I love seeing all the different ways LLMs can be used. This is how we learn what works and what doesn't.

It reminded me of an experiment I ran back in 2023. I used ChatGPT to synthesize insights from interviews, first-hand experience, and secondary market data into a set of proto-personas for my collectors of Second Realm's artwork. Then I pushed it further. I asked the LLM how each persona might respond to influence using Robert Greene’s 48 Laws of Power.

That work was not just a creative exercise. I've spent nearly two decades in human-centered design, leading service design and customer experience research. I've worked with real users across messy, high-stakes environments and paradigm-shifting emerging technology products. So when I ran that experiment, I wasn't trying to replace people. I was trying to understand what this tech could actually do.

And here's what I've learned since. LLMs are good at surfacing patterns. They're fast. They're directional. But once you step into ambiguity, they start to break down and need human intervention.

Recent studies back that up. In AbsenceBench, researchers found LLMs are much better at spotting inserted facts than noticing when something important is missing. They're built to predict what should come next, not question what isn't there. They work best in environments where the rules are clear. But the world doesn't operate like that.

Even the team behind Synthetic Users admits this. In their own comparison study, they found simulated responses often lack depth unless you ask for it directly. Personal stories, emotional nuance, contradiction, all the things that tend to unlock real product insights, need to be forced into the conversation.

They claim 85 to 92 percent fidelity when comparing synthetic responses to real interviews. But if you read closely, that number comes from structural and thematic overlap. Not emotional realism. Not subtext. Not the awkward silence that happens when a real person gets asked a hard question.

Humans are still better at handling complexity. We pick up on tone, timing, hesitation. We notice when something feels off, even if we can't explain why. As I previously wrote, LLMs are flooding the market with cheap competence. But what they are not replacing is our ability to move through layered, context-dependent situations and come out the other side with insight.

Simulated user feedback might help you get started. They are fast, cheap, and sometimes good enough to move things forward. But they're still mirrors. They reflect what's already been said, not what's been left unsaid.

And that's where the truth usually lives.

In my experience, observational research is still the most reliable way to find early signals. Watching real users interact with your product or service tells you more than a dozen simulated interviews ever will. Start with five people. That is usually all it takes to surface patterns and unexpected truths.

I can see specific use cases for synthetic people, especially when you are testing a strict set of conditions or validating edge-case logic. But those are the exception, not the rule.

Who comes to mind when you read this post?

Send it their way. It might say what they’ve been needing to hear.

Have you missed other posts from Eric's Blog?

Don’t let the next one slip by.

Follow Eric: X | Farcaster | SuperRare

"No, your brain does not perform better after LLM or during LLM use."

But the study didn’t test how people typically use AI to think or write. It tested what happens when you hand over the task and check out completely. Of course you don’t remember what you didn’t take part in. This isn’t new to cognitive science. And it’s not a flaw in ChatGPT. It’s a setup that invites passivity and then blames the result on LLM technology more broadly, even though the study only tested a narrow, constrained use of ChatGPT.

I don’t find this work as insightful as some folks in academia seem to think it is, judging by the comments I’ve seen here on LinkedIn. A lot of teachers are still trying to make sense of how to incorporate AI tools and understand how this changes their pedagogical approach. So this study feels more like it confirms a pre-existing bias than offers real insight.

In fact, it's wildly misleading. The research is built on a framework of bias, from how it defines “typical” ChatGPT use, to how the task was designed, to how far the conclusions stretch to support a narrative about cognitive decline linked to LLMs. It says more about the limits of the study than about the limits of human cognition or AI.

What I’d like to see is two more groups added to the three already in the study: brain only, internet only, and ChatGPT only.

One should mimic what happens when you ask a tutor or a friend to write the essay for you. That would give us a baseline for full handoff. If you're going to claim cognitive decline, you need to show it’s worse than that.

The other should reflect how people actually use AI when it has access to real sources. Let ChatGPT pull from the internet or supporting material. Let people search, revise, compare, and actually think with the tool in context. That is the in-between space where thinking and offloading mix. And it is how a lot of people use these tools in practice.

I suggested this to the researchers. But their response to this and other similar criticism has been some blanket version of "we state this in the limitations section of the paper."

This study shows what happens when you treat AI like a friend who writes your paper for you. You skip the thinking, so nothing sticks. That’s fine to observe, but let’s not pretend it tells us something deeper about how people think with AI.

Who comes to mind when you read this post?

Send it their way. It might say what they’ve been needing to hear.

Have you missed other posts from Eric's Blog?

Don’t let the next one slip by.

Follow Eric: X | Farcaster | SuperRare

I love seeing all the different ways LLMs can be used. This is how we learn what works and what doesn't.

It reminded me of an experiment I ran back in 2023. I used ChatGPT to synthesize insights from interviews, first-hand experience, and secondary market data into a set of proto-personas for my collectors of Second Realm's artwork. Then I pushed it further. I asked the LLM how each persona might respond to influence using Robert Greene’s 48 Laws of Power.

That work was not just a creative exercise. I've spent nearly two decades in human-centered design, leading service design and customer experience research. I've worked with real users across messy, high-stakes environments and paradigm-shifting emerging technology products. So when I ran that experiment, I wasn't trying to replace people. I was trying to understand what this tech could actually do.

And here's what I've learned since. LLMs are good at surfacing patterns. They're fast. They're directional. But once you step into ambiguity, they start to break down and need human intervention.

Recent studies back that up. In AbsenceBench, researchers found LLMs are much better at spotting inserted facts than noticing when something important is missing. They're built to predict what should come next, not question what isn't there. They work best in environments where the rules are clear. But the world doesn't operate like that.

Even the team behind Synthetic Users admits this. In their own comparison study, they found simulated responses often lack depth unless you ask for it directly. Personal stories, emotional nuance, contradiction, all the things that tend to unlock real product insights, need to be forced into the conversation.

They claim 85 to 92 percent fidelity when comparing synthetic responses to real interviews. But if you read closely, that number comes from structural and thematic overlap. Not emotional realism. Not subtext. Not the awkward silence that happens when a real person gets asked a hard question.

Humans are still better at handling complexity. We pick up on tone, timing, hesitation. We notice when something feels off, even if we can't explain why. As I previously wrote, LLMs are flooding the market with cheap competence. But what they are not replacing is our ability to move through layered, context-dependent situations and come out the other side with insight.

Simulated user feedback might help you get started. They are fast, cheap, and sometimes good enough to move things forward. But they're still mirrors. They reflect what's already been said, not what's been left unsaid.

And that's where the truth usually lives.

In my experience, observational research is still the most reliable way to find early signals. Watching real users interact with your product or service tells you more than a dozen simulated interviews ever will. Start with five people. That is usually all it takes to surface patterns and unexpected truths.

I can see specific use cases for synthetic people, especially when you are testing a strict set of conditions or validating edge-case logic. But those are the exception, not the rule.

As some of you know, I’ve been deep in my own research on the future of work and AI. But that’s not why I picked up Range. I’ll probably get into that in another post.

What struck me right away was how Epstein lays out one of the clearest and most grounded arguments I’ve seen for why humans will continue to have an advantage over AI. Put simply, we’re better at navigating ambiguity, especially when the rules of the game are not clear.

Also, through a story about Kasparov and freestyle chess (chess with teams of humans and computers working together dubbed "centaurs"), Epstein makes another compelling point.

“If Deep Blue’s victory over Kasparov signaled the transfer of chess power from humans to computers, the victory of centaurs over Hydra symbolized something more interesting still: humans empowered to do what they do best, without the prerequisite of specialized pattern recognition.”

The most effective players were not grandmasters or supercomputers. They were amateurs with decent machines and strong strategic skills. They knew how to think big picture while using both the technology and the experts. They knew how to coach the system.

Although Kasparov was speaking about Chess-playing computers and not LLMs, I think the point he's making holds true for AI.

As I said on X, AI is flooding the market with cheap competence. Expertise is no longer gatekept. And in that kind of landscape, those who can think strategically, coach humans and computers together, and navigate ambiguity are in a prime position going forward.

This further confirms what I’ve understood so clearly over the last four years. That my broad skillset, big picture thinking, and strong strategic abilities are not diminished by AI. They are amplified by it.

Who comes to mind when you read this post?

Send it their way. It might say what they’ve been needing to hear.

Have you missed other posts from Eric's Blog?

Don’t let the next one slip by.

Follow Eric: X | Farcaster | SuperRare

What began in grad school at Rutgers evolved into an independent research initiative based on a cross-sector survey of nearly 300 workers.

Yesterday, I learned that my paper was ranked a top 5 download in Organizational Learning on SSRN

The study centered on a few key findings, including:

1. AI use often correlates with drops in team trust.

2. As AI automates more analytical tasks, organizations begin to place greater value on interpersonal work.

These patterns were especially pronounced among knowledge workers, those typically assumed to benefit most from AI, who reported more negative impacts than those in service or civic roles.

Last week, Stanford independently confirmed both of these findings with a new study of 1,500 workers. Their data shows the same pattern: AI boosts efficiency but weakens trust and connection. And as automation spreads, it’s the human side of work, collaboration, communication, emotional intelligence, that becomes more valuable and more at risk.

It’s really validating to see this kind of alignment across independent studies.

But more importantly, it’s a signal:

The future of work won’t be decided by technology alone. It’ll be shaped by how we choose to design for people.

More soon.

Who comes to mind when you read this post?

Send it their way. It might say what they’ve been needing to hear.

Have you missed other posts from Eric's Blog?

Don’t let the next one slip by.

Follow Eric: X | Farcaster | SuperRare

But that’s not the full story.

In the original version, Daedalus, Icarus's father, didn’t just tell Icarus not to fly too high. He also warned him not to fly too low. If he flew too close to the sea, the water would drag him down. The point wasn’t to avoid risk. It was a lesson about balance. About awareness. Not about fear.

Over time, though, the tale was flattened. We kept the part about the sun. But the part about flying too low is rarely told. So all that's left is the warning: don’t fly too high or you’ll get burned.

That’s the deception.

Somewhere along the way, we became convinced that the greatest danger in life is making a mistake. That playing it safe, following the rules, and avoiding failure is how we survive. That’s how we stay whole.

But it’s a lie. A beautiful, seductive lie. One that trains us to believe that smallness is safe.

That’s not safety. That’s stagnation. That’s hiding.

I know that space well.

I stayed small for a long time. I played it safe, avoided risks, convinced myself that caution was wisdom. I thought if I could just avoid mistakes, I’d be okay. But the longer I waited, the heavier the silence got. Safe started to feel like lost.

The few times I let myself stretch, everything changed. It was uncomfortable, sometimes even painful, but it woke something up in me. I started to see that growth doesn’t happen in the knowing. It happens in the doing.

Now I know what it’s like to build things I believed in, things that were bigger than me at the time. And I know what it’s like for it all to fall apart. But every time, I come back clearer. Stronger. Wiser. A little closer to the version of my life I continue to aim for.

The greatest trick ever pulled is convincing us that playing it safe is better than making mistakes.

Being ambitious and making mistakes are how we grow. They sharpen us. They show us who we are when “the script” falls apart.

I'm done with the lie.

Does someone come to mind when you read this?

Send it their way. It might say what they’ve been needing to hear.

Have you missed other posts from Eric's Blog?

Don’t let the next one slip by.

Follow Eric: X | Farcaster | SuperRare

Listen to the episode on Pods, Spotify, Apple, YouTube, and everywhere else you get your podcasts.

In this episode, Darren and I unpack what really defines an artist, why that definition matters, and who gets left out when gatekeepers draw the line.

We also break down Apple’s quiet shift on NFTs and crypto payments, the true cost of always-on tech, and what new AI-at-work data says about autonomy and stress on the job.

In this episode, we discuss:

• Why the “only world-builders are artists” take falls apart

• What Apple’s updated crypto policy actually signals

• How passive listening is built into your daily tech

• The rise of tokenized real-world assets and the MSTY YieldMax ETF

• What new survey data reveals about AI and workplace anxiety

Your Hosts:

🎙Darren Kleine: X/Twitter, Farcaster
🎙Eric P. Rhodes: X/Twitter, Farcaster

Rising Tide Links:

🌐 Website: rising-tide.xyz
💬 X/Twitter: @risingtidexyz
💜 Farcaster: /risingtide

But in the middle of it all, one question kept sticking with me.

How is AI really changing the way we work?

Not in theory. In practice.

That question followed me over the last few years. I saw it everywhere. Artists raising concerns about stolen IP. Writers on strike in Hollywood. Headlines about potential unions, layoffs, and automation reshaping entire industries. And then the rise of LLMs. And vibe coding. It wasn’t just theory. It was happening in real time.

This project grew slowly. I had no intention of running an independent study. But a class assignment that helped me develop a survey instrument eventually led to piloting the survey, and the early findings were encouraging and surprising. I wanted to learn more.

So I kept going. What started as a quick course project became a standalone research effort. No one assigned it. No one required it. But I could not stop thinking about it, so I built it.

Now I am looking for 300 people to take part. The survey is short. It’s anonymous. It is open to anyone. Even if you don’t regularly use AI at work.

👉🏻 About 6 minutes: https://fowlab.net/survey

To show my appreciation, I’m giving away a limited edition (1 of 146) of Delta Sauce’s The Office to one survey participant. Details are inside.

This is the last thing I’ll complete before I graduate, and maybe the thing I am proudest of. It is not a thesis. And no one assigned it. But it does feel like the culmination of my degree.

If you have a few minutes, I would really value your perspective.

Thanks for being part of it! - Eric

And during that time, we published billions of books. We shared countless hours of music. We streamed sports to every corner of the world. We turned gaming into something multi-player and global.
Each of them evolved into full ecosystems. Indie scenes, global fandoms, and billion-dollar brands grew up around them.

As their distribution models evolved through technology, their cultural impact grew. They weren’t just about content. They were about connection.
You could read the same book. Watch the same match. Listen to the same music. Play the same game.
You were not just consuming. You were connecting.
They were hobbies, but also highways. They were how people found each other.

Art never really had that at the same scale.

Art was private. Niche.
A painting on someone’s wall. A baseball card in a box. A rare toy in someone's home. Comic books are displayed in an office. A first edition on someone's shelf. A sculpture in a museum gallery.
These were things you might feel something about, but rarely share with large audiences at the same time.
Collecting wasn’t even accessible unless you were of that world.

Then came NFTs.
And with them, something subtle but seismic happened.
Art got a social layer.

For the first time, visual art became networked.
You could collect it. Talk about it. Remix it.
You could show someone worldwide what you were drawn to and why.
You could build an identity around the art you loved.

This wasn’t just economic. It was emotional. Communal.
It gave art the same kind of cultural distribution that music and gaming have had for decades.

We’re in the middle of live experiments.
Platforms like Rarible, SuperRare, Zora, Rodeo, and Base aren’t just marketplaces. They’re exploring what distribution for visual art can look like in a networked world.

We’re watching the boundaries blur between high brow and low brow, between internet-native and institution-backed.

Galleries and collectors once rooted in traditional art are moving into digital, and digital artists are finding new relevance in the traditional art world.

These aren’t fringe experiments anymore. They’re shaping how art circulates, how it’s valued, and how it brings people together.

What we’re seeing isn’t just a new medium. It’s a new model.

This doesn’t mean art has changed.
It means what art can do has changed.

And that opens up everything.

When art becomes something we connect through, not just around,
we don’t just witness beauty. We share it.
We don’t just express ourselves. We build community.
We don’t just collect. We connect.

A decade ago, this wasn’t possible. Not at this scale.

Now it is.

It’s easy to get caught up in market cycles, gatekeeping, promotion, and who’s getting the spotlight. But if you take a step back, it’s kind of incredible. We’re alive at the exact moment visual art became truly social. For the first time in history.

And if we look at how much music, gaming, and media industries have evolved since they became networked, then this moment is not the peak.

It is the beginning.

We’re still early.

Know someone who might connect with this?

Send this to someone who might find meaning in it.
Share it so others can discover it, too.
This is how we grow. One connection at a time. 🌱

I do not create to be consumed.
I create to understand.
To reveal the world to itself.
To reflect what is, not just what signals or sells.

I believe in the sacredness of time.
In the marks it leaves behind without asking.
In what is unsaid, unseen, and incomplete
because it speaks too.

I will not mistake permanence for truth.
I will not sacrifice nuance for virality.
I will not offer my work as certainty
when it is, at its best, a question.

I owe the audience transparency, not access.
I owe myself integrity, not output.
And I owe the future art that honors my soul,
even if it risks being misunderstood.

When I create, I do so with intention.
When I speak, I do so knowing the cost.
When I share my vision, I do so with care
because every piece is a mirror.

I am not here to sell a version of myself.
I am here to bear witness.
I am here to leave behind a trail of meaning,
not a monument, but a direction others carry forward.

I will not be perfect.
I will make many, many mistakes.
But I choose to show up every day despite this,
because the work is what keeps me honest.

About The Artist's Oath:

The Artist’s Oath is a personal commitment to how I show up creatively. It is a reminder to lead with integrity, to resist the pressure to perform, and to make work that reflects truth, not just what gets attention. It is a compass for navigating the tension between expression and permanence, and a way to stay rooted in what matters: presence, process, and purpose, not perfection.

Know someone who might connect with this?

Send this to someone who might find meaning in it.
Share it so others can discover it, too.
This is how we grow. One connection at a time. 🌱

Many are full of moral certainty: protecting creators, defending innovation, upholding what’s “right.”

As someone studying AI’s impact on work, I’ve been thinking about what’s actually at stake in these debates.

Not just legally, but structurally.

Lately, I’ve been thinking about who can claim the moral high ground in the AI and IP law conversation.

The tension between AI and IP isn’t about ethics vs. theft.

It’s about a system struggling to control what it can’t contain.

When people speak about IP law with moral certainty, I always wonder whose morality they defend.

Creators? Or the systems that decide which creators matter?

Maybe the current system isn't about protecting creator IP.

Maybe, in practice, it's about deciding which company's IP gets to stay exclusive longer.

Certainty is easy when one outsources complexity to law and call it ethics.

Note: This isn’t a fully formed argument. I’m just poking at an idea that’s been bothering me.

Know someone who might connect with this?

Send this to someone who might find meaning in it.
Share it so others can discover it, too.
This is how we grow. One connection at a time. 🌱

The stories in Adam Grant's book Hidden Potential push back on the myth of the lone genius. They show what happens when expectations are high, and people are supported, not left to figure it out alone.

Initial Thoughts After Reading

Learning about the Golden 13 made me think of Bounce by Matthew Syed. Same idea: excellence doesn’t just appear. It builds in clusters under the right conditions.

It also brought the halo effect to mind. Adam Grant talks about how we often mistake polish for potential. We let résumé credentials like McKinsey, an MBA, or an Ivy League degree stand in for actual skill. That creates two realities. First, a lot of great people never get the shot they deserve. Second, some people learn how to work the system. They know what gatekeepers are looking for and how to speak their language. Once they’re in, they can start to shift what gets noticed.

That last idea made me think about artists. There’s a tendency to treat gatekeeping and discoverability issues as a wall instead of a challenge. This book doesn’t ignore those barriers. It asks whether the real work is finding a way through or finding a way in.

What Stuck With Me

• The systems we use to spot talent often miss it. They reward polish, not potential.

• Success is rarely about individual genius. It grows in clusters with the right support.

• High expectations can unlock performance, but only when people aren’t left to figure it out alone.

• Progress over time (trajectory) is a stronger signal than early achievement.

• Belief in potential is powerful, but it has to be paired with scaffolding, mentorship, and opportunity.

• People who don’t look like the “usual” candidates often need to over-qualify to get noticed.

• Once inside the system, those same people can shift what success looks like for everyone else.

Deeper Reflections

Scaffolding: Grant calls it scaffolding. I’ve always thought of it as stacking. Different word, same idea. Potential needs support. If you want people to grow, you build the environment around them. You don’t test what they can do alone. Support isn’t a crutch. It’s a launchpad.

Becoming a Cy Young Award Winner at 38: This one stayed with me. I know what it feels like to be written off on the ball field, even if not at the professional level, and to wonder if your window is closing. Dickey changed the game by learning the knuckleball, a pitch most pitchers avoid. He didn’t do it alone. With help from Charlie Hough, Phil Niekro, and Tim Wakefield, he mastered it. It wasn’t until his mid-thirties that things started to click. In 2012, at 38 years old and sixteen years into his pro career, he won the Cy Young. What stuck with me wasn’t the award. It was the mindset shift. He found mentors, changed his approach, and stayed in the game when most would have walked away.

Growth Mindset and Environment: Believing in yourself isn’t enough. You need to be in places that believe in you, too. Grant challenges the idea that mindset alone drives success. The system around you matters just as much. It made me think about the kinds of environments that have helped me grow best and what it might look like to find or build more of them.

Becoming the First Black Navy Officers in 1944: The Golden 13 are a group of Black Navy officers who thrived under intense pressure and what many saw as impossible odds. In 1944, they became the first African Americans commissioned as officers in a segregated Navy. What stood out was how they got there. They taught each other, studied together, and lifted one another through a system that was never built for them. It’s a story of preparation, community, and belief. High expectations paired with real backing.

Grade Point Trajectory: Grant shares research showing that grade point trajectory, or improvement over time, is a stronger predictor of future success than a flat but high GPA. Progress matters more than polish. It reflects growth and resilience, not just early performance. My own path fits that curve. I was a solid B minus student in high school, and my first shot at college went poorly. But over time, my direction changed. That shift, not the starting point, has shaped everything since.

From Migrant Farmer at 6 to Astronaut at 41: José Hernández spent his childhood working in the fields with his family. His college grades weren’t perfect at first, but he kept improving. He applied to NASA’s astronaut program twelve times before finally getting accepted. Instead of walking away, he took a job as an engineer at NASA and found another way in. Gatekeepers often miss potential because they expect it to look a certain way from the start. Sometimes the path in isn’t about being the perfect candidate. Sometimes it’s about getting close enough to be seen. At 41, after more than a decade of rejections, Hernández was finally accepted into the program. Sometimes the strategy isn’t charging through the front door. Sometimes it’s finding a side entrance and letting your work speak for itself.

Final Note

Still thinking about what this means for the environments I want to build and the systems I want to challenge.

But my gut tells me that I'll be on the right side of history.

Here goes:

AI didn’t break anything. It just makes one simple truth harder to ignore.

Attention matters more than skill.

Some artists have been acting like creation is the entire point. Art, for art’s sake. But as I've written before, while "alluring," it often falls short of reality. Before ChatGPT, that story was easier to believe when scarcity was part of the creative process when gatekeepers held the keys, when access was limited. Even digital had its own gatekeeping. The tools themselves.

Now, anyone can make anything, anytime, anywhere, in a hyper-connected world. And the issue isn’t that the art got worse. It didn’t. It’s that the mask finally came off. We can all see the game for what it really is. And realize it’s always been that way. The illusion broke. And we all collectively can’t unsee it. Attention matters more than skill. It always has.

Even the most celebrated artists in history weren’t above chasing attention. Da Vinci positioned himself for recognition and patronage. Van Gogh’s work didn’t gain traction until after his death—and that only happened because his sister-in-law championed it. The Mona Lisa wasn’t always iconic. It became the world's most famous painting after being stolen in 1911 (possibly by Picasso), and the media turned it into a global story.

Attention has always mattered.

AI agents and LLMs all make that truth harder to avoid. The myth of pure creation is coming undone. We can no longer pretend that creation happens in isolation or that it speaks for itself. It never did. Now we have to reckon with that.

Some people weren’t being dishonest. They just didn’t want to admit that attention mattered more than they were comfortable with. Others were fully lying to themselves, unwilling to accept that in the real world, attention often outweighs skill.

Even in the social media era, where that’s been so painfully and unequivocally obvious for so, so long, many still didn’t want to see it. Maybe AI helps force the conversation.

Because when anyone can make anything, and everyone’s chasing attention, maybe we finally stop pretending. Maybe we stop worrying about whose idea it was, or who did it first, or whether something “belongs” to someone at all.

And now, thanks to NFT technology, we can cryptographically track authorship and provenance. So maybe that frees us up from some of the concerns with ownership and credit. Maybe we stop clinging so tightly to originality and just get back to making things together. Not to prove anything, but because we understand that the real value in art is shaped by attention, not skill.

Then came the catch. In order to take the class, I had to enroll in the Computer Science degree program.

I had no real background in it. I’d taken one computer science class in high school, but we mostly just translated binary code. I did own a computer, but I had no idea how it actually worked. Up until that point, it was just something I used to write essays and chat on AOL. Beyond that it was a mystery to me. And now I was about to major in it.

But something about it pulled at me. There was a thread there. I didn’t know what it connected to. I just knew it was worth pulling. The first internet bubble was crashing as I enrolled in 2000. I didn’t know where I was going, but I knew the internet wasn’t going away. It felt important. And I wanted to understand it.

I ended up doing well. The math came easy enough. Learning how to speak "computer" and make it do things was empowering. For the first time, tech wasn’t just a mystery. It was something I could shape. That felt good. I liked it.

I also got selected for an experimental program at Hudson County Community College called the Interdisciplinary Learning Community. It blended psychology, writing, and speech. And it brought me right back to high school and the Palette Paradigm. That same sense of wonder, of seeing connections between things that weren’t supposed to be connected. It was familiar. It made learning feel like discovery again.

This wasn’t the moment I found my career path. I didn’t even know this technical education would play a role in being a designer just a few short years later. However, I started to notice that I preferred to focus on the interface elements rather than the application layer. I liked making things interactive. I liked the intersection of logic and creativity. And I was intuitively pulling that thread. I just didn’t know what to call it yet.

I would eventually go on to study computer science at a university, but ended up taking more art classes than technical ones. That caused some uncertainty. I had received a scholarship to study computer science, but I couldn’t ignore the pull toward creativity. And I didn’t yet know that being a UX designer was a possible career path.

The deeper I got into the technical side of things, the more I realized that pure engineering wasn’t it for me. I wanted to make things people interacted with. I wanted to understand why they make the choices they make. I didn’t know what that looked like yet, but I knew what it didn’t look like.

That’s when I met Tony Capparelli, a long-time mentor, friend, and one of the first people to encourage me to stick with the tech side of creativity. His guidance helped me land a job at a boutique marketing agency in book publishing, where I stepped into web design for the first time.

That’s where I discovered the intersection of front-end development and UI design. It was what I eventually came to understand as UX design. We were early. We pioneered the use of blogging, social media, and mobile platforms as primary marketing and audience engagement tools. That’s when it all clicked. I was designing user experiences with intention.

I spent eight years there, working my way up to Director of Design. And I would eventually go on to do experience design work for global brands like Google, Twitter, HarperCollins Publishers, and Major League Baseball

Looking back, my Associate's degree in Computer Science put me on a path few were traveling at the time. It showed me that tech needs creative types who can move comfortably in the gray space, in the ambiguity, using design thinking and human-centered design to serve as the connective tissue between engineers, stakeholders, and users.

As a design leader, this is where I am most at home.

It’s a fair question, but the most likely outcome isn’t that junior roles will vanish. It’s that the middle collapses.

As AI takes over more routine tasks, junior workers will be expected to take on more complex, higher-impact responsibilities earlier in their careers. In turn, they’ll build skills and experience that, by today’s standards, would qualify as “senior” much sooner.

But this shift doesn’t just push everyone upward; it compresses the entire structure. Mid-level roles shrink. Senior talent gets stretched thinner. Early-career employees are expected to leap into the deep end with less guidance than ever before.

AI doesn’t eliminate the need for training. It just raises the baseline for what “entry-level” looks like, without updating the systems that help people get there.

• When spreadsheets replaced calculators, analysts stopped crunching numbers and started interpreting trends.

• When search engines made information instantly accessible, knowledge work shifted from memorization to synthesis.

• When tools like Canva and Figma empowered non-designers, designers moved into systems thinking and brand strategy.

• When low-code platforms made app-building easier, developers focused less on syntax and more on system architecture.

In each case, the work didn’t disappear. It evolved. And more often than not, responsibility shifted downward. What used to be considered mid-level expectations became the new minimum.

They’re not just afraid of change. They’re afraid of being expected to perform at a higher level without the time, training, or support to get there.

In many fields, the middle is getting more compressed.

Roles like editors, project managers, coordinators, assistants, and analysts are increasingly augmented by tools. The roles that remain are burdened with more responsibility than ever. Early-career workers are left to figure things out on their own, while senior employees are stretched beyond their limits.

This isn’t the end of junior roles.

It’s the end of the slow ramp-up that made entry-level sustainable.

If AI is going to flatten the ladder, we need to build new rungs like training, mentorship, and structured support so early-career workers have something to climb.

Because the floor isn’t falling out from under us. It’s just rising.

The question is: who are we helping climb with it?

I didn’t step into a competition—even if it sometimes feels like one.

But I found something that helped me stay grounded and intact.

For most of my life, I created when no one was paying attention, and I’ll keep creating long after the noise quiets down.

Some people are built to chase the peak.

Others are built differently—from the ground up, in the margins, in connection.

Not better. Not worse.

It's just a different rhythm, a different kind of fuel.

If you’re giving this everything you’ve got, I’ve got nothing but respect for that.

Just don’t forget: burning out isn’t the only way to leave something behind.

Legacy can come from joy, too.

Wholeness can drive the work.

We’re not all reaching for the same thing.

That’s the point.

This is where I build from.

At first, I believed I was only telling that story.

The breakdown. The rebuilding. The fight for clarity.

And in a lot of ways, I was.

But sometime in 2021, during the early stages of divorce proceedings, I looked at the work again. I really looked at it. And something else surfaced.

Unintentionally, and without even being fully aware of it at the time, I had also told the story of the end of my 12-year relationship.

It was all there. The unraveling. The silence. The resentment. The longing. The slow emotional separation that started long before anything legal was in motion. My subconscious had put it all on the canvas before I had the language to explain it to myself.

Recently, I came across a post by @exlawyernft. It wasn’t about me, but it might as well have been. Their words hit hard. They spoke about how the right partner lifts, grounds, and holds you through the worst. And how the wrong one can quietly pull you apart, piece by piece.

exlawyer.eth/tez

@exlawyernft

The most important decision you will ever make is who you marry. If you marry the right person, they will lift you up higher than you would go alone, they will be there to catch you and hold you when you stumble and suffer, they will make you laugh and smile and find joy in even

100

7:00 PM • Mar 30, 2025

That post didn’t show me anything new. I'm well past grieving what was gone. The post just reminded me of something my ex used to say:

“There’s a reason and a season.”

She said it when things ended. Projects. Friendships. Even people. It was her way of finding meaning in the fall.

And I think she was right.

But I also think there’s a reason and a season for truth to arrive.

Sometimes it takes months. Sometimes years.

Sometimes the art knows before you do.

I later added two more pieces to the series in 2022, just as the divorce was finalized. One of them was called Pleasure (SuperRare). It wasn’t loud. It wasn’t triumphant. But it was honest. It marked a quiet return to myself. Something more grounded. More whole.

Now in 2025, I’m sitting with all of it.

Not trying to rewrite the past. Not trying to make it neater than it was.

Just reflecting on what this series became, and what it helped me survive.

"I don't feel guilty about any of my pleasures" - Adwoa Aboah

I talked more about that in a 2023 podcast interview with The Angels’ Wing. It was one of the first times I publicly opened up about what the series really meant, including how the mannequins became a way to safely express myself when words weren’t enough.

Most of the mannequins in the series represent me, especially the female figures. I’ve always felt more connected to my feminine energy, and when I need to express something deeply emotional, that’s who shows up in the work.

If you’re curious about the deeper story behind Brutalist Mannequins, how I moved from hiding in surrealism to expressing more openly, or why I continue working with the mannequin form today, you can listen here:

🎙 Space #33 – Chat with Eric Rhodes, an artist in search of himself

These videos bring me back to when I saw my HS art teachers every day for four years in the 90’s (that’s me in high school). It also reminds me of the hours I spent quietly honing my skills under the guidance of Mrs. Bartoszek, Mr. DePice, and the Art Department head, Mr. S.

Mr. DePice also opened my eyes to the interdisciplinary relationship between art and other fields of study. I was among his first-generation of students in my junior and senior years to participate in what would become known as "The Palette Paradigm" program.

This was an invigorating and eye-opening revelation at the time. It was like some secret knowledge had been passed to me, a door unlocked, and much of the world suddenly made sense. We explored and experimented with the symbol (art) as metaphor (English), structure (science), and proportion (math). These were early lessons in open-mindedness and critical thinking through the lens of art—but I had not realized it yet.

Today, almost 30 years later, the process of exploring the interdisciplinary relationships between seemingly unrelated philosophies, subjects, ideas, and more continues to be a significant part of my art practice. Beyond that, viewing the world through an interdisciplinary, openminded but critical lens has impacted my life in even more profound ways outside of art. It has helped to drive decisions about academics, hobbies, interests, career paths and more.

At the heart of my early educational experience, we've got The Palette Paradigm, a visionary approach developed by artist and educator Doug DePice. It was later detailed in the book, "Interdisciplinary Education: Art & Social Studies Across the Curriculum" (2013), which he co-authored with Social Studies educator Amanda Jones. The program aims to break free from the usual educational mold and mix up the rich, varied colors of academic disciplines into one vibrant, unified vision of learning.

DePice created The Palette Paradigm because he noticed something was off with the traditional education system: it's organized into separate subject groups, which doesn't really reflect how ideas are experienced in the real world. He saw an opportunity to make learning more holistic, helping students see the ties between art, science, math, and the humanities. The program's aim? To spark a sense of wonder and discovery, teaching kids not just to learn, but to connect and create.

The Palette Paradigm is all about:

• Fostering Interdisciplinary Learning: It's about mixing up subjects so students can see the connections across different areas, boosting their understanding and sparking innovative thinking.

• Developing Critical and Creative Thinking: The idea is to beef up students' skills in thinking both critically and creatively, using Problem-Finding Questions (PFQs) to challenge them to identify and explore new problems, thereby prepping them to come up with cool, innovative solutions to tricky situations.

• Encouraging Lifelong Learning: With its focus on mixing disciplines, The Palette Paradigm kindles a love for learning that goes way beyond the classroom walls, getting students ready for a lifetime of curiosity and exploration.

• Preparing for a Complex World: By seeing knowledge as a big, interconnected web rather than isolated bits, students are better equipped to deal with the complex world we live in.

In short, The Palette Paradigm isn't just an educational framework; it's a rally cry for educators and students to think outside the box when it comes to learning. With its holistic approach, it's all about not just educating, but inspiring—setting the stage for a future where learning knows no bounds, is deeply connected, and driven by an endless curiosity.

Mr. DePice’s teachings, soaked in the wisdom of the Palette Paradigm, are timeless. His videos welcome anyone, from newbies to experienced artists, to dive into or revisit art's basics. For those who've followed my artistic journey, these videos shed light on the early influences that sculpted my creative and personal identity.

Doug teaches how to re-see the possibilities of line in order to create a free form work of art.

Doug teaches the basics of creating trees.

Doug shows you the basics of printmaking using easy and simple techniques.

Watercolor painting and floral studies.

Watercolor painting and landscape studies.

I hope you enjoyed these art studio lessons from Doug! Here's a bonus art history video for you on Leonardo da Vinci's Mona Lisa.

Originally posted on Twitter/X as a thread.

We called the mechanism Social Liquidity Mining (SLM).

The idea was simple: reward people who spread value, not hoard it.

At the time, it felt experimental. In hindsight, it looks inevitable.

$TATR was a token distributed through SLM. It was an early attempt to design tokenized incentives for cultural engagement.

• You could only earn $TATR by gifting “The People’s Potato” NFT to someone who had never owned one.

• Both sender and receiver were rewarded, but only if it was a first-time interaction.

• The protocol included a reward split, anti-gaming mechanics, and a Social Distribution Score (SDS) model to measure organic behavior.

In total, 695,000 tokens were distributed.

A provisional patent was filed, then later abandoned in favor of an open-source, protocol-first ethos.

The contract was later renounced. 90% of supply burned.

Social tokens are better earned than bought
$TATR wasn’t just airdropped. They were unlocked through interaction.

Distribution > Scarcity
I designed the protocol to incentivize sharing, not hoarding.

NFTs as community bridges
Each Potato was a conversation starter, not just a collectible.

Decentralized Patreon was coming
Anticipated token-based membership before it hit mainstream Web3.

I underestimated gas friction
Airdropping rewards became too expensive to scale on Ethereum at the time. L2s like Base and Optimism eventually solved this, but the timing was off.

I got claiming wrong
I assumed no one would want to bother. But the opposite happened. Claiming became the new like button. A small hit of validation and control.

I thought protocol-first was enough
I believed Social Liquidity Mining could thrive at first without a platform. Technically it did. But in hindsight, platforms create social gravity. Protocols still need places to gather.

I walked away from $TATR in 2022. Not because the idea was wrong, but because the space wasn’t ready.

In 2021, I filed a provisional patent for Social Liquidity Mining. It was a protocol designed to reward peer-to-peer participation through NFT distribution, token incentives, and anti-gaming mechanics.

Today, that thinking is everywhere.

Every week, I see platforms rolling out features first explored through Social Liquidity Mining. Peer-to-peer distribution. Earned rewards. Social scoring. Cultural tokens with no roadmap but the people holding them. Platforms like Farcaster, friend.tech, and Stack are exploring patterns I tested in 2020.

The protocol may be dormant, but the ideas are alive. They’ve shaped how I think about engagement, coordination, and what meaningful participation looks like in Web3.

This was not just an experiment. It was a signal.

Explore the $TATR archive
Let’s talk on X or LinkedIn

Both ideas stuck with me. They’re circling the same theme from different angles, and here’s where I landed.

I could see this going in two ways.

When digital cameras became mainstream, photography didn’t die; it raised the bar. Anyone could take a decent photo, so standing out meant developing a sharper eye and a stronger sense of craft.

Same with calculators. Math didn’t disappear, it just moved to a higher level for younger generations once the basics were automated.

I think prompting tools are doing something similar. As the baseline rises, people will need to push further to stay ahead.

At the same time, as Jack noted, exceptional skill might be appreciated more like fine taste, especially in circles that value craft for its own sake. So it’s not just “skill is the new taste.” Elevated skill might also become the new status signal.

In our conversation, he brought up something else that stuck with me.

Widespread access doesn’t necessarily dilute craft. It can actually enrich the upper tiers of it. When more people can participate, we often see a deeper appreciation emerge for the refined, the niche, and the truly intentional. Cookbooks and cooking shows didn’t replace fine dining, they helped elevate it. Streaming didn’t kill arthouse films, it expanded their audience. MP3s and digital downloads led to a vinyl resurgence. The more accessible creation becomes, the more we seem to value what feels crafted and enduring. Democratization fuels connoisseurship.

The issue, I think, with LLMs and AI in general is that they’re impacting multiple areas all at once. Calculators, digital cameras, streaming, and mp3s all had niche appeal at first. AI prompting has a broader appeal that touches learning and knowledge, skill building, product development, social media, art, language, writing, and more.

It can feel almost impossible for us to keep up because the ground is shifting so quickly in so many places and we humans hate change. It’s overwhelming, and there’s a sense that we don’t have time to process or respond from a cultural or societal standpoint. This isn’t a shift in just one domain. It’s a global, tectonic change in how humans interact with computers, and it affects how we move through the world.

A few of us seem to be circling similar ideas from different angles. Skill, craft, and artistry all still matter. Maybe more than ever.

In a way, as AI takes on more of the process, we might be pushed or allowed to return to what makes something truly great: the art of craft.