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Founders in Arms

Founders in Arms

Hosted by Immad Akhund and Rajat Suri

TechnologyScienceSocietyCultureInterviews guests
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Episodes

109

Latest episode

Jul 2026

Language

EN

About the show

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. YouTube: youtube.com/@FoundersInArms Substack: foundersinarms.substack.com Instagram: instagram.com/foundersinarms TikTok: tiktok.com/@foundersinarms_

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60 recent
August 14, 202655 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 segmentHow direct indexing creates "tax alpha" by harvesting capital losses, and why that's a deferral of taxes rather than an elimination of themHow a step-up in cost basis at death effectively forgives the deferred tax billThe concrete numbers: how much a $100k investment can harvest in losses via a classic direct index versus a long-short direct indexWhy long-term, sophisticated investors have proven far less fee-sensitive than the market assumesMo's path from Twitvid — an early video app built on top of Twitter — to five years working inside Twitter itselfHow a frustrating experience with a wealth manager who charged 1% fees for little added value planted the idea for FrecWhy 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 FrecWhy Frec had to resequence its roadmap when rising interest rates undercut its original plan to lead with a cheap line-of-credit productImmad's framework for company OKRs (which he calls "COR") and why he insists on including non-measurable resultsWhy Frec has deliberately stayed out of banking, unlike some robo-advisor competitorsMo and Immad's picks for financial products that should already be obsoleteChapters: (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

July 10, 202628 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 needHow to build an ethical framework for operating in an industry full of shortcuts and noiseHow to extract real signal from investors without letting them drive your roadmapReal pivot stories from Presto (restaurant tablets to voice AI), Lyft (carpooling to peer-to-peer ride-hailing), and Mercury's early product-market-fit signalWhy blaming distribution is often easier than blaming the product — and why that's a trapHow founders can get their teams to think about prioritization the way they doHow Mercury created early demand by deliberately recruiting a broad, vocal set of seed investorsWhat "the future of work" looks like when your team's job shifts from producing outcomes directly to building the systems that produce themHow growing up outside Silicon Valley shaped each panelist's belief that they could build something from scratchChapters:(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

June 26, 202649 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 easierWhy winning a market can be a trap when the TAM is smaller than you thoughtThe real story behind the WeWork acquisition: a three-year relationship, a theatric walkout, and why great exits are always principal-to-principalWhy over-capitalization was more ruinous to Managed by Q than any external factorHow 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 outcomesThe mismatch between what early-stage founders need to raise and what top VC funds are mandated to deployWhy founders should play the hype game — but stay ruthlessly honest with themselves about what game they're playingDan's take on Adam Neumann: what made him exceptional, where he fell short, and why Dan wouldn't bet against himThe "leaders eat last" philosophy — and why holding people to high standards and having their backs aren't in conflictChapters:[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

June 5, 202652 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 accessHow iRobot funded itself for eight years through customer contracts instead of investorsThe sales tactic Colin used to get Fortune 500 CTOs to fund iRobot's R&DHow DoD mine-hunting algorithms and a Hasbro partnership became the technology inside RoombaThe wallet share framework for evaluating whether a consumer robot idea can actually workWhy adding features to a consumer robot often reduces perceived valueHow iRobot priced Roomba at $199 with a $42 BOM — and what that discipline requiredWhat it felt like to go public, and how everything changes when what you say can be monetizedThe full story behind the Amazon acquisition attempt and why the EU and FTC blocked itWhat Familiar Machines and Magic is building and why the pet economy is the target compChapters:00:00 – Regulators celebrate blocked deals — what Colin saw on FTC examiners' doors00:53 – Introducing Colin Angle, co-founder of iRobot and Familiar Machines and Magic02:00 – The "if not us, who?" moment that started iRobot03:54 – First business model: privately fund a moon mission, sell the movie rights07:03 – Eight years without VC: "completely unfundable"08:09 – The CTO sales tactic: present a problem half a step from their real one09:00 – "Work for no profit, cancel anytime" — the deal structure they used five times12:05 – Built for 10,000 units, sold 70,000 Roombas in three months15:03 – "If I had VC early, iRobot would have failed"18:40 – $199 retail, $42 BOM — the Roomba economics20:31 – 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 falling42:09 – Familiar Machines and Magic: the new company and the original vision46:12 – Building robots for human connection, not task automation

May 29, 202651 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 feedbackThe "pain discovery" method he uses to extract what's actually brokenHow Next.js started as a personal solution and became a wedge into the entire cloudWhy he deliberately ignores competitors when buildingThe three chapters of Vercel's growth and what drove each inflection pointHow customer-led innovation produced some of Vercel's biggest revenue linesWhy your second product has a higher bar than your firstThe iPhone and AirPods framework for thinking about adjacenciesWhat "brand permission" means and why it explains Google's failuresWhy consensus around an idea is a signal to walk awayChapters:00:00 – Managing your own psychology as a founder00:51 – Welcome + live event intro02:55 – Vercel's web stack vs. agent stack04:04 – Guillermo's background and first exit to WordPress05:15 – Spotting the waves: cloud and front end in 201308:49 – Everything is feedback; the pain discovery method10:40 – Short-term pessimism, long-term optimism13:14 – Opinions vs. ideas: the Jony Ive mental model16:40 – Chapter 1: Finding focus — how Next.js became the wedge21:03 – Why consensus is a red flag for startup ideas21:40 – The MacBook moment: simplicity wins25:37 – Chapter 2: Repeatability — e-commerce as the GTM unlock29:30 – Chapter 3: Scaling the company as the product34:41 – iPhone and AirPods: smart adjacencies to a strong core38:41 – Brand permission: why Google keeps failing at social40:18 – The SF culture divide: AI optimists vs. AI anxious43:09 – The AI gentrification of San Francisco49:05 – Being your own coach; founder loneliness and burnout50:46 – What fundraising actually feels like

May 22, 202654 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 agentsWhy speed and quality — not features — were Linear's winning strategy in a crowded marketHow Karri thinks about AI's role in design and why average startup design is getting worseWhy designers rarely become founders and whether AI will change thatThe "Quality Wednesday" ritual Linear uses to keep polish standards high at 120 peopleHow Linear's feature roast process catches blind spots before anything shipsWhat Linear borrowed from Coinbase's hiring playbook — and how work trials outperform interviewsHow Linear built an open agent platform and why it now hosts more agents than any tool in its categoryKarri's take on whether designers should write code — and where design thinking matters mostWhy Linear intentionally pushed PM thinking to engineers and designers instead of hiring traditional PMsIn 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

May 1, 202654 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 backwardsHow WorkOS became the default enterprise-ready layer for AI-native companiesThe Stripe parallel: why developer infrastructure compounds the same way payments didWhat a failed first startup taught Michael about idea validationHow keeping a daily idea notebook — volume, not quality — led to WorkOSWhy second-time founders approach conviction and validation completely differentlyThe do-or-die bond between developer tools and their customersHow Michael taught himself enterprise sales after starting as a purely technical founderWhy building for developers is the ultimate boss battle in techWhat AI getting to Renaissance-printing-press level actually means for softwareChapters:(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

April 21, 202625 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 holdImmad's take on whether he'd invest in OpenAI or Anthropic at $800B todayHow Anthropic is growing 3x in revenue in three months — and whether it's even possibleThe new engineering team model: fewer engineers, more autonomy, OKR-driven executionWhy design still matters — and why Mercury embeds designers directly into product teamsHow to time IPO investments: why Raj waits 3-4 months post-listing to buyWhat the SpaceX S-1 signals about the new AI hype cycleWhy Apple is undervalued (or not) — the edge computing argumentHow 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 likeWhere 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

April 3, 202652 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 timeHow 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 learnWhat 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 Anymore05:00 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 Now09:30 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 Trade16:30 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 Brokers20:10 Better Alternatives to Robinhood 21:40 AI, Data, and the Future of Investing 23:00 Why Intent Data Could Change Everything24:40 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 Risk30:00 Mercury’s Investing Strategy (Simple ETFs) 31:30 Why They Avoid Complexity in Investing Products31:45 Fundraising Journey: From Angels to Crowdfunding 33:00 Lessons from Running a Crowdfund 34:10 When Crowdfunding Actually Works36:00 Mercury’s Acquisition Strategy Explained 38:00 Building an All-in-One Financial Platform41:00 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, Purpose47:00 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 About52:05 Closing Thoughts

April 1, 202638 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 outcomesThe data behind why strangers can make better co-foundersWhat actually makes a billion-dollar founding teamWhy Andy evaluates the team before the idea when investingThe key ingredients: skill, interest, and timing alignmentWhy solo founders rarely build generational companiesHow AI is enabling a new wave of high-revenue, small-team businessesThe evolution of venture capital — and what might come nextAndy’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

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