Markets are looking fragile.

In Crypto, investors are turning risk-off with BTC down -20% over the month at time of writing, with ETH and many altcoins experiencing drawdown of 30%+. Meanwhile, the SP500 continues its climb to new all-time highs, driven by expanding growth projections for a cluster of AI-related stocks.

Any developments that undermine those projections cause large swings. For example, Friday’s stronger than expected US job report led to $1.8T in value erased from the SP500 in a day, and an earnings miss from chip manufacturer Broadcom resulted in a -14% hit to a single stock. The volatility is a symptom of underlying uncertainty.

But we remain long-term optimists. Just this week $17B payroll giant Deel announced the launch of its DLUSD stablecoin to be gradually rolled out to the 1.5M workers being paid on its platform. The dollar stablecoin is built on Stripe’s new Tempo blockchain and enables workers to shelter their income from volatile local currencies while simultaneously earning yield from on-chain DeFi markets.

This week, we dig into the size of the opportunity for Deel, unpack its chosen infrastructure stack, and explore how DeFi yield can become a differentiator. Today’s agenda below.

1. FINTECH: Behind $17B Payroll Platform Deel’s New Stablecoin Bet

2. ANALYSIS: Analysis: The Missing Half of Agentic Payments

3. CURATED UPDATES: Paytech, Neobanks, Lending, Digital Investing

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Digital Investment & Banking Short Takes

Behind $17B Payroll Platform Deel’s New Stablecoin Bet

Deel was founded in 2019 by Alex Bouaziz and Shuo Wang, who met at MIT in 2013.

The pair had something in common: Bouaziz watched MIT classmates land $300k Bay Area offers while equally talented friends were locked out by visa barriers, and Wang kept hitting the same wall when trying to hire and pay people across borders. The friction was both expensive and slow. Hiring a single employee in a new country traditionally meant opening a local entity, government approvals, local bank accounts, and payroll registrations before the first contract is even signed.

So they started a company that solves this problem. Deel acts as the Employer of Record (EOR) on its customer’s behalf and collapses the entire process of hiring abroad into a few clicks in exchange for a fixed monthly fee. The company enables onboarding staff in over 150 countries without the customer having to set up an entity.

The business model piggybacks off a broader trend, which is that firms are increasingly generating revenue outside their domestic markets.

Stripe’s annual letter pointed out how its clients that operate internationally generate 30% of their revenues from a long-tail of markets that are outside both their home countries and the top 10 global economies. For Deel, this means a growing TAM of companies requiring local hiring and workforce solutions.

Today, Deel serves 35K+ businesses and processes $22B in payroll annually.

The company has been profitable since 2023 and last year reported a $1B+ annual revenue run-rate; faster than Stripe or Square (but slower than AI services like Lovable and Cursor). Its recent funding round, led by Ribbit Capital, valued it at ~$17.3B. Since launch, it has expanded to serve other needs of a global workforce such as payroll, benefits, and mobility.

The largest slice of revenue comes from EOR ($300M+), but 25-30% now comes from commissions on FX and payments.

This week, the company announced the launch of its own stablecoin wallet that lets contractors hold their earnings in DLUSD, a dollar-backed stablecoin balance, delivered within the existing app.

It is rolling out first in Argentina before expanding to other markets. The focus on LatAm makes sense. Converting volatile local currencies into US dollars is a common practice to shelter against inflation, and stablecoin payments have seen a much larger uptake in the region relative to the rest of the world. Chainalysis found that 62% of Argentina’s retail crypto transactions are stablecoins vs. the 44% global average.

DLUSD now gives workers a native way to access dollar exposure without withdrawing funds to another fintech platform. The question that comes to mind is why Deel chose to offer a native stablecoin over traditional USD balances.

We see two key reasons.

The first is owning the economics. The reserves backing DLUSD sit in short-term cash equivalents (Treasury bills) that are currently yielding roughly 3.6% (the higher interest rate environment has led to a huge windfall for stablecoin issuers more broadly).

Deel keeps the full amount after a small issuance fee paid to its partner Bridge. That is typically a better deal than the alternative: custodying dollars with partner banks who keep a large chunk of the float or spending resources acquiring their own banking licenses. The potential income is significant.

Using Revolut as an (imperfect) comparison, we can project how many idle balances Deel could attract relative to its payments volume.

Revolut reportedly ran £985B in annual transaction volume against £50.2B in deposits last year, a 20:1 ratio. Apply that to Deel’s $22B in annual payroll flow and you get around $1B in potential DLUSD balances. At ~3%, that is roughly $30M in annual revenue.

This also gives Deel a deposit base it can monetize further via adjacent financial products e.g. interchange income on the Deel Card it is launching next month.

The second is access to DeFi yield.

Through the stablecoin Earn feature, a worker opts in with a single tap and routes their DLUSD into an on-chain vault. The vault is similar to a DeFi investment fund but controlled by publicly visible smart contracts that restrict where the manager can allocate. In Deel’s case, the vault manager or “curator” is Sentora, and the whitelisted markets are lending pools on Morpho. The vault lends deposits to borrowers who post collateral, and the balance earns yield as interest payments are made. Morpho itself has grown to support over $10B in vault deposits, powering Earn products for both Coinbase, Kraken, and other major exchanges.

There are two things to double-click into: What this yield is today and where it could go in the future. Right now, Deel is advertising rewards of up to ~4% but that number is partly engineered. A new stablecoin lacks organic borrow demand, so in the short-term bootstraps yield using the reserve income on DLUSD balances.

Paypal, who uses the same curator Sentora for its PYUSD vaults on Solana, funds ~3% of the 5% advertised yield using these rewards. The remaining 2% comes from actual borrowers. The playbook is to bootstrap the market with an upfront incentive budget, pull in deposits, attract borrowers, then wean off the subsidy as organic activity builds.

In our view, where this organic activity will come from is a function of Deel’s chosen stack. DLUSD, together with Klarna’s USD stablecoin, are one of the first built end-to-end on Stripe’s architecture: Bridge as the issuance and orchestration layer, Privy for abstracting away wallet complexity, and Tempo for underlying blockchain settlement.

We think Tempo and the Stripe brand will attract high-quality yield opportunities as institutions continue to tokenize assets.

Tokenized real-world assets (excluding stablecoins) have reached roughly $29B on-chain, up almost 3x since last year. The majority is made up of tokenized treasury products and commodities, but other sectors like equities are growing fast. In fact, tokenization platform Securitize is set to go public this year via SPAC.

This is significant because the on-chain yield powering vaults like Deel’s is currently mostly confined to lending against highly liquid crypto collateral. That market is deep, but it has problems: the yield tends to compress over time as more capital crowds in, and sourcing anything higher means reaching into strategies that stack on unfamiliar risk e.g. oracle risk, bridge exploits, etc.

RWAs live within a risk framework that traditional institutions already understand (at least the underlying assets do), and we believe fintechs will increasingly demand them as part of yield portfolios when bringing users on-chain. With Tempo already securing the top-of-funnel with Deel and other players, the network is likely betting that institutional RWAs will follow.

The caveat is that it is very early. Tempo only launched in March and currently has negligible value locked ($11M). Over half of RWAs still sit on Ethereum that offers neutral and decentralised infrastructure. It’s also where the majority of DeFi capital is still allocated.

These details will become important as the wallet scales — but also shouldn’t take away from broader trend, which is that fintech platforms are beginning to offer real DeFi products to users.

That’s at least one reassuring sign in the depths of the current bear market.

👑 Related Coverage 👑

Analysis: The Missing Half of Agentic Payments

AWS, Circle, and Coinbase all shipped agent-payment infrastructure — but all of it is buyer-side, letting agents hold and spend money. Almost nobody is building the merchant side: the acceptance layer that lets a business take payment from an agent across a mess of competing schemes, tokens, stablecoins, chains, and PSPs. Card history says issuance scales fast and acceptance scales slow, so this gap is probably the work of the next decade.

Read More

Curated Updates

Here are the rest of the updates hitting our radar.

Paytech

• ⭐ Ramp raises $750M at $44B valuation - Techcrunch

• MoneyGram Launches MGUSD, a Stablecoin to Power Its Own Global Network - Morningstar

• SoFi rolls out SoFiUSD stablecoin to banking app users - The Block

Neobanks

• Mercury hits $5.2 billion valuation after funding round - CNBC

• Monzo spends heavily on ‘refer a friend’ rewards to boost market share - FT

• Revolut launches mobile telecoms service in the Netherlands - The Paypers

Lending

• JPMorgan, Citi and Big Banks Plan New Tokenized Deposit System - WSJ

• FCA and Bank of England set out shared vision for tokenisation - Bank of England

• Monzo’s FY26 Results Proves Neobank Profitability - Fintech Magazine

Digital Investing

• ⭐ Anthropic confidentially submits draft S-1 to the SEC - Anthropic

• Standard Chartered to acquire Zodia Custody - SG

• Kalshi valued at $22 billion in latest funding round - Reuters

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

Today, we are excited to share with you the latest report from Plaid and The Harris Poll.

Do you know what your customers expect from AI?

Over half of Americans used AI to manage their finances in the past 12 months. And they’re not going back.

The State of Intelligent Finance, Plaid’s latest report in partnership with the Harris Poll, surveyed 2,000+ American adults on how AI is changing what people expect from financial products. What stands out isn’t just how quickly adoption has grown. It’s how quickly expectations have followed.

Consumers want products that understand their situation, guide their decisions, and increasingly act on their behalf. Younger consumers are increasingly ready to hand bigger financial decisions over to AI. And trust hinges on transparency and accountability, not just capability.

The report covers:

• Where consumers are most ready to let AI take action

• What builds trust and what breaks it

• How AI is expanding financial access for underserved consumers

The companies that build for both intelligence and trust will define personal finance for the next generation.

Read the Full Report

Intelligent is the new Digital.

AI has moved from novelty to daily utility. As it becomes embedded across various parts of financial life, it’s fundamentally reshaping what consumers consider modern money management. And AI in finances is becoming more and more normalized.

This adoption is driven by a broader trend: 63% say AI is finding its way into many areas of their lives (rising to 69% for Gen Z and Millennials).

Intelligence is becoming a must-have: 48% of consumers say AI tools are becoming necessary to manage money effectively in today’s economy. And for Gen Z and Millennials, that number jumps to 57%.

Get the Insights

Financial literacy for all

AI is making money questions easier to ask and answer in real time, offering judgment-free, always-available guidance that meets people where they are.

In fact, Plaid’s report shows that:

• 64% of Americans agree that AI is making financial advice more accessible

• 51% of consumers turn to AI over human advisors for its 24/7 availability

• 40% of consumers say AI feels less judgmental than talking to a person

This is starting to make a real difference in people’s lives. From detecting scams to reducing financial stress and understanding financial products, AI tools are helping people navigate complex situations in real ways.

Yet only 45% of surveyed companies say financial insights and personalization are a top AI investment priority for the next 12 months (and just 34% say they’re already applying AI to that use case). In other words, demand for always-on, judgment-free guidance has arrived, but broad productization has not.

The era of intelligent finance is here

In a very short period of time, AI adoption has made “intelligent” the new “digital.” Consumers, particularly younger cohorts and power users, are looking for AI-powered tools that can navigate market volatility and optimize money in real time.

And because consumers already see intelligent finance as a way to a brighter financial future, the roadmap is clear: the most effective AI experience will be built around tangible consumer benefits, not the technology itself.

Read Plaid’s full report to get the insights on where consumers are most ready to let AI take action, what builds trust and what breaks it, and how teams can turn rising expectations into a competitive advantage.

Additional key topics include:

• Factors that increase comfort with financial apps utilizing AI

• Trust in autonomous AI for financial activities

• Expectations for AI-powered money management
  

Learn More

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

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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

To support this writing and access our full archive of newsletters, analyses, and guides to building in Fintech & DeFi, subscribe below (if you haven’t yet).

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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, we have a guest post from Don Gossen, CEO of Nevermined, an agentic payments company in the Generative Ventures portfolio.

Agentic finance has gotten very hot, so I asked him to dig into what the recent agentic payments launches are missing and where he sees the opportunity still to come.

• Summary: AWS, Circle, and Coinbase all shipped agent-payment infrastructure — but all of it is buyer-side, letting agents hold and spend money. Almost nobody is building the merchant side: the acceptance layer that lets a business take payment from an agent across a mess of competing schemes, tokens, stablecoins, chains, and PSPs. Card history says issuance scales fast and acceptance scales slow, so this gap is probably the work of the next decade.

• Topics: AWS, Amazon, Circle, Coinbase, Cloudflare, Stripe, PayPal, Braintree, Visa, Mastercard, American Express, Shopify, Spreedly, Primer, BlackRock, a16z, OpenAI, Adyen, Microsoft

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The Decade-Long Checkout

The agent economy has built the buyer side. But the merchant side has barely started.

In September 1958, Bank of America mailed 65,000 unsolicited cards to residents of Fresno, California.

This launched BankAmericard, the first successful all-purpose credit card.

Prior to this, credit cards were merchant-specific, requiring consumers to carry a card for each merchant where they had revolving credit.

The genius of the idea, which ultimately ended up being VISA, was to consolidate those separate cards into one. Two decades later, in 1980, roughly 52% of US households carried a credit card. By 1990 it was 72%. The buyer side of card payments, which includes issuance, wallets, and credit, reached saturation.

The merchant side, however, definitely had not.

By 1990, the Federal Reserve estimated that only around 2 to 3 million US merchants accepted Visa or Mastercard cards. And even then, merchant acceptance was largely restricted to travel, dining, and department stores. It was not until the late 90s and early 2000s that the ubiquity of credit cards began, largely due to the Internet and the growth of e-commerce.

We are watching the same process repeat with AI agents.

In a 96-hour window, between May 7 and May 11, 2026, AWS, Circle, and Coinbase shipped their answers to the agent-payment question. They distributed the equivalent of BankAmericards to the agentic masses. But the side of the transaction that has to receive money via a new form of payment has largely been ignored.

The buyer side

On May 7, AWS announced Amazon Bedrock AgentCore Payments, built with Coinbase and Stripe. AI agents inside AWS infrastructure can now pay for APIs, MCP servers, web content, and other agents. Developers pick a Coinbase wallet or a Stripe Privy wallet. Users top up funds in stablecoins while settlement takes place on-chain.

AWS did not build their own rail.

They picked from existing or emerging standards and existing wallet solutions. The largest cloud provider in the world is treating x402, Coinbase, and Stripe as consolidation points for agent payments.

Four days later, Circle shipped Agent Stack, including Circle CLI, Agent Wallets, an Agent Marketplace, and Circle Gateway-powered nanopayments. Nanopayments are USDC transfers as small as $0.000001, gas-free, at machine speed. Circle also raised $222M in an Arc token presale from BlackRock and a16z, which we covered here.

As for Coinbase, x402 had cleared roughly 165 million transactions and about $50 million in cumulative settlement volume by late April 2026, mostly across Base and Solana, with around 69,000 active agents. Governance moved to the x402 Foundation, formed by Coinbase and Cloudflare. A year ago, x402 was a whitepaper.

By April, it was clearing nine-figure transaction counts on live rails — though one on-chain analysis shows that daily volume has fallen sharply from a December peak.

The buy-side primitives include wallets, spending controls, micropayments, protocol standards, programmable mandates and cryptographically signed authorizations. Every major infrastructure player who has shipped agent-payment infrastructure in the past month has converged on the same buyer-side primitives.

The question of whether AI agents can hold and spend money has been answered. The unsolicited cards have now been distributed.

The merchant matrix multiplication

What is not solved well is the part of the network that has to accept the payment.

A merchant who wants to accept agents as consumers today faces a fragmentation matrix the card era never produced. The schemes are splintering:

• Visa Intelligent Commerce, Mastercard Agent Pay, American Express Agentic Commerce Experiences, with the other card networks quickly following with their own offerings.

• On the PSP side, Stripe leads the agentic charge. Secure Payment Tokens, Link for agents, Stripe Agent Toolkit, Machine Payment Protocol, and more have Stripe taking up positions across the entire agentic payments landscape.

• PayPal has leaned into agentic payments through partnerships with the likes of OpenAI.

The solutions are a jumble from a merchant perspective. Each has a different mandate format, a different token issuance flow, different validation rules at authorization time, and a different fee structure depending on the type of agentic transaction.

A merchant who supports Visa agentic tokens but not Mastercard Agent Pay loses the Mastercard agent population. The reverse is also true.

Then there are the token types. Network agentic tokens, like Visa’s VIC tokens carry mandate metadata, are issued by the network, store agent spend policy information, and are designed to route across any PSP that supports the network scheme. They are intended to be portable.

Gateway tokens, on the other hand, like Stripe’s or Braintree’s vaulted tokens, are scoped to a specific gateway, carry less metadata, and lock the merchant into that gateway’s environment. Most agentic merchants will want to accept both. Few are configured to.

The stablecoin side adds another axis. USDC, USDT, PYUSD, Circle Gateway-issued instruments. Each settles on a different chain: Base, Solana, Ethereum, Polygon, Arc, Tempo, with other agent-focused L1s and L2s in flight. Each chain carries different gas costs, finality periods, and acceptance fingerprints.

The issue for merchants is complexity, both in set up and in support. Most merchants, even those built for an AI powered world, don’t know where to start. Lack support for specific agentic tokens or PSPs, and you risk losing agent buyers. Accept too many and the cost of monitoring, risk underwriting, and book reconciliation balloons.

This means four to six new integrations and two or three new vendor contracts. Most merchants will pick one rail, accept a small slice of agent traffic, and wait for someone to solve the consolidation problem, rather than doing it themselves.

Issuance fast, acceptance slow

Buyer-side payment infrastructure has natural distribution. A wallet ships to a user. The user adopts. The product is consumer software with viral coefficients. Issuance scales like a startup.

Merchant acceptance does not. Every new merchant is a bespoke integration, a contract negotiation, a settlement reconciliation, a risk underwriting decision. The economics are different as well. There are merchant acquirers and aggregator platforms, but they are incumbents with existing solutions.

US credit card issuance went from one bank in 1958 to 72% household penetration by 1990, which took about thirty years. Merchant acceptance went from 400,000 US merchants in 1969, to 3 million in 1990, to 10 million-plus today. And online merchant acceptance was not really solved until Stripe and PayPal Braintree consolidated the integration cost and simplified network scheme adoption in the 2010s.

Issuance took three decades. Acceptance has taken more than six and still isn’t done.

The same asymmetry is now visible in agents. AgentCore Payments went from announcement to preview product in a single quarter. Circle Agent Stack shipped four products at once. The buyer side moves fast because the products are simpler to deploy: a wallet, a CLI, a marketplace.

The merchant side does not, because the products are complex infrastructure and harder to sell and integrate. We should expect the agent acceptance buildout to take years, not quarters.

The question is who builds it. So what are the candidates?

• The PSPs. Stripe already sits in the middle of the AgentCore announcement and processed nearly $2 trillion in payment volume in 2025, around 2% of the global total. They have deep merchant relationships. They have Bridge for stablecoin disbursements and Privy for wallet integration. PayPal Braintree is the parallel candidate, with 430 million consumer accounts and several million active merchants, plus an announced agentic checkout and their own stablecoin offering, PYUSD, to lean on.

• The commerce platforms. Shopify sits on roughly $300 billion in 2024 gross merchandise volume across 2 million merchants, with Shop Pay built into the checkout. The platform model means Shopify can update the checkout experience across millions of stores in a single product release, the way Apple Pay rolled out across iOS in a quarter. If Shopify ships agent acceptance as a default inside Shop Pay, the merchant side gets several million stores in a quarter.

• The card networks. Visa and Mastercard have spent fifty years operating the dual-network model that solved card acceptance. Each has jumped into the agentic payments space with agent-specific token issuance through Visa Intelligent Commerce and Mastercard Agent Pay. Today, Visa reports more than 130 million merchant acceptance locations globally; Mastercard reports comparable scale.

An agentic acceptance product needs to translate between network agentic tokens and gateway tokens, so a merchant contracted with Stripe can accept a buyer whose mandate was issued by Mastercard.

It should route stablecoin payments across chains and issuers based on cost, finality, and the merchant’s policy. It must surface agent identity to the bank’s risk engine at authorization, in a format the underwriter can attach policy to, regardless of whether the credential is a card token or a wallet signature.

Finally, it maintains a single audit trail and a kill-switch that propagates through every downstream wallet, PSP, and network in seconds.

That is the spec. No one has shipped it yet.

Which leads to a fourth possibility:

• New horizontal orchestrators sitting above the network schemes and gateways. Agentic payment orchestration is a different business from operating any one rail. In cards, that was Spreedly and Primer, both of which built on top of the schemes without competing with them.

The question for agents is whether the fragmentation is large enough to create a new venture-scale neutral player.

What happens now?

If the card analog holds, building the merchant side is the work of the next decade.

The candidates are already visible. Stripe, Braintree, Shopify, Visa, Mastercard, Coinbase Commerce, and a small set of horizontal orchestrators. Cards showed that issuance and acceptance scale on different curves, with different economics.

The company that solves acceptance is not always the company that solves issuance.

Fifty-two years after Fresno, Stripe was an answer to a problem that still had not been solved. We will see how long it takes this time.

🚀 Postscript

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

In this episode, Lex chats with Mike Milotich — Chief Executive Officer of Marqeta, the modern card issuing platform that processed nearly $400 billion in payments volume in 2025, and is certified to operate in 40+ countries, growing over 30% for the third straight year. They discuss how Marqeta's separation of bank, processor, and brand armed fintech's largest winners across buy now pay later, on-demand delivery, neo-banking, and expense management with the Lego blocks to build their own card programs. Mike explains how the company's growth is shifting from enabling new use cases to displacing volume on legacy bank platforms, and they explore why card issuing is going multinational, what the agentic commerce wave actually requires to clear security and behavioural hurdles, and how Marqeta's continued growth runs through embedded finance, real-time personalisation, and the forced modernisation of the banks themselves.

For those that want to subscribe to the podcast in your app of choice, you can now find us at Apple, Spotify, or on RSS. Or to support this writing and access our full archive of IPO primers, financial analyses, and guides to building in the Fintech & DeFi industries, see subscription options here. Our current price point is only $2/week.

Thanks for your time and attention,

Matt & Lex 🙌

Key notable takeaways:

1. The BNPL business model is flipping from merchant rails to consumer cards. Marqeta originally solved the merchant scale problem for buy now pay later via virtual cards, removing the need for tens of millions of merchants to integrate a new button at checkout. The current shift is more important: BNPL players are now issuing consumers their own physical and virtual cards usable anywhere cards are accepted, turning BNPL from a merchant-acceptance game into a direct consumer value proposition. BNPL volume has grown over 50% year-on-year for Marqeta in recent quarters.

2. Card issuing is going multinational, and that breaks the legacy bank model. Banks have always been local on the consumer side, with only a handful multinational on the commercial treasury side. The next generation of card issuers, neo-banks like Revolut and Nubank, plus large global platforms embedding financial products into existing user bases, are global by default. A single platform that issues cards, and is certified to operate across 40+ countries, becomes the strategic moat, and legacy processors built to serve domestic bank programs aren’t structured to compete.

3. The growth story is moving from expanding the pie to displacing the incumbents. To date, Marqeta has mostly powered new card use cases that didn’t exist before — on-demand delivery, BNPL, neo-banking, expense management. Mike’s forward thesis is a phase change: pressure from fintech winners is forcing banks to modernise, and the next leg of growth comes from displacing volume sitting on legacy bank-controlled platforms. Real-time personalised rewards, where the same card delivers different offers to different cardholders based on live data, is the wedge that legacy infrastructure can’t deliver.

Background

Mike Milotich is a payments veteran with more than two decades of experience in financial services, drawn to the industry by its quantitative, data-driven core. He began in business analysis roles at American Express and PayPal, supporting new product launches, portfolio strategy, partner pricing, and cost optimisation. In 2011 he joined Visa just as the company was standing up its mobile payments division at the dawn of the smartphone era, and spent a decade there across multiple finance leadership roles, ultimately serving as Senior Vice President of Corporate Finance and Investor Relations, where he managed the corporate outlook, evaluated M&A opportunities, and led engagement with the global investor community. Mike holds a bachelor's degree from UC Santa Barbara and an MBA from New York University. He joined Marqeta as CFO in February 2022, stepped into the interim CEO seat in February 2025, and was appointed permanent CEO in September 2025.

👑Related coverage👑

Topics:

Marqeta, Visa, Mastercard, American Express, PayPal, Payments, card issuing, embedded finance, fintech, BNPL, neobank, agentic commerce, e-commerce, crypto, stablecoins, programmable money, machine economy, agentic AI

Timestamps

• 1’04: From Math Brain to Payments Career : Finding the Nuance in How Money Actually Moves

• 7’05: The Narrative Gets Ahead of Reality : Why Agentic Commerce Will Move Slower Than the Technologists Think

• 10’08: Global But Local : The Balancing Act That Kept Visa on Top of the Payments Network for Decades

• 12’58: Carve It Out or Watch It Get Trampled : How Visa Incubates Mobile, Crypto and Agentic Without Killing Them

• 15’03: $400 Billion in Volume, 30% Growth, Three Years Running : The Numbers Behind Marqeta's Compounding Scale

• 17’05: The Pandemic Poured Gasoline on Everything : Why DoorDash, BNPL, Expense and Neo-Banking All Exploded at Once

• 24’24: The Lego Blocks for Payments : How Marqeta Armed the Innovators Who Couldn't Build Through Banks

• 29’19: Visibility as a Weapon : Why Being Public Helps Marqeta Win Customers Against Private and Embedded Competitors

• 33’15: Fewer Bets, Higher Probability : How Public Market Discipline Reshaped Marqeta's Risk and Profitability Model

• 36’35: The Legacy Platforms Were Built for Banks : Why Embedded Finance, Multinational Card Issuing and Personalisation Reshape the Pie

• 41’50: Prompted, Not Replaced : The Ten-Year View on Whether Volume Comes From People or Robots

• 43’56: The channels used to connect with Mike & learn more about Marqeta

Illustrated Transcript

Lex Sokolin:
Hi, everybody, and welcome to today’s conversation. We are very lucky to have with us today, Mike Milotich, who is the Chief Executive Officer at Marqeta. Marqeta is one of the original payment infrastructure companies and has been a fantastic success. So, I’m really excited to dig into its story as well as learn more from Mike about his own career. With that, welcome to the conversation.

Mike Milotich:
Thanks so much for having me. Good morning.

Lex Sokolin:
I want to start a little bit back in time focused on you. And I’ve noticed in your career that we have a little bit of overlap, that you were at Barclays Global Investors in the early part of your finance career? When I was at Lehman Brothers and we had been bought by Barclays, I remember that Barclays had sold BGI in order to buy Lehman Brothers, which I think is probably one of the historically worst corporate transactions you could do.

But it’s a fantastic business and now it is, you know, a huge ETF business and iShares, I believe. How do you remember your early career? What brought you to finance? What got you interested in finance? And maybe just pull on some of those early threads?

Mike Milotich:
I got started in finance mostly because I was quantitative in my orientation. So, I’m more of a math brain, if you will. And, you know, always was, you know, going through my school years. And I’ve always been inherently curious and analytical. So, I’ve always just noticed things and thought like, how does that work? Or, you know, wanting to explore more. And I feel like a lot of components of finance are more analytical. A lot of it is like peeling an onion and problem solving. And that’s just always something that I was interested in. And so, you can combine that with my sort of more data orientation.

Lex Sokolin:
What is inside the onion, and where do you use the onion? What do you season with it?

Mike Milotich:
Apply that, in particularly in the payments business, as an example, there are a lot of subtleties and nuances that matter. And so, the more you understand, the more you can connect the dots to add value. So, by peeling the onion and really getting down to really understanding how certain things work or interactions happen, the more you can apply that knowledge in ways that are advantageous, both from a career perspective, but of course always about adding value for the company and helping the company be more successful. That’s what helps your career. And so that’s how it’s really served me well, is that ability to connect the dots and then apply that to add value.

Lex Sokolin:
You did finance inside of financial services companies, right?

Mike Milotich:
Correct. So, I worked, I would say the better part of my career, American Express, PayPal, and Visa before I came to Marqeta. So, I’ve also been in payments a long time. But yes, all are financial services related.

Lex Sokolin:
What kind of things did you get exposed to, and did you see from that period — like the 2007 through that decade, starting with a great financial crisis — is really fascinating in terms of shaping, you know, the business models of whether it’s asset managers or whether it is payments companies. From the seat that you had early on, like with that fresh view, what did you notice? What did you see?

Mike Milotich:
I wish I could tell you that then I could really see how this whole financial services space was going to really modernize and get very innovative. I did not have that foresight. I targeted American Express as a place to work, as I had understood from many people that it was just a great company. And I was fortunate in that sense, that I sort of fell into it and then really embraced it, really liked it. I think what has served financial services very well is that it has always been very data driven. So inherently there’s more data in that business. And so, data-driven decision making and data-driven risk management, for example, has been around for decades, long before you had the kind of technology tools and capabilities that you have today. So, I would say the raw ingredients were always there.

And I think as you started to see technology come about — like, for example, my first job at Visa in 2011, when I joined, they had just created a mobile payments division. And, you know, at that time the smartphone was only a couple of years old, right? And so, Visa recognized that mobile is going to be very impactful in payments, but what exactly that’s going to look like is unknown, right? How it’s going to evolve is unknown. And what’s going to happen in the US is going to be different than what happens in Asia, which is also going to be different than what happens in Africa.

And so, you know, understanding that influence of how technology is going to impact the business, but having all this data and understanding about how to make better decisions and be smarter about some of the bets you’re making as a business, I think, is what makes financial services maybe more unique or ahead of its time right now. Everything in digital advertising and marketplaces is also all about data and data mining. But I think financial services was probably, you know, ahead in that regard, just because of the nature of how those business models work.

Lex Sokolin:
You brought up a really interesting example. So let me meditate on it for a moment. You know, the sort of moment when proximity payments and mobile phones became a real way that commerce could happen. Visa played an enormous role in that, but within some of the developments from Apple Pay and Google Pay. And it reminds me a little bit of where we are, and I know this is sort of a sidetrack, but where we are with agentic commerce, where you have, you know, everybody’s sort of like pregnant with the feeling that it’s coming. But it probably felt a little bit like that mobile moment. Were there parallels? How would you compare the two?

Mike Milotich:
Yeah, I think what’s similar is that when the technologists drive the conversation, they tend to get a little bit ahead — the narrative gets ahead of the reality, right? Because what’s possible with the technology is always very exciting. And I would say those two things are very similar in terms of the early days of mobile and the early days of agentic. So, what’s possible is very exciting, and the people who are driving the technology, I think, get ahead of the — again, some of the challenges and nuances of payments in terms of security, first and foremost. Like, how do you — you know, fraud is a huge issue and factor in everything related to payments. And it affects both the people who are trying to provide those payment options, who take the liability, but also in terms of consumer behavior. Consumers tend to be slower to change their behavior than I think a lot of technology enthusiasts think, because it’s money. People inherently are quite conservative. And so changing behavior takes a long time.

So, I would say security is something that has to be solved. And then there are other things that are just, again, practical things that nonpayment people don’t tend to think about. So, as an example, I often use, like between e-commerce and what’s happening with agentic, in the early days of e-commerce, a lot of retailers would talk about that they were sort of — they had to evolve to deal with the number of returns, right? When you were buying everything in the store, you tried it on, you saw it, you put it on, you looked in the mirror.

And so, the returns were relatively low. But in the early days of e-commerce, when you’re buying everything online and you’ve never put it on, you’ve never seen what it looks like, there was an increase in the returns rate. And that was like an operational challenge — then the impact of what was happening in payments became an operational challenge in retail. And so there are just things like that that have to be worked out. And that’s why things don’t happen with the snap of your fingers. It takes some time for not only people to work out the user experience and making it secure, but also some of the operational differences that have to be worked through. Investments have to be made; problems have to be solved.

Lex Sokolin:
I think it’s a blind spot for sure, in the people who invest in categories and tell stories, versus folks who actually have to make it happen. If we spend just a bit more time on your rise at Visa and the different parts of the business that you saw, I mean, Visa is a company that has stayed on top as a network of networks really successfully. And, you know, that’s a really difficult thing to do. What do you think allowed it to, you know, persist in this way? And as you kind of got more and more visibility into the company’s machinery, like, what are some of the things you admire about it?

Mike Milotich:
Two things in particular. So one is, there are very few businesses that are as global as Visa, in terms of — I think they operate in every country that isn’t sanctioned by the US government. So, they truly are, you know, everywhere, I guess, as the tagline says. But yet payments inherently — and you know this, you’re a student of the business — is local, right? So how things work on a local level is quite different as you move from country to country.

And so being able to do both is really a unique skill set: be global, but yet local at the same time. So, what things have to be done consistently for the good of having a global platform, but what things need to be local to cater to the local habits, tastes, laws, regulations, etc.? That’s a challenging balancing act, and I think Visa and Mastercard both have perfected that — you know, or honed that. Maybe not perfected it, but honed it over decades, right, of learning how to operate that way. And I think that was something that I took away and was very impressed with: the ability to balance both those things.

The second one that I would point to is a really healthy paranoia, right? You know, when you’re the leader in the space, you have a great position, competitive position. You have a great business model. There are many, many examples in history of businesses that get complacent, right, and sort of rest on their laurels. And I would say one of the things I’ve always admired about Visa when I was there is that there’s a very healthy dose of paranoia and a pride in kind of continuing that legacy, carrying that torch and not being the one who drops it, right? So every day, that sort of willingness to grind and say, I’m going to keep working to stay on top — you know, I think is something that I took away, that makes the, you know, the business one of the more special businesses out there.

Lex Sokolin:
Were there any organizational principles that you think allowed it to be global but local, or sort of traditional but nimble? People try matrix organizations; they try the pizza-size team. I think right now the idea is, let’s have everything inside of an AI giga-brain, and people are just nodes in the machine. Like, what was the organizing structure of the company that you think worked?

Mike Milotich:
The leadership, inherently the way the company is organized, is regional. So that’s what keeps them local. But all the functional expertise is global. So, they’ve struck the right balance of people on the ground to understand the local nuances, but then there are certain functions like the people running the network, or, you know, finance, risk, some of these other functions that are global in nature. So, you can’t go too local. I think they do that well.

I think what I saw as successful is they’re good at incubating new ideas. So even when, as I’d referenced, when I first joined in mobile, they recognized that — at that time they separated what they called an emerging products function away from the core product, away from the big machine of, you know, credit, debit, and prepaid. We need to kind of carve this out so that it doesn’t get kind of trampled or become an afterthought in comparison to the big business that’s being run.

And so I think that’s what they do very well. And whether it was some of those early emerging products or now with, you know, crypto and stablecoin and agentic — all these things, when you look at how they organize in those areas, they try to create separate teams that can really focus, and give them, you know, visibility at the most senior levels, right? It really is carved out with a lot of visibility, because otherwise it’ll get trampled by the big machine that’s being run of the day-to-day business.

Lex Sokolin:
Yeah, that makes perfect sense. Let’s transition to Marqeta, which is sort of the spotlight of the conversation. Can you give us a high level — what are the latest figures of the company? What’s the scale of the company? And maybe just what are the chunky leverage points?

Mike Milotich:
So, in 2025, we processed just shy of $400 billion in payments volume. And so, we’re getting to be, you know, pretty good size now. And that grew over 30% for the third straight year. And, you know, as you get bigger, right, it’s hard to compound at that kind of rate as the base, or the denominator, keeps getting bigger. So that’s something we’re quite proud of, is that ability to scale and get to that level.

And what makes us unique is that we service a lot of different use cases. So, a lot of the, you know, more modern players like ourselves tend to be more narrow in their focus. So, they only tend to do like one type of use case, for example. And what makes Marqeta unique and has allowed us to grow at that rate is that we support many use cases, whether it’s, you know, neo-banking, buy now pay later, expense management, on-demand delivery. But we do credit and debit, we do consumer and commercial, and we do it in over 40 countries.

And I’m sure one of the trends we’re going to talk about at some point is how more and more people are going multinational, where, you know, card issuing tends to be very local and individualized in the past. I think that’s changing with some of the developments in the market and some of the types of innovators and disruptors that we power. But that’s what kind of gives you a sense of the unique scale of the business and the way it’s growing.

Lex Sokolin:
When was the company founded? And if you think about its history, you know, were there a couple of turning points that really got you here? And I know you kind of stepped into the leadership role recently, but I’m sure there’s a sort of a myth of things that turned on, whether it’s, you know, the Square relationship or Klarna. How does that internal myth come together?

Mike Milotich:
So technically the company was founded about 15 years ago, but I would say it was more like ten years ago — the 2015, 2016 time — when it started to become a real business. And the real innovation that I feel that Marqeta brought is — in the past, like before Marqeta, if you wanted to do something in card issuing, the bank controlled every aspect of that card program.

You know, if you think about some of the largest co-branded cards that you use today, probably that are, you know, affiliated with premier technology companies or major airlines or hotels, but, you know, you still manage those cards through the bank’s app, as an example. Like, that to me shows you — gives you a little insight into the history of what was possible. It had to be done that way in the past.

And what Marqeta came along and said is, you know, there should really be a better division of labor here. Because, you know, let’s just be honest, banks, because of the regulatory nature and the safeguarding priority that they have, they tend to not be super innovative. You know, they tend to not be very focused on user experience and product. Like, those are not their strengths. And so why are we having them control those things?

And so, what Marqeta did was really say, let’s have the bank focus on what they’re good at, which is the safeguarding of funds, making sure you’re compliant with, you know, laws and regulations. And let’s leave the user experience and the value proposition up to the brand, the company that really is going to touch the user and allow them to embed it in their experience, and have the bank be a little bit more in the background, playing a very important role but a little less visible to the end user.

And then in the middle, what we can do as Marqeta is provide the technology and the expertise and the scale to help our customers. So, by us providing the processor and program management role, we can allow our customers, for example, to use different banks for different products, because each bank might only be comfortable with certain types of businesses. So, we can help our customers roll out multiple product types across different banks. But we can obscure a lot of that complexity, because they’re only dealing with the Marqeta platform, and they don’t see sort of what we’re doing behind the scenes to provide all that variety to them. And so, I think that’s what really separated Marqeta.

And so it was in that, you know, 2016, 2017, 2018, that’s when, you know, fintech really started getting momentum. And some of these companies that are now household names were really starting to, you know, figure out and hone their value propositions and start to get traction. And then it was, of course, the pandemic that I would say really, you know, poured gasoline on everything. In terms of — if you look at the major use cases we support, you know, on-demand delivery — I mean, Instacart, DoorDash, Uber Eats, all those businesses exploded with the boom of e-commerce during the pandemic. You know, buy now pay later went mainstream. You know, in expense management, everyone has been talking about for a long time about how to take cash and check, how to — B2B payments. It became more of a priority, in just a matter of survival in the pandemic, and that really provided a catalyst in that space.

And then finally, neo-banking, right — really having an embedded banking experience, either in a platform that you use for other reasons or just a very unique way of engaging you. You know, that became much more valuable once everybody was, you know, at home and on computers and devices more frequently. And so that was really the catalyst that really changed the trajectory of the company and our partners.

And then what’s happened in the last couple of years is, as the, you know, the fintech boom has, you know, sort of calmed — you know, the winners have been crowned, as I always say it. You know, there used to be 10 or 15 companies chasing the same thing. Now there’s been a little bit of a shakeout and it’s clear who the winners are in this space, and they’re becoming big businesses. And as they become big businesses and there aren’t 15 people chasing the same thing, they are spreading their wings, moving into new products, new geographies. And the Marqeta platform really makes that fairly seamless for them. And so that’s really what has, the last couple of years, fueled our growth — that expansion of what used to be, you know, kind of fintech disruptors into, you know, very big companies.

Lex Sokolin:
I think there is a through-line to something we talked about before, which is your experience seeing mobile become a new channel and kind of Visa positioning ahead of it. And then the agentic commerce wave where, like, the demand is not there, but, you know, it’s time to plant a flag in the ground and position ahead of it. To me, it seems Marqeta did a really similar thing, because it was an embedded finance company before there was embedded finance as, as like this category, right, where instead of selling financial products to consumers or to like a partnership manager that would sign a legal contract with you, you’re effectively selling to developers and people who are focused on building software.

You know, obviously Stripe’s been a huge success in that on a very particular use case. But then there are companies in different verticals, whether it’s, you know, renting a bank or card issuance or capital markets and so on and so forth, where selling and embedding your fintech services or your financial services with a sort of like regulated chassis is the whole game. And, you know, to me, there’s a big symmetry to Marqeta’s experience. I looked at it in detail quite a few years ago, and the thing that really struck me about the company is, as you said, it was founded pretty early on, and the product had been live for three years until Cash App and DoorDash launched. You know, like, CashApp and DoorDash launched in 2013. And these are huge drivers of the business. You know, I imagine just like the stress of sitting there for three years, having this feeling of like, we have it totally right, like the wave is coming, there’s no demand of any kind. You know, and doing that for three years and having the conviction to get to the other side of that, and then your clients just like materialize, right? To me, that’s such a powerful entrepreneurial story.

Mike Milotich:
That’s right. I mean, we really armed, it’s a little bit of a cliché analogy in business, but I really think the way our business works is we’ve really provided the Lego blocks for, you know, innovative companies in payments to build their own service and their own offering. And that just wasn’t possible before. Again, the bank kind of controlled everything and you had to go through them. And so once you, you know, you give these, you know, brilliant and creative people the Lego blocks and you set them free, they’re going to build some pretty incredible things.

And again, consumer behavior in payments takes some time. So you don’t just launch something and everyone flocks. Like, it usually doesn’t work that way, because you’ve got to change their behavior. But more and more people started seeing the value, right? And they were delivering the value. And then you, you know, you start getting more and more momentum, and good things start to happen, and they start to evolve, right?

I think if you look at, like, buy now pay later, for example, is one of our fastest-growing use cases. You know, it’s a good-size business for us, and it’s growing incredibly fast. The last couple of quarters have all been over 50% growth on a year-over-year basis. And the transformation that’s happening there is quite interesting, where the original problem we helped solve for the buy now pay later companies was merchant scale, right — rolling out a product that’s more merchant facing, because you needed to click on the button on the checkout page. You know, there are tens and tens of millions of merchants. And that’s a very challenging endeavor to get the footprint you need. And we solved that problem with virtual card, and essentially eliminated that need for all those merchants to do, you know, technology integrations, but still allow them to accept this new form of payment.

And so that helped the buy now pay later companies’ scale. But then the last couple of years they’ve been shifting to be more of a consumer value proposition. So, in those early days, it was all about getting their mark on the website, right? And the merchant was pulling their business through. The emerging trend that’s now happening is, more and more they’re issuing the consumer the card and saying, a feature of the card is to be able to buy now pay later, and that card can be used anywhere that cards are accepted. So, I don’t have a merchant acceptance problem now — I can just engage the consumer to deliver my value proposition. And so, you know, those are also the things that have happened during this time — these businesses are also evolving, you know, as times change and they get bigger and they become, you know, again, more trusted and have more brand recognition. Their business models are evolving, and we, you know, do our best to make sure we’re on the forefront of that, and we enable them to make those evolutions and have that flexibility.

Lex Sokolin:
So, let’s transition to today. I’m really interested at maybe a somewhat prickly topic: public versus private markets. Like, as you said, Marqeta is one of the largest winners in this space, and it’s now a public company. It is generating more volume than ever before and has more business lines and kind of commercial traction than ever before. And yet, I think it’s so much harder to be a public company than a private one, because in the private world, you’re only valued based on sort of a conversation with venture investors. You might get 20 to 50 times revenue as your valuation. The story is kind of enough to get you through, and people are just chasing growth. And then you get to the public markets. And I think a lot of the public markets are just bigger. And so no matter how much venture backing you have and how many growth investors are behind you, you are going to run into people who are just like, I’m a banks investor and this is a bank, and I only look at this company through a spreadsheet, you know, and they don’t maybe appreciate the color and the richness of the struggle and the story.

What has been the experience within the company for, like, adjusting to that reality? Is there an operational reflection in terms of what the company can do, in terms of how it makes its choices? You know, and especially given your experience in investor relations and corporate finance and kind of like that core skill set of communicating with the public equity world, what is it like for a really hot fintech unicorn to jump into the sobering pool of the public markets?

Mike Milotich:
It’s an experience, right? The analogy I often use — you know, we were relatively young as a company when we went public. And so, the analogy I often use is that we’re a child star. We’ve been growing up in the public eye. And I think it’s a good analogy, because you have to adjust your behavior in terms of, yeah, how you communicate and how clearly you do that and how you do it on a consistent and regular basis. As opposed to when you’re private, there’ll be certain points or junctures or certain meetings where you have to be very articulate and communicate your case well, but those are more infrequent. Versus once you become public, every day you’re being evaluated and measured on a day-to-day basis. And so, the clarity with which you communicate and the consistency with which you do it becomes very, very important.

Once you embrace it, I guess it makes you stronger, right? So, the key is — and I think something that we’ve done well the last couple of years is to, rather than, you know, sort of say, you know, woe is me, I’ve got, it’s so hard. It’s more like that scrutiny and that competition — because essentially the market is, it’s supply and demand. So, there’s only so many investment dollars out there. You’re competing for them. That really makes you stronger and makes you better if you embrace it.

It’s a challenge. And I think that’s what we’ve done well, particularly because we really view that we have the ingredients to be a very big business. And so if we execute well — and so sort of accepting and embracing that challenge, I think, does make you better and helps you maybe prioritize and not, you know — one of the things I saw, for example, when I first joined that were more from our, you know, private, more startup roots, is trying to chase like seven things at one time, for example. That can be very challenging. You spread yourself thin. You tend to waste a lot of investment dollars, and it’s very hard to execute. Versus now, I would say we try to do three things at a time. And you have a list of, okay, once one of those is done, you’ve got the next one up, right? That you slot in, and allow you to focus and execute a lot better.

I also think that it’s served us well with customers, because they can see exactly the health of the business. So we can’t hide in terms of where we are financially. You know what we’re up to every quarter. We have to talk about what’s happening in the business and where we are and things we’re focused on. And I think for the most part that helps us with our customers and our prospects, because it’s very visible, you know, what we’re up to as a company and the strength of the business. Versus many of our competitors are either private or are a small piece of a much larger public machine, so there’s not a lot of visibility into what’s happening and how well they’re performing. And so, we’ve tried to embrace these things and turn it into a strength, rather than, you know, feeling sorry for ourselves, if you will, as the market fluctuates as it has, particularly in fintech, over the last few years.

Lex Sokolin:
Does it change how you think about risk for the company? You mentioned sort of finding focus and being more transparent, but in terms of, you know, going back to that question of a successful organizational structure and how much responsibility you give to individual product leads, or, you know, how you do budget allocations — has the discipline of being public changed that operating texture of the company?

Mike Milotich:
A little bit, I would say. It’s all about calculated risk, right? I think you can be a little bit — if you’re private, you know, no one’s going to know if, you know, if you try something and it really doesn’t work. And so, there’s something freeing about that. But also, that can, you know, lead to more variability. Like, sure, you can have home runs, but you also have a lot of strikeouts, right? Because you didn’t necessarily do as much work and sort of pre-planning and thoughtfulness before you just started running.

So, there’s good and bad. But I would say, you know, we still do a lot of innovative things. We’re always looking to push boundaries, both things we think of or things that, you know, customers or prospects may bring to us and we work on together. And I think it really just comes down to calculated risk: how do you really think through the things you’ve got to watch out for, and what are the gating mechanisms? So, what’s the progress? What are the milestones I’m going to be looking for to make sure we’re on the right track and this is leading to something — as opposed to, this is really just a science project that, you know, isn’t going to get traction or lead to something that really helps our customers grow their businesses. Because ultimately, we’re an enabler, and that’s what we’re looking to do.

So, I think it just gets you a little bit more disciplined in terms of, let’s really think through this, and let’s consistently evaluate the progress we’re making, and just be honest about it. And that’s worked well for us, where we can strike that balance between improving, you know, our profitability — because that’s the one thing, when we went public that was fine at that time, that, you know, we didn’t make money, but we were, you know, driving growth and causing disruption, and that was enough for shareholders. But that has changed.

And so, you know, over time, as we’ve gotten more and more scale, we get the benefits of that scale, and that shows up on the bottom line. But also, the discipline with which we’re operating means that if we pursue fewer things, but they become higher-probability successes, we actually are better off, not just on the top line of the business, but also how much of that trickles down to the bottom line in terms of profitability.

Lex Sokolin:
I spent a bit of time in equity research, so I apologize on the behalf of all analysts out there who think that they can manage a company better through spreadsheet assumptions than by actually running it. If we fast forward — and this is obviously like an imagination exercise, not anything specific — but you mentioned that there is a huge opportunity for the business in the future. And you know, that’s demonstrably true. There are several large payment networks that are each worth about half a trillion. You know, the payment processing companies that are in the hundreds of billions. At a moment of time when Marqeta is a $100 billion company, what does the world look like? Like, what are the things in the environment that have changed that you think get the company there?

Mike Milotich:
There’s a few. And I would say, you know, the seeds are already planted and being nurtured right now. So, there’s a few things happening. So one is, if I were to, I guess, summarize it, it’s non-banks providing financial services. And you said it earlier, embedded finance. Like, we’re still in the early days of that.

And what we see happening is, fintech showed what was possible, right? So that was the definition of fintech. And they showed what was possible. But a lot of the challenge that fintechs ran into is they had to build their business from scratch, and the cost of acquisition to build their business became unwieldy. But what’s happening now is, big companies that already have platforms that have tens of millions of users are saying, well, I could inject a financial product, like I saw fintechs do, and really drive engagement and loyalty. And so that’s one of the next waves. And a lot of our more legacy competitors — we have a diverse set of competitors, but the ones that are the biggest and have been around the longest, their platforms are really set up to serve banks. And so, there’s a real opportunity to serve this emerging business that’s being driven by non-financial-service companies.

The second thing I would say is, as those non-financial-service companies move into this space, they’re global already, almost by nature. So, if you think about the way banks work — banks are really local. Like, there are some multinational banks, but they’re more multinational on the commercial side in terms of treasury management and things like that. You know, there’s only a handful that are truly multinational on the consumer side. But most businesses, whether they’re large retailers or their tech platforms, are global. And so that ability to have one platform that can serve them across many different geographies and do that in a relatively seamless way is a big opportunity. And you see some of the neo-banks, I would say, are probably the furthest along in terms of being multinational, right? When you look at, you know, Revolut or Nubank, some of these, you know, businesses, they are going more multinational.

The other thing that I think will drive a lot of success for us is, as these fintechs and embedded-finance companies keep having success, they’re putting more and more pressure on the establishment, which is the banks, who, you know, still control the bulk of this business and have, you know, owned it since the beginning. There is more and more momentum for them to modernize, because they have to compete. And so, what I think is the exciting opportunity for Marqeta is that up until this point, we have mostly grown the pie. So, we’ve mostly served use cases that maybe weren’t on a card before, or servicing a customer base that, you know, couldn’t get kind of mainstream financial products. But I believe we’re on the verge of entering the era where there’s a modernization that comes to the industry, where we start displacing volume that is on platforms that are less capable and don’t have the flexibility and capabilities that Marqeta provides.

So that’s what we’re working towards. And there’s a number of ways we’ll do that in terms of different product categories. But if I were to think forward, in terms of what makes us a much bigger business, that’s what it is. Like, an example — I’ll just give you one example, and then I’m sure you’ll have other things you want to ask me — but personalization hasn’t really come to the card business and maybe most of financial services. Versus with everything being so much more digital now, many other aspects of your day-to-day interactions outside of payments are personalized. But that hasn’t come yet to payments. And we believe it is coming, and it requires, though, you know, real-time data and being able to analyze things in real time and respond. And so, you need modern technology to do this. But this would come in the form of rewards that are customized to you versus me. We might have the same card, but the way we get rewards on that card is different based on what’s valuable to us. And that’s a trend that, like, we believe is going to come to the card business. But it requires really modern technology to make that happen. And so those are the kinds of things that, you know, we can enable, and can help us, you know, fuel growth and become a much bigger company, you know, five years from now.

Lex Sokolin:
Last question to close us out. So, in that world, you’re a $100 billion company. You’ve got all these things going on. If you look at your volume, will more of it come from humans or from robots? And I use the word robots, you know, pretty liberally — algorithms, programmable money, software, right? But are we in a world where using a card, even if it’s a virtual card on a mobile phone, is sort of the equivalent of sending a letter by post? If you fast forward to this future where you’re at the scale that we’re talking about, everything you could do for humans has been done, and you look at your volume — is it going to be still people, or is it going to be this machine economy?

Mike Milotich:
Obviously, I don’t have a crystal ball, but the limited one I have maybe only gone out, say, ten years. I think more of it is still controlled by humans. I think there are things that, no doubt, agentic is going to help people do, and it’s still going to be human-controlled, but it might just be that you talk to it, or it prompts you, right? You’re still going to make the decision, right? But it might prompt you. You know, hey, you’re getting low on this — should I get more? And you say yes, right? So, I guess it depends on how you define it.

So, I think we’ll still have a lot of human-based decision making, just because it’s money, and it’s hard for people to completely relinquish control of how they use and spend their money. I just think that’s going to take a long time to get people comfortable with that. But will a lot of robots, to use your word, you know, sort of be guiding you through that and prompting you? I do think that, you know, five or ten years from now, there’ll be a lot more of that. That’ll make it a lot easier for you to make those decisions, as opposed to now, you have to do some work to make that happen.

Lex Sokolin:
Despite your experience as a financial professional in financial services, I think you’re still a romantic and believe in the human spirit, which is nice to see. If our listeners want to learn more about you or about Marqeta, where should they go?

Mike Milotich:
The easiest place to go — well, certainly about Marqeta — would be to go to Marqeta, and also podcasts like yourself and other, you know, fintech industry publications. That’s where, you know, we do power many of the fintech winners and disruptors. So that’s a great place to look. And I’m probably less interesting, so I’ll leave it at that.

Lex Sokolin:
Fantastic. Thanks so much for joining us today.

Mike Milotich:
Thank you for having me.

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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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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.

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Today, we highlight the below items.

Also notable are the drastic moves both in the US and UK to allow the trading of tokenized securities, to be covered in a later issue.

1. DIGITAL ASSETS: Beyond the Rate Trade — Circle’s Infrastructure Bet

2. ANALYSIS: The Fintech Blueprint AI & Robotics Report Q1 2026

3. CURATED UPDATES: Financial Institutions and Adoption; DeFi and Digital Assets; Blockchain Protocols; NFTs, DAOs and the Metaverse

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DIGITAL ASSETS: Beyond the Rate Trade — Circle’s Infrastructure Bet

Circle has had a busy week.

Alongside its Q1 2026 results — nearly $700 million in total revenue and reserve income, up 20% year-over-year, with USDC in circulation reaching $77 billion and onchain transaction volume hitting $21.5 trillion — the company dropped two significant product announcements and closed a $222 million token presale.

Circle has long carried the label of interest rate proxy: 99% of its 2024 revenue came from interest earned on USDC reserves, making the business acutely sensitive to rate cycles and leaving equity investors with little to price beyond spread income and USDC float growth. Arc, Circle Agent Stack, and the Payments Network are a concerted attempt to change that — to diversify the revenue base and re-rate the stock on infrastructure multiples rather than yield.

Probably most unusual is the following: Circle, a public company with traditional equity ownership, has raised $222 million in a presale for its new stablecoin-focused layer-1 blockchain, achieving a $3 billion fully diluted valuation.

There are financial instruments that go on the regular cap table, and then there are these tokens for a protocol. Notably, Base, the Coinbase Ethereum L2, still does not have a token. The fact that a multi-billion dollar public company was able to do this fundraise means that tokens have arrived on Wall Street.

The round was led by Andreessen Horowitz with a $75 million commitment, with BlackRock and Apollo also participating. The presale has multi-year lockups and repayment rights if key Arc network milestones are not achieved.

Circle holds 25% of the 10 billion token initial supply, with 60% allocated to network participants and 15% to a long-term reserve. The Arc mainnet is expected live in summer 2026, with the testnet having processed 244 million transactions as of early May.

ARC token utility remains in an exploratory phase. That means you don’t really need to design tokenomics to raise $200MM+ still. And if we are paying real attention, you don’t need $200MM to build a Layer 1 blockchain either.

Alongside Arc, Circle unveiled Circle Agent Stack — a developer toolkit for building AI agents that transact in USDC, comprising wallets, a marketplace, and a nanopayments layer capable of transfers as small as $0.000001.

The company joins Stripe, Coinbase, Visa, Mastercard, Shopify, Fiserv, and Brex in the race to bank the robots.

Arc is a defensive play

Today, USDC runs across Ethereum, Solana, and dozens of other chains and wallets. Circle earns reserve income on all of it. The problem is how much of that income it actually keeps. Under the 2023 Collaboration Agreement with Coinbase (signed when the Centre Consortium was dissolved and Coinbase held significant negotiating leverage as Circle’s largest distributor) the reserve income is split in three steps.

• Circle takes a small issuer fee off the top.

• Each party then receives the reserve income proportional to USDC held in their own custodial products.

• Whatever remains — Coinbase takes 50% of. The result is that Coinbase earns a cut of reserve income from USDC it has no custodial relationship with.

In 2024, that totalled $908 million paid to Coinbase out of $1.68 billion in gross revenue. The agreement auto-renews every three years and Circle has no unilateral right to exit it. Arc is, in part, an attempt to build infrastructure Circle actually owns and earns fees on directly.

Just to say this again: Coinbase has a 50% net haircut on effectively all Circle revenue, and there is no way out except through a clever backdoor.

The pitch is straightforward: a layer-1 blockchain built specifically for stablecoin-native finance, with USDC as the gas token, sub-second finality, opt-in privacy, EVM compatibility, and quantum-resistant architecture. Settlement infrastructure for institutions that already move money for a living and a substitute for ACH, SWIFT, and correspondent banking. The testnet, which launched in October 2025, has already drawn over 100 institutional participants including BlackRock, Goldman Sachs, Visa, and State Street, and processed 244 million transactions.

To be fair, similar participants joined Tempo and the various AI payments and agentic protocols we covered in the past. That suggests a multi-polar world for rebuilding the rails.

The $3 billion fully diluted valuation attached to the presale is harder to square. ARC token utility remains in an exploratory phase. What investors are pricing is the option value of Circle owning the chain its stablecoin settles on, closing the vertical loop that currently leaks value to third parties. Whether that option is worth $3 billion is a question about transaction volume. Specifically, whether Circle can migrate enough of USDC’s $77 billion in circulation onto Arc to generate fee revenue that justifies the valuation.

The regulatory backdrop sharpens the urgency.

The GENIUS Act, signed into law in July 2025, explicitly creates a path for banks to issue their own payment stablecoins through subsidiaries, supervised by their existing federal regulators. JPMorgan and Bank of New York are already running tokenised deposit pilots. If regulated bank-issued dollar tokens reach scale, the need for a third-party stablecoin issuer like Circle narrows.

Arc does not solve that problem directly, but owning proprietary chain infrastructure creates network effects and switching costs. It is a hedge against everyone from Canton to Ripple to JPM’s Kinexys taking their cut, or consolidating vertically.

Circle Agent Stack is the offensive play

Agent Stack is a developer toolkit for building AI agents that transact in USDC — wallets, a marketplace, and a nanopayments layer capable of transfers as small as $0.000001. The thesis is that as AI agents take on more operational and financial work autonomously, they will need to transact at a scale and granularity that existing payment infrastructure cannot support.

As we say, Robot Money.

The nanopayments layer is the most interesting piece. Card networks, ACH, and SWIFT all carry fixed costs that make sub-cent transactions economically unviable. A USDC-native chain with programmable micropayments does not have that floor. An AI agent paying per API call, per second of compute, or per data query has no clean solution today.

We covered Ramp launching Agent Cards in March 2026. In short, it allows businesses to issue virtual cards for autonomous agent spending. Stripe, through its acquisition of Bridge in late 2024, has its own answer: agent-specific card issuing via Bridge, wallet infrastructure via Privy, and stablecoin payment acceptance across 32 markets. Where Ramp’s Agent Cards are built for corporate spend control, and Circle’s Agent Stack is built for USDC-native micropayments on Arc, Stripe is positioning itself as the full-stack layer (fiat, stablecoin, and wallet infrastructure under one API).

Circle vs Stripe

Where Circle has a structural advantage is in the asset itself.

USDC is the dominant regulated stablecoin, already the unit of account for a large portion of onchain activity. Stripe’s Bridge issues its own stablecoins through Open Issuance. USDH, one of Bridge’s flagship issuances, was sunset this week after failing to gain traction against $5 billion in USDC on Hyperliquid, with Coinbase stepping in as the official USDC treasury deployer. Building agent infrastructure on top of USDC means agents inherit existing liquidity and network depth from day one. That advantage is proving harder to replicate than it looks.'

As discussed, Stripe have also incubated Tempo, a layer-1 blockchain purpose-built for payments. Whereas Tempo is built for general payment settlement across any stablecoin, Arc is built specifically around USDC. Both are bets that the future of payments clears on purpose-built chains rather than general-purpose ones like Ethereum. The capital structure question is also worth noting. Circle raised $222 million for Arc at a $3 billion FDV from a presale. Stripe is private, profitable, and last valued at $70 billion — it can fund Tempo and Bridge expansion from its balance sheet without diluting into a token.

That is a meaningful difference in how each company can absorb the cost of building and subsidising a new chain ecosystem.

End of the day, the capabilities and inclinations of (1) a Payment Processor and (2) an issuer of a cash equivalent financial instrument are profoundly different. One knows distribution and has merchants and customers in its ecosystem. The other one has an asset in every exchange and crypto wallet. We think it will be a mistake to vertically integrate and get into expensive warfare.

The revenue math

Circle’s business today is simple: $77 billion of USDC in circulation, earning a reserve return rate of roughly 4.1%, of which a large portion flows to Coinbase under the distribution agreement. Full-year 2025 revenue was $2.75 billion.

Analysts are forecasting roughly $3.2 billion for 2026, implying around 15% growth — modest by the standards of a company that grew 64% last year, and reflecting two real headwinds: falling interest rates compressing reserve yield, and the GENIUS Act’s restrictions on how reserve income can be shared with distribution partners, putting the Coinbase arrangement under regulatory scrutiny.

The new products need to be understood against that backdrop. Circle guided to $150–170 million in non-reserve revenue for 2026, up from $110 million in 2025, but still less than 6% of total revenue. Arc transaction fees, Agent Stack developer revenue, and CPN fees are all early-stage. The re-rating thesis, from interest rate proxy to infrastructure platform, requires those lines to grow materially as a share of revenue, not just in absolute terms. At current trajectory, the narrative is still ahead of the numbers.

The stock reflects that tension. CRCL IPO’d at $31 in June 2025, briefly approached $300, and has since settled around $114. Following Q1 2026 results, JPMorgan raised its price target to $155, Needham to $150, and Deutsche Bank to $101. The consensus sits around $125–130, implying very cautious upside from current levels.

The Bull and The Bear

The bull case requires three things going right simultaneously:

• USDC circulation grows fast enough to absorb falling reserve yields;

• Arc generates meaningful fee revenue and partially displaces the Coinbase agreement;

• Agent Stack captures early infrastructure position in agentic payments before Stripe outbuilds it.

If all three happen, Circle re-rates as a payments infrastructure company, where multiples are driven by transaction volume and network effects rather than interest rate cycles.

The bear case is simpler. Rates fall faster than circulation grows, the Coinbase agreement gets restructured in a way that reduces distribution without replacing volume, Arc fails to migrate meaningful USDC onto its chain, and Stripe or Ramp outbuilds Circle on agent infrastructure at lower cost.

The announcements are the right strategic moves, but right now they are bets, not yet businesses. Circle is asking investors to price the option value of all three paying off at once, while the core model faces genuine structural headwinds. That is not an unreasonable ask — just expensive at current levels.

👑Related Coverage👑

Analysis: The Fintech Blueprint AI & Robotics Report Q1 2026

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.

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.

Read Analysis

Curated Updates

Here are the rest of the updates hitting our radar.

Financial Institutions and Adoption

Hana Bank to acquire $670 million stake in Upbit operator Dunamu - CoinDesk

Minnesota signs law allowing banks, credit unions to offer crypto custody services - The Block

SEC’s innovation exemption for tokenized stocks to come as early as this week: Bloomberg - The Block

Gemini Stock Jumps on Revenue Rise, $100M Bitcoin Investment From Winklevoss Capital - Yahoo Finance

DeFi and Digital Assets

Hyperliquid Policy Arm Rejects Market Integrity Concerns Amid Oil Futures Surge - Decyrpt

Fasset raises $51m for stablecoin-powered digital banking - Finextra

Blockchain Protocols

HYPE pops 7%, beating bitcoin declines, as SpaceX pre-IPO lands on Hyperliquid - CoinDesk

BTCFi protocol Echo exploited, targeting eBTC market on Monad: onchain analysts - The Block

NFTs, DAOs and the Metaverse

Aave restores ether borrowing limits after $230 million exploit - CoinDesk

NFL All Day Stops Issuing NFTs as Dapper Labs Signals Future Plans With League - Decrypt

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In this episode, Lex chats with Evan Malanga — Chief Revenue Officer of Yuma, a subsidiary of Digital Currency Group focused on growing the Bittensor ecosystem. They discuss how Bittensor's $6 billion protocol incentivises AI builders worldwide through token emissions across 128 competing subnets, and why the network has produced real commercial outputs — including a 72 billion parameter model trained on-chain and a coding agent rivalling Claude at a fraction of the cost. Evan explains Yuma's role as the institutional gateway to Bittensor through its validator, accelerator, and asset management products, and they explore why the concentration of AI in OpenAI and Anthropic is a systemic risk, and whether Bittensor's future extends beyond AI into a broader coordination engine for decentralised work.

For those that want to subscribe to the podcast in your app of choice, you can now find us at Apple, Spotify, or on RSS. Or to support this writing and access our full archive of IPO primers, financial analyses, and guides to building in the Fintech & DeFi industries, see subscription options here. Our current price point is only $2/week.

Thanks for your time and attention,

Matt & Lex 🙌

Key notable takeaways:

1. Bittensor has crossed from experimentation into shipping benchmark-competitive work at a fraction of centralized cost. Three recent proof points: Templar (subnet 3) completed the largest decentralized pre-training run of a 72B parameter model using only the network’s token incentives. Ridges, an AI agent platform, is hitting 88–90% on software engineering benchmarks, on par with Claude-class agents at ~5x cheaper, built by a 3-to-5-person team under $10M of token emissions. Score (subnet 44) is doing computer vision 200x faster than centralized counterparts. Small distributed teams are producing outputs competitive with frontier labs without raising venture capital or hiring staff.

2. Dynamic TAO restructured emissions from validator-curated to market-curated, making each subnet its own tradeable asset. Previously, dominant validators assigned weights that determined how the 7,200 daily TAO emission flowed across subnets. Under Dynamic TAO, each of the 128 subnets has its own token denominated in TAO, and any holder can buy or sell into specific subnets, pricing them like a market rather than a committee vote. Subnet owners, miners, and validators earn fees in the respective subnet token. Distribution has settled into a power law: the top ten subnets hold ~80% of market cap. This is the move that turned Bittensor from “decentralized AI protocol” into a financial hyperstructure with hundreds of tokenized work markets layered on top.

3. The economics for subnet owners are genuinely unusual — hundreds of millions in annual incentives, fully subsidized labor, no fundraising. A subnet owner gets access to up to ~256 miners globally competing to satisfy their problem statement, with miner compensation paid by protocol emissions rather than the subnet owner. At current TAO prices, annual incentives across the network run into hundreds of millions; at higher prices, this approaches $1B/year up for grabs. No hiring, no benefits, no recruiting, the network runs as a continuous adversarial competition where validators rank miner outputs. This is the mechanical answer to “why would an AI researcher choose Bittensor over Silicon Valley”, and explains why researchers at Meta and Google reportedly mine Bittensor on nights and weekends, with top miners on subnets like Ridges earning ~$30,000/day.

Background

Before becoming CRO at Yuma, Evan Malanga served as a platoon leader and battalion officer in the U.S. Army's 75th Ranger Regiment. After completing his MBA at Columbia Business School, he moved into private equity at Gotham Consulting Partners before joining ConsenSys as a founding team member of its digital securities division, one of the earliest efforts to tokenise real-world assets on Ethereum. He went on to become Senior Director of Business Development at Securitize, where he led institutional partnerships during the early days of security token offerings.

In 2021 he joined Digital Currency Group as Director of Strategy & Operations, rising to Vice President of Investment Strategy, where he helped shape DCG's positioning across its $20 billion subsidiary portfolio including Grayscale, Foundry, and CoinDesk, before leading the incubation of Yuma as DCG's decentralised AI subsidiary focused on Bittensor.

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Topics:

Yuma, Bittensor, Digital Currency Group, DCG, OpenAI, Anthropic, Foundry, Templar, Ridges, Bitcoin, Meta, Google, BlackRock, JPMorgan, Decentralized AI, Crypto, Blockchain, AI, Tokenomics, Decentralized Science, DeSci, AI Agents, Computer Vision, Proof of Work, Tokenization, Real World Assets, RWA, Machine Economy

Timestamps

• 1’09: The World Wide Web of Intelligence : How Bittensor Turns AI Into Open Competition

• 9’48: Decentralized AI or Financial Hyperstructure : Unpacking Bittensor's Tokenomics and the Shift to Dynamic TAO

• 15’04: 256 Miners, Zero Payroll : How Bittensor Subsidizes the Labor Behind Every Subnet

• 18’03: The Olympics of AI : How Subnet Competitions Replace Bitcoin's Proof of Work

• 20’09: The Grayscale Playbook for Bittensor : How Yuma Is Building the Institutional On-Ramp

• 23’19: AI Is the Wedge, Not the Ceiling : Bittensor's 3-to-5-Year Path to Coordinating All Work

• 28’03: Right but Early : Why the Vision for Decentralized AI May Take 15 Years to Realize

• 30’52: Decentralized Science as the Next Wedge : Why DeSci Could Be Bittensor's Most Underrated Use Case

• 34’10: $30,000/Day Mining on Nights and Weekends : Why Meta and Google Researchers Are Quietly on Bittensor

• 35’56: The channels used to connect with Evan & learn more about Yuma and Bittensor

Illustrated Transcript

Lex Sokolin:
Hi everybody. Welcome to today’s conversation. I’m super excited to have with us today, Evan Malanga who is the Chief Revenue Officer for Yuma. Yuma is a really interesting company. It’s a subsidiary of Digital Currency Group, of course, which is one of the pioneers in crypto based investment management. But Yuma in particular is a decentralized AI company affiliated with Bittensor, and we’re going to dive into what that means. So, with that, Evan, welcome to the conversation.

Evan Malanga:
Lex, thanks for having me. My pleasure.

Lex Sokolin:
I kind of just want to get to the conclusion, which is to ask you what is Bittensor and what is Yuma and why are you working on it?

Evan Malanga:
We can spend a lot of time on all of those things. So, let’s start with what is Bittensor. Bittensor is a protocol. We like to describe it as the World Wide Web of Intelligence, its decentralized, open-source incentive mechanism that enables market participants from all over the world in a permissionless environment to contribute or build towards artificial intelligence projects. You can kind of hear sort of the rhyme with Bitcoin in a lot of ways. It’s, you know, it’s non-sovereign, it’s immutable. Hard cap supply, decentralized, global, etc. And instead of doing proof of work in the Bitcoin sense, which is random number generation, market participants are establishing competitions and competing across a variety of use cases to build across the AI value chain, which I think is fascinating and that’s why I’m working in it.

Lex Sokolin:
We’ve talked in the past here about decentralized AI and kind of the value proposition there, and we certainly written a bunch about it in the blueprint. And then out of my fund in Generative Ventures, we invest in the machine economy and decentralized AI. And, you know, I’ll start with the sharp question, which is, has decentralized AI worked right? Because we’re in this world where, like all of the value of, like every single market, from infrastructure investment to the price of components, to ram to the price of energy, to like sovereign debt.

Literally every single market is going to capitalize OpenAI and Anthropic and maybe a bit of Elon Musk’s ambitions. My hope was that decentralized AI was going to be this kind of open-source counterweight where privacy, decentralization, all these things matter. Are we in that world and what has happened over the last few years in Bittensor?

Evan Malanga:
So, I would say, like philosophically, I am very much aligned with you. And I think the point you just made around OpenAI and Anthropic, I mean, that is the risk here is that the world across all markets will leverage two companies and the decision making of a few individuals at those companies to determine, like what AI model parameters, outputs, training data, etc. influence our everyday decision making, be it in personal or business use. I mean, look at in recent weeks, I mean, the US Department of War going to battle between these two companies. And then, you know, you could think about all the different use cases around deepfakes, elections and things of the like all over the world that, you know, having an open-source counterweight really, really matters.

And so, I would say, you know, kind of over the last two years that I’ve really been focused in this, we’ve gone from sort of the promise of the ethos. I think we saw a lot of different projects in the decentralized AI space raise money about two years ago, that we’re still kind of waiting to see what those projects bring to market when they’re to TG events occur, but specifically in the Bittensor world. You know, this protocol. If I take a step back…

Lex Sokolin:
Just to be clear, I appreciate, like, the vote of confidence that these projects, both that they have a product to bring to market and that their TG will happen. I as a crypto VC, I really enjoy those assumptions. Thank you.

Evan Malanga:
I think you’re reinforcing the point I’m trying to make is that Bittensor has a massive first mover advantage. The founders first met and started tinkering on this in 2016. I think Bittensor has already tried and failed, and learned the lessons that many of these other projects are trying to set out do so there are multiple cycles ahead of the rest of the market in many ways.

And so what I would say two years ago would kind of be like the initial discovery, the initial excitement, that ChatGPT moment where everyone was like, okay, AI is here now, two years later, we’ve got themes around rebuilding the American industrial base, reshoring, you know, this need for energy compute, data centers, small nuclear reactors, right. Like there’s this new industrial revolution happening around AI. And I think in the Bittensor world, we’re growing up. We’re moving away from what I would describe as the experimentation phase into the tipping point of maturation. And so, to give you like a few succinct highlights in the last few weeks, one subnet called Templar completed the largest decentralized pre-training run of a 72 billion parameter model. I mean, fully leveraging the token incentive mechanism of a blockchain on the Bittensor network. That’s subnet three. I mean, that’s massive and no one’s really talking about it yet, but it’s starting to break into the dialogue. Ridges, which is a AI agent platform, is hitting like 88, 90% of the software engineering benchmarks.

It is effectively on par with like a Claude type agent for 5X cheaper the cost and was built with under $10 million of token emissions on the Bittensor blockchain. Right. So, here’s a small 3-to-5-person team being able to do this at a fraction of the cost. Fully decentralized across the globe amongst miner’s validators in the subnet owners. Another example would be score subnet 44. They’re doing computer vision over 200 times faster than centralized counterparts. Specifically, they’re doing a variety of different verticals. But where they started off was sports specifically soccer. Doing like post-game play analysis for managers, coaches to think through game strategy and stuff like that, but would take a lot of people weeks if not days, if not weeks, is taking the team a handful of minutes and now has natural language prompting to ask questions around, you know, what occurred in the game and how to prep for like, you know, the next match, so to speak. So, there is a lot of real-world use cases, a lot of tangible businesses being built, generating revenue with real customers across the board Bittensor ecosystem. And that’s just, you know, 3 or 4 upwards of 128 subnets going to 256 subnets.

Lex Sokolin:
These are kind of cool spot checks of capability I want to rewind a little bit, just to kind of set the groundwork for a bit tenser for an audience that might not be familiar with it. So, Bittensor is a token-based project. It is around 3 billion, give or take, in terms of market cap, or between five and a half and 6 billion in fully diluted values. So, there’s still about, give or take, a little bit less than half of emissions that are going to happen. It’s AI themed. And I mean, one of the directions I want to talk about is like, is Bittensor decentralized AI or is it like this financial hyper structure? And so, I’ll just describe what the financial hyper structure looks like. And then I’d love to get an update because it’s been a bit since I’ve touched the tokenomics. They’ve gotten more complicated in the beginning. You’ve got the emissions curve in the same way that you have an emissions curve for Bitcoin, and people can get rewards of these tokens.

Obviously, they’ve got no value unless they have value. And today they have $6 billion of FTV of value. And then the emissions that come out are then directed to different projects which are called subnets that you had just mentioned. And they’re directed through this mechanism where effectively other participants in network are able to, using their existing holdings, kind of decide which things are worth funding, kind of on a periodic basis through the Yuma consensus, which I’d love to hear about. And then people can get effectively millions of dollars of token rewards for doing work. That sort of aligns with the decentralized AI mission. And then since then, I think the emissions have gotten a lot more programmatic and attached to business outcomes. So, to me, that’s like a financial product. It’s less of an AI product. Can you guide us as to what the current token infrastructure looks like, and then what’s the connection here to AI? Other than sort of like what the subnets decide they want to do or not do.

Evan Malanga:
To take it one step back before I dive into this. It’s important to know that when this protocol was created and they first launched the token similar to Bitcoin, there was no venture capital round. There was no like, you know, large investment with, you know, vesting schedules and things like that. Right. Like the founders just launched the token. In order to accumulate TAO, you had to provide work to the network and earn it through the emissions. Right. And so that sort of community aspect of people just showing up here, naturally, organic growth was really important to our thesis. In addition to the parallels to Bitcoin. 21 million tokens hard caps apply for your halving schedule. The first halving just occurred in December. So, you’re right. We’ve just passed 50% of the supply have been emitted. And so, what has changed over time, kind of diving into your question more specifically, is what we went was from emissions determined by the validators, the validators would assign effectively some sort of weighting scheme. It varied validator to validator to determine the 77,200 tokens admitted the way.

Lex Sokolin:
How did one become a validator in the first place? And is Yuma a validator?

Evan Malanga:
Yuma is a validator. To become the validator. You have to create a wallet, you register in the network as a validator, but then you have to provide from subnet to subnet the validation function. It varies. So, it’s not it’s not uniform across the network. Each subnet has different validation requirements. And you have to provide that hardware, that infrastructure to validate the work of the miners on each respective subnet. So today there’s 128. So, it’s basically like validating 128 different protocols concurrently. And so, there’s you know there’s a couple dozen validators in the network I’d say roughly like the top ten, top 12 have the majority of the dominant in terms of assets staked. We’re about number three, number four, depending on the day of the week in terms of TVL on the network. And so, you know, the change in Tokamacs you’re referring to is called dynamic time. So, prior to that the validators would have basically assess these weights.

And the community realized and the foundation realized that, you know, this was this wasn’t sufficiently decentralized, and we needed to move to more of a market-based approach. And so, in order to do that, to allow any token holder anywhere in the world provide some level of input through their assets as to where emissions should flow. We created Dynamic TAO and effectively, what that is, is each of the 128 subnets has its own subnet token denominated in TAO, and market participants can buy or sell. Any token holder can kind of come in permissionless trade their TAO for a respective subnet token. The subnet owners, the miners, and the validators earn their fees on that respective subnet in that subnet token. And so effectively, what you have is what you’re seeing is more of a Curato distribution, as you would imagine, where market participants are ascribing value to you, call it. You know, the top ten-ish roughly have 80% of the market cap versus, you know, the long tail, 20%, 80% of the rest have, you know, the rest remaining 20% of market cap.

And so, it’s further decentralized the protocol into the hands of token holders, to your point of like this seems more of like a financial instrument than an AI instrument. It does introduce complexity for sure, to a new entrant coming to Bittensor. Part of the reason we created Yuma Asset Management, to simplify that entry point process for investors and token holders, to get sort of a simplified one click trade approach to getting exposure to the subnets. But overall, I would say this is allowed other subnets that may have gone unnoticed to really build their businesses and rocket themselves into the top ten because they don’t have to deal with, you know, pitching a handful of teams. They can make their announcements publicly. They can manage their own communities publicly, be it through X or discord both, and the market’s reacting accordingly to various business updates. Be it technical or commercial.

Lex Sokolin:
Looking at that financial hyper structure again, like to me it feels I’m sure you won’t like this a bit like transitioning to like a virtual model, right? So, a virtual model is you’ve got the main token for virtual and then you’ve got an agent launcher. But the agent launcher has like infinite agents, like you can have as many as you want, and they can be as thin or as thick, sort of in terms of their functionality. And then each one is tokenized. And then in order to kind of support the creation of that agent and the trading of that agent and the market infrastructure like the virtual token is required. And so, you kind of have the platform and then you have speculation on the components.

Now, in the case of Bittensor, you have a limited number of subnets. And it’s actually quite expensive to become a subnet, and once you become a subnet, you’re eligible to receive distributions from token rewards. But there’s also stuff about your own token floating and then creating markets between using TAO and your own subnet token. I guess what kind of profile is the right profile for somebody who wants to participate as either a subnet? If I’m an AI researcher and I’m like, this is the best, I want to do it. What are the ways that they would engage?

Evan Malanga:
Yeah. So, I would say, first of all, you don’t always have to be a subnet owner to participate in the protocols if you’re an AI researcher. The attractive angle here, right, is that you can come into this network without going out to Silicon Valley and having to fundraise a business, hire employees or anything like that. You get companies like Yuma, there are others that provide funding to get on, so we’ll pay the subnet registration fee. You can also buy one from the protocol directly. It de-registered an existing subnet. That’s sort of the design. And there’s talk of the cap extending from 128 up to 256. I don’t know when that will occur, but it is being discussed in the community. But effectively what you get is a handful of things. Number one is you can tap in to a few hundred million dollars of annual incentives, right. So that’s paid to the subnet owners. It’s paid to the miners. It’s paid to the validators. Right. So as asset prices and a dollar, you know, TAO to USD basis move with the markets.

You know, TAO at 300 versus TAO at, you know, 500 to 1000 whatever. You know that starts encroaching in like $1 billion of annual incentives up for grabs every year. Right? So there’s meaningful dollars at stake if you compare that to some of the more centralized kind of web2, it’s actually quite comparable. Second is you get access immediately. If you’re a subnet owner and you define your problem statement, you get access to, you know, 256 miners anywhere in the world competing Against each other to earn as much of your specific token, and you don’t have to pay them right. It’s fully subsidized by the network. It’s fully incentivized by the network. You don’t have to go hire them. You don’t have to pay them or do anything. Recruit them, provide them employee benefits or anything like that. It’s fully decentralized and it’s continuous, right. So, you get this continuous iterative model where you can constantly improve whatever it is you’re doing. So, most subnet owners, you know, kind of get punched in the face a little bit when they first launch.

It’s naturally kind of a exploitive type network where, you know, another term would be like a GAN, an adversarial type network by design to have the best competitors, you know, sort of rise to the top. And so, you’re getting the best outputs for whatever your prompt is. And those miners are getting rewarded, and the validators serve as sort of the judge. Right. Sort of ranking the miners against the prompt statement and allocating tokens accordingly. And so, you get all of this in a totally permissionless environment. You also are starting to see network effects across Bittensor, where other subnets are tapping into the work of adjacent subnets to build upon to succinctly describe it. I mean, you get access to hundreds of millions, if not billions of dollars of annual missions, continuous improving system access to hundreds of miners all over the world, fully subsidized by the protocol, and immense and growing network effects between you and other subnet owners as well.

Lex Sokolin:
Can you elaborate on how the sort of proof of work like mechanism is actually evaluated, like this evaluation of work done against the problem statement? Like technically, how does that work?

Evan Malanga:
Yeah. So, this is slightly where it departs against kind of the Bitcoin analogy. The example we use is you kind of have to think of the subnets as like the Olympics right. Everyone’s competing but no sport is the same. So, if you’re let’s use the Winter Olympics as an example like a cross-country race is different than figure skating, right? Like how you win, how you get the gold medal, and what is required of you as a competitor is totally different. And so, you can think of subnet owners as like the Olympic Committee. They’re setting out the competition. So, you’ve got training at home. You’ve got data scraping, data validation. You’ve got computer vision, AI agents, prediction markets, kind of science type things like protein folding, drug compounding. So, you have you have all these different use cases. And within that the competition is not necessarily linear. Some of these competitions are winner take all. Some of them are more designed to be more of a Pareto distribution. There’s different sort of designs within the incentive mechanism in of itself.

But your miners are your competitors. They’re your athletes and the validators are the judges. So, when you think about like if I’m asking a subnet, if a subnet is asking miners to produce social media data within a certain time window or a certain topic, the validators are just verifying is that data? Does that meet the prompt, and how quickly were those queries returned to the subnet owner? It’s a tough question to answer because it’s not so uniform across the network, but the I think the Olympics analogy is a helpful framework to understand.

Lex Sokolin:
Let’s talk about Yuma for a moment. In my mind, thinking about Yuma as sort of similar to thinking about a Bitcoin miner in 2012. We’ve had a couple of years. There’s been returns. We know what the infrastructure is, but these things can grow into tremendous size. But the business model is kind of still evolving and it’s new for a lot of people. How do you think about it and what was the origin of creating this entity?

Evan Malanga:
I think kind of going back to the sort of the DCG Bittensor origin story. I mean, this was a, I think 21 or 2022. We made our first investment buying tokens OTC from the foundation and at the time our other wholly owned subsidiary, foundry, was running all of our infrastructure, and foundry has the largest bitcoin mining pool in the world. They did equipment financing for mining machines and stuff like that, so it naturally fit within that business at first. And so they spun up a validator and started sort of validating on the network, kind of fast forward to 2024, as we were thinking through kind of the strategy of what we wanted to incubate next Bittensor was something we already had high conviction in and was like a natural starting point of let’s, let’s look there and see where you know, what we can do, what we can play. And the short answer really is, at the time, we realized there was a lot of barriers to entry to get on the network as a subnet owner. And so, running a subnet acceleration business made a lot of sense by providing the TAO to get teams on the network.

And that’s sort of where we started to find initial product market fit. We started to see a lot of demand of subnet teams coming to us for capital. The validator was a natural kind of complement to that business. And so, we combined those two efforts to form Yuma and Yuma’s objective is to. Is to grow the Bittensor ecosystem and increase network adoption. And so, we’re doing that in a variety of different ways. As I mentioned, you know, we offer an accelerator to lower barriers to entry for summit teams to get on the network. The validator. And we have a partnerships effort with other crypto providers, you know, custodians, wallets, exchanges to get institutional staking available to more sophisticated platforms versus using sort of the command line interface, which is what it was originally a couple of years ago. We self-mined. So, we have a we have a one-person mining team that’s, you know, doing some proprietary mining across the network. But we don’t have a broader sort of externally facing mining business at the moment.

And then we launched when Dynamic TAO happened, we launched Yuma Asset Management, which is, you know, we have two fun products, a subnet composite and a large cap product, which effectively are like, you know, kind of you can kind of akin to like the S&P 500 of subnets where it’s, you know, market cap weighted rebalances on a regular basis to give investors to reduce the barriers to entry for investors to get exposure to the subnets. So, it’s a simplified product. And you in addition to not only the price appreciation you get, you get the benefit of the staking rewards as well within that. And so we’ve seen a lot of success across all four business lines. But you know the nexus was really about growing. The ecosystem is like how can we be a force for good? How can we continue to support this protocol and its growth trajectory and get more teams, more builders, more investors excited about it? And, you know, a lot of that playbook comes from what grayscale did with Bitcoin and applying it here with Bittensor with Yuma.

Lex Sokolin:
If you were to sketch out the future of the bittensor protocol and Yuma, you know, DCG is famous for being first to a real large scale Bitcoin trust product that defined a a good seven years probably of the crypto industry is almost like the heart of institutional investment. What do you think things look like for bittensor and for Yuma from here.

Evan Malanga:
For Bittensor. We’ve been you know, the community talks about this. I talk about it. We’ve talked about the days like AI is the main use case. But if you really think about what this protocol has designed is really like a global coordination engine. And so like although AI is sort of rooted in the founder’s core DNA from their career experiences prior to starting the protocol, and it’s attracted to AI builders at first is I really think if you look 3 to 5 years out, I mean, Bittensor could be coordinating all types of work, all types of use cases. It doesn’t have to just be AI. I mean, you may be an existing business that has some R&D challenges and you spin up a subnet, you run some prompts, you kind of run some modeling, and then maybe you don’t need any more and you shut it down, but it provides value for you.

Or maybe you’re, you know, a startup that’s looking to kind of create something new and novel that never existed. And, you know, TensorFlow enables you to do so in a very cost-efficient way. Right? So, AI, I think is kind of where we’re finding the wedge currently. But I think this can be much bigger than, than AI specifically. And I think the role of Yuma within that is, is to continue to be that that institutional entry point. We want to be the go-to phone call for anyone looking to build, invest, get sharp and get smart about this. You know, we published reports. We just launched our second state of Bittensor. We’re publishing regular kind of data. What we’re seeing in sort of the asset management products. And so, we want to be we want to be a data resource for parties interested all over the world about what is going on and continue to support the growth of the network.

Lex Sokolin:
We talked before the call that you and I actually share quite a bit of, let’s say, institutional history between the Columbia MBA, the time of Consensys. I mean, you were also a securitize. You’ve had this background of seeing where things are going, right? And kind of positioning ahead of the curve. And I think in many ways Bittensor is trying to do that. Like it’s imagining a world where the trillion dollars of value isn’t going to the Musk organization or the Sam Altman organization, but is rather going to all of the holders of a particular token that want to guide the development of these profoundly fruitful technologies. There is this danger. And I think this is a bit of just my own thinking recently of figuring out where the value accrual is in these plays and how to position for them and how to find them. Right. So like if we think back to the early consensus days or the securitized days where 2019, 2018, 2020, all this work is being done in terms of pioneering research and proofs of concepts with every single bank around digital assets and central bank digital currencies at the time, you know, like working with the payment networks and issuance of private credit, like all the ideas that we see today were happening back then.

But then you fast forward into 2026, and I think the biggest winners of tokenization, as far as I can tell to date, are like Circle at 50 billion and Tether at 200 billion or whatever it is, right? Whereas a lot of the other companies of that time, I wouldn’t say got left behind, but are looking at smaller outcomes. And then similarly, like you look at the pioneering work that DCG is doing, and now a lot of the large institutions have come into the space. And like whenever we talk about Bitcoin as an investment vehicle, you’ve got kind of like Larry Fink and Jamie Dimon as the poster stars for that, which to me is like obscene, but nonetheless is sort of what it is. And now we’re in this third wave that you’re seeing around AI and the value expansion there. And if we look at kind of like investment returns at least in 25 and in the beginning of 26 for AI. And I’ve sort of set this up a little bit before. But if you really look at the value chain, most of the sort of value accrual is going to energy companies.

It’s going to component companies, to the chip manufacturers and to the folks who train models. And doing that in sort of like an extreme way. And I guess just in terms of soul searching and thinking about the future, how do you square this? Like being right, but being early and building exposure, but maybe doing it in a way that’s philosophical or, you know, focused on ideals versus how these things actually end up playing out, how the winners actually end up growing into these behemoths. Like, any thoughts on just that story and then any ideas on how to navigate it?

Evan Malanga:
Yeah. No, I mean, the story really resonates. I mean, we were talking before the call started, but the industry uses the term real world assets, or RWA, but back then we call them STOs or security token offerings in like, you know, we were doing a lot of novel things like tokenizing a hotel and whatnot. And, you know, you kind of needed that moment to happen first before we figured out how to tokenize treasuries and, you know, and whatnot as the sort of evolution has matured over time.

And I think the same thing will happen in these markets, too. I mean, just as much as there’s been shake outs in crypto, albeit they seem to be more newsworthy, they seem to be shorter, like every 2 to 4 years than, say, other industries like there will still be shake outs in, you know, the regular web2 AI industry. And you know, there’s a lot of teams raising a lot of dollars. And that value will not accrue to every single one of those teams. And I think the markets parallel each other. Just maybe the timeline is, is a little bit longer versus a little bit shorter. And that said, I think generally speaking, there’s the vision has always been there. It’s just the distance to realizing that vision may not be tomorrow, like most people think. You know, Bitcoin was, you know, 2009. I mean, we’re, you know, 15 years into this and we finally have, as you mentioned, like Larry Fink and Jamie Dimon talking about this.

It may be 15 years before Elon Musk and Sam Altman are talking about Bittensor. That’s totally feasible. And there will be peaks and valleys in between. And there will be teams that come and go. But I think, you know, there will be things that happen in society, in the world that will continue to drive more desire for open-source AI. The token-based incentive is a great structure to bring people into the ecosystem. And moreover, I think we’re going to see a lot of incidences with AI kind of impacting people’s lives in a negative way. I mean, I’m still a bull for the technology, but, you know, if we’re leveraging AI for, say, medical decisions or therapeutic decisions or whatever the use case you may have. Or maybe it’s, you know, legal analysis and the output is wrong. You know, that maybe that boils up to a decision made at, you know, one of these two companies, right. And so, people will want inevitably over time, there to be, you know, greater competition and more transparency when it comes to this type of stuff. And so, I think I think it’s a, it is a matter of time. But yeah, it does it does feel very early. So, we’ve got the vision, as you said.

Lex Sokolin:
When you think about other things on the horizon, are there any other green shoots that you’re seeing beyond kind of the story of creating an AI alternative? Like, are there any weird and novel and interesting things that are popping up for you that you’re starting to get kind of like your second sense on?

Evan Malanga:
One of the things that has kind of been a bit of a start and stop within this ecosystem and probably maybe crypto. Crypto more broadly is kind of the I think the industry lingo term is called DeSci or decentralized science, but what I really do think is quite interesting. Some teams have tried and failed. Some teams still exist within the network. But you know, leveraging this as a way to, you know, do something you couldn’t otherwise do in sort of the real world, like if you wanted to, you know, compound DNA sequences or drugs or proteins or whatever.

Right. Like in Bittensor, you can continuously, every 12 seconds issue a new block where you’re having miners respond to queries and just like very quickly building up massive, massive data sets on things that would otherwise be like very manual, very labor intensive. And I think I think that’s really unique. We haven’t kind of figured out the business model around it yet, which is why some teams have struggled. But I think that kind of similar to like kind of like the hotel analogy I used earlier is kind of like we need that to come and, you know, we need to fail and learn the lesson. But when it comes around again. I think it’ll be immensely valuable. And, you know, teams be a decentralized or centralized players may want to leverage that type of data and that type of experimentation for whatever, you know, business purposes they may have.

Lex Sokolin:
Yeah, I think we’re also like just to riff on that. We’re seeing a lot of open-source AI papers coming out of the top, researchers who sort of, you know, there’s been like this reshuffling in the world because Web2 is funny actually, to see, like how people’s roles have changed just even in the last 20 years. Right. Like, you used to have finance people at the front of the house and they’re making all the money, and then you’ve got it. People in the back in the basement running like the servers for Wall Street banks. And obviously that’s flipped completely where the technologists and the developers became huge superstars. And the finance people are now like doing special projects in finance and operations inside of their companies. But I feel like over the last few years, we’ve had another switch from the Web2 model, where the people that had the most traction and visibility were like these empathetic product leaders, or go to market people who had intuition for where demand was. And then between both crypto and AI, I think that has flipped completely. So, like the people who are extremely deep technically in either cryptography or machine learning or, you know, neural network architecture and then transformers and so on, like the heavy technical people are getting recruited by Facebook for like whatever it is, $100 million contract, right? So, like the value has finally gone back to like the highly technical creative people.

And I feel like a bunch of them made enough money now that they just get to research what they want. And the stuff that’s coming out is, you know, how do we get a rapidly improving recursive mathematics researcher or protein folding researcher or like, you know, biotech stuff. And we’re just sitting in front of this completely insane explosion of technology that we couldn’t even imagine.

Evan Malanga:
One of the one of the learnings we’ve had is, is there are a lot of people sitting at the Meta’s and the Googles of the world that are mining Bittensor on their nights and weekends. And you’re right, like if you are that technical individual, that researcher, you have this opportunity cost. If I can do something and maybe get hired by meta for $20 million and with, like, you know, an insane compensation package. But if I’m also the top miner on, say, ridges or shoots, I can be making $30,000 a day kind of thing. So, there’s there are parallels. It’s just it’s just not it’s just not well known enough yet. And it’s still very much in sort of the cypherpunks kind of crypto World that it’s slowly starting to break out into more of the mainstream.

Lex Sokolin:
We’re definitely in science fiction land, and it’s one of these things where the more you know the stranger it feels and is and I think biosensor, it can be a really confusing project if you’re coming at it from the outside. But at the same time, it has been a project that has maintained its value in a pretty bad bear market and through it and then recovered, which, you know, is not the case for a lot of other decentralized AI protocols that went up really fast and then melted. And I think that speaks to a large amount of different activity that’s on biosensor as well as the capital depth of the players involved. So, you know, for anyone listening, I would definitely recommend that you go deeper and check out what the protocol does as well as what Yuma does. So, Evan, if our listeners wanted to learn more. Where should they go?

Evan Malanga:
I’d say the best starting point would go to Yuma. Go to our website. We have the state of Bittensor volume one and volume two. They’re volume two we released about ten days ago. It’s kind of got the latest and greatest coverage of the protocol. Follow us on X. We’re posting kind of updates market color that we’re seeing on a regular basis as well. Those are two great places to start. And if you want to go deeper, fill out the contact us form and we can figure out a way to work together.

Lex Sokolin:
Fantastic. Thank you so much for joining me today.

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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.

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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

Both Robinhood and Coinbase missed analyst estimates last week as a prolonged crypto downturn dragged on trading activity, sending their shares down -13% and -5% on the news respectively, wiping off $12B of value.

We believe it exposes a deeper challenge for exchanges and neobrokers, who could be at a structural disadvantage as fintechs race to become holistic financial platforms for users. With the majority of revenue tied to volatile transaction fees on retail trading, investing into other financial products becomes much harder. We explore this theme through the lens of Coinbase’s exchange model, the broader consumer trading business of Robinhood, and the payments-first model of Revolut.

Today’s agenda below.

1. FINTECH: The $12B hole in Coinbase and Robinhood’s 1Q Earnings

2. ANALYSIS: Coinbase and Ramp restructure as AI-Native Financial Companies (link here)

3. CURATED UPDATES: Paytech, Neobanks, Lending, Digital Investing

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Digital Investment & Banking Short Takes

The $12B hole in Coinbase and Robinhood’s 1Q Earnings

Crypto is in the depths of a bear market.

Bitcoin is hovering around $80K, down roughly 36% from its October 2025 peak near $126K. Spot volumes across centralized exchanges have collapsed to their lowest level since September 2019, and are down 44% YoY in Q1, according to Coinbase.

Some on-chain analysts suggest the recent rally off of $60K might not have much steam. It is now the longest bear-market rally of the last two cycles, but it looks more technical than fundamental. A rise in open interest for derivatives (perps) but lower spot activity, indicates that the move higher is being driven by short liquidations and unwinds in speculative positioning rather than durable buying.

The fall in activity is eating into trading platforms’ revenue. Coinbase saw revenues decline 31% YoY to $1.41B and posted a $394M net loss, compared to a $66M profit a year ago. Management also announced a 700-person layoff (~14% of the workforce) the same week, citing both crypto cyclicality and an “AI-era” cost reset.

Trading sits at the centre of the decline.

Transaction revenue made up 56% of total revenue in Q1 and fell 40% YoY, with consumer transaction revenue down 48% to $567M. Institutional transaction revenue actually grew in this period, but that increase is almost entirely attributable to the $4.3B Deribit acquisition that closed in August 2025; organic institutional volumes were actually down 48%.

The remainder of revenue comes from subscription and services, which is distributed across stablecoin revenue (interest income earned on customer USDC balances through Coinbase’s partnership with Circle), blockchain rewards, interest and finance fees, and other subscription products like Coinbase One.

This segment now makes up 44% of total revenue, which management frames as a “durable buffer” against trading volatility. But that is slightly misleading. Stablecoin revenue is the single largest line at 22% of net revenue and grew 11% YoY, but it’s also highly correlated to trading volume. Customers move into USDC when they want to sit out volatility or rotate between assets, but will redeploy back into volatile assets as soon as markets turn. This dynamic is partly why subscription and services as a % of total revenue look quite flat over the past 3 years.

Meanwhile, Robinhood posted stronger numbers.

Revenue grew 15% YoY to $1.07B and net income of $350M, but still missed analyst revenue expectations. Like Coinbase, the miss was driven by crypto, where related transaction revenue dropped 47% YoY to $134M. Strikingly, it was the only major income line that fell year-on-year.

Trading still makes up 58% of Robinhood’s revenue, broadly unchanged from a year ago. But the company has held up better throughout the bear markets due to diversity in the asset classes being traded. Total transaction revenue grew 7% YoY to $623M, driven by a 320% surge in prediction market revenue through Robinhood’s partnership with Kalshi, a 46% jump in equities revenue, and 8% growth in options.

Derivatives like prediction markets and perpetual futures (perps) are proving more resilient in the downturn. Kalshi raised $1B last week at a $22B valuation, doubling its valuation and tripling annualized trading volume to $178B within just 6 months.

Event-driven trading, like predictions are often focused on sports, elections, and economic data, so are less sensitive to broader markets. But growth has also stemmed from institutions beginning to use them as hedging tools when markets get choppy. There is an organic adoption tailwind that is hiding the cyclicality.

Perps have shown a similar pattern. The total value of open leveraged bets from traders on Hyperliquid, measured as “Open Interest”, was $4.3B by end of April, up +9% in the past two months despite the broader spot market collapse. This metric still dropped from the October peak alongside the rest of crypto, but it’s held up materially better.

This makes a difference for trading platforms that have these features.

Prediction markets now make up 17% of total transaction revenue for Robinhood!

And while it doesn’t offer perps directly, it offers similar margin trading on equities and crypto, on which it earns interest. In Q1 2026, margin interest revenue grew 75% YoY to $193M, making up 18% of total revenue.

Coinbase has been late to this shift. While it launched both prediction markets and perps for retail customers in January 2026, it didn’t materially impact its PnL yet. As a result, the exchange has been left more exposed to spot trading.

Financial platforms like Revolut, which have major trading activity but center on payments and banking, are much less affected. Revenue grew a whopping +45% in 2025 to $6.1B and is evenly balanced across major revenue streams, each making up between 13-22% of total.

Card interchange fees and interest income are the largest at ~$1.3B each. Crypto trading is wrapped under the Wealth segment together with equities and CFDs, which amounted to 15% of total revenue, a fraction of Robinhood’s exposure and a sliver of Coinbase’s.

It’s also worth noting that Revolut’s interest income is similar to Coinbase’s stablecoin revenue, with both monetizing idle customer balances. Revolut held 90% of its $68B customer balances in cash and Treasury investments at year-end. But the behaviour driving those balances is fundamentally different. Revolut’s deposits grew with growth in primary banking relationships and direct deposits (up 45% YoY) while Coinbase’s USDC balances grew with reduced trading appetite. If crypto markets turn more bullish, Coinbase is more likely to see a fall in balances.

The challenge for trading-first platforms like Coinbase and Robinhood is whether they can meaningfully expand into adjacent financial products while staying tied to market cycles. Robinhood has shown that diversity in tradable asset classes, especially prediction markets and derivatives, can act as a hedge.

Coinbase is moving in a similar direction. The risk is a prolonged bear market hindering their ability to grow while fintech competitors like Revolut, Nubank, and Cash App grow their share of customer deposits.

👑 Related Coverage 👑

Long Take: Coinbase and Ramp restructure as AI-Native Financial Companies

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.

Read Long Take

Curated Updates

Here are the rest of the updates hitting our radar.

Paytech

• ⭐ Circle raises $222 million from BlackRock, Apollo and others in Arc token presale valued at $3 billion - CNBC

• Meta begins paying creators in USDC via Stripe - CoinDesk

• Ramp acquires Swedish payments platform Billhop - FinTech Futures

Neobanks

• Mercury Wins Conditional OCC Approval For Banking License - Fintech Futures

• UK Digital Bank Monzo to Exit US - WSJ

• Chime moves further up market with prime offering - American Banker

Lending

• SoFi Reports Record Net Revenue of $1.1 Billion - BusinessWire

• Revolut’s AI Credit model PRAGMA - Finextra

• Commerzbank to axe 3000 jobs as it fends off UniCredit takeover - Finextra

Digital Investing

• ⭐ US sues Wisconsin in legal battle over prediction markets - Reuters

• Kalshi to offer perpetual futures trading - Coindesk

• KelpDAO/LayerZero Exploit Drains $290m, Freezes DeFi Markets - Galaxy

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

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’s agenda is below.

1. AI: AI regulation has always been complicated, but more speed is essential to ensure the next crises aren’t AI-built

2. ANALYSIS: The Machines are working, while their Banks are getting robbed

3. PODCAST: Inside Mercury’s $650M Revenue Machine, with CEO Immad Akhund

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

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The Regulator Lag

The Cambridge Centre for Alternative Finance’s (CCAF) 2026 Global AI in Financial Services Report — 140 pages published 29 April with the BIS, IMF, World Bank, and World Economic Forum, drawing on 800+ respondents across financial institutions, vendors, and regulators — produced four findings that stick out to us:

• 4 in 5 firms now deploy AI at some level

• 76% of industry uses OpenAI as their primary foundation model

• The top three cloud providers serve more than 80% of the industry

• 43% of financial regulators are not collecting AI adoption data and have no near-term plans to start

Industry is moving roughly twice as fast as the supervisors meant to oversee it, and the supervisors don’t have the data, infrastructure, or training to catch up: 46% of surveyed regulators use no cloud infrastructure at all, 24% use no foundation models. Among regulators flagging constraints to their own AI adoption, 48% cite a lack of training and capacity building, 47% a lack of talent, and 45% a lack of technology and infrastructure.

This is not a stress-test problem. No capital adequacy ratio, liquidity coverage requirement, or operational resilience framework captures it. You can see the data below in the chart tracking active AI adoption by stakeholder.

The firms that regulators oversee are deploying AI in credit decisioning, fraud detection, customer communications, trading strategies, and compliance infrastructure. These oversight institutions are operating without the tooling, data, or capabilities to monitor and interrogate these companies.

We highlight several of the additional conclusions below and recommend you check out the whole report.

An Information Problem

Regulation often lags technology by design, so some delay is a reasonable trade-off for preserving innovation incentives for private companies.

But that logic rests on regulators at least observing what is happening, even if not yet acting on it. When 43% of the world’s financial regulators are not collecting AI adoption data and have no near-term plans to start, it becomes an information problem which won’t self-correct.

Regulator Collection of Data on AI Adoption and Related Risks

The Financial Stability Board (FSB) flagged this in October 2025, explicitly acknowledging the early-stage monitoring efforts and data gaps which are preventing authorities from adequately quantifying the risks they are responsible for managing. Building on its November 2024 work, the FSB acknowledged that most authorities remain at an early stage of monitoring, citing data gaps including the lack of agreed definitions for AI, difficulty assessing the criticality of AI services, and no consistent way to track third-party dependencies. The FSB identified four categories of AI-related systemic vulnerability:

1. Third-party dependencies and service provider concentration

2. Market correlations from common models

3. Cyber risks

4. Model risk, data quality, and governance failures

Three Jurisdictions, Three Ideas for Regulation

The advanced economies are ahead in exploring solutions to the AI transformation relative to the rest of the world, but unfortunately, the three major regulatory blocs have reached materially different conclusions about how to respond.

• The EU has focused on prescriptive measures. The AI Act’s obligations for financial services take effect on 2 August 2026, covering AI systems used in credit scoring, insurance pricing, and AML profiling. Non-compliance carries fines of up to 7% of global annual turnover or €35 million, whichever is greater. Institutions using third-party AI models are classified as deployers, not providers, carrying different regulatory obligations.

• The UK is trying to be more flexible in its regulations. The FCA has been explicit that it will not introduce AI-specific rules. FCA chief executive Nikhil Rathi reaffirmed this in December 2025, citing the technology’s pace of change as justification for resisting prescriptive rule making. The Supercharged Sandbox, AI Live Testing programme (its second cohort commencing April 2026), and AI LAB represent the frameworks for supervised experimentation rather than codified prohibition.
  
  The problem is that the UK Parliament’s Treasury Select Committee’s January report concluded that the FCA, the Bank of England, and HM Treasury are exposing the public and the financial system to potentially serious harm. It called for the FCA to publish practical guidance on AI by the end of 2026.

• The US is using examination as a way to enforce its ideas of AI use in financial services. The SEC’s 2026 Examination Priorities, released in November 2025, made AI a primary focus across fraud detection, AML, trading, portfolio management, and customer services. Currently, only 40% of S&P 500 companies provide AI-related disclosures. Just 15% disclose board oversight of AI. There is still a gap.

Risks and Model Concentration

There are many risks to consider, but among the CCAF’s most striking findings is the degree to which the global financial system’s AI dependency has concentrated around a single provider.

The 76% OpenAI dependency among industry respondents is the single most exposed concentration in the system. Google sits at 57%, Anthropic at 35%, DeepSeek at 15%. Among regulators, OpenAI usage is 48% — meaning supervisors are partly dependent on the same provider their firms are using. On cloud, AWS leads industry at 46% and vendors at 55%; among regulators that do use cloud, Azure leads at 39%.

This can easily expose the financial system to resilience vulnerabilities, pricing shocks, or supply disruptions.

What breaks if OpenAI or Anthropic have a sustained outage? Credit decisioning pipelines stall. AML alert generation pauses. Customer-facing chatbots stop. Trading systems that depend on GPT-based signal extraction lose inputs. Compliance functions that auto-summarise filings or transcripts go dark.

The European Systemic Risk Board’s (ESRB) Advisory Scientific Committee published Artificial Intelligence and Systemic Risk in December 2025. The report analysed 11 AI features and identified five as potential amplifiers of systemic risk:

• Concentration and entry barriers

• Model uniformity

• Monitoring challenges

• Overreliance and excessive trust

• Speed

The ESRB's proposed policy response covers three categories: capital and liquidity regulation adjustments; "skin-in-the-game" and "level of sophistication" requirements; and enhanced supervision combined with transparency labelling on financial products that use AI. The skin-in-the-game proposal is arguably the sharpest policy idea in the current regulatory landscape, addressing a fundamental misalignment that firms deploy AI systems they did not build, cannot fully explain, and are not financially liable for when they fail.

The UK’s Critical Third Parties Regime was designed to address exactly this class of systemic dependency, giving the FCA and the Bank of England powers of investigation and enforcement over firms providing critical services such as AI and cloud providers. However, despite being operational for more than a year, no organisation has yet been designated under it.

The financial system’s 76% dependence on a single AI provider does not appear on any stress test. No regulator has formally quantified what a disruption to that provider would mean for the firms it supervises.

Intent to Execution Misalignment

Different surveys put agentic AI deployment in different places, but they point in the same direction.

The Neurons Lab synthesis of recent agentic AI research finds that 99% of companies plan to put autonomous AI agents into production. Only 11% have actually done so. Among those that have started, 57% report lacking the internal capabilities to take advantage of agentic AI.

This matters for the regulatory lag argument.

Gartner projects that 40% of business software will include AI capable of completing end-to-end tasks independently by the end of 2026. Finastra’s research puts the operational efficiency gain from agentic AI at 20%, with banks leveraging it earning a 15% greater market share. These are the commercial incentives that will accelerate deployment beyond the current 11%.

The buffer between where firms are and where they are going is the policy window for regulation changes.

What This Means in Practice

For investors, a portfolio of financial services firms with 76% dependence on a single AI provider is carrying a correlated operational risk that is neither well-priced nor well-governed.

For CEOs, the FCA has been clear that personal accountability for AI failures sits with named senior managers, specifically SMF24 (Chief Operations) and SMF4 (Chief Risk), not with systems or vendors.

In the EU, the provider-vs-deployer distinction established by the AI Act creates an additional board-level decision on how your institution sources its AI and what determines your regulatory classification and liability exposure.

Some regulatory lag is unavoidable. But the absence of supervisory data, capability, and legal clarity is leaving firms inside a thin oversight structure. The commercial incentives to accelerate AI adoption are substantial. The safety structures around that acceleration are not.

👑Related Coverage👑

Analysis: The Machines are working, while their Banks are getting robbed (link here)

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 capitalisation. This vehicle holds a bunch of private company shares, while charging a 3%+ management fee.

Read Long Take

🎙️ Podcast: Inside Mercury’s $650M Revenue Machine, with CEO Immad Akhund (link here)

In this episode, Lex chats with Immad Akhund, CEO and founder of Mercury, a leading neobank for businesses. Immad shares his entrepreneurial journey, explaining how frustrating banking experiences inspired Mercury’s creation.

They discuss Banking as a Service, open banking, embedded finance, and core banking systems. Immad details Mercury’s product philosophy, team structure, and migration away from Synapse before its collapse. He also outlines Mercury’s impressive growth, with 300,000 customers, $650M in annual revenue, and three years of profitability.

The conversation concludes with Mercury’s future plans, including lending expansion, a bank charter application, and hopes for smarter AI-driven regulatory compliance.

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Curated Updates

Here are the rest of the updates hitting our radar.

Machine Models

Ethical and Bias Considerations in Artificial Intelligence/Machine Learning - Matthew G. Hanna & Liron Pantanowitz & Brian Jackson & Octavia Palmer & Shyam Visweswaran & Joshua Pantanowitz & Mustafa Deebajah & Hooman H. Rashidi

A Critical Field Guide for Working with Machine Learning Datasets - Sarah Ciston & Mike Ananny & Kate Crawford

AI Applications in Finance

⭐ AI-Driven Payment Systems: From Innovation To Market Success - Merve Ozkurt Bas

The Rise Of Generative Ai Agents In Finance: Operational Disruption And Strategic Evolution - Inesh Hettiarachchi

Financial Modeling in Corporate Strategy: A Review of AI Applications For Investment Optimization - Olufunmilayo Ogunwole & Ekene Cynthia Onukwulu & Micah Oghale Joel & Ejuma Martha Adaga & Augustine Ifeanyi Ibeh

Investment Outlook

⭐ Private Equity Outlook 2025: Is a Recovery Starting to Take Shape? - Bain & Company

⭐ Global Venture Capital Outlook: The Latest Trends - Bain & Company

⭐ Global Private Markets Report 2025: Braced for shifting weather - McKinsey & Company

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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.

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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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

Most fintech founders I meet have read all the right things and still can’t put together a plan.

• A podcast on Tuesday

• A Substack on Wednesday

• A YC video on Thursday

• A thoughtful Twitter thread on tokenomics that made the rounds Friday

Each piece is fine on its own. None of them turns into a company you can run on Monday morning. Information overload is not a plan.

For those of you who just want to know what I am talking about, click below.

The Fintech Unicorn Company Builder

For everyone else, read on!

When I started my first fintech, I would have paid serious money for a single, ordered map of the territory — what to do in month one versus month thirty-six, when to think about tokens and when to ignore them, how to model a Series A, when to start planning the exit. There wasn’t one. So I cobbled it together from mentors, mistakes, and a few hard exits.

Looking back across my own companies and the ones I’ve now backed from the investor seat, the patterns are clear. Founders who hit unicorn outcomes run a more focused process than everyone else. There is luck, but there is also organization and understanding which goals to target.

So I built the process.

It’s now an app — Launch A Fintech Unicorn From Scratch — live on Studio.

The app walks through the nine stages I now teach as an investor: business model design, B2C and B2B go-to-market, software product strategy, financial modeling and venture fundraising, tokenomics, AI integration, the difference between real growth and the dark-arts version, and exit planning.

You might recognize this as our own Company Building Playbook series, available to premium subscribers. With the help of some smart software, we have been able to bring the content to life, and build a personalized experience for entrepreneurs.

Each day, you get a personalized lesson, workshop, or strategic exercise tied to what you’re actually building. The AI coach adapts to your sector, stage, and timeline. You’re meant to work alongside it daily.

If your idea has been sitting in the same Notes file for two years, this is the nudge to take things to the next level.

Take quiz to get your personalized plan

We are running a limited-time discount with the launch of this app, grab it while it's available for the next 10 days.

Thanks as always for your time and attention,

Lex

In this episode, Lex chats with Immad Akhund, CEO and founder of Mercury, a leading neobank for businesses. Immad shares his entrepreneurial journey, explaining how frustrating banking experiences inspired Mercury's creation.

They discuss Banking as a Service, open banking, embedded finance, and core banking systems. Immad details Mercury's product philosophy, team structure, and migration away from Synapse before its collapse. He also outlines Mercury's impressive growth, with 300,000 customers, $650M in annual revenue, and three years of profitability.

The conversation concludes with Mercury's future plans, including lending expansion, a bank charter application, and hopes for smarter AI-driven regulatory compliance.

For those that want to subscribe to the podcast in your app of choice, you can now find us at Apple, Spotify, or on RSS. Or to support this writing and access our full archive of IPO primers, financial analyses, and guides to building in the Fintech & DeFi industries, see subscription options here. Our current price point is only $2/week.

Thanks for your time and attention,

Matt & Lex 🙌

Key notable takeaways:

1. Banking-as-a-Service Has Been Completely Restructured - and the Original Model Is Dead

   The fintech BaaS layer that enabled the 2019–2021 neobank boom - middleware providers like Synapse, Unit, and Bond sitting between fintechs and partner banks - has effectively collapsed. The replacement model is banks themselves exposing modern APIs directly, with Column Bank and Lead Bank emerging as the new infrastructure layer. Mercury navigated this shift early, moving entirely off Synapse months before its April 2024 failure, but the broader lesson is that the hundred-program BaaS model broke under the weight of compliance and reconciliation complexity.

2. Mercury’s 40% Startup Market Share Is Just the Entry Point to a $2 Trillion Opportunity

   Mercury captures over 35% of early-stage US startups, but broader SMB banking represents 30% of all banking revenue - a $2 trillion market. The company is now expanding into personal banking (launched December 2025), lending (bank charter application filed), and subscription software. Akhund frames Mercury not as a bank but as a financial operating system - the “Google suite of banking” - where deposits are the entry point to invoicing, bill pay, spend management, and eventually underwriting.

3. Stablecoins Don’t Magically Solve the Ledger Problem

   Akhund pushes back on the narrative that stablecoins eliminate reconciliation risk. In practice, most stablecoin providers pool customer funds into shared wallets and run their own abstraction layers and internal ledgers - recreating the same reconciliation challenges that exist in traditional banking. The benefit only holds in the narrow case where users truly own their own keys and wallets, which is rarely how scaled fintech products operate.

Background

Immad Akhund studied at university in the UK before joining Bloomberg as an engineer - his only traditional job. Starting in 2006, he launched three startups in rapid succession: two ended as quick failures or talent acquisitions, and a third, an ad-tech business that processed large volumes of advertiser-to-publisher payments, ran from 2009 to 2016 when it was sold.

It was during that company that the pain of managing business banking - hiring people to manually log in, reconcile payments, and process hundreds of payouts over days - planted the idea for Mercury. An immigrant and serial founder, Akhund spent years watching every other piece of the entrepreneurial stack modernise while banking stood still, waiting until the emergence of banking-as-a-service infrastructure in 2017 made it possible to build without a charter.

👑Related coverage👑

Topics:

Mercury, Synapse, Chase, Evolve Bank, Column Bank, Stripe, Plaid, Coinbase, neobank, neobanking, banking-as-a-service, BAAS, fintech, fintech regulation, reconciliation, product development, stablecoins, API, blockchain, VCs, embedded finance

Timestamps

• 1’05: Signing up for six bank accounts and asking for an API nobody understood : how the idea for Mercury was born

• 4’57: Why depository banking was fintech's last untouched frontier : partner banks, BaaS, and the gap Mercury filled

• 6’54: BaaS, open banking, embedded finance, and banking cores : a plain-English breakdown of fintech's alphabet soup

• 13’50: Competing against Chase and Wells Fargo : why Mercury's best advantage is how bad banks still are

• 18’28: Checkbox banking versus handcrafted product : how Mercury built a unified experience that incumbents can't replicate

• 20’52: Autonomous product teams and customer-first engineering : how Mercury structures 300 people to ship like a startup

• 23’21: The right unit of speed : why Mercury bets on autonomy over coordination in product development

• 26’14: Navigating the Synapse collapse : how Mercury moved off early and reconciled every transaction

• 29’42: Stablecoins as the new embedded finance : why blockchain ledgers don't magically solve reconciliation

• 31’52: $650M in revenue and still just getting started : Mercury's vision for the Google suite of banking

• 35’04: Why profitability beats begging VCs : Mercury's business model and the case for financial independence

• 37’42: 40% of startups and a bank charter application : Mercury's roadmap inside a $2 trillion market

• 41’28: The path to a national bank charter : why AI will reshape compliance costs for fintechs

• 44’00: The channels used to connect with Immad & learn more about Mercury

Illustrated Transcript

Lex Sokolin:
Hi everybody, and welcome to today's Fintech Blueprint conversation. We are extremely fortunate to have with us. Immad Akhund, who is the CEO and founder of Mercury. Mercury is one of the largest neobanks for businesses and also now individuals, that has been created over the last decade. It's a huge success story, and I am super excited to chat with him about how he's done it. Welcome to the conversation!

Immad Akhund:
Thanks, Lex. Excited to be here.

Lex Sokolin:
My pleasure. So, let's jump in to your early career and your first introduction to the beautiful world of financial services and software development. How did it happen to you? What was your first kind of step in this direction?

Immad Akhund:
You know, funnily, I guess the first step is like the first bank accounts you get as a customer, right? So, I remember I mean, I always had like a tiny bank account that my parents had set up with it, like, you know, put some money into it.

And then when I went to university, there was a bunch of deals that the banks were doing. Like, if you're a university graduate, if you sign up to the bank, you get like a, you know, $500 or something. I think you were only supposed to sign up for like, one bank, and I signed up for like six of them because I was like, oh, yeah, if there's a deal, let's get let's get all of them. The only job I really had was after college. I worked one year at Bloomberg, which is obviously like an old school fintech company. Yeah. After that point, I wasn't necessarily into fintech, but I started doing startups. So, I did one startup in 2006 and did another one in 2007, basically. And I did the third startup in 2009. So, you know, there was like kind of two relatively quick failures slash talent acquisitions. And then my third startup I did for a long time from 2009 to 2016 when we sold it.

And during that startup is when I had this idea for Mercury, and it really started from something simple. I was we were obviously using banking as entrepreneurs. That previous company would receive a lot of money from advertisers and pay out publishers at the end of the month, and it was super frustrating to do that with kind of these incumbent banks. We would literally hire someone to go login to the bank every day, see who had paid us, copy paste that number into our back end. And then it took like 3 or 4 days of 3 or 4 people working to do manual payouts to hundreds of publishers, and I thought that was ridiculous. So, you know, I went to our bank and I was like, hey, can we get an API? And they were I mean, really honestly, they didn't know what I meant.

Lex Sokolin:
Did the police come to your house?

Immad Akhund:
They were just, like, mostly confused and, like, eventually they dug someone up and they send me some SFTP thing. It was just so, like, they just didn't know what I wanted and like, how to enable it. And it was just pretty obvious that, you know, in the years I was doing startups from, you know, 2006 to 2016, literally nothing had changed in banking, right? They had a mobile app and that was it. But, every other aspect of entrepreneurship and B2B software had changed, right? There was like Slack and Stripe and Gusto and. Yeah, and just like so much innovation where, you know, we have much better software to run businesses and much better software for entrepreneurs. So, it seemed obvious that someone would do it in banking. But so, I had this idea in 2013, but it was very much on the, you know, mindset that someone else would do it because I knew nothing about banking or fintech.

Lex Sokolin:
Yeah, if you kind of go to that moment in fintech, right, which is you're talking about kind of this ‘14 to ‘17 moment where we're out of the first wave of fintechs, right.

So mint.com has happened. Investnet has happened. PayPal certainly happened. Yeah. But you've also got sort of like the second generation of stuff like Plaid. You've got the roboadvisors. And so, people are playing with and making progress on these concepts. But all of it is like read only like nobody can actually write into a core banking platform. You can consume data, but if you consume data, you have to steal it from the banks. You have to crowbar out of it with some sort of screen scraping like offshore password farm. And that's like best in class right at the moment. So do you remember like these companies like Finicity, you know, how did you navigate this and say, okay, that's not enough. Like, I want to build from scratch.

Immad Akhund:
Yeah. So, if you think a little bit about like, what is a bank and what our financial services, they're, they're actually like a bundle of different things. Right. There's like lending there's payments and the, you know, the different kind of payment rails you could integrate with.

There's infrastructure you can provide to banking obviously. And then there's depository banking. And if you think about kind of 2017 when Mercury started, a lot of innovation had happened on a lot of other pieces. But depository banking was the was the least innovated on. I think there were a few reasons for that. Number one, there was no way to really do depository banking without working with a bank or being chartered as a bank. Whereas most of the other things you could get lending licenses, you could do money transmitter license for payments. So, another part of it was it was probably the most regulated space, like depository banking just has like a, you know, you're dealing with the FDIC, you're dealing with people's real money. So, it's like a heavily regulated. Kind of part of it. So really came down to working with partner banks. Even to access the card networks you have to work with partner banks. So, this idea of fintechs working with partner banks has been around since like 1980s or something with the original kind of store cards, but it hadn't been done too much on the depository side.

Like Chime was like one example, but Chime in some ways, especially back in 2017, was a relatively simple neobank. It was just a debit card with a wallet. Basically, one of the enabling factors for Mercury was this kind of rise off base. And at that point, like base stands for banking as a service. At that point, the base providers were kind of other fintechs that would go integrate to the bank core, would provide you payment systems and things like that.

Lex Sokolin:
So, let me stop you for the definitions, because this is always wonderful. How would you describe banking-as-a-service versus open banking Versus embedded finance. And then maybe also like what is a banking core.

Immad Akhund:
So one of the fundamental things is when I started like thinking about Mercury and like investigating fintech, I was like, wow, there's just so many acronyms and so many terms. And there's like for some reason; fintech loves its terms. So it's not that complicated. So BaaS stands for banking as a service.

There are actually two forms of BaaS. The original form that I'm talking about from 2017 ish where there was like Synapse and Unit and Bond and there's like 2 or 3 other ones, was like fintech providers that would sit in between people like Mercury and other people that want to integrate to a bank and the banks. And the idea was they would do all the hard work of integrating into the bank. They would provide you nice kind of, you know, Rest APIs that would help you bypass the tricky work of like figuring out how to do bank integration. And they actually went a little further. They provide you kind of the compliance rails. They would they would really take care of a lot of things for you, and they were like an enabler to kind of a thousand flowers booming in fintech. You know, in 2021, there was this idea that everyone could be a bank and that was really enabled by these fintech-based providers that would like, provide the connection, but really actually give you a path to do it.

Right before these kinds of fintech pass providers came along. To do integrate with a bank partner, you would have to not only figure out the weird back end, but you also just have to persuade them that it was worth doing the integration. And from a partner bank perspective, integrating with the tiny fintech was just too much work. Whereas if they integrated with a fintech, they could get 100 programs live. At least that was the idea.

Lex Sokolin:
Surely no risk in that?

Immad Akhund:
Yes, exactly. And unfortunately, in 2022 and 2023, that kind of idea of like having 100 programs kind of done by this fintech kind of crashed against the fact that, like, it was very hard to regulate that many regulators do compliance and everything across that many programs, and there was a lot of problems with it. And so that as a category kind of declined, maybe even disappeared. The fintech base, the new base is banks themselves. You know, now that time has gone on, they've learned how to build some of this technology.

Some really well-known Silicon Valley people have gone and acquired banks, and they've exposed kind of modern APIs to their banking stacks. The two big ones, there are column Bank, which we work with, which my group works with, and then lead Bank, which is another bank done by someone who started or helped start the square Bank. So, bass as a category still exists, but now it's like the sponsor banks themselves exposing APIs for fintechs to integrate with. All right. So that's pass. Open banking is mostly I would say you have existing bank accounts and you before banks would not really create good APIs. I mean I was talking about this earlier about how it's ever so hard to get an API into our bank. Open banking is this kind of initiative. It's actually like a regulated thing that Congress passed where it would force banks to open APIs for fintechs to integrate into. And, you know, if you think about what you get from plaid, which is a connection to a bank and authentication, and then a bunch of mostly read only data, you could get that from open banking.

Some other countries, like the EU, has gone much deeper with open banking, and they force banks to not only expose read APIs, but also write APIs through open banking. So, you can do things like, you know, send payments and things like that. Yeah, which would enable some use cases in fintech, but I don't it doesn't enable necessarily a full neobank. You said okay. Another term banking corps. There are these two big companies. I mean three actually FIIs, Fiserv and Jack Henry that almost every bank in the US uses for what is called the banking core, which is the banking core itself, is kind of the it's basically the database that holds the accounts for all of the customers. So, account numbers, reading numbers, things like that. But then once you have that, you have the accounts, the money in the account, then you have transactions, then you have payments. So, these banking goes actually go all fairly full stack. So, if you're a small bank, you know, 6000 banks in the US and a bunch of credit unions, they are now building their own mobile app.

All of the mobile app, the website, all of that's coming from these banking core providers that started off with just like the ledger for where the money was. And in some ways, I mean, these are big companies. I think they vary between like $20 billion and $100 billion in valuation. But in some ways, they both provide all this infrastructure to banks, but they also hold back the banks in terms of innovation. Just because, you know, most banks are not building their own mobile apps. So, the you know, it's not it's not really the case that there's thousands of banks building, you know, with like amazing engineering teams building modern infrastructure. It's actually only the top banks are building their own kind of customer facing experiences. A lot of it is coming from these banking core providers. Then the last time you said is embedded banking, embedded finance. And this is just the idea that, you know, any non-fintech company could provide some fintech products via APIs in an embedded way.

So, there is like an overlap between bars. The big difference is often the company providing the fintechs like services is not really a fintech company. It's like a Shopify has payments that they provide via Stripe. You could call stripe a embedded finance player in that case. For Shopify, there's a lot of like these vertical SaaS companies that want to provide something like, you know, either payments like I just described or lending or sometimes depository functions, and they want to embed it within the service. So yeah, all of these things have a little bit of overlap in them. But they are like kind of distinctly used.

Lex Sokolin:
Okay. So now we know the basics super straightforward. So, there you are, 2017, right? You're thinking about building out this new experience. We actually had the former CEO of synapse on. So, I'm interested in some of that, although it's a painful topic. But before kind of getting into that, like when you're thinking of doing the sort of like de novo company, right? You have an enormous existing market, like there's infinite revenue in business banking.

And at the same time, it's really, really saturated. And famously, customer acquisition costs are high, you know, and so you at the same time have the revenue, but you also have to find ways to beat, like Bank of America and JP Morgan, which obviously you found a way to do in 2017. You don't have the clients; you don't have the infrastructure. How did you take the first step and then connected to that? Like, was Mercury really the go to market and distribution motion, or was it the core engine, like, which of those parts of the barbell did you lean into?

Immad Akhund:
You know, I was thought about like there's kind of two types of startups. There are the startups where it's a new category and you're entering the new category and there's no incumbents, but also there's no customers potentially. Right. Like you're trying to invent a category where, you know, most of crypto fits that bell, right? At least the initial wave of crypto, right. Like with Bitcoin existed, there wasn't a category and it was inventing a category.

You could say by the time Solana came in, it was actually the second kind of startup, which is like a category exists, but you think and there's like really big popular incumbents in that category, but you think there's like a flaw in it, or there's some reason that's not innovative, or you have some new angle in that category, and you feel like you can build that second kind of startup and actually, like if you look at a lot of successes, they fit that second one right? Even something like Facebook, right? There was a lot of social networks that had existed before then. And they, you know, they had this new angle around, like using real names and things like that. So, I wasn't particularly intimidated by the fact that there were all of these incumbents that in theory, had business banking. Right. It was actually for me. When you think about that second kind of category, was the best kind of circumstances for the second category, which is you have a lot of incumbents.

The market is freaking huge. But these incumbents are very much old school. They're not innovative. They haven't been disrupted for a long time. You know, from my perspective, I was like, this is the best kind of competitors I could have. And every day today, I'm still like, oh my God, this is amazing. I think, give me these competitors any day, right. I would wish these competitors on my on my friends. So yeah, I wasn't like intimidated by that. And I really thought the reason the market didn't have much innovation was really a regulatory problem. Right? There was it was it's just very hard to become a chartered bank. You have to, you know, probably if you wanted to do it day one. And there's people who've tried it, like you kind of have to raise like $30 million to $100 million. Like day one, before you even could serve customers to be a chartered bank. And alternatively, you could obviously do this best thing. But, you know, in 2017 passed was like super new.

So, we were very early to it. And you know, what we felt was if you own the customer experience and you'd have to figure out distribution and what does that customer experience. And there's a lot to figure out actually to own the customer experience around banking. It's not especially depository banking for businesses. It's not just like, here's your money. You have to have like five different payment types, right? We had debit cards, wires, international wires, check. And all of those have both input and output kind of modes to them. So, it did take us a year and a half to launch. And a lot of things we did when we launched, we were the first people to do it. We were the first people to have wires. We were the first people to kind of international onboarding for us businesses. So, you could live in France and have a US business. And, you know, for us, I'm an immigrant. A lot of other entrepreneurs are immigrants, so I thought it was an important feature for us to support.

So, you know, having, like a fully featured banking service that, like, had wires and all these things was actually like, it doesn't sound like it's super innovative, but like being the first to it and obviously like kind of having an amazing design, having an experience that people really loved, being able to do it online. I mean, that also doesn't sound like it should be a big deal, but the main alternative to using Mercury, even today is actually you have to walk to a Chase branch or Wells Fargo branch, right? Like that's a to me, like getting out of my house to do something when I'm, when I'm like in the middle of coding or whatever. It's like a, it's not something that I'll be easily persuaded to do. So, all these little things ended up being like these kinds of big innovations in the space.

Lex Sokolin:
I'm curious, actually, how that works, right? Because if you're a large bank, you're often gluing these interfaces together, even if they're fully on the web.

You might have, you know, wires and all this stuff that's available through a web interface. But then, like, different elements will look different, you know. So, if you're like in the credit area of the site versus the ad recipient element of the site, or if you're looking at like treasury management, you will feel yourself kind of clinking between different third-party systems that have just been skinned with brand colors. You know, and like there's different like authentication issues and all this stuff. But that's partly because end of the day, they are different infrastructures. Like people don't know this intuitively, but like whether you're trading or paying or lending or depositing, they are going to sit inside of often different entities and different ledgers and so on. How did you get around or how did you build around that challenge, like in terms of your product development process and product management and sort of architecture? How did you actually get that done in the beginning.

Immad Akhund:
You know, initially we didn't have that many features to begin with. Right. But every feature we did have, we kind of handcrafted. And, you know, the back end is APIs. Or actually, you know, even calling them APIs is sometimes generous. But the front end is a fully crafted experience where every element of it we own. And we were very specific about this. I completely agree with you that this kind of experience you get at most banks where you log in and every, every click you do sends you somewhere else and sometimes it doesn't work. And if you ever call them and say, hey, I noticed a bug and like, you know, this screen, they're like, you know, that's just obvious. They have nothing. They don't know what to do with that. We were really very deliberate about not having infinite features, like a bank has like a ton of features. Having these kinds of core features that you really needed to run your business, which is like, give me, you know, online, sign up, give me an account, let me do the basic kind of payments and then give me good user management around it and controls.

So, we started with that basic set. And then obviously eight years later We've actually added a lot of software and financial features to the product, but we always wanted to kind of own that user experience element. And it's not that it's not possible. I mean, you know, to go back to these kinds of banking calls, a lot of the time, the banking core is, you know, can you imagine this, like super enterprise sales process where the banking core comes to the regional bank. It's like, hey, do you want to include some feature like investment or something like that? And the, you know, the bank CEO is like, oh yeah, I'd love that feature. And then the banking core has kind of stitched together this feature sometimes from an acquisition. And you know, they're like, yeah, they've sold the feature. But like no one no one in that process is really like okay, what does that customer experience look like? How is it how are we going to make this an amazing, fluid and intuitive process? It's very much like a kind of checkbox.

Yes, I have this feature. And like, you know, then you kind of leave it to some product manager to figure out, like how to theme it and put it into the interface. It's just a it's just a super different experience to like something like BigQuery. When we think about a new feature, we're going, okay, you know, how do we interlace this with the rest of Mercury and make it like, smooth and seamless and all of this kind of stuff?

Lex Sokolin:
Got it. When you look at your product development organization, what percent of the team is dedicated to the sort of things related to keeping integrations live? The ledger, like reconciliation, that kind of stuff. And what percent of the team is really building out the consumer experience.

Immad Akhund:
You know, all of the team is thinking customer first. So it's not you know, I've tried to avoid having this idea of we do have some of these, but the general idea of the team that only does some backend process and doesn't care about the customer experience, I don't like, I don't love that because at the end of the day, even if it's something cool like wires, how do you deliver an amazing wires experiences not just someone you know.

One team is continuously like thinking about the back end of the wires and then the team that deals with the front end. Because when it comes to changes, if you want to say, oh, now we want to collect addresses. If there's like two separate teams, I have to coordinate, that's when you end up with kind of disjoint experiences and it works on every level. So, I do think about every team as like, yes, they're going to touch customers and they have to think about that. We have split up the EPD engineering product design team into what we call the banking and risk team. And then the experiences team and the banking and risk team are much more kind of working on the core. And I'd say it's like it's not quite 50/50. I think the banking and risk team is a little bigger, but they work on kind of the core banking, onboarding wires, all of the pieces that enable like this kind of core banking and the experiences team often does. The more software related pieces like we have bill pay and invoicing and personal banking, some of our kind of newest software products, and in the banking and risk teams.

Yeah, there's some dedicated there's one dedicated to kind of the ledger. As a separate team kind of dedicated to payments. So just to enable the experience, yes, there's a bunch of it. But we do think about these teams as like, hey, you know, how are you delivering the best value to the customers? So even the, you know, the wise team is trying to push to like, make sure that the wire cutoff is like as long as possible. And, you know, if we do something like a undo feature where, you know, maybe you send a wire and you want like five minutes kind of time to be able to undo it, like the wise team would build that end to end. It's not going to be the kind of a separate experience to this team.

Lex Sokolin:
Super interesting. What is your product development philosophy as it gets applied to the organization? Like, do you have the Amazon two pizza team? You know, keep things small and focused on specific like use cases approach. Or do you have more of a, you know, matrix organization, like what do you think is the right way to be fast in shipping software and be customer aligned from setting up the product organization.

Immad Akhund:
You know, my general philosophy and advice that I give to entrepreneurs is you have to do what makes sense for you and your product, which doesn't make sense for everyone. Right. For Mercury. You know, one of our core tendencies, it has to be like a very unified, very smooth experience between all of our products. So yeah, the design team has to be like one design team, and the engineering team has to be one engineering team. So, in that sense it is matrix. But I'm also a big believer in giving people like broad responsibility and giving small teams kind of ability to go execute things without collaborating with a bunch of people. So, so we have these like what we call what I call autonomous product teams, that something like eight people, 1 p.m., one designer, six engineers that that own some remit.

So, you know, they might own invoicing or they might own wires or something like that. And they can, you know, they can run their own roadmap, they can fix their own bugs. And like the design on that team still has to work within their design team and their design, create and design standards and things like that. And same with engineers. But they do have a broad remit when it comes to big projects. Often multiple teams have to collaborate together. And we have some function of like figuring out who collaborates and how. But even within the collaboration, like each team as a collaborating will own some chunk of it. And I kind of think about it as like, you know, what is the most fast and functional unit in the world, which is like, I think a startup, right? Like a ten-person startup can do amazing things. And, you know, to some extent, I've just been doing startups for such a long time that I have this like model where I'm like, okay, how do we even in a like a engineering class product and design org is about 300 people.

So even in a 300-person org, how do we have like enough kind of startup units that can go do things and do them without like too much coordination, headache and like execute really fast?

Lex Sokolin:
Yeah, that a lot of that resonates. We mentioned before Synapse. So I want to just take a moment to learn your takeaways in navigating that with the company. The background is that synapse was the ledger between I think it was Jack Henry on top of evolve Bank that you guys were plugged into. They had a whole ledger issue between evolve and Jack Henry, and a whole bunch of files didn't end up making it to their engine. And as a result of that, something like 50 million was unaccounted because it disappeared in the fog of war, which was like payments and so on. And there was obviously a lot of blame and difficulty that went around. I'm interested in how you, as a leader and an organization dealt with that, right? Like, so you have this thing that is in your infrastructure and it's effectively starting to throw off smoke and catch fire. How do you troubleshoot that? You know, what was the situation like and how did you navigate it?

Immad Akhund:
In many ways, we weren't at the heart of that situation because we'd actually left Synapse much earlier. So, you know, the final I just looked it up. The final failure for Synapse, April 2024, and we'd actually moved entirely off them around September 2023, so at least 6 or 7 months before then. So yeah, we were not in the middle of like, oh, where are where are customer funds and things like that. We had moved entirely off and we were confident in kind of the, you know, the ledger for our customers and things like that. So, the rough history goes. We started working with them in 2018 and we launched our platform in 2019. It was with Synapse as our kind of software provider to our partner bank. And then in 2022, we stopped onboarding customers to them. And then 2023 we moved off entirely. And then yeah, 2024 had failed.

So firstly, I'm kind of grateful for Synapse, right? Like there was a when we launched, there wasn't that many ways to work with banks. That synapse had built, you know, the most sophisticated system that did allow us to kind of launch with that full feature set that we really needed and made that possible. And this is true for many other fintechs, I think, towards the end. So that kind of got complicated. They started working with multiple partner banks on their side. We actually kind of stuck with just one partner bank. But I think a lot of the kind of confusion that came from their ledger came from fintechs that they distributed across many partner banks, and keeping all of that kind of clean and seamless is non-trivial. We only ever worked with one of their partner banks evolve, and we would, you know, talk to them directly. We could kind of keep an idea of the ledger between us and Synapse and Evolve. And it was just like, you know, one system rather than lots of partner banks were like money going between them in the background.

And yeah, so we moved completely off them in, you know, before their failure. And, you know, during that process, we spent a lot of time going through every single transaction that we'd ever done with Evolving Synapse and making sure that was all reconciled. And, you know, how reconciliation works is the fintech has an idea of how much money is in each account. And then, yeah, the partner bank also has an idea of like, what's the total level of money in the program? And you kind of want to make those numbers match, which sounds simple, but there's so many edge cases when it comes to every payment type, right? It's like just to give you an example, it's a debit card. And you go in a hotel and they put like a $2,000 hold on your account, but then they release that hold after two weeks. Like, all of those things come in as like separate transactions. And you have to, like, make sure that you release the right amount of money in each person's funds at the right time, and that all these systems kind of match up with each other.

Lex Sokolin:
An inevitable point of view that I've come to is around the benefits of large scale blockchain ledgers as a solution to reconciliation and finality in financial holdings. What's your view on the recent rise in stablecoins as an alternative to embedded finance? I feel like a lot of the excitement that people had about embedding banking product into consumer interfaces are now getting repeated, but with kind of a stablecoin first pitch. And one of the benefits is that you get to avoid synapse types of outcomes. Is that area something that you're thinking about? You know, is it demand led by your customers? Is it infrastructure led? How do you consider it?

Immad Akhund:
Yeah, I mean, we do think about it. But, you know, for our customers that are mostly kind of dealing with USD systems, it's easier for them to kind of just have US dollars and like send ACH and wires and, and things like that. So, we do have some customers that are more global. And for them it matters a little bit more to access kind of the US dollar system and maybe be able to send money easier than Swift and things like that.

So, a lot of these kinds of global stablecoin banks have been really focused on people that cannot get you as bank accounts at all. You know, that's an important service but different. I would say I don't think this like magically solves the idea of recon and lecturing and things like that. In a world where if you really own your keys and own the wallet and you have like full understanding of where the money is at, yes. But, you know, a lot of the time when you're dealing with these providers, they don't give you access to the key in the wallet. And, you know, they are running their own kind of abstraction layer on top of the blockchain, which is fine. I think that's often you have to do that to provide like a seamless service and make it cheaper. If you think about Coinbase, right. Like most of the time they're grouping together customer funds across lots of customers in the same wallet and things like that. And they're keeping their own ledger. So, I don't think this is like a magic solution in this scenario where, where it is really just a, you know, just an interface layer to the blockchain and you have your own wallet and you have your own keys.

Yes. It is like solving this core ledgering and a wallet problem. But in most of the cases, if you think of most of the fintechs that I think of that are in this space that, you know, they are doing some ledgering and recon work on their side, doesn't mean they're not doing it correctly. I'm just saying it's not like a magic solution to it.

Lex Sokolin:
Yeah. Of course. Let's go back to your vision for what the Bank of the future looks like in terms of scale and product suite. Your starting point, right, is with businesses with this very regulated and difficult product of deposits. Tell us about the scale of the company today. I mean, you've recently raised a very large round. And based on that, how you're thinking about the places where the company is going to go.

Immad Akhund:
Sure. So we're about 1100 people today. We have about 300,000 customers, you know, growing 50% per year. Number we announced in August was we were making $650 million per year, and we've been profitable for three and a half years.

That's the rough scale. One thing that, you know, when I was thinking about what company to do next. This was my fourth company. So, I really wanted to do something that I felt I could do for 20 plus years. And, you know, as I was saying earlier, I'm a very startup person, so I get bored of things if it's not like kind of continuous product innovation. And one of the things that got me really excited about Mercury is banking is both so simple, right? Every single business needs a bank. Every single customer needs a bank. It's, you know, it's got this kind of cool money in payments, but it's also universal. It's where your money is. And then there's so much kind of financial software that is provided to businesses and consumers that has been built on top of the bank account. Right. You know, on the business side, whether it's like bill pay or invoicing or credit card or spend management or, or payroll. Like it just keeps going on and on.

And if you, if you kind of imagine a world that there's this like true software company that's at scale, that's built on top of banking. It's just hard to believe, you know, if there's a Google service that is built. What is the Gmail, YouTube, Google Calendar, Google Maps equivalent of banking? It's kind of how I thought about it when I was starting Mercury, and there's just so much to build. So that's what really got me excited about Mercury, which wasn't just like, hey, let's build this kind of cool new bank that replaces, you know, GMC or something like that. I really wanted to build a new type of service where, yes, banking is the core feature, and that's how people will think about it. They'll be like, oh, I'm signing up for getting a bank account, but there's just so much more we can build on top of that. I feel like we're still just getting started. Like, you know, we've been doing this for eight years, and it's only just now that we launched some of these features like invoicing and bill pay, but there's just so much more to build here.

We only just in December launched Mercury Personal, which, you know, gives the same kind of features around kind of collaboration and copayments and wires and. Holding your money in automation and APIs, all of that kind of stuff. We built on business for personal. So that went live in December. So yeah, I get really excited about all the things, things we're building. And I really think what people, you know, in ten years from now, if we do a good job, what people will think is, is banking will be very different. It won't be like, oh, this is kind of this stodgy institution where I get my auto loan and my mortgage and kind of a bank account, and the software barely works, which is, I think, how people think about banking today. But I think we can redefine that as like, oh, this is like a core part of running my business where I do all my finances. And I really it helps me understand my business and make good decisions. And yeah, to me there's like infinite potential here, which is kind of the fun part.

Lex Sokolin:
You mentioned being profitable now for the last three years. What is the sort of a naive question, but what is the business model to get to that 650 million of revenue is its interchange net interest, and what licenses do you need to be able to do these things?

Immad Akhund:
Yeah. One fun thing about fintech is you can provide a lot of free services and still make money. So, we make money on float where, you know, when we have money in checking accounts, you know, those accounts don't give you to customers our treasury accounts. And from the personal side, our high yield savings accounts give interest rates and yield. So, on the checking account, we make money on the float, give both credit cards and debit card, and we make money on the interchange. So, every time someone swipes, we make some money. We have FX fees. When you're doing currency transfers, we have AUM fees on the Treasury side, and we also have a subscription fee for some of the advanced features in invoicing and a few other software pieces.

And yeah, I really like being profitable. It's super annoying to go begging to VCs every year. So, I love just having that kind of independence as a company, but I think it's also good for our customers to know that we're set up for the long term. And we've been profitable for three years and we have almost twice as much money as we've ever raised from investors in the bank.

Lex Sokolin:
That's beautiful. There is a big difference between being in the hole and being free. Yeah. So, congratulations. When did that diversification of revenue start? Was there like one that started to snowball and you're like, okay, now I can reinvest and build these other paths? Or did you try to do it as a portfolio from the start?

Immad Akhund:
So, we had both the float revenue and interchange on the initially just a debit card from the start. And then in the last few years, we've kind of spun up more of these revenue streams and diversified more. But, you know, we tried to do it in ways that are very natural to customers, you know, things that they already pay for other places in general, we don't want to be.

I think one of the problems that I would have said most SMB banking has, there's tons of fees. You don't know when you're going to get charged a fee and it's going to you're going to be surprised about it. So, we wanted to make that much more kind of seamless and easy to understand. So, we have fewer fees and we try to be more seamless about it. But we you know, there's still things people are used to paying for. And there's some things that people used to paying for that we don't charge for. So, for example, why is it free? But yeah, other places we charge and make it seamless, like something like FX.

Lex Sokolin:
So, when Mercury is a $10 billion revenue company, right, like and you've got everything you've talked about, you're a huge competitor in personal accounts. Now you're maybe an underwriting, what do you think that full business looks like. And maybe also what does the rest of the field look like. Do you think the traditional banks sort of catch up, or do you think the industry will segment between high quality neo banks like Take Us Forward into the future a bit?

Immad Akhund:
The only place where we have a large market share is in startups of 40%, or a little. More than 35% of early-stage startups use Mercury. You know, outside that we're still super early. Right. Like the broader small business banking is huge. It's like 30% of all banking revenue, which is a $2 trillion kind of banking space. Obviously, consumer we only just launched. So even if you look at just the basics of what we do, they could easily scale to being kind of multi $10 billion plus revenue. And then you know we're very early to lending. You know we just applied for a bank charter in December. Once we do all the things and that's approved there's a big potential for us to get into lending. And that's obviously actually one of the main ways they're kind of traditional banks make money. And then on this kind of software and subscription side, you know, there's a ton of financial software out there that makes a ton of money. That's actually like a whole separate tab for us, which is different and distinct from banking. And again, we're early. Intuit is $130 billion company that just makes money from kind of software.

And yeah, there's lots of other examples there. So, I guess like overall I think it will be doing the same things we're doing, which have a lot of ways to scale and then like some of these kinds of new revenue streams around lending and subscription that'll scale up to get to 10 billion. In terms of what does the space look like? I think it's really hard to predict the future. You know, how I've thought about it is, you know, if you think about the last kind of 15 or 20 years of banking, there's been a ton of consolidation where I think there was like 12,000 banks or something pre 2008 crisis, and now there's 4500 banks in the other 2000 credit unions. Don't quote me on those numbers. I think that's like roughly right. So, there's been a ton of consolidation. I think that will continue. And the bigger banks will get bigger. But at the same time, there'll be kind of this wave of fintechs and neo banks that will capture more and more of the of the market share, especially on the kind of what does the user see and what you know, what is the main kind of distribution and interaction with the users.

So I think the, you know, if you look at like, what are the top ten kind of places people get financial services, they'll probably still be like the big, big banks that will be part of that. But then there'll be people like kind of Mercury and other players in the neobanking and fintech space. They'll be like pretty dominant players there. And banking is just so huge and so diverse. I don't think it's like a full winner takes all. Obviously in some segments of it, there will be kind of fierce competition. There'll be 1 or 2 kinds of big winners. But you know, overall, I think most of these things will exist in parallel. I think the skew will be moved towards more fintechs that are like scaled up. I think the biggest fintechs today are I guess Visa and Mastercard are the biggest. But if you ignore those too, since they're pretty old, you know, the biggest are still likes, you know, sub 150 billion, whereas the biggest banks go way bigger than that.

Lex Sokolin:
The last place I want to take us to is you mentioned the license and the regulatory environment in the US, of course, has gotten much more favorable towards startups and fintechs and all of this over the last few years that's come with its own associated cost. But at the same time, it's really exciting to see new types of licenses and sort of access to the window. And so being granted to young companies and, and young companies, you know, there's financial innovation, there's technology innovation. So, when you have banks with technology innovation in them, I mean, it makes all the sense in the world to make them into national champions. If you were to have a wish list for how regulators would look at our industry, how would you like them to think about the sector? I think there's just a lot of change and updating of prior assumptions. What do you think are the right ways to move forward?

Immad Akhund:
You know, in general, I would say fintech and most innovators like we want to operate within a regulatory framework. I think, you know, obviously when we're talking to customers or we're talking to investors, being able to say, hey, we're licensed and this is how it works, is really important to us. And one thing that you know. Especially in crypto was tricky, is there wasn't really a good framework to operate within which, you know, I think the genius act and the other stuff being worked on helps with a lot. It's nice to see the OCC especially, but, you know, these other banking regulators are open to work with fintechs. And I still think it's actually hard if you're a tiny fintech like, yeah, if you have five-person team and you've raised $3 million, I think being directly regulated is still probably out of reach. But at least when you've scaled up and you had Mercury's size or, you know, some of these other people applying for the national charter, it makes sense. And that's like a good, you know, if you think about what might be the ideal framework for the US, maybe there's kind of fintechs that can innovate in the small side.

Working with partner banks and some of them reaching of scale, obviously when they're innovating, even on the small side, they have to obey the laws. But when enough of them reach a scale, there's kind of this path to being chartered. So, I think that's like very healthy. Obviously, it would be nice if the kind of cost of being regulated and working was, was cheaper. I actually think AI will potentially help with this, where, you know, a lot of lower things that are expensive are things that require humans to create reports and review reports and like all these things that are very AI able and actually hopeful that the cost of doing compliance, actually you can be way more compliant as well, right? Like, you don't have to decide which customers to review. You could review them all. Just have an AI do it kind of thing. So I think there's a lot of potential that AI can bring to that actually improves compliance and regulation as well. So yeah, that's kind of how I see this kind of future playing out. I'm hopeful that we can continue on this and make it easier to get chartered and work in this way.

Lex Sokolin:
I think the dream is all the compliance documents are AI generated. And then on the other side, all of those AI generated documents, AI read and people can have their lifetime.

Immad Akhund:
Yeah. And then like actually the I still think there's a world for humans there, right. Like you're still going to need someone to review the AI set policies and things like that, that just the human does even more leverage work. And I think that's like a much better system for everyone.

Lex Sokolin:
Absolutely. Immad, thank you so much for joining us today. If our audience wants to learn more about you or about Mercury, where should they go?

Immad Akhund:
Yeah. Of course. Check out Mercury.com. It's all online and you can sign up easily. I'm on X just Immad, and that's probably the main place. I'm active.

Lex Sokolin:
Fantastic. Thank you for joining today.

Immad Akhund:
Yeah, thanks for having me.

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Hi Fintech Futurists —

We are excited to share with you an analysis today looking at the evolution of Open Banking, from experiment to a trusted financial infrastructure.

We partnered with Mastercard on this piece to share some of their research on how businesses and consumers navigate data sharing, security, and the expanding industry adoption of open finance. We tie it to the broader arc of technological development and the importance of trust in financial enablement.

Read on for our general takeaways from the report, and find out more about Mastercard’s Open Finance initiative here and below:

Learn More

Thanks as always for your time and attention,

Lex

Open banking has become an expectation

In the past, you would need to go to a bank branch or website to have access to financial services. And if you wanted to move assets and information from one bank to another, it was time to fill out a form.

Over the last decade, technology and regulation have pushed financial services companies to open up. Users can now not only aggregate their data across financial providers into dashboards, but cause money to move and take action programmatically. We call this robot money.

This has been a boon for both consumers and merchants, but a pretty tricky thing for banks — which are focused on security and compliance — to navigate. In some jurisdictions, this change has been catalyzed through government intervention on behalf of bank customers.

Across the UK, Europe, Australia, and increasingly the United States, the regulatory frameworks for consumer-permissioned data access have been built, rebuilt, and argued over in courts. The UK now has over 11 million active open banking users as of early 2025, growing at roughly 26% year-over-year. In Europe, reportedly more than 94% of licensed banks are PSD2-compliant. In Australia, the Consumer Data Right framework has expanded beyond banking into energy and telecommunications.

The United States, characteristically, has taken a different path.

Rather than a top-down mandate, the US has been market-led. Aggregators like Plaid and Yodlee built bilateral data-sharing agreements with banks to move forward from the data scraping era. The CFPB finalized a Personal Financial Data Rights rule under Section 1033 of Dodd-Frank in October 2024, aiming to bring API-standardized, consumer-authorized data sharing into federal law.

But the rule became politically contested.

The current administration vacated the original rule in 2025, then reversed course and reopened rulemaking in August with an Advanced Notice of Proposed Rulemaking that drew nearly 14,000 public comments. JPMorgan Chase and Plaid subsequently struck a new data-sharing deal that includes a pricing structure. That is the first clear signal that US open banking may develop around paid API access rather than the free-access model Europe codified. The compliance timeline for the largest banks, originally set for April 2026, has almost certainly slipped to 2027 or beyond.

This regulatory divergence sets the context, but it also obscures a simpler reality.

In 2026, consumers and businesses are already sharing financial data overwhelmingly.

The real question is who they trust to handle permissions, identity verification, security, and execution when that data moves between institutions.

Mint, scraping, and tolerated fragility

When Mint.com launched in 2006, there was no regulatory framework for what it did. There were no APIs. There was no concept of “open banking.” But there was screen scraping.

A third-party app logged into your bank’s website on your behalf, using your actual credentials, and read the data off the page. By any security standard, it was pretty insane. You gave a startup your bank password. The startup’s servers pretended to be you, every day, harvesting transaction data to build a dashboard of your financial life.

And yet 20 million people signed up. For the first time, a fragmented financial picture — checking account here, credit card there, brokerage somewhere else — could be consolidated into a single view. Budgeting, net worth tracking, and spending analysis became automated. The utility was so obvious that consumers tolerated a trust model built entirely on shared credentials and the good faith of Web2 entrepreneurs.

Mint’s story ended in early 2024 when Intuit shut it down, years after the app had stagnated and screen scraping became untenable. We wrote the obituary ourselves, back in November 2023. Banks were tightening security, implementing multi-factor authentication, and deploying bot detection. The connections broke constantly. And from a business model perspective, advertising against free PFM data never worked at scale.

Mint’s legacy matters, though. It proved that consumers will share financial data if you give them something valuable in return. It proved that the middleware between you and your bank — the aggregation software, the routing, the reconciliation — is where the product lives. And it proved, painfully, that fragility is not the same thing as trusting a financial institution.

PSD2 and open banking as regulated trust

Europe decided to solve this trust problem through regulation.

The Second Payment Services Directive, PSD2, came into effect in 2018 and required banks across the European Union to provide third-party access to customer account data through secure APIs. The shift from screen scraping to APIs changed the architecture entirely. Instead of handing over your bank password, you authorized a regulated third party to access specific data through a standardized interface, with explicit consent, auditable access logs, and the ability to revoke access at any time.

This restructured what could be built. Under PSD2, consumers gained two new categories of access:

• Account Information Services (AIS), which let authorized third parties read account data, and

• Payment Initiation Services (PIS), which let them trigger payments directly from a bank account.

New entity types — the AISP and the PISP — had to be licensed and supervised. Banks were required to build APIs that met performance and uptime standards.

APIs replaced credential sharing with explicit consent, auditable access, and clearer boundaries. Instead of a read-only dashboard, open banking now enabled account verification for onboarding, income and affordability checks for lending, payment initiation that bypassed card rails entirely, and real-time balance checks for cash management.

Consumers could decide exactly which data to share, with whom, and for how long. And most importantly, they could revoke access whenever they wanted.

PSD2 was not perfect. Bank implementation and API quality varied across jurisdictions. But it established a principle: open banking is about consumers choosing who sees their financial data and on what terms.

What consumers and businesses actually trust

This brings us to the present.

Mastercard commissioned the Harris Poll to do research across the US, UK, Nordics, and Australia to find a clear read on what people using open banking actually care about. All of the following statistics come from the Rise of Open Banking: The Trust Imperative report.

The numbers are clear

• 85% of B2B respondents are already using open banking today, and

• 93% expect industry momentum to increase over the next five years.

• On the consumer side, 76% connect financial accounts directly to tools for conducting financial tasks.

This is mainstream adoption, even if most people don’t know they are already using Open Banking tools in their apps.

The top B2B use cases right now are online checkout payments, paying credit card balances, and transferring money between accounts. On the consumer side: sending money to others, banking tasks, and paying bills. But the appetite extends well beyond simple transactions. Among businesses, 87% want to use open banking for recurring payments and 86% for invoices. Among consumers, 70% want to automate financial tasks and 59% want it for subscription and recurring payments.

We are past the “view your balance” era. This is financial orchestration across institutions.

The convenience numbers tell a parallel story.

• 93% of B2B respondents agree that convenient financial experiences for customers are critical to their organization’s success.

• 71% of consumers will walk away from an online transaction that redirects them to another platform to complete a payment. Friction kills revenue.

• Businesses using open banking report results: 64% say profitability improved, 63% say it helped revenue and strategic decision-making.

But this isn’t free.

Security is the number one priority for both businesses and consumers, and the number one barrier to wider adoption.

• 93% of B2B respondents say financial data security is top of mind.

• Among consumers, 94% say it matters that their bank keeps their financial information secure,

• 86% will walk away from an online transaction that is not verified by a brand they trust.

The biggest thing preventing wider consumer adoption? Thirty-three percent cited concerns about being hacked.

Consumers trust banks (66%) and credit card companies (56%) most with their financial data. The entities they most trust to protect account connections are credit card companies (50%). For businesses choosing an open banking partner, the top motivator is trust in the company, followed by security capabilities. The top barrier is concern about protecting customer data.

Embedded finance and the Middleware problem

These trust findings are not abstract. We have recent, painful evidence of what happens when the intermediary fails.

Between 2019 and 2024, venture capital investors funded several embedded finance platforms that connected to sponsor banks and core banking platforms, created middleware ledgers, and allowed third-party applications to build experiences on top. This feels a lot like Open Banking, except it puts another reconciliation ledger between the money and the user and into the hands of small start-up companies with limited runways.

April 2024 was the defining cautionary tale of the embedded finance era, bringing the collapse of a key industry player. When it filed for Chapter 11 bankruptcy, over 100,000 people lost access to more than $200 million in funds. A shortfall of between $65 million and $95 million emerged between what banks held and what customers were owed.

This shows what happens to financial infrastructure when the middleware is operated by an undercapitalized, loosely-regulated startup. Since then, the FDIC proposed what became known as the “Synapse rule”, requiring banks to verify the accuracy of their custodial accounts on a daily basis.

When data flows, payments initiate, and identities are verified across institutional boundaries, the entity coordinating that activity must be regulated, experienced, well-capitalized, and accountable. That is what the survey data shows consumers and businesses demand. And there are material problems when they don’t get it.

From API permission to shared financial state

Where does open banking go from here?

• Phase I was aggregation: give me a read-only view of my data.

• Phase II was permissioned access: let me authorize specific uses through regulated APIs.

• Phase III, which is emerging now, is about shared financial state — the ability for multiple institutions, services, and increasingly autonomous agents to operate on a common, real-time understanding of a consumer’s or business’s financial position.

Think about what AI agents need to work in financial services.

An AI acting on your behalf to optimize spending, negotiate bills, rebalance portfolios, or initiate payments needs concurrent access to data across multiple institutions, in real time, with permissions that are granular, revocable, and secure.

Screen scraping cannot support this.

Even basic API access, as currently implemented, may not be enough. What you need is something closer to a shared ledger of permissions, identity, and financial state that multiple parties can reference without each maintaining their own incomplete copy.

This is where the blockchain thesis, for all its excesses, remains relevant. A shared, auditable state that multiple parties can trust without trusting each other is what the next generation of open finance requires. We have also heard this positioned as a network of networks, or the Internet of Money. Payment rails must work across institutional boundaries without bespoke integrations per participant.

Who do you trust to run it

The scale of Mastercard’s position here is worth understanding.

Their Open Finance platform connects to 95% of US deposit accounts, all Australian banks, and nearly 3,000 European banks via API. The network handles over 15 billion API calls globally per year. That is infrastructure at enormous scale, run by a company with 60+ years of operating payment systems, where security failures are not acceptable.

On the product side, the platform addresses the specific problems the survey data highlights. Account Owner Verification (AOV) confirms that identity data provided by banks — name, address, email, phone — matches the business’s records when a customer links a third-party bank account. One financial institution reduced fraud losses by $100,000 within three months of implementing AOV. The enhanced version, AOV+, adds device intelligence and IP signals to triangulate identity across multiple factors. Mastercard’s Identity Network comprises more than 8 billion elements of compliant, global identity data.

For businesses trying to turn open finance data into customer engagement, Mastercard’s Engagement Intelligence Suite enriches raw transaction data into categorized, usable insight. Their categorization engine classifies 97% of transactions with 90% accuracy. Consumer Foresight, built on top of that, applies predictive modeling to benchmarked spending data from the Mastercard network to forecast behavior and inform campaigns.

For small business lending, Mastercard Credit Intelligence, integrated into the Open Finance platform, combines network-level transaction insights with permissioned banking and cash flow data to give lenders a fuller picture of a borrower’s financial health. Saxo Bank saw funding conversion rates rise 26% after implementing Mastercard Open Banking for account verification and onboarding.

Startups can build APIs. Building the identity graph, the compliance infrastructure, the bank relationships covering 95% of US deposits, and the institutional credibility that makes a consumer comfortable authorizing data access is a material competitive moat.

Open banking in 2026 is about who consumers trust to manage permission, identity, security, and execution when their financial data moves between institutions. The survey says they want institutions with track records and accountability. Mastercard, with the specific products and network scale to back it, is built for that.

In Partnership

Learn more below about how Mastercard shapes the future of digital finance by powering secure payments and data connections between people and businesses
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Explore Open Banking Offerings

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Today, we highlight the following:

1. DIGITAL ASSETS: TradFi Wraps Crypto. DeFi Wraps TradFi.

2. ANALYSIS: Google, Shopify, and the Infrastructure Layer for AI Shopping

3. CURATED UPDATES: Financial Institutions and Adoption; DeFi and Digital Assets; Blockchain Protocols; NFTs, DAOs and the Metaverse

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DIGITAL ASSETS: TradFi Wraps Crypto. DeFi Wraps TradFi.

A year ago, Morgan Stanley's 15,000 financial advisors couldn't recommend a single Bitcoin product to clients.

Now the firm has the market's cheapest spot Bitcoin ETF and Goldman is filing for one. Schwab is building a crypto trading desk. Legal & General is bringing $68 billion in money market funds on-chain. And the S&P 500 trades 24/7 on a decentralized exchange.

The question we keep coming back to: who ends up owning this infrastructure — the banks who own the clients, or the chains that can settle anything at 3am on a Sunday?

The TradFi wrapper

Morgan Stanley's spot Bitcoin ETF, MSBT, pulled in more than $100 million in its first week. This has been the firm's most successful ETF launch to date, priced at 0.14% annually, and the first spot Bitcoin ETF issued by a major US bank.

Even so, it is far from the biggest. BlackRock's IBIT crossed $1 billion in inflows within its first week of trading in January 2024 and has since accumulated over $65 billion in total net inflows. MSBT is on track for over $7 billion this year.

Morgan Stanley’s wealth management network oversees trillions of dollars in client assets. It’s a channel for investors who would never open a Coinbase account, but will buy an ETF through their existing advisor.

Wall Street firms travel in packs, and we saw Goldman Sachs file for its own Bitcoin ETF within days of MSBT’s launch.

Charles Schwab also confirmed it remains on track to launch spot Bitcoin and Ethereum trading in the first half of 2026, via its Charles Schwab Premier Bank unit. Schwab manages $12 trillion in client assets across 39MM active brokerage accounts.

Importantly, it famously drove stock trading commissions to zero in 2019.

Will it do the same to crypto? This is particularly relevant as the fee gap with native crypto exchanges is significant

• Coinbase Advanced charges makers up to 0.40% and takers up to 0.60%

• Gemini charges a flat 1.49% on basic orders

If Schwab applies its equities pricing to crypto, the compression on native exchange margins could be significant, particularly when considering the cost of acquiring these assets directly on-chain.

The ETF, the custody account, the brokerage interface, these are all wrappers that distribute product. They are the store that sell the sandwiches, the sandwiches being financial products. The incumbents are competing to own the client relationship, and the underlying asset is incidental to that contest.

The Perpetual Trade

DeFi is running the same play in reverse, pulling traditional assets onto the chain rather than wrapping crypto into brokerage accounts.

On March 18, S&P Dow Jones Indices licensed the S&P 500 to Trade[XYZ] to launch the first officially licensed perpetual derivative contract based on the index, trading on Hyperliquid. Earlier attempts relied on synthetic pricing. This contract runs on institutional-quality index data from the source, and S&P DJI powers trillions in ETF and index fund assets globally. That data provenance is what institutional confidence follows.

HIP-3 is Hyperliquid’s framework for permissionless perpetual futures on tokenized equities and commodities, where builders spin up new markets by staking HYPE.

Open interest hit a record $2.38 billion last week, up roughly 580% year-to-date from around $280 million at the start of the year — crossing $1 billion within the first month. TradeXYZ accounts for over 90% of that. We’d watch the $5 billion mark. At that level, HIP-3 starts generating enough volume and liquidity to attract the market makers who currently operate on CME and CBOE.

A note: this product is currently restricted to eligible non-US investors, so it won’t compete with Morgan Stanley or Schwab for the time being.

As of the week of the S&P announcement, only seven of the top 30 markets on Trade[XYZ] by open interest were crypto pairs. The rest were tokenized or synthetic versions of commodities and equity indexes, like oil, gold, silver, and now the S&P 500. The crude oil contract alone saw $1.7 billion in peak daily volume and $300 million in open interest, driven in large part by CME traders who found they could trade oil on weekends during the Iran conflict when conventional markets were closed.

When a major macro event breaks on a Saturday, traditional exchanges are shut. On-chain perpetuals aren’t. All of a sudden, all of our equities and commodities are digitally native and globally available.

Where This Converges

Morgan Stanley, Goldman, and Schwab are wrapping crypto into existing brokerage infrastructure. Hyperliquid is absorbing equities and commodities into on-chain settlement — oil, gold, the S&P 500, all trading 24/7 without intermediaries. Only three of the top ten markets on Hyperliquid by volume are crypto pairs. Traditional finance volumes are arriving on-chain and will increasingly make up the bulk of blockchain activity.

Morgan Stanley still has its clients. S&P DJI still licenses its index. But the S&P 500 now trades on a decentralised blockchain at 3am on a Sunday.

How long before someone asks why the NYSE doesn’t?

👑 Related Coverage 👑

Analysis: From Boom to Bust in the $30B DAT Trade

We look back at 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.

Read Analysis

Curated Updates

Here are the rest of the updates hitting our radar.

Financial Institutions and Adoption

American Express Debuts Agentic Commerce Experiences (ACE)™ Developer Kit and Announces Industry-First Protection for Registered Agent Purchases - American Express

Visa Launches Validator Node on Tempo Blockchain - Visa

EToro to acquire crypto wallet Zengo in $70 million deal - CoinDesk

UK asset manager puts $68 billion of funds on-chain via Calastone token network - CoinDesk

Goldman Sachs files for bitcoin income ETF in crypto push - CoinDesk

Payward to acquire Bitnomial, creating a fully CFTC-licensed derivatives platform - Kraken

DeFi and Digital Assets

HIP-3 open interest tops $2 billion as demand for 24/7 tokenized equity exposure surges - The Block

Coinbase adds ‘DeFi mullet’ to UK offering in drive to become an everything exchange - DLNews

Blockchain Protocols

Drift degen sues Circle, alleging stablecoin giant ‘did nothing’ during $295m hack - DLNews

Aave launches v4 on Ethereum as founder eyes ‘opportunities in the real world’ - DLNews

NFTs, DAOs and the Metaverse

Kelp DAO $293 Million Hack Largest DeFi Theft of 2026 - PYMNTS

Polymarket seeks $400 million raise at $15 billion valuation: report - The Block

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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

This week we dig into the growing role of crypto as a neutral settlement layer for governments and everyday citizens amidst rising geopolitical tensions. The most recent example is Iran demanding crypto as toll fees for tankers crossing the Strait of Hormuz. We link this to the rise of the ruble-backed A7A5 stablecoin in Russia as well as the crypto-denominated aid donations raised by Ukraine and what this could mean for the global dollar system.

Today’s agenda below.

1. FINTECH: Why Iran demands crypto for ships crossing the Strait of Hormuz

2. ANALYSIS: Why Agentic Finance is not the Metaverse all over again (link here)

3. CURATED UPDATES: Paytech, Neobanks, Lending, Digital Investing

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Digital Investment & Banking Short Takes

Why Iran demands crypto for ships crossing the Strait of Hormuz

In 2025, 20 million barrels of oil passed through the Strait of Hormuz every day, representing about 20% of global petroleum consumption. Following US and Israeli attacks on Iran on February 28th, Iran’s Revolutionary Guard Corps shut the Strait to Western-allied shipping, causing traffic to collapse by over 90% within days.

The result is the current global oil crisis. Brent crude surged from $71 to above $112 per barrel in under a month, and around 175 million barrels of crude and refined products remain loaded on tankers stuck in the Gulf. The below diagram from the FT neatly shows the Strait’s strategic significance and the scale of the impact.

As part of last week’s fragile ceasefire agreement, Iran has agreed to let some westbound tankers through in return for a $1-per-barrel toll in bitcoin.

A lot of newsflow has centred around the toll itself, but this is not actually that novel.

Paying for passage dates back to the Ottoman levies on ships transiting the Dardanelles and Denmark’s “sound dues” on the Øresund, which ran from 1429 until the US pushed to abolish them in 1857. Today, the Suez and Panama canals continue to charge billions annually under agreed treaty regimes. But demanding crypto assets for transit is something entirely new.

The motivation lies in the infrastructure powering international trade.

Every year $150T (circa 40%) of global cross-border payments settle via SWIFT, the interbank payments network overseen by the G10 countries and the key plumbing that moves US dollar trade. Iran has already been disconnected from the network twice, first in 2012 and again after the US withdrew from the JCPOA in 2018. It is one of a handful of cases where US Treasury sanctions cover an entire country rather than specific entities. This means that Iran is, in effect, completely closed off to the dollar system.

Tehran has spent over a decade building financial workarounds to keep its economy afloat. These range from (1) shadow banks and front companies in the UAE and Hong Kong, to (2) hawala networks, and (3) an integration with Russia’s Mir payment system.

China has become the most significant of these channels, purchasing over 80% of Iran’s seaborne oil exports since the war began in February. These payments are reportedly settled in yuan via intermediaries and non-dollar banks with funds staying in controlled accounts. Settlements on China’s cross-border payments rails (CIPS) have grown sharply since March. But even these channels have limits. They often tie Iran to a single counterparty or keep funds locked in a bilateral loop.

Crypto offers an alternative and neutral settlement layer. Underlying blockchain rails can transfer value instantly around the world, without intermediaries, and with users preserving unique control over their assets. Its use in Iran has been growing.

According to Chainalysis, the total value of crypto received by Iranian-linked addresses reached $7.8B in 2025, with the IRGC accounting for roughly half of all activity by Q4.

Historically, the aggregate use of crypto in Iran has spiked in line with geopolitical conflicts.

The other half are everyday Iranian citizens who have fled to crypto to shelter from local currency fluctuations (the Rial is down 90% since 2018) and move value off local rails. This is important. We want to emphasise that the value of financial sovereignty here extends far beyond the evasion of sanctions. It offers capital preservation and control to everyday people on either side of a war.

The IRGC has historically used stablecoins like USDT for their oil sales, weapons procurement, and proxy financing because they preserve USD-denominated value and have deep liquidity. The reason for demanding bitcoin now is likely a response to issuers like Tether increasingly freezing wallets linked to sanctioned entities.

Side note: stablecoin issuers have a legal obligation to freeze OFAC sanctioned addresses, but have sometimes been slow. Just last week, Circle was accused of $420M+ in alleged compliance failures for the untimely freezing of addresses used for illicit funds.

A similar dynamic has played out in Russia following its invasion of Ukraine in 2022. Moscow’s banks were disconnected from SWIFT and major card networks like Visa and Mastercard suspended operations. Russian businesses needing to settle cross-border invoices and citizens looking to move value offshore turned to A7A5, a ruble-backed stablecoin.

A7A5 is issued via a Kyrgyz entity called Old Vector and primarily operates on Tron and Ethereum. The ruble reserves backing it sit in Promsvyazbank (PSB), a sanctioned Russian state-owned bank. The typical flow for users looks like this:

1. A Russian business converts local rubles into A7A5 tokens via Grinex, a Kyrgyz exchange.

2. They then swap those tokens for globally liquid crypto assets like USDT or BTC on a DEX like Uniswap.

3. The entity now has dollar-denominated liquidity without ever touching the dollar banking system directly.

The outstanding value of A7A5 sits at roughly $520M, making it the largest non-USD denominated stablecoin and a top-20 stablecoin overall. Last Summer, the stablecoin was used to transfer over $1B-equivalent in value a day.

The US recently tried to shut it down. Grinex was sanctioned in August 2025, but A7A5 administrators destroyed and reminted over 80% of circulating tokens to break the on-chain link to sanctioned wallets and resumed operations.

On the other side of the same conflict, Ukraine has become a top-10 country globally for crypto adoption. When the National Bank of Ukraine imposed strict capital controls to prevent capital flight after the invasion, citizens turned to digital assets to preserve savings and move value abroad.

The government also received over $80 million in crypto donations in the weeks following the invasion, fast-tracked digital asset legislation, and now holds an estimated $5.6 billion in bitcoin. This ranks it behind only the US, China, and the UK among sovereign holders. In the first 12 months of the war, pro-Ukrainian fundraising totalled over $212 million across government, blockchain projects, military charities, and humanitarian causes.

The common thread behind these three stories is a demonstrable demand for a neutral, global, instant settlement layer. The risk of weaponising the dollar system through SWIFT, capital controls on local currencies, or enforcing extra-territorial compliance is that it gives users of your product an incentive to find alternatives.

The dollar’s share of global foreign exchange reserves has declined from over 70% at the turn of the century to below 58% today, its lowest level in decades.

At the same time, the US has been at the forefront of stablecoin regulation with USD-pegged instruments making up 99.8% of the entire $320B asset class. This strategically significant as commerce in crypto assets to continues to grow. Advances in regulation, infrastructure, and new demand unlocked by AI agents all point to a shift towards borderless financial infrastructure.

All governments should be paying attention.

👑 Related Coverage 👑

Long Take: Why Agentic Finance is not the Metaverse all over again

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.

Read Long Take

Curated Updates

Here are the rest of the updates hitting our radar.

Paytech

• ⭐ White House says stablecoin yield won’t hurt bank deposits - American Banker

• Mastercard acquiring stablecoin startup BVNK for $1.8 billion - CNBC

• FX payments startup OpenFX raises $94 million - Reuters

• Ramp’s New CLI Sparked a Wave of AI-Finance Speculation - Surf AI

Neobanks

• Stablecoin Payments Firm KAST Raises $80 Million in Funding - Bloomberg

• Revolut introduces AI assistant for money management - Finextra

• Coinbase wins conditional approval for national trust bank charter - Finextra

Lending

• Cash App launches ‘pay later’ feature for P2P transfers - Techcrunch

• Revolut acquires full UK banking license after years-long wait - CNBC

• Bolt lays off third of staff - Finextra

Digital Investing

• ⭐ Robinhood, BNY to build Trump accounts app - CNBC

• The $20bn+ exodus from private credit - FT

• Morgan Stanley launches Bitcoin ETF - Finextra

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

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.

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