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

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

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

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

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

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

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

What you'll learn:

1. Why the marketplace model fundamentally fails for skilled labor and home services
2. The counterintuitive insight behind every successful consumer business (the Airbnb lesson)
3. How Vishwas discovered workers were shocked that "nobody's yelled at me yet" after joining Honey Homes
4. Why solving both sides of the market—customer experience AND worker quality of life—is essential
5. The role of AI in leveling up service workers and automating operations without replacing humans
6. Why early compromises on hiring and standards compound into major problems later
7. The distribution challenge: getting consumers to prioritize chronic home maintenance needs
8. How altruism, not just incentives, drives consumer referrals and growth
9. Why companies like Yelp, Peloton, and Lyft deserve more respect for building culturally relevant businesses
10. The mental model shift required to sell subscription home services vs. one-time fixes

In this episode, we cover:

(00:00) Introduction and the respect successful companies deserve

(01:12) YC batch memories and feeling "late" to tech trends

(03:05) The genesis of Honey Homes and why Immad and Raj invested

(04:50) Growing up with a handy dad and discovering the home services gap

(06:30) The counterintuitive consumer insight behind Honey Homes

(07:03) "Nobody's yelled at me yet"—the worker experience problem

(08:11) Why marketplaces don't work for skilled home services

(09:48) Hiring only 1% of handyman applicants

(14:07) Building trust through consistent quality and W2 employment

(19:31) How altruism drives consumer referrals, not just incentives

(21:51) Getting AI-pilled at Vinod Khosla's CEO retreat

(23:01) Using AI to level up workers and automate operations

(27:54) Overcoming the mental model barrier for subscription home services

(30:07) The vision compromise lesson: don't settle on quality early

(31:44) The critical importance of distribution for consumer businesses

(32:26) Why partnerships aren't the answer (yet) for Honey Homes

(38:41) Defending Yelp, Peloton, and Lyft against Silicon Valley discourse

(42:18) Unit economics challenges in services businesses

(47:10) Role models: Jeremy Stoppelman and Ramit Sethi

(48:08) Hope that divisiveness is a passing trend

(49:35) The daily challenge of building before the world sees it

(51:04) Getting feedback about being "unpredictable" and staying in your head

(52:33) Bringing people along for the journey in your mind

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.

They explore why the SaaS business model is under attack (hint: it's not just AI building software faster), the shift from per-seat pricing to API-driven usage, and why enterprises actually buy SaaS products—spoiler, it's not about the software. The conversation reveals how startups can now stay lean with fewer employees for much longer, with companies like OpenAI reaching $500B valuations with just 4,000 people.

Immad and Raj also dive into their personal experiences with AI agents, discussing what actually works versus the hype, why they're skeptical of consumer AI hardware, and how AI is changing daily productivity for founders. They debate Google's quiet win with the Apple-Gemini deal, why Siri is dead, and whether one AI model controlling all handsets should concern us.

The episode wraps with practical advice on what makes a compelling VC pitch in 2026, why crazy promises still work (even when timelines are wildly optimistic), and how to think about your startup's valuation as a call option rather than current worth. From Elon's humanoid robot bet to the new growth expectations (0 to $5M in 12 months), this conversation offers an honest founder-to-founder take on navigating the current landscape.

Key Topics:

1. Why SaaS companies are struggling and what survives
2. The real reason enterprises buy software (risk offloading, not features)
3. AI agents in practice: what works, what doesn't
4. Google's strategic win with Apple's Gemini integration
5. How to pitch VCs when expectations are 5x higher than before
6. Why crazy promises and long timelines still attract capital
7. The shift to leaner startups and API-first business models

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 work
• The difference between automation and true intelligence in home robots
• Why negative-NPS categories can hide massive opportunities
• How Matic beat entrenched incumbents like Roomba by fixing fundamentals, not adding features
• Why vision-only robotics was a risky but necessary bet
• The real reason humanoid robots are still far from consumer-ready
• Lessons from Nest on why some hardware categories stay defensible for decades
• Why creating a new market can be fatal for hardware startups
• How Matic built robots in-house in California instead of outsourcing manufacturing
• The tradeoffs between subscriptions, ownership, and consumer trust
• Why great hardware products must earn word-of-mouth before growth

In 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

What you'll learn:

1. Why cross-border payments remain broken despite decades of fintech innovation
2. How Airwallex competes against Stripe and other established payment platforms
3. The challenge of building financial infrastructure across multiple countries and regulations
4. Jack's perspective on fair competition versus FUD (fear, uncertainty, doubt) tactics in business
5. Why Airwallex is deploying $1 billion in the US market over the next three years
6. The reality of being a foreign founder building in America during geopolitical tensions
7. How payment infrastructure for global businesses differs from consumer fintech
8. The trade-offs between growth velocity and sustainable business building
9. Jack's philosophy on money, success, and what matters after achieving wealth at 30
10. Why he chose to stay in Melbourne instead of relocating to San Francisco

In this episode, we cover:

(00:00) Introduction to Jack Zhang and Airwallex

(02:34) Early days of Airwallex and the founding story

(05:12) The problem with cross-border payments

(08:45) Competing with Stripe and other payment platforms

(12:18) Building in regulated markets and compliance challenges

(16:23) FUD (fear, uncertainty, doubt) tactics in business competition

(19:13) Raj's experience with FUD at Lyft vs Uber

(22:47) Navigating geopolitical tensions as a Chinese-Australian founder

(25:36) The $1 billion US market investment commitment

(27:41) Product philosophy and fair competition

(31:15) Going upmarket vs staying with SMBs

(35:22) Life choices: Melbourne vs San Francisco

(37:49) Perspective on wealth - "not about the money"

(42:18) The future of payments infrastructure

(45:30) Advice for founders building in competitive markets

(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:

1. Why robotics is a systems discipline where progress stacks rather than explodes
2. How to bootstrap a hardware company to $10M revenue before raising venture capital
3. Why robotics follows 20-50% sustained growth for decades vs. software's boom-bust cycles
4. The "promise problem" with humanoid robots and why form factor shapes user expectations
5. How manufacturing in Canada (not China) became a strategic advantage for Clearpath
6. Why founders overestimate 2-year progress but underestimate 10-year impact in robotics
7. The real economics of humanoid robots: $20K cost becomes $80K landed price
8. How robotics investment differs from software: less competitive, more defensible
9. Why experience compounds in hardware but expires in software careers
10. 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

What you'll learn:

1. Why AI alignment and AGI are fundamentally the same problem
2. How theory of mind is the critical missing piece in current AI systems
3. Why continuous learning requires self-modeling capabilities
4. The dangerous truth: alignment is a capacity for both great good and great evil
5. Why "aligned AI" really means "aligned to me"—and why that's concerning
6. How societies of smaller AIs will outcompete singleton superintelligences
7. Why AI needs to be integrated with humans, not segregated into AI-only societies
8. The Twitch lesson: people don't want easy, they want good
9. Why 99% of AI startups are building labor-saving tools instead of value-creating products
10. How parenting and AI development mirror each other in surprising ways
11. Why current AI labs are confused about continuous learning
12. Conway's Law applied to AI: you ship your org chart
13. The problem with mode collapse in self-learning systems
14. Why emotions are training signals, not irrational noise
15. Emmett's biggest mistake at Twitch: chasing new products instead of perfecting the core

In this episode, we cover:

(00:00) The dangerous truth about AI alignment

(01:13) Introduction to Softmax and organic alignment

(02:05) What alignment actually means (and why most people are confused)

(03:33) The output: training environments for theory of mind

(05:01) Continuous learning and why it's so hard

(06:25) Multiplayer reasoning training in open-ended environments

(07:14) Aligned to what? The critical question everyone ignores

(08:40) Why alignment is always relative to the aligning being

(11:07) Cooperation vs. competition: training for the real world

(12:56) Is AGI an urgent problem or do we have time?

(13:15) AGI and alignment are the same problem

(15:25) Alignment capacity enables both good and evil

(17:13) The singleton problem and why societies of AIs make sense

(20:41) Building alignment between AIs and humans

(22:09) Why Elon's "biggest cluster" strategy might be wrong

(23:06) AI must be aligned to individual humans, not humanity

(25:03) What does the atomic unit of AI look like?

(28:02) Adding a new kind of person to society

(29:06) Everything will be alive: from spreadsheets to cars

(30:00) From Twitch retirement to Softmax founding

(31:26) Research vs. product engineering at early-stage startups

(32:41) Raising money for AI research in the current era

(34:30) Why Softmax will ship products

(34:50) Ilya's closed-loop research vs. open-loop learning

(36:36) How you do anything is how you do everything

(37:28) The continuous learning problem explained simply

(38:29) Mode collapse: why AIs become stereotypes of themselves

(39:33) The reward problem and why humans need emotions

(40:48) Why LLMs are trained to avoid emotions

(41:52) Watching children learn while building learning AI

(43:04) Advice for first-time AI founders

(45:08) Treat AI as clay to be molded, not a genie granting wishes

(45:50) The Twitch lesson: people want good things, not easy things

(47:22) Why 99% of AI companies are building the wrong thing

(48:16) Rapid fire: biggest career mistake at Twitch

(50:15) Which founders inspire Emmett most

(50:56) The passing fad: AI slop generators

What you'll learn:

Why Immad launched Mercury Personal despite investor and team skepticism—and the founder lesson about following conviction

How 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 advantages

Raj'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 platform

The 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 2025

Why self-driving technology (Waymo, Tesla FSD) represents a more immediate transformation than AGI

The best and worst of 2025: renewed tech energy vs. immigration scapegoating and Doge's failure to deliver government efficiency

Why supply constraints (chips, power) signal AI demand is real, not a bubble

In 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

What you'll learn:

1. Why Olo's first 10 years required extreme "pain tolerance" waiting for product-market fit
2. The B2C to B2B pivot that transformed their unit economics from burning $15 per customer to earning revenue while scaling
3. How "embrace the suck"—borrowed from the Marine Corps—became the cultural mantra that kept the team going
4. Why going public was about customer confidence and long-term credibility, not exit or liquidity
5. The role of industry advisors in bridging credibility gaps when selling to traditional enterprises
6. How adding delivery-as-a-service (Dispatch) in 2015 unlocked escape velocity and scale advantage
7. The challenges and benefits of operating as a public company in a misunderstood industry
8. Why partnering with Thoma Bravo PE offers better alignment than quarterly public market pressures
9. Noah's philosophy on founder loyalty and the lifelong bonds formed with early team members
10. Why the current "homegrown tech stack" trend in enterprise is a passing fad that misses SaaS fundamentals

In this episode, we cover:

(00:00) Introduction and the "embrace the suck" mentality

(01:03) Early days and the long wait for product-market fit

(05:30) Why YC's "grow fast or quit" advice doesn't apply to every company

(08:06) The deep bonds formed with early team members

(12:14) Deciding between B2B vs B2C business models

(13:34) The B2C beginning and Good Morning America moment

(16:08) The pivot to B2B enterprise software

(20:43) How third-party delivery and DoorDash changed the industry

(23:04) The journey as a public company (2021-2024)

(27:49) Why going public signaled long-term stability to enterprise customers

(30:15) Operating under private equity with Thoma Bravo

(36:10) Breaking into enterprise sales with industry advisors

(44:45) The importance of reliability at scale for enterprise

(46:58) Thinking about market size and expansion in vertical software

(48:25) Rapid fire: Which founder inspires you most

(49:01) Uncomfortable feedback on being overly loyal

(50:48) Current trend prediction: Homegrown enterprise software is a fad

What you'll learn:

Why everything built for human web consumption will become irrelevant when agents become the primary users

How Parallel's APIs enable agents to search, fetch, and monitor the web with unprecedented scale and speed

The evolution from simple tool calls to autonomous sub-agents with real decision-making capability

Why 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 web

Parag's counterintuitive approach to fundraising: why VC rejections don't sting but customer rejections do

The rational game VCs play that founders misinterpret as genuine enthusiasm

Why 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 explosion

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

What you'll learn:

1. How Sphere is becoming the "Deel of revenue compliance" for global businesses
2. Why Nicholas pivoted from EdTech after 18 months and what made him choose tax compliance
3. The strategy of selling contracts with a high-fidelity Figma prototype before building the product
4. How to convince investors to back a pivot when your co-founder has left
5. Why businesses struggle with international tax compliance and how AI solves it
6. The importance of hiring an internal recruiter once you raise significant funding
7. Why San Francisco remains the best place to build a startup despite the challenges
8. How YC's network helped navigate a critical health insurance crisis
9. The advantage of being a solo founder when recruiting high-quality founding engineers
10. Why raising from a position of strength creates better fundraising dynamics

In this episode, we cover:

(00:00) Introduction to Nicholas Rudder and Sphere

(01:10) The EdTech marketplace that didn't work

(03:08) Why EdTech is such a difficult market

(09:16) The hard pivot to tax compliance

(10:56) Selling five contracts with a Figma prototype

(13:10) When the co-founder left and twins arrived early

(21:58) Why international tax compliance is broken

(27:10) Sphere's vision as the "Deel of revenue compliance"

(31:34) The unintentional path to Andreessen Horowitz

(38:54) Why VCs all know when you're raising

(41:37) Building Sphere in SF vs. the UK or Australia

(46:23) Immad's advice on hiring internal recruiters

(51:14) Rapid fire: Founder inspirations and lessons learned

What you'll learn:

1. Why starting "boring and narrow" (one city, one job type) is the key to marketplace success
2. How Instawork is building a "LinkedIn for hourly workers" with hundreds of data points per profile
3. The hidden costs of 100%+ annual turnover in restaurants and hospitality
4. Why people actually want to work MORE hours when friction is removed
5. The concept of "robot wranglers" as the next major labor category
6. How Instawork is using its worker pool to train physical AI and robotics models
7. The difference between "leading by disappointment" vs. celebrating wins as a CEO
8. Why labor costs range from 30% (restaurants) to 80% (hospitals) of revenue
9. The labor market as a "Tetris board" of micro-jobs and available workers
10. Why Silicon Valley undervalues hourly work despite 100 million workers depending on it

In 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

Eric Migicovsky is the Founder of Core Devices, and the original founder of Pebble, the pioneering smartwatch that raised $10 million on Kickstarter before being acquired. Eric has launched Core Devices to continue building the smartwatch platform he believes in, complete with Google's newly open-sourced Pebble operating system.

What you'll learn:

1. The Kickstarter phenomenon: How Pebble became one of the first massive Kickstarter successes, raising $600K in the first day with a $100K goal

2. Hardware's inventory trap: Why missing revenue projections by 20% ($80M vs $100M target) created a $20M inventory crisis that nearly sank Pebble

3. The sustainable hardware model: Eric's new approach of targeting profitability at 5,000 units and eliminating inventory risk through pre-orders

4. Inventor vs. founder mindset: The difference between building products you love versus building scalable companies

5. Fighting Big Tech: How Eric's Beeper Mini challenged Apple's iMessage monopoly and led to DOJ antitrust action

6. Getting software from Google: The surprising story of how Google open-sourced Pebble's operating system to enable Core Devices

7. Hardware manufacturing today: Why building smartwatches is easier now than in 2011, and what's still challenging

8. The artisanal hardware movement: Building premium, limited-run products for passionate niche audiences

9. Regulatory battles: Apple's API restrictions and how they limit third-party smartwatch functionality

10. AI integration: Adding ChatGPT and voice capabilities to modern smartwatches

In this episode, we cover:

(00:00) Introduction and reconnecting with Eric

(01:18) The Core Devices relaunch and getting Pebble IP from Google

(02:33) Eric and Raj's Waterloo connection and early entrepreneurship

(04:48) From Pebble's precursor to YC and the smartwatch vision

(09:30) The legendary Kickstarter launch day and calling Raj at 2am

(14:00) Five years of overnight success and authentic marketing

(16:07) Inventor vs. founder mindset and product obsession

(19:21) The 2015 inventory crisis that changed everything

(27:20) Eric's new sustainable hardware model with Core Devices

(32:00) Using existing Pebble cases and Google's open-source software

(36:58) The artisanal approach: 3 people, no VCs, limited production runs

(41:14) AI integration and ChatGPT on the wrist

(49:36) Secondary markets and public company trading restrictions

What you'll learn:

1. How Venmo started from forgetting a wallet at dinner and evolved into a cultural phenomenon
2. The near-shutdown moments when Wells Fargo threatened to close their account
3. Why Venmo raised only $3.4M total before the Braintree acquisition
4. The strategy behind keeping Venmo invite-only for five years
5. How the team's close friendship shaped Venmo's personality as a product
6. Why Iqram believes AI made startups polished but soulless
7. The shift from building for purpose (helping musicians) to building for metrics (ARR, funding)
8. What it's like working at PayPal after selling your startup
9. How Jelly uses crypto infrastructure to enable global money movement through video
10. Why immigrant founders bring a different hunger and work ethic to building companies

In this episode, we cover:

(00:00) Introduction to Iqram and his founder journey

(00:49) The origin story of Venmo - forgetting a wallet

(03:08) Building on Google Voice and eating credit card fees

(08:40) The near-death moment with Wells Fargo

(12:01) How the Braintree acquisition saved Venmo

(16:56) Working with Bryan Johnson at Braintree

(18:57) The regret of not having more equity in Venmo's success

(21:06) What makes Venmo feel different than other payment apps

(22:16) Why modern startups lost their personality and purpose

(26:00) Life at PayPal after the acquisition

(27:38) Consumer vs B2B founder-product fit

(30:23) Social media became a nightmare of ads and flexing

(32:20) Demo and vision for Jelly

(39:06) Using crypto and meme coins in social apps

(41:15) Why invite-only launches create quality users

(42:38) Rapid fire questions on inspiration and mistakes

(45:28) What it means to be an immigrant entrepreneur

Fresh from DC, Immad shares a surprising disconnect he observed about AI regulation and market sentiment. The conversation moves through whether we're in another bubble, the startup metrics that are being gamed again, and the infrastructure realities that might change everyone's timelines.

They also get into the fundamentals that separate sustainable companies from hype-driven ones: why retention matters more than growth, what to do after raising at a high valuation, and the frameworks they actually use to make spending decisions.

Plus updates on what they're building at Mercury, Tribe, and Lima—and the lessons they're learning along the way.

A candid founder-to-founder conversation about navigating uncertainty and building for the long term.

What you'll learn:

1. Dan's framework of "engineering societies vs lawyerly societies" for understanding the US-China competition
2. How China deliberately promoted engineers to power—by 2002, all nine Politburo Standing Committee members had engineering degrees
3. Why the one-child policy and zero-COVID demonstrate the dangers of literal-minded engineering applied to society
4. How America transformed from building the transcontinental railroad and Apollo missions to being unable to fix its subway systems
5. Why lawyers took over American governance in the 1960s and created a self-reinforcing system
6. The stark reality: China builds 500 gigawatts of solar capacity annually vs America's 50, and has 30 nuclear plants under construction vs zero
7. Why China's electricity advantage could determine who wins the AI race—not just better models
8. How American AI leadership is threatened by power constraints and Chinese researchers potentially returning home
9. Why robotics applications of AI matter more than reasoning models for geopolitical competition
10. The dual reality of America: trillion-dollar tech companies exist alongside broken infrastructure that only works for the wealthy
11. Dan's writing process: traveling, eating (twice), reading novels and history, and being deliberately provocative
12. The future of US-China competition in semiconductors, aviation, manufacturing, and whether America's technological lead is sustainable

In this episode, we cover:

(00:00) Introduction and Dan's AI/electricity thesis

(01:15) Dan's journey from San Francisco tech to China analyst

(03:40) Engineering society vs lawyerly society framework

(04:21) Why engineers running governments can be dangerous

(05:46) The one-child policy: designed by a missile scientist

(06:56) China's path from Mao to engineering-focused leadership

(09:51) America's transformation from builder to regulator (1960s shift)

(11:08) Can the pendulum swing back? Housing, transit, and infrastructure failures

(13:12) The self-reinforcing nature of lawyerly societies

(14:12) Yale Law ambition vs Stanford engineering ambition

(16:13) Is there bipartisan consensus on building?

(17:41) Why left and right can't agree on solutions

(19:32) China's engineering design flaws and authoritarian feedback loops

(22:19) US technological advantages: semiconductors, AI, aviation

(23:07) The electricity bottleneck: China's massive power advantage

(24:31) If AI is everything, what should America do?

(26:29) Why Dan doesn't buy the "AI is everything" premise

(27:27) Robotics as the real AI battleground

(29:35) Silicon Valley codes, China builds power plants

(30:37) Anti-AI populism emerging on left and right

(33:41) Dan's meta process: philosophy, eating, traveling, reading, being provocative

(37:20) China's rural infrastructure and redistribution through building

(40:39) Peter Thiel question: acknowledging China's dual reality

(44:54) America's core tension: works great for the rich, broken for everyone else

(46:35) Will China get stuck in the 2010s like Japan in the 1980s?

What you'll learn:

1. How Iñaki built CoverWallet from 0 to $100M in premium revenue and 400 employees in just 4 years
2. Why he'd rebuild his 250-person company with only 10 people in the AI era
3. The hidden time cost of scaling teams: 40% of CEO time spent on HR, hiring, and one-on-ones
4. How strategic partnerships with potential acquirers create acquisition optionality
5. Why investment bankers matter: the difference between 3-month and 8-month due diligence timelines
6. The critical mistake of taking common stock vs. preferred in acquisition deals
7. Why "paranoid optimist" is the ideal founder mindset
8. The lifestyle reality check: VC work vs. founder intensity and what actually counts as "high pressure"
9. Reference check strategies that reveal integrity under pressure
10. How luck and timing determine exits more than founders want to admit

In this episode, we cover:

(00:51) Iñaki's journey: three companies, three exits across different industries

(03:21) Why Pixable's "always on" consumer product was harder than enterprise

(09:04) The decision to sell CoverWallet despite investor pressure to keep building

(12:20) Product-market fit doesn't exist in AI: markets change faster than products

(19:43) How Iñaki would rebuild differently: from 250 employees to AI agents

(22:32) The real time cost of hiring: 100 employees = 1,000 interviews

(27:16) M&A lessons: why time kills deals and investment bankers matter

(29:03) Building optionality through strategic partnerships with potential acquirers

(32:37) The fulfillment of building vs. investing: team wins and external validation

(36:23) Why founders struggle to celebrate wins that took years to achieve

(40:25) The "paranoid optimist" mindset: assuming someone is always working harder

(42:14) AI in healthcare: the most underhyped opportunity

(45:20) Comparing entrepreneurial cultures: Silicon Valley vs. New York vs. Europe

(46:16) The biggest mistake: not doing enough reference checks on people

(48:44) What drives founders: proving doubters wrong, not money

What you'll learn:

1. How Andrew pivoted Clearco through three different markets before finding product-market fit with e-commerce financing
2. Why customers who "want to be found" create fundamentally easier go-to-market strategies
3. The psychology behind why financial incentives destroy natural networking behaviors and trust
4. How the best companies now generate inbound investor demand instead of running traditional fundraising processes
5. Why Andrew learned to trust founder intuition even when he couldn't articulate it to stakeholders
6. The technical and business model evolution from merchant cash advances to AI-powered networking
7. How Boardy uses voice AI to create more human-like relationship building at scale
8. Strategic insights on building in regulated industries like financial services
9. The transition from scaling a fintech business to creating AI characters with their own objectives
10. Why VCs don't actually remember your previous pitches and how to leverage that reality

In this episode, we cover:

(00:00) Introduction and Andrew's journey from Waterloo to YC

(02:32) Clearco's evolution from Uber driver financing to e-commerce

(04:27) The pivotal board meeting and Series A pivot decision

(05:25) Finding product-market fit with customers who "want to be found"

(09:35) Scaling Clearco to $100M+ revenue and 600 employees

(11:08) The COVID boom and building Clear Angel with GPT-3

(13:31) Andrew's decision to step down as CEO

(15:58) Introduction to Boardy AI and the AI super connector concept

(18:43) Live demonstration of Boardy's voice capabilities

(26:33) Business model and the "economy of Boardy" vision

(29:09) Why financial incentives destroy network effects

(33:36) Fundraising evolution from process-driven to inbound demand

(37:13) The reality of investor relationships and memory

(43:00) Rapid fire: biggest mistakes, inspiration, and founder psychology

(47:29) The creative expression of building AI characters

We're bringing back one of our best episodes featuring Jesse Zhang (Decagon AI CEO). This is a strategic conversation that centers on the business challenges of building an enterprise AI company that can sustain beyond the current hype cycle.

Jesse reveals how AI moves beyond being a mere chatbot to become a "system of intelligence" that encodes complex business logic, creating moats for companies that implement it effectively. The conversation also explores the realities of implementation, adoption, and enterprise sales strategies that actually work.

Technology founders, customer experience leaders, and investors will find this episode particularly illuminating as it bridges the gap between AI hype and practical implementation. Whether you're evaluating customer service AI, selling enterprise technology, or navigating fundraising, this conversation provides the strategic context and tactical insights needed to make better decisions.

What you'll learn:

How David pivoted from application to infrastructure after spending 80% of engineering time on recording problems

Why the social shift toward recording acceptance created a massive infrastructure opportunity

The systematic approach David used to learn enterprise sales with zero experience

How Recall's desktop recording SDK eliminates the need for bots in meetings

Why Series B fundraising still requires a 100x growth vision even at scale

The framework David uses to validate new products and channels before investing time

How Amanda Gu built 45,000 LinkedIn followers and turned social media into a lead generation engine

Why David records and reviews every sales pitch to improve his closing rate

The mental shift from seeking external validation to embracing continuous failure

How the conversation data revolution will transform every B2B software application

In this episode, we cover:

(00:00) Introduction and David's Y Combinator background

(01:30) Announcing Recall.ai's $38M Series B funding round

(03:18) The pivot from call recording app to infrastructure platform

(09:42) Why recording infrastructure became their nightmare and salvation

(15:36) Learning enterprise sales as a technical founder

(22:13) Amanda's LinkedIn growth and social media lead generation

(28:26) Systematic approach to testing new products and channels

(34:52) Why Series B still requires 100x vision and growth story

(42:17) The social transformation that made recording acceptable

(48:23) Working seven days a week for three years in the early days

(53:05) The framework for embracing failure as a learning tool

What you'll learn:

Why VCs avoiding entire sectors often signals the biggest opportunities

The unconventional partnership strategy that led to WePay's $400M JPMorgan acquisition

How to position strategic partnerships as pathways to acquisition rather than just revenue

Why WePay's delayed pivot from consumer to developer APIs cost them market leadership

The specific tactics for getting enterprise buyers excited about acquisition vs. partnerships

How to navigate the early fintech landscape without established banking infrastructure

Why timing strategic decisions matters more than perfecting the original plan

The 12-18 month timeline required for enterprise acquisition conversations

How crisis-driven industries create first-time openings for technology adoption

Bill's contrarian thesis on investing in utilities, insurance, and government sectors

In this episode, we cover:

(00:00) Introduction and Bill's journey from investment banking to entrepreneurship

(08:13) Starting WePay during the 2008 financial crisis in Boston

(12:00) Getting into Y Combinator and the early pivot struggles

(17:37) The acquisition strategy and JPMorgan partnership approach

(24:38) Lessons on founder burnout and sustainable company building

(36:19) Convective Capital's thesis on physical risk management

(42:38) Building an insurance company for high-risk California properties

(46:35) The future of wildfire risk and climate resilience investing

What you'll learn:

Why "Default Alive" means cash flow positive, not just having runway or theoretical profitability

How to avoid false trade-offs by asking "why not both?" instead of accepting either/or decisions

Why the biggest startup mistakes are always people, especially bad executive hires

The hidden advantages of grinding through difficult early years versus overnight success

How to maintain frugal company culture while scaling from 4 to 200+ employees

Strategic approaches to long-term R&D investments, including AI physics and Department of Defense contracts

The difference between executives who built systems versus those who just ran them

Why promoting internal talent often works better than external executive hires

How to structure resource allocation decisions to force proper prioritization

The psychology of founder endurance and why some businesses are intentionally harder to build

In this episode, we cover:

(00:00) Introduction to Joris Poort and Rescale

(01:04) Meeting Raj at Paul Graham's place in England

(04:36) What Rescale does: supercomputing platform for engineering

(08:02) How modern AI impacts scientific computing and physics

(12:31) Building for 15 years: the ups and downs of long-term company building

(14:20) The moment of becoming "Default Alive" and what it really means

(16:49) VCs versus founders on spending and growth philosophy

(22:03) Implementing frugal culture and budget discipline at scale

(26:56) The challenge of promoting internal talent to executive roles

(32:28) Interviewing every hire up to 200 people and building relationships

(34:30) Rapid fire: riskiest bets, biggest mistakes, and hard-won lessons

(42:31) AI investments, Department of Defense contracts, and strategic moats

(48:45) Why the hardest path often creates the strongest business

What you'll learn:

1. How Mattermost pivoted from gaming to defense tech through bottom-up adoption
2. The concept of "sovereign collaboration" and why governments need independent AI systems
3. Ian's framework for hiring executives with "scar tissue" from diverse experiences
4. Why he regrets spending $270,000 on internal swag after taking VC money
5. The unique language and procurement challenges of selling to government customers
6. How open source enables PLG motion in highly secure environments
7. The difference between "butts and seats" contracting vs. commercial off-the-shelf solutions
8. Why most successful founders are in their 30s and 40s, not college dropouts
9. Ian's "daddy daughter days" approach to work-life balance as a CEO
10. The emerging landscape of sovereign AI and national security technology

In this episode, we cover:

(00:00) Introduction and Ian's Tools for Tech Leaders podcast

(04:05) Mattermost's evolution from Slack alternative to sovereign collaboration

(08:09) Bottom-up adoption in national security environments

(14:23) Learning to sell to government customers and navigate defense acronyms

(24:49) Executive hiring philosophy and building long-term relationships

(30:47) The sovereign AI landscape and government investment trends

(38:53) Defense tech opportunities and getting started in the space

(42:57) Palantir's business model and government contracting dynamics

(51:18) Rapid fire: Walt Disney as tech founder inspiration and personal lessons

What you'll learn:

1. How Glean evolved from search to conversational AI without pivoting by riding AI model capabilities
2. Why making it easy for customers to leave can be a competitive advantage in enterprise sales
3. The challenges of competing against every major software company building similar products
4. How to recruit from FAANG companies when you can't match their compensation
5. The difference between building for established markets vs. creating new product categories
6. Why enterprise buyers are tired of AI overpromising and prefer honest positioning
7. How to stay close to product development while scaling to 1,000 employees
8. The transition from Google engineer to startup co-founder and the skills you have to relearn
9. Why customer success and shared roadmaps are critical for complex AI implementations
10. The decision framework for staying independent vs. acquisition when building at scale

In this episode, we cover:

(00:00) Introduction to Arvind Jain and his journey from Rubrik to Glean

(01:27) Lessons learned building two different types of enterprise companies

(03:09) How Glean works as enterprise search and AI assistant

(06:00) The natural evolution from search to conversational AI

(08:00) Navigating the AI hype cycle and competitive landscape

(11:04) Competition strategy and staying ahead of major software companies

(16:40) Honest positioning vs. overpromising in AI sales

(20:01) Building enterprise reputation and customer relationships

(24:41) Motivation for continuing to build after previous success

(26:56) Staying close to product at scale and avoiding bottlenecks

(31:08) Using Glean internally and rapid iteration cycles

(32:18) The decision to stay independent vs. acquisition

(37:24) Transition from Google to startup founder

(43:45) Rapid fire questions and leadership lessons

(48:31) Uncomfortable feedback about constraining team speed

What happens when the ultimate startup veteran joins a 3,000-person company after 20 years of founding his own?

In this candid conversation, Mercury CEO Immad Akhund and serial founder Raj Suri sit down with Hiten Shah (Crazy Egg, KISSmetrics, Nira—acquired by Dropbox) for an honest exploration of founder identity, business fundamentals, and the reality of life after exit.

Hiten brings a unique perspective as someone who's built both venture-backed and self-funded companies, including Crazy Egg, which has been running profitably for over 20 years. The discussion covers his contrarian takes on fundraising strategy, why he believes "there's no better hack to product market fit than someone else's product market fit," and the surprising challenges of transitioning from founder to employee.

The conversation explores practical frameworks for choosing customers, the hidden truths about venture-backed versus bootstrapped companies, and why Hiten's official title at Dropbox is "Chief Troublemaker." Whether you're building your first startup or considering an exit, you'll gain insights from three founders who challenge conventional wisdom while sharing hard-won lessons from decades of company building.

This episode offers essential lessons for entrepreneurs at any stage, covering everything from the psychology of startup building to the practical realities of product-market fit, customer development, and maintaining founder DNA within larger organizations.

Steve Huffman is the co-founder and CEO of Reddit, one of the internet's most essential platforms with 100 million daily active users and a $20 billion public market valuation. Over 20 years, Steve has guided Reddit through multiple existential challenges, from early acquisition to content moderation crises to competing with AI-generated content.

What you'll learn:

- How Reddit achieved product-market fit in just 2 months and the early signs that indicated massive potential

- Why Steve sold Reddit for $10 million after 18 months and what he learned during his 9-year absence

- The existential crisis that brought him back as CEO in 2015 and how he rebuilt the platform

- Reddit's evolution through three distinct eras: link aggregator, intentionally not social media, and authentic human content in an AI world

- The strategic decision in 2008 to let users create subreddits and how it transformed the platform

- Building content moderation and safety infrastructure from scratch while preserving free speech values

- The challenges and benefits of taking Reddit public while maintaining community ownership

- How Reddit is addressing AI-generated content and the fight for human authenticity online

- The business model evolution from advertising to AI data licensing partnerships

- Steve's vision to make Reddit "universal" and expand from 100 million to billions of users

In this episode, we cover: 

(00:00) Introduction and Reddit's 20-year milestone

(02:40) Early product-market fit signals and the $10M acquisition 

(06:25) Steve's 9-year absence and return in 2015 

(08:30) Content moderation challenges and building safety infrastructure 

(14:10) Reddit's three evolutionary eras and strategic positioning 

(16:40) The subreddit explosion and community self-organization 

(18:08) Positioning as the human platform in an artificial world 

(19:15) Human verification challenges and potential solutions 

(25:22) AI partnerships, data licensing, and monetization strategy 

(29:02) Search evolution and Reddit's role in the new ecosystem 

(35:07) Leadership philosophy and staying true to company values 

(37:43) The decision to go public and community ownership 

(42:18) Managing public company pressures while maintaining long-term vision 

(48:27) Steve's vision for Reddit's universal future

In this episode, Immad and Raj welcome Silicon Valley legend Paul Buchheit.

As the creator of Gmail, an early Google pioneer, and a Y Combinator partner, Paul shares his unique perspectives on the rapidly evolving AI landscape and its implications for startups and society.

The conversation covers the current state of large language models and their potential future developments, opportunities for new social apps in the age of AI, and the impact of AI on search engines and Google's business model. Paul highlights the importance of open-source AI in preventing centralized control, and shares thoughts on how the interplay between truth and narrative shapes our perception of reality.

This episode offers valuable insights for founders and aspiring entrepreneurs from one of the most experienced builders in the tech industry, discussing common pitfalls in startup narratives and the importance of seeking disconfirming evidence.

What you'll learn:

Why the future of AI and human creativity depends on solving copyright licensing

How Created by Humans is building the "Spotify for AI rights"

Why books are the starting point—and the most complicated use case—in AI licensing

The three types of AI rights: training, reference (RAG), and transformative use

Why authors—not publishers—hold the keys to AI rights for books

How lawsuits between AI companies and authors are shaping the market

The long game of building two-sided marketplaces in emerging industries

Why Trip is prioritizing standards over fast ARR growth

Lessons from Scribd and why founding a second company feels different

How AI-human collaboration could reshape the creative economy

In this episode, we cover:

(00:00) Introducing Trip Adler and Created by Humans

(01:10) The explosion of startup culture since 2006

(03:26) Copyright in the AI era: the core problem

(07:13) Building a licensing marketplace for AI rights

(09:47) Author reactions: skepticism, enthusiasm, and early adoption

(10:48) Why this is like the music industry’s Napster-to-Spotify shift

(12:23) The future of licensing for video, music, and social media content

(15:34) The value hierarchy of data: books vs. tweets

(16:47) The three AI use cases: training, reference, and transformation

(20:32) Will AI replace authors—or collaborate with them?

(23:30) How AI might change the way we read and write books

(27:09) Lessons from Scribd: Why starting a second company feels different

(31:09) Avoiding short-term thinking and playing the long game

(35:25) Why founder journeys are marathons, not sprints

(37:40) Licensing clauses AI engineers need to read

(38:23) Authors reviving dead characters with AI

(39:13) Final thoughts: Building a creative economy that lasts

What you'll learn:

1. How Turion's "droid" satellites provide space domain awareness and anomaly resolution for other spacecraft
2. Why orbital debris removal serves as the stepping stone to asteroid mining operations
3. The engineering culture principles Ryan brought from SpaceX to build a flat, department-driven organization
4. How Space Force contracts provided early validation and multi-million dollar revenue opportunities
5. The difference between hardware and software engineering talent pools and motivation levels
6. Why Ryan believes asteroid mining could be the economic forcing function for humanity's galactic expansion
7. The technical challenges of capturing uncooperative space objects at 35,000 mph
8. How a $400 quintillion asteroid could reshape precious metals markets like aluminum did historically
9. The 15-year timeline to meaningful asteroid mining revenue and building for the long term
10. Why mission-driven engineering culture attracts top talent willing to work on decade-spanning projects

In this episode, we cover: 

(00:00) Introduction to Ryan Westerdahl and Turion Space 

(01:31) Turion's satellite operations and space infrastructure mission 

(07:18) Long development timelines and staying motivated through multi-year projects 

(08:27) Ryan's journey from age 11 space obsession to SpaceX to founding Turion 

(14:21) Engineering culture and the "hardcore" mentality required at SpaceX 

(17:01) Building flat organizational structures and engineering-driven decision making 

(22:30) The economic case for asteroid mining as humanity's galactic forcing function 

(25:16) Hardware vs software engineering talent dynamics and motivation 

(28:06) Early fundraising challenges and the importance of actual contracts over LOIs 

(31:21) Current satellite operations: anomaly resolution and space domain awareness 

(33:06) Immad's space investing thesis and evaluation criteria 

(35:33) Science fiction culture at Turion and book recommendations

What you'll learn:

1. How donor-advised funds represent a $557 billion market that most people have never heard of
2. Wealthfront's counterintuitive strategy for building trust by targeting tech workers first
3. Why subscription business models create better customer relationships than transactional ones
4. The three key metrics that subscription businesses should obsess over: acquisition, retention, and churn
5. How to design organic network effects that make customers want to share your product
6. Why emotional connection is the hidden driver behind every successful scaled product
7. The importance of finding the "humanity underneath" when building financial products
8. How to turn a financial task into something people genuinely care about
9. The behavioral finance insight that separates "how much to give" from "who to give to"
10. Why putting money aside for charity (even without knowing the recipient) is the most important first step
11. The trust-building power of getting one prestigious customer in B2B sales
12. How geographic restrictions can actually help remote companies maintain culture and reduce fraud risk

In this episode, we cover:

(00:00) Introduction to Adam Nash and Daffy

(04:19) What donor-advised funds are and why they matter

(06:32) How Daffy is democratizing charitable giving

(12:23) The emotional connection behind successful products

(15:05) Wealthfront's journey from $80M to $80B

(21:14) The trust-building strategy that worked for fintech

(28:12) Subscription vs. transactional business models

(34:08) Designing organic network effects

(38:20) Adam's giving philosophy and approach

(44:18) The remote work fraud problem and trust issues

(52:00) Building ownership culture in startups

What you'll learn:

1. Why cutting R&D spending won't meaningfully reduce the national debt (it's less than 3% of federal budget)
2. How the U.S. is winning the global AI competition and what could derail our lead
3. The bipartisan immigration deal that 75% of Americans support but politicians won't pursue
4. Why solar + battery storage is changing the renewable energy equation for Republicans
5. The lawsuit that could revolutionize small nuclear reactor regulation
6. How Alec's "X Labs" proposal could transform government-funded science
7. Why naval shipbuilding reform represents billions in potential savings
8. The real story behind U.S.-China relations from someone who's traveled extensively in both countries
9. How progress studies differs from progressive politics in Washington
10. Why energy abundance is key to everything from AI to transportation costs

In this episode, we cover: 

(00:00) Introduction to Alec Stapp and Institute for Progress 

(02:02) Progress studies vs. progressive politics clarification

(05:08) What America is doing right in AI competition 

(08:05) Immigration policy disappointments and opportunities 

(10:02) Why government science funding matters for innovation 

(13:52) The federal budget reality: entitlements vs. R&D spending 

(16:20) National debt concerns and productivity growth solutions 

(18:00) AI's economic disruption and the care economy transition 

(23:16) Building stronger institutions for technological change 

(25:47) Fertility rates, immigration, and America's competitive advantage 

(29:13) U.S.-China relations and cultural similarities 

(33:09) Solar energy, battery storage, and Republican energy policy 

(39:06) Nuclear power's regulatory challenges and state-level solutions 

(42:03) The Techno-Industrial Policy Playbook overview 

(47:04) X Labs: Reforming how we fund scientific breakthroughs 

(49:11) Naval shipbuilding procurement reform opportunities

What you'll learn:

1. How VLEO satellites capture imagery quality that replaces planes and drones commercially
2. Why atmospheric drag and atomic oxygen create unique engineering challenges at 275km altitude
3. Topher's journey from Lockheed Martin engineer to Y Combinator space startup founder
4. The pre-sales strategy that secured double-digit millions in binding contracts before launch
5. Why they pivoted from outsourcing to vertical integration mid-development
6. How to navigate defense sales as a startup without traditional prime contractor relationships
7. The surprising accessibility of space—getting a satellite up for $1M in just months
8. Why only imaging and communications make business sense in today's space economy
9. The capital journey from founding in 2020 to $100M raised before first satellite launch
10. How SpaceX's ecosystem is enabling a new generation of space companies

In this episode, we cover:

(00:00) Introduction to Topher Haddad and Albedo

(01:04) What is VLEO and why fly satellites so low

(03:09) Engineering challenges of atmospheric drag and atomic oxygen

(06:12) Solar activity cycles and satellite lifetime considerations

(07:30) Commercial use cases replacing planes and drones

(11:41) Timeline from quitting Lockheed to first satellite launch

(13:24) The surprisingly low barrier to entry for simple satellites

(14:22) What space markets actually work today vs. future bets

(17:08) How launch cost reductions enable new business models

(19:04) Navigating defense sales and government relationships

(25:23) Why space startups survived the 2022 market crash

(38:41) The importance of pre-sales and customer deposits for deep tech

(46:03) Rapid fire: skateboarding, support systems, and space observations

What you'll learn:

1. The Kickstarter phenomenon: How Pebble became one of the first massive Kickstarter successes, raising $600K in the first day with a $100K goal

2. Hardware's inventory trap: Why missing revenue projections by 20% ($80M vs $100M target) created a $20M inventory crisis that nearly sank Pebble

3. The sustainable hardware model: Eric's new approach of targeting profitability at 5,000 units and eliminating inventory risk through pre-orders

4. Inventor vs. founder mindset: The difference between building products you love versus building scalable companies

5. Fighting Big Tech: How Eric's Beeper Mini challenged Apple's iMessage monopoly and led to DOJ antitrust action

6. Getting software from Google: The surprising story of how Google open-sourced Pebble's operating system to enable Core Devices

7. Hardware manufacturing today: Why building smartwatches is easier now than in 2011, and what's still challenging

8. The artisanal hardware movement: Building premium, limited-run products for passionate niche audiences

9. Regulatory battles: Apple's API restrictions and how they limit third-party smartwatch functionality

10. AI integration: Adding ChatGPT and voice capabilities to modern smartwatches

In this episode, we cover:

(00:00) Introduction and reconnecting with Eric

(01:18) The Core Devices relaunch and getting Pebble IP from Google

(02:33) Eric and Raj's Waterloo connection and early entrepreneurship

(04:48) From Pebble's precursor to YC and the smartwatch vision

(09:30) The legendary Kickstarter launch day and calling Raj at 2am

(14:00) Five years of overnight success and authentic marketing

(16:07) Inventor vs. founder mindset and product obsession

(19:21) The 2015 inventory crisis that changed everything

(27:20) Eric's new sustainable hardware model with Core Devices

(32:00) Using existing Pebble cases and Google's open-source software

(36:58) The artisanal approach: 3 people, no VCs, limited production runs

(41:14) AI integration and ChatGPT on the wrist

(49:36) Secondary markets and public company trading restrictions

What you'll learn:

1. How Sahil transitioned Gumroad from venture-backed to profitable without selling or shutting down
2. The remarkable story of Kleiner Perkins selling their stake back for just $1 and the tax implications
3. Why AI coding tools like Cursor and Devin are making senior engineers more valuable while challenging junior developers
4. What it's really like working inside the federal government as a Silicon Valley software engineer
5. How Sahil automated contract reviews at the VA and helped identify $1.6 billion in cuts
6. The three-pronged DOGE strategy: cutting contracts, workforce reduction, and shipping software
7. Why radical transparency (public board meetings, open-source code) became Gumroad's competitive advantage
8. How to implement a dividend model for private companies instead of traditional growth/exit strategies
9. The challenges and opportunities of bringing private sector expertise to government agencies
10. Why cooperation beats disruption when trying to modernize massive bureaucratic systems

In this episode, we cover:

(00:00) Introduction and Sahil's early Pinterest days

(05:26) The decision to drop out of college for startups

(08:12) Building Gumroad and raising from Kleiner Perkins

(25:26) How AI is changing software engineering and hiring

(39:02) The 60-day DOGE experience at the Department of Veterans Affairs

(50:15) Transitioning from venture-backed to profitable company

(57:16) The Kleiner Perkins $1 buyback story

(1:02:45) Implementing dividends and radical transparency

(1:09:04) Why transparency makes you better at everything

What you'll learn:

1. How Reddit achieved product-market fit in just 2 months and the early signs that indicated massive potential
2. Why Steve sold Reddit for $10 million after 18 months and what he learned during his 9-year absence
3. The existential crisis that brought him back as CEO in 2015 and how he rebuilt the platform
4. Reddit's evolution through three distinct eras: link aggregator, intentionally not social media, and authentic human content in an AI world
5. The strategic decision in 2008 to let users create subreddits and how it transformed the platform
6. Building content moderation and safety infrastructure from scratch while preserving free speech values
7. The challenges and benefits of taking Reddit public while maintaining community ownership
8. How Reddit is addressing AI-generated content and the fight for human authenticity online
9. The business model evolution from advertising to AI data licensing partnerships
10. Steve's vision to make Reddit "universal" and expand from 100 million to billions of users

In this episode, we cover: 

(00:00) Introduction and Reddit's 20-year milestone

(02:40) Early product-market fit signals and the $10M acquisition

(06:25) Steve's 9-year absence and return in 2015

(08:30) Content moderation challenges and building safety infrastructure

(14:10) Reddit's three evolutionary eras and strategic positioning

(16:40) The subreddit explosion and community self-organization

(18:08) Positioning as the human platform in an artificial world

(19:15) Human verification challenges and potential solutions

(25:22) AI partnerships, data licensing, and monetization strategy

(29:02) Search evolution and Reddit's role in the new ecosystem

(35:07) Leadership philosophy and staying true to company values

(37:43) The decision to go public and community ownership

(42:18) Managing public company pressures while maintaining long-term vision

(48:27) Steve's vision for Reddit's universal future

What you'll learn:

1. How COVID-19 staffing shortages sparked the idea for drone-powered emergency response
2. Why public safety tech has been overlooked by VCs and is now experiencing a renaissance
3. The challenge of selling to 10,000+ small police departments across America
4. How to leverage domain expertise as a competitive moat in specialized industries
5. Strategic advice on when to take a "smaller" exit vs. swinging for bigger outcomes
6. Why investor incentives aren't always aligned with founder success during acquisition talks
7. The regulatory breakthrough that made beyond-visual-line-of-sight drone operations possible
8. How Flock Safety is building American-made drones to compete with Chinese manufacturers
9. The infrastructure challenges and national security implications of drone technology
10. What AI and robotics mean for the future of law enforcement and public safety

What you'll learn:

1. How Aalo's liquid metal cooled reactors could transform clean energy production
2. Why data centers are the perfect first customer for next-generation nuclear power
3. Matt's transition from selling his software company Hume to pursuing nuclear innovation
4. The regulatory strategies that could enable faster nuclear deployment
5. Why factory mass manufacturing could solve nuclear's historical cost and timeline challenges
6. The safety advantages of liquid metal cooling and small modular designs
7. How the US compares to China and Russia in nuclear innovation
8. Matt's surprising perspective on fusion vs. fission energy investment
9. The capital journey from $6M seed to $30M Series A to upcoming $100M round
10. A vision for how abundant nuclear energy could enable everything from desalination to vertical farming

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They explore how Duolingo leverages AI to personalize learning, the company's obsession with retention, and why relentless experimentation is at the heart of its product strategy. 

Severin also shares insights on building a mission-driven culture, the immigrant founder advantage, and how to scale design thinking without losing your edge.

The conversation begins with how Zuckerberg's "buy or get crushed" approach to acquisitions leaves founders with an impossible choice: accept a lucrative buyout or face ruthless competition. They note how "every founder who comes out of Meta regrets selling their company," yet Meta's willingness to clone features makes rejecting their offers extremely difficult.

The founders then dissect the Epic vs. Apple lawsuit ruling that ended Apple's 30% App Store monopoly and examine antitrust actions against tech giants, questioning whether surgical regulation targeting specific practices might be more effective than breaking up companies entirely. The discussion highlights how AI foundation models have rapidly commoditized, making monopoly concerns in that space less pressing.

The episode concludes with Immad's sobering economic prediction that Trump's tariffs will trigger a recession by late 2025, creating a market where only AI companies secure funding while other promising startups struggle despite solid fundamentals - potentially creating opportunities for contrarian investors and resilient founders.

The conversation explores how Protex leverages existing CCTV infrastructure to detect dangerous behavior patterns, the advantages of being "outsiders" disrupting traditional industries, and how the AI boom has transformed both fundraising and customer conversations. Dan offers insights on navigating enterprise sales, building trust-based relationships with clients, and managing an international team across Ireland, Boston, and London.

From pivoting through multiple startup ideas to selling multi-million dollar contracts, this episode provides valuable perspectives on computer vision applications, scaling an AI company in today's market, and the unique challenges of leadership growth. Whether you're interested in AI applications, enterprise sales strategies, or the founder journey, Dan's candid experiences offer practical wisdom for entrepreneurs at any stage.

Jay reflects on lessons from BufferBox, the realities of manufacturing and tariffs, and the power of network effects in consumer hardware. The conversation spans everything from AI in education to emotional resilience as a second-time founder, the state of Canadian tech, and how Y Combinator continues to shape founder journeys.

Ryan shares insights on how businesses are responding to this trade crisis, strategies for supply chain adaptation, and the broader implications for US-China commerce.

The conversation explores how Flexport is helping customers navigate these challenges, the economic impact of protectionist policies, and Ryan's personal journey as a founder.

From global trade dynamics to startup leadership wisdom, this episode offers valuable perspective on managing through economic uncertainty.

ClassDojo CEO Sam Chaudhary shares how his education platform reached 50 million students by making the counterintuitive decision to spend seven years building a network before monetizing. In this revealing conversation with Immad Akhund and Raj Suri, Sam explains why education companies typically fail and how Class Dojo broke through by focusing on parents as true customers rather than selling to institutions.

The discussion explores their unique growth strategy and delves into the psychology of education, with Sam sharing insights on how children develop best in "cozy bubbles" where they can explore interests without constant comparison, and why the balance between challenge and support is crucial for both children and high-performing teams.

This episode offers essential lessons for founders tackling challenging markets, building consumer networks, and creating environments where authentic growth trumps comparison.

Mercury CEO Immad Akhund reveals how a focus on solving basic user experience problems enabled his company to secure a $300M Series C at a $3.5B valuation.

In this revealing episode, Hiten Shah (founder of Crazy Egg, KISSmetrics, and Nira—acquired by Dropbox) joins Raj Suri to mine insights from Mercury's journey from founding in 2017 to becoming a fintech powerhouseThe conversation explores how Mercury identified underserved segments in financial services and built distinctive features for startups, e-commerce businesses, and professional services.

This episode offers essential lessons for entrepreneurs building in regulated industries, product leaders working on complex user experiences, and founders evaluating fundraising strategies. It’s a masterclass in identifying market opportunities, creating differentiated experiences, and building a profitable business in a highly competitive sector while maintaining focus on customer experience above all else.

Hosts Immad Akhund (Mercury CEO) and Raj Suri (Lima/Tribe co-founder) welcome back Paul Buchheit—Gmail creator and former Y Combinator partner—for our most requested follow-up episode. Together they dive into how exponentially increasing intelligence will transform our civilization, with Paul bringing his uniquely optimistic yet pragmatic perspective to the profound questions facing humanity.

This wide-ranging conversation includes thought exercises like why the transition from scarcity to abundance creates an existential meaning crisis, how intelligence without intention changes our understanding of consciousness, and why current political systems may be fundamentally incompatible with AI-accelerated progress.

This episode is essential listening for anyone seeking to understand how technology will reshape human experience. 

As a successful founder turned investor, Auren brings a unique dual perspective on how to build elite teams, properly compensate founders, and navigate fundraising. He reveals why he deliberately structures Flex Capital to do two seed investments per week, argues for rethinking founder equity after the initial four-year vest, and shares controversial takes on why most VCs give advice they don't follow themselves. For founders looking to optimize their leadership teams, compensation structures, and fundraising approaches, this episode delivers practical frameworks that challenge conventional Silicon Valley wisdom.

Just one provocative sentiment from this episode that challenges conventional startup wisdom and delves into the mindset behind entrepreneurial success.

Waseem Daher brings insights from his journey across three successful startups—KSplice (acquired by Oracle), Zulip (acquired by Dropbox), and now Pilot, where he recently transitioned from CEO to executive chair. Host Raj Suri, himself a multi-time founder, creates an environment where honest founder-to-founder exchange flourishes beyond the typical success narratives.

The conversation unpacks frameworks such as the "David vs. Goliath" approach to competition, why unpopular decisions often signal good leadership, and how to maintain the delicate balance of the Stockdale paradox—confronting brutal facts while maintaining unwavering faith in eventual success. 

This episode offers rare insights into the psychological tools that sustain founders through years of uncertainty and challenge. The mental models discussed here apply beyond startups to any leadership position where resilience, decision-making, and maintaining perspective are crucial to success.

This episode brings together Jesse Zhang (Decagon AI CEO), Immad Akhund (Mercury CEO), and Raj Suri (Lima/Tribe co-founder) for a strategic conversation that centers on the business challenges of building an enterprise AI company that can sustain beyond the current hype cycle.

Jesse reveals how AI moves beyond being a mere chatbot to become a "system of intelligence" that encodes complex business logic, creating moats for companies that implement it effectively. The conversation also explores the realities of implementation, adoption, and enterprise sales strategies that actually work.

Technology founders, customer experience leaders, and investors will find this episode particularly illuminating as it bridges the gap between AI hype and practical implementation. Whether you're evaluating customer service AI, selling enterprise technology, or navigating fundraising, this conversation provides the strategic context and tactical insights needed to make better decisions.

Enter Hannu Rajaniemi, who brings a refreshingly clear perspective to the world of biotech. As both the CEO of Helix Nanotechnologies and a bestselling science fiction author, he bridges the gap between cutting-edge science and practical business insights. In conversation with Mercury CEO Immad Akhund and Lima co-founder Raj Suri, Rajaniemi reveals the real dynamics shaping the future of medicine.

The discussion unpacks several game-changing trends that every tech entrepreneur should understand: how AI reasoning models are revolutionizing research and development, why global competition is making traditional biotech business models obsolete, and what really matters when evaluating biotech investments. 

In this gripping episode, Mercury CEO Immad Akhund and serial founder Rajat Suri sit down with serial entrepreneur Hiten Shah (Crazy Egg, KISSmetrics, Nira, sold to Dropbox) for a candid conversation about founder identity over the course of an entire career. 

Get practical insights on choosing the right customers, surprising parallels between AI and the AWS revolution, and the hidden truths about venture-backed versus bootstrapped companies. 

Whether you're building your first startup or considering an exit, you'll learn from three founders who challenge conventional wisdom while sharing hard-won lessons from decades of company building. 

Jesse is a prominent entrepreneur and investor in the HR technology and recruitment industry. He is Founder and CEO of multiple successful companies, including Employer.com and Recruiter.com.

In this conversation with Mercury CEO Immad Akhund and Tribe co-founder Raj Suri, Jesse unveils his counterintuitive strategy—from how he finds companies, why VCs actually love working with him, and how he structures deals with limited capital.

He shares candid details about his recent acquisition of Bench Accounting and even discusses his current bid for TikTok, offering a masterclass in seeing opportunity where others see distress.

The conversation explores the fascinating shift in AI development costs, with DeepSeek challenging assumptions about the resources needed to build frontier models.

Richard shares insights about Academia.edu's work applying AI to academic research, while the trio discusses the geopolitical dynamics between China and the US in the AI race.

Through candid exchanges about synthetic data generation, regulatory challenges, and the future of human-AI integration, this episode offers a unique perspective on the advancing AI frontier from three tech leaders at the intersection of startups and artificial intelligence.

From his early days at MIT Media Lab to founding innovative startups, Jeevan shares insights on how OpenSpace is transforming construction site management through spatial AI and 360-degree documentation.

Discover why Jeevan believes we're still in the early stages of a camera revolution that will reshape how we interact with physical spaces.

Whether you're interested in AI, tech for the physical world, or the future of spatial computing, this episode offers a compelling look at how computer vision is becoming an invisible but essential part of our world.

From Biden's "tech industrial complex" remarks to the changing power dynamics between tech companies and their employees, they discuss how the industry's traditional political neutrality is giving way to partisan alignment.

The conversation delves into startup fundraising strategies, the importance of market size, and hard-earned lessons about the five-year journey to product-market fit.

Whether you're a founder navigating the VC landscape or interested in tech's growing political influence, this episode offers valuable insights from two seasoned entrepreneurs.

Connect with us on Tribe:

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Connect with us on Tribe:

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Want to join the conversation? Connect with us on Tribe:

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Want to join the conversation? Connect with us on Tribe:

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Roger Dickey’s Startup Ideas List: https://docs.google.com/document/d/1XgBM6DyFjH77lyobMgJep2C6YjIyAl9hwNAe7faoOaI/edit?usp=sharing 

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Connect with Paul here: https://x.com/paultoo?lang=en

Read “Loving What Is” here: https://www.amazon.com/Loving-What-Four-Questions-Change/dp/1400045371

Read “The Beginning of Infinity” here: https://a.co/d/hFvJBvl

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Listen to the Startup Ideas Podcast with Greg Isenberg here: https://podcasts.apple.com/us/podcast/the-startup-ideas-podcast/id1593424985 

Read Raj’s essay, “How to Grow Your Startup while being Cash-Flow Positive,” here: https://rajatsuri.substack.com/p/how-to-grow-your-startup-while-being

Read "Founder Mode" here:

https://paulgraham.com/foundermode.html

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[00:00:00] Introduction

[00:06:49] Documentary Filmmaking

[00:13:43] Programming

[00:17:24] The future of filmmaking

[00:24:51] Art and Award Shows

[00:28:28] Movie theaters

[00:29:30] Different roles in filmmaking

[00:34:26] Budgeting time in filmmaking

[00:37:01] Recent Work and Creative Process

[00:41:30] Funding creative film projects

[00:44:16] Dealing with rejection

[00:47:28] Building community

[00:52:41] Filmmaking and startups

[00:54:12] Evolution of filmmaking technology

[00:00:00] Introduction

[00:04:26] Miles’ Early Career

[00:09:54] Time at the DHS.

[00:13:37] National security priorities

[00:16:05] The future of democracy

[00:17:58] The future of national security

[00:23:52] US foreign policy implications

[00:27:46] Current political climate in the US

[00:34:31] Ranked Choice Voting and Proportional Representation

[00:43:54] Immigration laws and security

[00:53:17] 2024 Election possible outcomes

[00:55:20] Immigration bill bipartisan struggles

Timestamps:

00:00:00 - Introduction

00:05:27 - Viral Gene Therapy

00:11:17 - Universal Gene Therapy Concept

00:13:58 - Applications in Cancer Treatment and Common Diseases

00:16:17 - Development Challenges

00:19:46 - Insulin Production

00:21:13 - Viral Gene Therapy Industry

00:24:03 - FDA's Role in Gene Therapy Development

00:30:21 - Potential of Viral Gene Therapy

00:34:07 - Biotech Company Paths and Acquisitions

00:38:39 - Future of Cancer Treatment

00:42:33 - Futuristic Possibilities in Viral Gene Therapy

Timestamps:

00:00:00 - Introduction 

00:03:45 - Amira and the Terra System

00:06:47 - Predicting Hot Flashes

00:08:24 - Future Plans for Hormonal Imbalance Treatment

00:09:48 - Understanding Bioelectricity

00:11:20 - Amira's Product Availability and Impact

00:13:04 - Hot Flashes

00:16:35 - Women's Health

00:20:51 - The Potential of Sweat Analysis

00:26:15 - Inspirational Products in Women's Health

00:29:23 - The Future of Women's Health Products

00:32:27 - Complexity of Women's Hormonal System

00:34:24 - Healthcare Systems and Menopause

00:40:06 - Amira's Business Model and Self-Pay

00:41:21 - The Taboo Nature of Menopause

00:42:12 - Amira's Go-to-Market Strategy

00:44:23 - AI and Women’s Healthcare

Timestamps:

00:00:00 - Introduction

00:03:41 - Impulse Labs Origin

00:05:59 - Comparing Induction and Gas Stoves

00:06:42 - Battery Integration and Installation

00:08:08 - High-Power Cooking and Temperature Sensing

00:10:03 - Smart Appliances and Market Impact

00:13:47 - Manufacturing and Distributing Hardware

00:15:05 - Energy Savings and Battery Functionality

00:18:06 - Marketing and Industry Attention

00:20:46 - Copycats and Unique Selling Points

00:22:27 - Backup Power System and Energy Rates

00:30:44 - Consumer Electronics Manufacturing

00:35:04 - Future of Smart Appliances and Home Energy

00:37:08 - Hardware Startup Costs

00:38:08 - Future Home Innovations and HVAC Changes

00:42:31 - Future of the Power Grid

Timestamps:

[00:00:00] Intro

[00:07:25] Prolific writing and reading

[00:10:18] Learning to code in Silicon Valley

[00:14:22] Life-changing income transformations

[00:17:29] AI revolution in engineering

[00:24:40] Predispositions to programming

[00:27:30] Learning to code through trial

[00:30:40] Career orientation vs. social life

[00:34:20] Rethinking Traditional Education

[00:38:16] Rethinking the value of degrees

[00:44:29] Struggles and triumphs of fatherhood

[00:45:47] Life-changing job offers.

[00:50:20] How AI is transforming job search

[00:56:23] Programming and AI integration

[00:58:42] The evolving role of engineering

Timestamps:

[00:00:00] Intro

[00:02:13] The influence of small groups

[00:06:47] Local governance vs. national politics

[00:10:33] Challenges facing San Francisco

[00:14:39] Local Elections in San Francisco

[00:18:10] San Francisco's far left contingent

[00:23:39] Unifying Bay Area governments

[00:26:37] Creating a “Rule of Law” city

[00:29:06] Insufficient public schools

[00:34:03] Public safety and traffic concerns

[00:37:26] Political factions

[00:41:23] Voter behavior analysis

[00:44:19] Politics in democratic societies

[00:49:00] Fixing voting issues

[00:54:21] Crime in San Francisco

[00:56:05] High stakes in democracy

[00:00:00] Intro

[00:04:29] Prometheus and Process

[00:07:30] Liquid fuel and energy infrastructure

[00:11:13] Electrolyzer and carbon chains

[00:13:38] Nanotube membrane filtration

[00:18:59] Renewable gasoline and uncertainty

[00:19:31] Liquid e-fuels and battery alternatives

[00:23:20] Electrochemical stack advancements

[00:27:26] Solving energy grid challenges

[00:30:22] Underutilized solar and wind resources

[00:33:08] The cost of RNG and hydrogen

[00:36:09] Scaling up renewables quickly

[00:41:18] Energy security and climate change

[00:43:26] Taking on ambitious challenges

[00:49:13] Burying carbon in the ground

[00:54:09] United States senior level conversations

[00:57:37] Geopolitical battles in clean energy

[01:00:06] Solid-state batteries and electrification

[01:04:19] Energy sources and origins

[01:06:24] Solving climate change through technology

[01:10:13] Capital and future bets

[01:14:36] Government involvement in technologies

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Timestamps:

00:02:02 Intro

00:08:52 Nanorobotics and future possibilities

00:11:05 Modeling Biological functions

00:16:04 AI design for immunotherapy

00:22:02 Protein synthesis process

00:26:29 Turning cells into protein factories

00:30:00 mRNA modifications and antiviral sensors

00:32:24 mRNA manufacturing and IP disputes

00:36:26 Liquid biopsies for early cancer detection

00:40:24 Using mRNA for cultured meat

00:45:09 Protein structure prediction

00:49:05 Evolution of SARS-CoV-2

00:52:19 The deadliness of viruses

00:55:55 mRNA wearable and global immune system

01:02:26 Storing information through viruses

01:04:07 Exponential technology and genetics

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Timestamps:

00:00:00 - Introduction

00:03:10 - Delian's path from VC to Space Tech

00:08:25 - The Advantages of VC Experience in Aerospace

00:10:12 - Economic Incentives for Human Presence in Space

00:12:19 - Industrializating Outer Space

00:16:09 - Manufacturing and Micro-Gravity

00:21:24 - Pharmaceutical Applications in Space Manufacturing

00:24:38 - The Market Potential for Space Manufacturing

00:26:41 - The Importance of Crystal Structure in Pharmaceuticals

00:28:14 - Merck's ISS Experiment and Commercialization Challenges

00:30:20 - The Re-Entry Challenge in Space Manufacturing

00:36:12 - Automation vs Human Presence in Space Manufacturing

00:38:19 - Scaling Varda's Space Manufacturing Operations

00:40:14 - Varda's Mission Economics and Future Goals

00:49:42 - Over-engineered components for missions

00:52:10 - Permanent human presence in space

00:56:00 - Economics in space

01:02:10 - Varda's far-fetched mission

01:06:18 - Robotic aviation and Mars

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Time stamps:

00:00:00 Intro

00:05:49 Rapid advancement of wildfire technology.

00:11:49 Autonomous aircraft for wildfire suppression.

00:30:16 Why new technology is critical to fighting wildfires.

00:35:03 Wildfires are a global climate issue.

00:41:07 Adapting to climate change challenges.

00:44:15 Developing multiple solutions.

00:49:27 Early detection technology for wildfires.

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Time stamps:

00:00:00 Intro

00:05:23 Tools for augmenting human imagination

00:10:36 Creativity is an exploration process

00:14:48 The future of creative tools

00:23:40 Creative explosion through AI augmentation.

00:26:00 Positives and negatives of Deepfakes

00:29:17 Early stages of video generation technology

00:34:00 Equipping teams with the right tools

00:40:09 Benefits of collaboration

00:42:52 Long-term, value-driven bets

00:47:38 How technology will revolutionize art

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Time stamps:

00:00:00 Intro

00:05:23 Carbon removal process

00:14:29 Growth of the carbon removal market

00:18:26 Machinery and biomass cost

00:24:05 The scale of reversing climate change

00:31:42 Carbon removal and reducing emissions.

00:32:16 Supply constraints 

00:38:30 Governments’ role

00:44:05 Advancements in carbon removal technologies

00:46:18 Geoengineering risks

00:51:16 Working with hardware

Key topics:

• The history of psychedelics
• Role of them in therapeutical settings
• Why psychedelics have been misunderstood
• What the future holds for this and other drugs

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Key topics:

• Inflation and the current state of the economy, plus future risks
• America's relationship with China and how we should play our cards
• Immigration and our labor market, and some thoughts for how things will change
• Rabbits!

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Key topics:

• What does the future of drug delivery and tissue engineering look like?
• How has Bob managed to make a career in academia so entrepreneurial?
• What can we do to maximize innovation in biotech?

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Transcription:

Amjad Masad (00:00:00):

Pain and pleasure are like core features of consciousness and they seem important for humans operating in the world. Can you actually construct that in a machine?

Immad Akhund (00:00:27):

Hi everyone. Welcome to the first ever podcast recording of curiosity podcast where we go deep with an expert in their field. The tagline is Delivering 10,000 hours of learning in one hour. So that's a <laugh> ambitious tag line. I'm ial, I'm the co-founder and CEO of Mercury. I've been doing kindness startups and investing in startups since 2006.

Raj Suri (00:00:49):

I'm Raj Suri. I'm the founder and CEO of Presto Automation, which delivers AI type applications for for traditional industries like restaurants. Also co-founded Lyft and yeah, very excited to be cotting this with you. Mad. This is an opportunity to go really deep into some really interesting areas with some of the smartest people and you know, most thoughtful people on the planet. So excited to be able to explore, you know, in depth.

Immad Akhund (00:01:14):

Yeah. And today we have Amjad Masad with us. He's the co-founder CEO and I believe now head of engineering at Relet. What's the one line of relet? Mjad.

Amjad Masad (00:01:27):

It's the fastest way to make software. We have a platform that provides an online programming environment that's collaborative and we have large community of developers making things for each other, for other people. And we're getting into supporting teams of developers in the same way that say, you know, Figma is a collaborative design program replica is kind of that for programming.

Immad Akhund (00:01:54):

What's kind of interesting is it started relatively small, right? This was like an ID online and I think you were like compiling whatever programming to JavaScript and running it on the browser, right? But now it's this kinda hosted package combined with the teams combined with like learning and template. Like was that always your vision that you're gonna like progressively get to this level of like product or was it kinda incremental?

Amjad Masad (00:02:21):

I actually sort of saw a lot of it in my mind's eye pretty early on and a lot of it is like pretty obvious stuff. I'm actually surprised that no one had built it because I started working on it in college in like 2009, something like that. And then when I came to the US based on an Oprah source project that's related, I worked at Code Academy as a founding engineer and used this sort of the same technology that I built to like make browser coding possible and then left that and went to work at Facebook, worked on React and React Native was founding engineer on React native. React native is like the best way to make cross-platform mobile apps. And in 2016 revisited the idea and had found that basically nobody had built it, which was really surprising. It is the opposite or efficient market hypothesis, like why is in the market not producing this?

(00:03:20):

And I actually did not wanna start a startup, but I was so compelled by this thing and I knew that this idea had to exist. Well it turns out I think the answer that why nobody had built it is because it's incredibly hard. But yeah, in terms of execution it was started very simple. But in terms of vision it was always, you know, I was an early GitHub user, like perhaps in their beta. I think I was so excited by GitHub. I was also so disappointed by how little they evolved beyond the initial sort of kernel of an idea. So I always thought about making collaborative developer community that's like more exciting that you can ma do more things in it. And yeah, you know, I've always imagined a lot of the features that we're building. Of course with time just what became available in terms of technology, we started adding a lot of these things that I didn't really think about at the start. But overall, like even like now we have this tipping mechanism where you can tip developers. That was like, I've thought about that like fairly early on. It was actually kind of frustrating cuz like as a founder developer, you're sort of like, everything feels like it's like one weekend of hacking away and it's like turns out, no, it's actually more than a decade <laugh> of hacking <laugh>.

Raj Suri (00:04:36):

Mj it's a really interesting description. You talked about the fastest way to make software, right? Is the primary user base people who don't normally make software or are not really comfortable setting up their own environment cuz that takes time Or is it more the power users who are probably already, you know, they already have a CS degree or something like that, they have a lot of knowledge. What's the primary user base for this type of application?

Amjad Masad (00:05:00):

One of the exciting things about running Relet is the user base is so diverse. It also makes it really hard to run the company because people like crave these simple personas. I'm sure you guys at, at your companies like the design team or the product team want to talk about one or two personas. But I always push back on sort of persona building because ultimately I tell them that we have one persona and that persona is the developer, the software creator. Because like you start segmenting people into students or professionals or hobbyists or, and all these segmentations are true to some extent ultimately, like if you're familiar with Clay Christensen's jobs to be done, I think that inversed the question. It's not like a customer segmentation thing, which is totally arbitrary kind of based on user characteristics. It might be actually interesting to go into jobs to be done a little bit.

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Transcription:

Amjad Masad (00:00:00):

Pain and pleasure are like core features of consciousness and they seem important for humans operating in the world. Can you actually construct that in a machine?

Immad Akhund (00:00:27):

Hi everyone. Welcome to the first ever podcast recording of curiosity podcast where we go deep with an expert in their field. The tagline is Delivering 10,000 hours of learning in one hour. So that's a <laugh> ambitious tag line. I'm ial, I'm the co-founder and CEO of Mercury. I've been doing kindness startups and investing in startups since 2006.

Raj Suri (00:00:49):

I'm Raj Suri. I'm the founder and CEO of Presto Automation, which delivers AI type applications for for traditional industries like restaurants. Also co-founded Lyft and yeah, very excited to be cotting this with you. Mad. This is an opportunity to go really deep into some really interesting areas with some of the smartest people and you know, most thoughtful people on the planet. So excited to be able to explore, you know, in depth.

Immad Akhund (00:01:14):

Yeah. And today we have Amjad Masad with us. He's the co-founder CEO and I believe now head of engineering at Relet. What's the one line of relet? Mjad.

Amjad Masad (00:01:27):

It's the fastest way to make software. We have a platform that provides an online programming environment that's collaborative and we have large community of developers making things for each other, for other people. And we're getting into supporting teams of developers in the same way that say, you know, Figma is a collaborative design program replica is kind of that for programming.

Immad Akhund (00:01:54):

What's kind of interesting is it started relatively small, right? This was like an ID online and I think you were like compiling whatever programming to JavaScript and running it on the browser, right? But now it's this kinda hosted package combined with the teams combined with like learning and template. Like was that always your vision that you're gonna like progressively get to this level of like product or was it kinda incremental?

Amjad Masad (00:02:21):

I actually sort of saw a lot of it in my mind's eye pretty early on and a lot of it is like pretty obvious stuff. I'm actually surprised that no one had built it because I started working on it in college in like 2009, something like that. And then when I came to the US based on an Oprah source project that's related, I worked at Code Academy as a founding engineer and used this sort of the same technology that I built to like make browser coding possible and then left that and went to work at Facebook, worked on React and React Native was founding engineer on React native. React native is like the best way to make cross-platform mobile apps. And in 2016 revisited the idea and had found that basically nobody had built it, which was really surprising. It is the opposite or efficient market hypothesis, like why is in the market not producing this?

(00:03:20):

And I actually did not wanna start a startup, but I was so compelled by this thing and I knew that this idea had to exist. Well it turns out I think the answer that why nobody had built it is because it's incredibly hard. But yeah, in terms of execution it was started very simple. But in terms of vision it was always, you know, I was an early GitHub user, like perhaps in their beta. I think I was so excited by GitHub. I was also so disappointed by how little they evolved beyond the initial sort of kernel of an idea. So I always thought about making collaborative developer community that's like more exciting that you can ma do more things in it. And yeah, you know, I've always imagined a lot of the features that we're building. Of course with time just what became available in terms of technology, we started adding a lot of these things that I didn't really think about at the start. But overall, like even like now we have this tipping mechanism where you can tip developers. That was like, I've thought about that like fairly early on. It was actually kind of frustrating cuz like as a founder developer, you're sort of like, everything feels like it's like one weekend of hacking away and it's like turns out, no, it's actually more than a decade <laugh> of hacking <laugh>.

Raj Suri (00:04:36):

Mj it's a really interesting description. You talked about the fastest way to make software, right? Is the primary user base people who don't normally make software or are not really comfortable setting up their own environment cuz that takes time Or is it more the power users who are probably already, you know, they already have a CS degree or something like that, they have a lot of knowledge. What's the primary user base for this type of application?

Amjad Masad (00:05:00):

One of the exciting things about running Relet is the user base is so diverse. It also makes it really hard to run the company because people like crave these simple personas. I'm sure you guys at, at your companies like the design team or the product team want to talk about one or two personas. But I always push back on sort of persona building because ultimately I tell them that we have one persona and that persona is the developer, the software creator. Because like you start segmenting people into students or professionals or hobbyists or, and all these segmentations are true to some extent ultimately, like if you're familiar with Clay Christensen's jobs to be done, I think that inversed the question. It's not like a customer segmentation thing, which is totally arbitrary kind of based on user characteristics. It might be actually interesting to go into jobs to be done a little bit.

(00:05:58):

But basically the idea is that most companies, the way they think about bringing a product to market as they think about a customer persona and that really confuses people about what they should be building and what the actual need that they're doing it for. And so Clay Christensen switched that question, like his primary insight is that people hire products in the same way that you hire people. When you go and you want some accounting done, you don't go look for like a middle-aged man in New York. You know, you don't do that, right? You go and you say, I want some accounting done. And you find someone who's competent at accounting in the same way that you want to get software done or you want to make software, you go to a place that makes it really easy to make software. And from that lens, I just think about our community as as sort of really the main jobs to be done here is that you wanna make something.

(00:07:03):

And it turns out students wanna make something. They wanna learn to make something hobbyists wanna make something, they wanna crunch some data, they wanna make a fun app, they want to make a game. Professional developers wanna make software to bring it to their customers. And so that's the sort of shared goal of our community in terms of like the makeup of our community. It's cues younger and there's partly cultural, partly some product limitations. The cultural aspect is that developers tend to be very conservative people. They don't want to change their tools and they're actually quite haughty and quite, you know, I love them. My best friends are developers, I'm a developer, but we're actually like difficult people. And you know, you see it in Stack Overflow comments, it can be a little, people kind of look down on things and people kind of tend to not change too much with time. They stick to one language or one technology. And

Immad Akhund (00:08:05):

I was just thinking why someone didn't like make this innovation between like when you started in 2016. And I think it's partly because of this, right? Like developers are like, they're often build like little incremental tools for themselves to make their life a little easier, but they don't think about like transforming their trade. Like you don't go like, oh I want to like do it in this new way. Completely.

Amjad Masad (00:08:26):

I think that's right. Which is surprising because developers are the agent of change for the larger economy. Or maybe it's not developers that are the agent of change. It's crazy founders that recruit developers to, to make changes <laugh> it's a tough market because the habits really change really slow. And like there's common saying that, you know, programming changes one generation at a time and that is sort of true. But today, rep is is has some product limitations that we're working through mostly on the technology side, like making an immediately accessible institute, producible cloud development environment. Turns out it's like a very tough like distributor systems problem, like one Yep. Problem that we've like dealt with that's sort of introduced like a big product limitation is that if you're introducing this multiplayer programming environment, there's a a potential for what we call race conditions, which is like people like trying to edit the project at the same time and introduces data corruption and all of that.

(00:09:33):

So we use the file system, basically the system that manages your files, that was what we call atomics. So basically every time you do a right, you create a snapshot of your entire system and that is really good for distributed systems to protect against data races and all of that. It also turns out it just introduced like a really hard limitations on scale. Like we can't have like a one terabyte desk per project. So some of the trade offs we made early on created limitations to how much power the platform has. But all of these are solvable. It just requires a ton of innovations and so we're, we're working through those right now. Yeah,

Raj Suri (00:10:15):

I mean that's really interesting. So you talk about again, the fastest way to build software is on rep. What do you envision will be the fastest? Give us some examples of like, you know, how fast someone could build something, how much faster they could build something in the future versus now. And I know you know you're thinking about AI or probably already working on it, you know, in terms of now we can support coding. It'd be really interesting to hear you as the expert kind of what the future looks like in terms of how fast someone can build something.

Amjad Masad (00:10:41):

The fastest story I've heard on Relet is someone coming into Relet with an idea for a product and getting it 30 minutes later. And the way they got it is without writing a single line of code is by relying on a human machine. Centar of sorts, <laugh> basically we have this bounties program where you can like post, you can pay some money Wow. And post like a description of what you want to build. Someone posted like a Figma. I actually tweeted about it. I was like hey, I want to build this. And literally someone got them a prototype in like 30 minutes and that person who's getting them a prototype is not just a person. They're also powered by ai cuz we have a Ghost Rider product similar to GitHub co-pilot. It's actually a little more advanced than GitHub co-pilot because not only has the auto complete thing, you can also chat, you can talk with AI that's writing code with you.

(00:11:37):

And so it's like, feels more like a chat G P T that understands your code and that sits right there with your editor. And so we're really trying to be un ideological about how people make software and the idea is like really trying to reduce the distance between an idea and a, and a piece of product. Like that's been the trajectory of sort of a human history where if you're sort of hunter gatherer and you have an idea for a product that's sort of impossible to do it right. Like you, let's say you wanna build a spear, that in itself is like really, really hard. You have an idea for a hammer for example. It's like impossible to find the rock the right size and do all the right. These things go into, you know, agrarian societies and it's still pretty difficult to make things probably a little easier. Industrial society got way easier, you now had factories, you know, had accessible labor. Capitalism becomes a thing where you can organize people in groups to create things, but it's still fairly difficult to get a product on the market. And I think the information age is reduction of this idea of like having an idea in your head and getting a product in the hands of people. And I think we can get it down to like on the order minutes, <laugh> like to get something on the market.

Immad Akhund (00:12:56):

Like right now you said like there was a human that was like potentially using an AI to like generate this thing, this kind of prototype. Is there eventual state that there isn't a human involved and like the AI is just generating something good enough? Or do you think there'll always be a human in the loop with like our current AI at least?

Amjad Masad (00:13:12):

So the way I think about automation is that it sort of like takes the, takes each of the jobs from the bottom up. That's like not always true, but for the most part true. I believe it's gonna be true for software, meaning the low skill software creation I think will get fully automated. So I think pretty soon probably this year there's gonna be...]]>7689915c-cbd4-4d26-a4b0-c550ae112e9aMon, 27 Mar 2023 09:00:00 -070001:03:22falsefull11