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Founders in Arms

Immad Akhund and Rajat Suri

In this weekly series, fellow startup founders Immad Akhund (Mercury) and Rajat Suri (Presto, Lima, and Lyft) explore current events in the world of tech, startup, and policy, offering insights from their distinguished careers and an array of expert guests.

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  • 21 episodes
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  • Avg 48 min
  • English

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  • S2 · E90
    September 18 · 1 hr 2 min

    Stephen Balaban on 14 Years of Lambda and the Future of AI

    Stephen Balaban is co-founder and, as of a recent leadership change, CTO of Lambda, the AI cloud infrastructure company he and his twin brother started in 2012. It took five pivots — augmented reality, a facial-recognition contact book, a camera embedded in a baseball cap, the AI image app Dreamscope, then finally workstations and servers — before Lambda found a business that made money in 2017. Along the way, Stephen kept the company alive by consulting on the side, including projects with Airbus and the creators of South Park. What you'll learn: Why it took five pivots and 14 years for Lambda to find product-market fit How side consulting work — for Airbus and even South Park's creators — funded the company through its leanest years Why mainline Silicon Valley VCs kept passing on Lambda even while it was profitable, and why that didn't change after ChatGPT How a Series D round pulled largely from Taiwanese manufacturers and family offices instead of traditional venture capital What pushed Stephen to step down as CEO and bring in Michel Combes, a former CEO of Sprint and SoftBank International Why Stephen thinks the disinformation around data centers — noise, water use — doesn't hold up Chapters: (01:10) Meet Stephen Balaban, co-founder and CTO of Lambda (02:40) Palo Alto in 2012 and Lambda's earliest days (03:42) Building Heads Up, a facial-recognition contact book for iOS (05:06) The ImageNet moment and training neural nets on NVIDIA GPUs (06:14) Five pivots: from augmented reality to Dreamscope (08:55) Funding the company through consulting, including work with Airbus and South Park's creators (12:13) Writing down the goal to IPO back in a 2012 notebook (14:28) Raising a first $600K, including Austin Russell's $20K check at a $400K valuation (24:29) Lambda's climb from $3M in revenue to a $1B run rate (26:57) Why Silicon Valley VCs kept saying no — even after ChatGPT (34:04) A Series D built largely on Taiwanese manufacturers and family offices (37:13) Immad on Mercury Books, Mercury's new bookkeeping product (44:12) Stepping down from CEO to CTO and bringing in Michel Combes (52:34) Debunking data center disinformation, from water use to noise

  • S2 · E89
    August 31 · 53 min

    Why AI's Next Problem is Data | Garrett Lord on Training Real-World Models

    What if the company students use to find their first internship became one of the most important players in training AI? Garrett Lord, co-founder and CEO of Handshake, joins Immad Akhund and Raj Suri to break down Handshake's unlikely pivot. Handshake started as a way to help college students — regardless of where they went to school — find internships and jobs, and grew into a $200M+ ARR business used by most students in America. But over the last 18 months, Garrett has built a second business inside Handshake: using the company's network of 30 million students and alumni to help AI labs train their models on real, high-quality, professional-domain data — from oil and gas to finance to scientific research. That business alone has gone from zero to nearly $2 billion in revenue in about a year. The conversation goes deep on how this actually works: recruiting domain experts, building task environments that function like video games, scoring model performance against expert-validated tasks, and why 70% of the money spent training a model today goes toward reinforcement learning rather than pre-training. Garrett, Immad, and Raj also cover the open-weight vs. frontier model debate, why China may already be ahead on robotics, and what jobs might look like in a world where AI models can eventually learn continuously, on the job. The episode closes with a genuinely open-ended debate between Garrett and Immad about what humans will actually do for a living, and for meaning, if knowledge work is mostly automated — and how disruptive that transition might be along the way. What you'll learn: How Handshake used its network of 30 million students and alumni to build a second, multi-billion-dollar business training AI models Why 70% of AI training spend now goes toward reinforcement learning, not pre-training How AI labs identify gaps in their models and commission the specific data needed to close them Why data, not algorithms, may be the real long-term moat for AI companies Why computer use — AI navigating real software and websites — has recently gotten dramatically better Why China may already be ahead of the U.S. in deploying real-world robotics How enterprises like Mercury are likely to use a mix of frontier and open-source models going forward What Handshake learned scaling a data business from zero to nearly $2B in under two years Garret and Immad's differing views on what human work and meaning look like if knowledge work becomes automated Timestamps: (00:19) Introduction and Handshake's origin story (01:39) Handshake's new business: training AI models on real-world data (02:30) How Handshake's 30M-person network became a moat (04:24) Inside the "video game" environments used to train agents (05:00) Why 70% of AI training spend now goes to reinforcement learning (08:18) How AI labs commission specific data from Handshake (12:10) Why computer use has finally gotten good (14:11) What models are still bad at, and why (17:00) The "8 people can agree" test for what AI can be trained to do (18:23) China's 2 million working robots, and why the US is behind (22:12) Open-weight vs. frontier models, and how enterprises will use both (31:15) Scaling from zero to $2B: what broke along the way (34:02) Handshake's "Olympic pace" culture value (39:41) Why continuous learning is the next frontier for AI (42:07) Bill Gates' essay on AI, job loss, and taxing tokens (45:23) Immad and Garret debate what jobs and meaning look like in an AI-driven future

  • S2 · E88
    August 21 · 40 min

    Founders in Arms #101: Cursor, OpenRouter, and What's Next in AI

    This week, Immad and Raj sat down for a wide-ranging catch-up on the biggest stories in tech right now — from record-breaking acquisitions to what they're each giving AI access to in their own lives. The conversation kicks off with the OpenRouter-Stripe acquisition and Cursor's $60B deal, and what both say about investing in "obvious" ideas when the underlying trend is right. From there, Immad and Raj get into the economics of secondary markets (including Immad's own purchases of SpaceX and Anthropic shares pre-IPO), why staying private longer might be bad for retail investors, and the case for making it easier for smaller companies to go public. They also dig into consumer AI hardware — why simple, single-purpose devices like Pocket are breaking through where more complicated products haven't — and trade notes on what they've each connected their own AI assistants to, from email and calendars to health results and scheduled tasks. What you'll learn: Why "obvious" ideas can still be some of the best investments, if the trend is right What's driving the OpenRouter-Stripe and Cursor acquisitions, and why they matter for developer tools How Immad and Raj think about the risks and opportunities in secondary markets Why Immad believes deep secondary liquidity could be bad for retail investors and the broader economy What's made simple, single-purpose AI hardware devices succeed where more ambitious ones have struggled How Immad and Raj are using AI assistants in their own lives, from productivity to personal health Why "PMF doesn't exist anymore" in consumer products, according to a recent conversation Raj had with Character AI's CEO What it will take for AI to handle more complex, multi-step tasks like buying insurance How Anthropic and OpenAI's revenue numbers compare going into the back half of the year Timestamps: (00:47) Introduction (01:21) OpenRouter's acquisition by Stripe (02:18) Cursor's $60B deal and the case for "obvious" ideas (06:03) Why big exits justify high seed valuations (08:47) AI adoption is still low — why Immad is bullish on the next 5-10 years (12:56) Buying into SpaceX and Anthropic pre-IPO (15:17) The case against deep secondary markets (18:21) Why Pocket is winning in consumer AI hardware (20:02) Talking to Matic's robot vacuum (22:07) An idea for family video, and why photo frames haven't solved it (24:14) What Character AI's CEO said about PMF at a recent Tribe event (28:28) What Immad and Raj have given their AI assistants access to (33:08) Scheduled AI tasks, and why AI still can't do the last mile (35:59) Anthropic and OpenAI's latest revenue numbers (38:06) The debate over housing density and California's building laws

  • S2 · E87
    August 14 · 55 min

    Building Brokerage 2.0: Direct Indexing and Tax Alpha with Mo Al Adham

    Mo Al Adham is the founder and CEO of Frec, a brokerage platform he describes as "brokerage 2.0" — building on core trading primitives to offer more sophisticated strategies like direct indexing, long-short direct indexing, and options overlays. Before Frec, Mo co-founded Twitvid, an early video-for-Twitter startup, and later spent five years at Twitter. He founded Frec in 2021 and launched the product in October 2023. What you'll learn: Why the $1-30M wealth segment — about 10 million US households — controls 40% of all investable wealth in the country, and why it's the fastest-growing segment How direct indexing creates "tax alpha" by harvesting capital losses, and why that's a deferral of taxes rather than an elimination of them How a step-up in cost basis at death effectively forgives the deferred tax bill The concrete numbers: how much a $100k investment can harvest in losses via a classic direct index versus a long-short direct index Why long-term, sophisticated investors have proven far less fee-sensitive than the market assumes Mo's path from Twitvid — an early video app built on top of Twitter — to five years working inside Twitter itself How a frustrating experience with a wealth manager who charged 1% fees for little added value planted the idea for Frec Why Mo's six months of "top-down" market research largely failed, and why a "bottoms-up" approach — starting from what he actually cared about — led him to Frec Why Frec had to resequence its roadmap when rising interest rates undercut its original plan to lead with a cheap line-of-credit product Immad's framework for company OKRs (which he calls "COR") and why he insists on including non-measurable results Why Frec has deliberately stayed out of banking, unlike some robo-advisor competitors Mo and Immad's picks for financial products that should already be obsolete Chapters: (00:00) The $1-30M wealth segment and why it holds 40% of US investable wealth (01:03) Introducing Mo Al Adham and Frec, "brokerage 2.0" (02:07) Targeting sophisticated investors vs. democratizing access (03:12) Why long-term investors are stickier and less fee-sensitive than assumed (07:08) Tax alpha explained: deferral vs. elimination (09:20) How direct indexing lowers cost basis through loss harvesting (12:45) Long-short direct index and portfolio tilts (14:16) Mo's first startup, Twitvid, and getting outpaced by Twitter (16:29) The wealth manager experience that inspired Frec (19:49) Vetting the idea: six months of top-down research that failed (22:18) Switching to a bottoms-up approach and finding conviction (30:39) Immad's approach to OKRs, called "COR" (36:24) Frec's pivot from lending to investing as rates rose (52:16) Rapid fire: AI in fintech, obsolete products, and more

  • S2 · E86
    July 10 · 28 min

    Ethics, Pivots, and the Future of Work: A Live Q&A with Vercel's Guillermo Rauch

    Guillermo Rauch is the co-founder and CEO of Vercel, the company behind Next.js, and previously created the widely-used Socket.io library. In this special episode, recorded live in front of an audience, Guillermo joins Immad Akhund and Raj Suri for an open Q&A covering pivots, ethics, investors, and the future of work in the age of AI. What you'll learn: The difference between a "lowercase p" pivot (refining focus) and an "uppercase P" pivot (starting over) — and how to know which one you need How to build an ethical framework for operating in an industry full of shortcuts and noise How to extract real signal from investors without letting them drive your roadmap Real pivot stories from Presto (restaurant tablets to voice AI), Lyft (carpooling to peer-to-peer ride-hailing), and Mercury's early product-market-fit signal Why blaming distribution is often easier than blaming the product — and why that's a trap How founders can get their teams to think about prioritization the way they do How Mercury created early demand by deliberately recruiting a broad, vocal set of seed investors What "the future of work" looks like when your team's job shifts from producing outcomes directly to building the systems that produce them How growing up outside Silicon Valley shaped each panelist's belief that they could build something from scratch Chapters: (0:00) Lowercase p vs. uppercase P pivots (1:05) Q&A begins (1:23) Building an ethical framework in Silicon Valley (4:38) Balancing customer signal vs. investor advice (9:53) Pivot stories: Presto, Lyft, and Mercury's obvious PMF moment (15:34) Why founders blame distribution instead of the product (16:08) Getting your team to think about prioritization like you do (18:21) How Mercury created early demand with 60 seed investors (19:48) The future of work: agents, harnesses, and factories of output (24:25) Growing up outside the Valley: mentors and self-belief (28:04) Closing

  • S2 · E85
    June 26 · 49 min

    The New Rules of Startup Scale: Survival, TAM Illusions, and Opting into Excellence With Dan Teran

    Dan Teran is the co-founder and managing partner of Gutter Capital, an early-stage venture firm investing in vertical AI and marketplace businesses. He previously founded Managed by Q — an operating system for commercial spaces that grew to employ nearly 1,000 people, expanded nationally, and was acquired by WeWork in 2019. Dan joined WeWork as head of corporate development before leaving after a turbulent six months. He now runs Gutter Capital's third fund ($75M) and the Elbow Grease accelerator, sponsored by Mercury, which invests in early-stage founders in New York City. What you'll learn: How Managed by Q found extreme product-market fit in lower Manhattan — and why that made expansion harder, not easier Why winning a market can be a trap when the TAM is smaller than you thought The real story behind the WeWork acquisition: a three-year relationship, a theatric walkout, and why great exits are always principal-to-principal Why over-capitalization was more ruinous to Managed by Q than any external factor How to think about Series A benchmarks for non-AI companies today (2–3M ARR, renewals, one productive AE, 3x growth) Why AI-enabled services businesses can be great companies even if they're not venture-scale outcomes The mismatch between what early-stage founders need to raise and what top VC funds are mandated to deploy Why founders should play the hype game — but stay ruthlessly honest with themselves about what game they're playing Dan's take on Adam Neumann: what made him exceptional, where he fell short, and why Dan wouldn't bet against him The "leaders eat last" philosophy — and why holding people to high standards and having their backs aren't in conflict Chapters: [00:00] The hype trap founders fall into [01:31] Managed by Q: founding story and early growth [02:39] Scaling nationally and selling to WeWork [04:17] The state of co-working and commercial real estate post-WeWork [07:18] In-person vs. remote — what actually matters pre-PMF [11:16] How the WeWork acquisition really happened [15:06] Realizing the TAM was smaller than expected [17:09] Raj's parallel experience at Presto [20:04] FOMO-driven investing and the AI diligence problem [22:04] Series A benchmarks for applied AI companies today [25:27] Why founders should aim for break-even before raising [28:56] The mismatch between venture fund mandates and founder needs [34:32] What Dan learned about fundraising after becoming an investor [37:30] Adam Neumann, WeWork, and Flow [39:30] Leadership, high standards, and the "leaders eat last" philosophy [42:12] Why founders learn the wrong lessons from Steve Jobs [47:31] FarmEvo: the drone ag company Dan flew to Karachi to diligence

  • S2 · E84
    June 5 · 52 min

    Before Robots Were Cool: The 33-Year Journey of iRobot's Founder, Colin Angle

    Colin Angle spent 33 years building iRobot — bootstrapping for eight years without venture capital, surviving 15 failed business models, and ultimately launching Roomba in year 12. What followed was a decade of overcoming consumer skepticism, 70%+ global market share, a public offering on Nasdaq, and eventually a blocked acquisition by Amazon. Now he's back with a new company, Familiar Machines and Magic, building robots designed for human connection — priced to compete with the cost of owning a pet. What you'll learn: Why Colin believes iRobot would have failed with early VC access How iRobot funded itself for eight years through customer contracts instead of investors The sales tactic Colin used to get Fortune 500 CTOs to fund iRobot's R&D How DoD mine-hunting algorithms and a Hasbro partnership became the technology inside Roomba The wallet share framework for evaluating whether a consumer robot idea can actually work Why adding features to a consumer robot often reduces perceived value How iRobot priced Roomba at $199 with a $42 BOM — and what that discipline required What it felt like to go public, and how everything changes when what you say can be monetized The full story behind the Amazon acquisition attempt and why the EU and FTC blocked it What Familiar Machines and Magic is building and why the pet economy is the target comp Chapters: 00:00 – Regulators celebrate blocked deals — what Colin saw on FTC examiners' doors 00:53 – Introducing Colin Angle, co-founder of iRobot and Familiar Machines and Magic 02:00 – The "if not us, who?" moment that started iRobot 03:54 – First business model: privately fund a moon mission, sell the movie rights 07:03 – Eight years without VC: "completely unfundable" 08:09 – The CTO sales tactic: present a problem half a step from their real one 09:00 – "Work for no profit, cancel anytime" — the deal structure they used five times 12:05 – Built for 10,000 units, sold 70,000 Roombas in three months 15:03 – "If I had VC early, iRobot would have failed" 18:40 – $199 retail, $42 BOM — the Roomba economics 20:31 – The wallet share framework: which consumer spend are you actually replacing? 32:39 – First interview as a public CEO: "My wife says Roomba doesn't work" 34:42 – The Amazon acquisition gets blocked — 15% market share and falling 42:09 – Familiar Machines and Magic: the new company and the original vision 46:12 – Building robots for human connection, not task automation

  • S2 · E83
    May 29 · 51 min

    Guillermo Rauch at Founders in Arms Live: Simplicity, Focus, and the Bet That Built Vercel

    Guillermo Rauch, CEO of Vercel, joins Immad Akhund and Raj Suri at a live Founders in Arms event to break down the full arc of building one of the most widely used developer platforms in the world—from a contrarian bet that VCs said was already solved, to a multi-product company powering the future of the web. Guillermo walks through the three chapters of Vercel's growth: finding focus (trimming a portfolio of open source projects down to the one that had undeniable traction), building repeatability (anchoring go-to-market around customer-led ROI stories), and scaling the company itself as the product. Along the way, he shares how he thinks about feedback, why consensus is a red flag for startup ideas, how customer-led innovation beats internal roadmaps, and what "brand permission" has to do with why Google keeps failing at social. The conversation also gets into the current moment in SF—the AI supercycle, the anxiety around who gets left behind, and why Guillermo's answer to all of it is the same: product market fit solves most problems. Just stay focused on building. What you'll learn: Why Guillermo treats everything—including silence—as feedback The "pain discovery" method he uses to extract what's actually broken How Next.js started as a personal solution and became a wedge into the entire cloud Why he deliberately ignores competitors when building The three chapters of Vercel's growth and what drove each inflection point How customer-led innovation produced some of Vercel's biggest revenue lines Why your second product has a higher bar than your first The iPhone and AirPods framework for thinking about adjacencies What "brand permission" means and why it explains Google's failures Why consensus around an idea is a signal to walk away Chapters: 00:00 – Managing your own psychology as a founder 00:51 – Welcome + live event intro 02:55 – Vercel's web stack vs. agent stack 04:04 – Guillermo's background and first exit to WordPress 05:15 – Spotting the waves: cloud and front end in 2013 08:49 – Everything is feedback; the pain discovery method 10:40 – Short-term pessimism, long-term optimism 13:14 – Opinions vs. ideas: the Jony Ive mental model 16:40 – Chapter 1: Finding focus — how Next.js became the wedge 21:03 – Why consensus is a red flag for startup ideas 21:40 – The MacBook moment: simplicity wins 25:37 – Chapter 2: Repeatability — e-commerce as the GTM unlock 29:30 – Chapter 3: Scaling the company as the product 34:41 – iPhone and AirPods: smart adjacencies to a strong core 38:41 – Brand permission: why Google keeps failing at social 40:18 – The SF culture divide: AI optimists vs. AI anxious 43:09 – The AI gentrification of San Francisco 49:05 – Being your own coach; founder loneliness and burnout 50:46 – What fundraising actually feels like

  • S2 · E82
    May 22 · 54 min

    Building for Quality in a World of AI Slop with Linear's Karri Saarinen

    Karri Saarinen is the co-founder and CEO of Linear, the product and issue tracking platform built for high-performing software teams. A designer by training — with stints at Airbnb and Coinbase — Karri took a different path to founding than most Silicon Valley CEOs. Linear has become one of the most beloved tools in the startup ecosystem, known for its speed, design quality, and now its deep integration with AI agents. What you'll learn: How Linear evolved from issue tracking to a full product-building system with AI agents Why speed and quality — not features — were Linear's winning strategy in a crowded market How Karri thinks about AI's role in design and why average startup design is getting worse Why designers rarely become founders and whether AI will change that The "Quality Wednesday" ritual Linear uses to keep polish standards high at 120 people How Linear's feature roast process catches blind spots before anything ships What Linear borrowed from Coinbase's hiring playbook — and how work trials outperform interviews How Linear built an open agent platform and why it now hosts more agents than any tool in its category Karri's take on whether designers should write code — and where design thinking matters most Why Linear intentionally pushed PM thinking to engineers and designers instead of hiring traditional PMs In this episode, we cover: (00:00) Why designers rarely become founders (00:53) Introducing Karri Saarinen and Linear (01:27) How Immad and Karri met 15 years ago (02:00) What Linear actually is — and where it's going (03:13) Mercury running compliance workflows on Linear (05:12) Immad's regret: not investing in Linear early (06:17) How Linear broke through a crowded market (08:08) Speed and quality as a product moat (09:26) Why Mercury and Linear win the same way (14:23) Linear's AI agent strategy and open platform (17:40) Coinbase and Ramp building custom agents on Linear (19:27) Linear's upcoming coding agent and PR review interface (21:31) Karri's background as a designer-CEO (23:33) Why designers don't start more companies (27:15) How AI is blurring the lines between design and engineering (31:03) What AI can't replace in design thinking (34:05) Bleeding roles without losing specialization (36:47) The AI slop problem in product features (37:02) Maintaining quality culture at 120 people (39:31) Quality Wednesdays explained (41:16) The feature roast process (44:18) How Linear collects user feedback (46:33) What Linear borrowed from Coinbase's culture (47:21) Work trials: how they work and why they're better (53:32) Why work trials benefit candidates too

  • S2 · E81
    May 1 · 54 min

    WorkOS's Michael Grinich on Becoming the Enterprise Layer for AI's Biggest Companies

    Michael Grinich is the co-founder and CEO of WorkOS, the enterprise authentication and identity infrastructure used by Anthropic, OpenAI, Cursor, xAI, and hundreds of fast-growing companies. Before WorkOS, Michael dropped out of MIT, worked at Dropbox, and founded Nihilus — where a painful first experience with enterprise features planted the seed for everything that came next. In this episode, Immad Akhund and Raj Suri sit down with Michael to talk about the SaaS apocalypse thesis, how WorkOS quietly became the enterprise layer for AI's biggest companies, and what it actually takes to build for developers. What you'll learn: Why the SaaS apocalypse narrative gets it completely backwards How WorkOS became the default enterprise-ready layer for AI-native companies The Stripe parallel: why developer infrastructure compounds the same way payments did What a failed first startup taught Michael about idea validation How keeping a daily idea notebook — volume, not quality — led to WorkOS Why second-time founders approach conviction and validation completely differently The do-or-die bond between developer tools and their customers How Michael taught himself enterprise sales after starting as a purely technical founder Why building for developers is the ultimate boss battle in tech What AI getting to Renaissance-printing-press level actually means for software Chapters: (00:00) The SaaS apocalypse thesis — and why Michael thinks it's wrong (01:09) Introducing Michael Grinich — MIT, Dropbox, and the road to WorkOS (05:14) The Stripe origin story and early MIT startup network (07:03) Drew Houston, Dropbox, and what convinced Michael to build (09:05) Founding Nihilus: three maxed credit cards and two days from missing rent (11:00) How to generate startup ideas: volume over quality, the notebook habit (14:05) Finding sticky ideas — the ones you keep coming back to (17:10) Why the energy behind an idea matters as much as the idea itself (20:16) What experience gives you: pattern recognition and a framework for new scenarios (24:05) The moment Michael saw the enterprise auth problem and knew it was real (27:02) How Anthropic, OpenAI, and Cursor ended up as WorkOS customers (31:16) Why WorkOS sits at the security and growth layer for AI companies (35:06) The ultimate boss battle: building developer tools for other developers (39:06) Why developer customers give the best product feedback — and why that's a gift (44:04) The SaaS apocalypse revisited — and what's actually happening to software (47:17) How AI compressed the timeline to enterprise-ready from months to a day (53:03) Tying company value to something durable through technology waves

  • S2 · E80
    April 21 · 25 min

    AI Winners, IPO Hype, and the Future of Engineering Teams With Raj and Immad

    In this candid one-on-one episode, Immad and Raj catch up on what's actually happening in tech right now — the AI narratives shifting under everyone's feet, which companies they'd bet on, and how they're thinking about building teams in an AI-native world. What you'll learn: Why Anthropic has taken the AI narrative from OpenAI — and whether that lead will hold Immad's take on whether he'd invest in OpenAI or Anthropic at $800B today How Anthropic is growing 3x in revenue in three months — and whether it's even possible The new engineering team model: fewer engineers, more autonomy, OKR-driven execution Why design still matters — and why Mercury embeds designers directly into product teams How to time IPO investments: why Raj waits 3-4 months post-listing to buy What the SpaceX S-1 signals about the new AI hype cycle Why Apple is undervalued (or not) — the edge computing argument How good Gemini's travel integration actually is (Raj tested it in Tokyo) Why AI real-time translation is still painfully clunky — and what the ideal experience looks like Where to find Immad and Raj: [00:00] Data centers in space: skeptical takes [01:02] Anthropic's moment: why the narrative has shifted [02:16] OpenAI vs. Anthropic at $800B: where would you invest? [04:12] Anthropic's 3x revenue growth in 3 months: how is that possible? [06:10] The future of engineering teams in an AI-native world [07:37] Design's role in product: why Mercury still embeds designers everywhere [13:44] SpaceX S-1 and the IPO watch list [14:37] Why post-IPO hype fades and when to actually buy [17:01] Gemini in Tokyo: surprisingly good travel integration [17:43] AI translation fails: what the phoneless experience actually needs [20:06] Apple's AI opportunity and the edge computing bet [22:07] Data centers in space: the only scenario it makes sense [24:19] Xai co-founder exodus and AI researcher retention

  • S2 · E79
    April 3 · 52 min

    The Future of Investing: Data, Signals, and Retail Power

    George Kailas is the CEO of Prospero AI, a platform helping retail investors make smarter decisions using simplified market signals and data-driven insights. In this episode, George joins Immad and Raj to break down one of the biggest debates in investing today: should you just buy ETFs, or can retail investors actually beat the market? They go deep into how modern markets really work, why retail investors are becoming more powerful than ever, and what most people get wrong about stock picking, AI tools, and “free” trading platforms. What you’ll learn: Why ETFs beat stock picking if you don’t have enough time How retail investors now make up a massive share of market movement The biggest mistake investors make: not knowing when to exit Why analyst ratings and price targets often can’t be trusted How platforms like Robinhood actually make money (and what it means for you) The shift from software → data as the real moat in AI Why AI stock-picking tools are dangerous in volatile markets The psychology of investing: why most people need to lose before they learn What we cover: 00:00 Should You Pick Stocks or Just Buy ETFs? 00:50 Meet George Kailas (Prospero AI) 01:30 Beating the Market with Data Signals 02:15 From Mortgage Models to AI Founder 03:20 Why Data Will Matter More Than Software 04:20 Why People Don’t Trust Analyst Ratings Anymore 05:00 Who Is Prospero Actually Built For? 05:45 Value Investing vs Modern Momentum 07:00 The Big Debate: ETFs vs Stock Picking 07:35 The 1-Hour Rule: When You Should NOT Pick Stocks 08:30 Retail Investors Are Driving the Market Now 09:30 How to Actually Learn Investing (Without Losing Everything) 10:40 Why Exiting Trades Is the Hardest Skill 11:25 Are Public Markets Really Mispriced? 11:55 Why Analyst Price Targets Can’t Be Trusted 13:05 Inside Prospero’s 10 Signals System 14:10 How They Simplify Complex Market Data 15:10 Risk Signals: When to Exit a Trade 16:30 How Traders Use Options, Sentiment & Dark Pools 17:30 Are Apps Like Robinhood Good or Bad? 18:10 The Hidden Cost of “Free” Trades 19:30 Why Retail Investors Lose Power Through Brokers 20:10 Better Alternatives to Robinhood 21:40 AI, Data, and the Future of Investing 23:00 Why Intent Data Could Change Everything 24:40 AI, Layoffs & Wealth Inequality 26:00 The Rise of Crypto Traders & Risk Culture 27:10 Why Some Investors Need to Lose First 29:00 Why AI Tools Are Bad at Risk 30:00 Mercury’s Investing Strategy (Simple ETFs) 31:30 Why They Avoid Complexity in Investing Products 31:45 Fundraising Journey: From Angels to Crowdfunding 33:00 Lessons from Running a Crowdfund 34:10 When Crowdfunding Actually Works 36:00 Mercury’s Acquisition Strategy Explained 38:00 Building an All-in-One Financial Platform 41:00 George’s Founder Journey & Early Exit 42:30 From “Sharky” to Self-Aware Leader 43:30 How Meditation Changed His Leadership Style 45:00 Managing Teams: Autonomy, Mastery, Purpose 47:00 Long-Term Vision for Prospero AI 49:30 Rapid Fire Begins 49:40 Founder He Admires (Jensen Huang) 50:40 Trends That Won’t Last 51:30 What He Changed His Mind About 52:05 Closing Thoughts

  • S2 · E78
    April 1 · 38 min

    Founding Teams: What Works, What Doesn’t — with Andy Chen

    Andy Chen is the co-founder of Outcast Ventures, an early-stage fund focused on rethinking how founding teams come together. Prior to Outcast, he worked across recruiting and venture capital, including roles at Riviera Partners, Kleiner Perkins, and Coatue, where he was a General Partner. At Outcast, he’s building a talent-first approach to company creation, including a co-founder matching program designed to help founders form stronger teams from the start. What you'll learn: Why choosing a co-founder from your existing network can lead to weaker outcomes The data behind why strangers can make better co-founders What actually makes a billion-dollar founding team Why Andy evaluates the team before the idea when investing The key ingredients: skill, interest, and timing alignment Why solo founders rarely build generational companies How AI is enabling a new wave of high-revenue, small-team businesses The evolution of venture capital — and what might come next Andy’s unconventional path into venture, including time in government (as shared in the episode) In this episode, we cover: (00:00) Why successful founders struggle to find co-founders (00:28) Introduction to Andy Chen and Outcast Ventures (01:17) Andy’s path into Silicon Valley (03:23) Building Outcast and rethinking founder formation (04:19) Research on co-founder success (and what most people get wrong) (06:25) Why working with your co-founder before can hurt outcomes (07:47) Skill, interest, and timing alignment in founding teams (08:22) Inside Outcast’s co-founder matching model (10:24) Why existing co-founder platforms often fall short (11:23) Talent vs. finance backgrounds in venture capital (13:37) Why the team matters more than the idea (14:47) How venture capital has evolved over time (17:48) Rethinking the “atomic unit” of startups (19:20) AI, enterprise vs. consumer, and new opportunities (24:49) The rise (and limits) of solo founders (27:48) The future of venture in the AI era (30:33) Rapid fire: trends, feedback, and lessons (34:20) Andy’s experience working in government (37:45) Why everyone should try building something

  • S2 · E77
    March 27 · 52 min

    The Long Game: David Rusenko on Building Weebly, Surviving Acquisitions, and Investing in Climate

    David Rusenko is the founder and CEO of Leap Forward Ventures, a pre-seed and seed climate tech fund investing in energy, deep tech, and the reinvention of industrial processes. Before that, he spent 14 years as co-founder and CEO of Weebly, growing it from a college project to a platform serving tens of millions of small businesses before selling to Square in 2018. What you'll learn: Why Weebly stayed cash flow positive from early 2009 and what that meant for how they built the company How David thinks about dilution — and why inefficient spending is where founders actually lose equity The three headcount breaking points every CEO hits and how your role has to change at each one Why small businesses need owned channels and how marketplaces eating their margin is the defining tension in that market What clean tech investing looked like during the Vinod Khosla era vs. how David approaches it now Why solar's cost curve looks nothing like oil's over the last 100 years — and what that means for timing How David thinks about nuclear's role alongside renewables What made the Weebly acquisition to Square work when most acquisitions don't How word of mouth drove 80%+ of Weebly's growth and why that's hard to explain to investors Why David moved from operating to investing — and what the coach-on-the-sidelines framing means to him In this episode, we cover: (00:00) Cash flow positivity and dilution (01:08) Introduction to David Rusenko and Leap Forward Ventures (04:11) What Leap Forward Ventures invests in (05:32) Why climate tech goes through investment cycles (07:09) Oil price vs. solar cost curves over 100 years (09:08) Clean tech timing and the dot-com parallel (10:31) David's take on nuclear energy (12:29) Why David moved from operating to investing (13:45) Reflections on the Weebly acquisition (15:13) The small business owned channel problem (17:57) CEO breaking points at 25, 75, and 175 people (20:02) What happens to your jokes at 75 employees (22:55) Designing culture intentionally as you scale (28:18) Keeping politics out of your organization (32:50) Weebly's lowest points and near-death moments (37:27) Bootstrapping vs. VC — David's actual view (40:18) How Weebly grew: mostly word of mouth (43:04) The three phases of an S-curve market (44:13) What made the Square acquisition work (48:30) Rapid fire

  • S2 · E76
    March 13 · 55 min

    The State of Robotics in 2026: Ryan Gariépy on Hype, Reality, and Long-Term Thinking

    This week, we're bringing back one of our most loved episodes on Founders in Arms. Ryan Gariépy is the co-founder and former CTO of Clearpath Robotics and Otto Motors, acquired by Rockwell Automation for $600M+ in 2023. He bootstrapped the company for five years with only $300K in funding, reached profitability in 18 months, and spent 14 years building mobile robotics platforms that became the industry standard for research and industrial automation. What you'll learn: Why robotics is a systems discipline where progress stacks rather than explodes How to bootstrap a hardware company to $10M revenue before raising venture capital Why robotics follows 20-50% sustained growth for decades vs. software's boom-bust cycles The "promise problem" with humanoid robots and why form factor shapes user expectations How manufacturing in Canada (not China) became a strategic advantage for Clearpath Why founders overestimate 2-year progress but underestimate 10-year impact in robotics The real economics of humanoid robots: $20K cost becomes $80K landed price How robotics investment differs from software: less competitive, more defensible Why experience compounds in hardware but expires in software careers Investment criteria for robotics: engineering risk vs. technical risk and go-to-market strategy In this episode, we cover: (00:00) Introduction and live event announcement (03:29) Ryan's background: Clearpath Robotics and Otto Motors (04:06) Building two brands under one company (06:29) The 14-year journey: challenges and non-linear growth (07:11) Bootstrapping robotics when "nobody thought you could make money" (08:17) Reaching profitability in 18 months with research customers (10:28) Building robotics platforms for MIT, universities, and research labs (11:03) Manufacturing in Canada vs. outsourcing to Asia (15:05) Reconnecting after 20 years: the Waterloo entrepreneurship connection (16:17) Working at Kiva Systems (now Amazon Robotics) (18:10) Why robotics is more exciting now than ever in history (19:21) Robotics as systems discipline: no single breakthrough technology (21:22) The overhype cycle and realistic expectations (22:14) Software explodes then crashes; robotics compounds for decades (23:36) Why hardware is harder but more mission-driven (25:27) The talent pool advantage: people irrationally love hardware (27:30) Physical AI and real-world impact beyond software optimization (28:07) Humanoid robots: incredible tech, miscalibrated expectations (32:41) The "promise problem": form factors make promises to users (34:35) Consumer robotics examples: Matic cleaning robot (35:59) Asia leading in restaurant and airport robotics deployment (38:37) Training challenges and precursor technologies needed (39:20) China's role in robotics and humanoid development (41:08) Venture capital structures forcing "ridiculous things" in robotics (42:36) Robotics for entertainment vs. utility as consumer use case (43:52) Imad's robotics investments: Embark, Gecko Robotics, vertical AVs (45:23) Why robotics is less competitive than software (47:21) Operational design domain and technology risk assessment (48:19) The AV journey: Waymo, Zoox, and the importance of experience (49:39) Experience compounds in hardware, expires in software (50:31) Rapid fire: biggest mistake, following gut over charisma (51:47) Founder inspiration: Rodney Brooks (52:20) Uncomfortable feedback at Honda co-op job (53:17) Investment criteria: engineering risk, go-to-market, team understanding

  • S2 · E75
    March 6 · 51 min

    Thumbtack’s Marco Zappacosta on AI, Trust, and the Future of Marketplaces

    Marco Zapacosta is the co-founder and CEO of Thumbtack, the home services marketplace connecting homeowners with local pros for everything from plumbing to renovation. Started three weeks before Lehman Brothers collapsed in 2008, Thumbtack has grown to over $500M in annual run rate across 17 years of building. What you'll learn: Why Marco believes Thumbtack is still pre-product market fit at $500M in revenue How AI is shifting Thumbtack from a search engine to a matchmaker Why word of mouth is still the biggest competitor to every home services marketplace — and how AI finally evens the score Why convenience doesn't win when someone's spending $1,000 and entering your home Marco's take on practitioners vs. projectors — and why he doesn't trust most AI predictions Why AI agents won't disintermediate high-trust marketplaces How Thumbtack's operating model evolved from Google to Facebook to their own matrix structure What's kept Marco going for 17 years — and why he scores zero on neuroticism Why Marco wants to stay private a little longer before an inevitable IPO Why AI applied to robotics is overhyped and synthetic biology is massively underrated In this episode, we cover: (00:00) AI as substitute vs. complement — the flaw in our collective thinking (01:00) Introduction to Marco Zapacosta (02:12) Practitioners vs. projectors on AI (04:14) Real anxiety about AI job loss — engineers at birthday parties (07:21) Why Marco doesn't trust Block's layoff messaging (09:46) How AI is a massive accelerant for Thumbtack (10:02) Why home services is still pre-product market fit at $500M (11:02) Word of mouth is Thumbtack's biggest competitor (12:40) Will AI agents disintermediate marketplaces? (15:17) Why choice still matters in high-trust purchases (17:34) Why humans still want to read reviews themselves (19:15) Thumbtack's origin story — starting 3 weeks before Lehman collapsed (23:16) What's kept Marco going for 17 years (24:42) Entrepreneur parents and raising entrepreneurial kids (30:20) How Marco runs the company — the matrix model explained (35:25) Four co-founders: how responsibilities divided over time (37:02) Is Thumbtack going public? (39:33) The real downsides of being a public company (45:21) Rapid fire: who inspires Marco, what's overhyped, what's underhyped (47:12) The hardest part of leadership is self-awareness, not skills (49:02) Why struggling early builds staying power

  • S2 · E74
    February 27 · 42 min

    What AI Will Actually Do to the Economy with Noah Smith

    Noah Smith is a writer and Substack blogger behind Noahpinion, known for his contrarian, data-grounded takes on economics, technology, and geopolitics. What you'll learn: Why the viral Citrini "2028 Global Intelligence Crisis" post moved markets — and whether it should have The psychology behind why "AI causes 2008" scared Wall Street more than killer robots Why Noah thinks an AI-driven financial crisis is possible but unlikely How a productivity boom could paradoxically trigger a mild recession through "sticky prices" Why AI-enabled bioterrorism — not economic disruption — is Noah's biggest fear What Block's 4,000-person layoff and Mercury's hiring shifts reveal about AI's real impact on jobs Why software job losses in 2023-24 may have been driven by uncertainty, not AI capability Noah's take on deflation, GDP growth, and where inflation goes from here Why intellectual humility has been Noah's biggest edge as a forecaster The "dinosaur and the meteor" theory — why we're worrying about the economy while a much bigger threat flies overhead In this episode, we cover: (00:00) The meteor meme — AI's real threat vs. the economy (01:07) Introduction to Noah Smith (02:14) What the Citrini post actually argued (04:30) Why markets missed Covid — and what that tells us about AI (06:17) Why Citrini moved markets: the power of pattern matching to 2008 (07:51) Breaking down Citrini's financial crisis domino theory (08:38) Noah's verdict: possible but unlikely (11:04) Block lays off 4,000 — how does AI-driven unemployment play out macro? (17:42) How a productivity boom could cause a recession: sticky prices explained (19:46) Noah's real AI fear: vibe-coded bioweapons (24:55) Has bioterror surpassed China-Taiwan as Noah's top worry? (25:10) The economy today: inflation, deflation, and GDP (28:44) What Mercury's hiring strategy reveals about AI's effect on headcount (31:32) Why software job losses in 2023-24 may have been forward-looking uncertainty (34:38) The threat to blue collar jobs — are truck drivers next? (35:52) Why intellectual humility is Noah's competitive edge (39:26) The meteor meme closing: we created zombie gods for a 2.7% productivity boost

  • S2 · E73
    February 20 · 55 min

    How AI Agents Will Reshape the Web with Parag Agrawal

    We're bringing back one of our most loved episode on Founders in Arms. Parag Agrawal is the co-founder and CEO of Parallel, building infrastructure for the agentic web. Previously CEO of Twitter, Parag now leads a company architecting how AI agents will interact with the open web at orders of magnitude beyond current human scale. Two years after founding in stealth mode, Parallel recently announced a $100M Series B co-led by Kleiner Perkins and Index Ventures. What you'll learn: Why everything built for human web consumption will become irrelevant when agents become the primary users How Parallel's APIs enable agents to search, fetch, and monitor the web with unprecedented scale and speed The evolution from simple tool calls to autonomous sub-agents with real decision-making capability Why the web must transition from "pull" (searching on demand) to "push" (alerting when conditions are met) The new business models needed to compensate content creators in an agent-driven web Parag's counterintuitive approach to fundraising: why VC rejections don't sting but customer rejections do The rational game VCs play that founders misinterpret as genuine enthusiasm Why Parag believes we're not in an AI bubble—but an overreaction is coming (and it'll be faster than dot-com) How Parallel built quietly for a year before product-market fit arrived with the agent explosion The operational philosophy of extreme in-person collaboration that shaped Parallel's early culture In this episode, we cover: (00:00) Introduction and Parallel's mission (01:02) What Parallel's APIs enable for AI agents (02:43) Practical examples: coding agents, sales automation, research (04:57) The conviction bet on agents before the market existed (10:54) New business models for content in the agentic web (20:22) The $100M Series B fundraise and going public (23:03) Why Parallel built in stealth with carefully chosen early customers (24:55) Current scale and product offerings (30:42) The evolution from tools to sub-agents to push-based web (33:13) Are we in an AI bubble? Parag's nuanced perspective (36:34) The mental models behind fundraising vs customer rejections (38:37) Why VC enthusiasm is rational strategy, not signal (45:37) Biggest career mistake: delaying Twitter's algorithmic timeline (48:28) The compounding cost of six-month delays (50:09) Finding inspiration in "re-founders" like Satya Nadella (51:54) The most rewarding part: watching customers do unexpected things (52:43) In-person culture and the transition to remote-friendly

  • S2 · E72
    February 13 · 53 min

    Building a Services Business in a Tech World with Honey Homes' Vishwas Prabhakara

    Vishwas Prabhakara is the co-founder and CEO of Honey Homes, a subscription home maintenance service that's reimagining how Americans care for their homes. After spending four years at Yelp running the restaurant business, Vishwas saw firsthand why marketplaces fail for skilled home services—and built a contrarian solution. Now operating across San Francisco, LA, Chicago, Dallas, and Austin with 3,000+ members, Honey Homes creates quality jobs for skilled workers while delivering consistent, reliable home maintenance to homeowners. What you'll learn: Why the marketplace model fundamentally fails for skilled labor and home services The counterintuitive insight behind every successful consumer business (the Airbnb lesson) How Vishwas discovered workers were shocked that "nobody's yelled at me yet" after joining Honey Homes Why solving both sides of the market—customer experience AND worker quality of life—is essential The role of AI in leveling up service workers and automating operations without replacing humans Why early compromises on hiring and standards compound into major problems later The distribution challenge: getting consumers to prioritize chronic home maintenance needs How altruism, not just incentives, drives consumer referrals and growth Why companies like Yelp, Peloton, and Lyft deserve more respect for building culturally relevant businesses The mental model shift required to sell subscription home services vs. one-time fixes In this episode, we cover: (00:00) Introduction and the respect successful companies deserve (01:12) YC batch memories and feeling "late" to tech trends (03:05) The genesis of Honey Homes and why Immad and Raj invested (04:50) Growing up with a handy dad and discovering the home services gap (06:30) The counterintuitive consumer insight behind Honey Homes (07:03) "Nobody's yelled at me yet"—the worker experience problem (08:11) Why marketplaces don't work for skilled home services (09:48) Hiring only 1% of handyman applicants (14:07) Building trust through consistent quality and W2 employment (19:31) How altruism drives consumer referrals, not just incentives (21:51) Getting AI-pilled at Vinod Khosla's CEO retreat (23:01) Using AI to level up workers and automate operations (27:54) Overcoming the mental model barrier for subscription home services (30:07) The vision compromise lesson: don't settle on quality early (31:44) The critical importance of distribution for consumer businesses (32:26) Why partnerships aren't the answer (yet) for Honey Homes (38:41) Defending Yelp, Peloton, and Lyft against Silicon Valley discourse (42:18) Unit economics challenges in services businesses (47:10) Role models: Jeremy Stoppelman and Ramit Sethi (48:08) Hope that divisiveness is a passing trend (49:35) The daily challenge of building before the world sees it (51:04) Getting feedback about being "unpredictable" and staying in your head (52:33) Bringing people along for the journey in your mind

  • S2 · E71
    February 10 · 42 min

    Instacart's Max Mullen on Building Instacart and the Future of AI: First Live Founders in Arms

    What does it take to build a company in a category where everyone says the idea is dead? In this special live recording from Mercury's San Francisco office, Immad Akhund and Raj Suri sit down with Max Mullen, co-founder and former Chief Product Officer at Instacart, for an honest conversation about the founder journey. Max shares how Instacart started in 2012 when there was no gig economy, no Uber X, and investors repeatedly told them grocery delivery was a dead idea after Webvan's failure. The conversation explores the controversial early days of building Instacart, why Max believes founder pain tolerance is the biggest moat, and the critical importance of market timing even when you're executing well. Max opens up about the challenges of being a technical co-founder without deep technical skills, navigating co-founder dynamics, and the reality that many startup outcomes are heavily influenced by timing and luck. The discussion shifts to AI's transformative potential, with Max offering a compelling framework: software engineers are experiencing "the tip of the spear" of AI capabilities today, and this same 10x productivity leap will soon apply to lawyers, doctors, accountants, and every other profession. He explores what AI-native companies will look like and why the next wave of startups will be built around professionals orchestrating fleets of agents. This episode offers essential insights for founders building in challenging markets, navigating co-founder relationships, timing market opportunities, and understanding where AI is creating the biggest opportunities for new companies.

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