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The Fintech Blueprint: Daily Summaries Delivered
by Lex Sokolin
Finance is being pulled apart by the forces of frontier technology. From AI, to blockchain and DeFi, mixed reality, chatbots, neobanks, and roboadvisors — the industry will never be the same. Here is the blueprint for navigating the shift.
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- 1 weeks ago47 min
The $5.1B Marketplace With More AI Agents Than Human Users, with Whop CEO Steven Schwartz
In this episode, Lex chats with Steven Schwartz, 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: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.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.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.TOPICSFintech, Stablecoins, Payments, Marketplace, DeFi, Web3, Crypto, Tether, USDT, AgenticAI, AIAgents, LLMs, DigitalAssets, Whop, Hyperliquid, Robinhood, Stripe, Shopify ABOUT THE FINTECH BLUEPRINT🔥Subscribe to the Fintech Blueprint newsletter to stay at the forefront of Fintech and DeFi: https://bit.ly/3hyhlC2🤝 Partner with Fintech Blueprint through sponsorships: https://bit.ly/3UZllsV👉 Twitter: https://twitter.com/LexSokolin TIMESTAMPS1’03: A Playbook for the Post-Cubicle Economy: Why Whop Wants to End the 9-to-53’44: A Decade of Side Projects Before Whop: How Two 13-Year-Olds Found Each Other on Facebook8’31: Everyone Is a Builder: Why the Creator vs. Consumer Divide Doesn’t Hold12’09: The Damaged Perception of Success: What Social Media Took From Builders14’57: Facebook Groups, Discord, and Bitcoin: Selling Software Before Whop Was a Website18’02: Abstracting Away the Boring Stuff: What Whop Took Off Every Builder’s Plate20’04: Top-of-Funnel Income-as-a-Product: Why Whop Isn’t a Normal Fintech22’44: Amazon Is for Search, Whop Is for Discovery: The Next Era of Entrepreneurship25’22: $1.5M a Day in Physical Goods Is the Smallest Segment: Whop’s SKU Explosion29’03: $500M a Month in Earner Income: The Hiring Bar That Kept Up31’33: 200M From Tether to Pay Earners in 241 Countries: Whop’s Stablecoin Bet36’37: Crypto Overcomplicates, Stablecoins Just Work: The Customer-Journey Case39’27: More Agent Users Than Human Users: Whop’s AI Traffic Mix44’05: From
- Sep 28, 202647 min
How Stripe's Tempo is Rebuilding On-Chain Cash Settlement, with Head of Market Development Simon Taylor
In this episode, Lex chats with Simon Taylor, 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, 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: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.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.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.TOPICSFintech, Stablecoins, Tokenization, DigitalAssets, Payments, Settlement, RWA, DeFi, TradFi, CapitalMarkets, AgenticCommerce, AgenticPayments, AIAgents, MachinePayments, Ethereum, Canton, Blockchain, Web3, Tempo, Stripe, Anthropic, Ramp, Brex, Deel ABOUT THE FINTECH BLUEPRINT🔥Subscribe to the Fintech Blueprint newsletter to stay at the forefront of Fintech and DeFi: https://bit.ly/3hyhlC2🤝 Partner with Fintech Blueprint through sponsorships: https://bit.ly/3UZllsV👉 Twitter: https://twitter.com/LexSokolin TIMESTAMPS0’57: How do you ground-truth the digital world: Why AI ultimately runs on a digital asset substrate5’46: The two-speed transformation: Why customer-facing tech is competitive and back-end plumbing is industry-led9’47: Limit the blast radius: How JPMorgan, HSBC and Citi turned tokenized deposits into a client retention play14’46: Finance is globalised, not global: Why Stripe built its own chain instead of stitching together every other one18’21: There is no Fedwire for the internet: The missing global settlement layer Tempo is built for21’18: On-chain cash settlement is less than 0.1% done: Why Tempo is where stablecoins and tokenized deposits converge26’20: The WAP-phone era of agentic commerce: Why nobody is actually paying with agents yet33’07: Everyone is your frenemy: Why Stripe and Coinbase both showed up to the agentic payments market36’44: 9 in 10 companies get zero AI productivity gain: What separates the enterprises that don't43’54: Harnesses and model routers: Where the durable value sits in the enterprise AI stack46’05: The channels used to connect with Simon & learn more about Tempo & Fintech Brain food <a href="https://www.fintechblueprint.com/disclaimer" target="_
- Sep 10, 202647 min
How zerohash won Morgan Stanley's crypto business
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: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.“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.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.TOPICSStablecoins, EmbeddedFinance, Tokenization, DigitalAssets, Payments, GENIUSAct, MiCA, AgenticPayments, DeFi, RWA, Web3, Fintech, zerohash, MorganStanley, Gusto, Stripe, Circle, Tether, Plaid, Mastercard ABOUT THE FINTECH BLUEPRINT🔥Subscribe to the Fintech Blueprint newsletter to stay at the forefront of Fintech and DeFi: https://bit.ly/3hyhlC2🤝 Partner with Fintech Blueprint through sponsorships: https://bit.ly/3UZllsV👉 Twitter: https://twitter.com/LexSokolin TIMESTAMPS2’54: From 5% of Ethereum to $65 billion settled: four years of scaling without trading off trust6’19: The everything app comes full circle: from embedded crypto to one infrastructure engine11’29: Interoperability as the value prop: bridging USDC across ETH, Polygon, and Canton16’06: Build, buy, or rent: how zerohash wins the decision inside a firm with billions in profit<li
- Aug 10, 202640 min
Building the AI Distribution Layer for 5000+ Banks, with Fiserv Co-Head of Financial Solutions Srini Krish
In this episode, Lex chats with Srini Krish — 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: 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. 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. 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 ABOUT THE FINTECH BLUEPRINT 🔥Subscribe to the Fintech Blueprint newsletter to stay at the forefront of Fintech and DeFi: https://bit.ly/3hyhlC2 🤝 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: Both Labs, All Three Clouds: Why the Distribution Channel Sits in the Middle 33’16: The Engineer Who Stops Writing Code: Why Replacement and Expansion Can Both Be True 36’50: It Has to Be 100% Correct Every Time: Why Money Demands Deterministic AI Disclaimer here — this newsletter does not provide investment advice and represents solely the views and opinions of FINTECH BLUEPRINT LTD.Contributors: Lex, Laurence, Matt, Farhad, Mike, <a href="https://www.linkedin.com/in/daniella-seberini-273aa6205/" t
- Jul 20, 202649 min
How Perplexity's Computer Is Replacing the Family Office, with Finance Lead Jeff Grimes
In this episode, Lex chats with Jeff Grimes — 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: 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. 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. 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 ABOUT THE FINTECH BLUEPRINT 🔥Subscribe to the Fintech Blueprint newsletter to stay at the forefront of Fintech and DeFi: https://bit.ly/3hyhlC2 🤝 Partner with Fintech Blueprint through sponsorships: https://bit.ly/3UZllsV 👉 Twitter: https://twitter.com/LexSokolin TIMESTAMPS 1’05: A company built on failed founders: Why the startup that didn't work out led here 5’10: A playing card company that became Nintendo: How curiosity carried Perplexity from answers to actions 9’52: We're basically at zero shot now: Why prompt engineering is disappearing from finance work 15’21: A thinking model that routes the job: How Perplexity picks Claude Opus for macro and Grok for tickers 20’12: Two tracks, one engine: Building for enterprise workflows and a personal CFO at once 25’25: Bring your own license, or use ours: How Perplexity gets institutions comfortable enough to adopt 32’54: 5x revenue on 34% more headcount: The productivity gain Perplexity lived firsthand 37’28: Connect everything from your mortgage to the painting on your wall: Building the true personal CFO 45’15: A family offi
- Jun 22, 202643 min
Inside the $1B-a-Day Stablecoin Market Maker for 1,500 Institutions, with B2C2's Cactus Raazi
In this episode, Lex chats with Cactus Raazi — 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: 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. 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. 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 ABOUT THE FINTECH BLUEPRINT 🔥Subscribe to the Fintech Blueprint newsletter to stay at the forefront of Fintech and DeFi: https://bit.ly/3hyhlC2 🤝 Partner with Fintech Blueprint through sponsorships: https://bit.ly/3UZllsV 👉 Twitter: https://twitter.com/LexSokolin TIMESTAMPS 1’17: Rejected by 30 firms: A cold-call advertising inquiry that became a Goldman career 6’43: "I'll go sell jet engines": Complexity as the through line from credit derivatives to crypto 8’53: Scale, speed, and dimensionality: The hardest investing environment in 28 years 12’33: A terrific idea, a brutal execution: Building an automated market maker in 2015 16’22: The used car dealership of bonds: How over-the-counter fixed income actually works 19’51: Price, time frame, and the art of liquidity: What a market maker actually does 24’52: Riskless principal or proprietary alpha: The two extremes of crypto market making 29’54: Priming the liquidity pump: Why new tokens hire market makers and large ones don't
- May 25, 202644 min
How Marqeta Built the $400B Modern Card Issuing Platform, with CEO Mike Milotich
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 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: 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. 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. 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 🔥Subscribe to the Fintech Blueprint newsletter to stay at the forefront of Fintech and DeFi: https://bit.ly/3hyhlC2 🤝 Partner with Fintech Blueprint through sponsorships: https://bit.ly/3UZllsV 👉 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 10’08: 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 fo
- May 15, 202636 min
The $6B Decentralized AI Network, with Yuma CRO Evan Malanga
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. NOTABLE DISCUSSION POINTS: 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. 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. 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 ABOUT THE FINTECH BLUEPRINT 🔥Subscribe to the Fintech Blueprint newsletter to stay at the forefront of Fintech and DeFi: https://bit.ly/3hyhlC2 🤝 Partner with Fintech Blueprint through sponsorships: https://bit.ly/3UZllsV 👉 Twitter: https://twitter.com/LexSokolin 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-
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Finance is being pulled apart by the forces of frontier technology. From AI, to blockchain and DeFi, mixed reality, chatbots, neobanks, and roboadvisors — the industry will never be the same. Here is the blueprint for navigating the shift.
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