Founders in Arms

Ryan Gariepy 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.(If you’re looking for inspiration and lessons from other founders, Founders in Arms is hosting a founders roundtable with Rajat Suri, Immad Akhund, and Max Mullen next Wed Jan 14th at Mercury HQ. Discussing war stories and sharing lessons with a group of founders, as part of Founders-in-Arms podcast. Will be food and drinks. Capacity strictly limited at 50 so apply early if you’re interested: https://luma.com/dk97inyk )What you'll learn:Why robotics is a systems discipline where progress stacks rather than explodesHow to bootstrap a hardware company to $10M revenue before raising venture capitalWhy robotics follows 20-50% sustained growth for decades vs. software's boom-bust cyclesThe "promise problem" with humanoid robots and why form factor shapes user expectationsHow manufacturing in Canada (not China) became a strategic advantage for ClearpathWhy founders overestimate 2-year progress but underestimate 10-year impact in roboticsThe real economics of humanoid robots: $20K cost becomes $80K landed priceHow robotics investment differs from software: less competitive, more defensibleWhy experience compounds in hardware but expires in software careersInvestment criteria for robotics: engineering risk vs. technical risk and go-to-market strategyIn 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...

Emmett Shear is the founder and CEO of Softmax, an alignment research company, and previously co-founded and led Twitch as CEO. He was also a Y Combinator partner and briefly served as interim CEO of OpenAI.What you'll learn:Why AI alignment and AGI are fundamentally the same problemHow theory of mind is the critical missing piece in current AI systemsWhy continuous learning requires self-modeling capabilitiesThe dangerous truth: alignment is a capacity for both great good and great evilWhy "aligned AI" really means "aligned to me"—and why that's concerningHow societies of smaller AIs will outcompete singleton superintelligencesWhy AI needs to be integrated with humans, not segregated into AI-only societiesThe Twitch lesson: people don't want easy, they want goodWhy 99% of AI startups are building labor-saving tools instead of value-creating productsHow parenting and AI development mirror each other in surprising waysWhy current AI labs are confused about continuous learningConway's Law applied to AI: you ship your org chartThe problem with mode collapse in self-learning systemsWhy emotions are training signals, not irrational noiseEmmett's biggest mistake at Twitch: chasing new products instead of perfecting the coreIn this episode, we cover:(00:00) The dangerous truth about AI alignment(01:13) Introduction to Softmax and organic alignment(02:05) What alignment actually means (and why most people are confused)(03:33) The output: training environments for theory of mind(05:01) Continuous learning and why it's so hard(06:25) Multiplayer reasoning training in open-ended environments(07:14) Aligned to what? The critical question everyone ignores(08:40) Why alignment is always relative to the aligning being(11:07) Cooperation vs. competition: training for the real world(12:56) Is AGI an urgent problem or do we have time?(13:15) AGI and alignment are the same problem(15:25) Alignment capacity enables both good and evil(17:13) The singleton problem and why societies of AIs make sense(20:41) Building alignment between AIs and humans(22:09) Why Elon's "biggest cluster" strategy might be wrong(23:06) AI must be aligned to individual humans, not humanity(25:03) What does the atomic unit of AI look like?(28:02) Adding a new kind of person to society(29:06) Everything will be alive: from spreadsheets to cars(30:00) From Twitch retirement to Softmax founding(31:26) Research vs. product engineering at early-stage startups(32:41) Raising money for AI research in the current era(34:30) Why Softmax will ship products(34:50) Ilya's closed-loop research vs. open-loop learning(36:36) How you do anything is how you do everything(37:28) The continuous learning problem explained simply(38:29) Mode collapse: why AIs become stereotypes of themselves(39:33) The reward problem and why humans need emotions(40:48) Why LLMs are trained to avoid emotions(41:52) Watching children learn while building learning AI(43:04) Advice for first-time AI founders(45:08) Treat AI as clay to be molded, not a genie granting wishes(45:50) The Twitch lesson: people want good things, not easy things(47:22) Why 99% of AI companies are building the wrong thing(48:16) Rapid fire: biggest career mistake at Twitch(50:15) Which founders inspire Emmett most(50:56) The passing fad: AI slop generators

Immad Akhund and Raj Suri reunite for a one-on-one conversation covering the biggest tech shifts of 2025, from Mercury's public launch of Personal Banking to the quieting of AGI doom discussions. This wide-ranging episode explores why self-driving cars may matter more than AGI, how vibe coding is changing software development, and the strategic decisions founders make when everyone else disagrees.What you'll learn:Why Immad launched Mercury Personal despite investor and team skepticism—and the founder lesson about following convictionHow Mercury Personal brings business-grade financial controls to personal banking (collaboration features, automatic categorization, 3.5% savings rates)The existential threat facing OpenAI and Anthropic as AI models commoditize and Google leverages distribution advantagesRaj's vibe coding experiment: Building a full-stack app with Postgres backend using just prompts (and why Replit won)Why Tribe is rejecting the $30/user ad model to build a premium, ad-free group chat platformThe retention metrics showing Tribe's product-market fit (20-40% six-month retention with minimal marketing)How AI hype shifted from AGI doom conversations to practical commercial applications in 2025Why self-driving technology (Waymo, Tesla FSD) represents a more immediate transformation than AGIThe best and worst of 2025: renewed tech energy vs. immigration scapegoating and Doge's failure to deliver government efficiencyWhy supply constraints (chips, power) signal AI demand is real, not a bubbleIn this episode, we cover:(00:00) AGI conversations cooling down in 2025(01:50) Mercury Personal launch after year-long waitlist(02:42) Business-grade controls for personal banking(04:30) 3.5% savings rates and Treasury/Invest products(06:15) Following founder conviction despite opposition(07:33) Balancing product shipping with polish(08:26) OpenAI's Code Red and focus strategy(09:23) Google's distribution advantage vs. OpenAI(10:33) The API commoditization threat to Anthropic(12:34) Why ad economics dominate the internet(14:58) Facebook's $30/user vs. subscription models(17:22) Tribe's progress: retention, AI features, monetization plans(21:42) Vibe coding experiment: Replit vs. Lovable vs. Wix(26:31) Why Replit might own the vibe coding market(28:05) Enterprise use cases for AI-generated apps(33:26) 2025's best: renewed tech energy and deregulation(34:51) 2025's worst: immigration scapegoating and Doge's failure(40:48) Self-driving breakthrough: Waymo and Tesla FSD(42:31) Why AGI talk has quieted down(43:43) Supply constraints proving AI demand is real

Noah Glass is the founder and CEO of Olo, an enterprise platform for mobile and online ordering that powers digital commerce for 800+ restaurant brands and nearly 90,000 locations. Founded in 2005, Olo went public in 2021 at a $3.5B valuation and was acquired by Thoma Bravo in 2024—a 20-year journey from scrappy startup to category leader.What you'll learn:Why Olo's first 10 years required extreme "pain tolerance" waiting for product-market fitThe B2C to B2B pivot that transformed their unit economics from burning $15 per customer to earning revenue while scalingHow "embrace the suck"—borrowed from the Marine Corps—became the cultural mantra that kept the team goingWhy going public was about customer confidence and long-term credibility, not exit or liquidityThe role of industry advisors in bridging credibility gaps when selling to traditional enterprisesHow adding delivery-as-a-service (Dispatch) in 2015 unlocked escape velocity and scale advantageThe challenges and benefits of operating as a public company in a misunderstood industryWhy partnering with Thoma Bravo PE offers better alignment than quarterly public market pressuresNoah's philosophy on founder loyalty and the lifelong bonds formed with early team membersWhy the current "homegrown tech stack" trend in enterprise is a passing fad that misses SaaS fundamentalsIn this episode, we cover:(00:00) Introduction and the "embrace the suck" mentality(01:03) Early days and the long wait for product-market fit(05:30) Why YC's "grow fast or quit" advice doesn't apply to every company(08:06) The deep bonds formed with early team members(12:14) Deciding between B2B vs B2C business models(13:34) The B2C beginning and Good Morning America moment(16:08) The pivot to B2B enterprise software(20:43) How third-party delivery and DoorDash changed the industry(23:04) The journey as a public company (2021-2024)(27:49) Why going public signaled long-term stability to enterprise customers(30:15) Operating under private equity with Thoma Bravo(36:10) Breaking into enterprise sales with industry advisors(44:45) The importance of reliability at scale for enterprise(46:58) Thinking about market size and expansion in vertical software(48:25) Rapid fire: Which founder inspires you most(49:01) Uncomfortable feedback on being overly loyal(50:48) Current trend prediction: Homegrown enterprise software is a fad

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 usersHow Parallel's APIs enable agents to search, fetch, and monitor the web with unprecedented scale and speedThe evolution from simple tool calls to autonomous sub-agents with real decision-making capabilityWhy 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 webParag's counterintuitive approach to fundraising: why VC rejections don't sting but customer rejections doThe rational game VCs play that founders misinterpret as genuine enthusiasmWhy 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 explosionThe operational philosophy of extreme in-person collaboration that shaped Parallel's early cultureIn 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