Founders in Arms

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

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

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