Founders in Arms

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

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

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

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

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