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:

1. Why Guillermo treats everything—including silence—as feedback
2. The "pain discovery" method he uses to extract what's actually broken
3. How Next.js started as a personal solution and became a wedge into the entire cloud
4. Why he deliberately ignores competitors when building
5. The three chapters of Vercel's growth and what drove each inflection point
6. How customer-led innovation produced some of Vercel's biggest revenue lines
7. Why your second product has a higher bar than your first
8. The iPhone and AirPods framework for thinking about adjacencies
9. What "brand permission" means and why it explains Google's failures
10. 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:

1. How Linear evolved from issue tracking to a full product-building system with AI agents
2. Why speed and quality — not features — were Linear's winning strategy in a crowded market
3. How Karri thinks about AI's role in design and why average startup design is getting worse
4. Why designers rarely become founders and whether AI will change that
5. The "Quality Wednesday" ritual Linear uses to keep polish standards high at 120 people
6. How Linear's feature roast process catches blind spots before anything ships
7. What Linear borrowed from Coinbase's hiring playbook — and how work trials outperform interviews
8. How Linear built an open agent platform and why it now hosts more agents than any tool in its category
9. Karri's take on whether designers should write code — and where design thinking matters most
10. 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:

1. Why the SaaS apocalypse narrative gets it completely backwards
2. How WorkOS became the default enterprise-ready layer for AI-native companies
3. The Stripe parallel: why developer infrastructure compounds the same way payments did
4. What a failed first startup taught Michael about idea validation
5. How keeping a daily idea notebook — volume, not quality — led to WorkOS
6. Why second-time founders approach conviction and validation completely differently
7. The do-or-die bond between developer tools and their customers
8. How Michael taught himself enterprise sales after starting as a purely technical founder
9. Why building for developers is the ultimate boss battle in tech
10. 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:

1. Why Anthropic has taken the AI narrative from OpenAI — and whether that lead will hold
2. Immad's take on whether he'd invest in OpenAI or Anthropic at $800B today
3. How Anthropic is growing 3x in revenue in three months — and whether it's even possible
4. The new engineering team model: fewer engineers, more autonomy, OKR-driven execution
5. Why design still matters — and why Mercury embeds designers directly into product teams
6. How to time IPO investments: why Raj waits 3-4 months post-listing to buy
7. What the SpaceX S-1 signals about the new AI hype cycle
8. Why Apple is undervalued (or not) — the edge computing argument
9. How good Gemini's travel integration actually is (Raj tested it in Tokyo)
10. 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:

1. Why ETFs beat stock picking if you don’t have enough time
2. How retail investors now make up a massive share of market movement
3. The biggest mistake investors make: not knowing when to exit
4. Why analyst ratings and price targets often can’t be trusted
5. How platforms like Robinhood actually make money (and what it means for you)
6. The shift from software → data as the real moat in AI
7. Why AI stock-picking tools are dangerous in volatile markets
8. 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:

1. Why choosing a co-founder from your existing network can lead to weaker outcomes
2. The data behind why strangers can make better co-founders
3. What actually makes a billion-dollar founding team
4. Why Andy evaluates the team before the idea when investing
5. The key ingredients: skill, interest, and timing alignment
6. Why solo founders rarely build generational companies
7. How AI is enabling a new wave of high-revenue, small-team businesses
8. The evolution of venture capital — and what might come next
9. 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

David Rusenko is the founder and CEO of Leap Forward Ventures, a pre-seed and seed climate tech fund investing in energy, deep tech, and the reinvention of industrial processes. Before that, he spent 14 years as co-founder and CEO of Weebly, growing it from a college project to a platform serving tens of millions of small businesses before selling to Square in 2018.

What you'll learn:

1. Why Weebly stayed cash flow positive from early 2009 and what that meant for how they built the company
2. How David thinks about dilution — and why inefficient spending is where founders actually lose equity
3. The three headcount breaking points every CEO hits and how your role has to change at each one
4. Why small businesses need owned channels and how marketplaces eating their margin is the defining tension in that market
5. What clean tech investing looked like during the Vinod Khosla era vs. how David approaches it now
6. Why solar's cost curve looks nothing like oil's over the last 100 years — and what that means for timing
7. How David thinks about nuclear's role alongside renewables
8. What made the Weebly acquisition to Square work when most acquisitions don't
9. How word of mouth drove 80%+ of Weebly's growth and why that's hard to explain to investors
10. Why David moved from operating to investing — and what the coach-on-the-sidelines framing means to him

In this episode, we cover:

(00:00) Cash flow positivity and dilution

(01:08) Introduction to David Rusenko and Leap Forward Ventures

(04:11) What Leap Forward Ventures invests in

(05:32) Why climate tech goes through investment cycles

(07:09) Oil price vs. solar cost curves over 100 years

(09:08) Clean tech timing and the dot-com parallel

(10:31) David's take on nuclear energy

(12:29) Why David moved from operating to investing

(13:45) Reflections on the Weebly acquisition

(15:13) The small business owned channel problem

(17:57) CEO breaking points at 25, 75, and 175 people

(20:02) What happens to your jokes at 75 employees

(22:55) Designing culture intentionally as you scale

(28:18) Keeping politics out of your organization

(32:50) Weebly's lowest points and near-death moments

(37:27) Bootstrapping vs. VC — David's actual view

(40:18) How Weebly grew: mostly word of mouth

(43:04) The three phases of an S-curve market

(44:13) What made the Square acquisition work

(48:30) Rapid fire

This week, we're bringing back one of our most loved episodes on Founders in Arms. Ryan Gariépy 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.

What you'll learn:

Why robotics is a systems discipline where progress stacks rather than explodes

How to bootstrap a hardware company to $10M revenue before raising venture capital

Why robotics follows 20-50% sustained growth for decades vs. software's boom-bust cycles

The "promise problem" with humanoid robots and why form factor shapes user expectations

How manufacturing in Canada (not China) became a strategic advantage for Clearpath

Why founders overestimate 2-year progress but underestimate 10-year impact in robotics

The real economics of humanoid robots: $20K cost becomes $80K landed price

How robotics investment differs from software: less competitive, more defensible

Why experience compounds in hardware but expires in software careers Investment criteria for robotics: engineering risk vs. technical risk and go-to-market strategy

In 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

Marco Zapacosta is the co-founder and CEO of Thumbtack, the home services marketplace connecting homeowners with local pros for everything from plumbing to renovation. Started three weeks before Lehman Brothers collapsed in 2008, Thumbtack has grown to over $500M in annual run rate across 17 years of building.

What you'll learn:

1. Why Marco believes Thumbtack is still pre-product market fit at $500M in revenue
2. How AI is shifting Thumbtack from a search engine to a matchmaker
3. Why word of mouth is still the biggest competitor to every home services marketplace — and how AI finally evens the score
4. Why convenience doesn't win when someone's spending $1,000 and entering your home
5. Marco's take on practitioners vs. projectors — and why he doesn't trust most AI predictions
6. Why AI agents won't disintermediate high-trust marketplaces
7. How Thumbtack's operating model evolved from Google to Facebook to their own matrix structure
8. What's kept Marco going for 17 years — and why he scores zero on neuroticism
9. Why Marco wants to stay private a little longer before an inevitable IPO
10. Why AI applied to robotics is overhyped and synthetic biology is massively underrated

In this episode, we cover:

(00:00) AI as substitute vs. complement — the flaw in our collective thinking

(01:00) Introduction to Marco Zapacosta

(02:12) Practitioners vs. projectors on AI

(04:14) Real anxiety about AI job loss — engineers at birthday parties

(07:21) Why Marco doesn't trust Block's layoff messaging

(09:46) How AI is a massive accelerant for Thumbtack

(10:02) Why home services is still pre-product market fit at $500M

(11:02) Word of mouth is Thumbtack's biggest competitor

(12:40) Will AI agents disintermediate marketplaces?

(15:17) Why choice still matters in high-trust purchases

(17:34) Why humans still want to read reviews themselves

(19:15) Thumbtack's origin story — starting 3 weeks before Lehman collapsed

(23:16) What's kept Marco going for 17 years

(24:42) Entrepreneur parents and raising entrepreneurial kids

(30:20) How Marco runs the company — the matrix model explained

(35:25) Four co-founders: how responsibilities divided over time

(37:02) Is Thumbtack going public?

(39:33) The real downsides of being a public company

(45:21) Rapid fire: who inspires Marco, what's overhyped, what's underhyped

(47:12) The hardest part of leadership is self-awareness, not skills

(49:02) Why struggling early builds staying power

Noah Smith is a writer and Substack blogger behind Noahpinion, known for his contrarian, data-grounded takes on economics, technology, and geopolitics.

What you'll learn:

1. Why the viral Citrini "2028 Global Intelligence Crisis" post moved markets — and whether it should have
2. The psychology behind why "AI causes 2008" scared Wall Street more than killer robots
3. Why Noah thinks an AI-driven financial crisis is possible but unlikely
4. How a productivity boom could paradoxically trigger a mild recession through "sticky prices"
5. Why AI-enabled bioterrorism — not economic disruption — is Noah's biggest fear
6. What Block's 4,000-person layoff and Mercury's hiring shifts reveal about AI's real impact on jobs
7. Why software job losses in 2023-24 may have been driven by uncertainty, not AI capability
8. Noah's take on deflation, GDP growth, and where inflation goes from here
9. Why intellectual humility has been Noah's biggest edge as a forecaster
10. The "dinosaur and the meteor" theory — why we're worrying about the economy while a much bigger threat flies overhead

In this episode, we cover:

(00:00) The meteor meme — AI's real threat vs. the economy

(01:07) Introduction to Noah Smith

(02:14) What the Citrini post actually argued

(04:30) Why markets missed Covid — and what that tells us about AI

(06:17) Why Citrini moved markets: the power of pattern matching to 2008

(07:51) Breaking down Citrini's financial crisis domino theory

(08:38) Noah's verdict: possible but unlikely

(11:04) Block lays off 4,000 — how does AI-driven unemployment play out macro?

(17:42) How a productivity boom could cause a recession: sticky prices explained

(19:46) Noah's real AI fear: vibe-coded bioweapons

(24:55) Has bioterror surpassed China-Taiwan as Noah's top worry?

(25:10) The economy today: inflation, deflation, and GDP

(28:44) What Mercury's hiring strategy reveals about AI's effect on headcount

(31:32) Why software job losses in 2023-24 may have been forward-looking uncertainty

(34:38) The threat to blue collar jobs — are truck drivers next?

(35:52) Why intellectual humility is Noah's competitive edge

(39:26) The meteor meme closing: we created zombie gods for a 2.7% productivity boost

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:

1. Why everything built for human web consumption will become irrelevant when agents become the primary users
2. How Parallel's APIs enable agents to search, fetch, and monitor the web with unprecedented scale and speed
3. The evolution from simple tool calls to autonomous sub-agents with real decision-making capability
4. Why the web must transition from "pull" (searching on demand) to "push" (alerting when conditions are met)
5. The new business models needed to compensate content creators in an agent-driven web
6. Parag's counterintuitive approach to fundraising: why VC rejections don't sting but customer rejections do
7. The rational game VCs play that founders misinterpret as genuine enthusiasm
8. Why Parag believes we're not in an AI bubble—but an overreaction is coming (and it'll be faster than dot-com)
9. How Parallel built quietly for a year before product-market fit arrived with the agent explosion
10. The operational philosophy of extreme in-person collaboration that shaped Parallel's early culture

In 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