That playbook has always had a mixed track record. Sometimes it worked brilliantly. Sometimes it burned billions of dollars proving that technology alone can't replace operational expertise. But either way, the underlying logic depended on a structural advantage: software companies had engineering capacity and traditional businesses didn't.

AI has eliminated that advantage. The cost of building software has collapsed, and you no longer need engineers to do it. But that shift doesn't automatically hand the keys to traditional operators. Most of them wouldn't have known how to direct a team of engineers even if they'd had one. The new bottleneck isn't access to engineering. It's the ability to combine deep operational knowledge with product development instinct, to understand both how a business runs and how to translate that understanding into software. That cross-functional skill set is rare, and it's becoming the most valuable capability in the market. What follows is a look at why the old model broke, what's replaced it, and where the opportunity sits for operators, software companies, and investors who can see the shift.

When Disruption Works and When It Doesn't

The "software eating the world" thesis was never wrong in principle. It was wrong in application about half the time, and the difference between success and failure comes down to a specific question: are you replacing an antiquated process, or are you trying to replace human judgment in a complex operating environment?

When the answer is the former, technology-first outsiders have built some of the most valuable companies of the last decade. Stripe replaced a genuinely broken payments infrastructure. Before Stripe, integrating payment processing into a product meant weeks of work with clunky gateway APIs, merchant account applications, and painful compliance hurdles. Stripe made it a few lines of code. The old process was purely technical friction with no redeeming complexity, and software eliminated it cleanly. Ramp attacked corporate expense management the same way. Legacy corporate card programs from the major issuers were built around manual reconciliation, paper receipts, and month-end close processes that hadn't materially changed in decades. Ramp replaced the entire workflow with automated categorization, real-time spend controls, and integrated accounting. The old way wasn't complex. It was just outdated. Shopify did the same thing for small business e-commerce, and Square did it for point-of-sale. In each case, the disruption succeeded because the incumbents were defending a process, not an expertise.

The failures happen when technology companies mistake operational complexity for technical inefficiency. Katerra raised over $2 billion from SoftBank to vertically integrate construction, from design through manufacturing to on-site assembly. The founding team came from electronics and tech investing, not construction. They burned nearly $1 million a day for six years before going bankrupt, because building a house is not a process problem you can engineer away from the outside. Zillow's iBuying program bet that its Zestimate algorithm could replace local market judgment when pricing homes. It wrote down over $500 million and laid off a quarter of its workforce when the algorithm kept overvaluing properties that any experienced local broker would have priced correctly.

The distinction matters because most of the traditional economy falls into the second category. The businesses that run logistics, construction, facilities management, skilled trades, and local services are operationally complex in ways that don't compress into elegant software abstractions. They require judgment, relationships, and an understanding of physical reality that technology alone has never been able to replicate.

But that doesn't mean technology can't transform them. It just means the transformation has to come from the inside.

Compass and the Full-Cycle Playbook

Compass is the most instructive example of this dynamic because it lived through both sides of it.

When Compass launched in 2012, the pitch was pure disruption. Technology-enabled brokerage. Better tools, better data, better margins. The company raised over $1.5 billion and went public in 2021 at an $8 billion valuation on the promise that it could turn residential real estate into a technology business. But real estate brokerage turned out to be one of those operationally complex industries. Homebuyers still chose agents based on personal relationships. Agents still split commissions the same way they always had. Compass wasn't replacing a broken process. It was layering technology onto a relationship-driven business where the technology wasn't the bottleneck.

What happened next is the interesting part. Rather than continuing to pretend it was a pure tech company, Compass evolved. It built what it calls the Agent Operating System, a suite of integrated tools covering CRM, marketing automation, transaction management, and lead routing, designed around how agents actually work. Not software as the product, but software as the operating backbone of the brokerage itself. That internal operating system became the foundation for something much bigger: a roll-up strategy.

Compass started acquiring high-performing independent brokerages across the country, integrating them onto its technology platform, and creating operational efficiencies that traditional brokerages couldn't match. In September 2025, Compass announced a $1.6 billion acquisition of Anywhere Real Estate, the parent company of Coldwell Banker, Century 21, and Sotheby's International Realty. The deal closed in January 2026, creating a 340,000-agent network and the largest residential brokerage in the world.

The trajectory is striking. Compass started by saying it would disrupt real estate from the outside with software. That didn't work. So it built an internal operating system around deep understanding of how the business actually runs, then used that operating system as the engine for a consolidation strategy that has fundamentally reshaped the industry. It went from failed disruptor to the most consequential operator in its sector in about a decade, and the technology was what made the roll-up economics work. Not technology as the product. Technology as the operating edge.

That arc is exactly the opportunity we see opening up across dozens of traditional industries right now. And AI is about to make it dramatically more accessible.

The Gap That No Longer Exists

The structural advantage of a technology company over a traditional business has always been engineering capacity. A software company has engineers embedded across the entire organization. The business itself is an engineering organization. A traditional business, by contrast, might have a small IT team keeping the lights on, with no capacity to build custom tools, automate workflows, or develop software products.

That gap is gone. Every business now has access to essentially unlimited engineering capacity through AI automation and agentic coding tools. Claude Code with Opus 4.6 and Codex with GPT-5.3, and a growing list of others, mean that a warehouse operator, a construction company, or a regional logistics firm can build and deploy functional software without hiring a single engineer. The cost of building discrete software tools has collapsed from hundreds of thousands of dollars and months of development time to an afternoon and a weekend.

This is a massive shift. The competitive moat of "we have engineers and you don't" no longer separates tech companies from everyone else.

The New Divide

But a new gap has opened up, and it may be even more consequential than the old one.

If every business now has engineering capacity on demand, the question becomes: do you know what to build? Do you have the product instinct and domain expertise to deploy these tools in ways that actually move the needle? The bottleneck has shifted from "can we build it" to "do we know what to build and how to think about it."

This is where operators with deep vertical expertise become the most valuable people in the room. Someone who has spent twenty years running warehouse operations understands the pain points, the workflows, the edge cases, and the places where even small efficiency gains create meaningful cash flow improvements. They know where the leverage is. What they often lack is an understanding of how to orchestrate AI agents as replacements for traditional engineering teams, or even awareness of what's now possible.

The interface is still evolving. Knowing how to instruct and direct AI tools to build what you need requires a product mindset that most traditional operators have not yet developed. It's a learnable skill, but right now, the people who have both domain knowledge and the ability to think like a product builder are incredibly rare. That's the operator gap.

Leapfrogging the Desktop Era

There's a useful parallel in how technology adoption played out across developing economies. In much of Asia and India, entire populations skipped the desktop computer entirely and went straight to mobile. They never had the intermediate step. As a result, they became mobile-first connected societies faster than we did in the West, building infrastructure and business models around mobile from day one rather than retrofitting desktop-era systems.

The same dynamic is playing out now in traditional industries. A business that has operated on pen and paper, with zero technology throughout its entire operation, doesn't need to go through fifteen years of SaaS adoption, cloud migration, and digital transformation consulting. It can go straight from nothing to deeply integrated, AI-built software tools in a matter of weeks.

The reason this works is simple: the workforce already has the hardware. Everyone carries a mobile phone. They use technology in their personal lives every day. If you build an inventory scanning app that runs on a warehouse worker's phone using QR codes, you don't need to train anyone on how to use a phone. You just need to build the app. And building that app, which would have cost six figures and taken months not long ago, can now happen in a weekend.

First Movers Win Twice

The operators and advisors who figure this out first will have a compounding advantage that becomes increasingly difficult to replicate with each passing month.

Every piece of software-driven leverage you build into a traditional business creates real cash flow. That cash flow creates options. You can offer the same products or services at lower prices while maintaining your margin, which puts pressure on competitors still running on pen and paper. Or you can use that cash flow as an acquisition tool, rolling up peers in your sector and migrating them onto the operating stack you've already built. On day one of an acquisition, you're creating value by deploying technology the acquired business never had access to. This is the Compass playbook at a smaller scale, and the economics have gotten dramatically more attractive now that the cost of building the technology stack is a fraction of what Compass spent.

The Software Company's New Option

This shift also creates an interesting strategic path for a category of software companies that are increasingly stuck.

Consider a vertical SaaS business doing $1 to $5 million in revenue. Good product, real customers, strong retention. But the Series A market has gotten brutally selective. Unless you can credibly pitch a path from $5 million to $50 million in the next 18 months, institutional capital isn't interested. These are perfectly good businesses that the current fundraising environment treats as uninvestable.

But what if the answer isn't raising more equity capital? What if the answer is becoming the acquirer?

A vertical SaaS company already has deep visibility into a fragmented customer base. It knows which businesses are using the product well, which ones are growing, and which sectors have the kind of sticky local relationships and physical operations that make them structurally sound. That's an intelligence advantage most PE roll-up shops would kill for. The software company has essentially been running diligence on its own customers for years.

The playbook goes like this. Instead of trying to sell a $500-per-month subscription to every plumbing company, landscaping firm, or regional logistics operator in your market, you buy the service business itself. You migrate it fully onto your operating system on day one. Every efficiency gain you've been offering through your software, the margin improvement, the workflow automation, the data-driven decision-making, you now capture 100% of that value creation instead of charging a small monthly fee for it. That margin expansion or multiple expansion funds the next acquisition. And the one after that.

The software doesn't have to be your revenue model. It can be your sourcing mechanism and your operating edge. You know exactly which businesses in your customer base would benefit most from full technology integration, because you already have the data. You know their operations, their growth trajectory, their willingness to adopt new tools. That's a roll-up thesis with built-in deal flow and a technology deployment advantage that a traditional acquirer simply can't replicate.

For software founders who have been banging their heads against a fundraising wall, this reframe could be transformative. You stop competing for a shrinking pool of venture dollars and start competing in a market where your technology gives you a structural edge no one else has.

Picking the Right Sectors

The opportunity is not uniform across all traditional industries. The most interesting targets are sectors that either benefit from AI tailwinds or are structurally insulated from direct AI disruption. A business that involves physical operations, local relationships, regulatory complexity, or real-world logistics has inherent defensibility that a purely digital business lacks. AI can make these businesses dramatically more efficient without threatening to replace them entirely.

The least interesting targets are businesses in sectors where AI itself can displace the core value proposition. If the primary service you provide is something an AI agent can do directly for the end customer, the moat you're building is temporary at best.

Where This Goes

We are in the very early innings of this transition. The capabilities are immense, but the awareness gap is still wide. Most operators of traditional businesses don't know where to start, and many don't yet realize what's become possible.

The "software eating the world" era created trillions of dollars in value by building software companies that sold tools to traditional industries. But the track record of those companies actually conquering those industries is far more nuanced than the narrative suggests. When the target was a broken process, technology-first companies won decisively. When the target was a complex operating environment, capital and code alone were insufficient.

What's changed is that the cost of building technology has collapsed to near zero, and you no longer need engineers to build it. You direct AI agents instead. But here's the subtlety that most people miss: collapsing the cost of engineering doesn't automatically hand the advantage to traditional operators. Most operators of traditional businesses wouldn't have known how to use a team of engineers even if they'd had one. They don't think in terms of product specifications, user workflows, or system architecture. They know their business inside and out, but they've never had to translate that knowledge into a software buildout.

The most valuable skill set right now is the ability to do both. Someone who understands the internal operations of a business deeply enough to know where the leverage is, and who also knows how to construct software applications, to define what needs to be built, scope the product, and direct the build process. That used to mean managing engineering teams. Now it means directing AI agents. But the product thinking is the same, and it's rare.

The next wave of value creation in traditional industries won't come from software companies disrupting from the outside. It will come from people who can bridge operational expertise and product development, embedding technology from within. And for the software companies already serving those industries, the most interesting move might not be raising the next round. It might be becoming the operator and using the tools as the edge.

"This is our books starting point..."

The pitch deck shows a massive TAM, the product roadmap sprawls across dozens of features, and the go-to-market strategy targets "all companies that need X." It's an understandable impulse, especially in a venture capital landscape where $8B+ multi-stage funds are consistently writing nine-figure checks into companies achieving $100M+ in ARR in months and ambitions of being the next “decacorn”.

The reality is more counterintuitive. Amazon demonstrated that the most successful companies rarely start by going broad. Instead, they identify a narrow market where they can become the undisputed leader, build something their initial customers genuinely love, and then systematically expand from that position of strength. This approach represents a fundamental way to build defensible, valuable businesses and yet is seemingly overlooked by many startup founders. They say "books" but they are often really non-committal on the narrow path they intend to definitively win.

The Virtues of Focus

The pattern is consistent across industries and decades. Start with a tightly defined segment where you can achieve market leadership. Deliver a unique solution to that specific audience. Use the resulting traction and revenue to fund expansion into adjacent markets. Repeat.

This approach works because narrow markets allow for precise targeting. When you know exactly who you're building for, you can understand their needs deeply, communicate your value clearly, and iterate quickly based on feedback. In short - you reduce your customer acquisition costs through better targeting. You're not trying to be all things to all people—you're trying to be indispensable to a specific group who understands exactly what they’re buying when they choose you.

The financial engine this creates is crucial. Early revenue from a focused market provides the resources to expand, but more importantly, it validates your ability to deliver value. You earn the right to serve broader markets by proving you can dominate narrower ones.

Software: The Stripe Case Study

Stripe offers a perfect example. Today, they process payments for businesses of every size and type, from startups to Fortune 500 companies, online and increasingly offline. They're one of the most valuable private companies in the world.

But they didn't start there. Stripe's initial wedge was elegantly narrow: developers who wanted to accept payments with minimal friction. The famous "accept payments in seven lines of code" positioning was a laser focus on solving a specific problem for a specific audience. Their early users were predominantly Y Combinator companies and similar startups where technical founders made purchasing decisions.

This narrow focus had several advantages. The product could be optimized for developer experience rather than enterprise procurement processes. Distribution happened through word-of-mouth in tight-knit developer communities. Integration was so simple that switching costs for experimentation were minimal, allowing rapid adoption.

From that foundation, Stripe could systematically expand. First to larger startups, then to established tech companies, then to traditional enterprises, and eventually to physical retail and end-to-end financial services. Each expansion built on the previous one, with the core product evolving to serve new use cases while maintaining the developer-friendly DNA that made them successful initially.

Consumer Brands: From Ties to Lifestyle

The pattern isn't limited to software. Consider Vineyard Vines, which started by selling ties—just ties. Not clothing, not a lifestyle brand, not a full wardrobe solution. Ties with distinctive patterns that were more affordable than Ferragamo but appealing to a specific demographic.

That narrow wedge allowed them to establish brand identity, build distribution relationships, and create customer loyalty. Once they owned that category for their target audience, expanding into shirts, pants, and eventually a full clothing line was a natural evolution. The same customers who trusted them for ties became buyers of their broader product range.

Under Armour followed a similar path. They started with performance undershirts designed for athletes in specific weather conditions. They focused on one category where they could be demonstrably better than alternatives. That narrow focus on performance and a specific use case gave them credibility that enabled expansion into a full-body athletic brand.

Yeti built their brand on premium coolers, a product category that barely seemed worthy of innovation. But by being the absolute best at that one thing—coolers that could keep ice frozen for days in extreme conditions—they created a loyal customer base that would follow them into drinkware, bags, and outdoor accessories. The narrow wedge established the brand's premium positioning and performance credentials.

The NVIDIA Blueprint

Perhaps the most striking example is NVIDIA. Fifteen years ago, they were primarily a gaming GPU company. High-performance graphics chips for gaming PCs and devices. It was a narrow, specialized market with demanding customers and clear performance metrics.

That specialization created an edge in GPU architecture and manufacturing that proved transferable to other compute-intensive applications. This success stemmed from strategic foresight rather than fortunate timing. Jensen Huang and the NVIDIA team recognized early that the future of computing would shift toward accelerated compute workloads. Their lengthy head start in GPU development and the scalable platform architecture they built gave them an insurmountable advantage when the market began its migration.

When the AI revolution emerged and the industry recognized that GPUs were ideal for machine learning workloads, NVIDIA was already years ahead. Rather than pivoting to AI, they were applying existing capabilities to an adjacent market where their core strengths were suddenly invaluable. The narrow wedge of gaming GPUs had given them both the technical foundation and the manufacturing scale to dominate an entirely new category.

Today, depending on the day, NVIDIA is the most valuable company in the world, powering everything from data centers to autonomous vehicles. That dominant position was built on years of focused execution in a narrower market that seemed much less consequential at the time, combined with visionary leadership that anticipated where computing was headed.

Implications for Today's Startups

This pattern has important implications for how startups should think about market opportunity and product strategy. The temptation to target large markets immediately is understandable, especially in fundraising contexts where TAM matters. But execution reality favors a different approach.

When you position your product as universally applicable, you create unexpected friction. Buyers don't know what to do with a solution that claims to solve every problem. Even ChatGPT faced this issue to an extent. Where their growth went truly vertical was when their image generation model went viral and mainstream, non-AI-inclined users now saw a personal use case for this generalized intelligence - to make their friends look like cartoons. It’s an example where the generalist approach paradoxically makes selling harder, not easier.

Founders often feel a sense of relief when they decide to be ruthlessly specific about who they serve and what problem they solve. "We are a company that does X and solves Y problem for companies like this." That clarity makes everything easier: customer acquisition, product development, messaging, partnerships. Customer qualification is simplified, with less cycles spent pitching the wrong buyers. And buyers can quickly assess fit, and early customers become reference accounts for similar prospects.

This doesn't mean building a narrow product with limited capabilities and upside. The examples above show you can architect for broader applicability while going to market with vertical or functional focus. The back-end might be a flexible platform guided by your intuition around how markets evolve over time. But your initial positioning and GTM motion should be sharp and targeted.

How This Plays Out

The narrow wedge creates optionality. Once you own your initial segment, you can choose between two valuable paths.

The first is systematic expansion. Use your foothold to attack adjacent markets, either by extending your product or by leveraging brand and distribution to serve related needs. This is the path Stripe took: start with developers, expand to enterprises. It requires capital and operational capability to execute multiple go-to-market motions, but it can build enormous businesses.

The second is vertical dominance. Decide that the narrow market you're serving is actually quite attractive, and focus on being the absolute best solution for that segment. Build deep features and workflows specific to that industry. Become so embedded in customer operations that switching is unthinkable. This can create highly profitable, defensible businesses even in markets that seem small initially.

Both outcomes can be excellent. The key is being honest about the capital intensity, funding partners, competitive dynamics, and operational complexity of each path. But you need to win the narrow wedge first before either option is available. And I think that early stage capital markets are going to need to evolve to better support option two over the next several years, as AI is increasingly creating a set of capital efficient, vertical champions.

Earning the Right to Expand

The fundamental insight is that market expansion is something you earn, not something you claim. Customers don't care about your vision of being a platform that serves everyone. They care about whether you can solve their specific problem better than alternatives.

By starting narrow, you prove you can deliver value. You build product velocity, customer relationships, and market credibility. You develop the operational capabilities and financial resources needed to expand. And you create the customer base that can pull you into adjacent markets based on their needs rather than your assumptions.

The companies that become broad platforms almost never start that way. They start by being exceptional at one thing for one audience. Then they systematically earn the right to do more.

For founders evaluating their strategy, the question isn't whether your eventual market is large enough. It's whether you can identify a narrow wedge where you can become the clear leader, and whether that wedge provides the foundation for the business you want to build. Start small, win decisively, then expand from strength. It's a less exciting pitch than targeting everyone immediately, but it's how many enduring companies are actually built.

A few months ago, I wrote about how crypto's future interface would be AI-forward - not another wallet redesign or slicker DEX, but something fundamentally different. I referenced the Rabbit R1 pin, which navigated the web on your behalf, interacting with websites and applications without traditional interfaces. While Rabbit ultimately stumbled, the point has proven directionally correct and is now materializing faster than expected via Model Context Protocol (MCP), introduced by Anthopic last year.

From Buttons to Conversations

To grasp the importance of MCP, we first have to understand the role of an Application Programming Interface (API). Think of an API as a set of rules that allows different software applications to talk to each other. Nearly every digital service you use today - from Google Drive to your banking app - has an API. It's what allows one program to programmatically request information or trigger an action in another. Any action you take by clicking a button in an app can often be replicated by sending a structured message to its API.

Historically, APIs have been the domain of engineers. They are the tools developers use to integrate services and build new features. MCP changes this entirely. It acts as a universal translator, taking all these disparate APIs and converting them into a standardized set of "tools" that Large Language Models (LLMs) can understand and use.

When you connect your Google Drive or Gmail to tools like Claude or ChatGPT, you're using MCP. This gives the LLM context and access to your private data within those services, turning the LLM itself into your primary interface. Want to search through thousands of emails for that contract from last March? Instead of clicking through Gmail's interface, you ask Claude. Planning a dinner party? Rather than juggling between recipe sites, shopping lists, and Instacart, you describe your vision to an AI that orchestrates everything behind the scenes.

Few people realize how useful this already is. Need to do your taxes: connect Gmail ask Claude or ChatGPT to find all charitable receipts or home improvement invoices from 2025. In seconds you’ll have exactly what you need. Apps like Shortwave exemplify this power really well.

The End of App Fatigue

Here's where things get interesting. We're approaching a world of UI-free applications - services that exist purely as MCP connectors without traditional interfaces.

Consider the Instacart example. Today, planning a Thanksgiving dinner means juggling between recipe websites, manually calculating portions, cross-referencing dietary restrictions, building shopping lists, and navigating Instacart's interface to find each item. It's exhausting.

Now imagine simply telling your AI: "I'm hosting 20 people for Thanksgiving. Three are vegetarian, one has a nut allergy, and my budget is $300."

Your AI orchestrates a symphony of MCP-connected services, but here's the crucial part - it knows you. If you’ve used ChatGPT or Perplexity recently you may have seen how the LLM grows to understand you over time through your chat history (more on this in a bit).

It remembers you prefer rustic sunflower arrangements over formal roses from past orders. It knows your nephew's nut allergy is specifically to tree nuts, not peanuts. It recalls that Aunt Jane only drinks oat milk and your father-in-law won't eat anything with rosemary.

Instacart's MCP server handles grocery orders from multiple stores, but your AI knows to get the organic cranberries from Whole Foods because you mentioned preferring them last year. DoorDash checks restaurants for the pecan pie you don't want to bake, but automatically orders the apple alternative for your nephew. Target's MCP server sources serving platters in that earthy aesthetic you gravitate toward. Amazon finds table decorations that match the "cozy autumn" vibe from your Pinterest boards (connected via MCP, naturally). Your favorite florist receives a request for a low, sprawling centerpiece because the AI remembers you hate when flowers block conversation.

The AI doesn't just execute tasks - it applies years of learned context. It understands portion scaling from past dinners, checks ingredient conflicts across all food orders against its detailed knowledge of your guests' restrictions, optimizes budget allocation based on your typical spending patterns, and even generates a cooking timeline that accounts for the fact you always underestimate how long turkey takes. Each service remains focused on what it does best, while your AI conducts the entire experience with the accumulated wisdom of every interaction you've ever had. No clicking through categories. No re-entering the same dietary restrictions for the hundredth time. No manual cart building. Just intent to execution, refined by relationship.

It feels like science fiction - but in reality it might be what we experience by next November at the rate that models and MCP is accelerating.

The enterprise implications are already profound. A support team lead can already ask Claude: "What's causing customer frustration this month?" The AI doesn't just search tickets - it identifies patterns across thousands of interactions, correlates issues with recent deployments, generates bug reports with reproduction steps, assigns them to the right engineers based on code ownership, and even submits pull requests with proposed fixes. What once required multiple teams and systems now happens in a single conversation.

For developers, MCP represents liberation from UI/UX constraints. Instead of spending months perfecting user flows and A/B testing button placements, they can focus on what their service actually does. Build robust APIs, define clear endpoints, and let AI handle the interaction layer. A payment processor doesn't need a dashboard - just endpoints for transactions, refunds, and reporting that AI agents can orchestrate.

For users, it means the end of app fatigue. No more downloading dozens of apps, remembering different interfaces, or hunting through settings. Your AI becomes a universal remote for your digital life - one conversation to rule them all.

As an investor, the implications are really exciting. Every SaaS product, every marketplace, every service platform could become an MCP-first business. The winners won't be those with the prettiest apps or the slickest onboarding flows, but those who best expose their capabilities to AI agents. In this new paradigm, the quality of your API documentation might matter more than your user interface - because your API is your user interface.

The Death of the Rectangle

Going back to the iPhone comment from earlier: MCP doesn't just change software - it obsoletes the entire foundation of modern computing interfaces. The smartphone, that we stare at for hours each day, is built on a fundamentally flawed premise for an AI-first world.

Think about it. Why do we need a touchscreen when our AI understands natural language? Why scroll through pages of apps when a single conversation can orchestrate dozens of services? The iPhone's grid of icons - each a portal to a different interface you must learn and navigate - looks as antiquated as a rotary phone in an MCP-enabled world.

In five years, I have a hard time believing we will be tapping glass screens the way we do today. Instead we will be getting stuff done through whatever form factor makes sense for the moment - glasses that overlay information, earpieces for private interactions, pins like that Rabbit device, or more likely something we haven't imagined yet. The device becomes secondary to the intelligence that flows through it. This shift is already occurring on desktop, where Perplexity (Comet), The Browser Company (Dia), and OpenAI (tba) are all releasing or working on AI native browsers that showcase what an agentic computing experience can and should look like with our PCs.

Hardware companies see this tsunami coming too. Apple's Vision Pro might seem like it is for watching movies in 3D. In reality it is probably their best attempt to own the next interface paradigm. OpenAI buying Jonny Ive’s IO for $1B, Meta's Ray-Bans with AI integration, Amazon's continued Alexa experiments, Google's various wearable attempts - they're all searching for the form factor that makes sense when your primary interface is conversation, not tapping.

The smartphone era is ending not because we found a better rectangle, but because we found a better way to interact with the digital world entirely. Just as the iPhone made physical keyboards seem clunky and limited, AI agents make app interfaces feel like unnecessary friction.

Why Stablecoins Win in an AI-First World

Of course there’s a crypto angle here too.

The crypto skeptics love to ask: "What problem is there really with credit cards for consumers?” and “Why would I give up my 2% credit card rewards for crypto payments?" Not accounting for what crypto payments offer to emerging markets in terms of access to stable currency and global digital payments, it's the wrong question for the wrong era.

First, let's address the rewards system. That 2% cash back isn't free money - it's your money, recycled through hidden fees. Every swipe costs merchants 3% plus $0.30 in interchange fees. Your local coffee shop isn't giving you rewards; they're marking up prices to cover Visa's toll. Eliminate those fees through stablecoin payments, and suddenly your $5 latte could be $4.75. Scale that across every transaction, and the savings dwarf any rewards program.

But that's still thinking in yesterday's paradigm. The real revolution isn't about replacing credit cards at the coffee shop - it's about enabling an entirely new commerce modality that credit cards can't touch.

When MCP allows AI agents execute thousands of micro-transactions on your behalf - searching paid databases, accessing premium APIs, commissioning specialized computations - the credit card model collapses. Imagine your AI assistant negotiating with a dozen different services to plan that dinner party, each charging fractions of a cent for specific data or capabilities. Traditional payment rails can't handle this volume or granularity efficiently.

This is where stablecoins connected via MCP become essential infrastructure. Your AI doesn't need a credit card number; it needs programmable money it can deploy instantly across countless services. No authorization holds, no batch processing, no $0.30 minimum fees eating up 90% of a micro-transaction's value.

The Sovereign Stack

Looking further ahead, MCP opens intriguing possibilities for on-chain identity and memory. Imagine your AI assistant's knowledge and context about you - your preferences, history, learned patterns - stored not in OpenAI's or Anthropic's servers, but in a self-custodial, blockchain-based memory layer. This feels critically important when you think about the power of these models and the unknowns that still exist. I love to use ChatGPT for many things but I prefer Claude for others and Gemini or Grok for still others. When a new state of the art model is released, I want to use it and I want my memories and personalization to come with me.

This sovereign AI stack would connect to LLMs via MCP while maintaining your data under your control. Switch between AI providers without losing context. Share specific memories or capabilities with services on a permissioned basis. Build a persistent, portable identity that grows more valuable over time - owned by you, not harvested by platforms.

The Payment Layer for Tomorrow's Commerce

In the context of MCP, the credit card networks may be fighting yesterday's war, optimizing for a world of human fingers tapping on screens. But when AI agents become our primary interface to the digital world, executing complex workflows across dozens of services in seconds, we need money that moves at the speed of software. When Shopify simultaneously begins exposing seller goods via ChatGPT and facilitating stablecoin payments in the same month, these things might just be connected actions.

Stablecoins aren't trying to win at credit cards' game. They're building the payment infrastructure for a game that hasn't fully started yet - one where AI orchestrates commerce at a scale and granularity that makes traditional payment methods look like dial-up internet.

The next time someone asks why they should give up their credit card rewards, remind them: crypto payments aren’t optimized for the world of apps and websites. They’re more likely for a world where AI handles the clicking, and money needs to flow as seamlessly as API calls.

The question isn't whether stablecoins can compete with credit cards for human-initiated purchases. It's whether credit cards can survive in a world where humans don't make purchases anymore - their AI agents do, thousands of times per day, for amounts too small for traditional systems to process profitably.

That world is closer than most realize. MCP is the bridge, and stablecoins are what will take us across it. As an investor exploring what’s next, I am closely watching how entrepreneurs begin to apply this new model and what new services are unlocked as a result.

After watching presentations from teams coming out of recent accelerator demo days, conversations with founders surprised me. The new normal was targeting $2-3 million raises at $10-15 million valuations—a notable shift from the $5 million rounds at $20-25 million caps that characterized previous markets. The driving factor wasn't market pessimism or lowered ambitions. Instead it was a reflection of a profound change in what it actually takes to build a profitable company today. This fundamental shift is at odds with the prevailing market narrative of the VC market’s continued expansion and the rise of the multi-billion dollar mega-fund and mega-round. I think it is useful to dissect what is happening.

Leverage

The catalyst is super clear if you’ve had a chance to play with Anthropic’s new Claude 4 models, now clearly state-of-the-art in code generation. If you haven’t tried sketching out an idea using these "vibe coding" platforms yet (v0, Replit, Bolt, Lovable, etc.), mostly powered by Claude 4, you should. I’ve mentioned it before, but it’s stunning how a simple prompt can accomplish what would have taken a dedicated engineering team weeks or months just a short while ago. Everyone is now a potential app developer and any business and individual can have a stable of custom built tools for whatever their needs may be, from marketing websites to family task lists.

This shift runs deeper than code-generation productivity gains though. Companies today are realizing they can achieve profitability with a fraction of the capital previously required. They're not raising smaller rounds because they can’t secure more—they simply don't need more.

The AWS Parallel

A similar version of this unfolded about 20 years ago. Between 2005 and 2010, AWS changed startup economics profoundly. Instead of spending millions on servers and maintenance, dependent on on-premise installs for adoption, founders could build cheaply, iterate rapidly, and scale effortlessly. That shift brought us the institutional seed fund and the “Lean Startup” movement. Firms like First Round Capital, Founder Collective, Floodgate, and many others were the vanguard that preceded the now thousands of seed funds that learned from these investors’ early insights, including Factor.

But with this unlock a new bottleneck emerged: engineering talent. The 10x expansion of software development opportunities, gave rise to a commensurate expansion in demand for people to build these applications. By the mid-2010s, acquihires routinely valued engineers at around $1 million each, which combined with the amazing profitability of big tech companies drove up these labor input costs across the board for the next decade. So the market adjusted, funds and rounds got bigger, and here we are with $20bn venture funds being raised and those same pioneering seed managers raising flagship funds that are 10-20x+ the size of their original offerings.

The Shift

I recently wrote about how what's unfolding now makes the AWS revolution look small by comparison. We’re witnessing simultaneous collapses in both infrastructure and labor costs:

• Crypto rails: Ready-made infrastructure for complex financial products.

• AI assistants: Instant expertise across multiple domains, especially coding.

• Automation tools: Handling personalized marketing, content creation, and even customer support.

At the extreme, a founder with unique market insight can design and "prompt" their vision into existence, then leverage AI tools to rapidly build revenue streams, perform customer research and support, and more. We’re already seeing tiny teams validate market demand and generate meaningful revenue in weeks, rather than years. And model performance is accelerating, not slowing down.

New Models for Capital Formation

This shift in startup economics is coinciding with innovation in capital formation itself. It’s often stated that many successful businesses start out looking like toys, only to later evolve into their widely accepted mature versions. Consider the evolution from NFTs. While they’re not often mocked as a speculative fad, they were actually the precursor to serious tokenization efforts now embraced by major financial institutions. The Blackrock or Hamilton Lane tokenized fund is really just an adaptation of NFTs, except instead of owning JPEGs you own a share of a private credit fund. The issuer of USDC, Circle, went public today and quickly traded up >200% showing the value of tokenization (in this case US dollars) first hand.

The next “toy” getting a makeover may be the ICO that dominated crypto markets from ~2017-18. For example, there’s a new crypto project called Believe that allows creators to launch tokens linked to their projects through simple social media interactions. Here’s how it works:

• Token Creation via Social Media: Users can initiate the creation of a token by replying to a specific post on X (formerly Twitter) with a desired token name.

• Automated Deployment: The platform handles the backend process, deploying the token on the Solana blockchain and providing a link to the newly created token.

• Revenue Sharing: As the token gains traction and trading volume increases, the platform shares a portion of the trading fees with the token creator, providing a potential revenue stream.

This model lowers the barriers to entry for capital formation, allowing creators to access funding and build communities around their projects without traditional financial intermediaries or venture funding.

It starts at this early stage of the market where the stakes are lower. However, it highlights how tokens could create entirely new capital market segments for established businesses. These segments may better align with the early capital requirements of these new, capital-efficient businesses or possibly become the liquidity pathway for these companies when they reach scale.

Imagine hitting $10 million in ARR with your SaaS business and, instead of selling or raising a traditional round, issuing compliant tokens with economic interest in your business performance—a new form of IPO powered by crypto rails. The market structure bill in the works (the GENIUS act) may be the catalyst to unlock this next wave of innovation.

What I believe strongly in is that investors are going to have to be properly positioned for this shift.

The Opportunity Ahead

The intersection of AI-driven efficiency and innovative capital structures makes this an extraordinary moment for early-stage investing. The companies we're meeting aren't raising less because they're thinking smaller. They're raising less because they're building smarter. Early stage investors will need to adapt to this reality, aligning more closer with founders’ new optionality and embracing the fact that they can now be builders not just investors if they have the right skill sets and are properly looking for opportunities to pursue.

Today, platforms like Anysphere’s Cursor, Codeium Windsurf, and emerging agentic tools such as Lovable, Replit, v0, Bolt, Ohara and a growing set of others allow individual, non-technical entrepreneurs to rapidly prototype, build, and deploy complex applications to their exact and unique specifications. Over the next year, powered by advancements from frontier AI models from OpenAI, Anthropic, Google and others, these agentic coding platforms will likely become 10x to 100x more powerful, allowing virtually anyone with a compelling idea to produce complex, fully functional, full-stack software applications in hours.

To fully grasp this evolution, let's draw on what's occurred in the content business over the past 20 years: historically, major publishers and media companies monopolized distribution channels, restricting opportunities for creators primarily to established organizations. If you wanted to be a journalist, you sought a job at a big news organization. If you wanted to make money as a comedian, you has to get a movie produced by Universal or a show on NBC. Then platforms like Facebook, Substack, and YouTube dramatically reduced these barriers to monetizing your content, enabling independent creators to reach both wide and niche audiences effectively. Initially, journalists left prestigious publications like The New York Times, bringing their audiences with them and independently attracting 10,000 subscribers willing to pay $10 per month, generating $1 million annually.

Then we quickly saw the opportunity for anyone to be a well compensated creator targeting specialized audiences like kids who love trick shots or any number of specialized fetishes on OnlyFans. This migration and these platforms shaped the creator economy we know today, where content can be created and monetized by anyone who can attract a following, even in a very targeted niche market.

Now software development is undergoing the same process, where not just professional developers but anyone with targeted ideas can deliver tailored software products to niche markets, creating substantial revenue streams previously exclusive to large SaaS players backed by VC dollars.

Just as the initial wave of content distribution enabled legacy media companies to reach broader audiences, advanced AI tools are significantly elevating incumbent engineering teams, transforming "B" engineers into "A" performers, accelerating system design, and dramatically shortening development and deployment cycles.

The next evolution brings a dramatic compression in both time-to-market and organizational structure through these new AI tools. Product-oriented, non-technical founders can now build an MVP in days, conduct market research, and iterate to product-market fit within weeks. AI enriches core business functions—from content creation to sales and personalization—enabling founders to generate engaging content, optimize sales strategies with rich customer insights, and deliver personalized marketing that adapts to user behavior. Finding those crucial "1,000 true fans" and turning an idea into a profitable business now takes months rather than years.

This transformation dramatically lowers barriers to entry and reduces the scale needed for sustainable businesses, fundamentally changing how founders evaluate opportunity costs and risks. Entrepreneurs can swiftly identify underserved niches and launch differentiated products using precise insights and targeted go-to-market strategies.

These micro-niche applications stand apart from general solutions by offering precisely tailored functionalities for specific operational needs. With the latest AI, entrepreneurs can prototype rapidly, validate concepts through immediate user feedback, and transition from idea to profitable operation with minimal upfront investment. We are even seeing this outside of software where these tools are cutting major time and expenses out of things like real estate development, through immediate renderings of property upgrades and renovations.

StripMallGuy

@realEstateTrent

We bought a strip center today.

We'd usually reach out to our architect, give them guidance, and they'd spent a few weeks putting together renderings.

Escrow closed a few hours ago, and the renderings are almost done.

In seconds. Using AI. For free.

747

8:21 PM • Mar 28, 2025

Success in these niches comes from identifying specific pain points that broad platforms inadequately address, targeting markets large enough to achieve meaningful results—even with single-digit market penetration. The key lies in thoroughly analyzing workflows, operational challenges, and customer interactions within these targeted micro-markets.

Consider specialized user groups—boutique fitness studios seeking precise client retention analytics or artisanal food vendors needing seamless hyper-local supply chain integration. By delivering superior, highly specialized solutions to such markets, entrepreneurs can build million-dollar businesses with modest penetration—say, 10,000 users paying $100 annually. These precisely customized solutions offer compelling alternatives to one-size-fits-all approaches.

Given the rapidly growing capabilities and accessibility of AI tools, several transformative implications are likely:

Democratizing Entrepreneurship: Launching software businesses will become dramatically simpler and more affordable, attracting thousands of new entrepreneurs who rapidly build profitable ventures targeting specialized niches. Many of these ventures will quickly scale to several million dollars in annual revenue with minimal, if any, outside investment.

Explosion of Customized Applications: Large, monolithic software platforms will lose their dominance, replaced by an explosion of highly specialized applications tailored to specific industries—like CRMs for veterinarians, inventory systems for craft breweries, or scheduling software for local theaters. Collectively, these niche applications will surpass today’s generalized platforms in total market size, and agentic tools will further enable users themselves to effortlessly customize applications.

However, as the entry barriers to entrepreneurship diminish, venture capital expectations simultaneously become more rigorous. Historically, SaaS companies targeted $100M ARR over five years, following a predictable growth trajectory (3x, 3x, 2x, 2x, 2x) starting from around $1M ARR. Now, we’re seeing startups hit $10-20M ARR within months. Companies demonstrating recurring and sustainable growth quickly secure funding rounds exceeding $100M at multi-billion-dollar valuations, given their potential for significant enterprise adoption.

Eric Simons

@EricSimons

In October, we launched @boltdotnew with just a tweet. We had no idea what destiny had in store, but it was nuts:

• $0-20m ARR in 2 months
• 2m+ registered users
• #1 web AI code app globally

Today, we're announcing we've raised $105.5m:

1,527

11:43 AM • Jan 22, 2025

This rapid evolution significantly reshapes the financial dynamics of startup financing, with clear implications:

Growing Bifurcation in Venture Capital: Investors, especially at the seed stage, must rethink traditional expectations. Seed-stage investors will increasingly fund profitable niche businesses with realistic ceilings ranging from tens to hundreds of millions of dollars. Meanwhile, large multi-stage funds will focus on capital-intensive sectors like space exploration, biotech, advanced manufacturing, or rapidly scaling breakout software ventures like those described above that are looking to raise $10M-100M+. The ease of bootstrapping the niche businesses could lead to different types of capital structures, where investors share in profits rather than relying on future changes of control to access liquidity.

New Dynamics and Buyers at Later Stages: A new type of buyer, including private equity groups, niche-focused holding companies, and decentralized investment collectives, will emerge. These entities will consolidate smaller profitable businesses overlooked by large traditional acquirers due to niche complexities. Models similar to what Tiny and Bending Spoons pioneered over the last several years—aggregating businesses like browser plugins and profitable niche ventures as a hold-co—could gain popularity and expand.

Innovative Financial Models through Tokenization: Tokenized equity and intellectual property will significantly reshape startup financing and operations. With anticipated regulatory clarity, tokenization will offer entrepreneurs liquidity, decentralized ownership, and transparent value sharing, potentially reshaping traditional IPO markets.

The last point is perhaps most interesting in the context of crypto markets. These niche businesses differ from those traditionally backed by venture dollars, and their smaller scale makes it harder to access financial services typically available to larger software companies. I expect DeFi to play an increasing role in filling these gaps, similar to how it has offered retail investors access to services previously exclusive to private bank clients—such as portfolio lines of credit, derivatives, and other bespoke financial services.

As an investor and former company builder, I find myself overflowing with ideas as these tools continue to improve. Over the coming months, we will witness rapid innovation as technology penetrates previously untouched markets. Niche specialization, along with revolutionary financing and growth models, offers extraordinary potential to those who dive in and experiment with these new tools. It’s never been easier to be creative and build new businesses, and this applies just as much to the financial side of the market as it does the founder side, looking ahead.

Year in Review

If you’ve been following the monthly updates, you’ve seen that 2024 was a typically wild year for the crypto markets. It began with the approval of the Bitcoin ETF in January and ended with the election of a seemingly highly pro-crypto congress in November. In between, the largest narrative was undoubtedly the clear emergence of stablecoins as the breakout example of product market fit for this technology and asset class.

The total circulating supply of stablecoins in the market grew by over 60 percent to over $200bn. In terms of transaction volume, roughly $20t in transfer volume occurred in 2024, with monthly volume growing 160% year over year. This now exceeds the volume of transactions and transaction value of Visa and is driven by adoption in emerging markets populations seeking access to the US dollar and growing numbers of merchants and corporations utilizing stablecoins for payments and international transfers.

The utility offered by stablecoins as a digital dollar alternative existed pre-2024. But the maturation of blockchain infrastructure, enabling these transactions to occur instantly and costlessly on networks like Solana and Tron has now made them a truly attractive asset and cemented their product market fit. I wrote about this in December, but we can think of this like the adoption of broadband internet creating a market for streaming video like YouTube and Netflix in the early 2000s.

With this non-speculative use cases growing in number, more and more users are finding their way on chain with exposure to digital assets and the compounding effects of this growing user base, more favorable US markets and the broad gains in token markets over the past year are setting up for a very active 2025 where more widespread adoption is very likely to occur.

The intersection of crypto and AI was a major theme in the latter half of the year, and I believe this will significantly boost crypto adoption. Although market hype has centered on AI agents, such as Truth Terminal, which created the meme coin Goatseus Maximus (reaching a $1.3 billion peak valuation), this seemingly frivolous event likely foreshadows the emergence of more advanced agent-driven experiences in 2025.

This combination of a chatbot with a memecoin is the perfect distillation of the more “degen” side of crypto in 2024. As I have written in the past, it is easy to look down your nose on these seemingly crazy behaviors as people essentially buy lottery tickets on worthless tokens like DOGE, I find there’s always something to learn and keep an eye on as the experimentation in this realm occurs and often leads to some more practical and valuable use cases longer term. 

For example, with NFTs we had a mania of activity that famously came crashing down in 2022. But NFTs themselves didn’t die. Just look at companies like our portfolio company Legitimate, offering digital experiences to buyers of collectibles like limited edition sneakers that just happen to be NFTs beneath the surface. Tokenized assets like real estate are also NFTs in a different form.

Investing out of Fund I, the goal has been to find companies that aren’t at the center of today’s prevailing trends, but rather to invest in great teams who have an insight on a market opportunity that today may feel small but that is likely to expand over the next several years. They seek to be early movers in these growing markets and take advantage of macro tailwinds that are likely to build and contribute to their success over time. 

For example, we’ve been quite active in energy markets, with investments in Neutral, Plural and Jasmine all in the portfolio. This aligns with the thesis that I articulated in a blog post earlier this year where we are seeing a massive new demand for energy resources underway through the immense buildout of compute capacity for AI workloads. This trend, along with the opportunities to innovate in a market that’s seeing the introduction of large numbers of distributed energy resources like solar panels, home battery storage, EVs, etc. creates an attractive convergence that we think intersects with crypto in exciting ways.

AI is another area we have been active in and written about here and here over the past few years, with our investments in Crunch, Nevermined, and 375ai playing roles in various parts of the stack, from model development to payments to data collection. Preparing for 2025 and beyond, the biggest areas of crypto opportunity I’m preparing for are the mundane extension of crypto efficiency in largely unseen ways and the possibility of some extreme seeming sci-fi style applications related to AI.

As mentioned above, stablecoins have evolved from niche financial instruments to essential tools in global digital finance. Their promise of price stability—pegged to fiat currencies or commodities—has made them indispensable for payments, remittances, and savings in many markets. There is about to be an explosion of stablecoin issuance as fintech and traditional financial services companies launch proprietary versions of these coins to lower their costs and improve efficiency of payments and transfers globally. This proliferation will commoditize stablecoins, driving widespread adoption and reshaping the digital payments landscape.

However, the current ecosystem remains fragmented and intimidating for many users. Custody solutions and crypto wallets can deter mainstream adoption as they try to be a gateway to all of crypto, rather than merely an easy to use home for your assets. This presents a significant opportunity for innovation: developing streamlined onboarding experiences and wallets with the simplicity of Venmo and creating ways for these stablecoins to be abstracted further from their crypto backend. This will onboard two sides of the population. First, the long tail small dollar consumers in both emerging and developed markets who become exposed to the simplicity and value of these assets. Second, the high net worth market where, if presented intelligently, stablecoins can offer an attractive mechanism to store and transfer money internationally without the arcane processes and restrictions of the traditional banking system.

Beyond generic stablecoins like USDC or USDT, the concept of branded currencies is where I expect the biggest growth. Companies like Starbucks, McDonalds and others save hundreds of millions of dollars by creating a closed payments system through their mobile apps, with customers loading money in bulk onto their Starbucks accounts and reducing transaction fees traditionally paid to credit card processors on a per-swipe basis. Instead of paying $.30 + 3% (9% aggregate processing cost) on every $5 in store purchase, the company incurs these costs only on a one time $25 top up (roughly 4% aggregate processing cost) and then each subsequent purchase occurs on their own internal ledger improving their margins by roughly 5%.

While Starbucks carries this out on their own centralized accounting and payments system, the opportunity is clear for this to be commercialized as a broader platform underpinned by stablecoin rails. While today we have USDC, USDE, USDT as generic dollar equivalents, I expect to see more branded stablecoins emerge, merging loyalty programs with digital payments. Imagine airline reward points that function as stablecoins or retail giants launching currencies tied to exclusive discounts. 

By integrating payments with customer loyalty through a stablecoin solution, businesses can build systems to incentivize repeat spending, recapturing lost margin on processing and international transfers. By moving this to decentralized stablecoin rails, they gain the benefits of lower administrative and operational costs to manage these programs. 

This could also become a trend in crypto markets. Instead of projects launching their own fixed-supply token, they might move toward a branded, uncapped stablecoin reward system. This would address regulatory issues and simplify reward and token programs for market participants where a variable price native token is often unnecessary.

Bitcoin (BTC) is transitioning from a speculative asset to a foundational component of diversified portfolios. Increasingly, individuals are holding Bitcoin as a hedge against inflation and macroeconomic uncertainty. Similarly, corporations are adding BTC to their treasuries, following high-profile examples like MicroStrategy and Tesla.

Governments are also beginning to recognize Bitcoin’s value, particularly in the face of geopolitical tensions and potential shifts in U.S. trade policy under the new administration. As a decentralized, borderless store of value, Bitcoin offers a hedge against dollar volatility, appealing to nations with significant exposure to U.S. economic policies.

This growing adoption creates a self-reinforcing cycle of legitimacy. As Bitcoin gains acceptance, its network effects strengthen, attracting more participants and increasing its utility.

Price action in Bitcoin markets has historically driven developer interest in crypto broadly. The increasingly crypto-friendly U.S. regulatory environment will accelerate this trend. A new generation of developers will enter the crypto space, bolstered by the halo effect from Bitcoin’s mainstream adoption that further legitimizes the broader crypto asset class and its underlying technology.

In response to a regulatory shift, the U.S. is entering a new era of crypto acceptance, particularly in the B2B space. In the past several years, regulated entities have shied away from servicing the crypto industry and meaningfully experimenting or implementing crypto solutions, in many cases due to outright threats from regulators. With the new administration, banks and other regulated entities are becoming more comfortable interacting with crypto companies, enabling safer operations and broader collaboration. 

Tokenization offers significant advantages that organizations like Blackrock and Franklin Templeton have already adopted. By reducing administrative overhead, tokenized assets can lower costs while providing broader distribution opportunities. For individuals, tokenized assets could redefine net worth, making previously illiquid assets (like real estate, watches, whiskey or intellectual property) tradeable and usable as collateral. Companies that are operating in these markets, bringing previously untapped financial assets on chain, become exciting opportunities in this new landscape.

Regulatory clarity in the U.S. will also unlock previously inaccessible consumer applications. Platforms like Polymarket—a decentralized prediction market—have operated in legal grey areas overseas but could become accessible to U.S. users under new guidelines. Similarly, decentralized futures and derivatives exchanges could gain traction, offering consumers innovative ways to manage risk and invest. I’m eager to see what types of new product experiences emerge from this new opportunity to experiment with novel token incentive structures for US users and build on the excitement that the DEPIN sector has catalyzed through physical infrastructure buildouts and virtual networks. Markets like energy, medicine, and others seem ripe for experimentation.

One of the most exciting developments in the U.S. market will be the evolution of crowdfunding through tokenization. Clearer classifications of securities and commodities will enable businesses to raise capital from a broader pool of investors. This could lead to entirely new models of corporate formation, where startups issue tokens instead of traditional equity, democratizing access to investment opportunities and sparking fresh innovation in capital markets. Businesses that are facilitating this movement become the next potential Coinbase in terms of the magnitude of the opportunity.

The rapid rise of autonomous AI agents calls for a paradigm shift in how these systems interact with digital ecosystems. Current browser-based interfaces, designed for human use, are ill-suited for agents. Instead, we’ll see the emergence of agent-specific interfaces akin to developer APIs, enabling seamless, natural interaction between agents and systems. Crypto is the payment rails for this evolution.

AI agents face a significant limitation: they cannot independently access traditional financial services like bank accounts. This makes cryptocurrencies the logical choice for enabling financial autonomy. By using crypto, humans can empower agents with the ability to manage funds, execute transactions, and perform services on their behalf. We saw this with the wide range of agents on X, like the aforementioned Truth Terminal. Put to more tactical and practical use, it will create an exciting experimental space to pay attention to.

One of the most innovative possibilities in this space is crowd-controlled AI agents. These agents would operate under the governance of token holders, who could collectively set rules, strategies, and limits. For example, an AI agent tasked with trading in financial markets could have its decisions shaped by the collective votes of its token holders, ensuring transparency and accountability. The new corporation might be simply an AI and the governing body, the holders of the tokens that oversee its incentive set and guardrails.

As AI agents become more autonomous, verifying human identity will be critical to prevent malicious agents from exerting undue influence. Proof-of-personhood systems with a crypto native ID (think Clear for everything) will ensure that only trusted humans control agents, maintaining the integrity of these systems.

Trust will be a cornerstone of AI-agent ecosystems. Unlike today, where users implicitly trust human-governed services, there will be skepticism toward AI counterparts. Decentralized governance mechanisms, enabled by crypto, can provide the necessary transparency and accountability. Governance tokens will likely play a role in this ecosystem, allowing stakeholders to enforce rules and align incentives.

As we turn the page on 2024 and look ahead to 2025, I anticipate the next 12 months will bring about some seismic changes in how we interact with technology in our everyday lives. Robotaxis are taking over cities like SF and LA, and soon many more cities. The capabilities of AI are accelerating and broadening at a breathtaking pace. A huge cultural shift is clearly occurring with the new administration coming into power in the US, with the Silicon Valley elite playing a central role in our federal government and policy agenda. All of this sets the stage for a volatile and exciting year. 

In the early days of the internet, few destinations offered deeply engaging, everyday utility. Browsing was sporadic, and persistent online activity was rare. Services like AOL and Yahoo were entry points, but they lacked platforms that could anchor users for hours at a time. Facebook changed that by creating an ecosystem where users scrolled, connected, and interacted for hours daily. This unprecedented level of engagement expanded the total addressable market (TAM) for internet businesses, enabling a new era of digital commerce. Businesses could reach billions of users with precision targeting based on rich personal profiles—a level of insight that was previously unimaginable.

This shift is analogous to how high-speed blockchains like Solana are redefining the TAM for crypto users today. Much like Facebook turned the internet into a dynamic, constantly engaged ecosystem, Solana’s low-cost, high-speed infrastructure is transforming blockchain from a niche space into a scalable digital economy.

The TAM Expansion Effect: From Social Feeds to Blockchain Transactions

Facebook’s model was simple yet revolutionary: by keeping users online longer, it turned sporadic web surfers into persistently engaged participants. The result was an ecosystem where businesses could reliably find and target consumers, driving platform-driven economic growth. Billions of people became addressable through one platform, creating an internet economy that generated trillions in value.

Similarly, blockchains like Solana are expanding crypto’s TAM by making decentralized services accessible, instant, and affordable. Earlier blockchains like Bitcoin and Ethereum established the foundation but were constrained by slower, costly transactions. While Bitcoin serves as a digital store of value and Ethereum powers decentralized apps, their high fees and limited scalability have historically kept them from mass-market adoption.

Solana breaks this barrier. Its near-instant, low-cost transactions make previously impractical use cases viable. Stablecoins, for example, become significantly more useful when transfers are virtually free and instantaneous. A small business in an emerging market can accept stablecoin payments without worrying about credit card fees or currency volatility—unlocking entirely new payment models.

Speculative Frenzy as Onboarding

Solana’s architecture has also enabled speculative, viral phenomena like memecoin mania. Platforms like Pump.fun, where anyone can create and trade meme tokens, wouldn’t be feasible on slower, more expensive chains. The result: over $300M in revenue generated by Pump.fun this year alone. This chaotic, speculative environment might seem trivial at first glance, but it serves a crucial purpose: onboarding millions of users who create wallets, transact, and explore decentralized ecosystems—often without realizing they’re using blockchain infrastructure.

Platforms Build Economies, Not Just Products

Facebook succeeded because it didn’t just offer one killer app; it built a platform where engagement bred utility, making opting out inconvenient. Similarly, high-speed blockchains like Solana are evolving from speculative playgrounds into robust economic platforms. They enable an interconnected ecosystem of apps, businesses, and financial services that collectively increase the value and utility of being on-chain.

In the same way that persistent online engagement unlocked vast new markets for internet businesses, persistent blockchain activity could unlock entirely new markets for decentralized applications, digital payments, and global finance. Blockchain’s TAM won’t expand through a single killer app—it will grow as the ecosystem itself becomes indispensable through cumulative, everyday use cases. The future of crypto isn’t about “getting people into blockchain.” It’s about making blockchain-powered services so useful that people can’t imagine life without them.

As a seed investor in crypto applications, this dynamic is exciting because it signals a massive, expanding TAM driven by the new foundational infrastructure high performance blockchains, like Solana, and others, offer. Just as Facebook’s persistent engagement unlocked unprecedented opportunities for internet businesses, high-speed blockchains are laying the groundwork for the next wave of decentralized apps. Every new wallet created through a speculative memecoin trade or a stablecoin transfer isn’t just a one-off event—it’s the start of a deeper engagement cycle. This growing base of active, transacting users creates fertile ground for builders to develop apps with real-world utility, sustainable business models, and scalable revenue streams. The opportunity lies not in chasing a single “killer app,” but in supporting the multiple builders creating compounding value for crypto users that ultimately leads to widespread usage without ever really "converting" people to crypto at all.

While the ESG/Climate Change/Green movements of the past decade+ have tried to rally support for new, cleaner forms of energy around a politically entangled message of saving the earth, economic incentives ultimately drive behavior. ATe energy dilemma created by these new technologies may finally be a catalyst to force the US and other nations to seek out new forms of energy supply. The demand for energy is set to outstrip supply, and supply will need to be asignificantly augmented with both traditional sources like natural gas and renewables, nuclear,and other sources.

Having spent the past few months examining this in the context of my role investing in blockchain startups, there’s an additional angle where I see an inevitable convergence of blockchain and energy playing a role in solving this problem over the coming years.

AI is not just changing how we work and live; it's fundamentally altering our energy landscape. The power demands of AI are staggering and growing at a pace that few anticipated.

Data centers accounted for about 4% of total U.S. electricity consumption in 2023, equivalent to 150 million megawatt-hours (MMWh). 80% of this consumption is coming from 15 states. For example, in 2023, data centers were estimated to comprise a quarter of Virginia's electric load.

According to Goldman Sachs Research, data center power demand is projected to grow by 160% by 2030 and AI expected to represent about 19% of data center power demand by 2028. This would imply data centers consuming as high as 6-9% of our energy supply in a short period of time. Let's look at this from a historical angle.

To fully appreciate the magnitude of the challenge posed by AI's energy demands, it's crucial to understand the historical trends in energy supply and demand.

1. Energy Supply Growth: U.S. primary energy production has been essentially flat since 2000, growing at an average of 1.7% annually.

2. Energy Demand Growth: The demand side has also been relatively stable, with a slightly negative annual growth rate based on data from the IEA.

The projected 160% growth in data center power demand by 2030 represents a significant departure from historical trends. This growth rate far outpaces both the long-term supply growth rate and the recent demand growth rate to where we could see a need for significantly more energy supply over the coming years. From the same Goldman report, then estimate that energy demand will grow by 2.4% from 2022-2030, with almost half of that coming from data center demand.

Beyond AI, we’re also contending with additional forces at work that are set to push the limits of our grid.

• In emerging economies, growing incomes mean a larger percentage of consumers in places like India can afford energy intensive appliances like air conditioners.

• Electric vehicles, which Bloomberg forecasts will add 5.2% to global electricity demand by 2040 as drivers start relying more on the electrical gris to power their cars than filling up at the pump.

• Climate change driving increased demand for energy, as more extreme temperatures (both hot and cold) are straining the grid and driving higher prices for home energy.

This unprecedented growth in energy demand presents both challenges and opportunities. It strains our current infrastructure and creates a pressing need for investment in new energy generation and distribution solutions.

The current energy infrastructure is ill-equipped to handle this surge in demand. The U.S. Department of Energy estimates that $50 billion is required for new generation capacity to meet data center demand by 2030. Additionally, the U.S. Energy Information Administration projects an increase of 3.3 billion cubic feet per day of new natural gas demand by 2030 for electricity generation.

Key challenges include:

• In some regions, like Northern Virginia's "Data Center Alley," new facilities are facing multi-year waits to connect to the grid.

• Countries like Ireland are seeing data centers consume up to a third of their total energy supply, surpassing the demand from the country’s home energy needs.

• The American Society of Civil Engineers reports that 700,000 miles of transmission lines need upgrades, and a total energy infrastructure investment of $637 billion is required by 2028.

The need for massive infrastructure upgrades is likely to drive up energy prices for both wholesale and retail customers. So while the narrative around energy has been muddied in recent years by those thinking this is just a climate change issue or a push by progressives towards a sustainable or green agenda, it is really rooted in practical economics. And this isn't just about building more of the same; it's about expanding and modernizing our entire energy ecosystem in a way that's more efficient and capable of meeting rapidly escalating demands.

While the surge in energy demand driven by AI and data centers may seem distant from everyday life, its effects are likely to ripple through to ordinary consumers in several significant ways:

1. Rising Electricity Prices: As utilities invest billions in new infrastructure to meet growing demand, these costs will likely be passed on to consumers. The U.S. Energy Information Administration projects that residential electricity prices could rise by 2.4% in 2024 alone, a trend that could accelerate as AI-driven demand increases.

2. Potential Grid Instability: The strain on existing grid infrastructure could lead to more frequent power outages or rolling blackouts, especially during peak usage times. This could affect everything from home appliances to electric vehicle charging.

3. Shift in Energy Usage Patterns: As utilities implement demand response programs to manage grid load, consumers may be incentivized or required to shift their energy usage to off-peak hours. This could mean running major appliances late at night or early in the morning.

4. Accelerated Adoption of Smart Home Technology: To manage energy consumption more efficiently, there may be a push towards smart home devices that can automatically adjust energy usage based on grid conditions and pricing.

5. Changes in Housing Markets: Areas with stable and abundant energy supplies could become more attractive for both residential and commercial real estate, potentially influencing property values and migration patterns.

6. Impact on Consumer Goods Prices: As energy costs rise for manufacturers and data-intensive services, these increases could be reflected in the prices of consumer goods and services, from electronics to cloud storage.

7. Job Market Shifts: The massive investment in energy infrastructure could create new job opportunities in the energy sector, while potentially leading to job losses in industries that struggle with higher energy costs.

These impacts underscore the far-reaching consequences of the AI-driven energy demand surge. They also highlight potential areas for innovation and investment, from energy-efficient consumer technologies to grid management solutions that can help mitigate these effects.

The integration of blockchain technology with the energy sector is paving the way for innovative solutions to longstanding challenges in energy distribution, trading, and sustainability. Several startups are at the forefront of this revolution, each addressing different aspects of the energy ecosystem. Jasmine Energy* is transforming the way Energy Attribute Certificates (EACs) are traded and managed. By leveraging blockchain technology, Jasmine enables 24/7 live trading of EACs with unprecedented efficiency. Their platform streamlines due diligence processes, offers rapid 9-second settlements, and provides a one-click retirement process for EACs. This approach not only accelerates cash flow for energy producers but also simplifies the process for climate-conscious actors to support high-impact, current vintage solar and wind projects.

Additionally Neutral* is introducing a new order book exchange for these energy assets, innovating in a market that’s overwhelmingly been run through inefficient brokerage models with over the counter trading.

While Jasmine and Neutral focus on certificate trading, Plural* is democratizing access to clean energy investments. Their platform allows individuals to purchase shares in solar projects and receive dividends from the energy sold. This model of fractional ownership opens up opportunities for a broader range of investors to participate in the clean energy transition, potentially accelerating the deployment of new solar installations. Beyond financing, we can envision a world where solar projects financed on Plural can lead to forms of community owned solar, where equity owners in the facilities directly draw from the facilities rather than sell into the grid, when it makes financial sense.

These types of Virtual power plants are being explored by companies like Daylight who is building a decentralized protocol that aims to revolutionize how the energy grid is programmed and managed. By leveraging distributed energy resources (DERs) such as solar panels, batteries, and smart thermostats, Daylight is creating a system where anyone could potentially build and operate a virtual power plant. Their initial focus on selling DER data to traditional energy companies for grid optimization represents a crucial step towards a more flexible and responsive energy grid.

Glow introduces a novel "recursive subsidy" mechanism designed to rapidly scale solar energy production. Their cryptoeconomic system produces high additionality carbon credits from cost-effective solar farms. By requiring participating solar farms to contribute 100% of their gross electricity revenue to the Glow incentive pool, they ensure that incentives are directed towards projects that truly need financial assistance. This approach, combined with their dual-token system (GLW and GCC), creates a powerful economic engine for accelerating solar deployment while also providing verifiable carbon credits.

These startups collectively represent a shift towards a more decentralized, transparent, and efficient energy ecosystem. Jasmine Energy's EAC trading platform could significantly increase liquidity and accessibility in the renewable energy certificate market. Plural's fractional ownership model could unlock new sources of capital for solar projects. Daylight's decentralized grid management protocol could lead to more resilient and responsive energy systems. And Glow's innovative incentive structure could dramatically accelerate the deployment of solar energy.

By leveraging blockchain technology, these companies are not just digitizing existing processes, but fundamentally reimagining how energy is produced, distributed, traded, and consumed. Their innovations have the potential to accelerate the transition to clean energy, improve grid stability, and create new economic opportunities in the energy sector.

As these technologies mature and integrate, we could see a future where energy systems are more democratic, responsive, and sustainable. Consumers might become prosumers, actively participating in energy markets. Grid operators could have real-time, granular control over distributed resources. And investors of all sizes could directly support and benefit from the clean energy transition. While challenges remain, particularly in terms of regulatory alignment and large-scale adoption, these blockchain-based solutions represent a promising path towards a more sustainable and efficient energy future.

For investors, the convergence of AI and the resulting energy demands presents a unique opportunity. The demand driver is clear and accelerating: the global appetite for energy isn't going away. The need for solutions is urgent, with current energy infrastructure straining under growing demands.

This convergence creates opportunities for several types of investments:

1. New Energy Generation: Companies involved in developing and deploying new energy generation capacity, including traditional and renewable sources.

2. Grid Infrastructure and Modernization: Firms focused on upgrading and expanding the grid to accommodate increased energy demands.

3. Energy Efficiency Solutions: Technologies and services that help measure reductions in energy consumption and improve efficiency in data centers and other AI-powered facilities.

4. Energy Trading and Management Platforms: Creating more efficient and transparent markets for energy trading.

5. Innovative Financing Models: Developing new ways to fund and incentivize energy projects.

However, success in this space requires more than just identifying promising technologies. Investors must also consider factors such as regulatory compliance, integration with existing systems, and scalability.

This intersection of cypto and energy has been a consistent investment theme for us at Factor, as we look for non-consensus ways to invest in AI and fund blockchain applications with mainstream impact. The energy sector is on the verge of a fundamental reshaping, driven primarily by the escalating energy needs of AI and growth in emerging market economies. This transformation presents both enormous challenges and unprecedented opportunities.

For entrepreneurs and companies operating in this space, the task is to create solutions that can be practically implemented to meet the rapidly growing energy demands. The examples of Jasmine Energy, Plural, Daylight, Glow and many others demonstrate that there's significant innovation already happening, but there's still ample room for new ideas and approaches.

For investors, the key lies in identifying those innovations that not only offer technological advancement but also demonstrate a clear path to real-world implementation and value creation, driven by the fundamental supply-demand imbalance in the energy market.

In conclusion, the convergence of AI and the resulting energy demands represents a rare alignment of urgent need and transformative potential. While the challenges are significant, so too are the potential rewards.

* Factor Portfolio company

Hanging on to the Origins

Many of the early adopters of crypto, and more specifically Bitcoin and Ethereum, were drawn to a set of core tenets, including maximal decentralization, ensuring personal privacy, non-sovereign currency, and self-custodied assets. These principles promised a future where reliance on centralized entities was minimized, personal data remained private, and individuals had full control over their assets and online identities. Some theories that exemplified this thinking:

• A decentralized social network was obviously superior to the nefarious centralized ones that are always changing the rules and harvesting user data.

• A decentralized global computer was better than AWS because you could now no longer be censored for hosting a website that the corporate bodies disagreed with.

• Banks can’t be trusted and the government is headed for fiscal implosion so we should have an independent monetary system since one day they will come for your gold and guns.

• Anonymity is a right as much as free speech and we should be able to live online without exposing our personal data and protecting our personal privacy as private citizens.

Behind these ideals lies some valid thinking. I largely agree that if we could roll back the clock and rebuild many aspects of the internet today, a decentralized, blockchain-based approach would have prevailed. This could have resulted in a more vibrant and broadly owned internet than we have now. Similarly, we can see how Wall Street is starting to notice the opportunities to remake their backend through blockchain applications, with Goldman as the latest big bank to announce their movement towards tokenized security products.

But the reality is the internet as we know it is now roughly 30 years old and people’s habits and rituals are entrenched. You can’t simply change the behavior of individuals based on ideals. A decentralized X/Twitter may be superior to the existing one in every respect, but all of your friends post to Instagram and Google has the best maps, so even if they are tracking your every move, the 99% of people in society default to what’s easy and, well, the default.

Most people don't prioritize privacy and security enough to avoid using convenient services like Google or Facebook, despite the data these companies collect. They continue to store passwords in plaintext, use major tech platforms daily, and share personal information freely to enhance their user experiences.

Think about how many people use Google Maps for navigation. Even though it tracks their location, most users prefer the convenience it offers over privacy concerns. Similarly, people use social media platforms like Facebook and Instagram daily, sharing personal details despite knowing these companies collect their data. It's a frequent joke that someone bought a product off their feed because “Instagram heard me talking about how I needed new kitchen tongs,” accepting a creepy assumed breach of trust and then not being turned off but instead rewarding it!

This inertia highlights a critical issue: people are more likely to stick with default apps on their phones and social networks, even though there are objectively superior alternatives, simply because it's easier to use what's already there and that’s where their networks exist.

Long Term Vision Achieved Through Near Term Wins

With the growing demand and openness to blockchain-based solutions, demonstrated by institutional examples like PayPal, Visa, Stripe, Puma, and Adidas, and many others, we can see a real market for companies to scale on crypto rails and value propositions. To succeed at the necessary scale for major outcomes beyond the cryptosphere, we need to focus on building businesses that compound utility and gain adoption through small wins, rather than presenting a fully developed crypto solution from day one. The best articulation of this strategy I have seen is Chris Dixon's blog post from 2010 entitled "The 'thin edge of the wedge' strategy."

While Chris is primarily know today as the founder and head of a16z Crypto, perhaps the most prominent investors in the crypto markets, he was previously an exceptionally successful generalist investor and founder. I think about this post often, as Chris lays out a playbook for product success, articulating a method used by startups to establish initial relationships with users by focusing on a small, often overlooked feature and then expanding. Once a wedge is established with a core value proposition, teams quickly iterate to build enduringly useful products. While critics often mistake wedge features for final products, successful startups plan for long-term value and the best founders recognize that this thin edge of the wedge is almost a trojan horse play to gain early adoption and earn your way into deeper relationships with users.

In crypto markets, I find this strategy to be often overlooked - or at best played in reverse. Many founders I speak to view the thin edge of the wedge to be crypto users. They identify problems faced by crypto teams and users, thinking that they'll demonstrate traction with this user base and then try to expand to traditional user markets. But this means architecting a product based on a set of users that barely resemble the users you'll seek to acquire in the real world.

The right approach may be instead to think about small wins that they can demonstrate by applying crypto and blockchain as solutions to small but acute problems faced by traditional users and then leveraging those relationships to broaden the utility that can be created with this captive, onboarded user base.

I have recently written about the opportunity in Zero-Knowledge (ZK) identity and on-chain personal data to facilitate online transactions. I am excited to see a number of companies working on this, but from what I have seen thus far, most seem focused either the crypto-native priorities of privacy and security, allowing users to do things while hiding their identity because they believe in the right to privacy versus the ability to accomplish something more easily through this technology. Guided by the thinking that individuals shouldn’t have to provide corporations with their personal information if avoidable they often target applications like AML and KYC for DeFi or crypto financial services, where existing solutions are already well established and hard to displace.

What might a wedge strategy for these services look like? Let's consider a practical application of this solution in one of the most innovative industries in the world: Pornography.

Porn has historically been among the most early adopting industry of any, going back to VHS and then later proving to be one of the first clear disruptors of the internet era.

But recent laws in 16 U.S. states now require age verification for adult content websites. These states mandate websites to verify users are at least 18, affecting sites with 33.33% or more content deemed "harmful to minors." Non-compliance can lead to fines and lawsuits. Websites face complex legal requirements to verify users' ages, often needing government IDs or digital driver's license apps, raising privacy and security concerns.

You can bet that porn consumers have zero interest in sharing their personal information with porn sites, risking being outed as users. But as discussed in Rethinking Data Ownership, really clear and excellent solutions to this problem are being created.

Enter on-chain identity storage and Zero-Knowledge (ZK) Know Your Customer (KYC) solutions.

ZK KYC allows users to input their KYC data into a secure system, verified by oracles, where they own and control it. The data is verified through an independent protocol and is not stored in a way that is accessible to the outside world. You now have a KYC record that can be used by any service, such as a porn site, to prove you’re 18 and allow them to comply with regulations. Costs are a fraction of traditional methods, and the user experience for both sides would be greatly improved.

Additionally, this ZK KYC provider can leverage its user base into more types of KYC solutions for various industries over time. This approach is far more interesting than proposing a way for people to gain access to KYC derivatives on-chain for 10,000 crypto users. I have no idea the real practicalities of this solution, but I use it as an example that recently caught my attention to help articulate the theory here. Companies like ZK.ME and the original, Civic, might be working on this or it might be that this is just a feature in iOS mobile Drivers Licenses. But to date, the focus has seemed to be these crypto-first wedges.

When you understand that people will not change their habits unless they encounter solutions that are ten times better than what they currently use, we should be seeking out these opportunities to demonstrate this innovation whenever possible in situations like this.

In conclusion, for blockchain to achieve mainstream adoption, founders must shift their focus from ideological purity to practical applications that address real-world needs. This involves understanding and catering to the spectrum of utility that each core tenet of blockchain offers. By focusing on solutions that enhance user experiences in meaningful ways, the industry can move beyond its current stagnation and realize its full potential.

I see this in the recent founders we’re backing. They largely come from outside of Web3 and understand how to build businesses with real consumers in mind, leveraging the benefits of this new technology for the masses rather than the crypto-twitter echo chamber.

As we apply secure, user-friendly mechanisms for managing personal data or create innovative financial products that seamlessly integrate with everyday life, the industry can rapidly drive adoption. Solving real-world problems like reducing transaction fees, enhancing privacy without compromising usability, and offering tangible benefits over traditional systems will make blockchain technology more appealing to the masses.

Ultimately, the future of blockchain depends on its ability to adapt and evolve beyond its ideological origins. By prioritizing the practical needs and values of end users, the industry can create a sustainable path to widespread adoption and unlock the transformative potential of blockchain technology.

Each time you move, or your details change, the onus is on you to update every individual or service that might need your new information. With each platform (e.g., social media, professional networks, personal contacts) potentially holding different versions of your data, there’s a significant risk of inconsistency. What if your old address is updated on Amazon but not the IRS?

Just today, I learned:

1. I am subscribed to HP Instant Ink

2. refill deliveries have been shipping to my old address for the past 3 years, where the new homeowner now apparently sits on a big supply of authentic HP cartridges while my pages are coming out blank.

We could seek a centralized solution to this problem. But if we depended on centralized counterparties, like Google or Facebook, now these entities can control your data and potentially exploit it for commercial gains. Moreover, the centralized nature of such databases makes them prime targets for data breaches - as has happened repeatedly.

Finally, in the current setup, individuals have limited control over who can access their personal information and how it is shared between platforms. This lack of control complicates data management and affects personal security and privacy.

The simple task of letting people and companies know where you live is not so simple anymore. This dynamic is just one example of the challenges of the current model of personal data management and centralized data systems.

In a decentralized data management system, individuals become the central hub of their personal data. Imagine your core information—name, address, and other key details—securely stored on a public, domain-specific blockchain. This 'digital vault' is under your control, with you deciding who can access your data and when.

Instead of repeatedly inputting your information for each service, you maintain it once in your digital vault. When a service or individual needs your data, it requests access from you, and you can grant this securely and transparently. It will be constantly kept up to date any time you update the central source. Think of it as a universal 'sign-in,' where your data is always up-to-date and consistent across services.

This shift transforms your interaction with digital services. Instead of constantly feeding data into disparate systems, you manage your information centrally, and the systems pull from your controlled source as needed. It’s a world where data management is seamless, secure, and user-centered, returning control to individuals and paving the way for innovative, efficient digital services.

This new model can take a few forms. Still, the one I hypothesize replaces many of the counterparties we rely on today is being replaced with decentralized data protocols housed on top of some decentralized data store, each serving a specific use case. I am eagerly watching to see who steps in to build the following solutions as examples of store once, populate anywhere:

• Identity Protocol: Today, crucial identity data has two forms: basic contact details and critical identifying information. Companies like Google and Facebook currently manage these data types. These two kinds of information would be stored on a secure blockchain in a decentralized model. For instance, you could share your actual contact information, like your mailing address, to receive packages or holiday cards without unnecessarily exposing other data.

  For businesses and services that need to verify your identity, they could request access to specific pieces of your critical identifying information. You hold the power to grant or deny these requests. This ensures that your data is shared only as needed, giving you control over its use. Farcaster and Lens are notable entrants in this game, as well as long-running protocol, Civic.

• Work Protocol: Professionals often rely on LinkedIn to showcase their work history, but a simple list of job titles fails to represent their full range of accomplishments. Instead, imagine a world where all of your career achievements - products launched, metrics improved, deals closed - are not manually input but tracked and verified in real-time. Your professional identity would extend beyond just job titles and be accessible to those you permit, while securely stored under your control. Grappa is among the teams building in this area.

• Credit Protocol: In the future, consolidating all your financial data on a public blockchain can help you manage your financial reputation better. Decentralized finance (DeFi) offers a preview of this world. But it becomes truly useful when you have complete control over your financial history, net worth, and other details that you can share with counterparties without compromising privacy. This can help lenders verify your creditworthiness while you can grant access to stored data and automate loan covenants.

• Health Protocol: Healthcare is another area ripe for transformation. Epic’s monopoly over EMRs means that our health data is centralized and often difficult to access. Apple Health actually represents the closest approximation of the model here, as you maintain a central store of health data on your phone's secure enclave. Now imagine this being unconstrained to your Apple device.

  Apps and wearables directly write to your health data, linking with your test history and other records. If you switch doctors or need to see a specialist, you can grant them access to your medical records without the cumbersome process of transferring files. This ensures that your healthcare providers have up-to-date information while you maintain control over your sensitive data.

The capabilities of decentralized, secure data management are amplified with Fully Homomorphic Encryption (FHE) and Zero-Knowledge Proofs (ZKP).

FHE lets us perform operations on encrypted data, keeping it secure. It enables services to interact with your data without exposing it. For instance, financial services could assess your creditworthiness or healthcare providers could make diagnoses without knowing the specifics, keeping your data private and secure.

ZKPs allow one party to prove they know a value without sharing it. They can be used to verify data without revealing it, adding an extra layer of privacy and security. For example, a lender could verify your income is above a threshold, or a doctor could confirm your vaccination status without knowing the specifics of your medical history.

Example: Consider applying for a loan. Today, this process is brutal.

You manually upload dozens of files (many of which the lender already has), and any time a new loan is requested, the profile needs to be rebuilt from scratch. Instead of sharing your income and credit history the old-fashioned way, you could provide encrypted data stored across all the previously mentioned protocols. The lender could perform calculations to determine your eligibility without seeing your information. If further proof is needed, such as verifying your income, you could provide a ZKP, confirming your income meets requirements, but no other details.

These technologies reduce data breach risks, streamline processes, and maintain privacy. With Decentralized Data, FHE, and ZKPs, a secure, decentralized data management system gives individuals control over their data and enables powerful new services and user experience gains.

The next big data category is beginning to manifest itself as we speak—our personal data for AI. Over the next year, we will see the introduction of more memory-capable LLMs, where the record of our interactions and the data we can supply them becomes increasingly powerful in driving a personalized and useful user experience.

Examples are emerging to build these personal contexts, like Limitless, where a small wearable clip passively records your life and creates a complete memory store of your conversations and interactions for superpowered recall. There’s really exciting potential in this model of conversational personal memory, even if the form factor here can be debated.

Today, AI systems rely on centralized vector databases to store and manage interactions. These databases, operated by companies like OpenAI, Weaviate, Pinecone, and major cloud providers, limit user control and interoperability unless you operate at an advanced technical level. For most users, their AI data is confined within these systems, making it difficult to integrate with other services or platforms. This lack of control and flexibility often leads to inefficiencies, such as the "cold start" issue when transitioning between different AI tools. While some companies, like LangChain are working on interoperability solutions, they still largely depend on traditional Web2 architectures and self-hosted data storage.

In the decentralized model we've discussed, your AI data would be stored on a public blockchain. This means you own your historical context data and the memory that informs your AI interactions, allowing you to control its use. Here are some practical examples of how this could revolutionize everyday technologies:

Seamlessly Transition Between Assistants: Imagine switching from ChatGPT to a Claude, while preserving full historical context. In a decentralized AI data management system, the new service can instantly access your historical interaction data stored on a blockchain, including past commands, music preferences, reminders, and frequently asked questions. This lets the new assistant offer personalized responses immediately, without the usual learning curve.

Enhanced Recommendation Systems: Consider AI in streaming services like Netflix or Spotify. When you switch to a new platform, it typically doesn't know your preferences and must learn your tastes over time. With decentralized AI data management, your viewing and listening history could be stored on a blockchain and shared with new service providers. A new streaming service could immediately access your watch history and start recommending content you are likely to enjoy, bypassing the "cold start" phase and providing a personalized experience from day one.

Educational and Learning Platforms: When users move between learning platforms, they often lose valuable data about their learning pace, strengths, and preferences. If educational data were managed in a decentralized system, a learner's history, including completed courses, quiz scores, and areas of interest, could be securely stored and shared with new platforms. This would allow educational AI systems to adapt to the learner's level and preferences right away, providing a tailored educational experience that builds on past progress without redundancy.

Achieving this today typically requires self-hosting data, using open-source LLMs, and manually integrating it into your models and services. In your decentralized memory model, the blockchain provides a permanent, always-on storage solution that is secure and accessible from anywhere. This ensures that your AI data is always available without the hassle of managing infrastructure. New products and services can immediately offer fully customized user experiences by leveraging your complete personal context, including traditional services and the growing data set of personalized memory and interaction data you build daily. With control over your data, the fear of giving it up to an AI diminishes, as the models can access knowledge without possessing it.

The transition to decentralized, secure data management on public blockchains promises to revolutionize the way we handle personal data. Individuals can regain control over their personal information by leveraging blockchain technology and zero-knowledge proofs, ensuring privacy, security, and interoperability.

This vision encompasses various domains, from core identity information to financial reputation, medical records, and personal AI data. By moving away from centralized custodianship, we can create a digital landscape where individuals truly own and manage their data. This enhances privacy and security and fosters innovation and efficiency in how services interact with personal data.

The rapid advancements in AI have made it more crucial and impactful to change the paradigm of data ownership. This might be the perfect time to try and succeed with this model, even though previous attempts may have been too early.

You can call an Uber, order food, research a flight, etc., all through this device, quickly interacting with standard websites and applications. This new agent model got me thinking about the evolution of UX, first on mobile. It stands to reason that with a sufficiently powerful Siri or Alexa, you don’t need all the apps on your phone that primarily serve to accept input (order a car for me, fill my shopping cart, etc.). The “rabbits” in the r1 follow through with agency on your behalf and perform actions to complete tasks like booking travel or answering questions about our surroundings.

Thanks for reading Factor Capital! Subscribe for free to receive new posts and support my work.

I suspect that with the next iteration of Siri, we will gain similar powers through our AirPods connected to our iPhones and, later, Vision Pro. Alexa is also getting a major upgrade soon to leverage these LLMs.

But the more exciting unlock for me is the potential for AI to serve as the missing UI for web3.

Today, one of the primary hurdles in web3 is the inaccessibility of decentralized applications that rely on browser extensions and private keys to sign transactions whenever you perform an action. While initial steps have been taken to abstract the login experience to one that is more familiar to mainstream users through email sign-on, etc., this feels like a near-term hack that doesn’t really improve upon UX and just substitutes one awkward paradigm that people are unfamiliar with one they have been conditioned towards over the past 20 years.

AI Agents are the next evolution of how Large Language Models (LLMs) like ChatGPT will capture mainstream attention. OpenAI is developing a set of these capabilities internally, lead developers at Google DeepMind recently left the company to found a company in this space, and companies like Adept, Sierra (founded by former Salesforce Co-CEO and Facebook Product Head, Bret Taylor), and other have raised hundreds of millions of dollars to pursue this opportunity. Evidence of the potential was on display just this week when Klarna said they are leveraging AI agents to do the work of 700 people in areas like customer support.

These enterprise use cases are just the beginning.

I imagine a paradigm where you soon have an AI agent with access to the complete set of web3 applications - most of which are open source and readily understandable for anyone to build upon through composability. Now, you link an agent with a wallet with value stored in it, and you can prompt your agent with intents.

For example, a simple one might be “buy 100 SOL.” The agent can look at your current balance of assets, explore the available services you’ve permissioned between centralized exchanges and decentralized apps, then work out the best way to transfer your assets or bridge them, perform the trade, and return with an executed activity all based on a single prompt. The UX improvement here is substantial - a single action is taken by the user, multiple actions are taken by the agent, and a task is effectively accomplished without the user needing to learn the mechanics of web3 any more than they need to understand the mechanics of the internet or their mobile device.

The manual process of asset bridging today

Taken further, the migration of our personal identity, credentials, and other data onto secure, privacy-preserved blockchain custody unlocks another level of capabilities entirely. This transition represents a significant change in how we manage and interact with our digital selves. With identities and credentials on-chain, AI agents can securely and efficiently manage our digital interactions, from verifying identity for online services to automating administrative tasks like signing documents or accessing secure areas.

This on-chain identity system ensures a higher level of security and privacy, as blockchain technology inherently provides tamper-proof and transparent record-keeping. Furthermore, it enables a more seamless and personalized user experience across various platforms and services. Users gain control over their data, deciding what to share and with whom, thereby enhancing privacy and data sovereignty. This on-chain migration of personal data paves the way for a more interconnected and efficient digital world, where AI acts not just as an interface but as a custodian of our digital identities, making our interactions more secure, efficient, and tailored to our individual needs and preferences.

Our new AI agent powered UX can significantly improve security by reducing human error in managing private keys and executing transactions. They can be programmed with security checks and encryption protocols to ensure every transaction is secure, thus decreasing the potential for fraud or theft. AI agents also enhance the discovery of products and services by searching various decentralized applications and platforms for the best options based on user preferences. This makes the discovery of utility far simpler. Furthermore, AI agents can optimize existing applications by simulating their performance on new blockchain networks and rollups. For instance, an AI agent could test a dApp on various Layer 2 solutions to determine the ideal balance of speed, cost, and security.

Lastly, this dynamic unlocks a whole new utility opportunity for tokens as native currencies of different applications. If you think about it, it is a situation that already exists - except these tokens come as rewards points, compute credits, Starbucks balances, and airline miles. But, as humans, our capacity to evaluate and transact across hundreds of currencies is limited, often defaulting to how they measure against standard benchmarks like the dollar. However, unfettered by these constraints, AI can adeptly manage multidimensional relative value assessments across a diverse array of tokens. This capability allows for a seamless interface with a multitude of native tokens, each representing unique assets or services, without burdening the user with complex "foreign exchange" calculations. The AI can automatically transact using these native tokens, providing access to a broader range of products and services while abstracting the intricate valuation and exchange processes from the human end beneficiary.

This situation comes with risks, and the implicit agency that this thesis prescribes may amplify the fears many express over AI’s taking over and leading to the end of the world. The more aware we are of these risks, the better prepared we will be for managing them, though.

That said, as these agents work on our behalf in web3, I believe the other knock-on effect will be the realization that as these agents become more prevalent across the Web2 landscape, developers will recognize that it is far easier to integrate payments and value capture for these tools through digital assets versus traditional payment models. As this unfolds, expect AI and Web3 to increasingly converge and compound one another's values.

I am already seeing components of this take shape in some of the products people are building today. At Factor, this intersection is a core focus of our investment thesis, where we believe blockchain applications are increasingly creating novel solutions to mainstream applications.

Today, I am excited to officially announce the launch of Factor Capital and the first close of Venture Fund I.

Factor Capital was formed earlier this year with a mission to apply lessons learned from 20+ years spent working on and studying businesses that successfully harness technology and data to drive progress, efficiency, and impact.

Thanks for reading Factor Capital! Subscribe for free to receive new posts and support my work.

Venture Fund I will write $500k-$1m checks to back exceptional, creative entrepreneurs at the earliest stages of company development, tackling problems of global importance using blockchain technology.  Fund I is backed by my former colleagues at GSR Markets and Two Sigma Investments, Theta Capital Management, founders of Multicoin Capital, Lattice Capital, Cosmos Network, and other inspiring individuals and family offices.  

Roughly once a decade, new technology platforms appear that ultimately touch every aspect of our society. When the public first becomes aware of them, many are unimpressed by the state of the art in its earliest form. 

The temptation is to either ignore this innovation or dismiss it as not solving a real problem because viable alternatives exist to accomplish everything this new technology purports to address. Meanwhile, early adopters and developers see unusual promise and potential in these platforms and excitedly commit themselves to expand upon their utility. On this spectrum from Skeptics vs. True Believers, I have generally gravitated toward a position of rational optimism.

It is often said that all overnight success is ten years in the making - and we have seen in each prior cycle that public perceptions of new technology can quickly change. After years of work by these early adopters to improve the technology, it is finally packaged in a way that focuses on mainstream utility and ease of use. The fabled “Netscape Moment” or “iPhone Moment” occurs. The technology suddenly fades into the background.

Yet despite these technologies digitally transforming many parts of our lives, numerous systems and institutions still feel woefully outdated. Since the introduction of Bitcoin over a decade ago, Blockchain technology has promised to change that dynamic. 

Similar to many disruptive technology platforms prior, the early applications most people associate with the technology come across as frivolous, impossible to understand, fraught with risk, and not generally useful. True believers cite the ability to send a Bitcoin to anyone worldwide and have an immediate settlement. But Venmo/AliPay/Paytm and other tools are easier and more accessible, even if you have to depend on banks and centralized service providers as intermediaries. The prevailing use cases have been so overtly “Crypto” and speculation-driven that they deter mainstream users.

The promise of an immutable ledger that enables digitizing data, identity, and assets and introduces trustless efficiencies in archaic analog systems is exciting in theory. Yet hardly a week goes by without someone asking me, “But what is it used for today, really?”

Having observed the evolution of this technology from launch, we are finally at the point where this question can reasonably be answered. For example, stablecoins backed by the US Dollar are now a >$120bn market and are being increasingly adopted in emerging markets. India and Nigeria top the 2023 Global Crypto Adoption Index. Turkey ranks fourth globally in raw crypto transaction volume, receiving around $170 billion last year, behind only the US, India, and the UK.

In Fund I, we have already made a number of investments in companies using this technology to solve real problems today. Companies like Koywe are driving significant efficiencies for businesses and consumers exchanging currency across Latin America. Jasmine Energy and Neutral are reducing costs in renewable energy markets, disintermediating costly brokers. Parcl is creating entirely new ways to participate in Real Estate investing.

These are just some of the daily examples I see, including innovative forms of financing for scientific research and advancements in AI and data science. The applications we’re investing in don’t require businesses or consumers to actively adopt or understand the technology and have already begun to generate actual revenue on the back of sustainable business models.

Our thesis is basic: now is a unique time to invest in this innovation's early stages as this mainstream impact accelerates. With advances in AI and the permissionless nature of decentralized blockchains, it is easier than ever for small teams to bring high-impact visions to life from anywhere in the world. I am excited to continue to partner with founders and investors who align with this vision - and to be back on a startup journey of my own through the launch of Factor.

To our LPs and our portfolio companies, thank you for joining us on this journey.  

If you’re a founder who is building in this area or interested in doing so, we are open for business, so please get in touch.

Thanks for reading Factor Capital! Subscribe for free to receive new posts and support my work.

I spoke with an investor last week who told me about one of their latest investments in an augmented reality (AR) game designed behind the scenes to teach sidewalk robots how to navigate city streets—akin to a drone version of Tesla's Autopilot training. The game leveraged a popular crypto incentive model of rewarding players with tokens based on the volume and quality of their play—a model broadly associated with Decentralized Physical Infrastructure Networks (DePIN for short). Blockchain networks form the backbone of these DePIN models, providing a secure, transparent ledger for transactions and interactions within the ecosystem. Projects like Helium exemplify the potential, where a decentralized wireless infrastructure is built and maintained by a community incentivized through blockchain tokens.

Other examples of this we've discussed include Dimo for automotive data, Hivemapper for street view data, and Render for computing, among others. The blockchain not only facilitates secure transactions but also fosters a sense of community and shared ownership among participants. As seen in the Helium project, Tokens act as a catalyst, incentivizing individuals and entities to contribute to and maintain the decentralized infrastructure, thereby creating a self-sustaining ecosystem.

The idea is that through these decentralized incentive models, you can bootstrap a marketplace by building out a supply of resources before demand comes online and defray the centralized cost of deploying infrastructure that would typically be borne by the offering party. Examples of the original model these businesses look to disrupt include Google Maps, where Google spent billions of dollars mapping and photographing the streets of the world to create an interactive on-the-ground view of Earth. Or telcos who invest billions of dollars in laying fiber or deploy 3/4/5G towers ahead of network launch so that when enough towers are online to cover a region, the latest cell service can be turned on.

In the DePIN model, we can have contributors of data/bandwidth/compute, etc., be incentivized with future network ownership that will ultimately be commercialized as the infrastructure reaches critical scale. This incentivization of data collection and infrastructure development, underpinned by blockchain technology, marks a paradigm shift from traditional centralized models.

These new models provide an exciting innovation in the area of infrastructure development. But I wonder if there isn't a model that we have missed through the more crypto-native approaches of these V1 entrants to the DePIN world.

During the summer of 2016, the streets of New York City were abuzz with many people seemingly chasing after celebrities for a photo op. However, I soon realized that they were chasing after imaginary creatures in the popular game Pokemon GO, which was the first big demonstration of Augmented Reality (AR) that I can remember. This game was fascinating to observe, as players could wander around the city and discover orbs and other virtual goods. The incentive was simply the gameplay and collecting these virtual goods, as seen in most popular games around the world today.

Around the same time, I recall businesses trying to pay people to take pictures of grocery store shelves for real-time insights into product placement and pricing. However, these businesses never took off due to the high costs of building out the speculative data set and the photographers getting bored doing an unnatural task.

Today, I wonder what we could achieve by considering the gameplay model attached to the experimentation with DePIN. Imagine if all those Pokemon GO players had modern iPhones equipped with cameras and sensors to record and feed data back to the game maker, Niantic, in real time. The gameplay could become a massive trojan horse, leading to the collection of an amazing real-time mapping, sensor, and information network that could be utilized in a variety of exciting ways.

In the traditional corporate model, this potential trove of real-world data given to a centralized body for free to monetize could be a frightful idea. But if the players feel they aren't being exploited in this process and ultimately become the beneficiaries of the monetization of their work, what kinds of interesting opportunities might this unlock?

Photo by Tayla Kohler on Unsplash

Examples already exist of versions of this in practice. Captcha forms serve as a trojan horse for AI training models, where we're constantly providing input on which pictures in a 3x3 matrix are cars or street lights or which letters in a faded piece of text spell words. The product (Captcha) makes a user perform a necessary act (prove I'm not a bot), and theoretically, I get a better user experience of the product since these protective measures are undertaken. What happens when, instead of introducing friction, we introduce games that people can play to generate information or infrastructure that ultimately becomes valuable over time—and instead of Google/Waymo benefiting exclusively from the training data we supply, our gameplay and fun are commercialized in a way that we benefit from as we're rewarded for work. Maybe this is where the whole Metaverse concept comes to life?

The concept requires some refinement, but the general idea of creative entrepreneurs introducing games or other mechanisms for users in the real world to participate in the development of key infrastructure and the long-term commercialization of this infrastructure—whether through airdrops or other incentive models—is intriguing.

If this is an area you're building in—the trojan horse model of driving natural user behaviors to generate data or other generated value from a core product or service with the ability for those users to ultimately share in the commercialization of their work, I'm eager to hear what you're building.

With the latest release of the iPhone 15, I am reminded of the early days of smartphones and the religious and ideological debates that occurred when people of different tribes talked about their phones of choice.

You were either an iPhone, Android, or Blackberry person. You identified with the choice between these operating systems to some degree and the people who made the same choice alongside you. There were ideological disagreements about open vs. closed ecosystems and walled gardens or security. But soon, the App Store had millions of apps, and the “walled garden” argument about iOS was mainly irrelevant to end users. iOS and Android built out credible security platforms to compete with Blackberry, and apps started highlighting the unique value of an actual touchscreen, so the Blackberry crowd finally converted - even if it took a while to get used to not having a keyboard at first.

Photo by Randy Lu on Unsplash

The best example of this I can recall is when my dad finally converted to an iPhone from Blackberry. He was golfing with a friend who, for $25, had downloaded the GolfShot app and showed him how he could use the phone mapping and GPS to track his distance to the hole while on the course instead of a $300 Garmin device. My dad has been glued to his iPhone ever since. It was the demonstration of the tangible capabilities of this new operating system and technology platform that drove the adoption at the application layer - not someone touting the technical sophistication of the device.

The revolutionary GolfShot for iOS

Fast forward 15 years, and there is no ideological debate about Android vs. iOS. People just buy a phone that they like for whatever reason. We have reached operational parity across these devices and operating systems. People focus entirely on the apps they use, not the operating system running them.

A similar religious dynamic has existed over the past few years in the crypto and blockchain space. You could hardly escape the din in the ecosystem debating which Layer 1 blockchain was superior and why you should use XYZ app because it was built on Ethereum vs. Solana (or Avalanche/Near/Cosmos/Polkadot…). These Layer 1 maximalists (”maxis”) argued over the religious choices between more or less decentralization, optimistic versus ZK rollups, and speed versus security. It was all too similar to those early days of the iPhone, and I think many people in the industry wasted a lot of time and energy focusing entirely on the wrong things.

In reality, no user should ultimately care about what blockchain someone built on as a means of differentiation versus the competition. In fact, many of these businesses were cutting off their nose to spite their face, not recognizing the insular audience of crypto users they were targeting on [insert blockchain here] was only limiting their potential user base to their fellow believers and specifically not focusing on the actual goal of creating a product that can appeal to as many users in society at large as humanly possible.

Bankless: Circa 2021

We’re finally seeing this dynamic subside a bit on the religious blockchain front for many of the same reasons we saw the debates decline in the mobile wars. All blockchains are trending towards some level of parity in terms of speed, reliability, security, and cost. People will build where they do because of the unique qualities the blockchain represents that appeal to their development objectives.

As the ideological battles in the blockchain sector fade away, we find ourselves in an exciting moment, similar to the transformative period experienced during the rise of smartphones based on the explosion of app-driven utility. The focus has shifted from debating blockchain protocols to developing user-centric applications, marking a promising phase of practical innovation. This transition creates opportunities within the crypto realm and broader technological and financial landscapes.

Most people I speak with do not doubt the potential of blockchain technology in theory - they just haven’t seen people apply it valuably in practice. The key now is for developers, investors, and industry stakeholders to pivot away from the focus on the ideology of the various blockchains and systems towards fostering solutions that resonate with mainstream users, thereby demonstrating the tangible potential of blockchain technology.

The convergence of creative entrepreneurs highlighting the applications that can uniquely be built on this new platform and the pragmatic presentation of these solutions will propel the blockchain ecosystem into a future where technology serves the many, not just a few - just as we ultimately saw in the maturation of the mobile phone era in the early 2010s. As an investor in the space, it is an exciting time to invest. We are seemingly on the verge of significant user-centric blockchain advancements - and if you’re building the next business with GolfShot-level influence on the adoption of this technology, I am excited to see how we can work together.

Thanks for reading Factor Capital! Subscribe for free to receive new posts.

Historically, the most prevalent onramps to the blockchain ecosystem have been financial in nature. Exchanges and stablecoin bridges like Circle's USDC have predominantly facilitated the conversion of fiat currency to digital assets, allowing people to invest, speculate, or transact in a decentralized manner. This specific onramp has been valuable in bringing capital into the market, and I already see a number of exciting startups using this stablecoin platform to create efficiencies in cross-border exchange, remittances, and access to USD exposure in emerging markets where costs and regulations make it otherwise challenging or prohibitive to access.

However, capital will only remain in the market and have a place to invest if new utility is created in digital assets.

As the blockchain universe expands, there's a burgeoning need for a new set of onramps that transcend currency and unlock a vast array of business applications. In fact, according to a report by McKinsey & Company, blockchain's value-add to business is estimated to exceed $3.1 trillion by 2030**. But what are these new onramps, and how do they serve to enrich the business landscape?

Firstly, let's consider financial data. Anyone who's ever applied for a mortgage knows the pain of this market, where information needs to be collected and entered from dozens of sources and submitted for review to prove assets, income, etc. and it is still highly imprecise. Existing technologies allow for basic financial data to be imported into blockchain networks, but the next evolution will feature more comprehensive financial histories and account statuses. For any deeply embedded crypto user, one can easily trace their digital financial twin and gain a clear picture of the financial activity, (for example here is Coinbase CEO, Brian Armstrong). Over time let's assume the specifics of their activity become abstracted through Zero Knowledge Proof or other privacy mechanisms that obscure the specific actions, we still will have the ability to gain incredible insight into a user's financial profile in real time. The potential market here is enormous; the global business process outsourcing market for financial services was valued at approximately $30 billion in 2021***. This bridge can allow businesses to execute real-time financial decision-making, such as dynamic pricing based on a client's financial health, significantly cutting down on the cost and time involved in risk assessments.

Recruitment is another lucrative market that could benefit from blockchain onramps. Companies spend an average of $4,000 to fill an open position****. By pulling verified professional histories and skills from networks like LinkedIn, businesses today can streamline the recruitment process and improve the quality of hires. Now let's imagine instead of relying on personally reported data on LinkedIn, every single professional accomplishment and achievement was verifiably committed to an immutable record (employee of the month, promotions, code commits, product contributions, etc.) where it was able to be made accessible to employers, recruiters, and partners. These onramps pave the way for human capital optimization algorithms that can even predict the future success of candidates based on multiple data touchpoints and a truly complete set of professional acumen versus what we rely on today.

With the global healthcare market size expected to reach $12.1 trillion by 2022****, the significance of health data cannot be understated. Secure bridges that bring verified health records onto the blockchain can revolutionize personalized healthcare. Pharmaceutical companies and insurers can develop more precise health plans, boosting the efficacy of treatments and potentially saving lives. Let's imagine your Apple Watch streaming data not exclusively to your Apple Health record but instead to a privately accessible data repository that you uniquely own and can verify on-chain. Blockchain onramps create a secure way of storing and verifying health records, enabling citizens to take control of their own health data. From here, the potential for artificial intelligence models to analyze vast swaths of data can be tapped into with the assurance that all data is verifiable and immutable and doctors are immediately empowered to make superior health recommendations to their patients based on all of the information that's available, not just what they directly collect.

The IoT market is forecasted to reach $1.39 trillion by 2026. IoT data onramps can feed real-time information from smart appliances, vehicles, and homes into blockchain networks. Coupled with transactional data from everyday activities, this opens up avenues for predictive consumer behavior models, fundamentally altering sectors like retail and utilities. One specific application of this is in the renewable energy market where companies like Jasmine are bridging renewable energy certificates on-chain to create more efficient energy markets pulling data directly from utilities and solar panels with companies like React Network plugging into your home energy systems as well.

Another less-discussed but highly critical onramp is legal and compliance data. The global market for Governance, Risk Management, and Compliance is expected to reach $64.6 billion by 2025*****. Just like the personal finance digital twin discussed above, companies can perform real-time due diligence during acquisitions and partnerships, significantly reducing risk and expediting transactions. We are already seeing green shoots in this area with states like West Virginia recently creating a nationwide digital vehicle title registry. As more of these types of digital assets are created and adopted the design space explodes for new efficiencies to be explored in digitizing marketplaces for assets of all types.

The development and widespread adoption of these onramps do face challenges, primarily around data protection and standardization. However, given the potential benefits, businesses and regulatory bodies are incentivized to find common ground and pioneer these bridges. The investment potential in powering the creators of these bridges is really exciting, as well as the opportunities to partner with founders who see unpredictable opportunities to leverage these bridges to construct new businesses that are significantly more efficient than existing markets through the uniqueness of the underlying technology.

While blockchain's beginnings were deeply rooted in the financial domain, its potential stretches far beyond. By creating a network of onramps that feed into a decentralized digital twin, blockchain promises to redefine business in ways we are just starting to comprehend. It's not just about currency anymore; it's about constructing an entire ecosystem that can drive unparalleled business innovation and efficiency.

---

**: McKinsey & Company, "Blockchain's Occam problem," 2019

**: Statista, "Global outsourcing market size," 2021

***: SHRM, "Average Cost-per-Hire for Companies Is $4,129," 2016

****: Deloitte, "2022 Global Health Care Outlook," 2022

Photo by Mariia Shalabaieva on Unsplash

AI has clearly captured the narrative in the tech world and mainstream tech coverage broadly. The accessibility of the technology through tools like ChatGPT is easy for the public to experiment with and grab ahold of. The potential for the continued evolution of these tools is easy to get excited about (and terrified by, depending upon who you speak with. What is equally exciting from an investment standpoint, yet far less focused upon, is the potential impact that blockchain technology can have in catalyzing this field and the sets of solutions that can be explored with AI and machine learning models beyond what LLMs are demonstrating today.

Subscribe now

Perhaps the leading idea that has been explored here surrounds the potential to solve the critical supply constraints in the market for the latest generation GPUs that are at the forefront of this acceleration of AI development by creating decentralized networks of computational resources. Demand for these newest generation chips is so acute that you now see venture funds reportedly investing $100M into their communal data centers as an enticement for founders to take their capital and access these scarce resources.

The past few years have seen companies like Render, primarily focused on graphic rendering via decentralized GPUs, Akash, and recently launched Gensyn, which enables anybody to provide access to compute resources for use in ML training and other computationally intensive activities all emerge to attempt to address this supply problem when it comes to accessing computing resources.

Another way we’ve seen businesses look to solve these computational bottlenecks is the Filecoin Virtual Machine (FVM). By functioning as an operating system for the Filecoin network, it allows more intricate operations to occur - including processing data directly at its source by AI models. This could greatly boost the development of AI and create new opportunities to harness its capabilities. The FVM also plays well with Ethereum, and AI systems built on Ethereum can tap into Filecoin's storage capabilities effortlessly, creating more innovation potential. It doesn’t grant builders access to the scarce GPU resources actively sought-after today. But it exemplifies some of the innovation occurring in the data and compute space as AI and data science continue to accelerate.

While decentralized GPU networks, used to train AI models, are an intriguing idea, but they have challenges and advantages. One of the main difficulties is related to the speed and reliability of data transfer. Since the GPUs are spread across different locations, sending data between them can sometimes be slow and uncertain, potentially slowing down the AI training process and impacting its effectiveness.

Another challenge is data security and privacy. Considering a network of GPUs strung together globally, ensuring that every GPU location is secure and trustworthy in a decentralized network can be difficult without a set design and compliance standard assigned to the network participants regarding the environments they enter into the network. Without some assurances of certain security standards, concerns arise about the safety and privacy of the data being processed.

Despite these challenges, decentralized GPU networks have their advantages. They are particularly beneficial when the focus is on preventing potential data censorship or when there's a need to ensure the utmost privacy of the data involved- i.e., a scenario where centralized actors like AWS, GCP, or Azure can censor or provide outside access to the data running through their systems. In these cases, the decentralized nature of the network provides a unique advantage by making it harder for any single party to control or access the data.

Lastly, it's important to note that running a decentralized GPU network requires significant computational power and energy. For some tasks, traditional centralized solutions (where all the data and processing power are located in one place) might be a more cost-effective and efficient choice.

So, while decentralized GPU networks come with some technical difficulties, they also offer unique benefits, and one can see a pathway to where these challenges are alleviated and where this model could have legs under the right design and oversight. Long term I would bet on some form of this model being a real alternative to the centralized systems we have access to today.

During my 4+ year stint at Two Sigma, I learned the immense value of data in constructing exclusive models that helped generate alpha. I also noticed an intriguing paradox: while copious amounts of data are up for analysis, there is just as much invaluable data out there securely stored away in corporate data centers and restricted databases (like HIPAA-compliant EMRs or corporate ERP systems).

For example, Electronic Medical Records (EMR) represent perhaps one of the more exciting design spaces considering how blockchain-based data models can be combined with cryptographic techniques to unlock new possibilities in AI and healthcare research. Current estimates are that any individual patient produces more than 80 Mb/year of electronic imaging and medical records data annually, which equates to >26 petabytes (26 million GB) of data in just the United States alone.

Bringing more data to the blockchain could break down these data silos, unlock sensitive data currently protected by privacy concerns and corporate secrets, and potentially address issues such as bias in AI and privacy concerns to ensure that AI systems are transparent and trustworthy.

These advancements could pave the way for a new era of responsible AI that respects individual rights and data privacy, demonstrating blockchain technology's potential in catalyzing AI and machine learning models.

It is worth exploring three main approaches to decentralized data analysis and AI that could uniquely benefit from decentralized approaches enabled by blockchain technology. These ideas include Multi-Party Computation (MPC), Zero Knowledge (ZK) Proofs, and Federated Learning.

Multi-Party Computation (MPC) is a cryptographic technique that enables joint computation on private inputs from multiple individuals. It is a powerful tool in the realm of blockchain for leveraging sensitive data to perform calculations in a secure and efficient distributed environment. Researchers across the globe can participate and provide their own data securely through MPC.

MPC can immensely benefit healthcare institutions striving to collaborate on scientific research while preserving the privacy of sensitive data from researchers at other institutions. Specific proprietary datasets can allow hospital systems to generate insightful analyses from larger datasets than they could access individually. By overcoming critical privacy limitations, MPC could pave the way for medical breakthroughs and foster collaborative innovation in various fields.

The second technique is Zero Knowledge (ZK) Proofs, which allow one party to prove to another that they know a value x without conveying any information apart from the fact they know the value x. In the context of AI and EMR data, ZK proofs can be used to verify the integrity and authenticity of the data without revealing the data itself. This can be used to ensure that AI models are trained on valid and relevant data while preserving the privacy of individual patients. Here we can imagine a scenario where one could reliably index vast medical records and identify individual datasets matching specific criteria without access to the underlying datasets. This could be especially interesting in the context of clinical trial recruitment or other research activities, and companies like Genobank provide examples where blockchain-based models for collecting, storing, and verifying genetic data while putting the power of this data in the hands of the individual instead of other third-party institutions.

The third technique is Federated Learning, which is not a cryptographic technique but can be combined with MPC and ZK proofs to train AI models on decentralized data. In federated learning, the AI model is trained on each user's device, and only the model updates (not the actual data) are sent back to the server. This allows the AI to learn from a large dataset without the data ever leaving the user's device. J.P. Morgan’s internal Onyx Blockchain team has demonstrated some unique applications here, a primary example of how this combination has unique potential in financial data applications.

Federated learning is also exciting in the context of “X to Earn” models that have proven to be successful in the Decentralized Physical Infrastructure Network (DePIN) market, where we could see collaborative modeling efforts rewarding individual researchers with shared ownership of IP they generate, verified cryptographically by attaching their signatures and identity to their work and then collectively sharing in the monetization of this work. In contrast, traditional research models have resulted in IP being held at a centralized entity level, as with OpenAI and other research labs.

Oasis Protocol is another group in this area, developing solutions for responsible AI, addressing fairness and bias in AI models, pipelines for AI that protect individual data, and flexible confidentiality tools for NFTs. They are also building infrastructure for Data DAOs to support individual data rights, reward data owners for data use, and handle data with confidentiality and privacy, with verifiable transparency in its operations. They or an upstart could emerge as a critical piece of this decentralized data and AI stack.

While these techniques provide strong privacy guarantees, they also come with computational and complexity costs. MPC and ZK proofs, in particular, can be computationally intensive, making them slower than traditional methods. Additionally, implementing these techniques requires careful attention to security to ensure that they don't introduce new vulnerabilities.

But by applying these privacy-preserving tools like MPC, ZK proofs, and federated learning, researchers can analyze sensitive EMR data without compromising privacy. These tools can pave the way for revolutionary blockchain-based solutions and broaden the applicability of generative AI models in healthcare and other privacy-focused fields. As the field advances, we can expect these techniques to become increasingly widespread, fuelled by the evolution of blockchain and hardware infrastructure. Costs will likely decrease, making these technologies more accessible and efficient.

As AI permeates all creative processes, it's becoming increasingly critical to differentiate between reality and what's generated through generative models like ChatGPT, Stable Diffusion, and an exponentially expanding set of generative tools.

Identity in the context of AI is of increasing concern as we see numerous examples of deep fakes emerging and generative AI leading to where it becomes increasingly difficult to ascertain whether everything from content to images to audio has originated from a human-generated source or a generative AI model. Proving authenticity will be just as consequential as the potential unlocked by using AI in society.

This image spread on Twitter and sent stocks tumbling.

Blockchains enable us to verify the validity of data and its provenance from either a generative model, a human contributor, or a content producer. Furthermore, this provenance and cryptographic proof of origin is similarly important when we consider the models that we’re seeing created globally - it will be important at some point soon for one to demonstrate not just whether the content you produce or consume came from a human or an AI - but also which AI model(s) were used to generate the content to ensure they aren’t generated by known malicious sources or biased actors. Yes, things are getting complicated…

We can utilize this provable identity model embedded in the immutable nature of blockchains to have all types of data and IP verifiably linked to their creators, which is critical in both assigning ownership and validity of data as well as combating misinformation that is likely to arise in greater volume online through the use of these powerful AI tools.

While much of the investment world has seemingly migrated away from blockchain and focused on AI as the next exciting area of investment, it is important to recognize the investment opportunities that are emerging in the blockchain ecosystem that specifically benefit from this renewed focus on AI and could be amazing accelerants to the work being done across the range of generative AI businesses that are emerging daily. They can also help us navigate an increasing set of challenges brought on by generative AI as it becomes a more ubiquitous element of our daily life.

At Factor, we’re actively looking to invest in founders building with this intersection in mind, as it is an area of specific interest and expertise. We hope to see more founders who share in the optimism that these technologies can be so complimentary and potentially impactful for society.

Thanks for reading - Subscribe for free to receive new posts and follow our investments and research.

Historically, applications with largely crypto-native impact rather than real-world impact dominate the blockchain development ecosystem. Crypto skeptics often cite this dynamic as why blockchain and Web3 are unimportant. In reality, this experimentation is a feature, not a bug. 

For blockchain technology to gain widespread adoption, this early experimentation has been critical in establishing widely used solutions to problems faced by crypto early adopters. Solutions like decentralized exchanges, money market protocols, NFTs, and DAOs have all successfully demonstrated strong product market fit within the ecosystem. Each successful experiment establishes a new proof of concept, inevitably inspiring new technologies, businesses, and services. These new applications take the concepts pioneered by “version 1.0” a step further to engage with a broader audience and open up new waves of adoption and possibilities.

Thanks for reading Factor Capital! Subscribe for free to receive new posts and support my work.

We have seen this with multiple primary technology cycles over the past 30 years. With the early internet, the first development of websites opened the door to e-commerce, online chat, digital advertising, and other early primitives. E-commerce spectacularly ballooned in the late 90s, leading to the collapse of numerous highly valued startups, like Pets.com and hundreds of others. But in their failure, they demonstrated people would come online, and they will buy things online. This behavior offered a fantastic proof of concept that was perfected by some of the companies of that era and after, like Amazon, eBay, and others, to where e-commerce is so ubiquitous we don’t even consider it a thing anymore. It is just shopping.

In the early days of mobile, hardware giants like Palm, Blackberry, Motorola, and others recognized a growing desire for more versatile mobile computing experiences. However, they were limited to a niche adoption among certain groups, such as bankers and consultants needing on-the-go email access. These pioneering companies served merely as a proof of concept. Nevertheless, their innovation would ultimately pave the way for Apple and Android to revolutionize the user experience and change the world forever.

Even with iOS and Android, the early days of mobile applications were experiments for early adopters of these new mobile devices, testing the capabilities of this new platform. Apps like Foursquare gamified the ability to show you where your friends were in the city on a map in real-time, playfully demonstrating mobile location awareness without any meaningful utility. But it wasn’t until Uber emerged as the defining breakthrough mobile app that people truly embraced the power of location awareness to create game-changing, mainstream applications that made the phone suddenly become a remote control for the world around you and inspired a whole industry of on-demand businesses utilizing mobile technology.

The crypto and blockchain ecosystem is now at a similar inflection point. The proof of concept phase has shown the critical elements of the platform that have product-market fit. It is time for the next wave of businesses to emerge beyond the early adopter crowd for mass-market applications. A handful of candidates here have emerged here that I expect will lay the groundwork for significant innovations in Web3 over the coming years:

• Decentralized Infrastructure Networks (DePIN) - demonstrated by Helium by deploying over 1M LoRaWan hotspots globally through crypto incentives.

• DAOs enable the global coordination of workers around a shared mission without the burdens or restrictions of traditional corporate formation and employment.

• Democratizing finance, offering any user access to types of financial products and services historically reserved for corporations and ultra-high net worth individuals

• NFTs as a new paradigm in tokenizing assets of all types to unlock new possibilities of ownership and value creation

Helium has been a fantastic proof of concept around the idea that you can globally coordinate individuals to perform collective action for shared benefit through crypto incentives. The network has deployed nearly a million hotspots globally, allowing users to “mine” tokens by providing “proof of coverage” for their LoRaWan network.

Helium's IoT Coverage

Much has been made about the declining rewards to deployers on the network. But this issue is more a function of the supply/demand imbalance of the chosen technology than anything structurally incorrect with Helium’s infrastructure incentivization model. The simple proof of their ability to catalyze the supply of resources in a global, physical network, based on this token model, is being adopted now in numerous markets by Helium and others. In these new markets, demand is already well established from prior industry models, and supply has historically been prohibitively expensive from a capital expenditure point of view for incumbent industries. Examples include 5G microcells, drone imagery, and renewable energy deployments, to name a few. As each new vertical is tested here, industries emerge on top of the successes and will create whole new categories of economic participation; the same way gig workers benefited from the “Uber-fication” of everything over the past decade.

In the crypto world, DAOs are the ubiquitous, amorphous coordinating standard of decentralized protocols. Decentralized Autonomous Organizations represent a new take on the corporation, without geographical borders or nexus and no constraints on their ability to hire, compensate and coordinate workers around shared missions or objectives. 

DAOs have become a catalyst for decentralized software development teams, empowering them to create decentralized applications while being remunerated in native governance tokens or fiat from the DAO treasuries. Approval, done democratically by the governance token holders (akin to shareholders in a traditional corporation), is crucial to this process.

This concept has great potential when applied across wider industries. In the same way that Helium utilized a token model to establish a bandwidth supply on their wireless network, DAOs have emerged to incentivize suppliers of research or data to join DAOs focused on decentralized research. Some ongoing examples are KlimaDAO for climate change, VitaDAO and Molecule for medical research, and Delphia for data science.

DeSci is in the early stages of testing product market fit

The DAO incentivizes individuals and corporations to provide data or work for the DAO through fiat grants or token distributions. The resulting impact of the contributors’ work, as it accrues to the DAO through monetizing this work and data, is effectively owned by token holders. This model creates many opportunities for decentralized coordination of real-world workers and assets for shared economic benefit.

DeFi has been the most significant success and the biggest failure of Web3 to date. The solutions created over the past few years demonstrate the massive potential for decentralized and democratized access to finance while focusing almost exclusively on facilitating speculation and wholly crypto-native benefits.

But the success of this proof of concept lies in the democratization of various forms of finance that would otherwise be entirely inaccessible to average investors or individuals. For example, today, one can easily take out an overcollateralized loan on their home equity - but few other assets they hold can be used as collateral in the same way one can as a corporation or ultra-high net worth investor can. UHNW investors can routinely borrow against illiquid or liquid equity positions in their private banking accounts, for example. Corporations and individuals can explore interest rate swaps and use complicated hedging techniques to mitigate personal financial exposure to rates, commodities, and other esoteric markets. Until now, this has been inaccessible to average individuals.

Parcl's real estate market

With decentralized finance, there is a legitimate opportunity to engage in financial tools with real-world applications to personal financial circumstances. For example, one could leverage a decentralized exchange to buy or sell exposure to specific housing markets based on prediction markets or decentralized oracles of offline real estate data. A homeowner might look to lock in an interest rate swap on dollars to hedge against rising interest rates on their adjustable-rate mortgage. These opportunities are near impossible to access in today's traditional banking industry. Using more user-focused applications of decentralized finance, I expect this type of financial democratization is just the tip of the iceberg, especially as NFTs and tokens become more ubiquitous assets in society.

Additionally, entirely new markets, like Renewable Finance (ReFi), are emerging where no legacy incumbent exists to disrupt. Many people agree that if you were to build NASDAQ or the NYSE from scratch today, they would be built on blockchain technology and leverage these ledgers as the immutable system of record for trade settlement and asset issuance. But the legacy systems will not be ripped and replaced by this new technology since too much-existing infrastructure investment has taken place, and entire industries are built around this sunk cost.

Jamie Dimon has been one of the loudest critics of crypto, but J.P. Morgan is still investing in developing blockchain solutions for financial offerings.

With ReFi, there is no incumbent, dominant exchange, primarily because these asset classes are so new to the market. With the lack of legacy tech to deal with, they can be built from scratch using the best available technology stack, which today is decentralized ledgers like Ethereum or its alternatives like Polygon, Solana, NEAR, etc. Without legacy incumbents to disrupt, the path to market accelerates, and new efficiencies can be realized to accelerate the growth of these important financial markets.

NFTs are probably the most well-known frenzy of crypto speculation, with profile pictures of Bored Apes and CryptoPunks trading at more than $1M each at the peak of the 2021 market. While the common meme was that you could right-click and save these images, the reality was the aggressive adoption of these NFTs firmly established product market fit for scarce and unique digital goods. Individuals paid seven figures for an item they could verifiably own, not controlled by a trusted third party. They had copyright ownership of the asset and any derivative work from its use. They also were effectively members of an exclusive club, one of only a few thousand people worldwide who could claim to own these scarce goods. This club membership gave them access to real-world events, experiences, and social standing online.

The evolution of this model to more mainstream applications is readily apparent. NFTs represent a valuable, non-fungible asset that creators can embed with unique qualities - scarce physical attributes or discrete rights to cash flows, experiences, underlying physical goods, or financial assets. The innovation in this context has already begun, but we have yet to come close to scratching the surface of this new platform for ownership that is only made possible by using blockchain technology.

Jasmine Energy is introducing new energy markets built on top of tokenized Renewable Energy Certificates (RECs)

Soon, expect to see the tokenization of illiquid commodities, alternative assets, security interests, and even concert tickets or fan club memberships. The ownership and patronage model we know today will likely be wholly upended as artists and influencers of all stripes can uniquely achieve a direct-to-fan/audience interaction and monetize their work or influence in new ways over the coming years with these new digital goods.

Beyond these early adopter proofs of concept, more will emerge and evolve with massive real-world impact. In these new versions, the crypto-native elements of these products and services will be secondarily important as an enabling technology that underpins their utility. The tokenization of more assets will combine with decentralized finance to give ordinary individuals much more financial flexibility and opportunities to monetize their time, data, and other assets creatively through these Web3 models. Combined with the improvements we’ve seen in decentralized infrastructure, it feels like we’re on the cusp of a massive explosion where Web3 will soon directly and positively impact the lives of ordinary individuals for the first time. As this occurs, it should heal the bruised reputation the industry has suffered and prove the value of blockchain and Web3 to the skeptics, often citing the lack of these real-world advances. In subsequent writing, I’ll expand the discussion more into the above categories.

Thanks for reading Factor Capital! Subscribe for free to receive new posts and support my work.

The emergence of blockchain technology has revolutionized the way we perceive and trade assets. It has enabled us to tokenize traditional assets and create digital assets not bound to geographical boundaries or regulatory frameworks. Tokenization offers many opportunities for investors, startups, and businesses looking to expand their offerings and access new markets.

Having seen countless examples of experimentation with tokenization, I have developed a framework for tokenizing various asset classes, including stocks, real estate, commodities, artwork, and more which I think guides the proper model for approaching this new opportunity space.

I believe three main qualities apply when judging whether an asset is appropriate for tokenization. They are:

1. The ability to create a digital native asset independent of any offline or off-chain asset. For instance, if we tokenize the ownership of a baseball card, it could provide a more efficient method of tracking its authenticity and trade history. However, if the card gets destroyed in a fire, there is no straightforward way of associating this unfortunate event with the digital asset in a precise and verifiable manner. Instead, we should look for assets that can be effectively "retired" or "burned" offline and fully reproduced with superior qualities through tokenization. Tickets may be a good example here where the physical ticket or mobile ticket market could easily be seen migrating to NFTs with promoters or artists placing inalterable rights on these tokens (max price, min price, secondary royalty, whitelisted addresses, etc.), which are virtually impossible to enforce in the current model.

2. The ability to construct a market that does not require a massive rip and replace incumbent participants. Transitioning the US Treasury or Equity markets to a tokenized utopia would require a significant change since the current financial system has existed for centuries. The challenge would be to convince established markets and stakeholders to embrace new technology without obvious benefits to them. On the other hand, in the renewable energy sector, we are dealing with a market that mostly serves early adopters and has an immature, inefficient, and low-tech existing market. This particular market has great potential for transitioning towards a tokenized future without requiring complete replacement of the currently established infrastructure.

3. UX improvement through tokenization. In this context, we focus on assets whose value relies heavily on their origin, accompanying data, ownership, and rarity level. For instance, when it comes to a piece of art, it's crucial to ensure that the artist genuinely created it and that it's part of an exclusive 1/100 edition with specific scarcity features. These elements are essential in determining the asset's worth and are why it's an ideal candidate for tokenization.

Some of the most popular categories of tokenization today, I feel, miss the mark - seeming more like a hammer searching for a nail than a truly native application of tokenization that meaningfully improves upon a problem. Tokenized treasuries, for example, address a problem that today serves just a small audience of crypto-native investors seeking access to higher yields than in risky DeFi markets. But the treasury market is unlikely to migrate on-chain anytime soon, meaningfully. Further, you're dealing with an asset of well-established provenance and minimal value from what amounts to a virtual avatar of an off-chain asset rather than a true replacement with a better version.

So, following the framework laid out above, what are a few examples where this model can be or has been implemented with more likely near-term success? I have seen a few examples that show promise:

Real Estate

Real estate is a massive market, but it has been traditionally inaccessible to most investors due to high transaction costs, lack of liquidity, and regulatory restrictions. Real estate tokenization can change all this by creating digital shares or tokens representing real estate asset ownership. Tokenization enables fractional ownership, enabling investors to buy and sell a fraction of a property, reducing the investment amount required to a fraction of the property's total value. It also enables the creation of a secondary market for real estate, enhancing liquidity and creating new investment opportunities.

It is important to note that when we talk about the tokenization of real estate, we are not talking about the tokenization of properties themselves; rather, we are discussing the tokenization of either the title of ownership in a manner that maintains real-world legal rights or the creation of a digital-native organization which would own the offline title and has its shares as the tokenized asset. Companies such as Homebase are interesting examples, experimenting with this model in its early stages.

Creator tokenization

Art and collectibles are some of the most valuable individual assets, fetching nine-figure valuations for some of the most expensive items. Still, their illiquidity often limits their value, and the industry is fraught with fraud and counterfeiting. Tokenization can create new opportunities for investing in art and collectibles by creating digital tokens representing ownership or contribution to a particular piece of art or collectible. Tokenization also simplifies the process of fractional ownership for a massive art collection, enabling investors to purchase and trade digital tokens representing a share of the collection. It also can mitigate the risks we often see from the counterfeit markets.

However, while the tokenization of artwork has been the mainstream force driving the narrative on NFTs and tokenized assets to date, the more exciting model here may be the concept of fan tokens. With fan tokens, we introduce a new model of tracking and investing in a creator and capitalizing on their success while actively offering rights to the token holder to the future outputs of the content creator. One example might be an emerging musician or painter who engages with their early fan base to offer a limited number of fan tokens at a nominal price, giving those token holders the right of first access to in-person experiences (concerts, art shows, etc.) or creative work (limited edition physical artwork runs). As an artist grows in popularity, this scarce access becomes increasingly valuable - as does the creative work they're producing or experiences they're offering. Here, both the creator and their audience benefit from the growth and success of the artist in a way that has not previously been possible.

Intellectual Property Tokenization

Intellectual property is an asset class with tremendous potential, but it is often difficult to value and monetize. Tokenization can increase accessibility and liquidity to intellectual property by creating digital tokens representing ownership or participation in a particular intellectual property. This can also create revenue-sharing models between creators and investors, making monetizing their intellectual properties easier for content creators. Two areas are especially notable in the context of IP.

First, music royalties, where companies like Royal and Anotherblock have partnered with royalty rights holders to sell the future revenue stream from their royalty rights through a fractionalized asset in the form of NFTs, offer a unique way for artists and collaborators to monetize their commercial rights. Here, the NFT holders get a yield-bearing asset tied to the value of the underlying royalty stream. Experiments are being conducted in scientific research with Intellectual Property (IP) forms such as IP-NFTs. These tokens represent the IP generated from scientific research, creating a more transparent and accessible market for patents and trademarks that can be used for drug discovery or AI modeling.

Renewable Energy Assets

The tokenization of carbon credits creates digital tokens representing credits derived from carbon offsetting projects. This offers a new market for carbon credits, making it possible for low-income investors or new market entrants to invest in carbon credit projects that were previously inaccessible. It also makes it easy for larger stakeholders to manage portfolios of carbon credits while avoiding administrative burdens.

Through the composability of these digital assets and the pre-existing models created in Decentralized Finance (DeFi), we can see how this climate market could easily become a truly digital native, on-chain asset class for real-world assets.

Conclusion:

The tokenization of new asset classes opens up new investment opportunities and creates a democratization of investment opportunities for both LPs and founders alike. The potential benefits of tokenization of these asset classes include enhanced liquidity, increased accessibility, and democratization, which can lead to increased investment in the asset classes and better returns for investors. It is an exciting time for the tokenization space as we continually see improvements and developments in tokenizing new asset classes. Feel free to get in touch if you're working on any of the above models or exploring other assets that could be ripe for tokenization.

Historically, applications with largely crypto-native impact rather than "real-world impact" have dominated Web3. Crypto skeptics often cite this dynamic as the reason why Web3 is unimportant. In reality, the real-world impact that's occurred is massive ($1T market cap), and this experimentation and development solving crypto-native problems is a feature and a catalyst, not a bug. 

For Web3 to gain widespread adoption, this early experimentation has been critical in establishing a series of widely used solutions to problems faced by crypto early adopters. Solutions like decentralized exchanges, money market protocols, NFTs, and DAOs have all successfully demonstrated strong product market fit within the ecosystem. Each successful experiment establishes a new proof of concept, which inevitably inspires the creation of new technologies, businesses, and services. These new applications take the concepts pioneered by “version 1.0” a step further to engage with a broader audience and open up new waves of adoption and possibilities.

We have seen this with multiple primary technology cycles over the past 30 years. With the early internet, the first development of websites opened the door to e-commerce, online chat, digital advertising, and other early primitives. E-commerce spectacularly ballooned in the late 90s, leading to the collapse of numerous highly valued startups, like Pets.com and hundreds of others. But in their failure, they demonstrated people would come online and they will buy things online. This behavior offered a fantastic proof of concept that was perfected by some of the companies of that era, and after, like Amazon, eBay, and others, to where e-commerce is so ubiquitous we don’t even consider it a thing anymore. It is just shopping.

In the early days of mobile, on the hardware side, companies like Palm, Blackberry, Motorola, and others proved that people craved more fully featured mobile computing experiences. But they were limited to the early adopter crowd who needed to get email on the go, like bankers and consultants. They served as proof of concept that (combined with the iPod’s successful proof of concept as a portable music player for the masses) Apple, and later Android, leveraged to perfect the user experience and change the world forever. 

Even with iOS and Android, the early days of mobile applications were experiments for early adopters of these new mobile devices, testing the capabilities of this new platform. Apps like Foursquare gamified the ability to show you where your friends were in the city on a map in real-time, playfully demonstrating mobile location awareness without any meaningful utility. But it wasn’t until Uber emerged as the defining breakthrough mobile app that people truly embraced the power of location awareness to create game-changing, mainstream applications that made the phone suddenly become a remote control for the world around you and inspired a whole industry of on-demand businesses utilizing mobile technology.

Web3 is now at a similar inflection point. The proof of concept phase has shown the critical elements of the platform that have product-market fit. It is time for the next wave of businesses to emerge beyond the early adopter crowd for mass-market applications. A handful of candidates here have emerged here that I expect will lay the groundwork for significant innovations in Web3 over the coming years:

• Decentralized Infrastructure Networks (DePIN) - demonstrated by Helium through their deployment of over 1M LoRaWan hotspots globally through crypto incentives

• DAOs enable the global coordination of workers around a shared mission without the burdens or restrictions of traditional corporate formation and employment.

• Democratizing finance, offering any user access to types of financial products and services historically reserved for corporations and ultra-high net worth individuals

• NFTs as a new paradigm in tokenizing assets of all types to unlock new possibilities of ownership and value creation

DePIN

Helium has been a fantastic proof of concept around the idea that you can globally coordinate individuals to perform collective action for shared benefit through crypto incentives. The network has deployed nearly a million hotspots globally, allowing users to “mine” tokens by providing “proof of coverage” for their LoRaWan network.

Helium's IoT Coverage

Much has been made about the declining rewards to deployers on the network. But this issue is more a function of the supply/demand imbalance of the chosen technology than anything structurally incorrect with Helium’s infrastructure incentivization model. The simple proof of their ability to catalyze the supply of resources in a global, physical network, based on this token model, is being adopted now in numerous markets by Helium and others. In these new markets, demand is already well established from prior industry models, and supply has historically been prohibitively expensive from a capital expenditure point of view for incumbent industries. Examples include 5G microcells, drone imagery, and renewable energy deployments, to name a few. As each new vertical is tested here, industries emerge on top of the successes and will create whole new categories of economic participation; the same way gig workers benefited from the “Uber-fication” of everything over the past decade.

DAOs

In Web3, DAOs are the ubiquitous, amorphous coordinating standard of decentralized protocols. Decentralized Autonomous Organizations represent a new take on the corporation, without geographical borders or nexus and no constraints on their ability to hire, compensate and coordinate workers around shared missions or objectives. 

The application of DAOs has been a catalyst for decentralized software development teams to produce decentralized applications and be compensated in native governance tokens or fiat from the DAO treasuries, subject to democratic approval by the governance token holders (the equivalent of shareholders in a traditional corporation). This proof of concept has significant potential when applied in a broader range of industries.

Similar to the way that Helium bootstrapped supply for bandwidth on their wireless network through their token model, DAOs have now sprung up to incentivize suppliers of data or research to join DAOs committed to decentralized research around climate change (KlimaDAO), medical research (VitaDAO, Molecule), and data science (Delphia). 

DeSci is in the early stages of testing product market fit

The DAO incentivizes individuals and corporations to provide data or work for the DAO through fiat grants or token distributions. The resulting impact of the contributors’ work, as it accrues to the DAO through monetizing this work and data, is effectively owned by token holders. This model creates a host of opportunities for decentralized coordination of real-world workers and assets for shared economic benefit.

Financial Markets

DeFi has been the most significant success and the biggest failure of Web3 to date. The solutions created over the past few years demonstrate the massive potential for decentralized and democratized access to finance while focusing almost exclusively on facilitating speculation and wholly crypto-native benefits.

But the success of this proof of concept lies in the democratization of various forms of finance that would otherwise be entirely inaccessible to average investors or individuals. For example, today, one can easily take out an overcollateralized loan on their home equity - but few other assets they hold can be used as collateral in the same way one can as a corporation or ultra-high net worth investor can. UHNW investors routinely can borrow against illiquid or liquid equity positions they hold in their private banking accounts, for example. Corporations and individuals can explore interest rate swaps and engage in complicated hedging techniques to mitigate personal financial exposure to rates, commodities, and other esoteric markets. Until now, this has been inaccessible to average individuals.

Parcl's real estate market

With decentralized finance, there is a legitimate opportunity to engage in financial tools with real-world applications to personal financial circumstances. For example, one could leverage a decentralized exchange to buy or sell exposure to specific housing markets based on prediction markets or decentralized oracles of offline real estate data. A homeowner might look to lock in an interest rate swap on dollars to hedge against rising interest rates on their adjustable-rate mortgage. These opportunities are near impossible to access in today's traditional banking industry. Using more user-focused applications of decentralized finance, I expect this type of financial democratization is just the tip of the iceberg, especially as NFTs and tokens become more ubiquitous assets in society.

Additionally, entirely new markets, like Renewable Finance (ReFi), are emerging where no legacy incumbent exists to disrupt. Many people agree that if you were to build NASDAQ or the NYSE from scratch today, they would be built on blockchain technology and leverage these ledgers as the immutable system of record for trade settlement and asset issuance. But the legacy systems will not be ripped and replaced by this new technology since too much-existing infrastructure investment has taken place, and entire industries are built around this sunk cost.

Jamie Dimon has been one of the loudest critics of crypto, but J.P. Morgan is still investing in developing blockchain solutions for financial offerings.

With ReFi, there is no incumbent, dominant exchange, primarily because these asset classes are so new to the market. With the lack of legacy tech to deal with, they can be built from scratch using the best available technology stack, which today is decentralized ledgers like Ethereum or its alternatives like Polygon, Solana, NEAR, etc. Without legacy incumbents to disrupt, the path to market accelerates, and new efficiencies can be realized to accelerate the growth of these important financial markets.

NFTs

NFTs are probably the most well-known frenzy of crypto speculation, with profile pictures of Bored Apes and CryptoPunks trading at more than $1M each at the peak of the 2021 market. While the common meme was that you could right-click and save these images, the reality was the aggressive adoption of these NFTs firmly established product market fit for scarce and unique digital goods. Individuals paid seven figures for an item they could verifiably own, not controlled by a trusted third party. They had copyright ownership of the asset and any derivative work from its use. They also were effectively members of an exclusive club, one of only a few thousand people worldwide who could claim to own these scarce goods. This club membership gave them access to real-world events, experiences, and social standing online.

The evolution of this model to more mainstream applications is readily apparent. NFTs represent a valuable, non-fungible asset that creators can embed with unique qualities - scarce physical attributes or discrete rights to cash flows, experiences, underlying physical goods, or financial assets. The innovation in this context has already begun, but we have yet to come close to scratching the surface of this new platform for ownership that is only made possible by the use of blockchain technology.

Jasmine Energy is introducing new energy markets built on top of tokenized Renewable Energy Certificates (RECs)

Soon, expect to see the tokenization of illiquid commodities, alternative assets, security interests, and even concert tickets or fan club memberships. The ownership and patronage model we know today is likely to be wholly upended as artists and influencers of all stripes can uniquely achieve a direct-to-fan/audience interaction and monetize their work or influence in new ways over the coming years with these new digital goods.

Conclusion

Beyond these early adopter proofs of concept, more are sure to emerge and evolve with massive real-world impact. In these new versions, the crypto-native elements of these products and services will be secondarily important as an enabling technology that underpins their utility. The tokenization of more assets will combine with decentralized finance to give ordinary individuals much more significant financial flexibility and opportunities to monetize their time, data, and other assets creatively through these Web3 models. Combined with the improvements we’ve seen in decentralized infrastructure, it feels like we’re on the cusp of a massive explosion where Web3 will soon directly and positively impact the lives of ordinary individuals for the first time. As this occurs, it should heal the bruised reputation the industry has suffered and prove the value of blockchain and Web3 to the skeptics, often citing the lack of these real-world advances. In subsequent writing, I’ll expand the discussion more into the above categories.

For Web3 to gain widespread adoption, this early experimentation has been critical in establishing a series of widely used solutions to problems faced by crypto early adopters. Solutions like decentralized exchanges, money market protocols, NFTs, and DAOs have all successfully demonstrated strong product market fit within the ecosystem. Each successful experiment establishes a new proof of concept, which inevitably inspires the creation of new technologies, businesses, and services. These new applications take the concepts pioneered by “version 1.0” a step further to engage with a broader audience and open up new waves of adoption and possibilities.

We have seen this with multiple primary technology cycles over the past 30 years. With the early internet, the first development of websites opened the door to e-commerce, online chat, digital advertising, and other early primitives. E-commerce spectacularly ballooned in the late 90s, leading to the collapse of numerous highly valued startups, like Pets.com and hundreds of others. But in their failure, they demonstrated people would come online and they will buy things online. This behavior offered a fantastic proof of concept that was perfected by some of the companies of that era, and after, like Amazon, eBay, and others, to where e-commerce is so ubiquitous we don’t even consider it a thing anymore. It is just shopping.

In the early days of mobile, on the hardware side, companies like Palm, Blackberry, Motorola, and others proved that people craved more fully featured mobile computing experiences. But they were limited to the early adopter crowd who needed to get email on the go, like bankers and consultants. They served as proof of concept that (combined with the iPod’s successful proof of concept as a portable music player for the masses) Apple, and later Android, leveraged to perfect the user experience and change the world forever. 

Even with iOS and Android, the early days of mobile applications were experiments for early adopters of these new mobile devices, testing the capabilities of this new platform. Apps like Foursquare gamified the ability to show you where your friends were in the city on a map in real-time, playfully demonstrating mobile location awareness without any meaningful utility. But it wasn’t until Uber emerged as the defining breakthrough mobile app that people truly embraced the power of location awareness to create game-changing, mainstream applications that made the phone suddenly become a remote control for the world around you and inspired a whole industry of on-demand businesses utilizing mobile technology.

Web3 is now at a similar inflection point. The proof of concept phase has shown the critical elements of the platform that have product-market fit. It is time for the next wave of businesses to emerge beyond the early adopter crowd for mass-market applications. A handful of candidates here have emerged here that I expect will lay the groundwork for significant innovations in Web3 over the coming years:

• Decentralized Infrastructure Networks (DePIN) - demonstrated by Helium through their deployment of over 1M LoRaWan hotspots globally through crypto incentives

• DAOs enable the global coordination of workers around a shared mission without the burdens or restrictions of traditional corporate formation and employment.

• Democratizing finance, offering any user access to types of financial products and services historically reserved for corporations and ultra-high net worth individuals

• NFTs as a new paradigm in tokenizing assets of all types to unlock new possibilities of ownership and value creation

Helium has been a fantastic proof of concept around the idea that you can globally coordinate individuals to perform collective action for shared benefit through crypto incentives. The network has deployed nearly a million hotspots globally, allowing users to “mine” tokens by providing “proof of coverage” for their LoRaWan network.

Much has been made about the declining rewards to deployers on the network. But this issue is more a function of the supply/demand imbalance of the chosen technology than anything structurally incorrect with Helium’s infrastructure incentivization model. The simple proof of their ability to catalyze the supply of resources in a global, physical network, based on this token model, is being adopted now in numerous markets by Helium and others. In these new markets, demand is already well established from prior industry models, and supply has historically been prohibitively expensive from a capital expenditure point of view for incumbent industries. Examples include 5G microcells, drone imagery, and renewable energy deployments, to name a few. As each new vertical is tested here, industries emerge on top of the successes and will create whole new categories of economic participation; the same way gig workers benefited from the “Uber-fication” of everything over the past decade.

In Web3, DAOs are the ubiquitous, amorphous coordinating standard of decentralized protocols. Decentralized Autonomous Organizations represent a new take on the corporation, without geographical borders or nexus and no constraints on their ability to hire, compensate and coordinate workers around shared missions or objectives. 

The application of DAOs has been a catalyst for decentralized software development teams to produce decentralized applications and be compensated in native governance tokens or fiat from the DAO treasuries, subject to democratic approval by the governance token holders (the equivalent of shareholders in a traditional corporation). This proof of concept has significant potential when applied in a broader range of industries.

Similar to the way that Helium bootstrapped supply for bandwidth on their wireless network through their token model, DAOs have now sprung up to incentivize suppliers of data or research to join DAOs committed to decentralized research around climate change (KlimaDAO), medical research (VitaDAO, Molecule), and data science (Delphia). 

The DAO incentivizes individuals and corporations to provide data or work for the DAO through fiat grants or token distributions. The resulting impact of the contributors’ work, as it accrues to the DAO through monetizing this work and data, is effectively owned by token holders. This model creates a host of opportunities for decentralized coordination of real-world workers and assets for shared economic benefit.

DeFi has been the most significant success and the biggest failure of Web3 to date. The solutions created over the past few years demonstrate the massive potential for decentralized and democratized access to finance while focusing almost exclusively on facilitating speculation and wholly crypto-native benefits.

But the success of this proof of concept lies in the democratization of various forms of finance that would otherwise be entirely inaccessible to average investors or individuals. For example, today, one can easily take out an overcollateralized loan on their home equity - but few other assets they hold can be used as collateral in the same way one can as a corporation or ultra-high net worth investor can. UHNW investors routinely can borrow against illiquid or liquid equity positions they hold in their private banking accounts, for example. Corporations and individuals can explore interest rate swaps and engage in complicated hedging techniques to mitigate personal financial exposure to rates, commodities, and other esoteric markets. Until now, this has been inaccessible to average individuals.

With decentralized finance, there is a legitimate opportunity to engage in financial tools with real-world applications to personal financial circumstances. For example, one could leverage a decentralized exchange to buy or sell exposure to specific housing markets based on prediction markets or decentralized oracles of offline real estate data. A homeowner might look to lock in an interest rate swap on dollars to hedge against rising interest rates on their adjustable-rate mortgage. These opportunities are near impossible to access in today's traditional banking industry. Using more user-focused applications of decentralized finance, I expect this type of financial democratization is just the tip of the iceberg, especially as NFTs and tokens become more ubiquitous assets in society.

Additionally, entirely new markets, like Renewable Finance (ReFi), are emerging where no legacy incumbent exists to disrupt. Many people agree that if you were to build NASDAQ or the NYSE from scratch today, they would be built on blockchain technology and leverage these ledgers as the immutable system of record for trade settlement and asset issuance. But the legacy systems will not be ripped and replaced by this new technology since too much-existing infrastructure investment has taken place, and entire industries are built around this sunk cost.

With ReFi, there is no incumbent, dominant exchange, primarily because these asset classes are so new to the market. With the lack of legacy tech to deal with, they can be built from scratch using the best available technology stack, which today is decentralized ledgers like Ethereum or its alternatives like Polygon, Solana, NEAR, etc. Without legacy incumbents to disrupt, the path to market accelerates, and new efficiencies can be realized to accelerate the growth of these important financial markets.

NFTs are probably the most well-known frenzy of crypto speculation, with profile pictures of Bored Apes and CryptoPunks trading at more than $1M each at the peak of the 2021 market. While the common meme was that you could right-click and save these images, the reality was the aggressive adoption of these NFTs firmly established product market fit for scarce and unique digital goods. Individuals paid seven figures for an item they could verifiably own, not controlled by a trusted third party. They had copyright ownership of the asset and any derivative work from its use. They also were effectively members of an exclusive club, one of only a few thousand people worldwide who could claim to own these scarce goods. This club membership gave them access to real-world events, experiences, and social standing online.

The evolution of this model to more mainstream applications is readily apparent. NFTs represent a valuable, non-fungible asset that creators can embed with unique qualities - scarce physical attributes or discrete rights to cash flows, experiences, underlying physical goods, or financial assets. The innovation in this context has already begun, but we have yet to come close to scratching the surface of this new platform for ownership that is only made possible by the use of blockchain technology.

Soon, expect to see the tokenization of illiquid commodities, alternative assets, security interests, and even concert tickets or fan club memberships. The ownership and patronage model we know today is likely to be wholly upended as artists and influencers of all stripes can uniquely achieve a direct-to-fan/audience interaction and monetize their work or influence in new ways over the coming years with these new digital goods.

Beyond these early adopter proofs of concept, more are sure to emerge and evolve with massive real-world impact. In these new versions, the crypto-native elements of these products and services will be secondarily important as an enabling technology that underpins their utility. The tokenization of more assets will combine with decentralized finance to give ordinary individuals much more significant financial flexibility and opportunities to monetize their time, data, and other assets creatively through these Web3 models. Combined with the improvements we’ve seen in decentralized infrastructure, it feels like we’re on the cusp of a massive explosion where Web3 will soon directly and positively impact the lives of ordinary individuals for the first time. As this occurs, it should heal the bruised reputation the industry has suffered and prove the value of blockchain and Web3 to the skeptics, often citing the lack of these real-world advances. In subsequent writing, I’ll expand the discussion more into the above categories.