Founders in Arms

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

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 time
How 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 learn

What 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 Anymore
05:00 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 Now
09:30 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 Trade
16:30 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 Brokers
20:10 Better Alternatives to Robinhood
21:40 AI, Data, and the Future of Investing
23:00 Why Intent Data Could Change Everything
24:40 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 Risk
30:00 Mercury’s Investing Strategy (Simple ETFs)
31:30 Why They Avoid Complexity in Investing Products
31:45 Fundraising Journey: From Angels to Crowdfunding
33:00 Lessons from Running a Crowdfund
34:10 When Crowdfunding Actually Works
36:00 Mercury’s Acquisition Strategy Explained
38:00 Building an All-in-One Financial Platform
41:00 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, Purpose
47:00 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 About
52:05 Closing Thoughts

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 outcomes
The data behind why strangers can make better co-founders
What actually makes a billion-dollar founding team
Why Andy evaluates the team before the idea when investing
The key ingredients: skill, interest, and timing alignment
Why solo founders rarely build generational companies
How AI is enabling a new wave of high-revenue, small-team businesses
The evolution of venture capital — and what might come next
Andy’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