Founders in Arms

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

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 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

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.

Mehul Nariyawala is the co-founder and President of Matic Robotics, a home robotics company building what he calls “robotics 2.0” — intelligent, vision-first robots designed to actually work in real homes. After early careers at Nest and a prior acquisition by Google, Mehul and his team spent seven years building Matic, challenging the assumptions behind robot vacuums, consumer hardware, and how robotics companies should scale.In this conversation, Mehul breaks down why robotics is far harder than software, why most home robots quietly fail, and how Matic approached everything differently — from vision-only robotics and in-house manufacturing to avoiding subscriptions, ads, and premature market creation.What you’ll learn:Why robotics is “100× harder than software” — and where most teams underestimate the workThe difference between automation and true intelligence in home robotsWhy negative-NPS categories can hide massive opportunitiesHow Matic beat entrenched incumbents like Roomba by fixing fundamentals, not adding featuresWhy vision-only robotics was a risky but necessary betThe real reason humanoid robots are still far from consumer-readyLessons from Nest on why some hardware categories stay defensible for decadesWhy creating a new market can be fatal for hardware startupsHow Matic built robots in-house in California instead of outsourcing manufacturingThe tradeoffs between subscriptions, ownership, and consumer trustWhy great hardware products must earn word-of-mouth before growthIn this episode, we cover:(00:00) Introduction to Mehul Naryawala and Matic Robotics(01:10) Why robotics is dramatically harder than software(03:00) The failure modes of early robot vacuums(05:10) Identifying opportunity in negative-NPS markets(07:45) Automation vs. intelligence in consumer robotics(10:15) Why vision-only robotics was a foundational bet(14:00) Lessons from Nest on defensible hardware categories(17:30) Why Matic avoided creating a new market(20:45) In-house manufacturing and vertical integration(24:30) Scaling hardware without inventory risk(28:10) The long road from demo to product(32:00) Why humanoid robots are still overhyped(36:20) Word-of-mouth, product-led growth, and brand trust(40:15) Subscription fatigue and consumer psychology(44:30) The future of home robotics and where Matic goes next

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 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

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

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

Sumir Meghani is the founder and CEO of Instawork, a staffing marketplace connecting 9 million hourly workers with businesses that need flexible labor. Starting with just line cooks in San Francisco restaurants, Instawork now serves warehouses, stadiums, hotels, and hospitality businesses across the country, creating what Sumir calls "employment at the touch of a button."What you'll learn:Why starting "boring and narrow" (one city, one job type) is the key to marketplace successHow Instawork is building a "LinkedIn for hourly workers" with hundreds of data points per profileThe hidden costs of 100%+ annual turnover in restaurants and hospitalityWhy people actually want to work MORE hours when friction is removedThe concept of "robot wranglers" as the next major labor categoryHow Instawork is using its worker pool to train physical AI and robotics modelsThe difference between "leading by disappointment" vs. celebrating wins as a CEOWhy labor costs range from 30% (restaurants) to 80% (hospitals) of revenueThe labor market as a "Tetris board" of micro-jobs and available workersWhy Silicon Valley undervalues hourly work despite 100 million workers depending on itIn this episode, we cover:(00:00) Introduction and YPO CEO forum discussion(03:42) Sumir's journey from Groupon to founding InstaWork(04:58) The restaurant visit that sparked the idea(06:28) Why the hourly labor shortage is a global problem(08:07) Building profiles for 9 million workers(08:54) Starting narrow: San Francisco restaurants and line cooks only(12:28) The hourly worker crisis in hospitality(13:04) Why wages haven't risen despite labor shortages(15:58) The true cost of labor beyond hourly rates(17:48) AI's role in reducing onboarding friction(19:42) Physical AI and the future of robotics(20:16) Introducing "robot wranglers" as a new labor category(22:36) Using InstaWork's workforce to train robot models(23:34) Navigating the AI hype cycle as a consumer(26:47) White collar vs. blue collar labor market dynamics(29:29) Why more jobs will shift to physical industries(30:43) The cultural bias against hourly work in Silicon Valley(32:11) Rapid fire: Biggest entrepreneurial mistakes(33:36) Most rewarding parts of the founder journey(34:30) Why Silicon Valley should start simple, not big(36:30) The uncomfortable feedback: "Leading by disappointment"(38:50) Balancing high standards with celebration(39:58) What inspires Sumir: Physical AI and robotics innovation