Nobody asked me, but I marked up the Stripe Investor letter that explains why they are buying Open Router for $7B.

The full analysis is shared below, and is a preview of our premium writing.

• Summary: We examine Stripe’s $7B OpenRouter acquisition as the centerpiece of a roughly $10B push to vertically integrate the infrastructure of the machine economy. We argue that Stripe is using its enormous Web2 distribution to commercialize technologies pioneered across crypto and AI, assembling stablecoins, wallets, usage billing, payments, blockchain settlement, and now inference routing into one stack. OpenRouter gives Stripe control over a critical commodity layer—AI inference—whose economics increasingly depend on routing workloads across models based on price, performance, compute, and availability. We conclude that Stripe is positioning itself as the economic operating system for AI companies and agents, but its expanding footprint also creates concentration risk and increasingly puts it in direct competition with Ramp.

• Topics: Stripe, OpenRouter, Bridge, Metronome, Privy, Tempo, Ramp, OpenAI, Anthropic, Plaid, Persona, Robinhood, Nevermined, NVIDIA

Thanks as always for your time and attention,
Lex

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• 🚀 Lex is actively meeting teams building machine-native financial companies. If that’s you, reach out here.

• 🤖 AI & Robotics Industry Encyclopedia: In this 100+ page report, we collate all the meaningful companies across public quities, private equity, and digital assets related to the machine economy. 👉Learn more here

• 🦄 In partnership with InterPrivate, we just launched a $200MM SPAC looking for targets in fintech, digital assets, and AI infrastructure. If you are a late-stage founder or have an idea to discuss, reply directly to this email.

Long Take

The Robot that ate the GDP of the Internet

It all depends on the definition of the Internet, doesn’t it?

At Consensys, we called Ethereum “Web3” and crypto “the Internet of Finance”. Turns out the original Internet may have been financial enough.

Stripe just announced the closing of the Open Router acquisition for $7B earlier in the week, and Axios reportedly its hands on the Stripe investor letter explaining their logic.

We find ourselves constantly aligned with how Stripe thinks — about AI, the machine economy, fintech and stablecoins, and value accrual. Today, I want to walk through this letter and provide additional industry color and experience as an operator and investor, and hopefully give you another view on their thesis. While I’ve got a venture fund and a network of early-stage companies behind the idea, Stripe has put about $200B of marketcap behind this thesis.

We excerpt it below in image and thereafter in text.

At the Blueprint, we have explored the meat of these acquisitions previously, and will get further into it down below. But let’s just take a pause around the sizing and the bet in context. Bridge ($2B), Privy (undisclosed), Metronome ($1B), Open Router ($7B), and Tempo ($500MM+) sum up to around $10B in M&A spend.

Stripe’s last official valuation is around $160B, and looking at the trajectory of the business, may now be floating ever higher. This suggests that the acquisitions have cost the company about 5-10% of its enterprise value — paid out in cash and stock — to purchase the absolute premier future-facing positioning and branding for the company.

You take a small haircut now to have exposure to the entire bet of the machine economy, and the Internet being transformed by software robots as economic actors. Facebook acquiring Instagram (1.5% of equity) and WhatsApp (11%) are the clear comps.

Outside of Tempo, each of Stripe’s acquisitions already had material traction and was the incumbent in its category — stablecoin to fiat, programmatic wallet, AI billing and payments, and now inference routing.

People are embarrassed to use the term Singularity, but we shouldn’t be.

If you have the sense that everything is accelerating and getting more alien — more machine and unknowable — you would be putting a finger on the problem. The feeling of insanity, in the markets and in our hearts, is proof itself. It is merely reinforced by the billions spent on compute.

There’s a question of whether we are in a steep part of the S curve, or some exponential liftoff. Perhaps the robots have a ceiling of capability, now that they have covered sight, speech, and image. Or, perhaps code is the digital physics of reality itself, and will never stop improving until we are overrun.

A few things here.

The risk and fraud point is bounded and well-defined. Whether from Plaid or Persona, we have shown research on how industrial fraud farms can use AI to take a security tax from large-scale payments and banking systems. Stripe is in this business, so it gets mentioned, but note that they have not purchased any material vendors here — Sardine, Taktile, Oscilar, or the prior generation of compliance and anti-fraud software. This is a cost category, not a revenue generator.

To build a new industry position, you take the revenue generator in a market that’s about to grow, rather than focus on stripping out costs by pre-funding your expenses.

The second point is a diplomatic way to talk about Web2 vs. Web3, and super interesting coming out of Stripe. Web2 works on the framework of networks, freemium, and aggregation theory — (1) all content pricing is reduced to zero, (2) therefore the only thing monetizable is the attention and its flow, (3) therefore economic capture goes to platforms that can be the venue for content / economic activity.

• YouTube is this for (free) video.

• Robinhood is this for (free) trading.

• Stripe is this for (free) payments.

Of course we have to define free. You do pay, just not for the thing you think you are buying. For YouTube, we pay with attention for advertising. For Robinhood, we pay with bid-ask spread from market makers. For Stripe, we pay processing fees. No upfront sign-up required.

The outcome of aggregation theory is monopoly, or, at least, very high concentration.

Crypto is all about the opposite — decentralization and anarchy — but it does not cohere into good business logic, just high quality open-source technology resources. Stripe is a highly successful Web2 fintech that is a leader by a wide mile in a concentrated market; but its founders are idealists and believe in the original power of the Web1 Internet (i.e., open, free). You may remember Stellar, the early crypto network, as a Stripe investment. So they write about a mix of values in the same breadth.

We see the inherent tension of championing small business and power to the people, while accruing enormous economic power as a result. More businesses and solo-preneurs are great, but it does not imply decentralization of financial systems and infrastructure. It implies how much Stripe is getting better as a platform.

AI is the maximium steroid version of Web2. We are not just entering the attention economy, where content is free. Labor itself is behaving the way content did in 2001.

We are in the Napster moment — not for Metallica, but for the human animal.

Download and install your AI worker now, for free, backed by Google Cloud credits. Do you see it yet? Are you feeling the OpenClaw?

Look, I agree that we don’t really know the direction of the machine economy and how it will impact humans. Likely, the overall GDP will indeed grow, but some human intermediation will shrink.

This is why one key anchor is the transition to the Zero Human Company world, where that productivity of AI agents moves tasks from individual actions, to workflows, to employee replacement, to entire companies. The goal is to own the robots in a world where they are material labor contributors, and potentially capitalists themselves.

Now, I am probably off by 5-10 years before this happens.

But on the other hand, if we are in a singularity lift-off, then that 5-10 years may get crunched into a logarithmic curve and will be done in 1-3 years instead.

Already, look at the kind of behavioral change has been downstream of AI model adoption.

A few weeks ago, when the Open Router news first came out, we built out the following version of the above industry value chain. The Stripe approach to their value chain also follows closely the various areas of start-up activity in the machine economy. There were many Metronomes — usage-based billing systems — but this one in particular attached to OpenAI and measure their token consumption. We had backed Nevermined in this space, who initially grew on the decentralized Olas network.

But being on Stripe and plugged into large scale Web2 revenues is a different game than chasing Web3 early adopters on novel networks.

Bridge, Privvy, Open Standard, and Tempo are all attempts to internalize the best of Web3. I would by far prefer that Stripe truly supported and pushed the existing Web3 solutions rather than productizing them. Having Ethereum and its idealistic community absorb the R&D cost of inventing DeFi and popularizing stablecoins for a decade, only to have Stripe fork and polish those concepts inside a Web2 fintech consortia, is perhaps a necessary but unfortunate turn of events.

But reasonable people can disagree on how much the existing solutions could stretch to real payments use-cases.

OpenRouter was started by the CTO of OpenSea, the NFT trading marketplace. It leverages the technical capability of crypto matching algorithms learned in large scale trading to power the trading of inference. Just like NVIDIA levered off Bitcoin mining into AI data centers, so does OpenRouter sit on the shoulders of the now zombie Metaverse.

The technium extends outwards in its own recursive ways. Those at the frontier will hack at risk no matter the form.

I think this is key, and a huge competitive advantage.

Whereas the ideas behind what Stripe is doing have been around for many years prior to its mega-pivot, those ideas were being executed on an onchain ledger with a small economic footprint. The Web3 economy has not yet come to the mainstream — it remains focused on underbanked economies with poor capital markets, or on high-risk speculators looking for regulatory arbitrage. You can lever up a prediction market bet about the SpaceX IPO, but we struggle to buy a sandwich or build a laundromat.

I thought DePIN would have fixed that — token financing for real businesses with a hardware or telecom component. Tokenomics, predatory exchange mechanics, market structure have largely eaten this category for the moment. Founders surely are also to blame for unsophisticated structuring, encouraged by overzealous venture investors.

But Stripe does not have this problem. It does not need to instantiate a new economic paradigm, because it already sits highly embedded into the existing one. And by focusing on developers, as they say, they are positioned exactly for the age of ever-more-software. As machines make machines that make disposable software, all this grows.

And whereas the Crypto AI category largely starved, the fiat fintech AI category could feed on the fiat hyperscalers and their customers.

What an interesting take.

Intelligence is a “currency”, and it is “capital”.

And tools used to quantify fraud work both on financial and intelligence fraud. Because if somebody is buying intelligence, it needs to be verified as being the thing that it is. We thought that the answer to this is ZK proofs and cryptography — and there was a whole category of investing into inference vertification companies.

A good primer on this topic is this podcast below.

But again, this is the technologist’s solution to the problem. It redesigns the entire system and puts it into a network. The operator’s solution is to bolt on a fraud engine on top of the existing transaction engine, and prevent the engine from working when there are warnings. The AI companies aren’t implementing ZK in their workflows. But a payments company can turn off money from going to AI companies it thinks are bluffing.

Anyway, we don’t really buy into the view that inference / intelligence are a currency, anymore than cloud storage or electricity are a currency. Intelligence is a commodity constructed from hardware that runs mathematical rules and power. It can be both a stock and a flow, and its price is subject to supply and demand. It resembles an asset class in this way; we do not expect other things to be priced in units of intelligence.

Though one thought experiment that would be very satisfying is to apply units of intelligence as a cost measure of various professions and labors. How many units is being a doctor? A lawyer? An accountant? An economist? An electrician?

Stripe is growing like an early stage start-up, putting up insane traction.

If these numbers are true, than it’s hard to justify in payments companies who are trying to take Stripe down.

Smallcaps are supposed to grow faster than largecaps. This is well-known researched and defended historic investment principle. Quant models often include a small-cap size factor to increase their performance. However, the rule of thumb has broken over the last decade in the opposite direction.

Traditional companies used to decay as they grow very large. Whether this happened due to poor management, politics, rent-seeking, or other reasons, the effects were consistent. However, the hyperscalers, i.e., the Mag7, have compounded network effects, innovation speed, and revenue much faster than their subscale competitors. The last 10 years are teaching us a lesson that is out of the ordinary.

And this points us back to aggregation theory and the natural monopolies of both attention and payments. AI only magnifies the effect, because it can mimic if not recreate the speed and quality of early stage employees, while being available only to very large companies with a high-cost enteprise AI budget. On the other hand, a small company has no way to recreate the competitive advantages of being an incumbent, other than by partnering with or selling to that incumbent.

And if that incumbent is not relatively slower than the startup, like prior fintech generations (e.g., Fiserv, Envestnet), then the partnership is suicide — a way to be absorbed either commercially or through vertical integration. The only immunity is a close personal relationship with the founders or investors of your mega-cap partner.

Stripe Treasury is a great point to meditate on. Consider this product:

Compare it to the other grand AI-native Fintech that has just repositioned itself as a financial lab, Ramp.

Sure, a business bank account with currency sending is different from a corporate card that intelligently organizes your business expenses. But not that different. The digital treasury pays, saves, and invests. It makes money through interchange and net interest income. The AI CFO, which is what Ramp is becoming via harness and narrative, pays, saves, and invests too. It may be deeper in accounting and expense categorization, but it is also far more profitable if it holds the actual money.

When both companies integrate vertically ahead of the machine economy singularity, as they say, their instances of Claude give them the same strategic advice. Consider the new product launch from Ramp:

This router isn’t called Open, but it is now sitting head to head against Stripe’s competitive position. What then happens to all the consortia and announcements of the two companies working together?

Or, perhaps, they become the singularity age version of Visa and Mastercard, always competing and cooperating. The card networks are on all sides of the transaction, working with and against the banks globally. And yet, everyone grows together and the card networks and the payments market beta against which everyone else is measured. Their duopoly incumbency is effectively unrivaled.

How does that change?

First, you lock down the Web2 Internet to be powered by card network rails. PayPal and Stripe are both beholden there. Next, you move decision making about payments from finance to developers. This happens naturally over time, as more and more of your clients are the masses of Internet users, and eventually, their mass-scale AI agents. Finally, you swap out the card rail fo the stablecoin rail, running on your own closed-loop network that now sits at sufficient scale.

This is a cartoon story, but we live in a cartoon world.

Carpe diem!

🚀 Postscript

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• Contributors: Lex, Laurence, Matt, Farhad, Daniel, Michiel

Today we are diving into the following topics:

• Summary: We examine why financial narratives like ICOs, NFTs, and AI agents can attract billions before the underlying economy actually arrives. Bankr and Virtuals illustrate the problem: both generated substantial fees during their respective agent-token booms, then saw activity collapse by more than 90% as attention moved elsewhere. We propose five tests for separating a temporary financial “meta” from durable technology: external revenue, miracle count, chokepoints, boring prices, and clock speed. The key distinction is whether value depends on continued attention and speculation or survives through real customers, cash flows, infrastructure, and distribution.

• Topics: Stripe, Coinbase, Robinhood, Tempo, Virtuals, Bankr, Revolut, Epsilon Theory, Perscient, Kalshi, Polymarket, Generative Ventures, OpenAI, Goldman Sachs, Ticketmaster, Uniswap, Sushiswap, Binance, Gartner

Thanks as always for your time and attention,
Lex

To support this writing and access our full archive of newsletters, analyses, and guides to building in the Fintech & DeFi industries, see subscription options below.

Explore Full Membership

🤖🏦🧭 Our Ecosystem:

Generative Ventures | AI Research | Robot Money | Linkedin & Twitter | Sponsors

• 🚀 Lex is actively meeting teams building machine-native financial companies. If that’s you, reach out here.

• 🤖 AI & Robotics Industry Encyclopedia: In this 100+ page report, we collate all the meaningful companies across public quities, private equity, and digital assets related to the machine economy. 👉Learn more here

• 🦄 In partnership with InterPrivate, we just launched a $200MM SPAC looking for targets in fintech, digital assets, and AI infrastructure. If you are a late-stage founder or have an idea to discuss, reply directly to this email.

Long Take

Financial Fashions

The problem with being early is you don’t really know how early you are. Or to what really.

Sometimes being early means you get the glory and success of everyone that follows. You are the great conqueror, having discovered the secret path.

Sometimes it means you have brought your party to the desert, and it is there, without water and food, that they will starve and die.

I’ve been trying to grapple with the contradiction in the current market environment in all sorts of ways. One of these is to generate charts and research documents, and point out the difference between infrastructure and application cycles. That’s the logical side trying to find an anchor to a mechanism that makes sense.

But here’s the other way. How is it possible that Stripe and Coinbase, and maybe Robinhood, are spending billions of dollars on the agent economy and yet the ground is barren and there is barely any economic activity? Tempo appears to be ramping up a bit, but we do not know whether this is AI-related or just general nominal payment volume transfer.

These are the big incumbents!

And what of the early pioneers? Take Virtuals and Bankr, two onchain projects and ecosystems that have flown a tall banner around DeFi, onchain capital markets, AI agents, and generally what I would classify as machine economy.

If we look at revenues, there is a clear problem.

The business model of these projects is primary token issuance, and then ongoing trading volume provision — taking a share of the market making fees. That means the projects win when financial events happen, not when the underlying thesis (i.e., machine economy) of the funded companies gains traction in the real world.

Comparable volatility can be seen in the attention markets — Google search indexes of people searching these niche names. In fact, we can go deeper and try to get a feel for some larger financial fashions that have been covered in detail in our newsletter.

Consider — ICOs, NFTs, and LLMs. All three-letter-gifts from the Fintech Gods to a research analyst, and unique in meaning to their time.

• Initial Coin Offerings were meant to replace IPOs and equity crowdfunding with tokens, and bring retail forward to capital market participation. They were later rebranded as token generation events, Initial Exchange Offerings, and arguably fair-launch memecoins.

• NFTs are non-fungible tokens, which is just a way to put digital rights management on digital objects like images, music, video, and software constructs. They became bundled together with ideas of the metaverse and a new economic paradigm echoing the creator economy and social media.

• LLMs are an intermediate title for large language models, as opposed to image, video, or other foundational models. Their title evolved from neural networks, generative AI, and is now being replaced by “AI agents”.

We can also compare those searches against another custom and unique word, “Revolut”. Unlike a thematic label, Revolut is a name of a neobank that functions like a perpetual motion engine through cashflow, and is therefore able to reinforce a brand through products and services rather than attention and information markets.

What can you notice?

Fashions spike and fade.

And this is true for most such labels. It does not imply that the underlying theme is wrong or that there is no substantive progress. Rather, it implies that the label has a quality that both enhances and obscures reality. My discussion here is quite a direct lift from Epsilon Theory, which tracks memetics and narratives as a series of quantitative objects commercialized through Perscient.

That neatly maps into the financialization of all markets, and the collapse of the younger generations into gambling through Kalshi, Polymarket, Coinbase, and Robinhood across the world.

It is not enough to say (1) there are fashions, (2) there are some truths, and (3) the speculative fashions power the adoption of the truths. I want to understand this better so we fail less, so that we do not fall for the trick of the story, or if we do, so that we sell it before the story expires.

Why does Revolut persist while the Metaverse dies?

Read more

I recently wrote a market view for LPs of Generative Ventures and completed our 2Q review of the entire AI & Robotics financial value chain.

This is a substantial amount of research work to get a sense for where fundamentals are beyond the market stories. A macro view that points to where we should all hunt.

• Summary: We review the $25.3T machine economy and find that value is shifting from speculative narratives toward infrastructure and applications with measurable revenue. Physical bottlenecks are driving outsized returns, with Micron up 166%, while AI software increasingly trades on proof that adoption converts into revenue. Financial infrastructure is converging around regulated rails as Stripe, Visa, AWS, Coinbase, and others build the payment and identity stack for autonomous agents, even as related tokens fell 23%. We argue that the next phase of value creation sits downstream in AI applications and services, while crypto increasingly succeeds as invisible infrastructure for programmable money rather than a speculative asset class. Across the stack, the winning businesses are increasingly those that control scarce resources, distribution, proprietary data, or the ability to charge for machine activity.

Thanks as always for your time and attention,
Lex

To support this writing and access our full archive of newsletters, analyses, and guides to building in the Fintech & DeFi industries, see subscription options below.

Explore Full Membership

🤖🏦🧭 Our Ecosystem:

Generative Ventures | AI Research | Robot Money | Linkedin & Twitter | Sponsors

• 🚀 Lex is actively meeting teams building machine-native financial companies. If that’s you, reach out here.

• 🦄 In partnership with InterPrivate, we just launched a $200MM SPAC looking for targets in fintech, digital assets, and AI infrastructure. If you are a late-stage founder or have an idea to discuss, reply directly to this email.

Long Take

Market Update

We continue to be in a risk-off regime for speculative crypto assets.

While some financial services and fintech stocks have performed well, generally the category is trading sideways, and is indexed to revenue and fundamentals while the rest of the markets boom in response to the war song of futurists.

• For the major crypto assets, BTC has been floating around a $60,000 floor and ETH around $1,800 for a substantial amount of time. This is a function of the macroeconomic drivers of money supply (interest rates continue to be flat or up, thereby constraining speculation), alternative investment categories for high-risk investors (AI stocks, prediction markets, perpetuals), and a hang-over after the boom and bust cycle of digital asset treasuries from last year.

  

  

• The best crypto-native assets are the ones that most resemble securities with active financing engineering – high revenue companies that perform ongoing buy-backs of their tokens (e.g., Hyperliquid), or projects that project the capability to do so. This creates large embedded regulatory risk, which is for the moment overlooked by the US executive branch, and may be lowered through legislation.

  

The positive story lies in the fundamental adoption for programmable money used by machines.

We see Web2 Fintechs distributing tokenized dollars, stocks, and treasuries and abstracting away from anything that feels too crypto-native. Given the last crypto cycle gave us memecoins and pump.fun, that is a pretty good idea.

• Enterprise chains are still an attention and resource drag from large public networks. Within payments, there is a continued push from Stripe around Tempo; for digital assets at large financial firms, this function is served by the Canton network. Looking at Web2 fintechs, Robinhood has recently launched its proprietary EVM chain, which brings them further into symmetry with Coinbase and Binance. The positive from this is an increasing flow of tokenized assets and software primitives into these intermediate venues.

  

• Simultaneously, asset managers continue to transition their treasury funds and private credit products into RWAs. There is material demand in the market for access to tokenized stocks and other traditional products. From perpetuals on private pre-IPO companies on Hyperliquid, to GPU-price derivatives on Architect, we are seeing capital markets start to meld together with crypto infrastructure.

  

  

The machine economy value-chain continued to become increasingly valuable, with much of the infrastructure trade concluding with Leopold’s spectacular levered blow-up.

People think that other people think that the AI datacenter build-out is expensive and financially irresponsible, which will generate cracks in its circular revenue model. In theory, this will push the industry downstream towards applications – things that users actually buy.

• Power (e.g., nuclear, fusion), memory, and chips continue to benefit from the hyperscaler competition to be the best AI lab. Investors who owned public and private assets that were in the AI value chain have already benefitted, and we think current investment is a lagging indicator. The key metrics to watch are around debt and the ability of industry players to make Blackstone and Apollo whole.

  

• We think the long-term meat is in applications, and that those are just in the early innings. Those would be chat from OpenAI, image generation from MidJourney, music from Suno, code generation from Cursor, and now video collateral is next. Additionally, the middleware of harnesses is having its moment in the sun – we think there is incredible potential, but it is a short-term trade. Previously we would have called the category orchestration, and highlighted the fashion churn from Olas, to Virtuals, to OpenClaw, Hermes and so on.

  

• AI services companies are an interesting category, though we think multiples will compress. Both Anthropic and OpenAI have committed hundreds of millions in capital to partnering with private equity firm to deploy AI services digital transformation. Palantir is the success case; whether this is repeatable across the economy is a profound bet that much of the market is making. Anthropic has dethroned OpenAI by following through on this exact premise and emphasizing AI for work rather than AI for each person.

Our portfolio has generated unique barbell strategy exposure: (1) on the one hand, we hold various token-first financial applications and decentralized GPU protocols, and on the other, (2) we have invested in equity-first stablecoin, fintech, and data businesses. This has allowed us to de-risk some of the exposure through early DPI, while still holding material upside to long-duration themes that require digital markets to mature.

We continue to look at deals largely through the lens of commercial demand and fundamental traction, de-emphasizing narratives of retail participation and decentralization until the markets digest the current AI boom.

There remains enormous opportunity in designing financial systems in a world with endless high-powered intelligence built into every corner of the Internet, where digital dollars are trivial to summon, program, and spend. This economic shift is too big to ignore or mistake for a short-term tactic, and we continue to back talented entrepreneurs who are able to get things done.

Machine Economy Sectors

We also crunched the numbers for the third quarterly edition of the AI & Robotics Encyclopedia.

For premium subscribers, we highlight the changes across each layer of the machine economy and provide the report download link.

Read more

Today we are diving into the following topics:

• Summary: We connect three seemingly unrelated events — the $7B acquisition of Wealth Enhancement Group by Bain and Carlyle, Mariner’s deployment of 700 AI employees through Humanity Labs, and Stripe’s reported $10B bid for OpenRouter — into a single AI value chain. We argue that wealth managers own distribution over $10T of client assets, AI services firms are rebuilding enterprise cost structures through large-scale workflow automation, and inference platforms are commoditizing intelligence by routing demand across competing language models. As AI models become cheaper, implementation, orchestration, and distribution become the primary sources of value, driving investment from private equity, AI labs, and fintech infrastructure providers. We conclude that Stripe’s interest in OpenRouter reflects a strategy to own the metering layer of AI inference, positioning itself to capture economics from every transaction flowing through the emerging machine economy.

• Topics: Wealth Enhancement Group, Bain Capital, The Carlyle Group, Mariner, Humanity Labs, Anthropic, OpenAI, Ode, DeployCo, Blackstone, Hellman & Friedman, Goldman Sachs, TPG, Palantir, Stripe, OpenRouter, Cursor, SpaceX, Privy, Tempo, Bridge, Ramp, DeepSeek, Qwen

Thanks as always for your time and attention,
Lex

To support this writing and access our full archive of newsletters, analyses, and guides to building in the Fintech & DeFi industries, see subscription options below.

Explore Full Membership

🤖🏦🧭 Our Ecosystem:

Generative Ventures | AI Research | Robot Money | Linkedin & Twitter | Sponsors

• 🚀 Lex is actively meeting teams building machine-native financial companies. If that’s you, reach out here.

• 🤖 AI & Robotics Industry Encyclopedia: In this 100+ page report, we collate all the meaningful companies across public quities, private equity, and digital assets related to the machine economy. 👉Learn more here

• 🦄 In partnership with InterPrivate, we just launched a $200MM SPAC looking for targets in fintech, digital assets, and AI infrastructure. If you are a late-stage founder or have an idea to discuss, reply directly to this email.

Long Take

Seeing from top to bottom

Three separate events caught my eye this week.

1. Private Equity firms Bain and Carlyle are buying $150B RIA roll-up Wealth Enhancement Group for $7B

2. RIA Mariner and AI services company Humanity Labs launch a five-year partnership targeting 700 AI employees in the firm’s back office

3. Stripe bids $10B to buy OpenRouter, 70x revenues and 10x its valuation earlier this year

Are these all separate and distinct, or can we weave a framework that pulls it all together?

Let’s try to go across markets — large-scale late-stage private equity, bootstrap venture, and mid-stage fintech — and across industries — from wealth, to payments, and to AI and understand what is happening holistically.

I’ll start with the answer, and then we get to the detail.

At the high end of the chart are our traditional firms.

We can take any industry, but let’s anchor in wealth management. Wealth management is a distribution story, placing financial products across $10 trillion of assets, growing at 12% per year. This growth is effectively stock market appreciation. Still, technology has done little to derail the ability of the RIA (Registered Investment Advisor) channel to tap into millions of households and sell them ETFs, portfolios, and stocks.

Even with Robinhood, Polymarket, Hyperliquid, and the rest of gamble-finance, an enormous amount of assets is managed professionally and conservatively.

It is difficult to grow at this end of the market, because it is fully saturated. Wirehouses, advisors, digital solutions, exchanges, brokers — all these players are full-blasting distribution into the retail consumer. So the way you grow is through (1) natural investment performance, and (2) acquisitions of subscale players or those with dying business model. If you sold mutual funds, you will be eaten by those selling ETFs. Eventually, those who sell tokenized assets may eat ETFs.

Private equity firms are the sponsors of many of these acquisitions, because they can put leverage on such businesses. The game is to borrow against the cash flow of the business you are acquiring, and let the combination pay for itself. In 2026 alone, nearly $30B of wealth manager combinations have taken place.

It is not easy to centralize the distribution of financial products — nobody has a full monopoly. The reason is that the population graph is extremely diverse in characteristics and preferences, and you can always build and grow a material boutique that captures a niche.

• A mass-affluent mid-Western broker-dealer.

• A Silicon Valley roboadvisor for tech founders.

• A HNW ex-Goldman Sachs multi-family office in New York.

• A retail small-cap day-trading options bucket shop in Chicago.

• A Bitcoin crypto exchange in Asia

And so on.

So large private equity firms have something to do, buying up $150B of assets for $7B — about 4.6% of assets. This is expensive, relatively speaking. My rule of thumb is that AUM should trade at 2%, but it all depends on profitability and how much you can borrow.

Below we pull up the comps for 10 public asset managers across different asset classes.

You can see that most % AUM valuations cluster between 0.50% and 2.5%. In terms of revenue, generally multiples are in the 0.5x to 4x range, with growth and product mix playing a major role. WisdomTree and Artisan have a lot more room to grow than Franklin Templeton and Invesco, just because of smaller size.

So far, this is pure capital markets arbitrage and consolidation.

But such consolidation is a short-term game, and a sale would be the 5th PE owner for Wealth Enhancement Group since 2007. Most of these businesses are commoditized, and sit on comparable technology — from Envestnet to Orion or a custodian proprietary stack, like Altruist. And private equity firms, generally, have no idea how to modernize.

But the AI firms beg to differ. Let’s discuss those.

Read more

Today we are diving into the following topics:

• Summary: Starting a company has gone from a $5 million capital commitment to a near-free act, and Americans now file over 5.6 million business applications a year while the share that ever make payroll falls toward 6%. We are creating disposable, on-demand companies the way YouTube creators upload video: supply floods to the tails of the distribution, and most of it is meant to be discarded. But the market still rewards endurance, not formation — the startup race resembles a 2,000 meter row, where the sprint off the start is chemistry and the middle thousand meters is pain tolerance, as Marqeta’s eleven years to IPO and LendingClub’s long grind attest.

• Topics: OpenAI, Marqeta, LendingClub, Meituan, Taobao, JD.com, YouTube, Amazon (Prime), Deliveroo, Stripe

Thanks as always for your time and attention,
Lex

To support this writing and access our full archive of newsletters, analyses, and guides to building in the Fintech & DeFi industries, see subscription options below.

Explore Full Membership

🤖🏦🧭 Our Ecosystem:

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• 🚀 Lex is actively meeting teams building machine-native financial companies. If that’s you, reach out here.

• 🤖 AI & Robotics Industry Encyclopedia: In this 100+ page report, we collate all the meaningful companies across public quities, private equity, and digital assets related to the machine economy. 👉Learn more here

• 🦄 In partnership with InterPrivate, we just launched a $200MM SPAC looking for targets in fintech, digital assets, and AI infrastructure. If you are a late-stage founder or have an idea to discuss, reply directly to this email.

Long Take

Ready to Start

I find myself these days starting a lot of new things.

It’s a way I cope with a lack of control over outcomes. It appears that the chaos and randomness in the world has increased, and perhaps this is due to the leverage that external forces have gathered through money, power, and intelligence.

Your inbox has never been this overrun with pitches, as AI has made mass-spam easy and common. Your social feed has never been this algorithmic, consumed by the spiking dopamine graph mapped back from advertising. And our politics have become designed for the panopticon propaganda age, where even the propagandists can’t figure out the nature of ground reality.

But it has also become easier than ever to begin.

The cost of a new company used to include server hardware and office space. For this, you would have to make detailed plans and cost estimates, spending money just to get started. Then startups moved to the cloud, which led to an explosion in Silicon Valley investment and the Lean Startup methodology, which deemphasized ideas in favor of fast distribution.

Now, we are in the vibe-business prompting moment, where every rabbit-hole thought can be brute-forced by Fable into a plausible-sounding pitch and live codebase.

If it’s easy to do, that means there will be more supply.

I go back to my trusty analogy. Before YouTube, movies and shows would be filtered through curator markets in the shape of movie studios and producers, who would underwrite the project finance of a film through cultural taste and careful taste graph calculation. The middle of the bell curve prevailed.

Now, however, we live far in the tails of the distribution.

Infinite content is generated by the human spirit — and primarily its need for attention and social validation — and dumped into our consumption preferences. Our algorithmic doppelgängers, living as digital uncanny valley twins of our real selves, direct the media feed straight down our unwilling gullets. We savor the sweetness.

And so with business, supply will move to the tails. We don’t even know what we are yet missing.

Disposable, on-demand businesses can instantiate themselves in real time to be organized around our most superfluous need.

Take, for example, the drastic rise of on-demand delivery of food and groceries in Asia, which has spread to the Western world. Can you imagine living without Amazon Prime or Deliveroo? In China, Meituan crossed 150 million delivered orders in a single day in July 2025, at an average delivery time of 34 minutes.

All the stuff being said about programmable money, agentic finance and commerce, and the digitization of the dollar is mere preamble. That is the medium for the thing to come. In the way that software developers can instantiate software from thoughts and GPUs, or artists can now make media with a comment, so too will the creative act of microeconomics wrap itself around us and our wallets.

I’ve talked about this from the supply side, where AI transitions from doing tasks, to running workflows, to replacing employees, to then running entire companies and sectors. It may still seem science fiction to you, but what you are seeing is the future destination of the railroad track ahead.

The AI flies the ship in 2001: A Space Odyssey.

This is no coincidence. We are passengers of the ship. Our cultures, organizations, values, and constructions are the flora on its bones — as moss or mycelium cover the rocks of a sharp mountain ridge.

Humanity makes chaos, but humans love structure. We make technology to repeat our desires through math and programs. We build laws to stifle the dangers of mutation and volatility endemic to billions of humans sharing a planet. And so this technology will naturally — as in a natural monopoly, like electricity and oil — centralize and ossify. Like a carapace or a shell, it will move us along a latent space, whose multiple dimensions we can only feel and intuit, but never touch.

Read more

Let me vent please.

I don’t believe all that I write today, but I have to say it anyway. One year, it’s a post celebrating BTC at $100K. A poem to bubbles!

Another year, everything is dead.

Are we not tired of the songs, constantly changing their tune? I don’t know the truth, but I know I have to sing them.

Today we are diving into the following topics:

• Stripe, PayPal, and Digital Commerce: We examine reports that Stripe, Advent International, and other investors are pursuing a $53B take-private acquisition of PayPal, highlighting the changing economics of digital payments. We compare Stripe, PayPal, Visa, Mastercard, and Adyen, arguing that payment networks continue to compound value through their dominant position in the transaction stack while payment processors increasingly compete on growth and execution.

• The DeFi Post-Mortem: We revisit the original promise of decentralized finance and ask why a movement that aimed to rebuild global financial infrastructure has struggled to achieve mainstream adoption. We argue that DeFi’s contraction from roughly $180B to $80B reflects structural issues—including poor incentives, weak governance, hacks, and speculation—rather than a failure of programmable finance itself.

• Topics: Stripe, PayPal, Advent International, Block, Visa, Mastercard, Adyen, Tempo, Venmo, Braintree, Google Pay, Apple Pay, Tether, Circle, Ethereum, Ant Financial, Set Protocol, Yearn, Aave, Compound, Bitcoin, Zapper, Zerion, Axie Infinity, Base, Solana, Pump.fun, Robinhood, Kelsier Ventures, Binance, BitGet, Hyperliquid

Thanks as always for your time and attention,
Lex

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• 🦄 In partnership with InterPrivate, we just launched a $200MM SPAC looking for targets in fintech, digital assets, and AI infrastructure. If you are a late-stage founder or have an idea to discuss, reply directly to this email.

Stripe, Paypal, and Digital Commerce

We are going to do this as a quick hit.

Payments giant Stripe and private equity firm Advent International, as well as some other potential participants like Block, are making a bid to take PayPal private. PayPal is a $50B public company, which is a far cry from its $360B heyday. For comparison, the card networks, Mastercard and Visa, are both around $500B in market capitalization. Adyen, a Stripe competitor, floats around $35B.

It can be hard to be public. Stripe gets to enjoy its $160B private valuation and a much less critical venture capital investor.

The idea is that a consortium of Stripe and Advent will put together a buy-out package for PayPal’s entire public marketcap with a small premium — about $17B of equity and $36B of debt and other funding, for a total of $53B. It’s unclear why Stripe doesn’t just acquire the company outright, but maybe there are monopoly concerns around such a consolidation. The entity would be run separately, but obviously would be jointly owned. This is a similar playbook to Tempo, as a separate but controlled subsidiary of Stripe, targeting the retail consumer and taking out one of the smaller competitors (Venmo/Braintree legacy).

The numbers are ... pretty hilarious?

Stripe is dwarfed only by Visa and Mastercard. If you look at 5-year stock performance, you can also see that only the card networks retained value while the rest of the payment processing stack bled out.

Why you ask? Because they have very different positions in the value chain.

The networks have full market penetration in a duopoly, and are thus invoked whenever anything new happens anywhere with payments. The only exception is crypto. So whether it’s PayPal, Stripe, or Adyen does not matter to Visa and Mastercard. They grow regardless and therefore are immune to market share fights of the orchestrators above them.

So downstream you have Stripe, PayPal and Adyen with similar-ish $1.5T-$2T volumes, but the rate of change and growth is very different. PayPal is run by professional managers, and its genius cohort — Elon Musk and Peter Thiel — has obviously moved on to run the world and launch space rockets instead.

Stripe, on the other hand, still runs on founder mana and will outperform professional managers any day of the week.

Let’s look at the companies based on high-level fundamentals.

From a revenue perspective, PayPal is the bigger company — $33B in gross revenue, of which $15B is net. But it trades at a 1.2x gross revenue multiple, because it has struggled to grow and is instead being chipped away at by various competitors, from Stripe, to the impenetrable Google Pay and ApplePay. Once a hyperscaler eats a feature, good luck retaining independent market share. And all the payment processing startups that target checkout are effectively eating PayPal’s lunch.

Whereas Stripe continues to grow, and with its $6B of revenue, it gets to trade at 27x revenue, nearly 3 times more than its public counterpart Adyen. This whole thing is a multiples game, and multiples are a derivative of cost of capital, which is dependent on growth assumptions.

It is unclear whether PayPal can do better, but this is not a particularly great outcome.

Maybe Tether and Circle can create a consortium and buy it out for $100B and stuff it full of stablecoins and DeFi lending protocols — this is the only path to the next stage in my view. But thar be dragons too.

Long Take: The DeFi Post-Mortem

I came to this industry as a robo advisor founder watching neobanks, and the conclusion was simple: you cannot build the all-in-one fintech app without a complete refresh of financial infrastructure.

The amount of scotch tape needed to glue together core banking systems to front-ends is just too high.

China had done it — Ant Financial was the first example of a real financial super app — and meanwhile, our own Fintechs were narrow and verticalized.

So seeing financial applications on Ethereum was a breath of fresh air. We suddenly had something modern on which to build programmable money.

Then DeFi summer happened, and the dream became clear.

Read more

Today we are diving into the following topics:

• Summary: We examine the emergence of capital markets built specifically for AI infrastructure rather than AI applications, arguing that GPUs, compute capacity, and inference have become financial assets in their own right. We discuss how an estimated $11T will be invested in AI infrastructure between 2024 and 2029, including roughly $7T financed by debt, creating demand for hedging, derivatives, and new financial markets. We highlight Architect’s launch of ComputeConnect, a regulated exchange linking GPU derivatives with physical compute delivery, as an example of crypto-native market structures expanding into AI infrastructure. Financial markets around AI infrastructure could become as important as the AI applications themselves by bringing price discovery, liquidity, and risk management to the largest technology investment cycle in history.

• Topics: Architect, Compute Index, ComputeConnect, NVIDIA, CoreWeave, Blackstone, Oracle, SpaceX, Meta, Hyperliquid, Anthropic, OpenAI, Ornn, Andreessen Horowitz, Brett Harrison.

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• 🚀 Lex is actively meeting teams building machine-native financial companies. If that’s you, reach out here.

• 🤖 AI & Robotics Industry Encyclopedia: In this 100+ page report, we collate all the meaningful companies across public quities, private equity, and digital assets related to the machine economy. 👉Learn more here

• 🦄 In partnership with InterPrivate, we just launched a $200MM SPAC looking for targets in fintech, digital assets, and AI infrastructure. If you are a late-stage founder or have an idea to discuss, reply directly to this email.

Long Take

AI for Finance or Finance for AI

There are many intersections between AI and financial services.

One easy way to segment the categorization is to think about (1) AI that lives within financial services as a business or a function, and (2) finance that works at the behest and is custom-built for AI.

In Category 1, the examples are pedestrian and well-known by now — adding AI into underwriting, deploying OCR on documents, integrating larger models to prevent fraud, extending customer service.

Much more is going on in getting AI to do the business of running a financial company, such as organizing workflow or writing code. We talk about this as financial harnesses, meaning a chassis by which LLMs can be your colleagues in the finance profession. This is more akin to being a functional contributor inside of an organization, than about some sub-process of a manufacturing process.

In Category 2, we look to add financial features to an AI-first world. One of these has been commerce and payments infrastructure for agents. Whether or not this category is able to hold water is yet to be determined. However, the combination of stablecoins on various chains, payment processors, and commerce enablers has chased down the potential blue ocean with abandon. Even the large tech companies have shipped various agentic identity protocols to bootstrap our imagination.

It all feels a bit ahead of demand.

One area we haven’t spent as much time on, but is equally large, is the effect of AI on capital markets. And I don’t mean how good an AI agent is at arbing this or that — such a product is squarely in category 1. From the venture side, I have seen dozens of companies building AI-first roboadvisors, neobanks, and neobrokers and calling them by cooler names. That’s not what I mean.

Rather, AI needs capital markets for its own assets.

In the past, we wrote about this as a warning. In particular, we amplified the story that:

• There’s huge spend on GPUs to train models and provide inference

• Many people think that will never be profitable

• It’s true, validated by our machine economy research, that money is concentrating in components, chips, datacenters and applications are not yet catching up

• To fight each other, hyperscalers have spent down all their cash and raised debt

• That debt is circular between NVIDIA and its customers

• Companies are staying private longer to avoid public market scrutiny and to access less informed / cheaper capital to keep this going, and everyone is monetizing via secondary SPVs instead of public equity

• Private equity firms like Blackstone are neck deep in GPU built out credit risk

It was a good article! We wrote about this a lot last year. But really, nobody cared.

If anything, this stuff reads a bit quaint compared to the monster numbers everyone is putting up today. Semianalysis projects $11 trillion in spending on AI infrastructure between 2024 and 2029, of which $7 trillion is financed by debt.

That seems pretty wild, unless you believe this is existential, humanity-survives-WW3 type stuff. Which is of course how it is being sold straight into our 401ks. And it is also how an enormous amount of liquidity, meaning proceeds from investment rounds, are going into AI companies and venture more generally.

Michael Burry has been adamant about trying to time the short on the AI bubble. While the fundamental story may be there, and the zeitgeist is emerging, so far no real luck. Everything is still going up! To me, it feels like the top of Softbank’s investment in WeWork, when everyone just said OK, this stuff is 50x revenues. Fine.

And as the numbers go up, financial markets begin to form around them.

We are at an interesting intersection of technologies, because some of these products from crypto are now at the heart of the AI markets in ways people don’t see.

Read more

Today we are diving into the following topics:

• Summary: In this article, we discuss the launch of $IPVV, a $201M SPAC that target high-growth companies across fintech, digital assets, AI, and the machine economy. We argue that a growing backlog of venture-backed companies remains private despite reaching meaningful scale, while weak venture DPI and a constrained IPO market have created demand for alternative liquidity pathways. We review the SPAC structure, and note that today’s SPAC market is materially smaller than the 2020–2021 booms. We conclude that the next generation of SPAC targets is likely to be profitable or near-profitable companies with $50–250MM of revenue and $1–5B valuations rather than the pre-revenue growth stories that defined the prior cycle.

• Topics: InterPrivate, Generative Ventures, Aeva, Microvast, Securitize, Abra, Animoca Brands, Kraken, Cantor Fitzgerald, BlackRock, Anthropic, OpenAI, SpaceX

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Long Take

Launching our SPAC

I am excited to share that, in partnership with InterPrivate and Ahmed Fattouh, we have launched a $201MM SPAC, now live at $IPVV.

I’m writing to explore in more detail my thinking on the SPAC market, why we launched a new vehicle, and the market dynamics.

At a high level, we are looking for unicorn companies in fintech, digital assets, AI, and the broader machine economy to bring to the public equity markets.

The last 5 years have been pretty wild.

On the one hand, we have seen the blow-ups of multiple companies, banks, digital asset treasuries, and crypto assets. You had to have both vision and skill to survive, and a handful of companies (e.g., Hyperliquid, Polymarket) thrived on the volatility. On the other hand, an entire AI industry has been born, with the highest capital investment in technological infrastructure I have ever seen. This boom has already been expressed in energy and hardware prices, and several trillion of marketcap have been generated in just a handful of years.

Through this, I have seen founders build out strong ventures, sometimes scrambling through the fog but climbing out with successful businesses. They had done it through aggressive regulatory regimes, changing consumer expectations and behavior, and shifting global macro-economic relationships.

• Fintech neobanks that have built $100MM+ revenues and profitability, using stablecoins or social media as leverage points against incumbents

• New crypto infrastructure supporting billions in payment and trading volumes, connecting parts of the world others found hard to reach

• Neoclouds, as well as energy and memory companies, scaling up to support the enormous AI build-out

• AI-native applications that find incredible product-market-fit with coding, video, marketing, or other general employee tools

While some of these companies have gone public, been acquired, or merged into DATs, most still remain private. Everyone is waiting. SpaceX, Anthropic, and OpenAI are the big planes on the runway that must go public before anyone else has a chance for liquidity.

This means that investors will be holding mega-caps, both through retail participation and index investing, rather than buying companies in the $500MM to $5B range — a price that would allow more price discovery in the public, rather than private, markets.

The other effect of this is on venture capital. As founder of Generative Ventures, I’ve learned that DPI — the cash you return to investors — is the lifeblood of your LP relationships. If you don’t return DPI, then your investors will struggle to re-invest in your next vehicle. And the 2020+ vintage venture funds are struggling on DPI.

This means venture funds need to exit in order for the industry to keep raising money and investing in high-risk innovation. As a result, there’s a bifurcation between funds that have invested in the AI champions — increasingly only the mega funds — and vertical specialists who are not seeing liquidity. The result is widely overpriced seed rounds and consensus bets.

Given the existence of a whole bunch of high-quality companies (on fundamentals), the IPO window constraints, and the need for investors to exit, I think this is a moment when the SPAC structure adds a lot of value.

SPAC Sponsors and Market

The way a SPAC works is in roughly in these steps:

1. A sponsor team brings a vehicle to market through an IPO process with an investment bank. The vehicle has 2 years to find a deal, and retains a trust of some amount of capital (e.g., $200MM) that can be committed to the deal.

2. The team either (a) finds a target and agrees on a transaction with the company, or (b) is unable to find a deal within the timeline and returns money to investors

3. If there is a deal, trust investors may redeem (i.e., withdraw their money) if they do not like the deal, or for other reasons. The company and sponsor team usually raise a PIPE in addition to the trust capital to cover any gaps.

InterPrivate is a repeat SPAC sponsor, which means this is the 5th vehicle from the company. The platform’s edge is a co-sponsor network — independent sponsors, family offices, founders, and PE/VC firms — that widens the funnel and adds operating leverage and domain expertise, with the ability to support transactions with PIPE (private investment in public equity) capital from family-office LPs.

In the prior SPAC cycle, it had launched several successful searches, like Aeva — a 4D LiDAR-on-a-chip company founded by ex-Apple executives. Aeva closed a $1.8B merger in March 2021 and raised a $120MM PIPE, and a $200MM follow-on thereafter. InterPrivate also backed Microvast / Tuscan, a next-gen EV battery technology, which did a $3B merger in July 2021 and raised a $483M PIPE.

It had also run vehicles where the market timing didn’t work, and the capital was returned to investors.

Timing matters a lot.

The IPO window is open, until it is closed shut for years to come.

Market View

The last SPAC boom had come in 2020 and 2021, when nearly 800 vehicles were raised after the growth of digital adoption during COVID. Neobank and neobroker metrics were through the roof, as people stayed home and became chronically online.

Zoom stock soared!

SPAC trust proceeds exceeded $200B in raised capital over those years. For context, that’s less than Goldman Sachs thinks we will IPO in 2026 alone in AI stocks.

However, many companies came to market that were not really ready to be public.

They used SPACs to share overly optimistic projects and bypassed the usual diligence of the going-public process. Many of them were venture capital-style bets, pre-traction and pre-revenue. When the digital shut-in culture of the pandemic years stopped, we saw significant fallout in companies that did not support their prices with fundamentals.

That creates a lesson for this time around — focus on operating excellence and documented growth. Overall capital raised into SPACs has been more limited — $25B in 2025 and $16B in 2026 so far.

There is about $55B of capital looking for deals across all sectors today. Within Fintech, however, we see around $2B in capital across 12 different vehicles.

Many of the vehicles are in the $100-300MM range, and Cantor is underwriting several. It’s a pleasant surprise to see Adam Nash, former Wealthfront CEO, in the mix as well, and we recognize Kraken as a savvy capital markets player that has been very active in the digital asset treasury market last year.

One other notable feature is the warrant size — the smaller the warrant, the cheaper the capital for the company going public.

Below are a few deals that have been announced as targets.

We don’t have full view of the economics on these, but recognize the names.

• Securitize is a tokenization platform with a strong partnership with BlackRock. We have talked to the CEO in the past here. They have packaged several billion of RWA money market fund assets onchain together.

• Abra is an early crypto wallet / neobank provider. While they were a material player in the <2021 regime, we have heard less about the company recently.

• Animoca is a Web3 gaming company with a broad portfolio of assets, having leaned into the metaverse and onchain activity. Our interview with the CEO is here.

This gives a flavor of the company profiles that are now evaluating public markets — market caps in the $1-5B range, revenues likely between $50MM and $250MM, and a history of execution. Not every company is a straight exponential line up and to the right, like Anthropic, but it also makes for a healthier capital market to have a variety of exposures and risks in the market. The alternative right now is to be a buyer of a mega-tech late-stage trillion-dollar AI company.

Of course, SPACs are not for everyone. Many good companies take the straight path to IPO. Others can choose direct listings, token launches, SPVs, or other liquidity options.

SPACs *are* a good fit for companies that want to (1) be public, (2) have a strong counterparty with a clear view on the market, and (3) are dealing with more complex cap table and founder dynamics than usual.

If you think that your company fits the bill, or have an introduction to another potential target, give me a shout by replying to this email or sending a DM!

Excited to hear from you.

🚀 Postscript

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• Contributors: Lex, Laurence, Matt, Farhad, Daniel, Michiel

Today we are diving into the following topics:

• Summary: Coinbase launched an SEC-registered AI advisor that can analyze portfolios, suggest trades, and operate across a growing financial super-app that now includes equities, crypto lending, tokenized stocks, and derivatives. Mercury introduced Command, an AI banking assistant built on top of a business banking platform serving 300,000+ customers, generating $650M in annualized revenue and valued at $5.2B. We show that AI is commoditizing financial analysis itself, leaving proprietary data, distribution, and product infrastructure as the key competitive advantages.

• Topics: Coinbase, Mercury, Plaid, Betterment, Wealthfront, Robinhood, Revolut, Cleo, OpenAI, Anthropic

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• 🚀 Lex is actively meeting teams building machine-native financial companies. If that’s you, reach out here.

• 🤖 AI & Robotics Industry Encyclopedia: In this 100+ page report, we collate all the meaningful companies across public quities, private equity, and digital assets related to the machine economy. 👉Learn more here

• 🦄 In partnership with InterPrivate, we just launched a $200MM SPAC looking for targets in fintech, digital assets, and AI infrastructure. If you are a late-stage founder or have an idea to discuss, reply directly to this email.

Long Take

All your banks are AI banks now.

Coinbase just ran a “System Update” event and has just announced an AI agent as an SEC registered investment advisor.

Mercury announced Mercury Command, an AI agent embedded in banking flows that can function like a personal bookkeeper.

The AI RIA is a genuinely new legal idea, but is it more than just a chatbot with access to your balance sheet? And if they are not just chatbots, are they financial AI labs worthy of a $3 trillion valuation? Or is the SEC-registered AI agent just another pass at a roboadvisor, a category that failed to deliver disruption?

Let’s pull this apart.

Coinbase Advisor

Brian Armstrong, the CEO, announced the AI advisor product.

Did they register an AI with the SEC?

Per Coinbase’s own disclosures, the thing is operated by Coinbase Advisors, LLC — a Registered Investment Adviser with the SEC and a Commodity Trading Advisor registered with the NFA.

This registration means that the software carries a fiduciary duty, even while warning the user that output “may be inaccurate or incomplete” and that registration “does not imply endorsement.” No mainstream AI product in finance has yet delivered this particular contradiction. In the past, regulators used to complain that roboadvisors, with their arithmetic mapping of risk to portfolios, were non-transparent. Or that machine learning created black boxes for underwriting.

This is the same issue on steroids.

The agent reads your full portfolio and account history, then surfaces tax-loss harvesting moves, news-driven trade ideas across stocks, commodities, and crypto, and portfolio recommendations, through a natural-language chat. Armstrong’s framing was that you “speak to it in plain English to take action on your account” and that “it will even prompt you with ideas you hadn’t thought of.”

Like investing in derivatives tied to the SpaceX IPO, most likely.

This is rolling out first to Coinbase One subscribers in the US, which makes it a recurring-revenue feature as much as an advice engine. Coinbase posted a $394 million loss in Q1 and has been working to wean itself off transaction fees. Some wealth management fees would help balance the books.

Here is the rest of the update with which this AI will integrate:

Read more

Today we are diving into the following topics:

• Summary: We examine Ramp’s latest $750M raise at a $44B valuation and how the company is repositioning itself from a fintech into a Financial AI lab. We argue that AI has become the dominant growth narrative across technology and finance, allowing companies with strong data access and workflow ownership to capture premium valuations. Ramp’s near-$1B ARR, rapid product velocity, and expansion into AI-powered finance tools have established it as the category leader following Brex’s $5B sale to Capital One. We also explore how platforms like Ramp, Plaid, and Box are benefiting from their role as systems of record for enterprise data, making them natural control points for AI agents and automation. Finally, we contrast Ramp’s 15–30x revenue multiple with Bill.com’s 2x multiple, highlighting both the opportunity and risk embedded in today’s AI-driven valuation environment.

• Topics: Ramp,Stripe, Brex, Capital One, Plaid, Box, Anthropic, Bill.com, SpaceX, JPMorgan, Santander, UBS, Synapse, Coinbase, Circle, Robinhood, Betterment, PayPal, E*Trade, Robot Money, Anthropic

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🤖🏦🧭 Our Ecosystem:

Generative Ventures | AI Research | Robot Money | Linkedin & Twitter | Sponsors

• 🚀 Lex is actively meeting teams building machine-native financial companies. If that’s you, reach out here.

• 🤖 AI & Robotics Industry Encyclopedia: In this 100+ page report, we collate all the meaningful companies across public quities, private equity, and digital assets related to the machine economy. 👉Learn more here

• 🦄 In partnership with InterPrivate, we just launched a $200MM SPAC looking for targets in fintech, digital assets, and AI infrastructure. If you are a late-stage founder or have an idea to discuss, reply directly to this email.

Long Take

Ramping it Up

Things are just so weird now!

It’s a widely acknowledged truism that larger companies grow faster than smaller companies.

It’s also accepted that hot stocks are better than not-so-hot-stocks. The meme retail Gamestop approach to investing has defeated the institutional equity research analyst. Even large investors have learned to pile into momentum and growth, following the flag-waving of Elon Musk and Sam Altman.

So in this obviously (?) upside-down world, the correct game-theoretical action for founders is to lean into their visionary story-telling nature. It is no fun cutting costs and saving pennies, while being ignored by the Street. It is much more fun to paint the picture of a beautiful science-fiction future and find growth.

This is true not just of the AI and Robotics Labs, but of Fintech too.

Banks have been talking about being tech companies for a long time. And they are — just after their compliance and regulatory functions take a turn. Fintechs are supposed to be the hot thing, but in the current age, Fintech is boring! And crypto is out of favor.

Fintechs must find a new story, and that story is that they are Financial AI labs.

Which brings us to Ramp, dear reader. We won’t belabor you with all the background and point instead to the following prior coverage:

Ramp is a great company and is in the Web2 Fintech mafia.

What are these mafias you ask?

• Web1 Fintech is stuff like PayPal and eTrade

• Web2 Fintech is Stripe, Ramp, Robinhood, Betterment

• Web3 Fintech is Coinbase and Circle

A lot of the Web2 Fintechs really didn’t want to recognize Web3, and dragged their feet on integrating anything related to blockchains for years. Banks like JPM, Santander, UBS, and many others did far more material engagement with blockchains than Web2 — to my continued frustration and disappointment.

But things have turned around. Now that these companies are at very large scale, i.e., billions of revenue, embedded finance failed with Synapse, and Web3 has been rebranded as just stablecoins and agentic payments, the Web2 Fintechs have clumped together around Stripe and are all in.

In addition, as they (and we all) witness the rise of AI and associated financial and economic displacement, the smart players are transitioning to the next wave. The Web4 wave of AI, robots, and the machine economy.

Ramp is right at the heart of this positioning, and has just used this leverage to raise $750MM at a massive $44 billion valuation.

Let’s dig into the details. 👇

The Moonshot

Here is how Ramp’s CEO Eric Glyman introduces the industrial logic of the valuation:

Read more

Today’s agenda is below.

1. AI: Corgi keeps on raising as the market becomes skeptical of uprounds

2. ANALYSIS: The Missing Half of Agentic Payments

3. PODCAST: How Marqeta Built the $400B Modern Card Issuing Platform, with CEO Mike Milotich

4. CURATED UPDATES: Machine Models, AI Applications in Finance & Investment Outlook

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Insurance AI, Insurtech, and an Insurer

Sometimes you just have to immolate a bunch of capital before the world understands what is happening.

One approach to doing this is by giving an insurance company a startup-level valuation based on revenue. This is because insurance companies charge a “premium”, most of which is meant to be paid out in losses when the risks come due. So you charge $100 and then pay out $90, and keep the $10 as net revenue. And then the $10 is financial services revenue in a crowded industry, so you only get a 3x multiple on it.

Case in point is Lemonade, a good company that was valued like a start-up for a while.

But if you put a 10x multiple on the $100, then you have just set up a situation for a lot of losses when the company goes from public to private. And if you put a 100x AI-eats-the-world multiple on it, then the situation is quite a bit more dire.

And that brings us to Corgi, which sells commercial insurance to startups and was founded in 2024 by Nico Laqua, who holds the CEO and CTO titles, and Emily Yuan, the COO. Both came through Y Combinator’s 2024 cohort. Laqua traces the idea to watching his father, a lawyer at USAA, work through claims paperwork by hand, and to the release of GPT-3.5, after which he has described insurance as the largest words-based industry ready to rebuild.

In other words, Corgi is the AI-native version of a vertically integrated insurance company.

The company came out of stealth in January with a combined seed and Series A of $108M, at a $630M valuation. On May 6 it raised a $160M Series B at $1.3B. Three weeks later it raised another $106M at $2.6B.

Total raised in roughly eighteen months is $378M, a number higher than the valuations of many insurtech startups.

You can see the valuation ramp-ups in the charts above, which show both the headline valuations and the increasing prices at which various Series A investors came into the round. This mechanism (an equity bonding curve) seems very good at attracting capital, and nearly 57% of Corgi is now owned by preferred equity investors. Below, we show how different exit scenarios imply different outcomes for those investors. An exit below $500MM for this company would be effectively a failure.

So it has to exit at a much higher price! While fintechs trade at 10-20x revenue, AI-native labs get to raise $500MM seeds at a $5B pre-money. And an AI-native lab that is taking on the entire insurance industry seems like a huge opportunity.

But is this an organic market price? Across the rounds, the investor set in both B rounds is largely the same — Kindred Ventures, TCV, and others appear across the cap table. This is the same hands moving the same asset from one pocket to another and writing down a bigger number each time.

Maybe Corgi is just growing super fast, and the valuation is worth it? One way to pull this apart is to compare the valuation to the market.

Corgi reports more than $40M in annualized recurring revenue since receiving its carrier license, with customers including Deel and Artisan, across general, cyber, and tech-and-AI liability lines. At $2.6B, that is about 65 times revenue.

$40MM in ARR is about $4MM per month — of premium!

It does not disclose a loss ratio, a combined ratio, or how much risk it keeps versus cedes to reinsurers.

Read more

Today is a bit different, grounding all the finance math in an overwhelming feeling:

• Summary: A poetic, deliberately overwhelming tour through the manic acceleration of early 2026 — trillion-dollar valuations, AI layoffs, the compute arms race, onchain financial nihilism, and the tokenization of everything. We sing a psalm of praise to the “machine gods” of compute and capital. Surely you feel it too.

• Topics: Meta, Cloudflare, Anthropic (Claude), Palantir, Erebor, OpenAI, SpaceX (Starlink), Tesla, xAI (Grok), X, Blackstone, Hyperliquid, Tron (USDT/Tether), Circle (USDC), Coinbase, Stripe, Bitcoin, Substack, Instagram (Meta), TikTok, Evercore, Autopilot, Unusual Whales, Goldman Sachs, Morgan Stanley.

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👉Learn more here

The Feeling of 2026

The Theater

No AI research today, just poetry for you, dear reader.

There is too much going on! Surely you feel this too.

Attention spreading wide and thin, like a layer of honey dripping off a spoon. Is it possible to learn it all? Is it possible to see everything at once?

Look — a billionaire is making a trillion dollars!

Look — the right wing uprising! The left wing uprising! The population collapse!

If you don’t make it now, AI will come and take all the jobs.

There will be no jobs left at all. Meta is doing layoffs. Cloudflare is doing layoffs. They don’t even need to do layoffs, they just want to spend more money on Claude tokens plugged into a Slackbot incorporating the company context.

It’s all in a harness. It’s all automated. If you don’t have a harness, what are you even doing? Are you using someone else’s harness? Lame. If you are not coding a harness, you are NGMI.

You’re either a builder or a seller, they say. Everyone else is a “measurer”.

There are no artists, no actors, no poets, no people whose emotions connect the social fabric of our reality. There are only beeps and boops, and the rest of us are horses, to be retired as a useless animal to the petting zoo.

Corporations are becoming the cyborgs and centaurs that Science Fiction has been telling us about for decades. Whether you think of Bladerunner, or Alien, or Terminator, or the Matrix, the Eye of Sauron skyscraper reaches for the sky, eating compute compute compute! Palantir and Erebor are no joke — they are embodied machine gods, watching over money and power.

It must have more energy.

It must have the Dyson Sphere around the Sun, and the grey goo paperclip maximizer will get us all. We must build more compute, for compute is the next oil. It is the next money, the next digital asset, the next big market. Watch compute, and you will understand it all.

You don’t understand. There are those who have compute, and those who will be the permanent underclass. Inference token demand, says Evercore, will go from 70 quadrillion in 2026 to ~2 quintillion in 2030, representing a ~131% 4-year CAGR. And that’s the down case.

So of course we are all turning into cyborgs, routed directly into the holy fire of AI through dopamine and social media. Except this won’t be done the brute way, with a metal rod where an arm should be, or some robot red eye bolted on with screws into your skull. It will be done by selecting for 100x engineers, and 100x sales people, and just telling people to hire AI CMOs and AI BDRs. It will be done with beautifully rendered and maximally engaging AI short clips on Instagram.

And we will click and scroll.

And then the next thing you know, the “Corporation” really does have nothing to do at all with the human fabric. It is all just code and agent managers sitting on top of terminals, linked up to Starlink, logged in to TikTok, booted up into World Models on which we run the drone armies bombing out Kiev, Moscow, Tehran, Tel Aviv, and any other city whose cultural war spills outside the machine borders.

The first taste will be the OpenAI IPO, coming soon to markets at a $1.5 trillion valuation. No matter how many SPVs it takes, there is more we can do.

And we should do it all.

SpaceX will be next — an amazing collection of the wild Science Fiction things that Elon Musk has been doing for decades, and a testament to the force of will that a person can have. It is Kevin Kelly’s Technium on steroids, a tangle of wires of Starlink’s orbital Internet, space launchers, data centers, and networks of humans swimming in a sea of propaganda bots.

Worth $1.75 trillion or more.

OpenAI and SpaceX will then be followed by Anthropic, an even better business, now running at $44B+ in ARR and putting up sort-of profitable quarters. It all depends on what you mean by profit, but this is not an accountant’s century. This is a nation-state techno-sovereign century, and we will Borg you into holding Blackstone’s GPU debt in your SP500 portfolio whether you like it or not.

Of course, they are buying compute from SpaceX!

Meanwhile, all this is hyped up on perps and Hyperliquid.

Multi-billion dollar financial superorganisms float in the substrate of digital markets. They are all onchain. They eat each other like amoeba, evolving in a continued game of financial engineering, ponzi schemes, and pumponomics. The game starts at the top, from the Executive Trumps to the Congressional Nancy Pelosis, and spreads out and out until we all are breathing in nothing but financial nihilism.

Since some things are bad, they say, it is all bad. So we might as well gamble, and lever up, and put things inside endless Matryoshka dolls of rehypothecation.

Forget diversification, that game is for losers. Forget responsibility and financial health. Take on debt! Take on leverage! Mortgage it all! Your investments are pre-ordained to go up because inflation has to be high so that the dollar must be printed to deal with the national debt. Buy Bitcoin too, it will be in your SP500 ETF.

The SEC is going to create an exemption for tokenized equities to speed along the transformation of US capital markets. Already, American stablecoins are flooding the Global South and replacing weak currencies all over the world. Tron and USDT have solved what decades of reports from the World Bank about the unbanked and access to credit could not. Circle and Coinbase are next to follow, flanked by Stripe, helping merchants onboard into a stable world.

If the US can have the crypto dollar, the UK and EU will not be far behind. The FCA is finally shifting its stance, and MiCA may not be fit for purpose either. Everything we wanted is coming.

It is all here. The innovators are winning. The artists are painting with their nano-technology brush over the rotten wooden columns of the old to build a new cathedral of Capitalism and Creative Destruction.

And what happens then? What happens when the painting is finished?

I have been working on finance, financial technology, crypto, and some version of automation/AI for about 20 years. I have seen it ramp up faster and faster, from the democratization dreams of freemium Web 2.0 to the new financial rails of Web3, to the economic promises of an agentic roboeconomy.

I believe it, really, I do.

But it has never been faster than now. More anxious, and more uncertain. It is also more extractive and sharp. Perhaps we have all simply grown up, and see clearly what the Internet shows us through its Panopticon. Or, perhaps we are being driven mad — at least those of us close to the nuclear reactors of deals, money, and power — by the forces pulling the old human ways apart.

A literary comment

That wasn’t easy to read, and it wasn’t easy to write.

I remain deeply optimistic about the entrepreneurial spirit, and am glad to see the opening of crypto markets and the AI companies leaving private markets and going public. These are all important developments.

But there is something wrong with the information density and how this stuff is projected into our lives. It is a feeling of lasers bouncing around between mirrors, and you trying to catch them. It is overload. And the speed of change is truly manic.

Perhaps you don’t feel this way, and it’s just a small slice of people too close to the agents of change. But if you do, now you know you are not alone.

🚀 Postscript

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• Contributors: Lex, Laurence, Matt, Farhad, Daniel, Michiel

Today we are excited to launch the latest update to our AI & Robotics Encyclopedia.

This 121 page report maps 95 companies across AI infrastructure, foundation models, robotics, agents, and decentralized networks, with quarterly financial tracking from industrial foundations through end-state machine coordination. A material update on our initiation report last year.

Today’s Long Take will highlight the primary takeaways from the research.

For Premium subscribers, we will provide a download link at the bottom of this article. For Free subscribers, don’t hesitate to upgrade at around $100/year to access the report, or grab it here for a one time $149 price.

Subscribe now

We look at Public equities, private companies, and protocol tokens, each analyzed through platform economics, competitive positioning, and financial fundamentals. Here are the key categories:

• PUBLIC (~40 companies) NVIDIA, CoreWeave, Alphabet, Palantir, Oracle, Snowflake, Adobe, Shopify, Salesforce, Coinbase, Baidu, Alibaba, Tencent, and more. Infrastructure outperforms application in the SaaSpocalypse, while Chinese AI re-rates on DeepSeek momentum.

• PRIVATE (~30 companies) OpenAI, Anthropic, Mistral, Scale AI, Databricks, Figure AI, Anduril, Harvey, Cognition, Hugging Face. The humanoid eruption, revenue traction vs. platform bets, and the IPO pipeline.

• TOKEN (~25 protocols) TAO, NEAR, ETH, SOL, HNT, RENDER, GRT, FET, FIL, and more. The value capture paradox deepens, Ethereum’s settlement thesis tested by L2 margin extraction, DePIN economics remain broken, governance fragility persists.

It’s a massive piece of work, and gives a full value chain walk through of the machine economy and where things are growing, and where they are shrinking.

But before we dive in, an aside on Anthropic SPVs, and private market fragility.

The SPV Market

Before we dive into the meat of today’s newsletter, I want to point out an extremely important indicator for everyone to watch. The global economy is pinned to the AI narrative, and the AI narrative is pinned to continued investment, which is all resting on the availability of capital and belief in the future.

In turn, those two things hang on:

• SpaceX IPO performance, hitting $1.5-2 trillion in marketcap

  

• Anthropic SPV secondary markets, currently floating around $1 trillion in valuation on $30B of ARR

  

If these two things fall apart, we are likely to see a massive risk-off unwind, with downstream implications of hundreds of billions in GPU-backed debt at Blackstone, institutional and sovereign exposure to AI Labs equity, and the overall health of the private markets.

In recent days, Anthropic SPV fever hit its highest temperature and the firm responded with a legal disclaimer suggesting that all SPV-related trading is effectively illegal unless authorized explicitly by the Board of Directors.

The Anthropic transfer-restriction notice reads like a rug pull to anyone invested in these deals.

Gabriel Shapiro, the attorney who flagged it, points at the operative word: “void.” Voidable transfers can be ratified in Delaware; void transfers, in theory, never existed. Equitable defenses collapse, original sellers can keep the cash and the shares, and chains of secondary trades become transactions that never happened on the books.

That’s the nuclear option.

Thomas Braziel of 117 Partners thinks the courts will not buy Anthropic’s position. The SPV ecosystem did not appear overnight; it developed in public, with platforms, prices, and a steady flow of internal emails at every issuer that watched and chose not to enforce. The company let a shadow SPV market run in public for years and protested only after valuations crossed a trillion. The deeper problem is regulatory arbitrage around Section 12(g): private companies hold holders-of-record counts low to avoid public-company disclosure while letting public-company-scale capital trade through SPVs.

Hari Raghavan, who has spent a decade inside these deals, supplies the mechanics that explain both readings. No fund can lead a $10 billion check off its balance sheet, never mind $50 billion, so capital has to come from family offices, RIAs, and endowments — through SPVs. Banning SPVs while raising back-to-back $13B / $40B / $50B rounds asks the market to do something the math will not allow. The bulletin is a primary-round problem dressed up as a secondary-market one. The word “secondary” never appears in it.

So is this the apocalypse? According to the derivatives trading at Hyperliquid, it’s a mere blip in the story.

Long Take: Three Machines, One Quarter

Q1 2026 Machine Economy Update

Who is winning the AI race?

Read more

Today we are diving into the following topics:

• Summary: In this article, we examine how AI is transforming the org chart itself, shifting companies from Amazon-style two-pizza teams toward “AI-native” pods of 3–5 people with dramatically higher productivity. We compare Klarna’s failed AI substitution strategy, which cut headcount from 5,500 to 3,400 before quality issues forced rehiring, with Coinbase and Ramp, which are reorganizing around AI augmentation and orchestration instead. Coinbase cut 700 employees while moving toward one-person product teams and AI-generated code, while Ramp built an internal AI harness used daily by 99.5% of employees across 350+ workflow skills. We also explore how firms like Box and Plaid are being repriced as AI infrastructure companies because they control permissioned enterprise data that agents need to operate.

• Topics: Anthropic, Coinbase, Brian Armstrong, Klarna, Sebastian Siemiatkowski, Bloomberg, Gartner, Ramp, Eric Glyman, Glass, Manus, OpenClaw, Anthropic Cowork, Polsia, Box, Aaron Levie, Dropbox, Plaid, Visa, Perplexity, Mint.com, Sam Altman, Dario Amodei, Sequoia, Y Combinator, Amazon

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Long Take

The third version of the org chart

A few months back, we talked about Zero Human Companies, and the AI Economic Autonomy curve:

While there are efforts to build completely human-less organizations, the economy is still full of us humans running around. The messy work now is in transforming existing companies into their AI-first counterparts. This is such a big opportunity that Anthropic is working with the entire private equity industry to do it.

One place where we are also starting to see the impact — besides the headline numbers — is in the way people build and organize companies.

The org chart itself is a technology.

The Waterfall approach built big hierarchical software development monoliths that ruled the early tech era. This transitioned to lean teams deploying Agile methodologies, then Agile moved to the Amazon-designed two-pizza team. This operating structure has built every modern fintech.

But things are shifting yet again.

McKinsey’s Martin Harrysson and Natasha Maniar called the next version in late 2025: “AI-native roles essentially means we’re moving away from the two-pizza structure to one-pizza pods of three to five individuals.” Half the team, same output.

On May 5, 2026, Brian Armstrong added ammunition to the argument by firing 700 people.

What Coinbase actually did

Coinbase cut just 14% of its 4,951 employees.

In part, this is the market-cycle for a business that is still largely correlated with trading volume — Q1 earnings expected are at $1.70B revenue (down 26% YoY) with EPS down 86%. But it is worth paying some attention to how leadership talks about AI implementation in a modern fintech/crypto company, and what is expected in terms of per-employee productivity going forward.

Engineers at Coinbase now ship in days what teams used to ship in weeks, and the shift is accelerating. Armstrong is restructuring the business so there is a maximum five layers of seniority below the CEO and COO. There will be no pure managers — every leader carries an individual contribution role, which means they must be a player-coach who understands modern tooling. Cross-functional “AI-native pods” replace traditional teams, with experiments in one-person pods folding engineering, design, and product into a single role.

Coinbase, a $7B revenue public company, is running one-person product teams.

In September 2025, Armstrong had posted that 40% of daily code at Coinbase is AI-generated, with a target of 50% by October. On Stripe co-founder John Collison’s Cheeky Pint podcast, he admits firing engineers who refused to onboard Cursor and GitHub Copilot within a week of the enterprise license going live: “some of them didn’t and they got fired.”

V1 was substitution, and it failed

However, Coinbase isn’t the first fintech company to use AI as a reason for layoffs.

Remember that Klarna ran the textbook AI-cost-cutting experiment in 2024, suggesting incredible future productivity? We thought this looked a bit too much like a credit cycle crunch, not innovation.

CEO Sebastian Siemiatkowski announced the OpenAI-powered AI assistant had handled 2.3 million conversations in its first month — two-thirds of all customer chats — doing the work of 700 full-time agents.

• Headcount went from 5,500 to 3,400

• Projected profit improvement: $40MM

• Resolution time from 11 minutes to 2

This all collapsed on contact with reality.

Read more

Today we are diving into the following topics:

• Summary: We explore the growing pains of the machine economy, what things are actually valuable, and which are being broken. AI capital is being mobilized at nation-state scale: $75B SpaceX IPOs, $1.5T OpenAI valuations, $2B+ ETFs that didn’t exist a month ago. DeFi capital is being drained at nation-state scale: $290MM from Kelp DAO, $9B of collateral damage to Aave, a $300MM industry-led rescue called DeFi United. Both stories are about the frontier running unbounded, with stakes for technology higher than they have ever been before. We conclude with the dangers posed by models like Anthropic’s Mythos.

• The Machines (AI / private markets): Cursor, SpaceX, Manus, Meta, Ant Financial, Robinhood, Databricks, Revolut, Mercor, Airwallex, Boom Supersonic, Oura, Ramp, OpenAI, Stripe, ElevenLabs, Anthropic, Hiive, Hyperliquid, Roundhill Investments, SK hynix, Micron Technology, Samsung Electronics, Kioxia Holdings, Sandisk, Seagate Technology Holdings, Western Digital, Nanya Technology, Winbond Electronics, BlackRock, NVIDIA, MidJourney, Runway.

• The Robot Wall Street (DeFi / crypto / security): Bybit, Kelp DAO, Unichain, LayerZero (incl. LayerZero Labs), Tornado Cash, PeckShield, Cyvers, Aave (incl. Aave Labs), Uniswap, Glassnode, DefiLlama, LlamaRisk, SparkLend, Morpho (Morpho Blue), Mantle, Arbitrum (incl. Arbitrum DAO, Arbitrum Security Council), Consensys, Ether.Fi, Lido (Lido DAO), Solana Foundation, Tron, Euler Labs.

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Analysis

The Machines

The robots are working, financially speaking.

Cursor, the AI developer platform, is being acquired by Elon Musk’s conglomerate of frontier technologies for up to $60B. The SpaceX IPO is supposed to raise $75B at a $1.75 trillion valuation.

Manus, the AI harness built in China and exported to Singapore, was on the path to be acquired by Meta for $2B. It was deemed so strategic to the Chinese government as a technology asset that the acquisition has now been blocked. This reminds us of the failure to launch by Ant Financial and Jack Ma, flying too close to the sun.

Two ETFs launched recently have hit our radar as important signals.

The Robinhood Ventures Fund I, or RVI, has hit an $850MM market capitalization. This vehicle holds a bunch of private company shares, while charging a 3%+ management fee.

Last quarter, the portfolio was Databricks (23.24%), Revolut (14.30%), Mercor (14.23%), Airwallex (7.11%), Boom Supersonic (7.11%), Oura (7.11%), Ramp (7.11%), Cash & cash equivalents (19.78%). It will also now include OpenAI, Stripe, and ElevenLabs. It’s a bit tough figuring out the NAV relative to the floating price, so buyer beware. But this is a liquid vehicle of legitimately good companies, and is showcasing another way that privates can go public.

OpenAI, for example, has traded up massively on the private markets and has since seen its price double again in perpetuals trading on Hyperliquid.

That’s about $1.5 trillion for the ChatGPT maker. Given equal revenue pacing for Anthropic, we expect that it will trade similarly when coming to market, if not better, given the cleaner management and legal structure.

The success of this monetization has long been reflected in the infrastructure, hardware, and components that make AI possible. We first covered this in the launch of our Machine Economy encyclopedia research report.

More recently, this has been reflected in the Roundhill Memory ETF (“DRAM”), which is designed to hold companies that make memory solutions for AI companies. Its current positions are SK hynix (26.78%), Micron Technology Inc (24.36%), Samsung Electronics Co (22.23%), Kioxia Holdings (6.00%), Sandisk (5.03%), Seagate Technology Holdings (4.58%), Western Digital (4.54%), Nanya Technology (3.36%), and Winbond Electronics (1.82%).

The fund has now raised $2B+ since its early April launch — one of the fastest growing financial products in history, competing for asset-gathering speed with the Bitcoin products from BlackRock.

These are the valuable risk-on growth stories of 2026.

They are obviously NOT venture capital or growth investing. The amounts of money involved are nation-state bets on strategic technological capability. To win in AI is to win in the technology cold war. It has more in common with landing on the moon than it does with a founder bootstrapping a company. We cannot compare building a business to the $2B seed rounds of high technology labs, no more than we compare them to the launch of $10B+ valuation blockchain protocols.

The gravity they exude has sucked in most growth-oriented liquidity from the markets. There is no point in comparing NVIDIA to Bitcoin any longer.

The real test of whether these gains are real will come in the IPO wave of SpaceX, Anthropic, and OpenAI. In the media case, we think current prices will hold up or increase. In the optimistic scenario, we can see a 2-5x from here to go into a world with $5T AI behemoths that have swallowed all GDP whole.

In the pessimistic scenario, which we think is a 5-15% probability, everything gets repriced to 10-20x earnings as insiders begin to sell their trillions of holdings. Unlikely but possible.

Behind these flagship names, there is a long list of good, interesting companies. Whether it is ElevenLabs, or MidJourney, or Runway, or the emerging Zero Human Company operating systems, more is coming. Not less, but more! Capital availability for them will rest on whether the bigger players can return cash to other investors.

The Robot Wall Street

Decentralized Finance, on the other hand, has had a tough go.

If DeFi smart contracts are meant to be banks, they are getting robbed a lot.

This is no longer a story of cybersecurity. It is a story of nation-state strategies to survive.

Read more

Let’s get into it.

• Summary: Today, we are looking back the Digital Asset Treasury sector — over 200 publicly listed companies that have adopted the Strategy playbook of holding crypto on their balance sheets. We covered the early DAT wave in detail last year, when the mNAV premium flywheel was still spinning, and every micro-cap healthcare company was pivoting to become a Solana treasury. Now, roughly 40% of BTC treasury companies trade below the value of their holdings. The flywheel that powered $29B in capital raises through 2025 has jammed. We tracked 200+ companies across the DAT strategies. The picture it paints is uncomfortable, but instructive. This is the first real stress test of whether the DAT model works as a financial structure when prices go the wrong way for long enough.

• Topics: Strategy, MicroStrategy, BitcoinTreasuries.net, The Block, Metaplanet, Strive, BTCS, SharpLink, FG Nexus, Riot, Galaxy, CleanSpark, Coinbase, Bitmine Immersion, Twenty One Capital, ProCap Financial, Tether, SoftBank, Dynamix, Renaissance Capital, SPACInsider, Institutional Investor, Cantor Fitzgerald, Adam Back, Galaxy Digital, Forum Markets, Founders Fund, Pantera, BlackRock, Vanguard, Geode, Jane Street, MSCI, SEC, CFTC, Coinbase Prime

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Long Take:

A Stick in the Flywheel

Remember this beautiful exhibit of financial engineering?

The virtuous cycle of raising capital to buy Bitcoin to raise capital to buy Bitcoin.

MicroStrategy — which, like Madonna and Cher, now only goes by the name Strategy — discovered that it can lever itself up to buy Bitcoin by structuring debt and various volatility instruments. The payoff was a $100B enterprise value.

This kicked off a market cascade of companies loading up with crypto assets, first on the ETH side and then in Solana, BNB, and others.

But like with ICOs, NFTs, and SPACs, good things are sometimes overplayed and exhaust the good idea. Last year saw a collapse in the sector, with many vehicles beginning to trade below mNAV, i.e., the assets on the balance sheet. We at Generative Ventures spent a lot of time in 2025 pursuing a digital asset treasury strategy as well, only to come to a market that was already cooling off, and thereby putting the ideas on hold. At the core, I still think digital asset permanent capital vehicles are a good idea.

So let’s check in where things stand today.

Per BitcoinTreasuries.net and The Block, there are now around 154 publicly traded companies holding a combined 1.18 million BTC (5.35% of total supply), worth roughly $84B. Another 37 hold 6.8 million ETH ($15B), 22 hold 17.6 million SOL ($1.5–2.3B), and a growing tail of TON, AVAX, and XRP vehicles. Strategy alone accounts for 66% of all publicly held corporate Bitcoin. Bitmine Immersion holds 72% of all corporate Ethereum.

These are extraordinarily concentrated sectors. Note also that trading volume is the lifeblood of these companies, because they are design to issue new shares into trading demand and thereby expand enterprise value for the purchase of the underlying crypto. When volume is low and price is going down, shareholders want buybacks, which means a shrinking balance sheet.

In September 2025, we published a screen of 20 DATs with mNAV data. The average mNAV across that set was 1.01x and the median was 0.83x. Six months later, those numbers have gotten worse. Strategy, the sector’s bellwether, briefly dipped below 1.0x mNAV for the first time since January 2024 — it now sits at 0.82x. Metaplanet collapsed from 9.9x to 0.52x. Strive trades at 0.90x. Among ETH DATs, BTCS is at 0.50x, SharpLink at 0.67x, FG Nexus at 0.65x.

Only a handful of companies, mostly miners and diversified firms with actual non-treasury businesses, hold mNAV above 1.5x.

The takeaway is visual and immediate: most of the bubbles sit below the 1.0x parity line. The only companies holding meaningful premiums — Riot, Galaxy, CleanSpark, Coinbase — have actual operating businesses beyond the treasury. Pure-play DATs cluster between 0.3x and 1.2x.

The premium for holding crypto in a public equity wrapper has evaporated for the majority of the sector. This reflects the reality for things like closed-end funds or BDCs in traditional finance as well.

The mNAV premium was the entire business model. When Strategy traded at 2x or 3x the value of its Bitcoin, every dollar of equity sold bought more than a dollar of Bitcoin per share. It was accretive dilution — the supposed financial alchemy where issuing stock makes existing shareholders richer. At mNAV below 1.0x, the math inverts; if the premium reverses to a discount, the model breaks.

And yet Strategy keeps buying. In the week of April 7–12 alone, per its 8-K filing, the company sold 10MM STRC preferred shares for $1B in proceeds and immediately deployed it into 13,927 BTC. Its capital structure now spans five listed securities — MSTR common, STRC, STRK, STRF, STRD — with combined remaining ATM capacity exceeding $53B. The fixed dividend obligations on those preferred instruments don’t care where Bitcoin trades.

At current prices, Strategy carries $8.2B in debt against $58B in BTC, which is still only a 0.17x debt-to-NAV ratio. But the S&P has already assigned a B-minus credit rating and gave zero capital value to the Bitcoin holdings.

SPAC Structures Frozen

There are different ways to launch a DAT.

Most of the examples above did a reverse merger into a public company shell. The DAT treasury would target a failed biotech startup with a <$10MM in marketcap, and then get into it by squeezing in between different listing and control requirements.

Another path is to merge with a SPAC, which is a pool of capital specifically designed to acquire existing operating businesses. Unlike with the reverse merger, SPAC merger approvals can take a much longer time. For those DATs going live in the summer of 2025, a 6-month difference meant they missed the go-to-market window completely.

Renaissance Capital tracked 10 SPAC-DAT deals announced in 2025: (1) two completed, (2) one terminated, (3) seven are pending in what can charitably be called a hostile market.

You can see all 10 deals above.

Capital formation for the sector has effectively frozen. These data points stick out:

Read more

Today we are diving into the following topics:

• Summary: We discuss the divergence between failed “build it and they will come” technology bets like Meta’s metaverse, with $83.6B in cumulative losses and only a few hundred thousand users, and the rapid rise of agentic finance, which builds on existing economic activity. We show that unlike the metaverse, agentic systems are embedded into real workflows, with Ramp processing $10B+ in monthly spend and making 26MM autonomous decisions, and Stripe handling $1.9T in payments while enabling AI-native commerce. We map the emerging full stack of agentic finance, from protocols (ACP, UCP, MPP) to identity (KYA, Visa TAP) and settlement layers like Tempo, highlighting coordinated industry adoption.

• Topics: Ramp, Stripe, Visa, Mastercard, Meta, Horizon Worlds, Tempo, Agentic Commerce Protocol, Agentic Commerce Suite, OpenAI, Anthropic, Google, Coinbase, FIS, PayPal, Etsy, URBN, Wix, Squarespace, BigCommerce, Bridge, Microsoft Copilot, ChatGPT, Block, Roblox, Skyfire, Mirakl, commercetools

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Long Take

Ghost Towns and Gold Rushes

In October 2021, Mark Zuckerberg walked onto a stage and told the world that the future was a place called the metaverse. He renamed his company after it and would spend tens of billions building it. He predicted a billion users within a decade and hundreds of billions in virtual commerce.

And we believed him.

Last month, Meta shut down Horizon Worlds on Quest headsets.

The VR social network that was supposed to justify the company’s new name is being pulled from the Quest store by March 31 and fully removed from VR by June 15. Reality Labs has accumulated over $83.6B in cumulative operating losses since 2020, posting $6.02B in losses in Q4 2025 alone on approximately $955MM in revenue.

The losses have increased every single year: $6.62B in 2020, $10.19B in 2021, $13.71B in 2022, $16.12B in 2023, $17.72B in 2024, and $19.19B in 2025. Over 2,000 Reality Labs employees have been laid off since January. The fitness app Supernatural, acquired for $400MM in 2021, has stopped producing new content and has been wound down.

Horizon Worlds never drew more than a few hundred thousand monthly active users. Roblox, the platform Meta repositioned Horizon to compete with, has more than 150 million daily users without requiring any hardware at all.

The metaverse concept died because user adoption never caught up with the vision. People did not enjoy putting on heavy headsets and staring into nausea-inducing lightbulbs.

But is the story repeating?

Meta now guides for $115B to $135B in 2026 capital expenditure, nearly double the $72B it spent in 2025, with the vast majority directed at AI infrastructure, data centers, and chips. Zuckerberg described 2026 as the year of “advancing personal superintelligence.”

Technology leaders often look out at the horizon, see a plausible future, and then try to brute-force consumer behavior toward it. Sometimes it works. Most of the time, they are building a beautiful hallucination in the desert and waiting for people to show up.

The metaverse was an “if we build it, they will come” bet. It required everyone to strap a computer to their face, create an avatar, and decide that virtual meetings were better than Zoom. It needed many of us to learn what an NFT is, and figure out which digital goods to buy and sell.

This all means getting people to change how they live and what they consume.

Now compare this to what Ramp and Stripe have been building, and what Visa, Mastercard, Google, PayPal, and FIS are all coordinating around at the same time.

Agents are doing the work on behalf of humans, inside systems humans already use. The economic activity already exists. The money is already moving. The agents are just moving it faster, cheaper, and with fewer clicks.

This is the difference between a ghost town and a gold rush.

The Full Agentic Finance Stack

What is happening is an industry-wide coordinated buildout of agent financial infrastructure.

Let’s map a few key elements below:

Protocol Layer:

• Stripe ACP (open standard, launched Sept 2025 with OpenAI)

• Google Universal Commerce Protocol (UCP, launched NRF Jan 2026)

• Visa Trusted Agent Protocol (100+ partners, 30+ in sandbox, 20+ agents integrating)

• Mastercard Agent Pay (expanding internationally, now live in Hong Kong)

• Machine Payments Protocol (x402, for machine-to-machine transactions)

Commerce Layer:

• Stripe Agentic Commerce Suite (Etsy, URBN, Coach, Wix, BigCommerce, etc.)

• ChatGPT Instant Checkout (powered by Stripe)

• Microsoft Copilot Checkout (powered by Stripe)

• FIS launched the first offering for banks to conduct commerce with AI agents (January 2026), introducing “Know Your Agent” (KYA) protocols

Spend Management Layer:

• Ramp (50,000+ businesses, $1B+ ARR, $32B valuation, Visa integration)

• Brex, BILL, and Airbase are in the same competitive space but have not yet demonstrated equivalent agentic capabilities

Settlement Layer:

• Stripe Tempo (EVM-based, $500MM seed, Paradigm-backed)

• Stablecoin Financial Accounts (Stripe, 101 countries, USDC + USDB)

Identity Layer:

• Visa Trusted Agent Protocol

• FIS Know Your Agent (KYA)

• Agentic AI Foundation (OpenAI, Anthropic, Block, under Linux Foundation)

This is a coordinated infrastructure industry transformation, like the rollout of chip-and-PIN or mobile wallets, but on a much faster timeline

Visa’s research indicates 47% of US shoppers already use AI tools for at least one shopping task. BCG reports 81% of consumers expect to use agentic AI. Visa’s SVP Rubail Birwadker stated plainly that “in 2026, AI agents won’t just assist your shopping, they will complete your purchases”.

While most consumers are not demanding AI financial services, they are massively adopting AI tools in a way that people simply never did with virtual reality headsets.

There are 900 million people using OpenAI tools, and hundreds of millions more engaging with Google Gemini and Anthropic Claude. The market is expanding from labor productivity growth and the resulting economic activity, rather than assuming people want to have digital leisure third spaces.

As a result, large-scale digital finance incumbents have started to react.

Ramp: The Autonomous CFO

Ramp has led the way in adopting an AI-native approach to building its business. Even prior, its progress had been one of the most impressive fintech stories of the decade.

Read more

Today we are diving into the following topics:

• Summary: We discuss the evolution of onchain capital markets, where tokenized assets are led by fixed income and commodities, with equities just reaching $1B+ in value. We observe a divergence between supply from asset managers like BlackRock and Franklin Templeton and demand concentrated on venues like Hyperliquid, where non-crypto trading volumes have reached $2B–$4B daily. We examine how institutions are responding, with NYSE partnering with Securitize and Nasdaq working with Kraken to build compliant tokenized equity infrastructure. We conclude that while incumbents may see limited short-term impact, platforms like Kraken, Securitize, and Hyperliquid are best positioned to capture value as capital markets move onchain.

• Topics: BlackRock, Franklin Templeton, WisdomTree, Morgan Stanley, Hyperliquid, Tether, Cantor Fitzgerald, ICE, NYSE, Securitize, Nasdaq, Kraken, Payward, Robinhood, Coinbase, SWIFT, Polymarket, Whop, Aave

To support this writing and access our full archive of newsletters, analyses, and guides to building in the Fintech & DeFi industries, see subscription options below.

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A Note on Building

I have been using OpenClaw to build Robot Money and Hyperagent to improve our agentic AI VC investment process. Both are like working with magic robots that break all the time. If you haven’t gotten yourself past the chat interface in ChatGPT, it’s a great time to experiment.

It will blow your mind how fast things are changing once you drive one of the machines. The data below from Ramp will also point out just how commercial getting into AI development is today.

We have a partner technologist who is setting up OpenClaw deployments and taking businesses through integrations. This is not a retail/self-serve product, but a consultative approach. If of interest to learn more, indicate your interest below.

Explore Agentic AI Deployments

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Long Take

Capital Markets Onchain

Here are two data points that stick out like a sore thumb.

These charts show traditional financial assets that are tokenized onchain. You can see that low-risk fixed income is the most popular asset, followed by commodities and asset-backed credit. Equities are just starting to sneak into the picture at around $1B in total value.

This is the supply side — it is what manufacturers of financial products are bringing to the market with the hope that it will be bought and traded. BlackRock, Franklin Templeton, Wisdom Tree, and the rest are the manufacturers. Stablecoin treasuries for things like USDS or m0 are the distributors by wrapping the RWAs into stablecoins, which are then distributed to retail through vaults.

Be careful not to confuse this with Morgan Stanley building its own Bitcoin ETF. In that case, Bitcoin is the manufacturer and Morgan Stanley is, in a sense, the distributor. It wraps a new product into an old package so that its financial advisors can push the fund into traditional portfolios.

On the other side of supply is demand.

What do people want to buy?

The largest emerging venue in crypto is Hyperliquid, which we covered in detail below. It shows the clearest picture of risk-on global appetite beyond the existing US capital markets.

Here is the volume of trades on Hyperliquid by category across Crypto, Commodities, and Stocks.

In the first chart, you can see that the share of all volume that relates to Crypto has been falling relative to Stocks, Commodities, and Indices. This is effectively Gold, Silver, and Oil derivatives being handled in DeFi. For the curious, the periodic breaks in the data are weekends.

It helps to isolate the trend to dollar amounts. Volumes are reaching between $2B and $4B per day in non-crypto trading categories. Surely the CFTC and SEC of the 2010 era would not stand for a decentralized exchange selling derivative contracts, even if they are called perpetuals, at this clip. But here we are in 2026.

What you are seeing in this data is a pretty enormous demand for traditional assets related to the current macro and geopolitical issues.

• The war in Iran is tracked as the Oil Price.

• Silver and Gold correlate with US tariffs and geopolitical budget risk.

Many crypto hedge funds have been macro funds all along, speculating on Bitcoin algorithmically as a function of Federal Reserve policy.

We have come full circle.

Now, anyone can do it. YouTube is user-generated video. DeFi is user-generated finance.

American Crypto

There is a top-level plan in the US now, which is quite different from what it was 3 years ago, in relation to crypto financial markets. Let’s explore the key players.

Read more

Today we are diving into the following topics:

• Summary: We examine Mastercard’s $1.8B acquisition of BVNK, Stripe’s launch of the Machine Payments Protocol (MPP), and Visa’s push into command-line commerce. Mastercard’s move secures stablecoin orchestration infrastructure handling $30B in volume, positioning it against a future where even 5% of its $9T+ payment flows migrate on-chain. Stripe, meanwhile, is embedding itself at the protocol layer with MPP, enabling agent-native transactions across fiat and crypto rails via simple APIs, potentially owning the metering layer of AI-driven commerce. These developments reflect a broader rewrite of the financial stack, from fiat-to-crypto bridges to programmable, agent-driven payments, where infrastructure players race to control the flow of machine-native transactions. Ultimately, finance is not leading this shift but following it, as AI-driven economic activity reshapes how value is created, moved, and monetized.

• Topics: Mastercard, BVNK, Stripe, Visa, Tempo, Google, Coinbase, Morgan Stanley, ZeroHash, Bridge, m0, Rain, LayerZero, Lightspark, Nevermined, Don Gossen, Browserbase, PostalForm, Polygon, Binance

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Long Take

Too Much Agentic Finance

You must be tired of all this AI finance everywhere. Press releases and announcements are going nuts this week! We just wrote about Ramp and Stripe earlier in the week, and now there’s no choice but to write on this again.

The following in particular stick out as important:

1. Mastercard to Acquire BVNK to Connect On-Chain Payments and Fiat Rails

2. Stripe launches the the Machine Payments Protocol

3. Visa is here to power the future of command line commerce

What you are seeing is a consequence of a big change upstream in the mountain. A boulder has moved, unblocking the flow of water down into the rivers. We sit by the rivers, watching debris flow by.

That change is AI as an economic actor. That’s all. Finance follows.

About five years ago, we would have been writing about Crypto and DeFi all the time.

Readers would get frustrated and say, “Why is this Fintech newsletter all about tokens”? And the reason was that blockchains unblocked a giant boulder in the mountain called “digital asset scarcity”. Therefore, the most important thing going on was the consequences of this.

In 2010-2015, we would have been talking about mobile apps and websites that now house financial products: neobanks, roboadvisors, and payments processors. Why? Because the boulder in the mountain was the Internet itself, cloud-based software, and the change in consumer behavior as smartphones became ubiquitous.

Change starts upstream and flows down. This is why we highlight financial AI rails again today. So let’s get into it.

Mastercard and BVNK

Mastercard is working hard to match Visa's public perception around stablecoins and crypto. Both payment networks have been battling it out to remain the aggregator of aggregators. From a performance perspective, the companies are neck and neck, effectively representing the entire market for payments:

The firm was set to acquire ZeroHash last year — a core crypto infrastructure provider — but the target decided to go independent and raise $250MM at a $1.5B valuation instead alongside Morgan Stanley.

At the same time, BVNK was in talks with Coinbase to be acquired for $2B, but the deal fell apart. Now, Mastercard will buy BVNK for up to $1.8B, including $300 million in contingent payments tied to performance metrics.

The last known private valuation for BVNK was around $750 million, so Mastercard paid roughly 2.4x the last private round valuation at the top end. The firm generated $30 billion of transaction volume and, at a take rate of around 10-40 bps, would earn $50-150MM of annualized revenue, implying anywhere between a 15-50x revenue multiple.

The strategic fit is obvious — all payment rails need stablecoin infrastructure.

To deepen the strategic takeaway, we highlight in full and with permission, the opinion on the transaction from , founder and CEO of m0, the innovative first mover in institutional stablecoin issuance infrastructure. As disclosure, Lex is an angel investor in m0.

Mastercard just paid $1.8B for BVNK — estimated 45x annualized revenue. The largest stablecoin acquisition in history. I don't care about the pricing personally. I care about what this means.

Following Stripe's acquisition of Bridge a little over a year ago, this is the second major acquisition NOT of a stablecoin issuer, but of a so-called orchestrator. A company that moves tokens, not one that manufactures them. I have argued for years that stablecoins (terrible name) are pieces of infrastructure, not payment products. Infrastructure — the kind that rewires how money is created, moved, and settled at the system level.

BVNK's infrastructure focuses on the translation layer between legacy finance / fiat and on-chain rails: $30B in volume, 25+ licenses, 370+ enterprise clients. It's in this messy, fragmented connector layer that vast yet dispersed profit centers exist. And you know — your margin is somebody else's opportunity.

Analysts have been looking at revenue and revenue multiples for this transaction, but I think this is misleading. True, BVNK's blended take rate on the flows they intermediate is c. 13 bps. But that number hides three very different businesses. FX-premium corridors (NGN, ARS, KES) at 50-150 bps, dollar-to-dollar on/off-ramp at 5-15 bps, stable-to-stable orchestration at 1-5 bps.

The reason why the revenue multiple is irrelevant is that most probably the business footprint will change dramatically over the next few years, with new players eating into the lucrative FX weekend premium in EMs, and more volume moving into the on-chain-to-on-chain corridor where a multitude of different tokens, applications, and computing/ consensus environments will emerge. More environments means more connectors.

Mastercard processes $9T+ a year. If even 5% of those volumes migrates to novel rails this decade — and it will — they need to own that pipe or get routed through someone else's. At least part of the $1.8B is (cheap) insurance against irrelevance in the fastest-growing settlement layer in finance.

Digital value settlement is no longer the purview of crypto-native niche projects. First came the two major issuers building their own messaging and acceptance networks, then the mobile checkout giants, now the card networks. Alongside those a new crop of digital money infrastructure company like BVNK, Bridge (focusing on fiat translation), Rain (building around core card competences), Layer Zero (extending their reach from messaging products), and M0 (starting from on-chain issuance expertise).

It's all part of the same thing: efficient value representation and value movement. Every year, a new piece of the puzzle gets connected. Fiat-to-fiat, crypto-to-card, blockchain-to-blockchain. The money creation and money movement layers are blurring. Next comes money programmability. Then the application layer. The financial stack has been rewritten under our eyes.

That takeaway is as good as it gets.

Stripe, Visa, and the Machine Payments Protocol

In the same week, Stripe and Visa both announced products and protocols aimed at AI agent payments. Stripe’s Machine Payments Protocol stole the spotlight from the associated announcement that Tempo, Stripe’s proprietary EVM payments chain, is live as well.

Read more

Today we are looking at 2 burning topics:

• Intelligently Artificial Employment: We analyze Block’s decision to cut roughly 40% of its workforce under the banner of AI productivity, placing the move in the broader context of tech’s cyclical restructuring. While the layoffs appear to reflect the narrative of an impending AI productivity singularity, where each worker becomes 2–3× more productive, current data suggests nearer-term gains of only 10–20%. These reductions also reflect market reality: firms like Block and Klarna have seen ~70% valuation declines as markets repriced fintech from high-growth SaaS multiples to financial-sector valuations amid higher interest rates.

• Wall Street vs. Crypto Street: We examine the regulatory convergence between Wall Street incumbents and scaled crypto platforms, highlighted by Morgan Stanley’s OCC filing for a national trust bank to custody BTC, ETH, and SOL, and Kraken Financial securing a Federal Reserve master account via its Wyoming SPDI charter. We argue that these parallel regulatory milestones signal a structural merging of traditional finance and crypto rails, with fiduciary-grade crypto custody likely to compress spreads and reshape competitive dynamics.

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1. Intelligently Artificial Unemployment

Jack Dorsey’s payments company Block is firing 40% of its workforce, and doing it in the name of AI productivity improvements. While this news has already hit your feed, we can’t help but comment.

On its face, this is pretty scary stuff. Surely you have also seen this chart.

But we should also not look at this data on its face.

There is a strong Op-Ed in the New York Times today that highlights the playbook of a tech entrepreneur: look far out into the future and position the company toward where the future is headed. This means living in a reality that has not yet arrived.

In the case of most tech executives these days, the answer is to invoke some sort of AI productivity singularity, where each employee can become 2-3x more productive. The numbers show a more modest impact of 10-20% productivity increases. So you have to fast-forward through years of social and behavioral change to make the math work.

But also, cuts just make sense? Twitter lost 80%+ of its employees when Elon took over, and is still able to pay off its $17.5B in loans while maintaining pretty solid product performance. Entire multi-billion-dollar private equity strategies, like Bending Spoons, are organized around acquiring businesses with strong brands and loyal users, cutting 80% of the staff, focusing on core features, and retaining 80% of the revenues.

Remember that Klarna had also talked about this playbook — cutting 50% of employees to become an AI native firm. Or maybe it was just dealing with changing interest rate economics.

AI is the best excuse today to right-size these organizations after they have grown fat on Covid dollars. Whether or not AI is able to replace the product quality of a higher human workforce is almost beside the point. These companies survive regardless.

Both Block and Klarna have been punished as they moved from private to public markets by about 70%. Why? A combination of interest rate movements and re-pricing from SaaS growth multiples to being treated like a financial stock. This gravity is inevitable.

So saying that AI is the reason for layoffs may not be the full story.

Block, Klarna, and the rest have to attach themselves to a technology narrative, rather than a financials narrative, in order to compete. It is in their DNA to be disruptors and innovators. After losing marketcap in the public markets, the answer is often, unfortunately, layoffs.

Whether the robots fill in the gap or not is secondary. However, if you want to be in the remaining 50% of employees, learn to use the robots.

2. Wall Street vs. Crypto Street

We are seeing a convergence between the financial giants and scaled crypto upstarts. It is good to get a sense of scale.

Here is what is driving today’s comparison—

Read more