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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👉 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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🤝 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

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

👉 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

 

ABOUT THE FINTECH BLUEPRINT

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

 

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

In this episode, Lex chats with Alex Gluchowski [https://www.linkedin.com/in/gluchowski/] — Cofounder and CEO of Matter Labs, about the transformative impact of zero-knowledge proofs (ZK proofs) on blockchain scalability and privacy. They discuss Matter Labs’ evolution, the development of zkSync, and how ZK proofs enable secure, private, and efficient blockchain transactions.

The conversation explores enterprise adoption, regulatory shifts, and the potential for blockchain to revolutionize global finance by enabling privacy-preserving, interoperable networks anchored to Ethereum, ultimately highlighting the growing role of cryptography in advancing financial sovereignty and innovation.

NOTABLE DISCUSSION POINTS:

 1. Incorruptibility is Blockchain’s Core Value—Not Consensus: Consensus mechanisms solve network liveness without central operators, but the guarantee that your assets can’t be spent without your permission comes from verification. Bitcoin’s “don’t trust, verify” mantra is literal: every node re-executes every transaction. Zero knowledge proofs achieve the same incorruptibility without requiring universal visibility—enabling both scale and privacy.
 2. The Regulatory Shift Has Unlocked an Entirely New Market: The post-Trump regulatory environment represents a “great divide” for crypto. Banks and enterprises that previously couldn’t engage are now actively piloting blockchain infrastructure. Matter Labs is working with Deutsche Bank, UBS, and 35+ global financial institutions through initiatives like Presidio Breakthrough. The focus has shifted from building systems to withstand regulatory hostility to integrating crypto into real business processes.
 3. Private Enterprise Chains Settling on Ethereum is the Institutional Path: Banks experimented with consortium blockchains (Hyperledger, Corda, R3) for years but failed due to privacy concerns—participants could see each other’s transactions. Zero knowledge proofs solve this by enabling private chains that interoperate trustlessly through Ethereum as a shared settlement layer. Each institution maintains sovereignty over its operations while gaining cryptographic guarantees when transacting with counterparties.

TOPICS

Matter Labs, zkSync, Ethereum, Consensys, Hyperledger, Arbitrum, Optimism, fintech, blockchain, zero-knowledge proofs, ZK proofs, privacy, institutional adoption, scalability, cryptography, interoperability

 

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

 

TIMESTAMPS

1’14: The Incorruptibility Problem: Why Zero Knowledge Proofs Are the Only Path to Private, Scalable Finance

5’19: From Soviet Ukraine to Zero Knowledge: How Hyperinflation and a Hunger for Freedom Built a Crypto Visionary

14’07: Freedom Has a Cost: Squaring Crypto's Libertarian Promise With a Decade of Market Abuse

17’11: The Post-Trump Paradigm Shift: Why Stablecoins Are the Shipping Container Moment for Global Finance

25’19: ZK Rollups Demystified: How a Few Kilobytes of Cryptographic Proof Inherit the Full Security of Ethereum

31’38: The Bank Stack of Ethereum: How Zero Knowledge Proofs Finally Solve the Problem Hyperledger and Co

Building Privacy Infrastructure for 35+ Global Financial Institutions, with Matter Labs CEO Alex Gluchowski

In this episode, Lex chats with Yoshi Yokokawa [https://www.linkedin.com/in/yoshiyokokawa/], CEO of Alpaca — a brokerage infrastructure company that provides API-based trading and custody services to fintechs and developers globally.

The conversation begins with their shared experience at Lehman Brothers during the 2008 financial crisis, where Yoshi worked in fixed income securitization and learned that even when market participants sense a bubble, they keep dancing because timing the exit is impossible. After Lehman's collapse, Yoshi pursued entrepreneurship, building a computer vision AI company acquired by Kyocera before founding Alpaca in 2017. Initially inspired by Robinhood, Yoshi pivoted after experiencing firsthand the friction of accessing brokerage infrastructure—realizing the deeper opportunity was building API-first brokerage rails for developers. Today Alpaca powers 9 million accounts through 300+ partners across 45 countries, recently raising $150 million at a unicorn valuation.

The discussion explores how Alpaca follows Robinhood's product roadmap to anticipate partner demand, the challenges of adding crypto, and Yoshi's thesis that finance is undergoing a generational shift from digital to on-chain operations. Lex shares examples of legacy infrastructure dysfunction—from faxing PDFs to TD Ameritrade in 2012 to the Synapse collapse caused by manual CSV uploads—illustrating why Alpaca built its own custody and ledger systems as a path to competing in the $350 trillion global securities custody market.

NOTABLE DISCUSSION POINTS:

 1. Alpaca’s biggest breakthrough was not a better investing app idea, but recognizing that the real bottleneck was brokerage infrastructure. Yokokawa and team initially explored B2C product concepts, but pivoted once they experienced firsthand how painful broker-dealer setup, custody, and clearing integrations were. For readers building fintech, this is a huge lesson: the highest-value opportunity is often the “invisible” infrastructure pain, not the user-facing feature set.
 2. They found product-market fit by starting with a narrow wedge (API for automated traders) and only then expanding into a broader platform (Broker API for fintech apps). Alpaca did not begin by serving large fintechs; it first attracted power users who urgently needed programmable execution, then used inbound demand (“can I build my own Robinhood?”) as proof to build account opening, reporting, and full brokerage APIs. This is a valuable go-to-market pattern for infrastructure startups: win with a sharp use case, then expand into the system of record.
 3. Yokokawa’s core strategic edge is full-stack control of licenses, memberships, and ledger technology rather than relying on legacy vendors. He explicitly ties this to lessons from historical fintech fragility (manual workflows, broken reconciliations, middleware failures) and argues that owning the custody/clearing layer is what makes Alpaca defensible long term. For readers, this is the key takeaway on moat-building in financial services: if you don’t control the ledger and operational core, your product may scale faster at first but remains structurally fragile.

TOPICS

Alpaca, Lehman Brothers, Barclays, Nomura, Neuberger Berman, Blackrock, Robinhood, Interactive Brokers, TD Ameritrade, BNY Mellon, Brokerage infrastructure, API, trading, tokenization, embedded finance, fintech, crypto, web3

 

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How Alpaca built the API brokerage for 300+ global fintechs across 45 Countries, with CEO Yoshi Yokokawa

In this episode, Lex chats to Joseph Chalom [https://www.linkedin.com/in/josephchalom/], CEO of SharpLink, a Nasdaq-listed leader in digital asset treasury management focused on Ethereum. Joseph shares his journey from BlackRock and the Aladdin platform to pioneering digital asset strategies, including staking and tokenization. The discussion explores the evolution of fintech, the integration of crypto into institutional finance, and the future of decentralized finance (DeFi) and AI-powered financial agents. 

Joseph highlights SharpLink approach to making Ether productive for investors and the growing institutional adoption of blockchain technologies.

NOTABLE DISCUSSION POINTS:

 1. SharpLink’s scale and “productivity” pitch for ETH
    We hear that SharpLink (Nasdaq listed since July 2025) has raised a little over $3B in equity, holds ~$3B of ETH, and claims it stakes nearly 100% of its ether—framing itself as a public equities “one click” way to get both ETH upside and yield.
 2. A rare behind the scenes look at BlackRock’s crypto playbook
    We get specifics on how BlackRock approached digital assets through three pillars—Circle/USDC reserves, the Coinbase integration (announced Aug 4, 2022) to make crypto trading “boring” for institutions, and tokenization via BUIDL on Ethereum with Securitize, which he calls the largest tokenized fund.
 3. The next wave thesis AI agents + Ethereum rails
    Chalom argues the underestimated unlock is autonomous AI agents using Ethereum for programmable settlement, continuously reallocating capital across staking, lending, liquidity, and DeFi while monitoring smart contract risk—replacing manual “yield farming” with always on optimization.

TOPICS

Sharplink, BlackRock, FutureAdvisor, Ethereum, ETH, Buidl, Aladdin, digital assets, treasury management, decentralized finance, tokenization, Bitcoin, AI, AI Agents, Roboadvisors, Autonomous Agents

 

ABOUT THE FINTECH BLUEPRINT

🔥Subscribe to the Fintech Blueprint newsletter to stay at the forefront of Fintech and DeFi: https://bit.ly/3hyhlC2

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

 

TIMESTAMPS

1’05: SharpLink’s Ethereum Treasury: $3B Raised to Make ETH Productive

4’53: BlackRock’s iShares Era and Aladdin Explained: Risk Tech at $14T Scale

9’07: From Aladdin to Robo Advisors: Why 320,000 Advisors Couldn’t Scale

16’32: The FutureAdvisor Culture Lesson: Balancing Product Builders and Institutional Know How

18’31: BlackRock’s Three Pillar Crypto Bet: Circle Coinbase and Tokenization

25’21: Why BlackRock Picked Coinbase: Making Crypto Trading “Boring” and Institutional

28’44: From Six Week Retirement to ETH Treasury: Why SharpLink Holds “Permanent Capital”

34’12: The Treasury Trade After the Hype: Why SharpLink Beats ETH ETFs on Staking

38’55: NAV Discounts and Mean Reversion: SharpLink’s Plan to Double ETH per Share

43’39: Ethereum’s Next Growth Stack: $300B Stablecoins $14T Tokenization and Institutional DeFi

48’23: From Copilots to Autonomous Agents: Ethereum as the Rails for Machine Finance

52’21: The channels used to connect with Joseph & learn more about SharpLink

Building the $3B Ethereum Treasury Company, with SharpLink CEO Joseph Chalom

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.

The Fintech Blueprint