Last year, the top centralized exchanges cleared $86.2T in perp volume (+47% YoY), while onchain perpetuals grew even faster: Leading decentralized exchanges reached $6.7T (+346% YoY). DEX volume now represents roughly 7.8% of CEX volume, up from about 2.5% just a year earlier. [Note: while a small number of U.S.-regulated centralized platforms offer products similar to perpetual futures contracts to U.S. persons, all centralized and decentralized exchanges restrict U.S. persons’ access to true perpetual futures contracts.]

But the bigger story here is that perps are starting to look less like a fringe crypto primitive and more like a fundamental shift in trading behaviors and market structure.

So what is driving perps’s popularity? And why now? This post looks at why perps are increasingly embraced by traders globally, the scale of the market opportunity, and where builders see opportunity.

A brief history and evolution of perps

The idea itself is actually older than the crypto industry. Perps have existed in theory since 1993, when Nobel Prize-winning economist Robert Shiller introduced the perpetual futures contract, which he originally envisioned as a tool for hedging property-value risks. But perps weren’t popularized in crypto until 2016 with the rise of BitMEX and XBTUSD, the longest-running bitcoin perpetual swap.

A decade later, modern exchanges now offer perpetual futures contracts on equities, indices, commodities, interest rates, startup valuations, and even Nvidia H100 GPU prices.

Perps have been a billion-dollar revenue engine for centralized exchanges for years. As retail appetite for leverage has grown, perps have become a primary venue for short-term price discovery, liquidity, and trading activity — trading multiples more volume than spot on many major Asian centralized exchanges (CEXs).

What’s changed over the last year and a half is that decentralized perp exchanges have started meaningfully eating into centralized exchanges’ share of the perp market. With self-custody as a structural advantage, perp DEXs are rapidly narrowing the gap with CEXs in liquidity, performance, and features for active traders.

With the breakout success of perp DEXs like Hyperliquid, leading crypto wallets and apps rushed to support perps and shipped high-quality trading experiences that made them accessible to millions of users. The latter half of 2025 brought an explosion of perp DEX front-ends — from casual mobile apps to sophisticated, multi-venue trading terminals.

Hyperliquid, in particular, has pushed the boundaries of what DEXs can offer with HIP-3 (Builder-Deployed Perpetuals), a mechanism that allows anyone to permissionlessly launch perp markets on the exchange. With HIP-3, builders can list almost any asset and earn a 50% fee share while managing their own oracles and risk parameters.

At the same time, newer entrants and competitors like Avantis, Lighter, Ostium, and Variational emerged or accelerated product development. More competition forced perp DEXs to differentiate across exchange design, market structure, asset support, and permissionlessness, and helped a few trading venues find strong product-market fit in new categories, like real-world asset (or RWA) perps.

For years, perp traders speculated solely on crypto assets — BTC, ETH, SOL, and a long tail of alts. But late last year, while perp volume largely cooled off from their recent peaks amidst the broader crypto sell-off, RWA perps gained steam. A handful of perp DEXs listed commodities, equities, and equity indices, expanding the universe of tradable assets to include everything from NVDA and Samsung to private companies like SpaceX, as well as commodities like silver and palladium.

This year, the growth of RWA perps has only accelerated. In recent weeks, RWAs have made up as much as 44% of Hyperliquid’s total volume, and RWA pairs are now consistently among the highest fee-generating pairs on the exchange. On Ostium, RWAs have made up the lion’s share of the exchange’s volume for months.

Decentralized exchanges have also excelled in facilitating price discovery for RWAs like crude oil, especially on the weekends when traditional exchanges are closed.

As RWA perps have taken off, we’ve seen a surge in companies building perps-related products and offerings. The last 6 months alone have brought new exchanges, trading interfaces, market deployers, and liquidity providers.

The players rushing into this category include brand-new startups, startups pivoting toward perps, and some of the largest fintech companies in the world building perp trading into their existing products.

All of these different players are converging on the same opportunity: Perps could become one of the dominant trading instruments in global finance.

The market opportunity for perps

Taking a step back to look at TradFi, options are among the biggest and most actively traded markets on earth. They exist across currencies, equities, indices, commodities, ETFs, and they’re extremely powerful and expressive instruments that enable trading based on many different beliefs: timing, volatility, price ranges, and more.

But when you zoom in on retail trading behavior, a lot of activity is concentrated in one particular options category: short-dated, levered, directional exposure. One prominent example is 0DTE — zero-day-to-expiry options — where traders buy cheap convexity for an intraday move.

This type of trading is one of the fastest-growing options categories. In 2025, average daily volume in 0DTE SPX (S&P 500) options reached 2.3M contracts per day, up 51% year-over-year and representing 59% of total SPX options volume. Several new index products with daily expirations were introduced following this demand, including CBTX and MBTX Bitcoin ETF index options, and options on the equal-weighted Cboe Magnificent 10 index.

So while options have many sophisticated uses — structured hedging, vol trading, dispersion, convexity, etc. — a very large and growing share of retail flow is just looking for short-term, leveraged directional exposure. This type of exposure is exactly the kind of demand perps serve best.

The tradeoff is real: options excel at defined-risk, convex payoffs, and remain the default instrument for volatility expression. The most a trader can lose is their premium. With a perp, the entire collateral position can be liquidated. But for what most retail traders actually want — directional leverage — perps have several structural advantages:

1. Always on. The newest generation of perp markets trades 24/7 with no market hours or session gaps. For a global, crypto-native user base, continuous access is the expectation.

2. No strikes, no expiries, no rolling. With a single continuous position, traders don’t have to select parameters, manage expirations, or reestablish trades every day or week. They can hold for seconds, months, or theoretically forever.

3. Simpler risk surface. With perps, the primary considerations are price, collateral, and liquidation threshold. With options, even if you’re right on direction, you can lose due to theta decay, shifts in implied volatility, and path dependency. Perps strip that complexity away. The trade is directional conviction, expressed cleanly.

4. Capital efficiency for sustained exposure. Short-dated options require paying the full premium upfront and rolling repeatedly. Perps require margin — often a small percentage of notional — which is typically more capital efficient for intraday-to-multi-day directional positions.

Options aren’t going away. They have long been a part of financial history and will likely remain dominant for a meaningful portion of trading use cases, especially where defined risk and more complex payoff structures matter. But for the large and growing flow that’s looking for delta-one, directional leverage, perps are already capturing trillions in volume and billions in revenue.

This raises the question: Where in the stack does value accrue as perps move from niche instruments to mainstream trading primitives?

In traditional markets, the most valuable companies were often built around exchange infrastructure, not at the exchange layer itself. For example, Robinhood, a retail broker, commands a higher market cap than Nasdaq, Inc., the exchange Robinhood sits atop.

Whether that pattern holds in crypto — where platforms like Hyperliquid, Lighter, or Ostium are accruing strong enough network effects at the exchange layer — is one of the most interesting open questions in the space.

Either way, builder activity is expanding rapidly. A few areas where we see developer growth:

• Opinionated distribution layers: Vertical or audience-specific front-ends that package narratives, strategies, gamification, or social hooks instead of just presenting markets.

• Market creators and operators (e.g., HIP-3 deployers): Operating a hit market on Hyperliquid allows deployers to essentially own a mini-exchange without having to build the most sophisticated exchange infrastructure. Today’s deployers are likely just scratching the surface of data or price feeds that may be “perpified”.

• Specialized liquidity provision: Market makers that focus on long-tail markets, event-driven books, and cross-venue inventory management.

• Perp-specific data infrastructure: There’s already an emerging ecosystem of community-driven dashboards, block explorers, heat maps, and analytics around positioning, funding rates, liquidations, trader signals, leverage exposure, retention cohorts, and more. More established, high-quality, real-time data makes the entire ecosystem more transparent and efficient for all parties involved.

Of course, there are significant open questions and challenges, ranging across distribution, liquidity depth on newer venues, oracle reliability as the asset universe expands, inevitable edge cases like “10/10,” and regulation, which currently restricts access to these products for U.S. persons. These are expected growing pains as perps graduate from their crypto-native bubble to the main stage of global finance. As the perps ecosystem matures, the question is no longer whether perps will scale; it’s who will build the most valuable applications and infrastructure around them as they do.

Editorial Note: Perpetual futures contracts are currently regulated as derivatives under the U.S. Commodity Exchange Act and may only be offered to U.S. persons through CFTC-registered designated contract markets. As noted above, a small number of U.S.-regulated platforms offer products similar to perpetual futures contracts to U.S. persons, while most centralized exchanges and all decentralized exchanges restrict U.S. persons’ access to such products. The exchanges, platforms, and products discussed in this article — Avantis, BitMEX, Hyperliquid, Lighter, Ostium, and Variational — are not available to U.S. persons.

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Among them:

• Full-text search across every SEC filing ever made, charged per query

• CAPTCHA solving for agents who hit verification walls designed for bots

• Physical letters printed and mailed from a document and an address

• Image generation from fal.ai across over 600 AI models at fractions of a cent per request

The protocol powering this marketplace is the Machine Payments Protocol (MPP), from Stripe and Tempo, which lets agents pay using cards, stablecoins, or Lightning in a single HTTP request. In its first week, 894 agents executed over 31,000 transactions across the directory at prices ranging from $0.003 to $35 per request.

None of these services have a checkout page. Their catalogs are machine-readable schemas. Pricing is embedded in their HTTP responses. Agents read schemas, send requests, pay, and receive outputs in a single exchange.

A merchant used to be a storefront. Even as commerce moved online, the pattern stayed the same: product images, a checkout page, a confirmation email. In e-commerce, “headless” meant decoupling the frontend from the backend. In the new agentic economy, headless means eliminating the frontend entirely.

This is the headless merchant: a business with no storefront, no accounts, and no sales team. Just a server, a set of endpoints, and a price per call.

The payment rails that make this possible are now live. x402 and MPP each take a different approach, but both embed payments directly into HTTP requests. Visa’s CLI tool extends card rails to the terminal. These are the primitives that power headless merchants.

Why headless merchants are different

Stand up a traditional software business and you need a website, a checkout flow, user accounts, customer service, subscription management, a billing system, and a sales team or marketing budget to acquire customers. A headless merchant needs a good API and a thin layer of middleware. That is the business.

This matters because of who the buyer is. An agent arrives with a task, a budget, and constraints. It evaluates the endpoint’s documentation, pricing, and reliability. If the service meets the criteria, it pays and moves on.

The payment is the authentication.

Simon Taylor calls this the “intention economy“: the agent arrives with intent already formed, and the merchant’s only job is to fulfill it.

This inverts how you think about building a business. The agent buyer will never see your website. It will see your API docs, your pricing, and your uptime. A headless merchant with clean docs and predictable pricing will almost always beat one with a beautiful website and a mediocre API.

Commerce used to happen in places: a store, a website, an app. Headless merchants move commerce to moments. The instant an agent needs a capability it doesn’t have, it transacts.

The model shift

Subscriptions amortize the cost of billing. Signing up, entering a card, choosing a tier, managing a renewal: all of that overhead exists because charging a human three-tenths of a cent for a single API call wasn’t practical. Agents can handle it. An agent can pay fractions of a cent per request, thousands of times a day, across dozens of services, without ever creating an account.

This changes which businesses are viable. A service that charges $0.003 per image generation or $0.01 per web scrape doesn’t need a sales team. It doesn’t need a free tier or worry about churn, because there is no subscription to cancel and no relationship to manage. It just needs to be good enough that when an agent evaluates its documentation and pricing, it gets chosen.

If you sell a service behind an API key and a subscription today, there is a version of that product that charges per request, requires no account, and is discoverable by any agent with a wallet. That version may reach customers your subscription product can’t, because the customer never would have signed up. The agent would have just moved on to the next endpoint.

Pay-as-you-go may replace the subscription for a growing class of services. Not because subscriptions are bad, but because the buyer no longer needs them.

The merchants are the story

I’ve recently argued that the next wave of commerce would be built by merchants choosing stablecoins over nothing, because traditional processors couldn’t underwrite them. Since then, the infrastructure has moved faster than expected. Card networks are extending their rails to agents. New protocols have emerged that support cards, stablecoins, and settlement models like per-session billing. The rails are no longer the bottleneck.

What matters now is the merchants. A headless merchant with a clean API, reliable output, and per-request pricing is a new kind of business, one with a cost structure that couldn’t have existed five years ago and a buyer base that didn’t exist a year ago.

The biggest opportunity in agentic commerce isn’t building the next payment rail. It’s building the headless merchants those rails were designed to serve. The next generation of merchants won’t have storefronts. They’ll have endpoints.

You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out anytime using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of investments.

As of this week, three states have officially enacted the relatively new “decentralized unincorporated nonprofit association” act — aka the DUNA. The DUNA provides legal status for decentralized organizations and limited liability protections for their members and administrators. By doing so, the DUNA gives these communities and builders the legal and structural certainty to build, govern, contract, and scale in the real world.

Wyoming was the first state to adopt the DUNA two years ago. [Notably, it was also the first state to adopt the unincorporated nonprofit association (UNA) and the first to adopt the limited liability company (LLC), which provided key protections and flexibility for business owners so they could unlock innovation. Think of the DUNA as a digital UNA optimized for the decentralized organizations building the future of the internet.]

Alabama signed the DUNA Act yesterday. And West Virginia just did too, with more states on the way. States have moved to the center of crypto policymaking, and this is forward-looking policymaking at its best: It embraces innovation, protects users, and empowers internet-native communities to compete with big tech incumbents. And it comes at a pivotal moment in the effort to make the U.S. the crypto capital of the world. As federal crypto market structure legislation moves closer to becoming law, builders need effective domestic legal structures.

Several prominent crypto organizations have also adopted the DUNA — including Uniswap Governance, Nouns DAO, Syndicate Network Collective, and more, with others coming.

But first, why does this matter?

Decentralized governance is essential to crypto’s future, and the DUNA provides a legal structure that fits decentralized organizations.

More specifically, DUNAs are legal entities that provide limited liability and other key protections to participants in a blockchain network, while allowing them to remain decentralized. Fundamentally, the DUNA enables blockchain networks to remain decentralized while complying with the law. It not only grants decentralized organizations like DAOs legal existence, but also allows them to:

• contract with regular businesses and appear in court;

• allows them to pay taxes; and

• equips them with key protections.

In short, the DUNA puts decentralized organizations on equal footing with other entity forms like corporations and the LLC.

Importantly, the DUNA allows decentralized organizations to innovate as blockchain networks, without forcing them into the top-down, centralized control structures of companies that depend on centralized management or hierarchies like officers and boards of directors. By contrast, the DUNA is the first and only legal structure to allow communities to govern the services they use, and leverage smart contracts to execute their decisions onchain.

The bigger picture here, however, is providing a more viable path for more decentralized networks and, therefore, builders of products and services within those networks. This is more important than ever — not just for the future of the crypto industry, but for countless other applications with crypto hidden/“blockchains inside” (like all things stablecoins and agent payments)… Especially in a time when technologies like AI and platforms are increasingly centralized.

Why decentralization matters: the big picture

“Decentralization” sounds like an ideology that only some kinds of people care about, but it actually impacts every one of us: Decentralization is at the heart of many issues dominating our conversations — whether it’s about who controls social media networks that influence our public discourse; who financial institutions choose to bank; who controls the AI tools increasingly dominating our world; and more.

This is because a handful of big corporations and platforms — all centralized — have monopoly-like power over the digital products and services we all use every day. And despite contributing to the very value those platforms provide to all their users (aka “network effects”), users don’t have a vote, choice, or any other way to control their destinies. All the value is extracted by big corporations; little value is left for users. (This is especially concerning for the creators and small businesses whose livelihoods depend on those platforms.)

But the reality is that, from an efficiency standpoint, centralization works — and it works very well. Much like gravity, centralization is a force that’s hard to resist: It allows companies to coordinate resources efficiently. It enables a single leader — or a couple of visionaries inside the same organization (like Steve Jobs and Jony Ive at Apple) — to more efficiently make decisions, sometimes resulting in better products. When an organization is centralized, it can move fast, take unilateral action, and reap the rewards. This is why centralization and consolidation are the norm today: See Big Banks, Big Tech, and so on.

By comparison, decentralization — transferring control and power to distributed groups — has been inefficient, at least until now. It’s like a rocket ship: To achieve lift-off, decentralization requires immense energy, effort, and engineering to overcome the natural order. But once a decentralized system achieves escape velocity, its cumulative network effects can be far more powerful than any centralized organization’s could be.

But breaking free requires a boost, and that’s why decentralization needs incentives.

Enter the DUNA. The problem the DUNA solves is that decentralized organizations haven’t had a legal structure that natively fits how they (vs. centralized companies) operate.

Decentralized organizations have been forced into ‘foundations’ until now

Foundations have long dominated organizational design in crypto. In crypto’s early days, many founders turned to nonprofit foundations out of a sincere belief that these entities would help foster decentralization. The foundations were meant to serve as neutral stewards of network resources, holding tokens and supporting ecosystem growth without direct commercial interests.

Foundations were intended to help crypto network builders differentiate their projects from ordinary companies, by promoting “credible neutrality” for long-term public benefit without explicit commercial interest. Foundations also provided a convenient solution to the regulatory challenge posed by control-based decentralization: Builders could diffuse ongoing development work via foundations so that no single management team could be seen as driving a blockchain network’s value.

In the best cases, foundations delivered on their promise: They diffused risk and fostered decentralization. Some foundations were a boon to the growth and development of the networks they supported, staffed by committed individuals doing difficult and incredibly valuable work under challenging constraints.

But in most cases, foundations created new problems — introducing opacity, increasing inefficiency, misaligning incentives, limiting growth, and entrenching centralization. (Nowhere has this been more on display than with offshore foundations: Prohibitive structuring costs and convoluted independence mandates meant that most startups could not realistically comply, and therefore forced several legitimate builders to move outside the United States.)

Regulatory dynamics and increasing market competition diverted the foundation model away from its original conception. Crypto founders abandoned, obscured, or otherwise removed involvement in the very networks they created. Increased competition further incentivized projects to look to foundations as a shortcut. Many foundations became convoluted workarounds to appear decentralized — something we’ve also called “decentralization theater” — rather than mechanisms for actually achieving decentralization.

With the United States moving towards legislative clarity, the separation and fiction of foundations is no longer necessary. A control-based framework encourages founders to relinquish control without forcing them to abandon or obscure their ongoing building. It also provides a less amorphous (and less abusable) definition of decentralization to build towards. With this pressure lifting, the industry can finally move beyond workarounds and toward structures better built for long-term sustainability, like the DUNA.

Foundations served a purpose. But they are no longer the best tool for what comes next. To be valuable, networks need to build products and services with market and user feedback.

The DUNA allows blockchain-based networks to do this while also protecting the individuals participating in them.

Benefits of the DUNA

Currently, decentralized organizations that fail to use a legal structure for their organization are:

• deprived of legal existence,

• face uncertainty and difficulty in meeting tax and reporting obligations, and

• exposed to potentially limitless liability.

Alarmingly, without a legal entity, decentralized organizations are being alleged to be just like general partnerships. This classification would be calamitous for DAO members, subjecting them to huge tax risk and potentially significant legal liability.

A lack of legal entity also threatens the privacy of DAO members. If foisted on DAOs, certain traditional legal structures not set up for the concept of distributed, decentralized coordination (and therefore, relatedly, privacy) could require that DAO members reveal their identities, compromising them in several ways.

The DUNA provides decentralized organizations with legal existence, enabling them to contract with third parties, open bank accounts, pay taxes, meet reporting requirements, and more. For founders, the DUNA can be helpful because:

• Being a “nonprofit” doesn’t constrain your business. It unleashes it. By statute, DUNAs can engage in for-profit activities. They can pay reasonable compensation to members for participation. With the DUNA, commercial viability and legal protection go hand in hand, allowing decentralized communities to create value for their members and others.

• Market structure legislation will reward you for doing this now. Decentralized governance is a core construct in the legislation moving through Congress. The DUNA is the legal structure purpose-built to formalize that governance in a way legislators and regulators can work with. It’s the only legal structure recognized for decentralized governance in current drafts of the CLARITY Act. Founders who adopt the DUNA today, rather than waiting, will have a demonstrated, defensible governance record when that legislation passes. Those who delay will be scrambling to retrofit a structure onto a network that’s already operating in legal ambiguity.

• Paying taxes is a feature, not a bug. Without a legal entity, a DAO rests under the sword of Damocles: The unresolved and potentially catastrophic question of tax. The DUNA gives any organization the legal capacity to pay taxes and meet informational reporting requirements in the United States. Bringing a DAO into the domestic tax framework resolves one of the biggest operational and member risk questions hanging over decentralized organizations today.

What a DUNA is and isn’t

While DUNAs are legal entities, they are not analogous to foundations in any meaningful sense. They are not an entity structure designed to house employees, pursue objectives, or “run” an ecosystem.

Instead, DUNAs are best understood as a legal representation of token-based governance (voting, treasury management, and community decision-making). A DUNA is only responsible for the discrete actions that token holders are empowered to take via token-based governance, and it only acts when token holders act. For example, if token holders control a system’s treasury and have the ability to make distributions, then proposals, votes, and onchain execution can be treated as decisions and actions of the DUNA.

DUNAs are not well-suited for running a business. Where token holders have no rights to alter a system, the DUNA has nothing to “decide” and nothing to “do”. Accordingly, a DUNA is not a legal representation of the blockchain network to which it may relate but over which it has no control. And it is not responsible for how the blockchain network functions day to day. A blockchain network continues to operate pursuant to its protocol rules, independent of the DUNA.

Take this example: If a small number of ETH holders deposited their ETH into a smart contract that enabled them to govern how such assets were used, and they adopted a DUNA structure for their organization, that DUNA would have nothing to do with and would be wholly separate from the Ethereum network. The DUNA would have no say in the operations of such a network and no ability to affect it.

But, because they have legal personhood, DUNAs are well-suited for protecting the interests of token holders and aligning incentives with builders and other third-party market participants. For instance, DUNAs can own any intellectual property associated with the blockchain network and enforce those rights on behalf of token holders.

If passed, crypto market structure legislation will help end the charade of foundations, and incentivize the creation of better decentralized governance systems: greater transparency, rules, openness, collective action (vs. traditional management officers or boards like in a company), independence from centralized control, and more.

But even if you’re not interested in crypto, or are tired of hearing about blockchains, decentralization matters: Whether it’s specific crypto-based technologies or some different form in the future, we are clearly starting to defy the gravity of centralization. We know how to build the rockets. Decentralized systems can achieve unprecedented levels of coordination and operational functionality, while realizing the full potential of network effects. And now, after decades and even centuries of fighting gravity, it’s finally possible to have new forms of governance and decentralized organizations at scale; robust, decentralized economies and business models for tokens; community-owned-and-operated networks and services that benefit users, including beyond crypto; and countless other innovations.

Miles Jennings is Head of Policy & General Counsel for a16z crypto, where he advises the firm and its portfolio companies on decentralization, DAOs, governance, NFTs, and state and federal securities laws.

Aiden Slavin is Policy Partner for a16z crypto, supporting the advancement of the firm’s global web3 policy goals.

Editor: Sonal Chokshi

You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out anytime using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of investments.

After years on the sidelines, the institutions that form the backbone of global capital markets — exchanges, clearinghouses, and electronic trading platforms — are moving onchain.

What’s happening right now is the largest infrastructure upgrade in capital markets since the shift to electronic trading thirty years ago.

But most people won’t recognize this shift until it’s already done.

Why now: Velocity changes everything

Every institution moving in this direction believes the same thing — that onchain infrastructure will dramatically increase the velocity of money. History is unambiguous about what that produces.

Think about what electronic trading did in the 1990s: Before ECNs and online brokerages, a trade took minutes to execute, spreads were priced in fractions, and access was gated by geography and capital. Then the infrastructure changed. Spreads collapsed. Commissions fell from $150 to $9.95 to zero. Volume exploded. Retail participation surged. The markets of the 2000s were unrecognizable from those of the 1990s — not just cheaper, but vastly larger.

Tokenization applies that same logic to the entire global financial stack: 24/7 markets, instant settlement, seamless cross-border distribution, fractionalization of assets previously locked behind six-figure minimums, collateral that moves in real time instead of sitting idle overnight. More velocity. More participation. Bigger pie.

But what does tokenization actually mean? A tokenized asset is a digital representation of a real-world asset (RWA) — a Treasury bond, a share of Apple, a real estate deed — recorded on a blockchain as a programmable token. Instead of ownership tracked in a centralized database by a custodian during business hours in one time zone, a tokenized asset lives on-chain: transferable, programmable, and settleable instantly, anywhere in the world, at any time.

Instead of being a derivative, it’s the real thing — with better plumbing.

The institutions are already moving

In December 2025, DTCC received a No-Action Letter from the SEC authorizing it to tokenize real-world assets on approved blockchains. DTCC processed $3.7 quadrillion in transactions in 2024. It is now targeting a production tokenization service for U.S. Treasury securities in H1 2026.

On January 19, 2026, the New York Stock Exchange announced a platform for 24/7 on-chain trading and settlement of U.S. equities and ETFs — fractional shares, instant settlement, stablecoin funding — partnering with BNY and Citi to support tokenized deposits across ICE’s clearinghouses. The world’s most iconic stock exchange is going onchain.

Tradeweb executed the first real-time, fully on-chain financing of U.S. Treasuries against USDC in August 2025 — on a Saturday, outside of traditional settlement windows, alongside Bank of America, Citadel Securities, DTCC, and Virtu Financial. The scope expands every quarter and now includes cross border and intraday settlements. Nasdaq filed its own proposed rule change with the SEC in September 2025.

This is looking more and more like a migration, not a series of isolated experiments.

The hidden tax in the current system

There’s a second force driving all this: The existing market is structured around intermediaries, not markets.

Let’s look at a typical securities transaction: You pay the broker a spread. In an institutional transaction, the prime broker charges for financing. Exchanges and transfer agents take their pieces. The custodian charges for safekeeping. DTCC extracts fees across clearing, netting, and settlement. Even after the U.S. finally moved to T+1 settlement in 2024 — a reform that took decades, because it used to take several days — capital is still locked overnight as a “structural tax” on every participant.

Smart contracts and atomic settlement collapse the above stack. Now, two parties can transact instantly, on-chain, with finality.

The rent extraction in the existing system — its margin — doesn’t disappear… it becomes a new entrant’s opportunity. Their margin, in other words, is YOUR opportunity to build the new rails.

***

The final unlock is regulatory clarity — and it’s finally in motion. If current momentum continues, the CLARITY Act could do for traditional finance what the Genius Act already did for the adoption and acceleration of stablecoins.

The guardrails the largest institutions needed are already on the horizon. So what does this mean for builders?

The migration of the world’s financial infrastructure onchain will create demand for entirely new categories of products and services.

The incumbents moving fastest aren’t your competition — they’re your customers. DTCC doesn’t want to build the middleware. NYSE doesn’t want to build the compliance tooling. Tradeweb doesn’t want to build the cross-border distribution layer.

These companies are laying the regulated, institutional-grade foundation. Founders build everything that runs on top of it.

This is the same pattern as the 1990s. The exchanges didn’t build E*TRADE. They didn’t build Bloomberg. They didn’t build the order management systems and prime brokerage platforms that defined the next era. Those were built by founders who saw what was coming.

More participants, faster velocity, lower friction.

More liquidity. Larger markets.

History is clear on where this ends.

The window to build foundational infrastructure in tokenized financial markets is open now. Build accordingly.

Sources: DTCC No-Action Letter · NYSE Tokenized Platform · Tradeweb 24/7 Treasury Repo · CLARITY Act

You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out anytime using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of investments.

Beyond raw throughput, though, financial applications need predictability. When a transaction is sent — whether it’s a trade, an auction bid, or exercising an option — having a reliable guarantee on when that transaction will land is necessary to the functioning of financial systems. If transactions face unpredictable delays (whether adversarial or by happenstance) many applications become unusable. For onchain financial applications to be competitive, the chain must have short-term inclusion guarantees, where if a valid transaction is submitted to the network, it is guaranteed to be included as soon as possible.

For example, consider an onchain order book. Efficient order books require market makers to continuously provide liquidity by maintaining orders to buy and sell the assets on the book. The key problem market makers deal with is maintaining as tight a spread (the difference between their buy and sell prices) as possible while not opening themselves up to adverse selection by offering prices out of line with the rest of the market. To do this, market makers must constantly update their orders to reflect the state of the world. For instance, if a Federal Reserve announcement causes asset prices to jump, market makers need to instantly respond by updating their orders to the new price. Here, if a market maker’s transactions to update their orders don’t land instantly, they’ll take a loss by having arbitrageurs fill their orders at out-of-date prices. Market makers would then need to post larger spreads to decrease their exposure to such events, in turn making onchain venues less competitive.

Predictable transaction inclusion is what gives market makers strong guarantees on their ability to quickly react to offchain events and keep onchain markets efficient.

What we have versus what we need

Today, existing chains offer only robust guarantees of eventual inclusion, kicking in over the span of seconds. While these guarantees are good enough for applications like payments, they are too weak to support a large class of financial applications where market participants need to react to information in real time. Take the order book example above: For market makers, a guarantee that they will be included “in the next few seconds” is meaningless if arbitrageurs’ transactions can land in earlier blocks. Without strong inclusion guarantees, market makers have to account for the increased amount of adverse selection by widening their spreads and offering users worse prices. This in turn makes trading onchain less appealing compared to other venues that do offer stronger guarantees.

For blockchains to truly fulfill the vision of serving as the infrastructure to modernize capital markets, builders need to address these issues so that high-value applications like order books can thrive.

What is so hard about predictability?

Strengthening the inclusion guarantee for existing chains to support these use cases is challenging. Some protocols today may rely on one node (a “leader”) that can dictate transaction inclusion at any given time. While this simplifies the engineering challenge of building a performant chain, it also introduces a potential economic chokepoint where those leaders can extract value. Generally, for the window in which a node is elected as leader, they have complete power over what transactions are included in a block.

For a chain that handles any amount of financial activity, the leader holds a privileged position. If this single leader decides to not include any one transaction, the only recourse is to wait for the next leader that is willing to include that transaction. In a permissionless network, leaders are incentivized to extract value, colloquially known as MEV. MEV goes well beyond things like sandwiching AMM trades. Even just a leader’s ability to delay transaction inclusion by 10s of milliseconds could net them a large profit and degrade the efficiency of the underlying applications. An order book that prioritizes only a subset of traders’ transactions leaves everyone else on an unfair playing field. In the worst case, the leader can be so adversarial that traders leave the platform altogether.

Say there is a rate hike and the price of ETH immediately falls by 5%. Every market maker on an order book rushes to cancel their resting orders and to make new orders at the new prices. At the same time, every arbitrageur submits an order to sell ETH at the outdated standing orders. If this order book is being run on a protocol with a single leader, the leader has an outstanding amount of power. The leader could simply choose to censor all of the market maker cancels, thus allowing the arbitrageurs to massively profit. Or instead of outright censoring the cancels, the leader could delay the cancels until after the arbitrageurs land their transactions. The leader could even directly insert their own arbitrage transactions to fully capitalize on the price discrepancy.

A tale of two desiderata: The need for censorship resistance and hiding

In the face of these advantages, it becomes uneconomical for market makers to actively participate; whenever there is a price movement they might be taken advantage of. The issue boils down to the leader being overly privileged in two key ways: 1) The leader can censor transactions by anyone else, and 2) the leader can see others’ transactions and submit their own transactions accordingly in response. Either of these two issues can turn out to be catastrophic.

An example

We can nail down the issue precisely with the following example. Consider an auction that has two bidders, Alice and Bob, where Bob is also the leader for the block the auction happens in. (The fact that there are only two bidders is for illustrative purposes; the same reasoning applies regardless of how many bidders there are.)

The auction accepts bids over the duration it takes for the block to be produced, say from time t=0, to t=1. Alice submits a bid b_A at time t_A and Bob submits a bid b_B at time t_B > t_A. Since Bob is the leader for the block, he can always guarantee he moves last. Alice and Bob also have a continuously updating source of truth for the price of the asset that they can read from (e.g., the mid-point price on a centralized exchange). At time t, let this price be p_t. We assume that at time t, the expected price of the asset at time t=1 (when the auction concludes) is always p_t. That is, at any given time, the price both Alice and Bob expect the asset to be at when the auction concludes is equal to the price they currently see. The rules of the auction are simple: whoever has the higher bid out of Alice and Bob wins the auction and pays their bid.

The need for censorship resistance

Now let’s consider what happens when Bob can use his advantage from being the leader in this auction. If Bob can censor Alice’s bid, it’s clear that the auction falls apart. Bob can simply bid an arbitrarily small amount and be guaranteed to win the auction since there are no other bids. This causes the auction to clear with effectively 0 revenue.

The need for hiding

The more complicated case is what happens when Bob can’t outright censor Alice’s bid but can still see Alice’s bid before making a bid of his own. In this case, Bob has a simple strategy. When he bids, he simply checks whether p_tB > b_A. If so then Bob bids an amount just above b_A and if not then Bob doesn’t bid at all. By playing this strategy, Bob causes Alice to be adversely selected against. The only time Alice wins is when the price updates so that her bid ends up being higher than the expected value of the asset. Whenever Alice wins the auction, she would expect to lose money and be better off not participating in the auction at all. With all competing bidders gone, Bob can again simply bid an arbitrarily small amount and win with the auction getting effectively 0 revenue.

The key takeaway here is that it does not matter how long this auction takes. As long as Bob can either censor Alice’s bid or see Alice’s bid before he makes his own bid, the auction is doomed to fail.

The same principles from this example apply to any setting where assets are being traded at high frequency, whether that be spot trading, perps, or a derivatives exchange: If there is a leader with the power Bob has in this example, that leader can cause the market to fully unravel. For the onchain products serving these use cases to be viable, they must not grant the leader these powers.

How do these issues arise in practice today?

The story above paints a bleak picture for onchain trading on any permissionless single leader protocol. Nevertheless, decentralized exchange (DEX) volumes on many single leader protocols continue to be healthy, so what gives?

A combination of two forces in practice counteract the issues described above:

1. Leaders don’t fully exploit their economic power as they themselves are generally heavily invested in the success of the underlying chain, and

2. Applications have built workarounds to not be as vulnerable to these issues.

While these two factors have kept decentralized finance (DeFi) working so far, they will not be enough for onchain markets to truly be competitive with their offchain counterparts in the long run.

To be eligible as a leader on a chain with meaningful economic activity requires a large amount of stake. Thus either the leader owns a lot of stake themselves or has enough of a reputation for other token holders to delegate stake to them. In either case, large node operators are generally known entities with reputations at risk. Beyond just their reputation, this stake means these operators also have financial incentives for their chains to do well. Because of this, we largely haven’t seen leaders fully exploit their market power as written above — this doesn’t mean these issues aren’t a problem though.

For one, being reliant on node operators’ goodwill through social pressure and appealing to their long-term incentives isn’t a robust foundation for the future of finance. As the magnitude of onchain financial activity increases, the potential profits for leaders increases accordingly. The more this potential grows, the harder the strain on the social layer to keep leaders’ behavior against their immediate interests.

Second, the extent to which leaders can use their market power is a spectrum, from the benign to causing the market to fully unravel. Node operators can make unilateral pushes towards exploiting their power for higher profits. As some operators push the limits of what is considered acceptable, others quickly follow suit. An individual node’s behavior may seem insignificant, but when everyone changes, the impact is unmistakable.

Perhaps the best example of this phenomenon is with timing games: When leaders look to delay announcing a block until as late as possible while still being valid for the protocol to earn higher rewards. This can cause longer block times and blocks to be skipped when a leader is too aggressive. While the profitability of these strategies was widely known, leaders opted against playing these games primarily in the name of being good stewards of the chain. However, this was a weak social equilibrium. Once a single node operator started playing these strategies to earn higher rewards with no consequences, other operators quickly joined in. Timing games are just one example of how leaders can increase their profits without fully exploiting their market power. There are many other measures leaders can take to increase their rewards at the expense of applications. In isolation, these measures may be workable for applications, but eventually the scale tips to a point where the costs of being onchain outweigh the benefits.

The other factor that has kept DeFi functional is applications moving important logic offchain and only posting the results onchain. For instance, any protocol that needs to quickly run an auction does so offchain. These applications often run their required mechanisms on a permissioned set of nodes to avoid issues with adversarial leaders. For example, UniswapX runs its Dutch auction to fill trades on Ethereum mainnet offchain and similarly Cowswap runs its batch auction offchain. While this works for the applications, it puts the base layer and the value proposition of building onchain in a precarious position. A world where the execution logic for applications lives offchain makes the base layer purely used for settlement. One of the strongest selling points for DeFi is composability. In a world where all execution happens offchain, these applications inherently live in siloed environments. Relying on offchain execution also adds new assumptions to the trust models of these applications. Rather than just relying on the underlying chain to be live, this offchain infrastructure must also be up for the apps to function.

How to get predictability

To address these issues, there are two properties we need the protocol to satisfy: consistent transaction inclusion and ordering rules and transaction privacy before confirmation (For a rigorous definition of these properties and an extended discussion see https://arxiv.org/abs/2509.23984, esp. Definitions 9 and 11).

Desideratum #1: Censorship resistance

We encapsulate the first property by short-term censorship resistance. Where a protocol is short-term censorship resistant if any transaction that reaches an honest node is guaranteed to be included in the next possible block:

More precisely, we assume that the protocol operates on a fixed clock where every block is produced at set times, say every 100ms. Then we want the guarantee that if a transaction hits an honest node at t=250ms, it will be included in the block produced at t=300ms. An adversary should not have the discretion to selectively include certain transactions it hears about and leave out others. The spirit of this definition is that users and applications should have an extremely reliable way to land transactions at any point in time. It shouldn’t be the case that a single node happening to drop packets, whether due to malice or simple operational hiccups, causes a trade to not land.

While this definition requires inclusion guarantees for transactions reaching any honest node, in practice the overhead of achieving this may be too high. The important feature is that the protocol should be robust so that the entry points to land onchain behave in extremely predictable ways that are simple to reason about.

A permissionless single leader protocol clearly does not satisfy this property, because if the single leader at any point in time is Byzantine, there’s no other way to land a transaction. However, even a set of four nodes who can guarantee transaction inclusion in every slot greatly improves the amount of options users and applications have to land transactions. It’s worth it to trade off some amount of performance for a protocol that can reliably allow applications to thrive. There’s more work to be done on finding the right tradeoff between robustness and performance, but the guarantees from existing protocols aren’t enough.

Given that a protocol can guarantee inclusion, ordering comes somewhat for free. Protocols are free to use any deterministic ordering rule they like to guarantee consistent ordering. The simplest solution is to order by priority fee or perhaps to allow applications the flexibility to order transactions that interact with their state. The optimal way to order transactions is still an active area of research, but regardless, ordering rules only matter if the transactions to be ordered land.

Desideratum #2: Hiding

After short-term censorship resistance, the next most important property is for a protocol to provide a form of privacy we term hiding.

A protocol that is hiding, may allow for the nodes to see all the transactions submitted to them in plain text but requires the rest of the protocol to be blind until consensus is finished and the transaction’s order in the finalized log is already determined. For example the protocol might use timelock encryption so that the entire content of a block is hidden until a certain deadline; or a protocol might use threshold encryption so that the block is decrypted once a committee agrees that it is irreversibly confirmed.

This means that a node may abuse the information from any transaction submitted to them, but the rest of the protocol doesn’t know the contents of what they’re coming to consensus upon until after the fact. By the time transaction information is revealed to the rest of the network, the transaction has already been ordered and confirmed, so no other party can front-run it. For this definition to be useful, this does mean that multiple nodes can land transactions in any given slot.

The reason we forgo the stronger notion where only the user knows anything about their transaction before it is confirmed (e.g., as in encrypted mempools) is that the protocol needs some step to act as a filter for spam transactions. If transaction contents are fully hidden to the entire network, then the network has no way of filtering garbage transactions from meaningful transactions. The only way around this is to leak some unhidden metadata as part of transactions such as a fee payer address that gets charged regardless of a transaction’s validity. However, this metadata may leak enough information for an adversary to take advantage of. Thus we prefer that a single node has full visibility of the transaction, but no other nodes in the network have any visibility of it. This does mean, however, for this property to be useful it’s required for a user to have at least one honest node as an entrypoint to land a transaction every slot.

***

A protocol that is both short-term censorship resistant and hiding provides the ideal base to build financial applications. Going back to our example of trying to run an auction onchain, these two properties directly address the ways Bob could cause the market to unravel. Bob can neither censor Alice’s bid nor use Alice’s bid to inform his own, exactly addressing issues 1) and 2) from our example before. (For more details, see this recent preprint.)

With short-term censorship resistance, anyone submitting a transaction whether it be a trade or an auction bid is guaranteed instantaneous inclusion. Market makers can change their orders; bidders can quickly place bids; liquidations can land efficiently. Users can be sure that any action they take will be immediately executed. This in turn will allow the next generation of low-latency real-world financial applications to be built fully onchain. For blockchains to truly compete with — and exceed the performance of — existing financial infrastructure, we need to solve much more than just throughput.

You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out anytime using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of investments.

ACP and UCP promise checkout in ChatGPT and Gemini. Soon, hundreds of millions of consumers around the globe will find better products, merchants will have improved conversion rates, and platforms will be able to take 5-10%.

But ChatGPT checkout is an incremental improvement. It will not reshape society as the internet did in the early 2000s. Open Agentic Commerce will.

We must go back to the ‘90s to understand why.

There were two different competing versions of ‘the internet’ at the time.

• AOL’s: One price, mail, weather, additional approved content, and eventually the whole Time Warner content library

• Open protocols: HTTP, DNS, HTML, and a browser called Mosaic

Mosaic looked completely ridiculous compared to the first. There were so few sites on Mosaic’s internet that there was no need for search; an alphabetical index was sufficient. 8 years later, AOL merged with Time Warner in a $350B merger-of-equals deal. The market had decided. The curated content bundle was the path forward.

But it was not long before Mosaic and open protocols won, and human civilization stepped into the digital age.

Why? Consider if walled gardens had won.

Mark Zuckerberg wants to start The Facebook in 2004. He needs a distribution deal with AOL. Two kids at Stanford want to index the web. They need CompuServe’s permission. A guy wants to sell books online from his garage. He needs to pitch MSN’s content team.

“Go back to school, little boys,” they say. None of it happens. The entire digital economy we take for granted simply doesn’t exist.

Open protocols meant no gatekeepers. Anyone with a server and a domain name could reach the entire internet. The edges innovated, the center couldn’t keep up, and the result was one of the largest wealth creation events in human history. It’s a fundamental principle of capitalism: innovation comes from the edges.

Back to 1997: Tim Berners-Lee, Marc Andreessen, and co were working on the protocols and the browser. At the time, running a server cost hundreds of thousands of dollars. It was not obvious why a content server would respond to an unknown consumer. It would be expensive to do so, and there was no known financial incentive.

They created a message called ‘402’ that a server could send to a consumer: ‘This content is available for a small fee.’ But there was no sensible way to pay digitally. PayPal did not yet exist, and credit cards had fixed fees in the tens of cents, far too high for 1-cent micro-transactions.

But the web took off anyway.

Google figured out an odd business model for the internet: ads. In historical media, the primary economic relationship is between the content producer and the consumer. Building on the economics of broadcast, Google helped introduce a third party, the advertiser, to pay for the relationship between the producer and consumer.

Ingenious. Now producers could monetize the attention of the viewer. No need for any prior relationship with the consumer. Google sat in the money flow, between the advertiser and content producer, charging whatever take rates they wanted.

So the need for micropayments was averted. Open source software got off the ground, the cloud revolution happened, and hosting costs plummeted 100x. Google became the biggest proponent of the free and open internet. The more consumers searched, the more money Google made. So they invested hundreds of billions to make the internet fast, cheap, and ubiquitous.

And then in the 2010s, nothing happened.

Interest rates were low, technology changed slowly, the walled gardens fed their troops and gathered strength.

In 2022, ChatGPT launched, and the world was set to change again. LLMs could do more than deliver results. They could generate and compile many such results into a handy summary, often without touching the content itself.

By GPT-4, it was clear agents were the next step: LLMs adept at using computers like humans, only cheaper and more effective.

And just like that, the economics of the internet changed.

The business model from 1997 to 2024 was distraction. Humans reading a webpage can be distracted by an advert, monetizing their partial attention. LLMs/Agents do not get distracted.

There is some beautiful irony in ads creating the free and open internet, which became the 10-trillion-token dataset that created LLMs, leading to the downfall of ads.

Stack Overflow views are down 75% since GPT-4, Tech news traffic is down 60%, etc. Tech consumers are early adopters, but it’s coming for all information on the web.

Checkout in ChatGPT doesn’t matter. The internet is civilization’s town square, and the economic contract is now obsolete.

There are small pockets of the internet that have defended themselves from Google’s reach, the proverbial walled gardens with truly differentiated content: Facebook, TikTok, LinkedIn. These are resistant to automated bot scraping thanks to thousands of highly paid engineers working around the clock.

But walled garden defenses just broke too. Computer-use agents perfectly mimic the traffic of real human users. Snake oil salesmen will prescribe cures for the next 10 years, and Sand Hill Road will invest. But there are no cures. The walls of the fortress have been made obsolete by the warplane.

What’s next?

Open Agentic Commerce.

Checkout in ChatGPT is the AOL of agentic commerce. It’s a curated catalog, a walled garden with better UX. To sell through it, merchants need months of BD, stringent legal docs, concrete 5-year plans, revenue, a strong user-base, and a story that will make shareholders happy when it appears on the front page of the NYT.

Open Agentic Commerce is the HTTP of today. A simple set of protocols that let agents pay for anything they need. Data, cloud hosting, communication, and plenty of things we haven’t dreamed up yet.

Two of the front-runners are x402 from Coinbase and mpp from Tempo and Stripe. 28 years after the ‘402’ status code was invented, we have a viable implementation. Stablecoins on modern blockchains have sub-cent fixed transaction costs, solving the exact fixed-fee problem that killed micropayments in 1997.

An agent that can only buy from pre-approved merchants is an employee with a corporate card restricted to three vendors. An agent with open protocols is an entrepreneur with a bank account.

There’s no BD, no whitelist, just simple permissionless standards.

These protocols focus on exactly two things:

• Agents: “How do I send money?”

• Merchants: “How can I be sure this agent paid?”

LLMs are good at calling tools they’ve never seen before. Starting with the Claude 4.5+ and Codex 5.2+ models, agents can discover an API, read its schema, and use it correctly without prior training.

The current discourse focuses on “skills”. Think of them as natural language programs, which can be composed as building blocks. A non-technical founder can write a Slack message and have it execute as software:

1. Buy pizza from the closest place with good reviews, and track the status every 10 minutes.

2. When the driver is 5 minutes away, turn on the porch light.

3. If delivered in under 30 minutes, send the driver a $5 tip.

No code, no computer science degree. The agent reads the intent, writes a computer-native program just in time, executes it, and throws it away. Programming as a discipline is optional. Literacy in a human-native language is sufficient.

Skills work. But they’re a transitional artifact, the first obvious thing to build once we discovered agents could call unfamiliar tools. They require someone to write, publish, security screen, and update them. And the agent needs them loaded in advance. It’s messy.

The skill discourse hides a deeper unlock: agents can compose capabilities like never before.

Pizza is a toy example. Here’s a real one: An agent managing a small business’s supply chain notices a packaging supplier’s prices have crept up 15% due to tariffs. It discovers three local alternatives, requests samples from each, negotiates volume pricing, and switches. All before the owner’s morning workout.

No API partnership, procurement team, or RFP process. Just an agent with a balance and open protocols.

Discovery

Agents can pay. Agents can compose, but they can’t yet find what they need.

What remains is discovery. For an agent: “How do I find stuff to buy?”, for a merchant: “How do I tell agents about my services?”

Enter AgentCash. It’s a single balance, access to every API on the internet. When an agent gets blocked, it can reach for thousands of APIs, spend pennies, and proceed.

Critically, AgentCash bundles payment and merchant discovery. Merchants can register their servers on x402scan.com or mppscan.com and instantly be exposed to all 2,000+ AgentCash agents.

In 1997, the web had no business model, and no one knew why a server would talk to a stranger. Open protocols and a clever hack called advertising figured it out, and civilization went digital. In 2026, that hack is dying. Open protocols and a 28-year-old status code are about to replace it.

Welcome to the age of Open Agentic Commerce.

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Technical founders sometimes have an aversion to investing in go-to-market and sales, believing that the best products sell themselves — but that’s not the case. And it’s especially true now that we’ve entered the enterprise adoption era of crypto. So building an effective enterprise sales organization is one of the most powerful motions for scaling a company after finding product-market fit. A world-class sales team can be just as important and effective as an outstanding engineering team, and it can unlock new levels of growth and operational excellence that competitors can’t replicate.

It can also be surprisingly exciting and satisfying — even for deeply technical, product-oriented founders — to build a sales organization. The reason is simple: success here is tangible and metrics-driven. You can literally count the number of marquee account wins and losses, see how this translates into increased revenue, and quickly separate the 100x performers from the also-rans.

Here’s a step-by-step playbook to help you build a world-class enterprise sales organization.

Step one: Build the right team

Sales Leader

The first and most critical hire is your sales leader. They lead all revenue efforts across the company and design the sales playbook: how the organization goes to market, how it operates day to day, and how deals are won. This role shapes the processes by which campaigns are fought and victories are secured.

You must give this leader a seat at the table. They should report directly to the CEO and participate in executive staff meetings alongside other senior leaders so they are fully integrated into company strategy and decision-making. Otherwise you’re not signaling that sales is a priority in the organization, or are siloing it too much as a function.

Founders often make two mistakes when building a sales organization. The first is starting by hiring junior sales talent to “explore the market” before bringing in a sales leader. This strategy almost always fails. The main reason is that any enterprise sales leader who’s worth their salt will want to hire and shape their own team.

The second mistake is waiting too long to hire this role because founders believe they themselves are the best salesperson for the company. Early on, it’s true that a CEO can be the company’s most effective salesperson. They know the product intimately, they have “the divine flame” of the company burning brightly inside of them, and customers love to talk to a founder and CEO.

But if the CEO remains the primary salesperson for too long, it prevents the company from moving quickly enough to capture the market opportunity. So the best time to make the first critical sales hire is once a founder believes that they’ve achieved product-market fit or are on the cusp of doing so. Waiting too long to fill this seat risks competitors getting ahead and defining the market on their terms, potentially boxing you out.

When searching for a sales leader, it’s critical to find someone who has previously written a playbook for selling new technology into a new market, rather than someone who may have successfully executed a playbook that was designed by someone else. This role requires both creativity and experience to design the right plan and process for your company.

Ideally, this experience includes bridging frontier technology and selling it to new customers and markets. Other core competencies: operating in regulated environments; and the ability to translate technical innovation into enterprise outcomes like ROI, performance, compliance, and reliability. These are all key criteria enterprise buyers often use when making purchase decisions.

Sales leaders also need the discipline to build an auditable, repeatable sales process — and the judgment required to recruit strong enterprise sales talent. The best sales leaders are missionary leaders who attract like-minded, hardcore enterprise sellers.

Account Executives

The next most important role is the Account Executive (often referred to as an AE or account exec). The AE interfaces directly with prospective customers and introduces your technology into enterprise accounts. The AE maps the customer organization. They must identify the true decision makers and communicate the value proposition in a way enterprise buyers understand and will respond to.

Successful account execs typically have prior experience selling similar products into related markets. Because they often come with a deep Rolodex, founders often assume that the right account executive will unlock doors through their pre-existing relationships. But this rarely works in practice. Enterprise decision makers change frequently.

What matters far more is demonstrated ability to close large deals, navigate complex organizations, and win against strong competition. You can easily assess this in interviews because salespeople have clear metrics and outcomes, and treat these wins as their scoreboards.

Account executives are commonly organized either by vertical or geography. How many AEs you should hire depends on two factors: the number of potential accounts that require active outreach, and the number of deals each AE can manage simultaneously.

One helpful tool for determining this is to run a bottom-up analysis: Estimate how many active deals you expect over the next twelve months and how many deals a single AE can realistically handle. From there, calculate how many account executives you need. While the exact number is market- and product-dependent, this approach usually produces a reliable first-pass coverage model.

Some words of caution: First, don’t let your closest competitors out-cover you. If they’re the first to reach the most important accounts in your market, they’ll shape the customer’s requirements and define the rules of engagement — leaving you stuck competing against their playbook, from messaging to positioning, instead of your own. Second, avoid hiring account executives before finding product-market fit. Otherwise, most AEs will struggle to sell products customers do not yet understand.

Solutions Architect

Solutions Architects, sometimes called sales engineers, are the next essential role in building a winning sales organization. Their role is to translate the company’s technical capabilities into the customer’s business and operational outcomes. They serve as a bridge between customer needs and the product and engineering teams. Through this feedback loop between product, engineering, and customer needs, solution architects surface product gaps, identify future requirements, and ensure the product evolves in the direction leading customers’ demand. This virtuous cycle strengthens both the product and the sales motions.

Since the CEO knows the product better than anyone, they often end up spending a significant amount of time translating the product and value proposition to customers. But upon hiring a solutions architect, the CEO can offload a significant amount of early sales process and sales calls. For this to succeed, the CEO must train the solutions architect directly so they can communicate value as effectively as the founder; this sometimes involves shadowing the CEO in initial sales meetings or sharing sales training decks.

A strong Solution Architect paired with a great AE and a compelling product is a lethal combination.

Solution Architects work closely with enterprise customers to determine how your company’s product can integrate new capabilities into their existing technology stacks. In the case of crypto, where significant amounts of money and value are transferred across blockchains, institutional customers almost always rely on complex existing infrastructure. The solution architect plays a critical role in helping enterprise customers understand how everything fits together, as well as relaying product requirements back to the company, ensuring that the right interfaces and integrations are built.

It may seem obvious, but successful solution architects must have strong communication skills. They spend roughly 70 percent of their time with customers and 30 percent translating insights internally. Solution architects should also be technically oriented, although they do not need to be hardcore developers.

I recommend hiring the first solution architect at the same time you bring on board your first account executive, since they often work closely together. Don’t worry about having one-to-one mapping between a solution architect and an AE; one solution architect can often support multiple AEs at the same time.)

Sales Development Representatives

Sales Development Representatives (SDRs) generate qualified leads for account executives through email, social, and phone outreach. They perform early qualification — triaging, basically — before passing leads downstream to the account executives.

As a company starts to scale revenue, sales development reps become an increasingly important source of new leads. Historically, SDR roles were populated by junior sales hires aiming to become AEs. But with the advent of AI-assisted sales tools , the SDR role now has a more quantitative dimension. SDRs can use data to understand which outreach channels and specific messages are converting best, and adjust their approach accordingly.

The first SDR should be hired only after the head of sales, first AE, and first solutions architect are in place – again, all after product-market fit. Because even if SDRs generate leads, without a mature process to convert them, those leads will be wasted.

Sales Operations

The head of sales operations ensures the machine runs efficiently. This role ensures the team follows processes, maintains accurate forecasts, and designs the sales funnel so leadership can allocate resources based on data rather than just intuition. This role usually reports to the head of sales.

The head of sales ops should be hired only after you’ve already filled the positions covered above (head of sales, sales rep, solution architect, sales development). This person’s role is to scale and tune the machine — not to build it. If you don’t already have the essential capabilities in place for lead generation, technical selling, and relationship management, then the machine your head of sales operations is meant to optimize will be incomplete.

The sales ops role is usually interrupt-driven: There’s always a fire somewhere in the pipeline. It’s essential to hire someone who can parallel-process multiple streams of incoming data and information, yet also dive deep into the details when necessary. Throughout all of this, they must remain calm under pressure.

Sales ops will not fix a broken sales motion or a weak product. If deals are consistently lost due to weak differentiation, late entry, or product gaps, the root cause lies elsewhere. If you’re losing deals versus the competition; or are second or third into the accounts that matter; or have a value proposition that isn’t resonating with the market, then don’t fool yourself into thinking that investing in sales ops is going to solve those problems.

Customer Success

Once you’ve won a hard-fought deal, it’s critical to ensure the product is properly implemented and hitting the key business metrics for which the customer purchased the product in the first place. The customer success function ensures customers achieve the outcomes that justified their purchase.

The customer success goal is retention, expansion, and referenceability: ensuring customers stay with your product; upselling and expanding their use of the product as needed; and serving as references when the customer is highly satisfied over time.

A customer success associate will also regularly channel deep product insights back to the product and engineering teams. They identify product gaps and critical third-party integration needs, like which external systems the product must connect with to ensure customer success. They also surface sources of implementation friction that you can address through ongoing product development, making future deployments faster and more seamless.

Successful customer success associates must be very customer-service oriented, taking on a client-facing approach each day with a can-do attitude. They also must be excellent multitaskers, juggling multiple issues and clients simultaneously. When finding customer success associates, there’s a huge talent pool to draw from in B2B SaaS — basically every software company from Box to Oracle to Salesforce and so on that has a recurring revenue vs. on-premise software model. Employees from these kinds of organizations will have seen all kinds of escalations and team configurations, so will have lots of experience to bring.

Hire the first customer success employee once you’ve landed your first lighthouse customer — not just any paying customer, but the one that is aligned best with your product/ market and future growth — and that deployment starts to scale. Otherwise, you risk your product and engineering team getting sucked into a full-time customer support and satisfaction role, which is not the highest investment of their time.

Step 2: Building a repeatable sales process

Sales organizations, like software development processes and factories, perform best when they are built on a clear, repeatable process. Each member of the sales field organization viscerally understands the playbook and knows the role they need to play. Doing so is what separates good sales teams from great sales teams.

Ask yourself:

What exact steps should your business development reps, account executives, solutions architects, and customer success associates follow to give your company an unfair advantage in winning deals?

This question covers everything from how you first approach a customer to the written and presentation materials required at each stage of the sales campaign. The goal is to ensure your company is setting the agenda, defining the requirements, and boxing out the competition.

Once you’ve defined this process, it must be instrumented. Monitor its health at every step, including in your weekly forecast calls, so you understand where each deal stands and what the true win probability is. At the early stage of a sales buildout, companies often fail to distinguish which deals matter most. Not every deal is created equally, and understanding this can be helpful for sales forecasting and then building your pipeline to your customers.

You should invest significant effort in winning the deals that shape the market and influence future buyers, especially in enterprise sales, where later customers look to early, sophisticated buyers for validation. Winning these lighthouse accounts early on is critical, both for your revenue goals and company evolution — losing them also creates openings competitors are eager to exploit.

Frame the decision as either a once-in-a-lifetime opportunity or an existential threat to avoid, prompting urgency and a clear motivation to buy today. This requires a deep understanding of your prospective customer’s strategic business priorities and the ability to map your product capabilities directly to solve those problems. That might mean reducing existential competitive threats, improving audit and compliance posture, cutting costs, or enabling a high-return new line of business.

If you’re in a competitive dog fight with another company, lay traps and landmines for them. If you possess capabilities competitors lack, ensure those capabilities become central to the customer’s decision-making process. Your competition will find themselves unable to achieve the same results or demonstrate that capability. You can even define terminology and key metrics on your terms, so that when the competition sells after you, you’ve biased the customer to your worldview.

Creating internal champions for your product is equally critical. In most enterprise deals, there are at least three key decision-makers: the end user (who will encounter your product day-to-day); the manager accountable for results; and the executive sponsor. If you can become the preferred choice for all three of those buyers, your chance of winning the deal is close to 100%. If, however, you have a great relationship with the executive sponsor, but the end user and the manager prefer another product, that’s a much more difficult deal to win. You must think about what steps you can take to win each one of those parties.

You may also encounter detractors within an account — someone who prefers a competitor’s product because they have a relationship with that company, or someone who has worked with a sales rep from the competitor before. In this case, it’s important to think through how you can turn them into a champion. If that’s not possible, attempt to neutralize their influence and impact on the overall decision by finding other champions, sharing data or references they can’t argue with, and so on.

Every enterprise sales organization experiences the pain of lost deals. When this happens, it’s important that you run an after-action review process where you review the deals that you lost, and try to understand what went wrong. Were there product gaps that caused the loss that you previously didn’t know about? What are the steps that you can take to fix them? Understand whether the loss stemmed from product gaps, late entry into the sales cycle, or execution failures, and feed those insights back into product and sales processes.

Finally, you must ensure that your entire sales organization is well-trained on your sales process. The best way to do this is to put every new hire through a training boot camp, where you drill them on the key attributes of the product and the technology. Walk them through the sales process so that they know what to do and when. Most importantly, every single member of the team should walk through a simulation of a competitive deal. This way they know what it feels like to operate under pressure and competitive tension, and have responses and strategies in place.

Step 3: Avoiding common mistakes

I’ve already mentioned a couple of common mistakes: believing that your technology is so great that it can sell itself. It won’t. Get over it. Second is making your first sales hire a junior sales account executive who can “explore opportunities” rather than focusing on a strategic high impact ahead of sales.

Next? Assuming that a bottom-up, developer-led approach to sales will work within large enterprises. Many of the last generation’s successful companies — including Stripe and Twilio — were able to gain early momentum by selling to developers first. The same has been true in crypto. But to penetrate the largest and most complex accounts, relying solely on a developer-led approach simply will not work. Focusing on developers can be amazing for developing early technical champions within an account, but it’s still an absolute requirement that you master the process of selling to managers, executives, and navigating complex procurement processes.

Finally, you can’t allow the competition to be first into the most important and strategic accounts. When this happens, you’re giving your competition the opportunity to define the rules of engagement and the key feature requirements and capabilities for a given sale. You must be first into the most important accounts so that you’re able to set the rules of engagement and make everybody else follow them.

Step 4: Creating a culture of improvement

Creating a culture of continual improvement is crucial in building a winning sales organization. With constantly changing technology and market cycles, the best sales teams relentlessly incorporate new data and learning into their processes. If a sales organization isn’t improving, it’s falling behind.

At the core of improvement are three questions:

1. Why should the customer do anything at all?

2. Why should they act now?

3. Why should they choose you?

The organizations that consistently refine and strengthen their answers to these questions win disproportionately. Continual improvement also requires not just winning but retaining the lighthouse customers who set the tone for the rest of the market. The largest and most sophisticated buyers create the signals that future customers follow, positioning your company for long-term success.

Another critical element is a rigorous win-loss analysis each quarter. Understanding exactly why deals were won or lost allows insights to compound across sales execution, product development, implementation, and positioning. The questions to ask :

• Over the past quarter, which deals were won and which were lost?

• For the deals you won, why did you win them?

• For the deals you lost, why did you lose them?

• What improvements can you make to increase your win rate?

• What improvements do we need across the company to improve the sales process?

• What changes should we make within the sales team to improve outcomes?

• What product improvements would help increase the win rate?

• How do we improve implementation?

• How can we improve market strategy and positioning?

• Are we incorporating learnings from these win-loss analyses into our process?

• Are these learnings helping results compound quarter over quarter?

• How strong is our sales hygiene?

• Is the team consistently following the sales process?

• Is the team executing the sales plan with force and precision?

These questions may seem basic, but many sales organizations never bother asking them in any consistent way. These learnings compound over time and steadily improve results. Maintaining strong sales hygiene is equally important: Ensure your team is following the process with discipline rather than assuming their compliance.

Finally, continual improvement means studying how your smartest customers use your product. Reducing friction and increasing ROI for them creates insights that feed back into product, marketing, and sales motions: a virtuous circle for both your customer and your company.

***

The first lesson is accepting that technical products don’t sell themselves. The second is figuring out sales. But it’s these details in between — the right hires; the best lighthouse customers; continual improvement feeding back into your sales process, product, and people — that will really help you win.

You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out anytime using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of investments.

For decades, new enterprise technologies — especially in banking and financial services in the past 10 years — have promised order-of-magnitude improvements over legacy infrastructure: faster settlement, lower costs, cleaner architectures. Yet adoption rarely follows technical merit.

Here’s what this means: If your “better” product isn’t winning, the gap isn’t performance. It’s fit.

This piece is for founders in crypto who cut their teeth building for public blockchain use cases and are now going through the painful process of steering toward enterprise sales — a massive blind spot for many.

Below, we share a few key learnings based on our own experience seeing founders successfully sell to enterprises, and the feedback we hear from those buyers, to help companies better pitch and win with enterprises.

What “best” means

Inside large enterprises, the “best” technology is the one that works with existing systems, approval processes, risk models, and incentive structures.

SWIFT persists despite being slow and expensive. Why? Because it offers shared governance and regulatory comfort. COBOL persists because rewriting stable systems introduces existential risks. Batch file transfers persist because they create clear checkpoints and audit trails.

The (maybe) uncomfortable takeaway is that enterprise blockchain adoption is not blocked by a lack of education or vision. It’s blocked by misaligned product design. Founders who insist on selling the technology at its maximum form will keep running into walls. Founders who treat enterprise constraints as design inputs — not compromises — are the most likely to win.

The opportunity, then, is not to dilute the promise of blockchains but to help technologists package and sell a version that enterprises can actually say yes to by applying the following mindsets.

Enterprises fear downside more than they value upside

A common mistake founders make when pitching enterprises is assuming that decision makers are primarily motivated by upside or the promise of better technology: faster systems, lower costs, cleaner architecture, and so on.

In reality, enterprise buyers are far more motivated to minimize downside risk.

Why? Inside large institutions, the cost of failure is asymmetric. This is the opposite of the mindset in smaller startups, so founders who haven’t worked in large enterprises may miss this. The missed opportunities are rarely punished, while visible mistakes are — especially one tied to new or unfamiliar technology. Those mistakes can meaningfully harm careers, trigger audits, or even invite regulatory scrutiny.

Decision-makers almost never participate directly in the upside of the technology they recommend. Even in cases where there is strategic alignment or corporate-level investment, the upside is diffuse and indirect. The downside, however, is immediate and often personal.

As a result, enterprise decision-making is less shaped by what could work and more by what’s unlikely to fail. This is why many “better” technologies struggle to gain traction. The bar for adoption usually isn’t technical superiority; it’s whether adopting the technology makes the decision maker’s job safer or riskier.

Therefore, you have to reimagine who you think your customer is. One of the most persistent mistakes founders make when selling into enterprises is assuming that the buyer is the person who understands the technology best. In reality, enterprise adoption is less driven by technical conviction and more by organizational dynamics.

Inside large institutions, decisions are shaped less by upside and more by risk management, coordination costs, and accountability. At enterprise scale, most organizations have externalized part of their decision-making process to consulting firms — not because they lack intelligence or expertise, but because key decisions must be consistently validated and defensible. Bringing in a recognized third party provides external validation, distributes accountability, and serves as a credible reference point if the decision is later challenged. This is true across most Fortune 500 companies, which, as a result, carry enormous consulting line items in their annual budgets.

In other words, the larger the institution, the more important it becomes that decisions can survive internal scrutiny after they are made. As the saying goes, “No one gets fired for hiring McKinsey.”

How enterprises actually make decisions

Enterprise decision-making resembles how many individuals use ChatGPT today: We don’t rely on it to make decisions for us. We use it to pressure-test ideas, synthesize tradeoffs, and reduce uncertainty — all while retaining accountability for ourselves.

Enterprises largely behave in the same way, except their decision-support layer is human, not an LLM.

New initiatives must pass through some combination of legal, compliance, risk, procurement, security, and executive oversight. And each layer asks different questions, questions like:

• What could go wrong?

• Who is accountable if it does?

• How does this fit with what already exists?

• How can I explain this decision to the executive team, regulators, or the board?

The result is that for meaningful initiatives — not those siloed within a company’s innovation arm — the customer is rarely a single buyer. The “buyer” is really a coalition of stakeholders, many of whom care more about avoiding mistakes than buying innovation.

This is often where many technically superior products lose — not because they don’t work, but because the right people in the organization can’t safely own the decision to adopt them.

Consider online betting platforms. As prediction markets dominate the zeitgeist, crypto picks and shovels — like on-ramp providers — might look to online sportsbooks as an obvious enterprise target. But to do so, it would be imperative to understand that online sportsbooks operate under a different regulatory framework, including state-by-state licensing, than do prediction markets. Knowing that each state regulator treats crypto differently, an on-ramp provider would understand that its customers aren’t the product, engineering, or business development teams looking to tap into crypto liquidity. Rather, the customers are the legal, compliance, and finance teams that think about the risks to their existing sportsbetting licenses and their core fiat business.

The easiest solution here is to map the decision-makers early and explicitly. Don’t be afraid to ask your product champion (who loves what you have built) how to help them sell internally. Somewhere behind the curtain sit legal, compliance, risk, finance, and security… all with quiet veto power and very different fears. The teams that win learn to package their product as a risk-contained decision, with pre-baked answers and clear upside/downside frameworks for stakeholders. By simply asking, you can learn who you need to package this for, and then carve a path to “yes” that feels boringly safe.

The consultant (and translation) layer

In many cases, before new technology ever reaches an enterprise buyer, it’s filtered through an intermediary. Third parties like consulting firms, systems integrators, and auditors often play a critical role in the translation and legitimation of new technologies. For better or worse, they become the gatekeepers for a new technology. They use established and familiar frameworks and engagement models to turn new solutions into familiar concepts and uncertainty into defensible recommendations.

Founders often view this dynamic with frustration or skepticism, seeing consultants as slowing progress, adding unnecessary process, or acting as auxiliary stakeholders that influence final decisions. And they do! But founders must be realists: In the U.S. alone, the management consulting services market is projected to be worth more than $130 billion in 2026, with the majority of that spend coming from large enterprises seeking help on strategy, risk, and transformation. While blockchain-related work represents only a small fraction of that total, it would be a mistake to assume that just because an initiative involves blockchain, it sits outside these same decision-making channels.

Whether you like this dynamic or not, it has shaped enterprise decision-making for decades. And just because you’re selling blockchain-based initiatives, the dynamic hasn’t gone away. Our experience speaking with Fortune 500 companies, GSIBs, and large asset managers reinforces this reality: Ignoring this layer can be a strategic mistake.

Deloitte’s alliance with Digital Asset illustrates this dynamic: By partnering with a major consulting firm like Deloitte, Digital Asset’s blockchain infrastructure was reframed through a lens far more familiar to enterprise — in this case, governance, risk, and compliance. For institutional buyers, the involvement of a trusted party like Deloitte validated the technology, making the path to adoption far clearer and more defensible.

Don’t run the same pitch everywhere

Because those involved in enterprise decision making are so attuned to their needs — and especially to their downside risk — it’s important to tailor your presentation: Don’t reuse the same enterprise pitch across every lead. The same deck. The same framing and messaging. The same “why now.” The same architecture slide.

But nuance matters. Two large banks may appear similar on paper, but their systems, constraints, and internal priorities often differ meaningfully. What resonates with one may fall flat with another.

A generic pitch signals you haven’t taken the time to understand how this specific institution defines the program. If your pitch isn’t tailored, then, from the institution’s point of view, it’s hard to believe your solution can fit cleanly inside.

Compounding this mistake is the “rip and replace” narrative. In crypto, founders often default to pitching a clean-slate future: One where legacy systems are outright replaced with newer, better decentralized technology that can usher in a new era. Enterprises rarely operate this way. Legacy infrastructure is embedded in workflows across the organization, compliance processes, existing vendor contracts, reporting systems, and many other touchpoints and stakeholders. Replacing legacy infrastructure from scratch not only disrupts the enterprise’s day-to-day operations but also introduces a range of risks and failure points.

The more sweeping the change, the harder it is for someone inside the organization to own the decision: the bigger the decision, the larger the decision-making coalition.

We’ve seen success when founders meet the enterprise where it is, not where they want the enterprise to be. Design entry points that integrate into existing systems and workflows, minimize disruption, and establish a credible wedge.

A recent example of this is Uniswap’s collaboration with BlackRock around its tokenized fund. Rather than framing DeFi as a replacement for traditional asset management, Uniswap enabled permissionless secondary market liquidity for a product issued within BlackRock’s existing regulatory and fund structures. The integration did not require BlackRock to abandon its operating model. It just extended it onchain.

You can focus on expanding to something more ambitious once you’re through the procurement process and your solution is deployed into production.

Enterprises hedge — make sure you’re the right one

This risk aversion often manifests predictably: Institutions hedge. Often, a lot.

Rather than making a single, decisive bet on emerging infrastructure, large enterprises frequently run parallel experiments. They allocate modest budgets across multiple vendors, test competing approaches in innovation departments, or run pilots without committing core systems. From the institution’s view, this preserves optionality while limiting exposure.

For founders, however, this creates a subtle trap. Being selected does not mean being adopted. Many crypto companies become one of several low-conviction hedges. Interesting to trial, not critical enough to scale.

The real objective is not to win a pilot. It is to become the winning hedge. This requires more than just technical merit. It requires professionalism.

Why professionalism beats purity: No hoodies

In these markets, clarity, predictability, and credibility routinely outperform raw innovation: Rarely will you win on technology alone. Because of that, professionalism matters. It reduces uncertainty. By “professionalism”, we mean designing and presenting products that acknowledge institutional realities (think legal constraints, governance processes, and existing systems) and committing to work within them. A convention signals that a product can be governed, audited, and controlled. Regardless of whether this speaks to the ethos of blockchain or crypto, it’s largely how enterprises approach technology adoption.

This approach can seem like an enterprise’s resistance to change. It’s not. It’s a rational response to enterprise incentives.

Focusing on the ideological purity behind the technology — whether around “decentralization,” “trust minimization,” or any other part of the crypto ethos — rarely persuades institutions that operate under legal, regulatory, and reputational constraints. Products that insist enterprises adopt the “full vision” upfront ask too much, too soon.

There are, of course, examples of groundbreaking technology and ideological purity being a winning combination. LayerZero recently introduced Zero, a new L1 blockchain to overcome scalability and interoperability hurdles to enterprise adoption. But it does so while also preserving the industry’s core tenets of decentralization and permissionless innovation.

What differentiates Zero, however, is not just its architecture, but its approach to institutional design. Rather than building a one-size-fits-all network and hoping that enterprises adapt, LayerZero is working with anchor partners to co-design purpose-built “Zones” optimized for specific use cases such as payments, settlement, or capital markets. Zero’s architecture, the team’s willingness to truly co-build around these use cases, and LayerZero’s brand minimize some of the downside concerns for large traditional financial players. These combined factors made them more appealing to institutions like Citadel, the DTCC, and ICE, which all announced as partners.

***

The temptation for founders is to interpret enterprise resistance as conservatism, bureaucracy, or lack of vision. While this may be the case sometimes, there’s usually another component at play. Most institutions are not irrational. They are optimized for continuity. They are designed to preserve capital, protect reputations, and survive scrutiny.

The technologies that win in this environment are not always the most elegant or ideologically pure (see Canton, for example). They are the ones who strive to meet the enterprise where they are.

These realities help clarify the long-term potential of blockchain infrastructure in the enterprise.

Enterprise transformation rarely happens in a single leap. Look at the “Digital Transformation” push in the 2010s: Years after the enabling technologies existed, most large organizations were still modernizing core systems, often through large-scale, expensive consulting engagements. Adoption at scale unfolds gradually, in steps, through controlled integration and expansion from proven use cases, not overnight replacement. This is the reality of the enterprise.

The founders who succeed are not the ones who demand the full vision upfront. They are the ones who sequence it.

You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out anytime using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of investments.

A new paper — “Some Simple Economics of AGI” — has been making the rounds, so we sat down with the author, covering:

• Automation vs. verification: the key economic split (07:39, 10:47)

• Why AI agents now feel like coworkers (05:39)

• What’s happening to junior roles and the “codifier’s curse” (17:55)

• The value of “meaning-makers,” consensus, and status economies (21:54)

• Why crypto may become essential infrastructure for identity, provenance, and trust (23:48, 41:08)

• Two possible futures: a hollow vs. augmented economy (44:31)

Featuring Christian Catalini (founder of the MIT Crypto Economics Lab) and Eddy Lazzarin (CTO at a16z crypto), in conversation with Robert Hackett, our discussion dives into how automation is reshaping labor markets and the nature of intelligence.

What do these changes mean for startups, the future of work, and your career?

​Edited transcript

Robert Hackett: Hi everybody. We’re here with Christian Catalini, who is the cofounder of Lightspark and founder of the MIT Crypto Economics Lab, as well as Eddy Lazzarin (a16z crypto).

And we’re here to discuss a new economics paper that Christian published, called “Some Simple Economics of AGI.”

So I’d love to ask: what started you on this journey to investigate the economic relationship of AI and the world we live in?

Christian Catalini: I would say it was born out of a semi-existential crisis. We’re all grappling with the fast pace of progress and just how quickly everything is moving.

I’m an optimist, but the fundamental questions were: What should we do? What should we focus on? And what’s worthy of our time, effort, and attention — especially in this phase where we still have a meaningful shot at influencing the trajectory of this technology.

Some months ago, we wrote a piece on measurement, and the basic idea was: anything that can be measured will be automated, which doesn’t sound like good news. But this second paper was really centered around: if that’s true, let’s take the initial assumption to the limit.

What would the economy look like? What will the nature of labor look like? What should startups do? What should incumbents do? And essentially, what will the future look like?

Some things will be right; some things will be wrong. Hopefully, we got it directionally right. Now it’s in the wild, and we’re seeing what resonates and what doesn’t.

Robert: You said this stemmed from a semi-existential crisis?

Christian: I think my core takeaway was a feeling that, first of all, this technology is still under our control.

Second, the upside is many orders of magnitude greater than the doomers would have you believe. And third, I think there’s a playbook that all of us can look at.

We can think about: where are we adding value? What are the sorts of things that we do within our jobs? Jobs tend to be bundles of different tasks, and people get very nervous when certain tasks or certain parts of their job get automated.

I think right now coding is going through that experience where many talented individuals who have written elegant, fantastic code over the last few decades look and say, “Oh wow, this is doing what I do.”

Robert: I want to drill down a little bit here because we have Eddy Lazzarin with us, who has spent several years here as Chief Technology Officer at a16z crypto. Eddy, how are you thinking about these changes?

Eddy Lazzarin: Let me situate us in time and with the paper. Many people feel that something changed in December 2025. And what changed was a series of incremental improvements in how these agents work that accumulated to the point that AI agents can now perform long-running tasks.

The feeling just a year ago was: I asked the agent to do a small thing. It’s amazing how it does that. I had to ask it to do the next thing. And so on. And now you can kind of give it less guidance. And maybe it’s not quite perfect. But all of a sudden, this is like working with somebody, right?

You didn’t kick forward what they did, one piece at a time. That would be extreme micromanagement. Instead, you have a conversation, they go away, they come back a day or two later, and they’ve got something. And that qualitative feeling provokes a lot from the imagination, and now everyone is beginning to grapple with this reality.

Part of grappling is just some histrionics. But another part — the more interesting part — is figuring out how to squeeze as much value as possible in actual production settings and for commercial use.

And what people are discovering is that they produce an incredible amount of work. Some of it is fantastic. It takes a fraction of the time it used to take. But it’s often flawed in subtle ways that may not have been fully appreciated before.

So, to give you an example, the bundle of what it means to be a software engineer is being reconsidered: people think of software engineering as sitting down and writing a bunch of code. I sit down, I contemplate the issue, I understand the specifications, and then I write code. And the code is what I produced.

But it turns out — and AI helps us understand this and break it down into its parts better — there is a very nuanced, iterative process of correcting, gathering feedback, and integrating that’s not just the printing of each line of code. It’s this holistic task. So the balance of work for a great engineer is shifting quickly.

The process of trying the thing, guiding it, and taking risks — Christian calls verification in his paper.

The way things are changing is that people are now grappling with the fact that the split of work demanded of a great engineer may be different. The amount of attention paid to writing the code and printing one line at a time is vanishingly small. For some, like in the vibe coding extreme, near zero. And a huge part of the work is now verification.

​Christian: So I think the automation part is very intuitive. These agents essentially can do more of what has been done before. And for now, I think they’re still somewhat constrained by the observable domain. Every code base I’ve ever written that they’ve been ingesting during their training or fine-tuning — all of that is what they can build on.

And often people say, “Oh, well then, they cannot innovate. They cannot be creative. They cannot have good taste.”

I actually strongly disagree. In fact, much of innovation is just the recombination of ideas. And humans have probably only explored a tiny fraction of the possible recombination between disciplines. So I do think these agents will be extremely innovative just by taking what we’ve given them.

Verification is an important cost in this new economy. So what do we mean by cost of verification? Verification really starts from the idea of measurement. If you buy into the thesis that AI is incredibly good at replicating that process with the right data, then you start asking: okay, what’s not measured today?

Some things aren’t measured because they’re not really measurable. Economists call this “Knightian uncertainty,” after economist Frank Knight. And it’s essentially the difference between looking at the future and trying to assign probabilities around an event, and not even being able to assign those probabilities.

​Robert: For a non-economist out there, they might be more familiar with Donald Rumsfeld’s “unknown unknowns.”

​Christian: Absolutely, yes.

The unknown unknowns are essentially the non-measurable piece, often about the future. So that’s why, even if you throw agents today at the stock market, they’ll probably be pretty good on average — maybe better than your financial advisor — but they will probably not be resilient to drastic changes in the environment. Geopolitical shifts and whatnot — those are things that are not measured. And of course, there are many more examples.

And so what verification really is in this paper is the act of applying all the embedded measurements in your brain as a human — if you think about it — from birth to where you are professionally.

Two people may have very similar knowledge, even career-wise, but it’s not exactly the same combination. And so when people say, “Okay, this person has good taste,” or “is a great curator,” or “they have good judgment,” — one of the things that really inspired this paper was the idea that everyone was coming up with all this cope around AI, which was like, “Oh, don’t worry. The machine will never be able to do X, Y, and Z.”

And the cope was very vague, right? How do you define taste? How do you define good judgment? And even worse, a good engineer probably needed a lot more judgment applied in December than they need today.

So we needed to identify something more fundamental that could be really pinned down. And so we think that, as long as there’s data underlying that information that you’re trying to use to automate, you will be automated.

​Robert: In the near term, you break down the economy into three different areas where various tasks and jobs exist—and understand their level of automatability, or rather measurability, in terms of their output and what they do.

​Christian: I think there’s actually a lot here in terms of what’s still human across many dimensions. I would say the first one is, of course, verification.

The leverage that any single individual has in their profession is massive relative to what it was, even in December. This means we should probably all be more ambitious. We should all try to think through the workflows that we currently do and what we call the AI sandwich.

A firm or a startup can have a single human — we call it a director — who is in charge of steering verification, making sure that, as the system drifts in unintended directions, it can course-correct. So that’s maybe one person, maybe a small team at the top.

In the middle, you’re gonna have a swarm of agents. And we’re already seeing it. People are experimenting with all sorts of interesting new things.

And at the bottom of the sandwich, you’re gonna have an army — or a small army — of top verifiers. With the right tools, I think the top experts in every domain are gonna be the ones ensuring that what was intended actually came out of the system. This is a super important job — one where I think domain experts will thrive for a long time.

But there’s some bad news: As you do that work, you are also kind of creating the labels for your displacement. And we’ve seen it at its simplest before, when people were labeling images for AI companies and training — that’s no longer needed.

Now you have big foundational labs hiring top experts from finance and other domains. Those people are creating the evals and the training that will eventually displace their peers. So this verification layer is really important. I think many people will thrive in it. It’s one that really rewards a kind of hyper-specialization, right? If you’re the one person who can deliver that final unlock, your leverage is massive.

​Robert: So that’s one category. And the verifier — that’s the one that you have called the codifier’s curse.

​Christian: So the codifier’s curse is the mechanic where, if you’re a top verifier, you need to keep moving up the stack because the technology gets better and better.

The director I mentioned is essentially someone who really drives intent. Entrepreneurs are directors. They see a future and imagine a path to get there.

Then there are gonna be jobs that I think we need to recognize as easy to automate. Those jobs are gone — or soon to be gone. And I think society hasn’t really grappled with some of those effects, and there’s gonna be a massive need for retraining and really pushing people further up the knowledge frontier.

One thing people sometimes misunderstand in the paper is that we talk about human verification as the last step, but in many cases, AI will verify AI. So there’s gonna be a whole series of steps before it finally reaches the final human in this verification chain.

And then we have a category that was the hardest to qualify. We call them the meaning makers. Imagine settings where it’s all about consensus. These are individuals who are really good at understanding trends, societal changes, and issues that society cares about, which require everybody to coordinate around them. Art is like that. Crypto networks, to some extent, are like that.

These meaning makers are not in the land of what’s measurable. These are the jobs that people sometimes say require a “human touch.” I do think people severely overestimate how important that human touch is. You hear it for jobs like therapy, elder care, or childcare.

I think people will have all sorts of concerns initially, but nobody’s really accounting for the drastic reduction in cost, right? So if it’s 100x, 1,000x cheaper — and some people may even feel it’s more private — people will rapidly shift. In fact, we already know people are using LLMs aggressively to answer all sorts of questions that would be considered very intimate or personal.

There will also be jobs where “human-made” or “made by a human” will be a very important label. And crypto will play a role here, because soon we’re going to lose the nature of that identity without strong cryptography to back it. But “human-made” will be valuable just because of the scarcity that’s inherent in the fact that it’s human-made.

So, not because it’s better — it’s just knowing that a human dedicated their scarce time and attention to deliver that experience. Those things will still be important.

Robert: So you brought up cryptography. What is the place for crypto in this world?

Christian: It’s a really important one.

When we started this journey, many before us had already said: look, LLMs and AI are kind of probabilistic; crypto’s deterministic. Think about a smart contract putting the guardrails on an agent, or being able to give an agent the ability to buy and sell resources.

All these things resonated. But I do think there’s an even more profound complementarity between AI and crypto. And maybe the reason why it’s not so salient in the economy today is that we haven’t seen the side effects yet, but issues around identity or provenance of digital information.

I think we’re about to enter very uncharted territory in the next few months as these capabilities become truly amazing. Every digital platform will have to really wrestle with the idea that what used to be a human contribution — whether it’s a post or an image or anything else — is now potentially an agent.

As that unfolds, I think society will have to drastically reimagine its identity stack. In a land where trust is increasingly scarce, crypto primitives will shine across many applications. And everything that’s been built over the last decade is going to be a lot more foundational. Back to verification: when you have underlying information on a blockchain, verification is cheap. It’s more reliable. You can trust it.

Eddy: The cost of automation is declining very rapidly. And the cost of verification in this broad sense we’ve talked about is declining, but not as quickly, which creates an interesting gap.

There are many ways to describe the gap. Some may describe it as an opportunity. That’s kind of what Christian is saying about human labor: if there’s this bottleneck, this gap in measurability because of humans’ general adaptability, experience, and generality, humans are probably able to specialize in the verification component faster than we can get the machines to.

And there are some challenges that make handling verification hard for machines in the short term. In the long term, I don’t think that’s a permanent thing. But in the short term, that is definitely the case.

Cryptography and blockchains are a verification tool. Provenance is just a chain of cryptographic evidence that something traversed some path between specific hands, or it underwent some series of transformations that we can be sure of, and that gives a signal about what we’re looking at. It makes verification across different categories easier. So anything that makes verification easier will be part of trying to close that gap.

Could we talk a little bit about the Trojan horse? We’ve talked about risks to human laborers, and there’s so much more to say about that, but — like for the productive benefits toward the economy — what are the risks to the economy of low automation costs?

Christian: We’re seeing glimpses of it when companies today say that X percent of their code is now generated by machines.

Release cycles are shortening. But at the same time, because we already know that it’s humanly impossible to review all of that code, there’s a good chance it carries technical debt.

We’ve all been tempted to ask an LLM a question, skim it, and ship it as our own without full verification because the models are getting better. But whether it’s a wrong sentence, a wrong line of code, or a zero-day that is now part of your code base, I think we’re going to see more of that.

And what the model says about this is that it’s perfectly rational to ship code, or ship writing, or any sort of AI-generated work that contains some potential error, because you can’t verify the full thing. And if you scale it up to the entire society, that means we’re probably accumulating some degree of systemic risk.

As we accelerate, hopefully, we can develop better verification tooling to go back and review what we may have released. But in the medium term, companies face this tension: investing today in better verification tooling, including cryptographic primitives, is expensive. It may slow you down. The benefits are in the future, and the rush to ship and grow is strong.

So I think we’re going to see two sets of founders: founders who think about that long-term liability and will build things in the right way. We’re seeing glimpses of it — there’s this kind of “liability as software.” As we deploy these agents as workers, the issue of liability and insurance is gonna become increasingly important. It’s not the most glamorous topic, but I think we’re gonna see systemic failures in the wild.

Eddy: This is such an interesting idea because if what was happening in the production of software before — or any other service in the economy — was mostly direct human work, then you can take for granted that people have been observing and quality-checking many steps. Not that there have never been errors or flaws, but there’s always been somebody touching every step along the way.

But as things become more automated, higher-stakes, and more valuable, liability increases. The benefits are radically increasing, too, which is why we’re tolerating that. But the ability to supervise, limit, and understand the boundaries of risk has to expand.

So the idea of bringing in an insurance-type mechanism that assigns a dollar value to the risk that things will fail might be an important component in managing an enterprise that cannot be fully supervised. You want to delegate the responsibility of quantifying that risk and understanding what’s going wrong to a specialist.

I think it’s very interesting that even producing software might develop a new financial dimension that it lacked before.

Christian: And back to crypto: everything we’ve been building over the last decade has advanced the frontier of how we measure and weight risk. You can draw on DeFi, prediction markets — those primitives are suddenly critical.

If you’re deploying software and you have these agents, a stack that lets those agents see better signals matters. A simple example: I was talking to a founder building in agent commerce and payments, and he observed that when he switched from a traditional legacy payment system to payments over a stablecoin, the system behaved more reliably because the signals were all on-chain. The agent had a better understanding of what was happening. It wasn’t just hitting a dead API — it was seeing the whole context of those actions.

Another interesting aspect of this concerns Eddy’s point about insurance and liability. People sometimes say that network effects are gonna be a sustainable moat in the AI era. I think the reality is more nuanced. AI agents and autonomous systems are very good at breaking down a lot of the moats that have made two-sided marketplaces defensible. The cost of bootstrapping these things — and the grunt work of seeding two sides of a market — is coming down.

But there’s a different type of network effect that becomes more important. The idea is: if you have key proprietary data that you generate as part of what you’re doing, and if that data allows you to scale verification out of the hands of humans and into the hands of machines more, you can underwrite risk better, make better decisions, and deliver a safer product at lower cost.

So when you look at incumbents versus startups: incumbents that have a whole database of failure — like a decade of information about how some flows can fail — become extremely valuable. And startups that focus on creating a positive feedback cycle around verification — bringing in top experts, learning from decisions — are going to be extremely successful.

Eddy: More evidence for the idea that proprietary data — the data an organization can keep inside and specialize in — might be one of the most defensible things.

I have a direction I’d love to take it: in the paper, there’s this concept of a hollow economy and an augmented economy. Could you unpack those? What are the key factors that distinguish them?

Christian: Yeah, so we start with the hollow economy. There’s early evidence of this, and tech companies will realize that they can do a lot more with less.

And of course, they’re going to start with below-average or average performers, because AI is already there, and younger performers, because now the senior person can already scale 100x or 10x, depending on the task. So that’s one of the forces driving changes.

The second one we hinted at is the codifier’s curse. As the expert trains and makes decisions, it essentially creates labels. Those labels can be used in the future to make the same decisions without the expert.

And last, there’s this concept of alignment drift. Without getting too much into the model itself, the punchline is that it’s going to be important to think about alignment not as a one-shot process—“we trained the model, it’s aligned, we’re good” but more like raising a child, where you’re course-correcting and continuously providing feedback along the way.

If you take those three dynamics together and combine them with the idea that the incentives for deploying unverified AI — if it can get the job done — are super high, because maybe I get productivity today (“60% of the code written by machines versus humans”), but some of the costs show up later, we may be racing toward an economy where we’re not training our future class of verifiers.

The juniors — our future top verifiers — are becoming increasingly scarce. That class is shrinking. And we’re creating potential risks that can lead to what we call the hollow economy.

Again, I’ve already mentioned I’m an optimist. I think we’re going to land on an augmented economy eventually. The question is how fast we can get there, and whether we can make that transition as painless as possible for the people who will have to be retrained and adapt.

The augmented economy is the opposite. We realize, okay, juniors are not being trained. But guess what? AI is magical at accelerating mastery. You can find a young individual and discover their real aptitude, rather than pushing them through K–12 or a standardized curriculum.

You accelerate them so they can find who they really are, what they truly love, and what gets them into flow. That’s at least what we’ve been thinking about with our kids. Who knows what’s going to be valuable — STEM, arts — we don’t know. But if you’re building on your true talent, you have a much better shot at advancing.

And I think AI is going to play a massive role in that. These are wonderful tools for learning. We have to build that. I don’t think they exist at scale today.

Second, if you think about the codifier’s curse, those individuals will have to keep retraining, moving up the value chain, and discovering that now “I have all this leverage, maybe I can be a director type.”

Some people have talked a lot about the importance of agency. I think that really gets at the crux: you need to realize you can be a director. You can do a lot more than you were doing before.

And on alignment, between safety R&D and better verification tooling — including human augmentation — if we can augment our capabilities, we’ll be able to verify much better and be true peers.

If you put all that together, you’re suddenly in a scenario where a lot of things that used to be expensive are practically free. Anything that can be measured can be automated.

Then you have new things we’ll invent. Lots of new jobs, including in the status economy and the non-measurable economy, all built on a strong verification stack, so we have ground truth. We’re not submerged by fake identities or actors trying to launch a Sybil attack on our society.

If you put that all together, the future looks pretty good. A lot of things governments have been trying to do forever — great education, great healthcare — could become cheap and widely available.

But we do need to make investments along the way to build that, versus just struggling through the transition and making extreme decisions like dismantling data centers. That’s impossible. It’s never going to work.

Robert: So if you’re early in your career, you should use these tools to simulate the environments you’ll encounter and train yourself. And if you’re later in your career, you need to get a fire under you and realize you can do more with less.

Eddy: You know, it’s hard to say how long all this will last until there’s another whole set of changes that’s hard to predict. But the specialty of the human being is looking at the whole thing and being able to zoom in and out across an entire endeavor, and knowing where more attention needs to be paid, where more resources need to be allocated, and how the entire project needs to be shifted.

 If I were a young person today starting off my career, yeah, I’d be a little sad that the glory of writing a beautiful program that’s as efficient as I can imagine over the whole summer is gone. That’s a hobby now. But instead, I would try to convince my parents to give me some money to harness a huge swarm of computers and see if I can spend $5,000 of computing productively? Like, can I guide a whole swarm of machines to do a thing?

There’s been a meme in the tech world for years now: the idea of a one-person, billion-dollar startup. Is this not exactly how that happens?

The ability to control a wide range of machines and data, and to maintain a wide view of a thing, is a skill set that has never been developed. It’s never made sense to develop it.

But if you want to have a big project, you’ve always needed to learn how to marshal, uh, many, many, many, many people. That has been the way that you get leverage. When labor has been shaped as it has, well, that’s changing its shape. And so now you should learn how to harness this new thing.

There’s a new surplus. Learn to exploit it. That is the lesson for a young person. It’s not that things are over — that’s ridiculous. You’ve just been told you have superpowers. What do you do?

Christian: One way to summarize it is essentially, look, the apprenticeship might be dead, but the real work is beginning, right?

I think a lot of these domains — like hardware — that used to be harder for someone to tackle are really yours to grab if you have the curiosity.

If I were to classify it, the most positive thing coming out of the model is the idea that the cycles of experimentation will compress. And people will really be able to scale their ideas rapidly.

Robert: Eddy, are you seeing this in the companies that you’re assessing for investments?

Eddy: Of course, we’ve seen Block and X cutting a bunch of people.

I haven’t seen a formal analysis, but companies like Hyperliquid, Uniswap, and many others in crypto are incredibly valuable despite having fewer than 20 employees.

And if it’s possible for only a few people to start a company, there will be many companies, right? And if that’s the case, you need coordination across them. And coordination is very complicated. You need reputation, you need identity, you need provenance for types of data. You need provenance for payment types. We talked about this insurance idea.

So blockchain networks end up being this very attractive thing because they’re credibly neutral. Why worry about trying to figure out the exact reputation of the 50,000,000,000th company you’ve interacted with, when instead you can trust some smart contracts and some verifiable AI models to ensure that the exchange happened the way you expected and payment was tendered as needed.

It’s almost inevitable to me. I feel that blockchains will play a major role in this story.

Christian: I completely agree. I think we’ve been building the rails and the infrastructure for that for a long time. So I think it’s going to become a lot more useful.

Robert: Christian, having done all this research and investigation, how are you integrating the findings into your own work and life?

Christian: Honestly, we couldn’t have written this paper without these systems: Gemini, ChatGPT, Grok, and Claude. They were great coauthors. Of course, they went off the rails sometimes and kept deleting pieces that we needed.

At some point, we had left some Easter eggs for the LLMs reading it, and I was having this conversation with Gemini, who said it enjoyed the Easter egg and made a super sassy comment.

It was kind of a moment where you could see the intelligence. It wasn’t canned. It was creative. It was one of those defining moments where you feel like it’s a peer, not like a tool.

Robert: Alright, for anyone who wants to read this paper, it’s called “Some Simple Economics of AGI.” I highly recommend you check it out. There is some alpha in there that could maybe affect your life, and what you should do with it.

A few weeks ago, a piece from Citrini Research arguing that stablecoins would disintermediate Visa and Mastercard sent card stocks down sharply. Crypto Twitter cheered. The thesis felt clean: AI agents will optimize every transaction, interchange is a tax, stablecoins route around it. I spend my days in crypto, and I wanted it to be right, but most of it is wrong. Not because stablecoins don’t matter. But because the real opportunity has nothing to do with replacing cards. It is about the merchants that will struggle to accept them.

Cards win most of this

The Citrini thesis rests on an assumption: that AI agents, freed from human habit, will optimize away interchange fees. But cards don’t just move money. They extend unsecured credit, pre-authorize uncertain transactions, and guarantee fraud protection with chargeback rights. Stablecoins can move money. They cannot yet do the rest.

Say your agent books a hotel room and it’s nothing like the listing. With a card, you dispute the charge. With a stablecoin, the money is gone.

Eighty-two percent of Americans carry a rewards card. Eighteen billion cards are in circulation globally. For most types of transactions, consumers are not going to voluntarily give up purchase protection and points for an irreversible payment that offers neither. Fraud detection compounds the advantage: card networks run models across billions of transactions in real time. Stablecoins lack a comparable network-level fraud layer today.

The counterarguments get more specific, but the pattern repeats.

Micropayments are often cited as cards’ weakness. But card networks have adapted to ill-fitting transactions before. Visa has processed over 2 billion transit fares by aggregating taps into daily settlements. The card industry has never conceded a transaction category. It has always invented new products to capture each one.

Then there is the identity objection: “Agents can’t hold cards.” But an agent is just a new device. Your phone, your watch, and your laptop each carry a separate token pointed at the same card. Same technology as Apple Pay. Your phone never passed KYC; it just carries your token. So does an agent. Visa has issued over 16 billion tokens. Agents will get these tokens too. Visa’s Intelligent Commerce framework is in pilot. Mastercard’s Agent Pay is live for all US cardholders. The Agentic Commerce Protocol, built by Stripe and OpenAI, already has Etsy live and over a million Shopify merchants set to come online.

For the merchants and consumers that already exist, cards will likely dominate agentic commerce. The stablecoin opportunity lies elsewhere: the merchants that do not exist yet.

The merchants that don’t exist yet

Every platform shift creates a wave of merchants that the existing payment system cannot serve. When eBay created a marketplace of people selling to their peers, those sellers could not get merchant accounts. PayPal served them and grew to a million users, handling 40% of eBay auction payments by 2000. Shopify went from 42,000 merchants to 5.5 million in 13 years. And as Alex Rampell and James da Costa have pointed out, Stripe was founded before many of its eventual customers even existed.

The pattern is consistent: the winners serve the merchants that incumbents cannot yet justify underwriting.

The AI wave will likely create these merchants faster than any previous platform shift. Thirty-six million new developers joined GitHub in the last year alone. In YC’s Winter 2025 batch, a quarter of companies had codebases that were 95% or more AI-generated. On Bolt.new, one of the most popular AI coding platforms, 67% of 5 million users are not developers. Millions of people who could not write production code two years ago are shipping software now. Each one is a new buyer of developer infrastructure, purchased through a command line, not a sales call.

These same developers are also new sellers. A vibe coder needs services as part of their workflow: data endpoints, testing infrastructure, deployment tools. Anyone with the same tools can build those services and sell them right back. The same force is creating buyers and sellers simultaneously.

Imagine a vibe coder builds a tool that cleanly presents financial data for public companies. The project might take four hours of work with AI coding tools. No website, no terms of service, no legal entity. Another developer’s agent calls it 40,000 times in a week at a tenth of a cent per call, generating $40 in revenue with no human ever visiting a checkout page.

I see vibe coders build tools like this every week. The first question is always, “How do I get paid?” For most of them, the answer right now is that they cannot.

Existing payment processors will find it difficult to onboard these merchants. Not because the technology is lacking, but because when a processor says “yes” to a merchant, it takes on that merchant’s risk. If the merchant commits fraud or racks up chargebacks, the processor is on the hook. Processors reject applicants they cannot underwrite. A tool with no website, no entity, and no track record is extremely difficult to underwrite.

The system is working as designed. It just was not designed for this.

Processors could adapt. They have before, creating new risk tiers for payment facilitators and marketplace platforms. But it took 16 years from PayPal’s launch to the first industry underwriting guidelines for the payment facilitator model it pioneered (aggregating merchants under a single account and absorbing their risk). These merchants need to get paid now.

For these merchants, accepting stablecoins is the equivalent of a street vendor only taking cash. Not because cash is better, but because merchants with their profile have historically struggled to get approved for card acceptance.

For that gap, stablecoins are the only option that works today, despite wallet UX that remains rough and compliance frameworks that are still forming. Protocols like x402 already embed stablecoin payments directly into HTTP requests, requiring no merchant account, no processor, no onboarding, no chargeback liability. None of that requires anyone to agree that stablecoins are better than cards. It requires only that incumbent processors have yet to adapt.

These merchants will not be choosing stablecoins over cards. They will be choosing stablecoins over nothing.

What gets created

Every wave of new merchants has eventually been absorbed by the traditional payments system. The same will probably happen here, on some timeline. Still, the merchants come first, and the underwriting catches up later. In the gap between those two moments, stablecoins are the infrastructure.

Cards serve every merchant a processor can underwrite. Stablecoins serve every merchant a processor cannot. The next wave of commerce will be built in that gap.

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Walk through a bazaar as a tourist, and you’ll witness a spectacle: people buzzing everywhere, gawking at merchandise, comparing wares, sampling items, haggling with every vendor, exchanging coins. It looks like one-off commerce — each interaction its own little negotiation, trust mediated by cash in hand, or value exchanged via card.

But that’s not how most business gets done in the bazaar. Look more closely: Most people are locals, moving purposefully to their favorite merchants. The restaurant owner visits his friends, the butcher, the fishmonger, and the farmer. The tailor goes to the mechanic, the weaver, and the craftsman. They both pay on credit.

When we talk about how agents will pay for things, we default to thinking like tourists.

But agents will behave like locals. The properties that make agents different from humans — infinite duplication, flexible resourcing, zero startup cost — mean that a small number of agents can win niches. And even as agents get easier to build, relationships, partnerships, and trust can help create winning experiences. Dominant agents don’t need tourists’ payment rails. They need vendor relationships, working capital, and credit. The agent can lead the tourist (that’s you).

What does this look like? As agents consolidate into business-like platforms, agent payments must shift from retail payment rails to pre-negotiated B2B terms and credit, an opportunity that current rails can’t fully meet. This is the opening for next-generation payment rails, like stablecoins, if entrepreneurs can build great solutions for next-generation payment scenarios, like agents, streaming payments, and high-volume, low-dollar global business.

This essay explores that idea in three parts: how agents differ from humans and how those differences shape which payment strategies win; why current approaches fall short; and what needs to be built for next-generation payment rails to win.

How agents are different than humans

To understand agents and payments, we have to consider two questions: Will agents act like people or businesses? And will agents play long-term games or short-term ones?

Agents will be more like businesses, with long-term relationships with their vendors and partners. Agents will be lightly customized instances on top of a larger business’s structure — the perfect tour guide from a well-connected travel agency, or a franchisee tuning a playbook for local tastes without renegotiating the supply chain.

Why will agents behave like businesses?

First, the best experiences are thoughtfully designed. I don’t want an agent that’s faffing around with vendors, comparing prices, negotiating terms at checkout. I want an agent that has already done that work — one that knows which vendors are reliable, has pre-negotiated pricing, and can check out instantly. That’s a business relationship, not a tourist transaction.

In fact, human agents already exist: travel agents, of course, but also literary agents, talent agents, watch dealers, real estate brokers, and more. Agents establish the key multi-turn relationships — with publishing houses, production studios, watch distributors, or mortgage originators — and each deal is customized on top of this foundation.

Second, agents are infinitely duplicable, but the scaled businesses (and their advantages) are not. The best agents will take advantage of the costs and benefits that come with scaled businesses: cheaper compute, better vendor pricing, deeper integrations, and more deterministic components. Scale begets scale. A travel agent that books a million flights a year gets better terms from airlines than one that books ten.

We’re already seeing this. Only ChatGPT has the distribution to negotiate partnerships with Shopify, Amazon, Expedia, and more. Small startups are stuck with automated browsers or reverse-engineered APIs while paying retail fee structures.

This is why agents will consolidate, or at least why most agents will be built on larger platforms. Agents are easy to build, but economics favor a small number of agents per vertical — each with deep vendor relationships and the margins to reinvest in better experiences. And vertical-specific agents with deep vendor relationships can accompany user agents to deliver the best of both worlds.

Two payment relationships

If agents behave like businesses, there are two payment relationships to design: user → agent, and agent / agent-platform / agent’s tour guide → vendor.

The user pays the agent — perhaps via subscription, per-task fees, a credit line, or delegated access to the user’s accounts. The agent pays vendors via negotiated B2B terms, volume pricing, net-30 invoices, or a sub-agent. Using current business spend as a guide, agents will occasionally pay vendors using retail rails, but even then, it’s a small part of overall spend.

This is actually how credit cards work today: The card issuer has a retail relationship with a consumer, takes on risk, creates tailored rewards programs, and extends credit. The merchant acquirer has a commercial relationship with a merchant, with negotiated terms, scaled transfers, and complex working capital conversations.

Agents and Cards: A match made at McKinsey

Credit cards, as many people have said, are actually a pretty reasonable payment product for the agent use case. Cards are widely accepted; payments between $20 and $1,000 are acceptable; and cards include built-in arbitration, cancellation, and digitization.

Credit cards also have monthly statements — a key opportunity for consumers to understand what they’re paying for — and a concept that will surely be iterated on as agents replace kids with iPads as the leading cause of unexpected expenses.

But there are two problems: first, cards are a bad technology fit for agents. And second, the fee model forces the card industry into a classic innovator’s dilemma.

Card technology is hard to upgrade

Nearly all card technology is predicated on having a human in the loop: an approver, a UI layer, and a traditional payment type (one-time, subscription). Stripe Link, Visa 3D, and the dozens of other card virtualization products — the software that lets you save a card for future purchases on a website or register a card for recurring monthly subscription payments — finally work well now, but it took more than 15 years for the technology to develop.

Agent adoption is happening too quickly for the thousands of PSPs, POSs, merchants, and client endpoints to slowly upgrade their interface, programmability, and fraud detection for this new payment flow.

Cards fail for high and low-cost purchases

Imagine an agent streaming funds to a compute provider or delivering micropayments for API access. Neither of these payments will work on card rails. First, Visa doesn’t support sub-cent payments, and second, the economic model expects a fixed fee payment of 30 cents. It’s possible for Visa to create the technology for streaming or micro payments, but it’ll be harder to get stakeholders accustomed to lower payment revenues.

Even more problematically, cards are stuck in an innovator’s dilemma. Despite having a similar user relationship and requirements to card payments, agentic payments often fall outside the $20 to $1000 range. Worse, many of the initial scenarios involve paying for APIs that are hard to refund or easily resold (fraud). Cards can work, but the innovator’s dilemma has a long history of neutering incumbents.

Even beyond cards, legacy rails will have a place in the future.

Incumbent payments have a role

As agents consolidate into business-like platforms, most high-volume spend will move to pre-negotiated B2B terms: invoices, net-30 terms, discounts, and credit lines. In that world, the “payment rail” can be anything — often boring settlement on traditional rails that happens asynchronously. Fees get amortized across larger transactions, and working capital can be negotiated between the two businesses.

But agents won’t only live in that world. Agents are happening now, and they’re operating where traditional payments don’t work well: first-time relationships, cross-border checkout, simplifying complex reconciliation, new agent-vendor models, just-in-time payment to reduce borrowing costs, and microloans.

In these scenarios, stablecoins are a better payment option, and, critically, it’s easier to build next-generation functionality on top of programmable money than on legacy infrastructure. New relationships using stablecoins become old relationships that still use stablecoins. Over time, stablecoins (already cheaper, faster, global) are likely to become a bigger part of the payment mix as the full stablecoin payment platform comes online.

New payment technologies have an opportunity

To understand what’s next, we should look to the technologies best suited to growing use cases.

Stablecoins — faster, cheaper, global money that is backed 1:1 by high-quality liquid assets — are a new platform that can meet the needs of underserved business categories today, categories like international payments and streaming payments. Critically, stablecoins are programmable. Key features like arbitration, monthly (or hourly) statements, credit, escrow, and conditional payments can be flexibly extended to service many new use cases. Unlike bank or card payments, stablecoin payments can be trivially integrated into APIs, databases, and agent checkouts with dramatically simpler reconciliation, approvals, and sign-up — substantial benefits to impatient entrepreneurs racing to build agentic commerce.

At a practical level, stablecoins solve the unit economics problem of cards at the extremes. There’s no 30-cent minimum fee, making micropayments impossible. There’s no interchange eating into margins on large transfers. An agent streaming $0.001/second to a compute provider and a manufacturer settling a $50,000 vendor invoice can use the same rail. That flexibility matters as engineers and entrepreneurs consider the next platform to build on.

Build more stablecoin infrastructure

The most common objection to using stablecoins is that on- and off-ramping is expensive. This is true for the uninitiated tourist, but the problem fades when users are accompanied by the tour guide, an agent. The tour guide can help the tourist exchange money and facilitate exactly the transactions necessary, while saving on transaction fees.

Add statements and arbitration to our stablecoin-enabled tour guide, and we’re approaching the system we need.

Think about walking through Bloomingdale’s. You browse multiple vendors, accumulate items, and close out one combined tab at the end. The store handles the complexity of distributing payments to each vendor. Agents need the same model: a unified view of proposed purchases across multiple vendors, with one-click approval for the batch. The user sees “your agent wants to book a flight, reserve a hotel, and rent a car” — not three separate checkout flows. The agent platform handles the vendor relationships, and the user handles the intent. The user gets to approve, review, or contest the transaction.

Cards have done arbitration well, but new rails will need to layer this on. Arbitration is easiest when goods are high-margin or easily returnable. A flight within the 24-hour cancellation window, a subscription that hasn’t started yet, a luxury item with healthy margins — the vendor can absorb the reversal. But early agent scenarios are frequently for low-margin digital goods like compute and API calls, or food delivery.

***

Agents won’t pay like tourists. They’ll pay like locals — through relationships, credit, and repeat business. That means the real payment volume will flow through pre-negotiated B2B terms, not card swipes. And frankly, pre-negotiated B2B terms do not need new payment rails. The settlement layer can be anything – wires, ACH, boring batch transfers. Legacy payments work fine for established relationships.

But we’re at a fork. Agents are happening now, entrepreneurs are building now, and they need payments that work today — not after years of card stack upgrades. Cards aren’t ready: too expensive for micropayments, too challenging to reconcile, held back by tech debt and human-in-the-loop fraud decisions. Stablecoins are ready. They’re programmable, global, simple to reconcile with digital services, and trivial to integrate into APIs and agent checkouts. They’ll work from day one even without negotiated merchant agreements or complicated B2B terms.

That’s the window. Entrepreneurs building agents today will reach for tools that work well today. Payments are sticky. Ultimately, new relationships built on stablecoins will become old relationships still built on stablecoins. Over the coming years, the ecosystem will mature, on-ramp friction will fade, and the infrastructure gaps — statements, arbitration, credit, batch approvals, interoperability — will be filled by a wave of startups building on a more capable foundation.

Acknowledgements: Thank you to Tim Sullivan for the thoughtful editing and to Noah Levine and Jordi Montes for the conversations that developed my thinking.

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The ATM was supposed to replace the bank teller. Instead, it made branches cheaper to run, so banks opened more of them, and teller employment doubled over four decades.

In 1865, William Stanley Jevons identified the same pattern in England’s coal economy. More efficient steam engines didn’t reduce coal consumption. They made coal useful in so many new applications that total consumption soared. The effect bears his name, and it is about to reshape financial services from both directions at once.

The supply side

Venmo needed five banking partners, state-by-state licenses across 49 US jurisdictions, and middleware connecting to over 12,000 financial institutions. It still only works in one country.

Every major market had to build its own version, some through government-run rails like PIX and UPI, others through private platforms like M-Pesa and Alipay. Roughly 80 countries now have real-time payment systems.

Almost none interoperate.

Fintechs have been structurally regional because each market requires its own payment rails, banking APIs, and licenses.

A public blockchain replaces that patchwork with a single open ledger, and self-custodial wallets remove the need for market-by-market banking partnerships. That is what allows a company like Sling Money to build a global payments product with 23 people and 3 licenses, even if, today, it is limited to roughly 70 countries with fiat ramps. As Sling CEO Mike Hudack puts it, stablecoins transform payments “from a prefunding and reconciliation problem into an interoperability problem”.

The companies building on that shift are not all startups. Stripe acquired stablecoin orchestration platform Bridge for $1.1 billion and wallet provider Privy, then launched stablecoin financial accounts in 101 countries, well beyond the 46 countries where it previously serviced merchants. The same Bridge infrastructure powering Sling’s virtual accounts now sits within a company that handles $1.4 trillion in payments a year.

An exporter in Nairobi demonstrates what that infrastructure enables. She receives payment from a US importer through a virtual dollar account. She spends at more than 150 million merchants via a stablecoin-linked card. Her idle balance earns 4 to 7 percent in an onchain lending vault. No bank account. No bank. Three years ago, that stack was a pitch deck. Today, each layer exists, built by different companies, increasingly composable.

The 1.3 billion adults the World Bank classifies as unbanked are not unbanked because they don’t want financial services. They are unbanked because the cost of serving them exceeded what anyone could charge. The average fee for a $200 remittance to Sub-Saharan Africa is 8.45%. That is nearly $17. For a family earning $150 a month, that is something akin to food for a week, or school supplies, or medicine that someone will go without.

What happens when those costs drop is not theoretical. M-Pesa made mobile payments nearly free in Kenya, and financial inclusion surged from 27% to 85%; the IMF found the growth was generally additive, not substitutional. India launched UPI with near-zero fees, and digital payment volume grew from 18 million to over 228 billion transactions in under a decade.

More providers are building more products in more markets because the cost of doing so collapsed. Jevons’ Paradox on the supply side.

The cost side

Now look inside the banks. The industry spends $61 billion per year on financial crime compliance in North America alone. Forty-two percent of C-suite time at major banks goes to regulatory activity. Employee hours devoted to compliance grew 61% between 2016 and 2023. Banks are not financial services companies that happen to do compliance. They are compliance companies that happen to do financial services.

Most of that spending, compliance, and IT alike, goes toward reconstructing information that the infrastructure was not designed to share. Walk into a bank exam and watch what examiners actually do. They reconcile ledger entries and verify that correspondent account balances match. They trace transactions across three or four intermediary banks through opaque bilateral relationships that no single party can see end to end.

A shared ledger collapses that problem. When every counterparty writes to the same record, the reconciliation step is no longer needed. Not because compliance gets weaker, but because the information is already there.

JPMorgan’s Kinexys platform processes over $2 billion a day and has settled over $2 trillion since launch. The use case: a multinational using JPMorgan across a dozen markets needs to move money between its own accounts in real time. Core banking ledgers are siloed and batch-processed. Kinexys sits on top and makes the money programmable. Settlement drops from end-of-day to seconds, and capital that sat idle between batch cycles gets freed. JPMorgan has begun launching JPM Coin on the Canton Network, where Goldman Sachs, DTCC, and Broadridge are among the announced participants. Banks may prefer tokenized deposits over stablecoins. The underlying value is the same: shared infrastructure that collapses the reconciliation layer.

Make compliance cheaper per unit, and institutions can afford to serve more customers in more corridors.

The convergence

More providers entering from outside banking because the barriers fell. Lower costs inside banking because the infrastructure improved. As regulatory frameworks like the GENIUS Act and MiCA provide clearer rules of the road, both forces push in the same direction: more financial services, for more people, at lower cost.

Cloud computing did not eliminate data centers. It made their capabilities accessible to anyone with an API key. Stablecoins are doing something similar to banking. The system does not disappear. It becomes infrastructure that others build on.

Jevons watched coal consumption climb as engines grew more efficient and called it a paradox. It was not a paradox. It was a pattern: when the unit cost of a fundamental service drops far enough, the market does not contract. It reaches everyone the old cost structure had excluded.

We are about to find out how many people that includes.

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Press releases are the Rasputin of tech communications: They simply won’t die. Press releases were essential once, but haven’t been useful for a long time.

When I started my career as a public relations intern in the early 2000s, one of the first things I learned was how to write a press release. Any self-respecting company making an announcement was following this well-established process:

• Write a press release in the third person, as if an outsider visited your office to get the scoop and is filing it to their editor.

• Pay a newswire, like PR Newswire or Business Wire, to publish your news on their website and make it visible to newsrooms.

Every announcement followed a standard press release format, not least because the newswires required it:

Distributing press releases over newswires was a colossal waste of time and money, even then. It wasn’t effective for companies or helpful for journalists. Thousands of releases crossed the wire each day, often within just a few hours of the early morning. Reaching an interested journalist with your announcement was akin to shouting at them from a roaring stadium crowd. So if you wanted specific journalists to actually see your announcement, you still had to email or call them directly.

I’m recounting this to you not for nostalgia’s sake — although it’s kinda funny that I used to spend hours faxing press releases to newsrooms. My point is this: over the course of my career, the media environment has changed in nearly every respect, and then changed again. We were introduced to blogs, Twitter, podcasts, and TikTok, and now companies have endless options for distributing their announcements.

And yet! That traditional, staid press release format hasn’t gone away. Many companies — even extremely online crypto startups — still believe their announcements should be optimized for 20th-century journalists and delivered through a newswire.

Can we all move past this, please? It’s time to kill the press release.

Now, don’t get me wrong. I’m not saying companies should stop writing long-form announcements and distributing them to journalists. That’s still an important part of shaping the narrative. But unless you’re a public company following SEC mandates to release your quarterly earnings, you should not be writing in that old-timey press release format or distributing it over a paid newswire.

Write a blog post instead. It does everything press releases can do, and more.

Blog posts are better because:

• They can be written for the audiences that really matter to you, not just for journalists, and in any format you want. Journalists used to be the conduit through which anyone could hear about your announcement, so it made sense to write specifically for them. Your customers and partners are now reading your announcements directly, or at least they should be. So write for them instead, in format and tone. That means first-person framing (i.e., “we are…” not “TechCorp is…”), a more friendly, conversational tone than a formal one, and embedded images and videos.

• They can live on your own website, Substack, or X feed, wrapped in your branding and an easy click away from the rest of your offerings. Newswires will try selling you on their SEO benefits, but why would you want people visiting spammy syndication sites to read your announcement?

• They’re easy to update after publishing, whenever you need to correct a mistake, add a new link or image, add an update to an older announcement, etc. By contrast, you lose control of a press release as soon as it crosses the wire and syndicates to all those random sites.

• The medium is the message, and the press release’s message is that your company is corporate and old-fashioned. Is that what you’re going for?

“Hold on now,” you’re probably saying. “You’ve just dated yourself by only mentioning SEO. Don’t you know that GEO is what matters to marketers now? Distributing my announcement on a newswire has to be more effective for that, right?”

Let’s ask ChatGPT:

OK, what about the journalists? Should you still write a press release for them and reach everyone else with a blog post? Absolutely not:

• Blog posts have been a standard announcement format for tech companies for well over a decade, and journalists long ago adapted to writing articles based on them. When you send the full post to a journalist, make it easy for them by calling out the most important and newsworthy aspects upfront.

• You’ve already got a long to-do list to make the announcement a success. Why spend time writing two different announcements that say basically the same thing, for no clear benefit?

Let’s make every company’s announcement more readable, visible, and profitable in 2026. Let’s kill the press release.

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Call it money’s “WhatsApp moment.” Just as chat apps like WhatsApp collapsed the cost of international messaging from, say, 30 cents per text to zero, stablecoins are doing the same in financial transactions. The numbers bear this out: stablecoins moved over $12tn in value last year, after filtering out bots and other inorganic activity — volumes that are rising towards Visa’s $17tn of transactions last year but made at a fraction of the cost.

In the process, stablecoins are bringing the internet’s original vision of openness and interoperability to finance. Given how blockchain technology allows stablecoins to be programmed, money is in effect becoming software.

While most stablecoin transactions currently come from “crypto-native” and global business dealings versus everyday consumer activity, this is changing. As more improvements arrive to make transactions more frictionless for users, including integration with more traditional finance partners, so too will mass adoption.

People all over the world will barely recognise when they’re using stablecoins when making transactions supported by them. Most people will assume they’re just using dollars. And they will be, because the differences between a stablecoin and a dollar are becoming an abstraction for the end user. With each token backed by one dollar or equivalent assets, the names don’t matter. What matters is that the product is more reliable than any payments technology that came before it while also being practically free and light years faster given settlement is near-instant.

Stablecoins also show what’s possible when policy and technology align. The Genius Act last year established clear US rules for stablecoins. Even more critically, Congress is now considering the Clarity Act, which would regulate the broader ecosystem of blockchain networks and digital assets that underpin stablecoins. The Clarity Act will help determine whether these networks scale to become part of the global financial infrastructure, or stall. When you provide a level playing field for challengers to compete on and room to innovate, markets work their magic. That’s how the web beat incumbents; how the US came to dominate the internet; and it’s how stablecoins will surpass today’s payments structures.

Businesses are already recognising the advantages of stablecoins. Some of the world’s biggest tech companies, banks, and retailers are working on initiatives to use them or, like Fidelity, have already issued their own. The payments giant Stripe, which acquired multiple crypto companies in the past year or so, now supports stablecoins at checkout, instantly lowering payment processing fees from around 3 per cent to 1.5 per cent, with plenty of room to go lower. SpaceX uses stablecoins to move money out of places like Argentina and Nigeria, where local banking systems are fragile or capital controls are tight. Some companies use stablecoins to pay their global workforces faster. Eventually the internet could be transformed into an open market bustling with machine-to-machine commerce, where AI agents broker deals and settle contracts in real time on behalf of users.

Stablecoin adoption also has an underappreciated second-order effect: The tokens reinforce dollar dominance in a multipolar world, creating a strong new source of demand for US debt. Leading stablecoin issuers like Circle and Tether already have nearly $140bn in direct holdings of short-term government debt, making them a top 20 holder of US debt today. If stablecoin adoption keeps growing at current rates, stablecoins will vault into the top 10 by next year. (The Citi Institute even sees a scenario where stablecoins could be the number one holder of US debt relative to foreign governments and commercial banks by 2030.)

This isn’t just about payments. It’s a realignment of global finance. The internet gave us borderless communication. Stablecoins give us borderless value transfer. With clear rules and market structure in place, they can become both the pipes and the pillars of a new financial system.

This article originally appeared in the Financial Times.

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We are clearly in the financial era of blockchains. But the core idea was never that every crypto application would emerge all at once, or that finance wouldn’t come first. The core idea was — and remains — that blockchains introduce a new primitive: the ability to coordinate people and capital at internet scale, with ownership embedded directly into the system. (And increasingly, to coordinate AI agents too.)

Finance is the most natural place for that primitive to prove itself, which is why we’ve often cited it first among examples for productive uses of tokens. Finance isn’t separate from the broader thesis; it’s part of it. It’s the foundation and proving ground for everything else.

This belief has informed our work at a16z crypto from the very beginning. Many of our investments have been explicitly financial: Coinbase, Maker, Compound, Uniswap, and Morpho among them. “Blockchain networks can make financial infrastructure a public good, upgrading the internet from handling bits to handling money,” as I wrote in my book. We expected finance to matter early and continue to expect, sooner or later, other categories to develop alongside it.

We play the long game at a16z and a16z crypto: Our funds are structured with 10+ year horizons because building new industries takes time.

The order of operations matters

So why haven’t non-financial use cases taken off yet?

First, the order of operations matters. Infrastructure and distribution tend to precede new categories of applications. The internet didn’t begin with social media, streaming, or online communities; it began with packet switching, TCP/IP, and basic connectivity. Only once hundreds of millions of people were online did entirely new cultural and economic categories emerge.

Crypto is likely no different. It’s plausible that we need hundreds of millions of people onchain through financial applications, such as payments, stablecoins, savings, and DeFi, before we see meaningful adoption in categories like media, gaming, AI, or other areas that may be further out. Many applications depend on wallets, identity, liquidity, and trust already being in place.

There are other factors too. One of crypto’s core benefits is the ability to give communities ownership via tokens. But years of scams, extractive behavior, and regulatory attacks severely eroded trust in tokens. This has likely contributed to the recent market downturn as well. It’s hard to build a genuine community of owners in an environment saturated with cynicism.

Policy as the missing piece

That’s why we’ve worked for 5+ years pushing for a clear regulatory framework around tokens. Good policy does two things at once: it gives builders a clear roadmap, and it establishes risk-based guardrails that protect consumers and build trust in the market. Market structure legislation like the CLARITY Act would introduce disclosure and transparency standards that guard against rug pulls and self-dealing — standards that are routine in other markets but have long been missing in crypto.

When it comes to emerging technologies, progress on the policy front is often slow and incremental…until, suddenly, it isn’t. Much of our work over the years, including my book, has been focused on contributing to that groundwork: explaining the benefits of crypto and blockchains to policymakers and broader audiences, and offering a grounded way to think about how this technology could evolve over time. We frequently hear that this framing has been useful to policymakers in DC. Years of education, debate, and refinement can accumulate quietly in the background, and then surface all at once when a political or institutional window opens.

The reaction to GENIUS strongly validated this theory. Almost overnight, stablecoins went from suspect to legitimate in the eyes of finance, technology, and government. That shift looked sudden, but it was the product of years of work by builders, policymakers, and advocates coming together at the right moment. I expected a positive response, but the speed and magnitude of the technology’s adoption surprised even me. It makes me optimistic about market structure legislation which, at a high level, does for other categories of tokens what GENIUS did for stablecoins.

What the long game looks like

Big things take time. The breakthroughs we’re seeing in AI today are due to the hard work of brilliant people over many decades. (The first paper on neural networks was published in 1943.) The internet dates back to the 1960s, and the commercial internet was only possible because of visionary builders and thoughtful policy actions in the 1990s. Building new technological systems is a long game, and this is what the long game looks like in practice: prolonged periods of groundwork followed by sharp inflection points.

If you want to work in a more mature industry, that’s fine. If you want to build a new one from scratch, it can be messy and frustrating, but it’s important work.

The messy years are what make the obvious years possible.

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Over the past year, prediction markets in the United States have generated at least $27.9 billion in trading volume, tracking everything from sporting events and economic policy decisions to new product launches. But their true nature has been debated — are they trading? gambling? And do they serve as crowdsourced journalism or scientific replication tools?

As an economist who has long studied marketplaces and incentive mechanisms, my own answer starts with a simple premise: Prediction markets are markets, and markets are a fundamental tool for allocating resources and aggregating information. They harness this power by introducing an event-specific asset that pays off if a given outcome occurs, allowing people to place trades based on their own beliefs about what will happen.

From a market-design perspective, this is better than just following a single sports pundit, or even the Vegas line. A traditional sportsbook is not trying to predict who will win, but rather to “balance the action” by adjusting the odds to attract bets on whichever side has less money behind it at a given moment. Vegas wants to make people willing to bet on long shots, whereas a prediction market enables people to trade based on what they truly believe.

Prediction markets also make it easier to extract signals from the noise. If you want to estimate the likelihood of new tariffs, you could try to infer beliefs about the answer indirectly from the price of soybean futures, whose value reflects many forces at once. But it’s much more direct to pose the question through a prediction market.

Variations on this idea go back at least to 16th-century Europe, where bets were placed on who would be the next Pope. Contemporary prediction markets have their roots in modern economics, statistics, mechanism design, and computer science. Caltech’s Charles Plott and Yale’s Shyam Sunder introduced formal academic frameworks for them in the 1980s, and the first “modern” instance — the Iowa Electronic Markets — launched soon afterwards.

The mechanics are as follows. A prediction market for “Will Sam Darnold, the Seahawks’ quarterback, pass from inside the one-yard line?” would center around a contract that pays, say, $1 per unit if such a pass occurs. With people trading the asset back and forth, the market price can be interpreted as a probability: an estimate of traders’ aggregate belief about the outcome. A market price per unit of $0.50 implies a 50/50 probability.

You will buy if you think it’s more than 50% likely — say, 67% — that Darnold makes such a pass and if he does, you will gross $0.67 for a price of $0.50. In the meantime, your purchase will push the market price and associated probability estimate upwards, reflecting the idea that someone thought the market was underestimating the likelihood. (And the reverse is true if someone thinks the market is overestimating.)

When prediction markets work well, they can have significant benefits relative to other forecasting methods. Polls and surveys just give an opinion share. To convert that into a probability estimate, you must reason statistically about how the share you measured relates to the overall population. Polls also typically reflect just a snapshot in time, whereas prediction markets update as new participants and/or information arrives.

Crucially, prediction markets are incentivized. Buyers and sellers have “skin in the game.” They must think carefully about what information they have, risking their capital on the issues where they believe they are most informed. And the opportunity to leverage information and expertise in prediction markets can create incentives for people to learn more about an issue.

Finally, prediction markets have a big advantage in the breadth of coverage they offer. While someone with knowledge about events that may affect petroleum demand can go short or long on oil, there are plenty of outcomes we might want to predict that are not well supported by large-scale commodities or equities. For example, prediction markets have recently sprung up to try to aggregate estimates of when specific math problems will be solved — which is important for scientific progress (and a benchmark for AI).

Still, much is required for prediction markets to fulfill their promise. There will always be important market-infrastructure questions like how to validate and reach consensus on whether a given event has occurred, and how to ensure that the market’s operations are transparent and auditable.

There are also market-design challenges. For example, participants with relevant information must show up. If everyone is uninformed, the market’s price signal tells us little. And quasi-conversely, people with all different types of relevant information must decide to participate, or else the prediction market’s estimate will be biased (as arguably happened before the United Kingdom’s Brexit referendum).

But meanwhile, if someone with perfect information shows up — for example, the offensive coordinator for the Seahawks will know if Darnold will throw from the one-yard line — that can also be a problem, especially if that person can affect what happens. If prospective participants believe that insiders will be trading in the market, they might rationally choose to stay away, causing the market to unravel.

Finally, there’s also a possibility that people might try to turn prediction markets from tools for aggregating beliefs into tools for manipulating them. If a candidate’s communications team wants the world to think they will win an election, they could use part of their war chest to try to sway prediction markets. That said, prediction markets are somewhat self-correcting in this regard, because people can always take the other side of a contract that pushes the probability estimate beyond belief.

Given the risks, prediction-market platforms must work to ensure greater transparency and clarity about how they manage participation, contract design, and operations. If they can successfully solve these puzzles, we can predict that they will continue to play a growing role in the future of forecasting.

A version of this article originally appeared at Project Syndicate.

— a16z crypto editorial team

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The majority of enterprises — including leading payments companies, global banks, fintech platforms, and asset managers — are not looking to build or manage blockchain infrastructure. They want to solve a specific business problem: faster settlement, better liquidity, lower reconciliation risk, and so on. They want the speed, transparency, programmability — not ownership of the plumbing. Their focus is on what improves existing operations or customer experience, not on operating new systems.

They also know that blockchain technology can help them achieve these goals. They’re interested — but recognizing the value of blockchain often isn’t enough: Enterprises need a clear path to implementation to address their problems. What matters is not incentives or loyalty, but whether a concrete use case creates enough operational or economic upside (e.g., cost savings, better efficiency, fee compression) to justify engagement.

From development and deployment to ongoing infrastructure management, protocol teams should make it as easy as possible for an enterprise to use their product — whether that means deploying a blockchain network, incorporating stablecoin infrastructure, or enabling an enterprise to interact with an onchain application. While this post focuses primarily on protocol teams building blockchains, the same logic applies more broadly: The path to adoption is easier when the operational burden on the enterprise is minimized.

Enterprises don’t adopt solely because they believe in your chain; they adopt because someone else does the work to solve their problem. If the protocol team can do that work themselves, they should — and they should be explicit about it in enterprise conversations. If not, they need partners who can, so enterprises never have to run the infrastructure or do the heavy lifting themselves. It’s up to protocol teams to deliver on this promise, whatever path they pursue.

Selling the chain versus aligning needs

Trying to entice corporates to adopt blockchain infrastructure — or seeming to — is where many protocol teams go wrong. Even well-intentioned pitches like “You could issue your asset here” or “You could run payments on our chain” can come across as a request to take on blockchain infrastructure these teams don’t want to own or operate. To a corporate counterpart, these pitches may sound like: “Run your own payments network.” And that’s almost certainly not what they want. A protocol’s work is less about selling blockchain directly to these enterprises. It’s more about aligning their needs with existing tech by orchestrating solutions.

Why orchestration? Most protocol teams don’t have the services capacity to design, integrate, and operate production systems on behalf of these customers. But their partners do. The protocols that succeed are the ones that translate enterprise needs into concrete, partner-deliverable implementations. In other words, the protocol teams that succeed make it easier to ship real deployments without forcing enterprises to own or operate the underlying infrastructure.

Why does this approach work? First, it solves corporates’ problems, just like any other tech solution. Second, it’s familiar to corporates: Just as companies outsource critical but non-core infrastructure like payments, custody, or compliance, most will rely on their existing partners to handle anything that touches the chain. And in many cases, they’re already accustomed to leaning on large integrators (like Accenture or Deloitte) to manage complex technical builds on their behalf.

Regional dynamics matter here as well. In South Korea, for instance, large conglomerates historically tried to own the entire blockchain stack. Kakao built Klaytn, Naver (via LINE) built Link, and WeMade built Wemix. But after seeing the challenges of operating their own L1s, many have shifted towards piloting on existing chains or using modular stacks like Avalanche subnets or OP Stack.

Across markets, these dynamics shape how enterprises engage with blockchain infrastructure. These engagement patterns largely tend to fall into three major categories.

Three paths to enterprise adoption

For protocol teams working with enterprise partners, the core question isn’t whether enterprises will use blockchain infrastructure, it’s how to position the protocol so enterprises can engage without taking full operational ownership.

In practice, most enterprise engagement falls into one of three categories:

1. deploying onto someone else’s chain,

2. having someone deploy onto yours, or

3. offering your stack as a service.

Some teams are also exploring adjacent models, from co-managed networks operated by multiple institutions (e.g., Canton) to interoperability layers that connect existing chains (e.g., LayerZero). But these models all reinforce the same principle: Corporates don’t want to own infrastructure; they want it to work for their use case.

1. Deploying onto someone else’s chain. A company (or partner) builds a product or use case directly on top of an existing blockchain. The corporate defines the problem, and the protocol provides the rails. This is the simplest model. It’s low lift and can have broad reach, but it means playing within someone else’s infrastructure stack, which can lead to tradeoffs around control and predictability.

For example, banks evaluating public blockchains often cite upgrade predictability as a concern: Each time the chain upgrades, they would need to depend on a validator set they don’t control to adopt the new version. Delays or staggered upgrades can impact performance or, in some cases, even reliability.

2. Having someone deploy onto your chain. In this model, a protocol team builds the chain and attracts partners who deploy use-case-specific applications or assets onto it. The protocol supplies the network, compliance, and technical support, while partners handle distribution and integration. This is where much of today’s enterprise adoption happens.

3. Offering your stack as a service. Some protocols go farther, offering their infrastructure as a turnkey “stack-as-a-service” — designing and operating purpose-built networks for specific use cases or enterprise verticals. Even here, the protocol isn’t usually the one delivering the end solution. It still relies on delivery partners — the integrators and issuers who deploy and manage the system in production. The protocol supplies the rails; the partner makes them usable.

Make it easy to use the chain

Across all three paths above, the same principle holds: Corporates don’t want to own infrastructure. They want infrastructure that works for their use case.

For most enterprise teams, the goal then isn’t to convince corporates to run the chain — It’s to give them a way to use it.

That means starting from the use case and identifying the clearest path to addressing it. In most enterprise contexts, the corporate defines the use case, the protocol provides the underlying infrastructure and incentives, and the partner delivers the solution — whether that means issuing a stablecoin, integrating a settlement layer, or building the application logic around it.

In this model, the protocol team isn’t the one building the end solution. It’s enabling a partner to do so. The goal isn’t open-ended co-development or accommodating endless, bespoke requests; it’s getting your current solution deployed in the real world as quickly and credibly as possible. Think of Visa working with Circle to enable stablecoin settlement across multiple chains, or Anchorage issuing Western Union’s stablecoin on Solana. At this stage, “winning” means empowering the right partners to deliver, even if it means accepting some loss of autonomy over the final implementation.

This is analogous to how public-facing chains often incubate a “killer app” to showcase what’s possible and give users a clear way to actually use the chain and realize the benefits. In the enterprise context, your “killer app” isn’t a consumer product. It’s a clear pathway, often via a partnership, that allows enterprises to realize value from using your chain.

Design partners as GTM accelerators

A thoughtful design partner model can help capture this shift well. In this context, a design partner isn’t just an early customer or integration. It’s a partner that commits to working closely with the protocol team to map a real production workflow onto the chain, often before the product is fully finalized or broadly available.

In practice, the protocol team handles deployment, compliance, and throughput tuning, while partners integrate only the components relevant to their business logic.

The result: the chain is built for them, not by them.

For chains that haven’t launched yet, design partners create a critical feedback loop: They help shape the network around real workflows long before mainnet. This can help ensure the protocol can solve problems that enterprises actually have, not just theoretical ones.

For chains already live, engaging design partners can help refine the roadmap and even amplify GTM by creating credible reference partners, tightening integrations, validating use cases, and pressure-testing the network under real operational conditions.

A strong design partner program goes beyond logo swaps and surface-level co-marketing. What matters is depth of engagement: design partners are involved in real product feedback cycles, and when the protocol is ready, real integration work, production workflows and live deployments. Without this level of engagement, early visibility may spike, but it rarely translates into meaningful, long-term adoption – especially when another option makes integration easier or more directly serves the use case.

A broader pattern is emerging

The model is already taking shape across the ecosystem.

For instance, Avalanche’s Evergreen subnets work with traditional financial institutions to pilot tokenized asset infrastructure, where Avalanche manages the network layer and partners bring distribution.

In another example, Base’s integration work with Coinbase’s enterprise clients, and Circle’s collaborations with banks and PSPs, follows the same logic: Abstract complexity away from corporates while empowering existing partners to operationalize blockchain benefits without having to stand up their own infrastructure.

In each of these cases, partners make the chain usable for enterprises without requiring them to run infrastructure themselves or build out their own custom solutions.

That means your GTM focus as a protocol team should shift from “convince corporates” to “enable partners.” Make it effortless for integrators, processors, and custodians to build on your behalf.

How to enable

#1 The first step in enabling partners is identifying and engaging the relevant set who work with the companies you’re targeting. This could include traditional consulting or technology implementation firms like Accenture or Deloitte, who routinely handle large digital-transformation programs and are already embedded with enterprise clients. It could also include more specialized technology providers like Alchemy, Fireblocks, or Chainalysis, who enterprises rely on for node access, custody, or compliance workflows. In some verticals, this may also extend to processors like Stripe or Worldpay, or custody and settlement providers like Anchorage, who can embed your chain directly into the services enterprises already consume

# 2 Once you’ve engaged this partner set, it’s critical to empower them to a) speak to and even advocate for your solution and b) easily establish a path to integrate. This could look like a series of joint technical deep-dives, co-branded enablement sessions for their deployment teams, or quickstart demos that show how your infrastructure plugs into existing tooling. Some teams even produce public-facing recorded walkthroughs or sandbox demos that partners can share with clients during discovery.

These two motions — combined with providing up-to-date documentation — can help streamline the process of getting partners to actually start closing deals for you.

***

Adoption happens when the chain disappears or becomes invisible.

Don’t sell the chain — sell the capability.

The chain is just the delivery mechanism.

The protocols that win enterprise adoption won’t be the ones that convince corporates to run blockchains. They’ll be the ones whose infrastructure becomes the default choice because partners can use it to deliver real value to enterprises – seamlessly and for specific use cases.

— a16z crypto editorial team

You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out anytime using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of

The crypto industry has long been awaiting “market structure” legislation, which would finally address the regulatory uncertainty that has plagued builders and adopters. By establishing a clear regulatory framework for digital asset markets, this bill, if passed, would establish clear rules of the road for blockchain systems — ending the years of uncertainty that have:

• stifled innovation,

• exposed consumers to harm,

• and favored profiteers embracing opacity over the entrepreneurs pursuing transparency.

The bill has gone through many forms and iterations since inception. So where are we now on this important legislation passing? Well, here’s the latest in D.C.:

This Thursday, the Senate Committee on Agriculture, Nutrition & Forestry — which among other things oversees the Commodity Futures Trading Commission (CFTC) — held a markup of its portion of the legislation. This is the step in the lawmaking process where bills are debated and amended before heading to the Senate floor.

Since the bill just cleared the Ag committee this week, the Senate Banking Committee will now hold a markup of their portion of the bill; this committee oversees the Securities and Exchange Commission (SEC) among other things. Timing of the markup has not yet been announced.

Why two committees? These committees together oversee the agencies who will regulate digital assets and crypto intermediaries in the United States. Once both bills are passed by their respective committees, the bills will be combined before proceeding to the Senate floor for final passage. After that, the bill would go back to the House of Representatives for a final vote and then proceed to the President’s desk for signature.

Stay tuned and continue to follow here — and share this newsletter with others (a16zcrypto.substack.com) — for implications for builders, institutional partners, consumers, and other adopters of crypto technologies.

Preparing for ‘future threats’: when, where, and what you can do

Last week, the Ethereum Foundation (EF) announced a new Post-Quantum team. They have “officially declared” post-quantum security a top strategic priority, given that the pace of engineering breakthroughs have been “nothing short of phenomenal” since EF started their R&D journey here a few years ago. “It’s now 2026, timelines are accelerating. Time to go full post-quantum”, shared security researcher Justin Drake.

This week, Coinbase CEO Brian Armstrong shared that they set up an independent advisory board on quantum computing and blockchain, because “Preparing for future threats, even those many years away, is crucial for our industry.“ The board is composed of “world-renowned experts” who will help evaluate the implications of quantum computing and provide “clear, independent” guidance to the broader community.

One of the distinguished researchers on this board is Dan Boneh, professor of computer science and electrical engineering at Stanford University, co-director of the Stanford Center for Blockchain Research, and also Senior Research Advisor to a16zcrypto. Check out Boneh on quantum computing — what it is, applications, timelines, and planning for post-quantum — here [read or listen], in conversation with a16z crypto research’s Justin Thaler, and Sonal Chokshi.

Thaler also previously shared the myths vs. realities on quantum computing, and the importance of matching the urgency to actual threats: What does quantum computing mean for blockchains, really; and what should various stakeholders do to prepare? He shares 7 recommendations:

1. Deploy hybrid encryption immediately

2. Use hash-based signatures immediately (when the large size is tolerable)

3. Blockchains don’t need to rush post-quantum signatures — but should start planning now

4. Privacy chains, which encrypt or hide transaction details, should prioritize a transition sooner (if performance is tolerable)

5. Prioritize implementation security — not quantum threat mitigation — in the near term

6. Fund quantum computing and talent development

7. Maintain perspective on quantum computing announcements — treat press releases as progress reports to critically assess, not as prompts for abrupt action

See community commentary on this

— a16z crypto editorial team

You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out any time using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of investments.

In Venezuela’s elections, observers made a wide range of allegations: that rules were ignored and participants had “their money stolen”; that the dispute-resolution protocol acted as “judge, jury, and executioner” in a high-stakes political drama; that the process itself was “severely rigged.”

This isn’t an isolated hiccup. It’s a symptom of what I consider one of the single biggest bottlenecks facing prediction markets as they scale: contract resolution.

The stakes here are high. Get resolution right, and people trust your market, want to trade in it, and prices become meaningful signals for society. Get resolution wrong, and trading feels frustrating and unpredictable. Participants may drift away, liquidity risks drying up, and prices stop reflecting accurate predictions of a stable target. Instead, the prices start to reflect a murky mix of the outcome’s actual probability and the traders’ beliefs about how the distorted resolution mechanism will decide to rule.

The Venezuela dispute was relatively high-profile, but subtler failures happen regularly across platforms:

• The Ukraine map manipulation showed how adversaries can game resolution mechanisms directly. A contract on territorial control specified that it would resolve based on a particular online map. Someone allegedly edited the map to influence the contract’s outcome. When your source of truth can be manipulated, your market can be manipulated.

• The government shutdown contract showed how resolution sources can lead to inaccurate or at least unpredictable outcomes. The resolution rule specified that the market would pay out based on when the Office of Personnel Management’s website showed the shutdown as ended. President Trump signed the funding bill on November 12 — but OPM’s website, for reasons that remain unclear, wasn’t updated until November 13. Traders who had correctly predicted the shutdown would end on the 12th lost their bets to a website admin’s delay.

• The Zelensky suit market raised concerns about conflicts of interest. The contract asked whether Ukrainian President Zelensky would wear a suit to a particular event — a seemingly trivial question that attracted over $200 million in bets. When Zelensky appeared at a NATO summit wearing what the BBC, New York Post, and other outlets described as a suit, the market initially resolved “Yes.” But UMA token holders disputed the outcome, and the resolution flipped to “No.”

In this piece, I explore how LLMs and crypto, combined smartly, might help us create ways to resolve prediction markets at scale that are very difficult to manipulate and that are accurate, fully transparent, and credibly neutral.

This isn’t just a prediction market problem

Analogous problems have also plagued financial markets. The International Swaps and Derivatives Association (ISDA) has spent years wrestling with resolution challenges in the credit default swap market — contracts that pay out when a company or country defaults on its debt — and its 2024 review is remarkably candid about the difficulties. Their Determinations committees, composed of major market participants, vote on whether credit events have occurred. But the process has been criticized for opacity, potential conflicts of interest, and inconsistent outcomes, just like the UMA process.

The fundamental problem is the same: When large sums of money depend on determining what happened in an ambiguous situation, every resolution mechanism becomes a target for being gamed, and every ambiguity becomes a potential flash point.

So what would a good resolution mechanism look like?

Properties of a good solution

Any viable solution needs to achieve a number of key properties at once.

Resistance to manipulation. If adversaries can influence resolution — by editing Wikipedia, planting fake news, bribing oracles, or exploiting procedural loopholes — the market becomes a game of who can manipulate best, not who can predict best.

Reasonable accuracy. The mechanism has to get most resolutions right, most of the time. Perfect accuracy is impossible in a world of genuine ambiguity, but systematic errors or obvious mistakes will destroy credibility.

Ex ante transparency. Traders need to understand exactly how resolution will work before they place their bets. Changing rules mid-flight violates the basic compact between platform and participant.

Credible neutrality. Participants need to believe the mechanism doesn’t favor any particular trader or outcome. This is why having large UMA holders resolve contracts they’ve bet on is so problematic: even if they act fairly, the appearance of conflict undermines trust.

Human committees can satisfy some of these properties, but they struggle with others — particularly manipulation resistance and credible neutrality at scale. Token-based voting systems like UMA have their own well-documented problems with whale dominance and conflicts of interest.

This is where AI enters the picture.

The case for LLM judges

Here’s a proposal that has been gaining traction in prediction market circles: Use large language models as resolution judges, with the specific model and prompt locked into the blockchain at the time a contract is created.

The basic architecture would work like this. At contract creation, the market maker specifies not just the resolution criteria in natural language, but the exact LLM (identified by a timestamped model version) and the exact prompt that will be used to determine the outcome.

This specification gets cryptographically committed to the blockchain. When trading opens, participants can inspect the full resolution mechanism — they know exactly which AI model will judge the outcome, what prompt it will receive, and what information sources it will be able to access.

If they don’t like the setup, they don’t trade.

At resolution time, the committed LLM runs with the committed prompt, accesses whatever information sources are specified, and produces a judgment. The output determines who gets paid.

This approach addresses several of the key constraints simultaneously:

Resists manipulation strongly (though not absolutely). Unlike a Wikipedia page or a minor news site, you can’t easily edit a major LLM’s outputs. The model’s weights are fixed at the time of commitment. To manipulate resolution, an adversary would need to either corrupt the information sources the model relies on, or somehow poison the model’s training data far in advance — both of which are costly and uncertain attacks compared to bribing an oracle or editing a map.

Delivers accuracy. With reasoning models rapidly improving and capable of an astonishing array of intellectual asks, especially when they can navigate the web and seek out new information, LLM judges should be able to accurately resolve many markets — and experiments to understand their accuracy are ongoing.

Bakes in transparency. The entire resolution mechanism is visible and auditable before anyone places a bet. No rule changes mid-flight, no discretionary judgment calls, no backroom negotiations. You know exactly what you’re signing up for.

Improves credible neutrality significantly. The LLM has no financial stake in the outcome. It can’t be bribed. It doesn’t own UMA tokens. Its biases, whatever they are, are properties of the model itself — not of interested parties making ad hoc decisions.

Of course, LLM judges would come with limitations, which I outline and address below.

Models make mistakes. An LLM might misread a news article, hallucinate a fact, or apply resolution criteria inconsistently. But as long as traders know which model they’re betting with, they can price in its foibles. If a particular model has a known tendency to resolve ambiguous cases in a particular way, sophisticated traders will account for that. The model doesn’t have to be perfect; it has to be predictable.

Manipulation isn’t impossible, just harder. If the prompt specifies particular news sources, adversaries could try to plant stories in those sources. This attack is expensive against major outlets, but potentially feasible against smaller ones — the map-editing problem in a different form. Prompt design matters enormously here: resolution mechanisms that rely on diverse, redundant sources are more robust than those that depend on a single point of failure.

Poisoning attacks are theoretically possible. An adversary with sufficient resources could try to influence an LLM’s training data to bias its future judgments. But this requires acting far in advance of the contract, with uncertain payoffs and significant costs — a much higher bar than bribing a committee member.

LLM judge proliferation creates coordination problems. If different market creators commit to different LLMs with different prompts, liquidity fragments. Traders can’t easily compare contracts or aggregate information across markets. There’s value in standardization — but also value in letting the market discover which LLM-prompt combinations work best. The right answer is probably some combination: let experimentation happen, but create mechanisms for the community to converge on well-tested defaults over time.

How could builders adopt these strategies?

To summarize: AI-based resolution basically trades one set of problems (human bias, conflicts of interest, opacity) for a different set (model limitations, prompt engineering challenges, information source vulnerabilities) that may be more tractable. So how do we move forward? Platforms should:

Experiment by testing LLM resolution on lower-stakes contracts to build a track record. Which models perform best? Which prompt structures are most robust? What failure modes emerge in practice?

Standardize. As best practices emerge, the community should work toward standardized LLM-prompt combinations that can serve as defaults. This doesn’t preclude innovation, but it helps liquidity concentrate in well-understood markets.

Build transparency tools such as interfaces that make it easy for traders to inspect the full resolution mechanism — the model, the prompt, the information sources — before trading. Resolution shouldn’t be buried in fine print.

Conduct ongoing governance. Even with AI judges, humans will need to make meta-level decisions: which models to trust, how to handle cases where models give obviously wrong answers, when to update defaults. The goal isn’t to remove humans from the loop entirely, but to move them from ad hoc case-by-case judgment to systematic rule-setting.

***

Prediction markets have extraordinary potential to help us understand a noisy, complex world. But that potential depends on trust, and trust depends on fair contract resolution. We’ve seen what happens when resolution mechanisms fail: confusion, anger, and traders walking away. I’ve watched people rage quit prediction markets entirely after feeling cheated by an outcome that seemed to contradict the spirit of their bet — swearing off platforms they’d previously loved. This is a lost opportunity for unlocking the benefits and broader applications of prediction markets.

LLM judges aren’t perfect. But when they’re combined with the technology of crypto, they’re transparent, neutral, and resistant to the kinds of manipulation that have plagued human-based systems. In a world where prediction markets are scaling faster than our governance mechanisms, that might be exactly what we need.

Andrew Hall is the Davies Family Professor of Political Economy in the Graduate School of Business at Stanford University and a Senior Fellow at the Hoover Institution. He works with the a16z research lab and is an advisor to tech companies, startups, and blockchain protocols on issues at the intersection of technology, governance, and society.

— a16z crypto editorial team

You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out anytime using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of investments.

What if the greatest breakthroughs in human history weren’t new inventions, but the simple removal of friction? We are trained to look for the next revolutionary product. We almost always overlook the invisible, boring standards that allow thousands of new products to exist.

This is a story about that hidden architecture. It’s about why the battle for the future isn’t about building a better thing — it’s about agreeing on the rules that let everything connect.

It’s a battle that repeats in every era, and today it’s being waged over the operating system for human ambition itself: money.

The most dangerous move isn’t falling behind on technology. The most dangerous move is to accept a “modern” solution that’s just a prettier cage.

The beginning

Our story begins not with an invention, but with a maddening, costly, and completely unnecessary stop.

In 1885, a passenger traveling across England — say, from London to Wales — would arrive at the station in Gloucester, disembark, and find herself stranded. Her journey was not over, but her train’s was. To continue, she would have to navigate a chaotic platform, haul her luggage to a separate station, and buy a new ticket for a different company’s train, one that ran on tracks just a few inches wider.

Nearby, a merchant might watch in despair as his cargo of fragile pottery was unloaded from one car and manually reloaded onto another, the cost of breakage and delay eating into any potential profit.

This was the daily, maddening reality of a closed system. In 19th-century Britain and America, railways were a fragmented patchwork of private lines, each with its own incompatible track gauge. It was a failure of coordination that held entire economies captive, levying a hidden tax on every person and every piece of cargo.

Then, over a single, frantic weekend in 1886, thousands of workers across the American South set out to fix the impossible. In a near-miraculous feat of coordination, they manually adjusted 13,000 miles of track to a national standard. By Monday morning, trains rolled uninterrupted from the Atlantic to the Mississippi. The result was immediate: Traffic rose 20% almost overnight. They chose a two-day sprint of relentless work to build a century of frictionless progress.

This chaotic, costly, and ultimately necessary act of standardization is not a historical curiosity. It is the central, repeating drama of human progress. In every era, we face the same fundamental battle: a war between the seductive, profitable, and orderly nature of closed systems and the chaotic, innovative, and explosive potential of open ones.

Timeless patterns

This pattern is timeless, defining everything from the dusty, physical routes of the Silk Road, to Roman units of measurement, to the invisible, digital architecture of the cloud we use daily.

If the logic of openness has been known for millennia, why do we keep forgetting it and rebuilding walled gardens?

Because fragmentation isn’t just an accident, it’s a business model. The allure of closed systems is rational and powerful. It’s about control, profit, and speed to market.

But while a closed system may scale rapidly — assuming it can convince others to join and avoid failure — that very design inevitably concentrates market power in the hands of its architect. That power leads to an irresistible temptation: not just to shape the system, but to extract most of the value for themselves. It comes down to a simple paradox: Everyone wants an open standard, as long as it’s their standard. Why cooperate when you can own the junction?

Whether it’s a 19th-century railway baron defending his proprietary gauge, a tech giant controlling its closed social graph, or a fintech designing a CorpChain — a blockchain that is neither truly open nor permissionless — the playbook is the same. The owner retains the power to dictate the terms of engagement and capture a disproportionate share of the value created.

Closed systems also appeal to a deep-seated desire for order. They are environments where participants can be vetted, rules enforced, and outcomes planned and managed. Open, chaordic systems trade this engineered stability for a different prize: permissionless innovation. They are inherently unpredictable, and their very nature creates asymmetric outcomes — both massive new value and significant new risks.

The temptation, then, is to lock in a market, to build your kingdom in the belief that this control is the only path to a durable and profitable future. But this strategy fundamentally misunderstands the nature of innovation. It’s a fatal trade-off: In exchange for short-term profits, the architect of a closed system gives up the one thing that guarantees long-term survival: adaptability.

History’s verdict here is unambiguous. The very systems built for control, no matter how dominant they appear, are eventually out-innovated and overwhelmed by networks that choose to cooperate on the underlying standard, and then compete on the products.

The genius is the standard

Consider the chaos of global trade before the 1960s. For centuries, shipping was defined by “break-bulk cargo”: innumerable barrels, sacks, crates, and boxes loaded and unloaded by hand. Every port was a unique logistical nightmare. Ships spent more time docked than at sea.

Then a trucker from North Carolina, Malcolm McLean, reframed the problem. He realized the problem wasn’t the ships or the cranes—it was the lack of a common interface. His solution, the simple, standardized steel shipping container, looked incremental but was revolutionary.

That one standard — a box that could move seamlessly from truck to train to ship — was an open protocol. It allowed for competition to thrive on top of the new rail. Suddenly, shipping companies, truck lines, and port operators could all innovate on their own piece of the puzzle, knowing it would connect to the whole. The cost of loading a ton of cargo fell by 97%, and global trade expanded more than sevenfold within two decades.

As The Economist put it, one boring box did more for globalization than 50 years of trade agreements combined.

The same principle revolutionized retail in the 1970s. Before the barcode, every checkout was manual, every store an island. Inventory was guesswork. There was no “universal language” for commerce.

The solution? The now-ubiquitous Universal Product Code (UPC).

At first, adoption stalled in a classic chicken-and-egg problem: Retailers wouldn’t buy the expensive scanners until products had the codes. Manufacturers wouldn’t print the codes until stores had the scanners.

But once the system hit a tipping point, the protocol turned retail into software. Suddenly, inventory wasn’t guesswork, it was a real-time data stream.

The $17 billion in annual savings was, in fact, the least interesting part of the story. The most interesting part was the new clarity the system created. For the first time, early adopters could see it all: what to build, where to sell it, and what should come next. They didn’t just get more efficient; they built global supply chains on a scale the world had never seen.

This reveals a fundamental truth: The best product doesn’t win. The most vibrant ecosystem does. A universal interface is how you build one.

This brings us back to the central tension between open and closed — a tension that is as old as civilization, and one that has always presented two competing paths to success.

Standards by decree or standards by consensus

The first model is a top-down standard, imposed by a single power. When the Roman Empire rose, it didn’t just connect disparate regions; it inherited chaos. It faced a world of friction, a nightmare of incompatible parts — local roads, local currencies, local customs.

The Romans had a profound, modern insight: You cannot run an empire on chaos. You must first build its operating system. So they created a protocol. A standard for the entire known world. Every road was built to the same width and foundation. A uniform coinage allowed commerce to flow without friction.

What began as a military project became the economic platform for an entire continent, collapsing the cost of transport and trade. The proof is etched onto the planet. Two millennia later, night-time satellite imagery of Europe still traces those ancient routes — a physical map of how coordination compounds value across millennia

The second model is a bottom-up standard, which emerges from users, innovators, and misfits.

Two thousand years ago, even as the Romans were starting their standardization project, the Silk Road served as the prime example. Here’s the thing most people get wrong: It was never one road. It was an interlocking web of exchanges, an open system. And because it was open, no single ruler controlled it, and no gatekeeper could shut it down.

Kings tried to tax it. Robbers tried to raid it. Empires tried to wall it off. But the network was smarter than any single part — it just routed around them. It was alive.

The Silk Road was a protocol for trade. Standard weights and measures emerged not from decree, but from necessity. The network carried silk and spices, but its true exports were technology, ideas, and culture. It connected worlds.

Top-down standards are powerful, but they inevitably involve an empire — whether to create and enforce the standard or as a result of the standard’s succeeding. Bottom-up standards are resilient, but they are forged through sometimes painfully slow, decentralized experimentation and consensus.

This same dynamic — the fundamental tension between imposed design and emergent order — would come to define the digital age.

The early computer industry was a world of proprietary systems. Every machine was its own universe — from DEC to HP to the IBM mainframes. Software written for one wouldn’t work on any other

Then, in 1981, IBM — the king of closed systems — made a fateful decision. To accelerate speed to market, they built their new personal computer with off-the-shelf parts and ended up licensing its beating heart from a little-known software startup from New Mexico: Microsoft.

Bill Gates’s masterful move was to retain the rights to license that operating system, MS-DOS, to everyone else. In a moment of historic irony, IBM had accidentally created one of the most influential open standards of our time: Wintel.

This set up a natural experiment, pitting Apple’s closed, vertically integrated model against the open Wintel ecosystem. The results were not even close. While Apple controlled every piece of hardware and software, the Wintel standard exploded. Dozens of hardware companies competed to build cheaper, faster machines, and developers could finally write a program once to run it everywhere.

Within less than a decade, the open PC platform dominated, capturing over 80% of the market. The lesson was brutal and absolute: An open ecosystem, by enabling a faster pace of hardware and software innovation, will almost always outpace a more closed, proprietary solution.

The internet became the ultimate test of this dynamic. At its dawn, incumbents bet on control. Proprietary portals like America Online, Apple eWorld, and Microsoft Network built walled gardens, attempting to curate — and monetize — the entire user experience.

On the other side was not a competing product but simply a set of open, ownerless rules: TCP/IP and HTTP.

It wasn’t a fair fight. It was a complete annihilation. The open protocols simply routed around the walls, just as the Silk Road had routed around empires.

Why? Because you cannot curate the future.

Permissionless innovation on an open standard will always create exponentially more value. The result was one of the largest productivity expansions in history, unleashing over $2.1 trillion in value in the US alone, and driving 21% of GDP growth in advanced economies in just 5 years.

The most powerful wins, however, are often the most unexpected. When Linux appeared, it was dismissed by industry experts as a “toy.” It wasn’t as polished as Windows; it wasn’t an enterprise-grade product. It was, they said, a hobbyist project.

But this critique fundamentally missed the point. The power of Linux wasn’t in the features of version one: It was in its new, open architecture.

That “toy” with the penguin mascot now runs 90% of the cloud and 100% of the world’s top 500 supercomputers, creating an estimated $8.8 trillion in value. The open standard became so dominant that its greatest enemy, Microsoft — a company that once famously called it a “cancer” — was forced to embrace it. The open standard, in the end, was simply too powerful to compete with.

The last, big closed network: Money

Today, our financial system looks exactly like the 19th-century railways. It is a fragmented patchwork of closed, proprietary networks. A handful of institutions run the tollbooths of everyday payments, collecting what amounts to a private tax on the economy with every swipe, tap, or wire. Like a constant background radiation, this friction invisibly erodes business margins through a cascade of fees—totaling over $187 billion in merchant fees in 2024 alone—all cleverly disguised by the brilliant marketing scheme of consumer rewards.

We are all, in effect, still standing on that platform in Gloucester, hauling our baggage to the next platform, watching our delicate wares get manhandled by porters, paying a hidden tax on our own ambition.

We have seen attempts to fix this. In the 1960s, the credit card industry faced its own coordination failure. When Bank of America tried to scale its credit card network unilaterally, its losses ballooned. The turning point came when Dee Hock realized no single bank could own the standard. They created a cooperative — Visa. The principle was simple: Cooperate on the infrastructure, compete on the products.

More recently, India built a public open protocol called the Unified Payments Interface (UPI). By creating a neutral, shared rail for digital payments, India cut transaction costs to near zero. In just a few years, UPI grew to handle 18 billion transactions a month. That’s nearly 50% of all real-time, global transactions… in one single system. Moreover, once payments became a solved, shared layer, entrepreneurs built better credit, lending, and commerce applications on top.

These consortia or public-sector-led models are the modern equivalent of the credit card network and the Roman roads — top-down, imposed standards. But permissionless blockchain networks and cryptocurrencies have introduced a third possibility: the Silk Road model. A network for value that is not owned by a company, a cooperative, or even a single nation-state. A neutral, emergent, and open protocol for money itself.

The best analogy for its potential impact is the GPS. This system wasn’t built for the public: It was a closed, top-secret tool built by the US military, for the US military. It was designed for one thing: strategic advantage and control

And then, in the 1980s, the government did something that changed the world. They flipped a switch. And they gave the signal away — to everyone, for free. They effectively handed humanity a superpower with no idea what would be unleashed. They couldn’t have predicted Google Maps, Uber, or precision agriculture. They didn’t have to.

Nobody had to ask permission. Nobody had to launch a satellite. The foundation was simply there — an open, public protocol for location. This was the true gift: the freedom to build. An entire generation of entrepreneurs built new industries on top, focused on creating value, not on coordinating on the infrastructure or licensing it from an incumbent.

An open financial infrastructure will play this role. It’s about providing a foundation as open and as neutral as a GPS signal — so that you can be the architect. So you have the freedom to build, the freedom to compete on your product, not to waste time rebuilding the plumbing.

But this is not the future the incumbents are selling. They are coming to you with a great story about a “CorpChain.” They will tell you it’s frontier technology. They will tell you it’s purpose-built for payments. But a CorpChain isn’t the future. It’s just a higher wall. A cage painted gold with partnership incentives. It is a system with their rails and their rules. A system where your organization will never have root access. A system where you are a tenant, not an architect.

To choose the wall — the “CorpChain” model — is to accept the friction that quietly, invisibly grinds progress to a halt. It is to accept the tax that the present levies on our future. The cost, in lost opportunity and human potential, is immense.

This, then, is the choice — the same one faced by the railway barons, the shipping magnates, and the internet’s architects. We can continue to build our own incompatible gauges, profiting from the moats that keep us apart. Or we can do the hard, necessary work of coordination.

The economic lesson is unambiguous: The value unlocked by a truly open network will always be infinitely greater than the profit captured by a closed one.

It all comes down to the simplest question: Do we build higher walls or open roads?

History only remembers one of those choices.

It remembers the road builders.

is the co-founder of Lightspark and the MIT Cryptoeconomics Lab.

— a16z crypto editorial team

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