In this episode, Lex chats with Steven Schwartz [https://www.linkedin.com/in/steven-schwartz-b72827b8/], CEO and co-founder of Whop, the Brooklyn-based marketplace where almost 100,000 people earn money each month selling software, education, communities, physical goods and clipping campaigns through a single API-first stack.

Lex and Steven discuss how Whop went from a license-check tool for a handful of Mac and Windows apps to a global commerce platform settling nearly $500M a month in top-of-funnel income to users, and why Tether’s $200M investment at a $1.6B valuation makes stablecoins the only unifying rail capable of paying earners in Morocco, Nigeria, Vietnam or El Salvador the same way as earners in Brooklyn.

Steven explains why Whop treats customer acquisition — not payments or user management — as the real wedge against Stripe and Shopify, why the platform now has more agent users than human users, and how an API that lets an LLM spin up an LLC, a storefront and a running ad campaign in one prompt reframes what “starting a business” means. They explore the trajectory from a billion earners to a public listing, and whether a creator-economy fintech can meaningfully invite its own users onto the cap table.

Recorded September 2026. In the weeks after this conversation, Whop closed a $200M Series D at a $5.1B valuation — a 3.2× markup on the $1.6B Tether round discussed in the episode — led by Sequoia Capital and Shopify Ventures, with Tether retaining its February stake.

NOTABLE DISCUSSION POINTS:

 1. Whop’s wedge is customer acquisition, not payments. Payments, user management and hosting are table stakes already solved by Stripe, Shopify and Discord. What sellers actually struggle with is their next incremental customer — and Whop’s take rate (3% on direct sales, 30% on Discover-sourced sales) only makes sense when read against that top-of-funnel claim.
 2. Tether’s $200M isn’t a crypto bet, it’s a global payouts bet. Whop services more countries than Shopify, Stripe, Square or Cash App because stablecoins are the only rail that works equivalently in Morocco, Nigeria, Vietnam and the US. Card-spend, DeFi access and local off-ramps all come “for free” once the balance is USDT; a bank-rail version of Whop can’t exist.
 3. Whop has more agent users than human users. On-platform traffic now skews toward LLMs, and Schwartz’s claim that he can tell a Claude instance to spin up a business “in one shot” is the product spec, not a demo. The strategic position is explicit: don’t play the model game, be the stack those models call out to.

TOPICS

Fintech, Stablecoins, Payments, Marketplace, DeFi, Web3, Crypto, Tether, USDT, AgenticAI, AIAgents, LLMs, DigitalAssets, Whop, Hyperliquid, Robinhood, Stripe, Shopify

 

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TIMESTAMPS

 * 1’03: A Playbook for the Post-Cubicle Economy: Why Whop Wants to End the 9-to-5
 * 3’44: A Decade of Side Projects Before Whop: How Two 13-Year-Olds Found Each Other on Facebook
 * 8’31: Everyone Is a Builder: Why the Creator vs. Consumer Divide Doesn’t Hold
 * 12’09: The Damaged Perception of Success: Wha

The $5.1B Marketplace With More AI Agents Than Human Users, with Whop CEO Steven Schwartz

In this episode, Lex chats with Simon Taylor [https://www.linkedin.com/in/sytaylor/], who heads up market development at Tempo, the payments-native blockchain built by Stripe as a high-performance settlement layer for stablecoins and tokenized money. Simon is also the founder of Fintech Brain Food [https://www.fintechbrainfood.com/], one of the sector's most widely read newsletters. They discuss why there is no Fedwire for the internet, and why the next wave of on-chain volume is coming from enterprise money movers like Deel rather than crypto-native builders. Simon explains why tokenized deposits, stablecoins and TradFi settlement will converge on a single chain, why agentic commerce is still stuck in its WAP-phone era, and how the Machine Payments Protocol is being designed as an IETF-grade standard that can settle across Tempo, Stripe or any card network.

NOTABLE DISCUSSION POINTS:

 1. The AI productivity gap is an operating-model gap, not a tooling gap. MIT found nine in ten companies get zero productivity gain from AI, while Ramp’s own data shows the heaviest users generate 2x higher revenue on 40% less capital. What separates them is four ingredients: AI evals as a cultural default, a shared library of skills (Ramp has 350+), non-engineers shipping production code (12% of Ramp’s human-initiated PRs), and treating AI fluency as the primary staff learning curve.
 2. Agentic commerce is in its WAP-phone era. Nobody is actually paying for things with agents at any scale - Walmart’s ChatGPT checkout converts worse than its dotcom, Target has told customers they are 100% liable for anything an agent buys, and Walmart and Amazon block third-party agents outright as fraud. The real volume is in B2B finance teams automating PDF-invoice-to-payment flows through Ramp and Brex, which is where Machine Payments Protocol and x402 will first find product-market fit.
 3. On-chain cash settlement is less than 0.1% done, and Tempo is being positioned as the missing Fedwire for the internet. The pitch is not a new L1 competing on TPS benchmarks - it is a settlement layer designed by payments veterans who understand that payments are all edge cases, with permissionless issuance combined with opt-in receive policies (TIP-20 upgrades on ERC-20, block/allow lists, embedded compliance) that let enterprises finally clear their RFP checklist.

TOPICS

Fintech, Stablecoins, Tokenization, DigitalAssets, Payments, Settlement, RWA, DeFi, TradFi, CapitalMarkets, AgenticCommerce, AgenticPayments, AIAgents, MachinePayments, Ethereum, Canton, Blockchain, Web3, Tempo, Stripe, Anthropic, Ramp, Brex, Deel

 

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TIMESTAMPS

 * 0’57: How do you ground-truth the digital world: Why AI ultimately runs on a digital asset substrate
 * 5’46: The two-speed transformation: Why customer-facing tech is competitive and back-end plumbing is industry-led
 * 9’47: Limit the blast radius: How JPMorgan, HSBC and Citi turned tokenized deposits into a client retention play
 * 14’46: Finance is globalised, not global: Why Stripe built its own chain inste

How Stripe's Tempo is Rebuilding On-Chain Cash Settlement, with Head of Market Development Simon Taylor

In this episode, Lex chats with Edward Woodford — Founder and CEO of Zerohash, a crypto and stablecoin infrastructure platform that lets banks, brokers, and fintechs embed digital-asset trading, payments, and tokenization through a single API. Four years on from their last conversation, Zerohash has settled over $65 billion in volume across 7 million customers, gone global under MiCA and EMI licensing in Europe, and onboarded institutions like Morgan Stanley.

They discuss the pivot from embedded crypto to pure-play B2B infrastructure, and why the product Zerohash actually sells is trust — with licensing treated as a bar, not a goal — in a world where state actors are now the primary threat.

Edward breaks down the three core rails (Trade, Transact at roughly 70% of revenue, and Tokenization), and unpacks the emergence of "on-chain money" as a legally fragmented category — stablecoins under GENIUS, tokenized deposits, tokenized money-market funds, and CBDCs, each a distinct form of dollar created inside twelve months. They explore how velocity of money and just-in-time funding reshape SME payroll, why the new Auth product aims to be the open banking of stablecoins, and where the industry sits on an S-curve Edward insists is still nowhere near maturity. Finally, they take a skeptical pass at the machine economy, landing on agent-to-knowledge payment — not consumer micropayments — as the durable intersection of stablecoins and AI, and on the convergence that will pull traditional and crypto-native payment firms into aggressive consolidation.

We recorded the podcast earlier in the year, and everything that Edward teased in his conversation has come to market. The E-Trade integration is live. The staking infrastructure has launched. The Treasury published the first proposed rules under the Genius Act, so you can see how those predictions came to market. Also, Stripe and Visa answered his M&A predictions with something even bigger: 140 Company Stablecoin Consortium.

NOTABLE DISCUSSION POINTS:

 1. Trust is the product; licensing is just table stakes. Edward’s sharpest framing is that “licensing is a bar, not the goal” - getting licensed actually opens you to new risks to manage at scale. For an FI like Morgan Stanley, whose crypto revenue is trivial next to tens of billions in quarterly profit, the deciding factor isn’t upside but de-risked entry: FIPS/government-grade compliance, an eight-year clean track record, and a threat model that now treats state actors as the primary adversary.
 2. “On-chain money” has fractured into distinct legal categories in under a year. Post-GENIUS and MiCA, stablecoins (backed 100% by short-term government debt) are now legally separate from tokenized bank deposits (e.g. JPMorgan), tokenized money-market funds, and CBDCs - each a different form of dollar. Edward predicts this taxonomy keeps multiplying, and treats the resulting complexity, including cross-chain stablecoin interoperability, as a widening moat rather than a nuisance.
 3. The real AI-stablecoin use case is agent-to-knowledge payment, not micropayments. Edward pushes back on the popular “sub-penny real-time micropayments” narrative - invoking iTunes, where payments got batched rather than charged per song. The durable edge, he argues, is a globally programmable rail where an agent in Mozambique can settle with a content creator in Brazil, with knowledge released on a DvP basis as payment clears. Sub-penny amounts get aggregated into daily or weekly batches.

TOPICS

Stablecoins, EmbeddedFinance, Tokenization, DigitalAssets, Payments, GENIUSAct, MiCA, AgenticPayments, DeFi, RWA, Web3, Fintech, Zerohash, MorganStanley, Gusto, Stripe, Circle, Tether, Plaid, Mastercard

 

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How Zerohash Grew Stablecoin Volume 690% and Won Over Morgan Stanley, with CEO Edward Woodford

In this episode, Lex chats with Srini Krish [https://www.linkedin.com/in/srini-krish-21b0b91/] — Co-Head of Financial Solutions at Fiserv, one of the original fintechs, in business for nearly five decades and sitting at the intersection of commerce and banking.

Lex and Srini discuss how Fiserv acts as the technology backbone for 5,000+ US banks and credit unions that lack the wherewithal to match JPMorgan or Wells Fargo on their own, and how the firm is packaging AI into that distribution layer through Agent OS and partnerships with OpenAI and Anthropic. Srini lays out his four-bucket framework for enterprise AI - better client service, internal productivity, AI embedded in products, and a platform banks can use to build their own agents - and explains why money demands deterministic outcomes rather than probabilistic guesses, keeping a human in the middle as commercial loan underwriting compresses from weeks to hours.

They explore the competitive race against challengers like Mercury and Ramp, the mainframe that has outlived thirty years of obituaries, and where power sits between the AI labs and their distribution channels once inference commoditizes.

NOTABLE DISCUSSION POINTS:

 1. MIPS became tokens. Srini frames the whole AI shift through continuity: engineers once measured effectiveness by MIPS consumed and how often they compiled code; today the metric is token consumption. Same discipline of doing more with minimal resource, thirty years apart.
 2. Money forces determinism. Probabilistic outputs are fine for many tasks but unacceptable for balances - a figure 1% or 5% off is a failure, it has to be right every time. So Fiserv’s Agent OS rollout starts with non-real-time, human-in-the-middle use cases and only graduates toward autonomy and eventually customer-built agents. It’s a crawl-walk-run path, and Fiserv says it’s clearly still crawling.
 3. The moat is distribution, not model access. Fiserv’s 5,000+ banks and credit unions can’t engage OpenAI or Anthropic directly at scale, so Fiserv becomes the platform that packages agentic workflows - turning commercial loan decisions from a multi-week process into hours, with the auditability and observability those institutions could never build alone.

TOPICS

Fintech, Fiserv, EmbeddedFinance, AgenticAI, EnterpriseAI, Banking, Payments, DigitalBanking, CommunityBanks, FinancialInfrastructure, AIAgents, OpenAI, Anthropic, ClaudeCode, JPMorganChase, FirstData, Mercury, Ramp, Plaid

 

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🤝 Partner with Fintech Blueprint through sponsorships: https://bit.ly/3UZllsV

👉 Twitter: https://twitter.com/LexSokolin

 

TIMESTAMPS

1’12: Fintech Before It Was Fashionable: Five Decades at the Intersection of Commerce and Banking

6’13: Access, Move, Trust: What Actually Defines a Fintech Across Three Decades

10’28: A Loan at the Mechanic's Shop: How Embedded Finance Widened the Market and the Money Behind It

13’22: Four Buckets for Enterprise AI: Where Agent OS and the OpenAI Partnership Actually Fit

20’47: Not Savviness but Wherewithal: Why 5,000 Institutions Can't Build JPMorgan's Stack Alone

25’39: Mercury, Ramp, and the Mainframe That Never Died: Why the Incumbents Aren't Going Anywhere

29’51:

Building the AI Distribution Layer for 5000+ Banks, with Fiserv Co-Head of Financial Solutions Srini Krish

In this episode, Lex chats with Jeff Grimes [https://www.linkedin.com/in/jeffgrimes9] — who leads the finance product at Perplexity, the AI company that has evolved from an "answer engine" into an "agent platform" built around Perplexity Computer, its multi-agent digital worker. They discuss how Perplexity has shifted financial research from the how to the what, letting a user describe an outcome in a single sentence while Computer orchestrates 20+ frontier models, direct tool calls to licensed live data, and finance-specific skills to produce the artifact.

Jeff explains the enterprise strategy behind traceability - the north star that 100% of every quantitative figure traces back to its source filing - alongside bring-your-own-license connections via MCP and the consumer "personal CFO" vision powered by Plaid. They explore what 5x revenue growth on a 34% headcount increase signals for finance jobs, and why the future looks like a 24/7 family office that proactively surfaces and, with permission, executes financial actions for everyone.

NOTABLE DISCUSSION POINTS:

 1. The “how to what” collapse is the real product thesis, not just better models. The shift to zero-shot rests on three stacked unlocks: direct tool calls to licensed live data (Quartr for earnings transcripts, unusual whales for insider and political holdings, SEC filings for historicals) instead of relying on web freshness; a thinking-model router that orchestrates 20+ frontier models in parallel, matching the model to the job (a heavy thinking model for macro analysis, a lighter one for ticker-matching 550 names); and ~20 opinionated finance skills (DCF, three-statement, LBO, comps) tuned through expert-led evals. Together they turn one sentence into a polished equity-research artifact.
 2. Traceability is the enterprise wedge, framed as “don’t trust and verify.” The stated north star is that 100% of every number in any output is hover-traceable back to the source filing - pre-scrolled to the page, highlighted, with the full chain of calculations exposed. The framing inverts the usual “trust but verify”: assume the user won’t trust the model, so trust must be earned per number. Paired with bring-your-own-license via MCP (FactSet, LSEG, Morningstar, CarbonArc, PitchBook), this is the concrete answer to why regulated institutions get comfortable adopting.
 3. The productivity and jobs signal is quantified and lived internally. Perplexity grew annual run rate 5x while increasing headcount only ~34%. Computer began as a company-wide Slack bot where every request was visible to all employees; Jeff now runs 9–10 scheduled cron jobs each morning and says essentially all code is written first by his agents. On the consumer side, the emergent pattern is build-your-own long-tail apps that no roadmap-bound product could serve - a DraftKings-addiction accountability system that emails a user’s spouse on any bet, or a GitHub-style heatmap of daily spending - which is the real substance of the personal-CFO bet.

TOPICS

Perplexity, Perplexity Computer, Perplexity AI, Google, Shadebot, Plaid, Yodlee, Claude, ChatGPT, AI, Artificial Intelligence, LLM, CFO, financial services, AI commerce

 

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👉 Twitter: https://twitter.com/LexSokolin

 

TIMESTAMPS

1’05: A

How Perplexity's Computer Is Replacing the Family Office, with Finance Lead Jeff Grimes

In this episode, Lex chats with Cactus Raazi [https://www.linkedin.com/in/cactusraazi/] — CEO Americas at B2C2, one of the original and largest institutional market makers in digital assets, serving roughly 1,500 institutions and pricing across more than 40 exchanges globally.

They discuss what a market maker actually does, how balance sheet and signal generation underpin roughly $1 billion a day of stablecoin flow at B2C2, and why the two extremes of crypto market making - riskless principal aggregation versus proprietary alpha - produce very different client outcomes that buyers rarely understand.

Cactus explains B2C2's 18-month bet that the Circle-versus-Tether debate would give way to a multi-issuer world, the launch of its PENNY product for instant zero-cost cross-stablecoin swaps, and they explore why programmability is the next frontier for digital dollars, why US capital markets have almost no structure for funding genuine risk-taking businesses, and whether the current combination of scale, speed, and complexity makes this the hardest investing environment Wall Street has ever faced.

NOTABLE DISCUSSION POINTS:

 1. Market makers aren’t a homogeneous category, and clients pay for the difference. At one extreme, a market maker is essentially a riskless agent - aggregating prices across 40+ exchanges and quoting on top with no real view. At the other extreme, a market maker is a proprietary quant shop running alpha signals on horizons from seconds to days, and the price you get is heavily conditioned by where the signal says the asset is going. B2C2 sits in the middle, partly because its public-company parent (SBI) constrains risk appetite. The implication for institutional buyers: who you trade with structurally determines the quality of execution, not just the spread.
 2. Algorithmic fixed income market making didn’t fail on technology, it failed on capital structure. US capital markets are excellent at funding venture, growth equity, private equity, and buyouts, but there is almost no domestic pool of “risk equity” - capital comfortable with the possibility that the machines (or the humans) lose money on a given day. Market makers need exactly that kind of balance sheet, and the mismatch between what the business requires and what the US capital base offers is a structural reason firms like Elefant struggled, regardless of execution quality.
 3. The Circle-vs-Tether framing is already obsolete; the next product wedge is interoperability. B2C2 made an 18-month-old contrarian bet that the duopoly narrative was wrong and that Stripe (via Bridge), Western Union, Revolut, and many other consumer and platform companies would issue their own stablecoins. PENNY - instant, zero-cost, zero-counterparty-risk stablecoin-to-stablecoin swaps - is the product expression of that view. The deeper claim is that stablecoins are software, and the SaaS analogy (a base layer plus an app store of programmable financial logic) is the real reason institutional adoption accelerates from here, not the transfer-of-value benefit on its own.

TOPICS

B2C2, Goldman Sachs, SBI Group, Binance, Coinbase, Circle, Tether, Stripe, Kraken, Credit Suisse, Market making, institutional liquidity, stablecoins, fixed income, risk management, algorithmic trading, crypto exchange infrastructure

 

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Inside the $1B-a-Day Stablecoin Market Maker for 1,500 Institutions, with B2C2's Cactus Raazi

In this episode, Lex chats with Mike Milotich [https://www.linkedin.com/in/] — Chief Executive Officer of Marqeta, the modern card issuing platform that processed nearly $400 billion in payments volume across 40+ countries in 2025, 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 path to a $100 billion business runs through embedded finance, real-time personalisation, and the forced modernisation of the banks themselves.

NOTABLE DISCUSSION POINTS:

 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 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.

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

 

ABOUT THE FINTECH BLUEPRINT

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👉 Twitter: https://twitter.com/LexSokolin

 

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

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

In this episode, Lex chats with Evan Malanga [https://www.linkedin.com/in/evan-malanga-a5904244/]— 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.

NOTABLE DISCUSSION POINTS:

 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.

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

 

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The $6B Decentralized AI Network, with Yuma CRO Evan Malanga

In this episode, Lex chats with Immad Akhund, [https://www.linkedin.com/in/iakhund/] 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.

NOTABLE DISCUSSION POINTS:

 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.

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

 

ABOUT THE FINTECH BLUEPRINT

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👉 Twitter: https://twitter.com/LexSokolin

 

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

Inside Mercury's $650M Revenue Machine, with CEO Immad Akhund

In this episode, Lex chats with Marc Boiron [https://www.linkedin.com/in/marcboiron/] — CEO of Polygon Labs. Marc shares his journey from law to blockchain, discussing the challenges of navigating crypto’s evolving legal landscape and the complexities of structuring compliant DeFi projects. He explains Polygon’s strategic pivot to focus on stablecoin payments, leveraging its proven blockchain and global partnerships.

Marc highlights Polygon’s real-world adoption, competitive edge, and vision to become the leading platform for on-chain payments. The episode offers insights into regulatory hurdles, industry trends, and Polygon’s mission to transform digital money movement.

NOTABLE DISCUSSION POINTS:

 1. The Labs-Foundation Structure Is a Frankenstein - and Its Creator Knows It: Marc helped architect the legal frameworks behind major DeFi token launches but openly calls the outcome a “complete Frankenstein.” The arm’s-length separation between labs and foundations was necessary to survive regulatory hostility, but makes coherent execution nearly impossible. He argues projects still copying this structure today are doing so out of habit, not legal necessity.
 2. Generalist Blockchains Are Dead - Polygon Is Betting Everything on Payments: As chain architectures converge, Boiron believes differentiation through speed and low fees is over. Polygon analysed its actual usage, found stablecoin payments was the standout vertical - $2.3 trillion already moved, fintechs across LatAm, Africa, and Southeast Asia already on-chain - and went all-in. The thesis is binary: if all money moves on-chain within a decade, even the 50th-best payments chain wins big.
 3. Polygon’s Real Moat Is Enterprise Trust Built During the NFT Era: The 2022–23 enterprise NFT push looked like a dead end after FTX collapsed, but it left behind institutional due diligence and credibility. Fintechs evaluating payments chains find that Polygon has years of live production use, Fortune 500 relationships, and Stripe already defaulting to it - a trust advantage no newly launched chain can replicate.

TOPICS

Polygon Labs, Polygon protocol, blockchain, crypto, decentralized finance, DeFi, legal frameworks, token launches, meme coins, stablecoins, payments, fintech, Ethereum, ICO boom, web3, NFT, Stripe, Circle

 

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TIMESTAMPS

1’09: From Spreadsheets to Smart Contracts: The Accidental Lawyer Who Found His Edge in Emerging Companies

4’40: Selling Your Soul for Low-Risk Capital: The Case For and Against the JD MBA

9’41: Fake It Till You Make It: How the ICO Craze of 2017 Turned One Niche Bet Into a Crypto Legal Career

13’01: Read the Actual Law: Why Memorizing the Securities Act Beat 20 Years of Legal Precedent in Crypto

18’19: The Crypto Legal Frankenstein: How Regulatory Survival, Not Business Logic, Built the Foundation-Labs-Token Structure

25’16: From Stockholm Syndrome to Meme Coin Mania: The Disorienting Cost of Crypto's Regulatory 180

30’05: The Dichotomy of Success: How Polygon's Most Celebrated Moment Was Secretly Its Most Broken

How Polygon Became the Payments Chain Moving $2.3T in Stablecoins, with CEO Marc Boiron

Lex Sokolin (@lexsokolin) · Zencastr

Lex Sokolin (@lexsokolin)

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