Over a video call, Adam and I talked about launching an NFT collection, creating the brand for EigenLayer, general design principles, and more. Adam even designed a new NFT to accompany this article, which you can collect for free on Foundation!

The following interview has been edited and condensed for length and clarity.

Katie Chiou: What was your initial foray into crypto like?

Adam Ho: My first time interacting with crypto was hearing about the coins they sold on Coinbase back in 2016-2017, like Bitcoin, Litecoin, Ethereum. I talked about it with my friends from college at the time. I didn’t really think about crypto again until around 2020 when I started seeing a lot of people talking about crypto in very cryptic ways I didn’t understand—like what does the acronym “NFT” mean?

Foundation actually reached out to me before they started using the term NFT, and I thought it was just kind of a way to securitize art. I didn’t really put 2 and 2 together until after Foundation launched, where I could see people uploading their art for other people to buy. This to me felt like a shift, a second phase of crypto based on art, whereas the first phase was based on money and speculation. Now, I feel like we’re in a third infrastructure phase right now.

I tend to have an obsessive personality, so when Loot came out in 2021, I was really into it. That’s actually how I first met Jon Yan at VectorDAO. He was working on a Loot project at the time, and we started talking about other NFT collections and communities like Solvency and Azuki. I thought the whole space was really fun and new. So that was my foray into crypto, it started off mostly as money speculation and treating it as stock and then later figuring out utility for crypto through NFTs.

KC: You launched your own NFT collection on Foundation called Liquidities in 2022. What made you want to launch a collection, and how was that process?

AH: I was watching an interview recently with Ezra Koening from Vampire Weekend and Zane Lowe, and Ezra talked about how musicians get into music through different ways like live music or the gear, but he’s always been interested in discographies and the idea of entire bodies of work. To some extent, that’s similar to how I approached Liquidities. My first few NFT uploads were a bit random, and then I thought to myself, “Wait a minute, this is on the blockchain, there’s going to be a history to this.” I didn’t want to continue to upload and mint just random things. For about a month or two after that, I really sat down and thought about the kind of work that I would want to produce that would be constrained and have a specific point of view—both in terms of the aesthetic but also the narrative.

Liquidities is a play on the word solvency, and it also sounds similar to Solidity. Once I had a name and concept I liked, I made the art feel almost liquid, like they were on loop in this liquid pattern. The art looks generative, but it’s not. I did everything in a lo-fi way frame by frame in Photoshop, similar to what you would do if you were making a GIF. When I exported the files, I constrained the amount of colors which is why they look both random and specific. That’s on the art side. When I decided to launch the collection, I wanted it to feel like an album. That's why actually there's only around 10 pieces. I haven’t touched the collection in a while because I’m focused more on client work nowadays.

KC: When you uploaded NFTs for the first time, do you remember it being particularly confusing or difficult or different from other platforms you had used before?

AH: It was way different. I think I lost around $600 the first time I tried to mint because the gas fee was like $300 and then everyone told me, “Yeah, that's just how it goes.” It actually felt like a massive barrier to entry. To some extent, there’s a charm to engaging with things that have a bit of friction behind it. After the first or the second time, it also gets a bit better and more familiar. There was definitely friction and a learning curve, and to be honest, I'm still learning more about crypto every day. But the more things are out in the market and the more people are working on tooling, the more processes become easier.

KC: You’re known to focus on working with emerging technology companies, specifically in crypto. Are there any considerations you take into account when you work with a crypto company versus our average tech startup?

AH: There’s still a lot I don’t know about crypto, I still ask you and Jon questions all the time. But that’s also something I enjoy, I like learning new things and facing new challenges. There’s a challenge to developing a net-new visual metaphor for an object that no one else has had to think about before—like an asset bridge for example. Or, how do you make visual metaphors for zero knowledge?

I also have a very specific point of view in my work, it’s very graphic design-driven. A lot of inspiration I pull from is from the 1960s and 1970s, so part of the fun is developing a visual metaphor for something entirely new, while also pulling from older references. This is a tangential point, but I love the design inspiration I can take from someone who may not have had the same tools as I do now, but still did incredible work that I couldn’t do today.

In terms of consideration for working with teams, I try to just make sure we are as aligned as possible. By alignment I mean that I’m very upfront about how I work and my point of view. What you see on my website is what you get. Once we’re aligned on process and method and there’s implicit trust, we can work together.

KC: There’s sometimes criticism about how crypto website copy and design tend to use a lot of very technical jargon that isn’t accessible to most people. Curious if you have thoughts or reactions to that discourse?

AH: I tend to not participate in these types of discussions publicly, but I have had private conversations about this. I think my work was even mentioned in a thread about this, but I don’t take offense to it. I understand where the perspective comes from, especially since the industry can seem convoluted to outsiders. However, many of these products are designed for insiders, they’re not meant for mass-market—particularly infrastructure companies aiming to improve the world through partnerships with other companies that later are consumer-facing. They need to be able to talk to developers in a certain way that makes sense to them, and then those companies can more easily build for consumers, be it Coinbase or MoonPay. It’s an evolution, I guess.

While we can always strive to make things more approachable and do a better job of it, my main goal isn't necessarily about accessibility. Rather, I aim to help differentiate the company I work with from its peers. This may involve making things more approachable as a byproduct, but it's not my primary focus.

KC: Walk me through the process of how you work with teams as clients.

AH: One of the challenges of working with emerging technology companies is that oftentimes, they’re proposing something entirely new to the market. Only, maybe 1,000 people actually understand what they’re building. Even the clients sometimes have trouble trying to explain to me what they’re building in simple language. They’ll apologize and say something like, “Sorry, this is really complicated, we just need a new brand.” I totally understand that, and it’s my job to be honest when I don’t understand something and to do my best.

For example, with EigenLayer, restaking just sounded like some kind of buzzword to me at first, but when I dug deeper into it, it actually was pretty profound—the thesis of expanding resources and non-zero-sum games around data availability and economic security. Even those terms are their own separate mouthfuls.

I feel more aligned with a company when they’re able to have a longer discussion with me about what they’re building. In the past, I would turn away clients because I didn’t understand what they were building. Now, I take on more clients than I would in the past because the learning curve has gotten easier. As time goes on, I build a better foundation of knowledge that allows me to better understand future clients. Especially working with foundational infrastructure companies in the past, it helps me better understand other companies both vertically and horizontally.

That all being said, I really don’t need to know that much about a company in order to work with them. With EigenLayer, I had to understand the baseline concept of restaking, they told me about their culture and how the team is very academia-driven. The branding reflects that, with my own twist on it, incorporating typefaces typically used in direct-to-consumer contexts.

KC: How do you think about the importance of a personal brand as a designer?

AH: I'm probably a bit of a purist in some way in that I want to be known for my work and for my work to speak for itself. That's the most important part. I try to do a lot of work so that I can show a lot of work and therefore get more work. Some people try to fill the gaps between work with more personal brand-oriented posting, but I just feel really weird when I try to act cool on the internet. I did a lot of that early in my career where I would respond to posts, and sometimes I would get hundreds of followers from that, so it definitely works.

But I think these days, I'm a little bit more focused on showing my work. Hopefully it’s good work that people resonate with. When I meet people that know of my work nowadays, it’s usually through my website. That’s also a part of personal brand, I think it’s important for designers to update their portfolios. You learn so much in a year, it’s like a year of studying or a year of school. I really enjoy having annual portfolios that reflect that growth, it’s meditative for me. So I think it’s a shame when people learn so much but don’t update their portfolio.

KC: What/who are your biggest design inspirations nowadays?

AH: There's a Japanese designer that I think passed away 4 or 5 years ago, Mitsuo Katsui. I found his work somewhere and he had such a deep body of work that I felt resonated with what I'm trying to do with my own work. I’m kind of speechless at how he was able to accomplish some of his work without the tools that exist today. Besides that, it’s no secret that I’m a massive fan of Karel Martens. I have books from both of them.

KC: Do you have any general design principles that you live by?

AH: The first is always maintaining a healthy balance of art and design. By that I mean, in the corporate branding world, art is usually reference and design is what you actually do. I think those two things can be much closer than people tend to think. I try to merge those worlds as much as possible.

It also doesn’t have to be profound, it’s just graphic imagery—like the Baldessari dots, for example. Brands need to be instinctively visually captivating, people crave unexpected experiences, even if they don’t think that they do.

Thirdly, there should be a method to the madness. Whenever something feels chaotic, make sure there’s a reason to it, so that you can scale that reasoning.

Next is similar to the first one, but conflict brings cohesion. I don’t mean interpersonal conflict, I mean playing with tension between concepts and understanding them.

Then lastly is paying homage. Especially as we’re talking about emerging technologies that can be extremely referential, I think it’s important to understand the past and why people made the decisions that they did.

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

Jared Madere is an artist and curator based in New York, as well as the Co-founder of Galerie Yeche Lange. The Yeche Lange team also includes Milo Conroy, Wretched Worm, Miles Peyton, Supermetal Bosch, Anastasios Karnazes, and Hikkimourning. Yeche Lange has collaborated with crypto-native communities including little swag world, Tojiba CPU Corp, and more. Yeche Lange is opening a physical gallery in New York on May 17, 2024.

Prior to founding Galerie Yeche Lange, Madere co-ran other galleries in Los Angeles and New York. His own works have been displayed at institutions such as the Whitney Museum of American Art, Le Magasin, La Panacée, The Watermill Center, and others.

Over a video call, Madere and I talked about the relationship between digital art and physical space, bridging traditional art and degen culture, the next generation of art collectors, and more.

The following interview has been edited and condensed for length and clarity.

Katie Chiou: For those unfamiliar with your work, can you share more about your background as an artist and curator? And specifically about your foray into digital art?

Jared Madere: I've always made digital work, but my baptism into the traditional art world was mostly large-scale, collaborative installations. The scale of this work requires institutional budgets and bureaucracies, so the process can be less than magical. You plan everything out, follow some sort of script, and the thing materializes exactly how it is in your mind. At a certain point, I became really bored with this direct 1/1 relationship, it lacked the sort of material improvisation and self reflective reaction associated with painting.

I became interested in this idea of working with other people to introduce an element of something that can't be anticipated into the process. I would come up with an initial script or prompt that was intentionally worded in a way to create ambiguity in its possible interpretation. So there would be material instructions like “Create xyz image out of bisected squid and sand” or “Create xyz image out of shredded beaded curtains and raspberries.” I was working a lot with tattoo artists and graffiti artists who were used to taking descriptions and very quickly interpreting them in a utilitarian way versus someone who comes from a conceptual art background that tries to be clever with their translation. I got interested in the way that something physically more reductive, i.e., the written word or a still image, could do that in a more portable way. The thought of compressing an idea into something that would fit into someone else's head and then could be unpacked according to their own idiosyncrasies. This happens commonly in literature, where there's this kind of transference between the author and the reader, where the vision gets compressed and committed to a textual format and then gets re-inflated in the mind of the reader. I became very hypnotized by how elegant that way of interacting with an audience was.

Also I think, with showing other people's art, there was always this interest in the democratization of ideas. I feel like that word often gets used to mean something specific related to class and accessibility. I don't necessarily mean it in that way. I guess to a certain extent I do, but I think about things like what Kanye and Miami Basel did for exporting ideas that were traditionally relegated to a less accessible field. But there’s also a perverse thing that happens when you round off an idea's edges and prepare it for mass export. I think about it in terms of the way fashion discourse transformed around 2011-2012, where labels like Margiela and Comme des Garçons participated. They weren't just luxury brands, but had commentary—like Comme des Garçons on deconstruction at a material level, or Margiela on deconstruction of the meaning of normative clothes with a wink. Around the birth of Instagram, these meanings flattened, becoming luxury goods like any other designer label. Despite this flattening the original seed is somehow still transmitted the same way that if you understand Skims you understand Judd at least on a subconscious level.

I think this idea that in that reduction, somehow the ghost of the idea remains in there if you can read between the lines or listen carefully enough. That was always an interest of mine—how to get these ideas, typically relegated to contemporary art, into the heads of more people.

KC: What about your interest in crypto?

JM: I had never been interested in crypto before NFTs. I didn't pay close attention to crypto beyond knowing it existed. But when NFTs appeared, it was immediately clear how they could be a very powerful vehicle for distributing art. I had always been envious of how music could penetrate people’s lives, while art felt less agile—you don't really bring a painting or sculpture on a road trip. When NFTs became a possibility, you had audiences curious about art that hadn’t explored it before. One example of this is Roman Signer, a Swiss sculptor with works like a kinetic sculpture of rain boots launching water or a white dress shirt mounted on an automatic zipline through a forest. By early 2021, I was in group chats where Apes were being discussed, and after 8 months of self-educating, the same group chats were talking about Roman Signer—this relatively obscure sculptor wasn't even on the radar of many trad art friends who live and breathe art.

NFTs provided a way for art to exist in people's lives differently, trafficking these ideas to new audiences. People who had been flipping coins were now interested in digging deeper into ideas previously relegated to other spheres. I started thinking about building Yeche Lange as a vehicle to bring my art friends into this new context with the idea of bridging them to the possibilities there.

KC: Earlier in our conversation, you referred a lot to the flattening of ideas especially due to speed of idea dissemination. In many ways, crypto culture is a manifestation of this type of speed.

JM: Yes.

KC: I’m curious if, by participating in crypto, you’re hoping to make some sort of commentary on that observation? Or if it’s generally intentional in any way?

JM: I never really think of it in terms of commentary. I guess what I'm hoping to do is bring ideas that have traditionally been relegated to less accessible corners of the contemporary art world to a new group of people so that they can unpack them from a different vantage point.

I think this is especially true with the new physical space we're doing with Yeche Lange in New York. It's a way of introducing traditional audiences to something that they haven't really had a way of viewing. With my traditional art friends, NFTs were just some sort of obscure esoteric interest that you really had to pour yourself into in order to understand. A lot of them didn't have the patience or didn't know where to begin. At the time, around 2021, NFTs also all looked the same—some pixelated animal with a rhyming name with different baseball hats and different 3D glasses on it. To me, this seemed so bizarrely reductive, it's like it's closer to stamp collecting if stamps had more limited imagery.

In the gallery, we're going to have a massive DriFella painting. To me, DriFellas represent some of the most radical image-making I've seen in the last 15 to 20 years. They stand out compared to what's been happening in the art world in recent decades, where the most advanced stances orbited around this sort of cynical celebration of the limpness of painting as an uninventive space, maybe seen most directly with artists like Michael Krebber or Merlin Carpenter.

DriFellas pick up from a very different place. The artists behind it aren't even engaging in those conversations, or if they are, it's through a completely different lens. The way they approach image-making, and the entire process, contrasts sharply. For instance, telling an artist to create a 3K or 10K piece collection is unfathomable to them. They may say, “I don't know if I'll ever make 3,000 works in my life. That seems psychotic.” DriFella’s approach involves creating a few thousand image components that are pseudo-randomly collaged together according to predefined weights. The resulting images feel like violent onslaughts of stickers piled on top of each other, creating complex foreground-background relationships. These are some of the most exciting images made in decades.

This crypto world has been opaque to the traditional art world—unless you're a degen or on crypto Twitter, you don't know where to look. You and I know how to browse collections on Tensor or OpenSea, but even then, you might miss something. Compared to the traditional art world, where you can visit a gallery website or do a Google search, this world is opaque to non-participants. Making the fruits of these cultures visible to audiences beyond collectors has been a huge driver for us to bring Yeche Lange into the form of a physical gallery as a bridge. We’re also launching this Yeche Lange archive platform, cataloging every collection these artists have done, including their IRL work to make all of this less opaque regardless.

KC: People have complained for years about how to display NFTs in physical spaces. How do you think about the relationship between digital art and physical space?

JM: I also think that’s a funny thing about the NFT space. Our gallery will also eventually have screens, as there’s motion work we want to show, but in our first show, everything is painting, sculpture, and prints. We're probably going to be the first NFT gallery to open a show without a single screen. You know how it goes—90% of NFT exhibitions are just flat screens with LED rimmed backs casting arbitrary colors onto the walls. I personally dislike being in dark rooms; I love sunlight, natural light, windows. It's important to me that when we present digital media, we don't confine it to caves. There's an added dimension when you put these things in a room with a window, where a breeze can come in, and you see leaves vibrating outside.

I've always been less interested in structuralism, meaning the belief that digital media should only be displayed digitally. This notion echoes through contemporary art and modernism in slogans like "the medium is the message," and similar ideas advocating that paintings should remain flat because that explores their full potential. In this modernist belief system introducing three-dimensional elements to a traditionally flat medium is a perversion of its essence. I’m more interested in them as substrates that carry communications. For me, these are historical conversations that demonstrated a point but should ultimately be moved beyond. Ultimately, the printing press wasn't just about the mechanics of printing but about distributing the Bible. I think of digital displays not just as screens but as another medium through which ideas are transported—like the literary form.

Many of the “sophisticated” NFTs in recent years have structurally engaged with blockchain, but I see those as inevitable conversations. Like in cinema, I'm not particularly fascinated by artworks like Tony Conrad’s sculptures where celluloid is put in pickle jars and this triple underlining this point that these too count as films. I'm more interested in Godzilla vs. Kong which engages with the core components of cinema from a less form and structural underpinning-obsessed place.

KC: Yeche Lange launched an NFT collection in 2022 “Galerie Yeche Lange Pie Keys” meant to act as a premium membership key of sorts. Any reflections or takeaways from that experience?

JM: Well, that was an interesting collection because it was released for free, serving as a way to introduce people to the gallery. I never expected it to become particularly popular. I imagined some would know about it, but many might remain unminted, with my friends being the primary audience. However, it minted out almost instantly, generating insane volume in a couple hours that basically funded the gallery treasury for two years.

There were a lot of interesting takeaways. Initially, I wasn't interested in making a 10K collection, I romanticized the idea of a single painting that takes 10 years, and we wanted to create a vehicle that allowed artists to work that way. I think a lot of us were thinking about this at the time. We wanted to have a vehicle that could allow artists to work within this medium that also had a way of bringing traditional artists onboard. For them, it might be unfathomable to create a 10K collection, but it was very easily conceivable to create a very labored single image or 3D object or whatever art object it is. However, between the Pie Keys and the gallery launch was the crash, and the meta changed drastically. Artists who had been market darlings in 2021, whose collections always minted instantly, suddenly saw 10 1-of-1 collections sitting for weeks. Our approach was to bridge worlds, aiming for price points comparable to what artists would get from traditional galleries. But post-crash, these prices seemed steep to even many degen collectors, and they were less interested in flipping single works. Our initial idea of bridging these worlds was shaken and has since remade its face many times.

KC: Launching an NFT collection at that time would’ve looked very different than launching one the year before or the year after, I think.

JM: A huge change we’ve seen as we’ve watched art scenes, particularly the scene that comes out of little swag world, is that there used to be a general degenship where people would buy Pepes and also generative art and also Murakami NFTs and also Petra Cortright because “someone in the trad art told me she’s in museums, so I should get that.”

There’s become much more focused collectorship recently, people who are really diehard. When I look at the traditional art world, I see a huge gulf generationally. You see the values of many traditional art collectors rooted in wanting to see their names on the steps of the opera house or have museum wings with their names above the entrance, but their kids aren’t really stepping up to fill those shoes in the same way. The majority of collectors that are interacting with projects like DriFella are participating at a very different price point but they are infinitely more engaged—eating, breathing, sleeping art from these scenes and spending huge swaths of their life in group chats focused on them.

I think collecting art is going to look different. Right now, a huge number of traditional collectors define the market by buying $200K paintings, but there are fewer degens willing to spend $200K on a 1-of-1. Comparatively, there's another economy where 500 people buy 100 things each for $10 to $100 a pop.

When I look at these two worlds, I see one slowly declining and in dire need of reinvention, and another burgeoning with extremely genuine engagement and consistent dedication that will grow as the scene matures. Many of these hyper-engaged younger collectors are under 30, which is a rarity in the traditional art world. These people are going to own homes and want art on their walls, but not the same decorative paintings you see in Chelsea, etc.

KC: You’ve mentioned in past interviews your belief that we shouldn’t necessarily laud the complexity of technology. How do you think people should engage with technology in the context of art?

JM: For myself, personally, when I first got out of school, it was very in vogue to transpose a structural conversation onto painting. There was a cliché from the late 80s into the 90s of "painting is dead," and a whole generation of painters reacted to the 90s with exhibitions like Sensation, which Damien Hirst, Tracey Emin, and others emerged from. These shows were sensationalistic, featuring things like a rotting cow carcass in a glass box being gnawed on by flies or a tent covered with the names of hundreds of people the artist slept with. You had this group of painters that were interested in structural conversations that looked up to conceptual artists like Tony Conrad. They aimed to show that painting didn't have to be a "dumb thing," it was like “I'm making paintings, but I'm doing it in this very self reflexive way.” You had these projects that had very indexical relationships to the labor that went into them. 20-foot canvases were being painted 24/7 by studio assistants in specific colors demarcating the length of their shifts and other metrics of their labor being visually charted out. All this felt like a very heavy justification, kind of like math class, where it was this kind of “show your work” approach.

It’s similar to why I'm more interested in DriFella than in code-based projects generated entirely onchain, which embody hardcore blockchain structuralism. DriFella engages with NFT cultural history in a very direct way, akin to how Godzilla reboots engage with cinema, not in a Derek Jarman’s Blue-like manner, where you stare at a blue screen for two hours to investigate materiality or mechanics.

KC: What do you hope Yeche Lange looks like 1, 2, 5 years from now?

JM: I guess I'd like to see these conversations just engaging with a wider audience, meaning, I think it's two things I'd like to see.

One is creating a bridge to make these conversations legible to a broader audience. We're working on a book, similar to the archive, cataloging these projects. These efforts speak to the diehard audience, degens or whatever you want to call them, but also serve to bridge to others not participating as directly.

I saw this at a party we did with Club Chess and Zora. We had Bosch's game set up due to its similarity to chess. A woman who had recently moved from Texas introduced herself saying she liked my work naming projects I had only really ever shown on Twitter. I asked if she collected NFTs, and she said, "No, I don't even have a wallet, but I love the art." She mentioned Wretched Worm and Bosch as favorites. This was exciting to hear because it showed that these projects have legs to reach outside the NFT sphere.

On the other side of this, many projects within the NFT sphere are still in their infancy. Most projects remain vaguely PFPs, representing something with eyes and a mouth, or engage in a structural way that can seem thin and lacks the ability to speak to a wider audience. They don't evoke the same emotional response as traditional art. I had a teacher in school who said their criteria for art was that their perception of the street was changed when they left the theater. I’m looking forward to seeing more of that onchain.

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

Verifiable compute is poised to become as important of a technological revolution as blockchain itself. It encompasses the commercialization of various cryptographic techniques that can be collectively as impactful for blockchains as HTTPS has been for the internet. While many technological hurdles and barriers to adoption exist, the density of sheer brain power pushing its limits points to an optimistic (or rather verifiable) future.

Verifiable compute and blockchains aren’t only groundbreaking technologies in their own right—they’re also uniquely complimentary. Where blockchains provide economically-guaranteed integrity by having multiple untrusted machines re-execute the same programs and agree on the end state, verifiable compute provides mathematically-guaranteed integrity by proving that some end state is the result of a set of inputs into some program.

Together, these two emerging technologies are poised to expand the design space of what is possible with blockchains, solve the issues present in today’s blockchain designs, and address broader societal concerns that arise from the proliferation of generative AI.

In this piece I will:

• Provide an overview and history of verifiable compute.

• Outline the use cases for verifiable compute.

• Introduce a framework for evaluating verifiable compute projects.

Note to the reader: there will be several abbreviations used throughout—if you are unfamiliar with these terms then you can reference the definitions at the end of the piece.

While verifiable compute is becoming an increasingly important tool for scaling blockchains, it is far from a new concept. We can trace its roots all the way back to the commercialization of the internet in the early 90s and, in fact, mathematical concepts even further back to the 1930s.

Kurt Gödel can be considered the grandfather of arithmetization, a key component of verifiable compute protocols. He figured out in 1931, before computers really existed, that you can efficiently encode complex mathematical expressions, logical proofs, or even entire programs into a numerical form. In other words, that’s right, you can turn code into math. While Gödel numbering played a foundational role in the development of mathematical concepts related to verifiable compute, the term as we know it today emerged because of two technological developments following the commercialization of the internet.

The first was distributed computation. Several projects emerged in the late 1990s and early 2000s that experimented with this idea, namely the Mersenne prime search in 1996, SETI@Home in 1999, and Folding@Home in 2000, all of which distributed computations to millions of internet clients to take advantage of their idle cycles. While the idea was novel, an issue that all of those projects faced was dishonest clients—end users could modify their client software to return plausible results without performing any actual work.

The second development was cloud computing. As cloud computing became more mainstream in the late 2000s, applications began outsourcing computation to commercial cloud computing services and increasingly wanted guarantees that the cloud performed the computation correctly to protect against faulty or malicious providers.

Before we continue, here is a quick refresher (or introduction) on computational complexity. Ideally we want constant or logarithmic complexity because that is when it becomes commercially viable at scale.

The initial cryptographic techniques for addressing these issues were introduced around the late 80s and early 90s, namely Interactive Proofs (IPs) in 1985 and (non-interactive) Probabilistically Checkable Proofs (PCPs) in 1992. In particular, Shamir’s IP=PSPACE paper in 1992 paved the way for people’s understanding around the potential of proof systems by demonstrating that any computation (which requires a generally practical amount of memory to solve) can be efficiently checked for correctness through the use of interactive proofs. These breakthroughs, however, were simply not commercially viable. IPs were exponential-time for the prover and PCPs were so long (superlinear-size) that it would have taken thousands of years to generate and check them.

Innovation began accelerating over a decade later in 2007 with two key advancements. One was the introduction of more performant Argument systems (IKO07) which were converted from PCPs and offered a succinct prover and more efficient verifier while maintaining computational soundness (i.e., making the assumption that a malicious actor does not have unlimited computing power). Another, GKR08, introduced an interactive proof protocol with a polynomial-time prover (prior protocols had superpolynomial-time provers) and a quasilinear-time verifier. That said, both were still completely impractical if implemented as-is.

It took five more years for researchers to propose notable improvements to these protocols in 2012, with Pepper refining IKO07 to achieve better costs (but still had a quadratic-time prover so did not scale to large computations) and CMT12 reducing the prover time in the GKR protocol from quartic-time to quasilinear-time. 2012 also saw an introduction of new “underlying cryptographic machinery”, which served as the theoretical foundation of the first SNARK that remains popular today-Groth16 (introduced in 2016).

Since 2016, advancements in proof systems have only continued to accelerate, with recursive SNARKs, STARKs in 2018, and folding schemes in 2021 making generating and verifying proofs orders of magnitude cheaper and less computationally intensive. Today, proofs for many common computational tasks can be generated in a matter of seconds using commodity hardware. While the performance still isn’t ideal for many blockchain applications, it is generally believed to be commercially viable at scale.

The easiest way to verify a computation is to simply repeat the computation, but that’s clearly inefficient and results in wasted time and energy. Techniques for verifiable compute aim to run computations on your behalf while providing assurances around the validity of the computation’s inputs, outputs, and methods used.

While people often think of zero-knowledge proofs (ZKPs) when they think of verifiable compute, it is important to note that these concepts are related but different. Verifiable compute is a broader concept that encompasses various techniques for ensuring computational integrity. It doesn't necessarily involve zero knowledge and the goal is moreso to prove the correctness of computation rather than to preserve privacy. Moreover, zero knowledge is an additional property that verifiable compute protocols can obtain.

That said, there are similarities between the concepts. Both ZKPs and verifiable compute utilize cryptographic techniques designed to establish trust and verify information. In addition, the primary constraint for both concepts is that the verification of the computation should require substantially less computational effort than performing the computation from scratch.

The traditional framing of zero-knowledge proofs is that they provide a way to cryptographically prove knowledge of a particular set of information without revealing what that information is. While this framing focused on proving simple statements, recent advances have unlocked breakthroughs in "general-purpose" ZKPs that allow you to prove arithmetic circuits, which can be encodings of real programs. These circuits are fundamentally a series of mathematical statements that are used as the basis for the proof. The circuit verifies details about the program’s execution, such as:

• Who called the program’s function?

• Did the caller pass correct inputs to the program?

• Did the execution use the correct program?

• Was the final output the result of correctly executing the program with the provided inputs?

In other words, you are effectively modeling the physical logic gates and circuits that make up a program as arithmetic gates and equations that give you an equivalent output for that program. The broader technique involves a programmer specifying a computation in a high-level language and a compiler transforming the computation to the formalism that the verification machinery uses, ultimately outputting executables that implement the verifier and prover. In short, ZKPs turn code into math.

There are other tangential technologies that are often associated with verifiable compute and it is important to clarify the differences. Fully homomorphic encryption (FHE) is a technique that allows computations to be performed on encrypted data without revealing the plaintext. While FHE is also used for privacy, and in fact provides stronger privacy properties because it ensures full-state privacy, the technology does not ensure the inherent correctness of the computation itself. Like FHE, trusted execution environments (TEEs) are also primarily used for confidential compute, but do not provide verifiable compute. TEEs ensure computational integrity with an attestation-based trust model that provides verification for three things: the application’s identity, its intactness (that it has not been tampered with), and that it is running securely within an enclave.

While researchers and developers have made tremendous strides in commercializing the technology over the last decade, we still have a long way to go.

Verifiability does not come for free. There is an inherent overhead for proving computation, and it is high for cryptography-based systems. From one source, it is currently approximately 1,000x to 100,000x more expensive to generate a ZKP for a computation compared to natively performing the computation. From another source, the Jolt prover, which is currently 6x faster than other currently deployed zkVMs, it is 500,000x slower relative to native execution of a RISC-V program. In other words, proving one step of the RISC-V CPU requires ~500,000 cycles of the RISC-V CPU.

The good news is that the efficiency of chips (power usage per unit of compute) improves exponentially over time, so we can expect costs to gradually converge to the cost of native compute. Even still, without additional advances in hardware (e.g. ASICs for ZKPs) and algorithms (i.e. even more performant proof systems), it is hard to imagine the overhead being less than 100x over the next decade. And for mainstream adoption, the power required to produce a proof likely needs to be a fraction of the percentage of power required to do the computation, rather than orders of magnitude more.

That said, I believe that there will be increased societal value placed on verifiability, which will translate into a willingness to pay for commercial use cases for verifiable compute technology, especially in low-trust environments like blockchains.

We already see this today with ZK rollups (ZKRUs). If it costs a ZKRU ~$200K/month to run a prover but they earn ~$1M/month in sequencer revenue, the economics clearly make sense. Naively, one could argue that the economics already make sense from a cryptonetwork perspective. If Ethereum has 7,500 nodes (at the time of this writing), each of which re-executes every transaction, one could argue that a ZKRU with 5,000x overhead is still cheaper for the system as a whole.

We will see more data points here over time. For example, if consumers are willing to pay more for a device (e.g. cameras, microphones) with an attested sensor (a TEE that stores a secret key for digital signatures), then we can say that GenAI is likely driving real concern over the authenticity of content.

Despite the power of verifiable compute, it remains largely inaccessible to the vast majority of developers. Domain specific languages (DSL) like Circom, Noir, Leo, Lurk, and Cairo are effective but require large communities and comprehensive libraries to be adopted at scale. Transpilers are slow, and virtual machines that provide abstraction come at the cost of an additional 100x to 1000x overhead.

Ideally developers can avoid thinking about circuits, constraints, and arithmetization altogether, and the developer experience is as close to that of the native blockchain on which the developer is building their application. The performance trade-off is glaring here, but I would argue that the experience as-is caters to too niche of a developer base to generate enough experimentation with new use cases (for if the use case is compelling enough, there will likely be a willingness to pay from users). Furthermore, developers need simpler deployment workflows that incorporate verifiable compute for their applications, including the underlying infrastructure and integration with onchain components.

The blockchain industry often frames use cases through an introspective lens—L1s, EVMs, defi, bridges, etc. But because the use cases for verifiable compute can extend beyond blockchains themselves, they deserve a broader framing that extends beyond those we see in production today.

The first production use case for verifiable compute has been privacy, starting with Zcash in 2014 and applied to projects like Aztec in 2017, TornadoCash in 2019, and Nocturne in 2022. These use cases were particularly focused on payments and aimed to make private the sender, receiver, and/or amount of a given transaction.

To provide a more tangible intuition of what exactly is being “proven” for privacy-related use cases, the zk SNARK for Zcash proves the following:

• Knowledge of Spending Key: The transaction creator knows the spending key corresponding to the notes they intend to spend without revealing the keys themselves.

• Unspent Note and No Double-Spending: Each note being spent has not been spent before, managed through nullifiers, which are cryptographic values derived uniquely and deterministically from the spending key and the note. The nullifiers for spent notes are revealed and checked against a list of all nullifiers from previously spent notes.

• Conservation of Value: The total value of inputs equals the total value of outputs.

• Merkle Tree Validity: The spent notes exist in the blockchain's current Merkle tree of all notes (spent and unspent).

The general issue with mainstream adoption of these solutions has been difficulty with regulatory compliance. In response, new projects have been leveraging verifiable compute to provide certain guarantees around this. For example, a user can generate a proof saying that “I didn’t interact with anyone on the OFAC list.” It is also possible to make the privacy properties adjustable and “pull back the privacy curtain” if necessary. For example, one can reveal the connections between addresses but keep the values and amounts hidden. This paper has a good overview of what can be done and what fundamentally can’t. This use case actually does demand the zero-knowledge property that ZKPs provide, but most other use cases rely on the succinctness properties of ZKPs.

The second production use case has been around succinctness, which can be reframed as state and compute compression. ZKPs can be used to post a proof onchain that says “here is some batch of valid transactions whose execution leads to some valid new global state.” There are several blockchain-native use cases here, notably ZKRUs and coprocessors. ZKRUs scale the execution throughput of blockchains by maintaining a partitioned persistent state that is periodically posted and finalized at the settlement layer. Coprocessors allow blockchain applications to use offchain compute while accessing the full state of the underlying chain without adding any trust assumptions to the application itself. The primary difference between ZKRUs and coprocessors is that the former is stateful while the latter is not.

Compute compression can also improve security for blockchain interoperability. Instead of relying on economic or game theoretic security, projects can efficiently verify proofs that the data posted on the destination chain has been signed by the validator set on the source chain, thus implementing “proofs of consensus.” In other words, instead of verifying a bunch of signatures onchain, which is generally cost-prohibitive, you verify a proof of a circuit that verifies a bunch of signatures. This is the basis for the “ZK bridges” we see coming to market today.

To provide a more tangible intuition of what exactly is being “proven” for compression-related use cases, a proof for a ZKRU proves the following:

• Validity of Transactions: Each transaction in the batch has been authorized by the owner of the funds.

• Correct State Transitions: The application of the state transition function is correct, including whether balances are updated correctly and ensuring that inputs, outputs, and fees are accounted for correctly.

• Conservation of Value: The sum of inputs equals the sum of outputs plus any transaction fees.

• Accurate State Root: Confirms that the transition from the old state root to the new state root is valid.

• No Double-Spending: Each account transaction must increment the nonce correctly.

Another example of an application using this property is onchain games. Instead of verifying individual actions of a game onchain, users can simulate the game locally and provide a proof of their game’s end state which can then be trustlessly verified. This also computationally enforces the game logic because the proof will be invalid if someone submits a forbidden action.

Verifiable compute can be used to prove that some piece of data from either onchain or offchain sources, as well as any additional computation on that data, is accurate and has not been tampered with. This enables a class of oracles that can be framed as verifiable oracles, which can be used in a wide range of applications, such as price feeds for decentralized exchanges and ingesting arbitrary web2 data and identity onchain. Verifiable compute can also be used to scale oracles in general by performing price updates and signature verification offchain in a circuit and posting proofs containing the signed updates onchain. It can also be used for building verifiable databases, which ensure data provenance by proving correctness of both data injection and retrieval.

To provide a more tangible intuition of what exactly is being “proven” for data integrity-related use cases, the proof for a zkTLS project like DECO proves the following:

• Data Possession: The user (prover) possesses a value that corresponds to specific data obtained via a secure TLS session.

• Data Origin: The data originated from a specific server that is verified through the server's TLS certificate.

• Data Integrity and Freshness: The data is unchanged from what the server sent and was retrieved in a timely manner.

A note on oracles: one can consider something an oracle if it breaks the chain of custody of the data that is being provided. Any data ingested onchain from offchain like ML models or exchange feeds is certainly considered an oracle, but what about offchain computation on L1 state or execution of L2 state? In an attempt to further categorize this, I suggest that all coprocessors are also oracles, specifically verifiable oracles, but not all oracles are coprocessors. In addition, verifiable oracles can be considered coprocessors if they perform compute on the data being provided along with a proof for the output of that computation.

A related use case is storage proofs, which proves the value in a storage slot for an address at a specific block. The “proof” here is not in the context of a ZKP but rather a light client proof which consists of the block header, state root, and Merkle-Patricia inclusion proofs for the key-value pairs of account data in the state trie and storage slot data in the storage trie. Verifying this light client proof requires checking that the block header, state trie proof, and storage trie proof is correct, which involves many Keccak hashes and is very expensive to do onchain. Instead, one can do each of these checks directly in a circuit and only need to verify ZK proof onchain, which is much cheaper.

Verifiable compute also has applications for machine learning (often referred to as “zkML”) by proving one or more steps along the “ML pipeline”. zkML can be used to delegate the execution of machine learning models to service providers and obtain proofs that a particular trained model produces the correct output given some inputs (i.e. verifiable inference), which ensures transparency and prevents model substitution. There are different flavors of verifiable inference: public model/private (but signed) input like Worldcoin, private model/public input like credit scores, and private model/private input like KYC.

It can also be used for verifiable training by generating proofs that the correct dataset and learning algorithm were used in the creation of a model (e.g. proving that no data poisoning occurred in a dataset of non-copyrighted works). zkML can also be used for proving specific properties of a model, such as fairness (e.g. the model is not biased), which can be important for certain applications, such as granting university admissions, assessing loan applications, and distributing medical resources. In a blockchain context, zkML effectively acts as an oracle that ensures the integrity of external models and their outputs that can be used to update mission-critical blockchain state.

In terms of the proofs themselves, the components being “proved” are generally:

• Correct Application: A specific model and set of parameters (e.g. weights of a neural network) were used to compute the output from given inputs.

• Integrity of Parameters: The parameters used are the ones claimed (i.e. they haven't been tampered with or altered).

• Correct Execution: Each step in the computation (e.g. for each layer in a model) was executed correctly.

To dive deeper into the intuition behind zkML, it is helpful to revisit ZKPs. In general, ZKPs enforce consistency between the circuit, committed inputs/outputs, public inputs/outputs, and intermediate values within the circuit (these are generally either committed to and/or not revealed at all to the verifier). Note that the only difference between a committed vs. public input/output is that the former is private (i.e. the prover says "I know some x such that...") while the latter is public (i.e. the prover says "here is x - you can check it for yourself!"). In other words, a ZKP shows that a circuit which takes in some committed/public inputs indeed produces the claimed committed/public outputs, with consistency between those and some set of intermediates which the verifier doesn't care about. Applying this framework to zkML, we have:

• Circuit ≈ model architecture.

• Committed inputs ≈ model weights, which are hidden but consistent between runs (enforcing the same parameters to be used each time), and potentially also private/hidden inputs.

• Committed outputs: not commonly used in zkML unless you want the outputs to be private and perhaps further used in another ZK computation (e.g. proving some property about the private output of a model).

• Public inputs ≈ model inputs, although this can also be model weights for public/open-source models too.

• Public outputs ≈ model outputs.

• Intermediate values within the circuit: typically not used in zkML.

One of the magical capabilities of certain verifiable compute systems, namely SNARKs and incrementally verifiable Computation (IVC) schemes, is recursion, which is the ability to generate “proofs-of-proofs” by using the output of one proof as the input for another proof. This can decrease the cost of verification by aggregating proofs from different sources and recursively proving them into a singular proof, amortizing the cost for all participants involved.

Proof aggregation can generally be applied to scalability (e.g. a ZKRU ecosystem aggregating proofs from its L3s and L2s to settle onto the L1) and interoperability (e.g. composing proofs from different ZKRU ecosystems to enable more secure and seamless value transfer across them).

It can also be looked at from the perspective of vertical scaling (i.e. rolling up lots of disconnected proofs for blockchain-level scaling) and horizontal scaling (i.e. providing more expressive and interconnected programs).

Verifiable compute expands the design space of blockchains by enabling them to interact with external systems and data in a secure and mathematically verifiable way. In addition, it scales blockchains by removing the need to re-execute the same computations while guaranteeing that the execution was done correctly and effectively, enabling infinitely complex smart contracts that can bypass a blockchain’s gas limits.

On top of all of this, recent advancements in generative AI like OpenAI’s Sora have placed a significant societal pressure on ensuring verifiability and further commercializing the underlying cryptographic techniques. The drive is a fundamentally human one; in a world where we do not know what is real and what is AI-generated, verifiability impacts the identity of both the species and the self.

Despite the issues with performance and cost, we are close to achieving the dream envisioned 33 years ago, where: “...a single reliable PC can monitor the operation of a herd of supercomputers working with possibly extremely powerful but unreliable software and untested hardware.” -Babai, Fortnow, Levin, Szegedy, 1991

If you are building infrastructure or applications around verifiable compute, please reach out!

You can find me on Twitter/X and Farcaster.

Many thanks to Srinath Setty, Thor Kampefner, David Wong, Shumo Chu, JD Kanani, Ben Livshits, Tom Walton-Pocock, Kobi Gurkan, and Ryan Cao for conversations and feedback on this piece.

• Verifiable Computation: Enables, through a variety of cryptographic techniques, a computationally weak client to “outsource” computation to a worker with more computational power, who then returns the result along with a proof that the computation was carried out correctly. Verifying the proof should require less effort than performing the computation from scratch.

• Arithmetization: The process of turning a generic statement or question into a set of equations to be verified or solved.

• Proof: a logical demonstration or evidence that establishes the validity of a statement.

• Interactive Proof (IP): An iterative exchange that permits the verifier to pose random queries and the prover to demonstrate the validity of a statement.

• Probabilistically Checkable Proof (PCP): A proof system where the proof's validity can be efficiently verified probabilistically and with high confidence by examining only a small, randomly chosen portion of the proof.

• Interactive Oracle Proof (IOP): A generalization of PCPs and IPs, where a prover interacts with a verifier by making queries to an oracle, aiming to convince the verifier of the correctness of a statement.

• Argument of Knowledge (the ARK in SNARK): A protocol where a prover can convince a verifier of the truth of a statement, while additionally demonstrating knowledge of some secret information related to that statement. An “argument” of knowledge, as opposed to a “proof” of knowledge, relaxes the inflexibility of proofs by stipulating that certain computational tasks are infeasible for the prover.

• Succinct Non-Interactive Argument of Knowledge (SNARK): A protocol where a prover can convince a verifier of the validity of a statement without revealing any underlying information, while maintaining a small and efficiently verifiable proof size. IPs, PCPs, and IOPs can all be compiled to a SNARK by utilizing polynomial commitments (for succinctness) and applying Fiat-Shamir (for non-interactivity).

• Incrementally Verifiable Computation (IVC): An ARK for incremental computations; enables producing proofs of correct execution of “long running” computations in a way that can be efficiently verified in an incremental manner.

• Folding scheme: A protocol used for IVC that is weaker, simpler, and more efficient compared to direct proof composition approaches where a verifier is implemented in a circuit. IVC proofs are usually constructed using a folding scheme and a SNARK at the end to finalize the proof.

• An incomplete guide to Folding

• Delegating Computation: Interactive Proofs for Muggles

• Do You Need a Zero Knowledge Proof?

• Efficient Arguments without Short PCPs

• Intro to Zero-Knowledge: A Guided Tour of the ZK Landscape, Part 1

• Incrementally Verifiable Computation or Proofs of Knowledge Imply Time/Space Efficiency

• KZG in Practice: Polynomial Commitment Schemes and Their Usage in Scaling Ethereum

• Making Argument Systems for Outsourced Computation Practical (Sometimes)

• Modern Verifiable Computation

• Non-Interactive Verifiable Computing: Outsourcing Computation to Untrusted Workers

• Polynomial Commitments

• Practical Verified Computation with Streaming Interactive Proofs

• Privacy-Protecting Regulatory Solutions Using Zero-Knowledge Proofs

• The Anatomy of Proof Generation

• The knowledge complexity of interactive proof-systems

• The Lifecycle of a ZK Proof

• Verifying Computations Without Reexecuting Them

• Zero knowledge proofs for gaming: how it works & why do we need them

• Zero-knowledge rollups | ethereum.org

• Zero Knowledge Virtual Machine Step by Step

• zkVMs are cool, but have you heard of zkCPUs?

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

In the nine years since Ethereum launched the first decentralized, programmable blockchain, crypto has faced multiple roadblocks in the quest to scale decentralized applications to billions of users. And in order to develop scaling solutions to address this, the crypto industry has continuously funded and developed entirely new types of blockchains to solve the “performance problem.”

However, the “performance problem” has been poorly defined and quantified. Synthetic memes such as “transactions per second” have neatly packaged what are really apples-to-oranges comparisons between transactions that do not require equivalent computational work. The lack of nuance in these metrics also shrouds our ability to assess the independent impacts of a blockchain’s components on performance, distracting us from a principled approach to identifying the sets of optimizations we can make to solving highly interdependent problems.

Despite this fog, we have seen credible, sustained improvements to blockchain scalability play out over the past few years. As Ethereum powers through its rollup-centric roadmap, a new wave of rollups, coprocessors, data availability (DA) layers, and competing L1s are emerging, each with unique design choices to provide developers with more performant environments for building scalable, user-friendly dapps.

Today, the introduction of EIP4844 and alternative DA layers have alleviated the critical DA bottleneck. Despite this critical milestone, evidence suggests other important bottlenecks must be solved. Last month, Base collected $1.57M in transaction fees in a single day while paying only $5K in data availability costs to Ethereum. This suggests that the computational work required to validate and process state updates remains a critical bottleneck and an opportunity for improvement.

This piece will evaluate the design choices made by both integrated and modular execution environments in their path to solving for higher performance and expanding the scope of applications that can live onchain.

The performance of an execution layer can be benchmarked according to the computational work that executing nodes achieve relative to their chains block time, or  "gas computed per second.”

With this in mind, we can narrow execution layer bottlenecks to two interconnected factors: inefficient state access and inefficient computation.

Inefficient state access refers to the overhead of retrieving and updating the blockchain's state, which can slow down transaction processing. On the other hand, inefficient computation is a function of the overhead incurred by the algorithms executing operations and state transitions, which can include everything from simple transfers to complex smart contracts and signature verifications.

These bottlenecks are mutually reinforcing—delays in state access can prolong the time to computation, while inefficient computational practices can strain state management. Moreover, proposed improvements to addressing these issues often require systemic improvements such as sharding or adopting stateless architectures, which enhance state access and computation efficiency to improve execution performance.

The cost and speed required to access the state of a blockchain are critical bottlenecks to performant execution environments and can be distilled to the issue of state bloat.

In blockchains, the state of the world is managed and updated through specific data structures called trees. Trees are integral to blockchains, providing a secure and efficient way to give parties external to the executing node guarantees around the blockchain's correct state. Each update within a trie generates a new root hash, which light clients can reference to verify transactions and account balances without maintaining the entire chain.

Ethereum specifically relies on a data structure known as the Merkle Patricia trie (MPT), which comprises four sub-tries.

As Ethereum adds more smart contracts and tokens to its state, its state trie becomes larger and more complex. As the state grows, it requires more storage space, more computational resources to process, and more bandwidth to transmit. At the same time, the node’s hardware constraints stay roughly the same.

This state growth directly impacts Ethereum's performance because the state is stored in disk, and disk operations incur a high overhead. While accessing data from a CPU register can take 0.1 nanoseconds, it can take between 10 and 100 microseconds (100x–1000x slower) to access data from a disk, roughly translating to 200,000 CPU instructions that could have been executed in that time. That equates to a conservative estimate of 36 ERC-20 transfers that could have been!

Exacerbating this issue, blockchains have many inefficient access patterns for reading and writing to state. For example, the non-sequential structure of the Merkle Patricia trie inherently leads to these disk input/output (I/O) operations reading from and writing to various unpredictable locations on the disk. The random nature of transaction inputs and the subsequent state changes they trigger lead to a scattered data access pattern which significantly slows the process of verifying and updating state and only utilizes a portion of a hardware device’s capacity.

All in all, the state management primitives for blockchains are far from achieving their absolute potential, and numerous advancements can be made to improve computational efficiency.

Execution layers also face the bottleneck of inefficient computation, which manifests in various ways.

For one, many process transactions sequentially, underutilizing modern multi-core processors capable of handling multiple operations simultaneously. This sequential execution leads to inevitable CPU idle times between transactions, wasting valuable computational resources.

Additionally, using virtual machines involves translating high-level smart contract operations into bytecode—a lower-level, platform-independent code—which is then executed instruction by instruction. This translation and execution process introduces significant overhead, especially for applications with complex and frequently repeated application-specific tasks.

These inefficiencies lead to suboptimal utilization of computational resources and hinder the performance of execution layers.

There are a few distinct ways that teams are improving the rate at which state can be retrieved and updated from an executing node’s hardware, including simplifying complex data structures and finding ways to reduce the costly disk I/O operations that lead to state bloat.

Some execution layers address state bloat by simply accepting it in the short run. They shift state data storage from slower disk-based systems to faster random access memory (RAM). Accessing state information in RAM significantly reduces the overhead associated with disk operations, which are slower and more resource-intensive.

However, this approach challenges the core principle of decentralization. Storing increasingly large amounts of state data in RAM necessitates more advanced and expensive hardware, which could limit the ability of individuals to participate as node operators. Consequently, as the hardware requirements escalate, fewer entities can afford to run these nodes

To balance the attractiveness of computing in memory with trust minimization, both L1s (such as Ethereum) and L2s are pursuing a scalability roadmap that relies on unbundling the role of a validator into separate, centralized executing nodes with many verifying nodes. In this model, highly performant block producers with the hardware requirements to compute in memory are responsible for generating blocks, and cryptographic proofs (fraud and validity proofs) are leveraged by verifying nodes to keep block producers accountable.

As a result, these systems should allow block producers to maximize their speed because they can be expected to compute in memory, eliminating disk I/Os completely during execution. Because the latency of RAM is typically under 100 nanoseconds, the latency of state access is reduced by up to 1000x relative to disk-based implementations.

In parallel, fraud and validity proofs are used in place of decentralized consensus to scale the trust-minimization properties of the system along with its throughput. As a result, powerful, centralized block-producing nodes are counterbalanced by verifying nodes that can be run on much less expensive hardware. These nodes perform the critical function of independently verifying proofs of state transitions (or invalid state transitions) to maintain an accurate view of the state without the burden of storing the entire blockchain state.

To facilitate this process in a trust-minimized fashion, execution layers must implement a degree of statelessness, the most popular being the concept of  "weak statelessness.” Weak statelessness is achieved by mandating that block producers provide a cryptographic attestation known as a witness to a verifying node. This witness encapsulates all the proposed state changes by the new block, enabling validators to verify these changes without additional historical data.

Although this concept can be applied using various tree structures, Verkle trees are often preferred to Merkle trees for their efficiency. Merkle trees require the inclusion of all sibling node hashes along the path from a data point (leaf) to the tree’s root to prove data integrity. This requirement means that the size of the witness (the proof of integrity) grows with the tree’s height, as each level necessitates additional hashes. Consequently, verifying data integrity in Merkle trees becomes computationally intensive and costly, especially for large datasets. In contrast, Verkle trees streamline this process, reducing the overhead associated with computation and storage in generating and verifying new blocks.

Verkle trees enhance the structure of traditional Merkle trees by streamlining the connections between leaves and the root and eliminating the need to include sibling nodes in the verification process. In a Verkle tree, verifying a proof involves only the value at the leaf node, a commitment to the root node, and a single vector commitment based on polynomial commitments, which replaces the multiple hash-based commitments found in Merkle trees. This shift allows Verkle trees to maintain a fixed-size witness, which does not increase with the tree’s height or the number of leaves verified, significantly improving the efficiency of storage and computation during data verification.

Over the coming years, we'll see implementations of statelessness happen at the L1 and L2 levels with varying configurations. According to the latest Ethereum roadmap, validators can rely on block builders to provide Verkle proofs regarding the state of certain blocks and verify these lightweight proofs instead of maintaining Ethereum's state directly.

At the L2 level, teams like MegaETH are actively applying the concept of statelessness to the design of optimistic rollups. In their design, the sequencer node generates a witness for each block containing the necessary state values and intermediate hashes while emitting a state delta representing the changes in the state. Verifier nodes can then re-execute any block by retrieving the witness from the DA layer or a peer-to-peer network without storing the entire state. In parallel, full nodes update their state by applying the state deltas disseminated through the network, allowing them to stay synchronized without re-executing transactions or storing the entire state history.

However, it’s also worth pointing out that the benefits of statelessness and the resulting ability to compute in memory isn’t a silver bullet for the performance of the execution layer.

As co-founder of MegaETH, Yilong Li, identifies in the following research presentation on Ethereum execution, there are other inefficiencies to the data structures and access patterns onchain that remain optimized.

Teams working on execution layers are finding ways to improve the structure of these databases themselves to eliminate some of the bottlenecks experienced by Ethereum and other EVM-compatible blockchains in dealing with inefficient state access, which has a domino effect on computational efficiency.

In fact, the limitations of existing database designs found in the EVM informed Monad's* decision to go beyond purely optimizing for computational efficiency to achieve parallelization. Monad found that even after implementing parallel execution, they only saw a small speedup in performance because multithreaded read and write requests to the database blocked each other. As a result, Monad implemented a database compatible with asynchronous IO (AIO), or parallel access, as a critical part of the solution.

I/O operations—such as reading from or writing to storage devices—often create bottlenecks, particularly with mechanical hard disk drives (HDDs). These drives require the physical movement of a read/write head to access data, which can significantly slow down data processing.

AIO addresses this challenge by allowing programs to perform I/O operations concurrently with other processes. Essentially, a program can initiate an I/O operation and move on without waiting for it to complete. It does this by registering a callback function or a promise that the operating system or an I/O library will fulfill once the I/O operation finishes. This asynchronous approach allows the main program to continue executing other tasks, improving overall efficiency by not stalling for I/O tasks to complete.

Asynchronous I/O can be implemented with both traditional HDDs and solid-state drives (SSDs), although the benefits are more pronounced with SSDs. HDDs can perform AIO, but their mechanical nature means they're inherently slower than SSDs which store data on flash memory and have no moving parts, resulting in faster access times.

For instance, Monad utilizes a custom state backend optimized for SSD storage, which supports high levels of parallel data processing and reduces I/O latency. This setup is more efficient than systems relying solely on traditional disk-based storage or those using in-memory databases, which may still face delays from frequent data writes to and reads from slower storage media.

Similarly, Reth employs a method that separates database operations from the core EVM execution engine. This setup allows the EVM bytecode to execute sequentially on a single thread to maintain consistency while database I/O tasks are offloaded to parallel processes. Reth uses the actor model—a software architecture pattern—to manage these parallel processes effectively, ensuring that I/O operations do not interrupt the EVM interpreter.

Another vector for optimization is the state merklization frequency. Ethereum's current model of merklizing state after every block introduces significant overhead, requiring frequent writes to and reads from the disk and continuous trie traversals. Merkle trees usually work by grouping intermediate hashes into sets of 16 (called a node) and storing them in a key-value store database where the key is the node hash and the value is the node itself.

Traversing this tree to find and update data requires one random disk access for each layer of the tree to be traversed, and traversing a naive Merkle tree will require roughly eight sequential database queries per entry.

Solana’s approach of updating the commitment to the state delta only at the end of each epoch allows for the amortization of write costs over many transactions within that period. If a state entry is modified multiple times within the same epoch, each write does not require an immediate update to the Merkle root. This reduces the overall computational overhead associated with state updates during the epoch. Consequently, the cost associated with reading from the state remains constant, or O(1), because the state can be read directly without needing to traverse a Merkle path each time.

Reducing the frequency of merklization in Ethereum could decrease the overhead from state reads and writes, enhancing performance. However, light clients would need to replay block changes to track state between epochs or submit onchain transactions for state verification, and such a change is not currently compatible with Ethereum.

Moreover, layered tree structures within existing Ethereum clients generally cause inefficient state access patterns, further contributing to state bloat. While Ethereum's state is structured as an MPT, it is also then stored in Ethereum client databases such as LevelDB, PebbleDB (utilized by go-ethereum), or MDBX (employed by Erigon) that store data in Merkle trees such as a B-Tree or LSM-Tree.

In this setup, a data structure is rooted into another data structure of a separate type, creating "read amplification" from navigating internal tree structures atop clients that operate under another Merkle tree-based system. Read amplification can be understood as the result of the multiple steps for accessing or updating information contained within a state, which requires navigating the outer tree to find the entry point into the MPT before executing the required operation. As a result, the number of disk accesses for a random read is multiplied by a log(n) factor.

To solve this, Monad natively harnesses a Patricia trie data structure on disk and in memory. From a technical perspective, Patricia tries are often superior to other Merkle tree structures due to their unique combination of space efficiency, efficient prefix matching, and minimal node traversal. The trie's design collapses nodes with single children and streamlines lookups, inserts, and deletions, reducing the number of disks or network I/O operations required. Moreover, a Patricia trie's adeptness at handling prefix matching enhances performance in applications needing rapid partial key searches.

Another bottleneck specific to tree-based structures is that accessing or updating data requires traversing multiple layers, leading to numerous sequential disk accesses. Sovereign Labs addresses this inefficiency by advocating for a binary Merkle tree configuration. This pivotal shift to a binary structure drastically reduces the number of potential paths during tree traversal, directly reducing the hash computations needed for updates, insertions, and cryptographic proofs.

An additional example in this category is the Reth team configuring Reth to pre-fetch intermediate trie nodes from the disk during execution by notifying the state root service about storage slots and accounts touched.

State expiry is a mechanism to manage and reduce the size of the blockchain state by removing data that hasn't been accessed for a set period of time. While expiry is often bucketed under the “statelessness” category, it’s critical to distinguish these concepts in the context of execution.

Statelessness improves execution by increasing an executing node's ability to compute in memory, but the improvements to execution stem from the beefier hardware requirements across fewer nodes that execute transactions. In contrast, state expiry can be applied to blockchains with both few and many executing nodes.

There are a couple of methods commonly discussed for implementing state expiry:

• Expiry by Rent: This method involves charging a maintenance fee, or "rent," to keep accounts active within the state database. If the rent isn't paid, the accounts are archived until a fee is paid to restore them.

• Expiry by Time: Here, accounts are deemed inactive if they haven't been accessed—meaning no transactions or interactions—for a specified duration.

Both methods aim to maintain only actively used data in the immediate, accessible state while pushing out the older, less frequently accessed data to an archived state that does not burden the main system.

By maintaining a smaller and more manageable state, state expiry reduces the "state bloat" that can severely hinder blockchain performance. A smaller state size allows nodes to quickly navigate and update the state, translating to faster execution because nodes spend less time scanning and more time processing.

Sharding optimizes resource utilization and performance by distributing tasks and data across a limited number of specialized nodes (not every node executes a global state).

In a sharded blockchain architecture, the global state is divided into distinct partitions called shards. Each shard maintains its portion of the state and is responsible for processing a subset of the network's transactions. Transactions are assigned to specific shards based on a deterministic sharding function, which considers various factors such as the sender's address, the recipient's address, and the hash of the transaction data. This minimizes the need for cross-shard communication and enables more efficient transaction execution.

This becomes evident when exploring NEAR Protocol’s sharding design, Nightshade, which achieves statelessness to scale sharding without compromising trust minimization.

In Nightshade, the blockchain is structured as a single logical chain, with each block composed of multiple "chunks" and one chunk being allocated per shard. These chunks contain the transactions and state transitions specific to each shard. Including chunks from all shards within a single block allows for a unified view of the entire blockchain state and simplifies the process of cross-shard communication.

Similarly to proper-builder separation (PBS) on Ethereum, Nightshade explicitly delineates the roles of stateful and stateless nodes. On NEAR, stateful validators are assigned to specific shards and are responsible for collecting transactions, executing them, and producing shard-specific chunks. They maintain the full state of their assigned shard and generate state witnesses for validators to use during the validation process.

Meanwhile, stateless validators are randomly assigned to validate specific shards on a per-block basis. They do not need to maintain the full-sharded state and rely on state witnesses provided by the block producers from other shards to validate the state transitions and transactions within a chunk. The random assignment of validators to shards helps ensure the network's security and integrity, as it makes it more difficult for malicious actors to collude and control a specific shard.

Since each node in the network only needs to handle the data for its respective shard rather than the entire network's data, the storage and computational burden on individual nodes is reduced.

Time to address the elephant in the room: parallelization. Parallelizing transaction execution enables processing multiple transactions by utilizing multiple computing resources concurrently. This allows for increased throughput as hardware resources are scaled up during periods of high demand.

However, it's important to consider that multiple execution components can be parallelized, many of which are implemented by coprocessors such as Lagrange* and alternative blockchain clients such as Firedancer to improve blockchains' performance significantly. Specifically, parallelization can involve:

1. Parallelizing State Access

2. Parallelizing Specific Operations

3. Parallelizing Consensus and Execution

Parallelizing state access brings two critical benefits:

1. Parallel EVMs distribute transaction processing across several CPU cores. This setup allows multiple transactions to be handled concurrently rather than forcing them to queue up for a single resource.

2. When a transaction waits for data from storage—which can introduce significant latency—the system doesn’t remain idle. Instead, it can switch to another transaction that is ready to execute. This is possible because multiple cores can handle different tasks independently and simultaneously.

The primary challenge in parallelizing transaction execution stems from managing concurrent access to the shared global state without violating the ACID rules for updating distributed systems. If a blockchain has a bunch of transactions executing in parallel, some of them will conflict. As a result, the two primary methodologies for parallelizing state access differ on when they dedicate resources to resolving conflicts: the pessimistic execution (or memory lock) model and the optimistic execution model.

The pessimistic execution model is a transaction processing approach requiring transactions to declare the state variables they will access (read or write) during execution. This information is included in the transaction's metadata, allowing the runtime to analyze the access patterns before execution.

By examining the read-write access patterns, the runtime can identify transactions with non-overlapping access sets, enabling parallel execution of non-overlapping and read-only transactions and improving throughput. The runtime creates parallel transaction queues for each CPU thread on a validator node, ensuring that transactions with non-conflicting access patterns are processed concurrently.

As a result of this design choice, the pessimistic execution model benefits from fine-grained control over resource allocation, allowing for segmenting or partitioning of a blockchain's state space.

Parallelization effectively creates multiple, synchronously composable independent execution shards underpinned by a unified security model. It helps address network congestion and optimize gas costs through precise resource management and dynamic fee markets. By identifying state-access "hotspots" (areas of high transactional demand), the system can implement targeted optimizations like differentiated fee pricing, rate limiting, or allocating additional resources to high-contention states. It's important to note that Solana's current implementation of parallelization does not fully realize the potential of localized fee markets.

To ensure data consistency in concurrent access, the pessimistic execution model utilizes a locking mechanism. Before a transaction can access a specific state variable, it must acquire a lock on that variable. The lock provides the transaction with exclusive access to the variable, preventing other transactions from modifying it simultaneously. The lock is released once the transaction is executed, allowing other transactions to access the variable.

In Solana's Sealevel runtime, which implements this pessimistic execution model, transactions specify the accounts they will read or write during execution. Sealevel analyzes the access patterns and constructs parallel transaction queues for each CPU thread on a validator node. If an account is accessed multiple times, it is listed sequentially in a single queue to prevent conflicts. Transactions not processed within the leader node's block time are bundled and forwarded to the next scheduled leader for processing.

Unspent transaction output (UTXO)-based systems improve computational efficiency similarly. UTXOs involve specific units of currency—UTXOs—associated with an individual’s wallet. For each of said wallet’s transactions, UTXOs are expended and replaced with new ones; one or more UTXOs are created for the receiver, representing the payment, and another is typically created for the initiator, representing any change due back.

By defining which contracts will be touched, transactions that touch disjoint sets of contracts can be executed in parallel by executing nodes (which can be accomplished in the “accounts”: data model with strict access lists). However, to gain compatibility with Ethereum-style smart contracts, UTXO schemes such as Fuel's constrain block-producing nodes to execute transactions with overlapping access lists sequentially.

Nevertheless, the pessimistic execution model has limitations. Transactions must accurately declare their access patterns upfront, which can be challenging for complex or dynamic transactions where the access patterns may depend on input data or conditional logic. Inaccurate or incomplete access pattern declarations can cause suboptimal performance and potential runtime errors. Additionally, the locking mechanism can introduce latency and reduce concurrency when many transactions compete for the same state variables. This contention can form performance bottlenecks, as transactions may spend a significant portion of their execution time waiting to acquire locks on high-demand state variables.

More importantly, this model places a considerable burden on developers, who must have a deep understanding of their contracts' data dependencies to specify necessary state accesses beforehand accurately. This complexity can introduce challenges, especially in designing applications with dynamic and complex state interactions, such as decentralized exchanges or automated market makers.

In contrast, the optimistic execution model adopts a “speculative” approach to transaction execution, allowing transactions to execute in parallel without needing upfront state access declarations.

Instead of preventing conflicts before they happen, transactions are optimistically executed in parallel, assuming they are independent. The runtime employs techniques like multi-version concurrency control (MVCC) and software transactional memory (STM) to track read and write sets during execution. After execution, the runtime detects any conflicts or dependencies. It takes corrective measures, such as aborting and re-executing conflicting transactions, but can do so by reading from memory instead of disk to identify conflicting transactions.

The optimistic execution model simplifies the development process, allowing programmers to focus on writing contract logic without worrying about declaring state access patterns. Because transactions do not need to declare their state interactions upfront, developers are afforded more freedom in designing their smart contracts, allowing for more complex and dynamic interactions with the blockchain's state. The optimistic execution model is particularly well suited for platforms that support a high volume of transactions and complex dapps, as it can offer higher throughput and scalability than the pessimistic model.

One notable implementation of this model is found in Aptos and the MoveVM of Movement Labs*, which employs a technique known as Block-STM. In Block-STM, transactions are first executed in parallel; then, conflicting transactions are identified and scheduled for re-execution based on the detected dependencies. This approach ensures processing resources are continuously utilized, improving throughput while maintaining the integrity of the transactional workflow.

Despite its advantages, the optimistic execution model also comes with challenges. The need for runtime conflict detection and the possibility of transaction aborts and retries introduce computational overhead and complexity. In addition, maintaining multiple versions of the state and managing the overhead associated with conflict resolution requires sophisticated system design and robust concurrency control mechanisms to ensure the blockchain's integrity and performance.

Block-STM leverages MVCC to effectively manage concurrent writes and maintain multiple versions of data, thereby preventing conflicts between simultaneous write operations. It incorporates a collaborative scheduler to coordinate the execution and validation tasks across multiple threads, ensuring that transactions are committed in the order they were started. This setup minimizes transaction aborts by using dynamic dependency estimation, which allows transactions with dependencies to efficiently wait and resolve these dependencies before proceeding.

Additionally, the account model used by MoveVM differs from that of Ethereum’s EVM, which leads to fewer collisions. In Ethereum, a token is typically managed by a single smart contract, potentially causing multiple token transactions to interact through the same contract address, increasing the likelihood of conflicts. In contrast, MoveVM assigns tokens to individual user accounts, reducing the chance of such conflicts as each transaction usually interacts with different account addresses.

In Monad, the initial set of transactions executed in parallel can be framed as an I/O phase, which may produce immediately-committable results, and the following “retry” phase, which requires a small amount of work to clear conflicting remaining transactions. These conflicting transitions are surfaced and pulled into cache, allowing for execution overhead to be reduced because they live in memory. While most state lives on disk, conflicting transactions are accessed quickly at execution time.

The pessimistic and optimistic execution models offer distinct approaches to handling transaction execution and state management in blockchains. The choice between these models involves tradeoffs between upfront complexity in state access specification and the computational overhead associated with dynamic conflict resolution.

Data and task parallelism focus on optimizing performance by distributing computational loads across multiple processors: data parallelism segments a dataset for simultaneous processing, while task parallelism assigns different tasks to various processors to operate concurrently.

These optimizations are distinct but interdependent with state access parallelism, which manages and synchronizes access to shared resources like memory or databases to prevent conflicts and ensure data integrity when multiple processes or threads operate simultaneously.

Data parallelism involves parallelizing specific operations across multiple data elements simultaneously. This approach is particularly beneficial when the same operation needs to be applied to a large dataset or when performing computationally intensive operations on multiple input values. The key unlock comes from distributing the data across multiple processing units and executing the same operation concurrently on different data elements.

One common technique for data parallelism is single-instruction, multiple data (SIMD), which allows a single instruction to be executed simultaneously on multiple data elements. Modern CPUs often have built-in SIMD capabilities, enabling them to perform parallel operations on multiple data points. By leveraging SIMD instructions, developers can achieve significant speedups for certain types of operations, such as mathematical computations, data transformations, or signal processing.

For example, consider a scenario where you must apply a complex mathematical function to a large array of numbers. Instead of processing each number sequentially, SIMD can operate on multiple numbers simultaneously. This simultaneous processing is achieved by loading a subset of the numbers into the CPU's SIMD registers, executing the mathematical function on all the loaded numbers in parallel, and then storing the results back into memory. By processing multiple numbers at once, SIMD can greatly reduce the overall execution time.

Firedancer’s work on ED25519 signature verification demonstrates the power of SIMD for optimizing complex computations. The signature verification process involves arithmetic operations within Galois Fields, which can be computationally intensive. By leveraging SIMD instructions, Firedancer can perform these operations on multiple data elements concurrently, resulting in significant performance improvements. These optimizations will be critical in improving the performance of Solana, which has already implemented parallelization of state access.

Task parallelism involves parallelizing different tasks or operations within a program across multiple processing units. This approach is useful when a program consists of multiple independent tasks that can be performed concurrently. By assigning each task to a separate processing unit, such as a CPU core or a GPU, the overall execution time can be reduced.

Task parallelism is commonly used in scenarios where a program needs to perform multiple complex operations simultaneously. For instance, consider a video processing application that needs to apply different filters and effects to a video stream in real-time. Instead of using every compute unit to collectively apply each filter sequentially, task parallelism can distribute the workload across the multiple processing units. One processing unit can be responsible for applying a blur filter while another unit applies a color correction filter, and so on. By executing these tasks in parallel, the application can achieve faster processing and maintain a smooth user experience.

Lagrange's ZK MapReduce (ZKMR) leverages data and task parallelism to efficiently parallelize and generate proofs of distributed computations on large datasets. In the map phase, the input dataset is partitioned into smaller chunks, and each chunk is processed independently by a separate mapper worker or machine in parallel (task parallelism). The "map" operation can be parallelized within each mapper task across multiple cores or processors (data parallelism). Similarly, in the reduce phase, the "reduce" operation on the values associated with each key can be parallelized within each reducer task (data parallelism). In contrast, the reducer tasks are executed parallel across multiple workers (task parallelism).

By combining data parallelism and task parallelism, ZKMR can achieve efficient scaling and performance for complex computations on massive datasets while maintaining zero-knowledge guarantees through recursive proof composition.

Lagrange’s ability to generate storage proofs for SQL computations over 888,888 storage slots in 1 minute and 20 seconds demonstrates the power of ZKMR, as well as the task and data parallelism that underpin it. Moreover, Lagrange’s recent Reckle Trees paper underscores the need for parallelism in that it ensures that batch proofs of onchain data are also computable in 𝑂(log𝑛), independent of the batch size.

While this piece doesn't address consensus, blockchains can also parallelize the process of consensus and execution. Traditional blockchains often process transactions sequentially, reaching a consensus on a block's transactions (block N) before executing them. Parallel processing of consensus and execution phases significantly boosts execution efficiency and is a technique exemplified by systems like Monad. As the network reaches consensus for block N, it concurrently executes transactions for the previous block (N-1).

This strategy ensures continuous, efficient use of computational resources, effectively reducing idle times and enhancing the network's ability to process transactions quickly. These improvements increase the system's throughput and the cost of capital required to spam the network.

When smart contracts are written in languages like Solidity, they are first compiled into the lower-level bytecode. Then, the EVM uses an interpreter to execute this bytecode. The interpreter reads and executes each instruction sequentially, akin to translating a foreign language in real-time as it is being spoken. Paradigm’s latest piece on Reth points out that this leads to overhead since each instruction must be processed individually and converted from bytecode to machine instructions during runtime.

Reth is addressing EVM inefficiencies by incorporating a just-in-time (JIT) compiler. This compiler translates bytecode into native machine code shortly before execution, circumventing the resource-intensive interpretation process typically required during runtime.

The Reth article mentions that 50% of EVM execution time under an interpreter-based system is dedicated to processes that JIT could theoretically optimize, suggesting the possibility of doubling execution speed with JIT implementation. However, as Yilong points out in this presentation, while JIT can significantly decrease the time needed for processing specific opcodes, it may not drastically impact overall execution. This is because a substantial portion of the 50% of EVM execution times that JIT takes up involves "host" and "system" operations (Slide 13), which are not amenable to JIT optimizations like "arithmetic" or "control" due to their non-computational nature.

While interpreters may limit performance, they do create the opportunity for “translation,” which increases the scope of code that can leverage new virtual machines, lowering the overhead for developers to use designer blockspace. For example, Movement Labs has developed Fractal, enabling developers to deploy their Solidity-based contracts on MoveVM. Fractal works by compiling Solidity into an Intermediate Language containing instructions articulated in EVM opcodes, which are then mapped to their MoveVM bytecode counterparts, allowing Solidity contracts to run in the MoveVM environment.

Customizing the execution layer involves designing specialized state machines optimized for specific applications. Not only does this mean that an execution environment can forgo the need for a virtual machine entirely, but it also enables applications to tailor the instruction set architecture (ISA), data structures, and execution model to their specific needs. The key performance benefit of tailoring an ISA to a specific application comes from reducing the overhead of translating the application's computational patterns into the general-purpose instructions of a traditional ISA. General-purpose CPUs use basic instruction sets (i.e., add, load, branch) to support running different types of software. However, when applications frequently repeat the same multistep operations, implementing these patterns using sequences of simple instructions becomes inefficient.

For example, database applications may need to constantly traverse tree data structures, look up entries, update values, and rebalance trees. On a normal CPU, mapping these higher-level operations requires breaking them into long sequences of low-level micro-ops, such as loads, stores, branches, and arithmetic, executing individually on the general hardware. In contrast, an ISA customized for databases can fuse these recurring patterns into optimized wider instructions that leverage specialized hardware. A "TraverseTree" instruction could calculate memory addresses, load relevant nodes, and compare keys using parallel comparison circuits designed for that operation. "UpdateEntry" could directly gather the entry from the optimized database storage layout, modify it, and commit the new state all in a single instruction.

This eliminates redundant overhead from translating high-level operations down to simple instructions. It also allows the hardware to optimally execute the application using fewer but wider, explicitly parallel instructions tailored precisely to its needs.

LayerN’s Nord demonstrates the performance benefits of specializing execution environments and data structures through their specific use case of a verifiable order book. LayerN's approach focuses on optimizing the placement of trades into the order book data structure, while their pipelining mechanism is designed to efficiently insert new orders into the appropriate position within the order book's data tree. By tailoring the data structure and insertion algorithm to the specific requirements of an order book, LayerN achieves low-latency order placement and high throughput.

Alternatively, it's possible to lean into general-purpose execution environments that enable arbitrarily programmable modules that apps can plug into to optimize their performance. This approach prioritizes the developer experience over raw performance.

Fluent and CWD utilize a strategy that balances the tradeoffs between optimizing for raw computational performance and enhancing the developer experience and compatibility of the ecosystem. This approach centers on using WebAssembly (Wasm) as the VM to execute code. Wasm has become a preferred choice in web development due to its broad language support and the wide degree to which it has been adopted.

A developer's decision to use Wasm rather than native client execution reflects a strategic preference for the versatility and broad accessibility of a general-purpose execution environment. Although native execution, which runs code directly on hardware without a virtual machine, can offer better performance, it restricts cross-platform compatibility and is less accessible to developers. In contrast, Wasm ensures a uniform and secure execution environment across different platforms despite not achieving the same raw speed as native execution. This tradeoff aligns with Fluent and CWD's design philosophies, prioritizing developer productivity and broader ecosystem integration over maximum performance efficiency.

CosmWasm deployment (CWD), in particular, exemplifies this approach by not just employing Wasm for smart contract execution but also incorporating it into a more extensive framework designed to support the intricacies of blockchain operations. Enriched with "periphery logic," this framework offers advanced account management, a customizable gas mechanism, and optimized transaction ordering. These features contribute to a flexible, efficient, secure development environment that empowers developers to build scalable and complex dapps relatively easily.

Stackr* takes a different approach by combining the benefits of customized execution environments with the flexibility of traditional smart contract platforms. Stackr allows developers to code applications as rollups, enabling them to define their own rules for transaction ordering, execution, and configuration. In the Stackr model, developers can choose the ISA, data structures, and execution model that best suit their application's requirements.

With Stackr, developers can apply state transition rules directly in the application's runtime rather than being constrained by the rules of a general-purpose VM, giving them the ability to streamline their instruction set to be more efficient and redefine the set of things that can be done in a runtime environment.

This results in more lightweight and efficient execution, as the business logic is implemented at the client level, eliminating the need for costly smart contract invocations and validation. As a result, the possibilities around how an application is configured expand in terms of the different types of languages, data structures, and signatures developers can use for a single app without sacrificing performance.

There are multiple paths to optimal execution layer performance.

No singular optimization to state access or parallelization stands out as a proprietary point of technical differentiation between execution layers when attempting to capture dapps. As we went through, the benefits of resource-based parallelization on Solana can be equally applied to Fuel’s UTXO model. Anyone can use Amazon’s insightful solutions to improve horizontal scalability through sharding and improve the execution layer performance.

While execution layer performance is a critical vector for winning over builders of decentralized applications, new L1s and L2s centered around improving execution must compete on other variables, including security, interoperability, and compatibility with existing tooling. For this reason, the proliferation of new interoperability layers—from Nebra to Statenet to Polygon’s AggLayer—will be critical to developers buying designer blockspace, as they can build or buy specialized blockspace without sacrificing the synchronous composability and shared liquidity of traditional, general-purpose L1s.

Improvements to state management and computational efficiency are interdependent.

Across the communities designing new execution layers, the parallelization of state access has become a defining meme for the performance improvements they promise to bring. While this is for a good reason, as it could lead to a 5x improvement in the execution of the EVM, evidence from Monad’s early experimentation with parallelization demonstrates that its role is overemphasized if other improvements, such as async I/O, aren’t developed in tandem.

Based on this, we can conclude that computational efficiency is often only achieved when we improve how state is accessed and stored. Efficient state management reduces the time and resources needed to access and manipulate data, which speeds up processing and reduces computational load.

Taking this a step further, incumbents may be making path-dependent choices that hinder their ability to compete with new blockchain designs that re-architect how state is managed and updated, given the inertia that a hard fork entails. As a result, specialized, modular execution layers and alternative L1s may be able to create defensibility around design choices for more efficient state storage and the protocols for reading from and writing to it. These design decisions offer a competitive advantage, as incumbents may encounter inertia in updating their database structures without a hard fork.

At the end of the day, a blockspace’s values impact the design space for execution layers.

In understanding how we can improve execution layers, we can now delineate that the classes of optimizations differ according to two critical design choices—who is executing transactions, and how many nodes need to be involved? The techniques available to developers for solving execution bottlenecks differ significantly depending on a team’s initial answers to these questions.

On one hand, monolithic L1s like Solana and Monad don't accept separating the validator role into heterogeneous powerful and weak nodes to accelerate performance. “Accepting” state bloat in the short-term isn't a viable solution, so they lean on improvements at the database layer and other components of the block production engine, such as consensus, to make up for the broader number of executing nodes deemed as a critical component and core value of the network. Because the security models of these L1s rely on the consensus of a more distributed set of validators with weaker hardware requirements, their data needs to be written to a database that lives on a disk, which is necessarily cheaper for a permissionless and maximally decentralized blockchain.

On the other hand, projects like Ethereum and its L2s are pursuing a roadmap that leans into centralization across their executing nodes through centralized block builders held accountable to weaker verifying proposer nodes through fraud or validity proofs.

Suppose centralized "executors" of transactions and state transitions are considered acceptable in pursuing a decentralized future. In that case, the law of physics states that systems that can 1) add blocks to a chain without requiring multiple actors to re-execute transactions, 2) increase validator requirements to maximize in-memory computation (and ignore the state bloat problem), and 3) reduce latency and consensus bottlenecks clearly win out compared to systems relying on extensive decentralization and consensus among nodes.

In seeking a balance between scalability and trust minimization, it's becoming apparent that the objective for execution layers should not be to optimize for decentralization blindly, nor must execution always be completely permissionless.

As we develop and implement a broader array of cryptographic tools, such as validity and fraud proofs, we effectively reduce the number of nodes necessary to resist censorship and maintain safety and liveness. This approach, however, involves tradeoffs, potentially impacting censorship resistance, ordering integrity, and liveness guarantees due to the possible centralization of executors.

As noted by Sreeram, the “minimum viable decentralization” does not mean that “validation should be permissionless” but that it should “just be rightly incentivized.” This implies that a well-monitored system, where validators face significant repercussions for misconduct, can maintain safety and liveness without the need for excessive decentralization (h/t Sreeram).

Such governance models are already being tested in practical applications. For instance, rollups like Arbitrum are exploring governance or committee-based systems to enforce transaction ordering and leader selection rules, and they are considering mechanisms where sequencers use onchain data to uphold transaction ordering policies.

Despite these advancements, there is no definitive "pareto optimal frontier" for balancing decentralization with performance.

Ideological and technical considerations continue to favor decentralizing executing nodes to validate the state. While centralizing nodes reduces consensus overhead and upgrading hardware can significantly enhance performance, it remains to be seen whether these optimizations will attract developers focused on creating censorship-resistant applications and to what extent censorship resistance remains a core value in the industry.

*denotes an Archetype portfolio company

Special thanks to all the people in the arena for the thoughtful conversations and feedback that went into putting this piece together: Ash Egan, Dmitriy Berenzon, Dima Romanov, Ismael Hishon-Rezaizadeh, Kautuk Kundan, Katie Chiou, Keone Hon, Larry0x, Movement Research Labs, Rushi Manche, Soumya Basu, Tyler Gehringer, Will Kantaros, and Yilong Li.

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

This post is divided into two sections. First, I describe my belief that chain abstraction infrastructure is vital for consumer adoption of crypto and that intent-based architecture is the best way to engineer it. Second, I describe the main obstacle toward broader adoption of intents: solver network liveness.

I end the piece with a solution and introduce the standard that Across and Uniswap have collaborated on, using feedback derived from the CAKE working group. This standard is designed to optimize for the solver UX, lower barriers to entry to a universal solver network that most intents can be routed to, and ultimately enable a larger, more competitive solver network to flourish.

0. Defining the end state: what makes crypto applications "usable"

1. Why "chain abstraction" is a solution to a UX problem that arises out of the fundamental topology of modular blockchains

2. Why usable crypto applications must be built on top of chain abstraction infrastructure

3. How intent-based architecture will give rise to chain abstraction

4. Understanding that intent marketplaces perform best when the solver network is large and competitive

5. Bootstrapping the intent solver network requires onboarding more applications that will produce intents

6. Why we need a cross-chain intents standard that prioritizes "solver UX" to grow the solver and intent marketplace to large enough scale to achieve network effects

Are our best and brightest building redundant infra?

Many have lamented that the best crypto engineers and most based thinkers are overallocating attention and energy toward offering more blockspace to end users. This criticism has merit; there are too many L2s available for end users relative to the demand for them.

However, I reject the notion that there aren’t any useful crypto applications in existence.

Decentralized finance offers individuals the ability to self-custody digital assets, allowing them to work around draconian service providers and use their digital assets to buy things valued in the real world. The promise of self-custodied data also offers a utopian alternative for individuals that are becoming increasingly wary of trusting FAANG monopolies to keep their data secure.

The real problem in my opinion is not lack of useful crypto applications but friction for end users trying to access them. End users should be able to experience the following when interacting with crypto applications:

• Speed: Applications should feel as fast as web2 applications.

• Cost: Unlike web2, all web3 interactions must incur some cost, but the "cost per click" should be negligible.

• Censorship resistance ("permissionless-ness"): Anyone with a wallet should be able to interact with the application if they can afford to click.

• Security: Clicks should do what users expect them to do and not revert—all web3 updates should be permanent.

These are the properties of "usable" crypto applications.

Today’s modular blockchain solutions offer consumers all of these properties, but they are not all available in the same place.

In 2020, blockchains were monolithic, offering two of three properties to end users: speed, cost, or security. We then envisioned a rollup-centric or modular future that would unlock all three properties simultaneously.

Today, we have built the groundwork for this rollup-centric infrastructure. L2s offer cheap and fast blockspace, and most of them offer permissionless blockspace. Conversely, L1 offers WW3-resistant, secure blockspace. (You can read more about the security-UX tradeoff offered by L1s and L2s in my short survey article). These L2s connect securely to L1 via canonical message paths, laying the foundation for a modular yet interoperable network. In the past four years, we've built the optical fiber between blockchains that will one day support useful crypto applications. But why are modular blockchains so unusable?

The inevitability of modular blockchain networks is that capital assets will accrue at the most secure layers while user clicks will accrue at the faster and cheaper layers.

Modular blockchain topology encourages secure blockspace to be offered on a different layer than cheap and fast blockspace. Users will naturally prefer to store their value on the most secure networks, but they will demand to interact most frequently with the cheap and fast ones. By design, the canonical paths between L2s and L1 are slow and/or expensive. These phenomena explain why users must traverse these canonical pathways to pay for L2 interactions using L1 assets. This results in "unusable" crypto UX.

The goal of chain abstraction is to reduce the friction of sending value across these in-protocol paths away from the user. Chain abstractors assume that users prefer to specify their desired end state to dapps as "intents" and it is the responsibility of the dapp to fulfill their intents. Users should not have to compromise secure custody of their assets in order to access low fees and low latency.

Therefore, chain abstraction depends critically on users being able to transfer value across networks securely, cheaply, and quickly. A common user flow today is that a user with a USDC balance on a "secure" chain like Ethereum wants to mint an NFT or swap for new tokens on a newer chain like Blast or Base. The way to do this in as few steps as possible is to sequentially execute a Bridge→Swap→Mint series of transactions (or Swap→Bridge→Mint).

In this example, the user's intent is to use their USDC on the secure chain to mint an NFT on another chain. The user will be satisfied as long as they receive the NFT and their USDC balance is charged wherever they choose to custody.

Chain abstraction relies on cross-chain value transfer, but sending value via canonical messaging paths is either expensive or slow. "Fast bridges" offer cheap and fast alternatives for users to send value across networks, but they introduce new trust assumptions. Message passing is the most intuitive way to build a fast bridge because it is modeled off of the TCP/IP architecture; it relies on a bridge protocol acting as the TCP Router to connect two chains.

Value transfer via message passing involves the bridge protocol sending messages between its contracts on the origin and destination chains. This message is triggered on the origin side by a user transaction and relayed to the destination side once the message's "validity" is verified.

A message can only be verified after the origin chain transaction initiating the message has finalized, meaning the transaction is permanently included in the origin chain’s canonical blockchain. This verification can be completed as a validity proof proving consensus inclusion of the transaction on the origin chain, as an optimistic proposal, or after a threshold of witness signatures attesting to its inclusion have accumulated on the origin side. Once the message is relayed to the bridge contract on the destination chain, tokens are released to the user.

There are several fundamental issues with this architecture:

• The verification mechanism must wait for full finality before sending the message to the destination chain protocol contract. This can take up to seven days for L2s with optimistic finality periods.

• One cross-chain message is sent per bridge transaction OR messages are batched together, but the batch can only be sent after the last message in the batch is finalized.

• The bridge has limited ability to source external liquidity to give user price improvements because it must be declarative about the fulfillment path of the user intent.

Message-passing fast bridges are going to be either unsecure, slow, or expensive depending on the verification mechanism. Intent marketplaces are an alternative architecture for fast bridging that arise from a key insight:

Can a bridge outsource the value transfer to a sophisticated agent to gain speed and lower cost? Liquidity is dynamic on and offchain and price improvement can be realized if the bridge mechanism has flexibility to choose an optimal execution path at the time of the bridge transfer.

Intent mechanisms allow users to specify precise conditions or covenants under which their value transfer transaction can be executed.

A minimal viable intent is an order to pay X token from chain A to receive Y token on chain B.

The bridge protocol does not need to send a message between domains per bridge transaction to fulfill a user's cross-domain intent. Instead, the protocol outsources the value transfer to an agent drawn from a permissionless solver network, and the individual solver will seek repayment later from the bridge protocol. In comparison, message-passing mechanisms specify exactly how their transactions should be executed and do not need to rely on the availability of an agent.

Intent-based bridge protocols can be labeled more precisely as intent settlement protocols that are responsible for ensuring that solvers do not violate the user-specified conditions. Intent settlement protocols offer solvers security that they will be repaid and rewarded for fulfilling user intents. To do so, intent settlement protocols need to appeal to an oracle to verify the authenticity of the intent fulfillment. The oracle's security can be grounded in an optimistic challenge period, a witness threshold, or be ZK validity proof based, for example.

Message-passing bridges can only communicate as quickly as finality is reached by the originating chain. Finality times are seven days on optimistic rollups and one hour on ZK rollups today. Even though these finality times should trend down following broader adoption of ZK light client technology and advancements in shared-sequencing pre-confirmation technology, it is unlikely that finality times for all blockchains will ever feel "instant" for users, suggesting a persistent need for fast bridging solutions. It is impossible to relay a message faster than the finality period without assuming finality risk—which is outside the scope of a message-passing bridge—unless the bridge wants to add an additional trusted agent to the relay path who will backstop losses due to chain reorganizations.

The speedup offered by intent-based architecture arises because individual solvers within a heterogenous solver network can assume more finality risk than a message-passing protocol can and fill a user's intent before chain-reorg risk completely disappears. Solvers will subsequently charge users for this finality risk they assume in exchange for faster fill times.

Outsourcing cross-chain intent fulfillment to an agent also leads to price improvement on average for users. In intent-based bridges, solvers who front user orders on the desired destination chain are repaid later by the system after their fulfillment is validated. These intent settlements can be batched together to amortize cost. Fillers, unlike users, do not demand instant repayment and will charge users accordingly for fronting them capital. Batch settlement is not unique to intent-based architecture, but the architecture is more synergistic with batch settlement because it separates the repayment step from the intent fulfillment step.

The larger source of price improvement comes from the intuition that value is fungible, and finding the best path just-in-time will usually outperform value transfer. (However, some paths will be impossible to beat on cost just-in-time, like when bridging USDC over CCTP.)

Message-passing bridges must encode how they will transfer value to the user. Some elect to send tokens out of a liquidity pool at a predetermined exchange rate, while others mint representative tokens to recipients who need to then swap for the desired canonical token asset.

When fulfilling a user's intent, an agent can source liquidity from a combination of onchain and offchain liquidity venues. Competitive solver networks offer users unlimited sources of liquidity in theory (but even these sources of liquidity can be depleted quickly when volume trends in one direction during high volatility onchain events like popular NFT mints, airdrops, and rug pulls).

Submitting a cross-chain order as an intent allows solvers to internalize the order's generated MEV as price-improvement.

Intent-based bridges can be built securely because they separate the urgent demands of the user from the complex demands of the settlement network. Solvers can wait for repayment, unlike users, and they will charge users for the amount of time that the settlement protocol makes them wait for repayment. Therefore, intent settlements can be validated using very robust mechanisms without a strict time constraint. This is preferable from a security standpoint because verifying an intent fulfillment is intuitively complex.

As an example of intent verification in production, Across validates and repays fillers in batches following a 90-minute optimistic challenge period. Of course, settlement networks should strive to repay fillers as quickly as possible to reduce end user fees. An improvement on the optimistic challenge mechanism would be a ZK validity proof mechanism, which would require encoding the intent validation logic into a ZK circuit. In my opinion, it’s an inevitability that validity proving mechanisms will replace optimistic challenge mechanisms and enable intent settlement networks to repay users faster.

Recall that chain abstraction requires fast and cheap cross-chain value transfer. It also shouldn't require the user to submit an onchain transaction on the network where their assets are stored.

A user's intent does not need to be submitted onchain by the user if it includes a Permit2 or EIP3074 signature. This is true for both message-passing and intent-based bridges. Both architectures can take advantage of the Permit2 pattern to allow the user to sign offchain the amount of tokens that they are willing to pay from their origin chain wallet.

Intent marketplaces best support chain abstraction because they offer cheap and fast cross-chain value transfer. Imagine a world where a user could request a solver to give them a quote to enter into a WETH staking position on Arbitrum, using their USDC on Optimism as payment. The user could send this intent offchain to an RFQ auction where solvers could bid on it. The winning solver of the auction could then receive the user’s signed intent, containing an allowance to spend their USDC on Optimism, their desired amount of WETH to receive on Arbitrum, and the calldata needed to deposit this WETH into a staking position on Arbitrum. The solver could subsequently submit this transaction on Optimism (on behalf of the user) to initiate the cross-chain intent and pull USDC from the user’s Optimism wallet. Finally, the solver could fill the user’s intent on Arbitrum by sending them WETH and forwarding the calldata to enter the user into the onchain staking position.

Building chain abstraction infrastructure means making this user flow feel instantaneous and cheap without requiring them to submit an onchain transaction. Let's conclude this article by discussing obstacles to broader adoption of intents.

Bridging with intents depends on solver network effects to perform better than message-passing variants. This is the core tradeoff of intent versus message-passing architectures. Realistically, not all applications producing intents will need access to a perfectly competitive set of solvers, and some might prefer routing their intents to oligopolistic solver networks. However, the current state of solver networks is immature and isn't close to fulfilling the assumptions of solver network liveness that intent marketplaces depend on.

We don't want a world where each dapp is routing intents to isolated solver networks. The best case for UX is that many dapps communicate with the same solver pools, and all dapps have the freedom to change which solver pools they send their intents to.

We must prioritize solver UX.

Running an intent solver is complicated and requires expertise in building highly performant software as well as managing cross-chain inventory risk. Naturally, there will be limited parties interested in paying the startup cost to run this code. In the best case scenario, a solver written for one dapp, like a UniswapX solver, could be reused to solve for other intent-producing dapps like Across and CowSwap.

We really need to increase the aggregate capital efficiency of the solver network for all intent-based dapps. This will require addressing the barriers to running a solver.

For this, we'll need dapps producing intents to be visible to any solver and ensure all solvers have access to many differentiated and competitive intent settlement networks. This would give confidence to solvers that they could choose to route their intent fulfillments to a settlement network that they trust. The competition between settlement networks would also lower costs for solvers.

The value proposition of intent settlement networks is offering security to solvers as well as other features that would affect the solver's ability to fill an intent.

The solver's choice of intent settlement network will affect their ability to offer fees and execution-time guarantees to users. Some settlement networks might offer solver exclusivity periods, which would support the development of offchain auctions where solvers and users could negotiate and commit to relay fees. (These intent auctions might moreover offer economically bonded pre-confirmations, further enhancing UX. To learn more about a user flow featuring intent discovery via auctions and pre-confirmations I recommend this talk by Karthik of Sorella.)

Some settlement networks might offer intent expiry (i.e., sending value back to users after some fulfillment deadline is passed), intent backstopping (i.e., the settlement network using its own balance sheet to fulfill a user's intent if no solver does), or flexible repayment chain (i.e., allowing the solver to get repaid on their chain of choice).

In the end, settlement networks will compete ferociously to repay solvers quickly and cheaply without compromising on security. In turn, solvers will send their orderflow to the settlement networks that allow them to offer the cheapest fees to users so that they can win the dapp orderflow. Competition in settlement and solver networks depends on all parties in the intent supply chain coordinating to speak the same language, and competition will lead to the best UX for cross-chain value transfer.

If solvers can assume that intents will share common elements, then they can reuse their code to solve intents originated by different dapps and subsequently lower their setup costs. If different dapps create intents that conform to the same standard, then they can all route their intents to the same solver pools. This would help onboard the next generation of dapps by giving them the ability to plug their cross-chain intents directly into an existing and mature solver pool. New dapps would not have to individually onboard solvers and, instead, would get access to cheap, fast, secure, and permissionless value transfers.

Third party tracking software would also be more easily able to track intent statuses for any new dapp if they conform to a standard.

I envision competing settlement protocols like SUAVE, Across, Anoma, and Khalani offering differentiated features to intent solvers. Depending on which settlement network is repaying the solver, the solver can offer different price and time guarantees to the owner of the intent. The dapp and solver could agree to route a user's intent to a settlement network they trust to avoid censorship, maintain data privacy, and also be secure enough to be trusted to repay the solver.

By enshrining the choice of settlement network into the intent order itself, the solver could bake this certainty into the quote that they would show to the user. The solver and user would eliminate upfront uncertainty about bridge pricing before submitting the intent onchain, reducing costs.

/// @title CrossChainOrder type /// @notice Standard order struct to be signed by swappers, disseminated to fillers, and submitted to settlement contracts struct CrossChainOrder { /// @dev The contract address that the order is meant to be settled by. /// Fillers send this order to this contract address on the origin chain address settlementContract; /// @dev The address of the user who is initiating the swap, /// whose input tokens will be taken and escrowed address swapper; /// @dev Nonce to be used as replay protection for the order uint256 nonce; /// @dev The chainId of the origin chain uint32 originChainId; /// @dev The timestamp by which the order must be initiated uint32 initiateDeadline; /// @dev The timestamp by which the order must be filled on the destination chain uint32 fillDeadline; /// @dev Arbitrary implementation-specific data /// Can be used to define tokens, amounts, destination chains, fees, settlement parameters, /// or any other order-type specific information bytes orderData; }

This standard is designed to make a solver’s job easier. One opinionated choice it makes is supporting Permit2/EIP3074 natively with the nonce and initiateDeadline and it gives fillers some guarantees, such as the amount they will be refunded from the settlement network and the format of the user intent that they can track. Moreover, an initiate function is defined in the standard that crucially allows the filler, the one who will bring the order onchain, to specify additional “fillerData” onchain that the user would not have known about at the time that they signed the CrossChainOrder. This allows the filler to make sure that they are rewarded by the settlement contract for submitting the user’s meta-transaction and also set repayment specific information such as repayment chain.

This standard is also designed to make it easier for dapps to track intent fulfillment status throughout its lifecycle. Any settlement contract implementing this standard should create a custom sub-type ResolvedCrossChainOrder that can be parsed from the arbitrary orderData field. This may include information such as the tokens involved in the swap, the destination chain(s), and other fulfillment constraints. A resolve function is included in the standard to enable dapps to understand how to display intent statuses to users and for solvers to know the exact intent order structure they are working with.

/// @title ResolvedCrossChainOrder type /// @notice An implementation-generic representation of an order /// @dev Defines all requirements for filling an order by unbundling the implementation-specific orderData. /// @dev Intended to improve integration generalization by allowing fillers to compute the exact input and output information of any order struct ResolvedCrossChainOrder { /// @dev The contract address that the order is meant to be settled by. address settlementContract; /// @dev The address of the user who is initiating the swap address swapper; /// @dev Nonce to be used as replay protection for the order uint256 nonce; /// @dev The chainId of the origin chain uint32 originChainId; /// @dev The timestamp by which the order must be initiated uint32 initiateDeadline; /// @dev The timestamp by which the order must be filled on the destination chain(s) uint32 fillDeadline; /// @dev The inputs to be taken from the swapper as part of order initiation Input[] swapperInputs; /// @dev The outputs to be given to the swapper as part of order fulfillment Output[] swapperOutputs; /// @dev The outputs to be given to the filler as part of order settlement Output[] fillerOutputs; } /// @notice Tokens sent by the swapper as inputs to the order struct Input { /// @dev The address of the ERC20 token on the origin chain address token; /// @dev The amount of the token to be sent uint256 amount; } /// @notice Tokens that must be receive for a valid order fulfillment struct Output { /// @dev The address of the ERC20 token on the destination chain /// @dev address(0) used as a sentinel for the native token address token; /// @dev The amount of the token to be sent uint256 amount; /// @dev The address to receive the output tokens address recipient; /// @dev The destination chain for this output uint32 chainId; }

A compliant settlement contract implementation MUST implement the ISettlementContract interface:

/// @title ISettlementContract /// @notice Standard interface for settlement contracts interface ISettlementContract { /// @notice Initiates the settlement of a cross-chain order /// @dev To be called by the filler /// @param order The CrossChainOrder definition /// @param signature The swapper's signature over the order /// @param fillerData Any filler-defined data required by the settler function initiate(CrossChainOrder order, bytes signature, bytes fillerData) external; /// @notice Resolves a specific CrossChainOrder into a generic ResolvedCrossChainOrder /// @dev Intended to improve standardized integration of various order types and settlement contracts /// @param order The CrossChainOrder definition /// @param fillerData Any filler-defined data required by the settler /// @returns ResolvedCrossChainOrder hydrated order data including the inputs and outputs of the order function resolve(CrossChainOrder order, bytes fillerData) external view returns (ResolvedCrossChainOrder); }

The design goals of this standard were to enhance solver UX, make it easier for them to support multiple settlement networks, and deterministically compute their rewards. I believe that this will allow them to give more accurate and tighter quotes to users. You can read more details about this standard, code-named ERC7683, in this X/Twitter post and the discussion surrounding it on the Ethereum Magicians forum.

"Intents" are confusing because they are not defined, and this lack of a definition is creating real UX defects.

Everyone wants everyone else to use their standard definition of an intent, so I fully acknowledge that standards are practically impossible to establish. I don’t think that defining an intent settlement system first and trying to attract orderflow second is the right approach to establishing an industry-wide standard.

In my opinion, the more tractable approach is for dapps that already own a lot of user flow and originate many user intents will agree to conform to some minimal standard that their existing solvers will adopt. This will form a new and larger solver pool. By getting access to merged orderflow from already prominent venues, this new solver pool will earn more profits and be able quote better prices to end users. Eventually, newer dapps will also demand to route their intents to this solver pool and will support its intent standard.

To get us kickstarted, Across and Uniswap are jointly proposing a standard for all intent supply chain parties to use when handling user orders to send X tokens from chain A and receive Y tokens on chain B. The orderflow running through UniswapX (having a comparative advantage in auction design and originating intents) and Across (having a comparative advantage in settling intent fulfillments) can merge and kick off the process of nurturing a larger, more competitive solver network.

This article was inspired by many IRL and online discussions with the following folks building actively within the intents supply chain:

Ankit Chiplunkar from frontier.tech, Mark Toda, Alice Henshaw, and Emily Williams from Uniswap, Arjun Chand from Li.Fi, Karthik Srinivasan from Sorella, Kevin Wang from Khalani, tumilet from The Spartan Group, Matt Rice, Ryan Carmen and Hart Lambur from Across, Benjamin Funk and Dmitriy Berenzon from Archetype, Will Kantaros from Decent, and Francesco from Flood.

Finally, the ideas in this article are summarized in the following ETH Denver talks that Hart Lambur and I gave:

https://youtu.be/qoDJxl7AR48

https://www.youtube.com/watch?v=S3WV_eBF45w

• UniswapX

• CAKE Framework by frontier.tech

• Anoma blog on Intents

• Intents, Suave, AA, XChain Bridging are all the same thing - Uma Roy

• Intent-based Architectures and their Risks

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

IN CONVERSATION WITH is a series from Archetype where we interview artists in/at the edges of crypto across music, visual art, design, curation, and more.

André Allen Anjos, professionally known as RAC, is a Grammy award-winning musician, record producer, and DJ. RAC is most notably known for pushing the boundaries of remixing, expanding the historically dance-forward genre to include indie and rock. RAC has released 200+ remixes over the course of his career, which you can listen to on SoundCloud. Artists RAC has collaborated with include The Shins, the Yeah Yeah Yeahs, Tegan and Sara, Phoenix, Kings of Leon, Lana Del Rey, and many more. RAC has also released three of his own solo albums, STRANGERS(2014), EGO (2017), and BOY (2020). Anjos has also been incredibly active in the crypto ecosystem, releasing an onchain, tokenized cassette tape known as the $TAPE token in 2020, as well as the $RAC community token launched the same year. He is currently building a new company at the intersection of music and crypto called Oscillator.

Over a video call, I asked Anjos a series of questions ranging from his experience as a major label musician, remixing and technology, personal brand, the opportunity to improve the music industry by leveraging crypto, and more.

The following interview has been edited and condensed for length and clarity.

Katie Chiou: When you think about the music business, the first thing that comes to mind is streaming because that’s where people interface the most with artists. Can you tell us a bit about the dynamics of music streaming?

André Anjos: Let’s break it down from recorded music, which includes streaming. You can cut it a few different ways depending on what type of recorded music it is. There’s physical sales and the costs of creating those artifacts—vinyls, CDs, cassette tapes, whatever. There’s a real cost to producing and distributing those artifacts, and we’ve taken that model and applied it to something digital which virtually has no distribution cost.

Spotify dominates digital distribution. For every play of a song, you receive a fraction of a cent, which is tiny. To give some concrete numbers, about a million plays is worth probably about $4,000. That’s assuming you don’t have a label, a manager, or a distributor that’s taking a cut. Now, let’s say you’re signed to a major label. They’ll typically take about 80% and give 20% to artists. Without getting super in the weeds, the artist is usually the last one to get paid after everyone else gets paid. If you’re on an indie label, there’s typically a 50/50 split. You get less visibility, usually in exchange for a higher percentage of ownership.Then, there’s the full independent route where perhaps you own everything or you use a digital distributor like DistroKid, but you get no support so you have to promote it yourself.

KC: Streaming isn’t usually an artist’s biggest revenue stream, there’s other things like touring and partnerships. Can you break some of these down?

AA: In general, I would say the three broad main categories are recorded music, publishing, and touring, and every artist has a slightly different version of that. I separate publishing from recorded music because that’s essentially like writing. You can be a writer on something, but not own the recording, so I treat them differently.

I talked about streaming, but on the publishing side, it's kind of its own industry. It involves a lot of government-mandated royalties and is a complex, convoluted industry. They are very litigious, they love to sue people. So, writing and publishing can actually be one of the more profitable sides of music, but you have to play their game and you can’t really be independent. You have no sway.

To talk about touring for a bit, I’ve done almost every type of touring. It generally depends, but I will say that touring can be profitable once you get to a certain level, but for most people it’s actually a loss or breakeven at best. There are a ton of hidden costs that people don't realize until they're too deep in it. You can get caught in this cycle where you're constantly touring just to make ends meet, and it can really spiral. I would say, right now, the only real way to be profitable is to be a solo artist who DJs and is a somewhat well-known person and tours non-stop.

I also don’t see enough people talk about how touring is just fundamentally different after COVID. There used to be a balance to it before where there was enough ebb and flow—people take a break and write an album, come back. The issue that happened was that everybody tried to come back all at once after not touring for 2 or 3 years. With limited supply of venues, that inevitably pushed everybody's fees down and the cost of touring is suddenly 3x more expensive. You basically have a supply shock.

KC: Artists today are increasingly personal brand-driven and are expected to form more social relationships/communities with their fans. Frankly, it feels unfair to me. Maybe some people just aren’t built to do that. Curious if you feel the same/if you have thoughts around that?

AA: I think that’s definitely become more true over time. I had this realization years ago that I'm effectively competing with Netflix for attention. Everybody's competing for the same thing. And with attention comes money, so it's like we're all chasing the same thing. So naturally, it doesn't surprise me that artists suddenly have to play this other game that maybe they didn't sign up for or that maybe they were promised some version of being an artist that now looks very different.

I think change is inevitable. You kind of have to adapt. There are aspects of that I’m not personally comfortable with, but you have to make those choices for yourself and do it in your own way. If your choice is to not play that game, then maybe that's cool too. But I definitely still empathize with newer artists today. If you asked me what an artist just starting out right now should do to succeed, I have zero clue. I think everybody's kind of in the same boat. You try different things, see what works and what doesn’t work, and trends emerge from there.

I remember when I first started doing remixes, I wasn't really touring for the first year or two. I was perfectly happy doing studio work. I love being in the studio; that's what I like doing. Then suddenly, I started getting offers to play shows. It wasn't really my thing, but I thought, "I guess I can learn." It's funny because I learned how to DJ while in the club, even though I never used to go to clubs.

KC: Yeah, I read in an early interview of yours that clubs aren’t your scene.

AA: Yeah, not at all. But I get booked for shows all the time, and maybe that's a good thing because I bring my own energy to it. I remember in 2009, there were all these bedroom producers suddenly being thrown into clubs and DJing. Performing is such a different environment, it’s like learning a whole other art form. Even to this day, it's not the most comfortable thing to do for me, but everybody has different comfort levels, and you have to find your own path. I empathize with people that have to do a lot more social media now than before, but it feels like it’s just the world that we live in now. I kind of just accept it.

KC: You livestreamed on Twitch a lot during COVID after you had to cancel your tour. What was that like?

AA: I was pretty aware of the culture on Twitch, so I think that gave me a little bit of a leg up when I started streaming. I knew that I had to solve a few things like filling a lot of space and being loose and improvisational. There were experimental musical things I did that sometimes worked and sometimes didn’t, but I got to have fun with it. But the music was just there to fill the dead space. The real powerful thing about Twitch is talking to people. During COVID, everybody rushed to do Twitch streams and “live” performances that were actually pre-recorded, but they just glossed over the point entirely or it’d just kind of miss the mark.

I'm really glad I did it, but I'm really glad I don't have to do it anymore because it was 4-hour sessions 3 times a week. It wasn't even that much money. Honestly, people were very generous with tipping but Twitch takes 50% unless you’re a huge streamer, and I just feel like that’s a lot.

But I enjoyed it. I think it was a way for me to interface with my fans and my audience in a more meaningful way and I still really appreciate it for that. Sometimes people will talk about that time very fondly, but I think everybody understands why I don't do it anymore.

KC: You’re an artist that’s always been at the forefront of technology. Part of your early legacy is transforming remixing. In the past few years, remixing has exploded even more because of things like TikTok and AI. As an artist, what’s your relationship to technology and these types of tools that make music creation easier?

AA: Obviously, I love technology. It'd be pretty hypocritical of me to criticize it because I feel like I owe a lot of my career to it. Recording technology, the rise of the internet—these things have enabled me to reach a wider audience than I ever could have as a local artist playing local gigs.

Back when I started, the remixing technology that existed was somewhat limited. Now, you can make a fully finished song in a day—it's crazy how things have changed. Especially with AI these days, I'm almost glad it wasn't around when I started. Picking a path like remixing and going deeper and doing different things with it than most people at the time, it gave me an edge in my niche.

Now, my moat around that was simply the fact that most people didn't have access to these files. Because, and I don't know if everybody knows this, every remix I've done has always been officially through the artist. I've never put out anything bootleg, even though that's a big part of remix culture. And I'm not saying that's a bad thing. I just mean that it was a source of income for me. I was paid by the artist to do it, which was, again, kind of a weird path to take. But now with some of these AI tools where you can extract a vocal from any track on YouTube, it’s hard to make a living from that type of work.

Technology, especially crypto, has served me well. It's become an interest of mine, a tool for expression. It doesn't feel forced—it's just something I'm genuinely into.

KC: Many of the original attempts to build at the intersection of music x crypto started with music NFTs. What are your thoughts on how music NFTs evolved and what are your thoughts on them today?

AA: Music NFTs were, I think, piggybacking off the narrative of scarcity that was happening in the fine art world at the time, and it didn't quite connect in the same way because people think of music differently.

People don't think of music as being a scarce asset. So why do I have to buy a music NFT? I don’t think anybody would deny that music is art, but people just think about it differently. They think about music as being everywhere, pervasive in the background. It's just not something that people think of as a scarce thing. So I think that narrative just didn't quite click for people, and there wasn't an analog to compare it to. With art NFTs, it's like a digital painting, there's only 10 prints and editions. But with music, there was never that same analog that resonated for people.

Aside from that, I really like the emergent behavior of uploading a song on the internet that is free of a platform and is universally compatible with everything else. That is exciting. That, I think, is a novel behavior that is so much more interesting than what we have currently. There are so many problems there to solve with that structure, but I think that is really cool. It’s just not yet compatible with the ways music works today with copyright, government-mandated royalties, performance royalties and all these other structures already in place. I still think there's merit to these ideas. We haven't quite figured them out yet, but I'm not ready to throw them away.

KC: What are your learnings and reflections from your own crypto projects like $TAPE and $RAC?

AA: They've all been unique in their own way.

$TAPE started as a concept I worked on with Jacob from Zora and Jack, my Co-founder. We basically came up with this question: If you let a market decide the price of music instead of a platform like Spotify, what does it become worth?

rac.eth

@RAC

$TAPE is apparently the most expensive cassette tape of all time. I'm speechless. Thank you.

147

4:32 PM • Sep 30, 2020

$TAPE is a physical cassette tape and the supply was completely arbitrary, but there's a hundred cassettes. Let's put them on a bonding curve and see how much they’re worth. People can trade $TAPE back and forth, and the trading created crazy price action. It made us realize that music is worth way more than a fraction of a cent. I don’t know the exact number, but it’s definitely higher. We learned that if you introduce some kind of artificial scarcity, you can use markets to kind of find a price. We also created a function where in order to get the actual physical cassette, you had to burn the token. Out of 100, only 31 were redeemed which is also interesting. Maybe the token is even more valuable than the actual thing.

With the $RAC token, it was basically like an airdrop to fans. It was a retroactive thing. Everybody that bought merch at a show or heard about tickets, bought an MP3 on Bandcamp in 2008, etc., got an email with some tokens. We didn't want to sell the token, it was meant to just be a community token. I thought it was a really interesting experiment. We ran into other issues such as people constantly in my DMs or in my replies asking why the price was moving.

ERC20s, for better or for worse, come with some baggage. No matter how often I try to tell people that the $RAC token is not meant to be financial, people still want to attribute a financial value to it. It was around this time that social and creator tokens were a big conversation, and the focus soon shifted to NFTs. Unlike ERC20s, NFTs don't carry the same liquidity provision baggage. I realize now that I may have been a little early with ERC20s. An NFT membership model may have been more sustainable for a smaller community. It's interesting now to observe the resurgence of memecoins and watch how narratives cycle.

KC: How are you taking those learnings to build Oscillator?

To back up a bit, when I first discovered Ethereum, I was excited about the programmatic nature, the ability to replace institutions with code, or middlemen with code. I think this idea is often overlooked in crypto discussions. While some focus on tokens and financialization, I think there's actually so much more interesting stuff you can do with blockchains and just programming business logic, if you want to think about it that way. That's the part that's more exciting to me than anything else. That’s where I'm coming from initially, and that's the future I want to see. I think this will be a better ecosystem to play in because open data keeps people honest. That's the vision I want to work towards, and it's what we're trying to do with Oscillator. In general, I think we just want to attract people that believe in that vision and that want to help. We want to recreate what a modern music industry could be onchain and that is built with the right foundation and built for artists. We want to cut out and trim a lot of fat and baggage that comes with the traditional music industry.

So if any of that resonates, reach out. We even launched our company with just a manifesto that said if you believe in these principles, come talk to us, like we want to work with you. We want to find the right people because there's no way we're doing this on our own. We want to put this vision out in the world and meme it into reality.

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.

The best part of ETHDenver 2024 was watching two opposing groups collide in a classic left-curve / right-curve debate.

One arrived with a risk-on, full send mentality in anticipation of a coming mega cycle.

The other came in struggling to find the fundamentals to make sense of today’s market euphoria.

In the short term, I think both are probably right. The setup for a massive, prolonged run up in crypto looks pretty good, even if the current momentum in public and private markets has almost certainly lost sight of fundamentals.

But a closer look suggests something different might be going on this time.

Where previous bull markets drove users, those cycles tended to look less like true adoption and more like a levered macro beta.

Today, those fluctuating waves of liquidity are set to give way to a permanent shift in the tides thanks to three major tailwinds:

The explosion in public and private markets—thanks to a mix of AI hype and economic optimism—masks several secular changes playing out.

In fact, rather than reverting to pre-pandemic norms, a very different geopolitical paradigm is emerging: one centered on fragmentation and cross-border competition.

While these new dynamics will materially challenge incumbents, they present a generational opportunity for crypto to make the jump to global adoption.

Crypto’s first decade was an extraordinary testnet marked by grassroots development, exuberant highs and difficult lows. Now in its second decade, crypto is ready for mainnet as the world’s interoperability layer: a neutral home for economic exchange and technological innovation in an age that desperately needs it.

Why?

For crypto, secular changes are positive tailwinds arriving at the perfect time.

Real structural shifts in the pipes that have underpinned global trade for the last two decades are being accelerated by very serious fractures in international relations. A restructuring of global supply chains and trade rails accelerated in 2023 and has become a core focus for companies and governments alike.

Growing Focus on Reshoring by Corporates:

Meanwhile, over 54% of the world’s population and close to 60% of global GDP are undergoing an election cycle where protectionism is already a major focus as several major military conflicts play out in real time.

Equally relevant to crypto is an evolution in the art of international competition. Countries no longer rely solely on rockets and bullets.

America weaponized international finance in response to Russia’s invasion of Ukraine, while OPEC and Russia have shown they are happy to respond in kind by manipulating energy supplies.

In parallel, techno-nationalism around semi-conductors and other critical inputs has seen rhetoric replaced with action in the form of sanctions and subsidies.

This fragmentation of global commerce is damaging to margins in the developed world, while for the 40% of the world living in Lower-Middle Income (LMI) countries, the impacts are even more existential.

For individuals in LMI countries in particular, crypto provides vital solutions to everyday problems, and its importance only grows alongside systemic challenges. The data confirms this story: grassroots crypto adoption is not only reinforced but accelerated when geopolitical strains rise.

Likewise, for private enterprises this confluence of factors introduces significantly greater costs and restricts access to new consumer markets, all of this in a world where the cost of capital is no longer zero.

The trade wars & tariffs of the last few years have already proven damaging to businesses and domestic economies, and those negative impacts risk compounding as companies adapt to a new reality.

As a result, enterprises and individuals increasingly need to make a choice:

This crossroads reminds me of an intriguing historical parallel:

When Constantinople fell to the Ottomans in the 1400s, its new conquerors inherited control over a geographic nexus connecting global commerce across the ‘Silk Road’.

Famously, the Ottomans would soon move to restrict the overland trade routes that had thrived for centuries. The result pushed European powers to take to the seas in search of new trade routes, thereby igniting the ‘Age of Exploration’ that shaped the modern world.

This time around, it will be blockchains where the riches and perils of the New World will be found by those brave enough to set sail.

There’s another important point worth hitting on: crypto has historically been a staging ground for large companies looking to experiment with a new technology.

Because of the factors covered here, the traditional enterprise’s exploration of crypto is now transitioning from R&D to production grade.

• Companies will accelerate efforts to explore digital assets and onchain ecosystems as a key source of greenfield markets. What was once a vanity project will increasingly become an existential mission.

• Capital allocators will scale up crypto-native deployment and participation to insulate against ‘beta’ exposure to global risk and uncertainty. There won’t be many places to hide in the old world.

• Systemic challenges in different regions (inflation, capital controls, cold / hot conflicts) will drive even greater relevance and need for digital assets. Permissionless blockchain infrastructure will start grassroots before going global.

To be sure, economic and geopolitical challenges have always driven users to crypto, particularly in developing markets.

But the scale and scope of challenges the world must navigate in the coming years presents a unique window for crypto to become the de facto system for free commerce and culture.

Of course, all of this is contingent on the final piece of the puzzle: bringing institutional capital onchain.

The approval of spot BTC ETFs marked a major turning point on this front, and it would seem a similar outcome is on the horizon for ETH.

Already, BTC ETFs have seen more than $7.5B in net inflows, and those launched by BlackRock and Fidelity marked the largest debut months of any ETF in the last 30 years.

This incredible momentum is what will finally allow the biggest institutions to join over 52M Americans and another 500M around the world in the onchain economy.

If the tectonic shifts in macro are providing the spark, the flow of institutional capital represents the gas that will light the fire underpinning crypto’s prime-time moment.

The exogenous setup is outstanding. Are we ready to seize the moment?

I believe the answer is yes.

Following the crashes of 2022 that sent tourists scattering, crypto natives worked through self-reflection of the excesses and shortcomings that enabled the bubble in the first place.

As capital and talent consolidated around what felt like a full systems upgrade, colossal progress was made across all layers of the stack, setting the stage for a major coming cycle of adoption at scale.

The flow of private funding throughout the year reflected that story. The year started with financial infrastructure commanding the largest share of funding, followed by wallets, and ended with the former dominant once again and L2/interoperability projects in second place.

Amidst all of this, what is particularly fascinating is the infrastructure-application flywheel is starting to catch fire with more concentrated purpose than ever before.

The needs of a growing class of crypto-native consumers are driving directional and focused improvements in the tech stack, which in turn is yielding new use cases and applications.

Q1 2023 saw the release of the ERC-4337 standard, designed to transform externally owned accounts (EOAs) into smart contract wallets to enable customizability, better private key recovery mechanisms, and a materially more streamlined user experience.

Even more impactfully, teams like Privy* made massive progress by simplifying onboarding with embedded wallets to minimize user friction while letting developers design more contextual experiences.

Privy’s efforts helped streamline Friend.Tech’s explosive early capture of 100K addresses in a matter of weeks, and they’ve gone on to power onboarding for OpenSea, Zora, Blackbird and others to the tune of 2M+ users across 150+ countries in the last 13 months.

Meanwhile, Farcaster’s* launch of Frames—a new primitive allowing people to embed interactive experiences directly within Casts—is a transformative move that is already supercharging activity on the platform.

Farcaster has over 8M+ reactions across 4M+ casts and may well represent early signs of a crypto-native consumer app hitting escape velocity.

Just as Ethereum sought to go beyond Bitcoin’s functional limitations, a new generation of projects are now targeting Ethereum’s own structural shortcomings in a wave of modularity.

Alternative L1s & side chains have been a feature of previous cycles, but none have succeeded in disrupting Ethereum’s (mainnet) dominance of users, TVL, developers and activity.

This changed with the launch of rollups like Arbitrum and Optimism—projects designed to enable better throughput and lower fees by offloading computation from Ethereum.

While these new layers have reached scale that rivals, or in some cases surpasses, Ethereum itself, builders are dreaming bigger as they seek to further optimize the L1 stack.

That’s because even though the number of daily active users on L2s has risen 8x in the last year, much of what users are actually doing looks largely similar to historical L1 activity. As a result, the emerging consensus is that offloading transactions to cheaper execution environments isn’t enough to enable truly novel onchain experiences.

We need to actually rearchitect the components underpinning blockchains, from Data Availability (DA) to state access bottlenecks and parallel execution.

Standalone data availability (DA) layers built to scale to web2 performance parity (i.e., EigenDA, Celestia, Avail) are coming to market alongside upgraded virtual machines, some based on the EVM and others using alternate engines like Move’s (Movement Labs*) or the Solana VM (Eclipse). Some of these are building L2s to optimize execution only (MegaETH), while others are launching net new L1s from the ground up (Monad).

Meanwhile, EigenLayer’s mission of providing a shared security layer via restaking is making it possible for a new generation of projects to launch in a way that minimizes the need to bootstrap native liquidity and therefore deviate from the core security model of Ethereum itself.

All of this means the underlying infrastructure, tooling, and design optionality on offer to web3 builders is approaching unprecedented levels of maturity and performance.

As crypto’s ethos increasingly resonates alongside upgrades in infrastructure, tooling, and middleware, we’re seeing a promising story in the data underpinning crypto’s paramount leading indicator: developer flows.

Building on blockchains should not just be a more meaningful exercise, but a more technically performant one that effectively empowers developers to design the future of the open internet.

Significantly better retention of existing developers and onboarding of new ones amidst difficult market conditions speaks volumes to the work that’s been done on this front.

And finally, our belief is that the intersection of crypto and AI—two paradigm shifts in their own right—represents one of the most transformative moments in modern history.

Blockchain rails have successfully undergone a multi-year pressure test in the design of a permissionless system fit for a digital age, and especially an age shaped by generative AI.

Crypto’s toolkit of solutions tackles pertinent problems ranging from resource and liquidity coordination, asset ownership, data provenance, attestations, and much more. Crucially, the maturity of the ecosystem and technology stack are arriving just in time to meet the demands of the AI revolution.

While there is ample room for blockchains to streamline existing machine learning (ML) flows, the most exciting opportunities will emerge where crypto and AI converge to enable completely novel outcomes.

The most exciting new design spaces will span areas like:

• Decentralized storage underpinning shared, permissionless repositories for data to enable better training or more performant models via Retrieval-Augmented Generation (RAG)

• Zero Knowledge Proofs for model or content verification, training or user data privacy, or enabling edge and local (client side) inference

• Novel information markets and better mechanisms for collecting higher quality data as foundation models require more specialized data inputs to continue evolving

• Autonomous agents transacting on smart contracts that uniquely enable them to accumulate resources, knowledge and assets using machine-operated private keys

As crypto rails impact everything from the supply of compute to markets for data to the collective creation and monetization of powerful foundation models, open-source AI/ML will be supercharged by crypto, fueling a renaissance in human productivity and open collaboration in the coming years.

Crypto will be the best way to get exposure to the rise of AI, as a proxy via blue chip assets like ETH or directly through ownership or speculation across agents, models, networks, and datasets.

We’re at an inflection point for this industry. After years of grinding in spite of market, incumbent, and regulatory resistance, the tides are finally shifting.

Crypto’s moment has arrived thanks to the convergence of a few significant tailwinds that are finally ushering in the decentralized future. Amidst this renaissance of infrastructure and middleware, which is already enabling a step function evolution in the onchain experience, there’s an important point we should keep in mind.

In a perfect world, modularity enables not just specialization but also a dissemination of control and points of failure across multiple contributors. However, each of these new puzzle pieces involve different technical and security assumptions, incentive mechanisms, token dissemination roadmaps, VCs, foundation setups, and internal politics.

I don’t want to detract from the inspiring efforts of builders across the space last year, especially in the face of a brutal economic downturn. But as activity and excitement pick back up, it’s imperative we escape the echo chambers and false signals of token grants masquerading as logical integration partnerships, effective PR as community approval, or incentivized behavior as a proxy for organic adoption.

In the coming years, we have a shared responsibility to keep this growing number of projects accountable across technical design choices, token concentration, value dissemination, ideology, and governance.

That’s how crypto wins the endgame.

*denotes an Archetype portfolio company

Thank you to my Archetype colleagues Katie Chiou, Benjamin Funk, Aadharsh Pannirselvam, Ash Egan, Tyler Gehringer, and Dmitriy Berenzon for thoughtful review and feedback.

Disclaimer:

This post is for general information purposes only. It does not constitute investment advice or a recommendation or solicitation to buy or sell any investment and should not be used in the evaluation of the merits of making any investment decision. It should not be relied upon for accounting, legal or tax advice or investment recommendations. You should consult your own advisers as to legal, business, tax, and other related matters concerning any investment or legal matters. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Archetype. This post reflects the current opinions of the authors and is not made on behalf of Archetype or its affiliates and does not necessarily reflect the opinions of Archetype, its affiliates or individuals associated with Archetype. The opinions reflected herein are subject to change without being updated.