All Guests

Jenny Wen, head of design for Claude Cowork at Anthropic and former design director at Figma, describes how the design profession is being structurally reorganized by AI-accelerated engineering. The old design process — diverge, converge, make beautiful mocks, hand off — is essentially dead for fast-moving teams. Designers are being forced into two new modes: real-time execution partner and short-horizon vision setter prototyping directional bets 3–6 months out.

Jeetu Patel, EVP of Product at Cisco, discusses what he learned from organizing an AI summit featuring Jensen Huang and Sam Altman, and how he's transforming a 90,000-person enterprise into an AI-first company. The episode covers his six-part framework for building great companies (timing > market > team > product > brand > distribution), his contrarian management philosophy of critiquing publicly and praising privately, and a personal story about his mother that became his most important life lesson.

Creator of Claude Code at Anthropic discusses how 100% of his code is now written by AI (with five agents running simultaneously during the podcast), how productivity per engineer at Anthropic has increased 200% since Claude Code launched, and how he built the product from a demo called 'Claude CLI' in late 2024. He shares principles like intentionally under-staffing projects to force AI adoption and giving engineers unlimited tokens.

HubSpot co-founder and Sequoia CEO coach discusses how starting a company has never been easier while scaling one has never been harder due to the explosion of competition AI enables. He shares his LOCKS framework for evaluating CEOs (Lovable, Obsessed, Credible, Knowledgeable, Student), why enterprise sales is the last job AI will replace, and how go-to-market will change as buyers research in AI chat interfaces instead of Google.

Sherwin Wu, an engineering manager at OpenAI, discusses how AI coding tools like Codex have transformed software engineering — 95% of OpenAI engineers use Codex daily and 100% of PRs are reviewed by it. The conversation covers how engineers are becoming "tech leads managing fleets of agents," the counterintuitive prediction that AI may usher in a golden age for B2B SaaS, and the challenges of deploying AI tools broadly across non-engineering organizations.

Lazar Jovanovic, the first official "professional vibe coder" at Lovable, describes his full-time job of building products without writing a line of code, and the specific frameworks he developed to get high-quality output from AI tools. The conversation covers his "parallel build" method (start 4-5 projects simultaneously), the importance of PRD documentation to manage LLM context windows, and why non-technical background is actually an advantage — you don't know what's supposed to be impossible.

Child psychologist and Good Inside founder Dr. Becky Kennedy applies her parenting frameworks to workplace leadership, showing that the principles for raising emotionally healthy kids map almost directly to managing high-performing teams. The conversation covers why optimizing for employee comfort creates fragility, how to set firm boundaries as an act of care, and the concrete script for validating someone's struggle while also projecting confidence in their capability.

Marc Andreessen argues that we are living through one of the most historically significant moments ever, with AI, demographic collapse, and political upheaval colliding simultaneously. He makes the case that AI is a genuine philosopher's stone — turning silicon into thought — and that its arrival is perfectly timed to compensate for 50 years of stalled productivity growth and a coming global population decline. The conversation covers how AI-forward founders are rethinking what a company even is, why career specialization is giving way to multi-domain individuals, and why AI's capacity to teach is its most underrated power.

Jason Cohen, two-time unicorn founder (WP Engine, Smart Bear), walks through his five-question diagnostic framework for stalled growth: logo retention, pricing, net revenue retention, marketing channel saturation (his "elephant curve" concept), and whether you actually need to grow at all. The discussion is grounded in specific mechanisms — why low prices signal low quality to enterprise buyers and why most A/B testing at normal scale produces mostly false positives.

Zevi Arnovitz, a non-technical PM at Meta, shares his complete workflow for shipping real products using Cursor with Claude Code, having built functional internal tools without engineering background. He covers his system for reviewing AI-generated code by having multiple AI tools review each other's work, how he uses vibe coding as an exploratory prototyping tool before handing off to engineers, and his view that product management as a title is collapsing as builders can now ship end-to-end.

Sam Lessin, VC at Slow Ventures and former VP at Facebook, makes the case that professional etiquette is a learnable tactical skill for founders — not about formality but about showing up with a "low heart rate" and an abundance mindset. The episode covers 10 social categories with specific, counterintuitive rules, arguing that Silicon Valley has incorrectly taught founders to focus only on product and be deliberately abrasive.

Product lead for OpenAI's Codex discusses how Codex evolved from a coding agent into something approaching a software engineering teammate, including how OpenAI built the Sora Android app in 18 days using Codex. He shares practical techniques for getting the most out of AI coding: writing a plan.md first, running tasks in parallel, and dogfooding carefully since internal usage patterns don't always match the broader market.

Two AI practitioners (one from Anthropic, one from OpenAI) who are also married discuss the fundamental differences between building AI products versus traditional software, specifically around non-determinism, the agency-control tradeoff, and why most teams fail by chasing hype over problems. They walk through a framework for shipping AI products successfully — starting small, building feedback flywheels, and developing robust evals.

Molly Graham, who has worked with Zuckerberg, Sheryl Sandberg, and Bret Taylor, shares her frameworks for high-growth leadership — most famously "giving away your Legos" and the J-curve model for career transitions. The conversation covers why 80% of company culture is defined by the founder's personality, how to give away things you're good at to unlock the next level, and the counterintuitive truth that chaos at a fast-growing company is a sign of success.

Jason Lemkin, founder of SaaStr, delivers a detailed playbook for B2B sales — covering when to hire your first salesperson, who specifically to hire, and why the vast majority of sales leaders today are burned out. The episode includes his argument that founders must close the first 10 customers personally, why you must hire two reps before a VP of sales, and why the "sell me this pen" demo assignment is still the only reliable hiring test.

Matt MacInnis, CPO and former COO at Rippling (B+ valuation), articulates a contrarian leadership philosophy centered on intensity, entropy, and power law dynamics — arguing that extraordinary outcomes require extraordinary effort and that leaders who try to "buffer" their teams from the founder's intensity are actively harming the company. He covers his "factory inspection" product review process, why withholding feedback is the most selfish thing a leader can do, and why point-solution SaaS is in existential trouble in the AI era.

Elena Verna, head of growth at Lovable (which hit M ARR in under a year with 100 people), argues that in AI companies, product-market fit is no longer a milestone — it's a treadmill you run every three months, as both LLM capabilities and consumer expectations shift with each model release. She covers Lovable's unconventional growth strategy of giving the product away free to developers in hackathons, why the traditional scaling playbook no longer applies, and her concern that women are dangerously under-represented in the AI adoption wave.

Edwin Chen, founder of Surge AI (which reached B+ in revenue with under 100 employees, entirely bootstrapped), explains how Surge became the primary data, evaluation, and RLHF infrastructure provider for Anthropic, Google, and other frontier labs. The conversation covers why quality in AI training data is almost impossibly misunderstood, why Surge kept the team tiny and elite by design, and what it actually takes to teach AI models what "good" and "bad" mean.

Tomer Cohen, LinkedIn's CPO, discusses how he led the transformation of LinkedIn's feed from an underperforming afterthought into a high-engagement professional content platform by going all-in on AI. He shares how he overhauled product strategy in fall 2022 — before ChatGPT was public — by dismantling existing roadmaps and rebuilding around LLMs, established an internal AI Academy, and explains why product leaders must personally own the AI strategy rather than delegating it to engineering.

Jeanne DeWitt Grosser, COO of Vercel and former head of sales at Stripe, delivers a highly tactical enterprise GTM playbook covering customer segmentation, why 80% of enterprise buyers act to avoid risk rather than capture upside, and how to design sales like a product. She shares how Vercel's GTM engineering team built AI agents — replacing a 10-person SDR function for roughly ,000/year.

Rachel Lockett, executive coach and former HR leader at Pinterest and Stripe, guides listeners through the human side of tech leadership with live coaching demonstrations using the GROW model and three levels of listening. She argues the most common leadership failure is defaulting to advice-giving over coaching, and covers burnout prevention through strengths-mapping and co-founder relationship dynamics.

Stewart Butterfield, co-founder of Slack and Flickr, shares mental models for building products people love — arguing truly great products must embarrass their creators because they can see every flaw, and that the only real measure of success is actual value created for customers. He makes a counterintuitive case that friction is often valuable and reducing clicks is almost always the wrong optimization.

Dr. Fei-Fei Li, the AI pioneer behind ImageNet and co-founder of World Labs, argues that spatial intelligence and world models represent the next critical frontier in AI that language models alone cannot address. She explains why the "bitter lesson" is harder to apply to robotics, shares the story of founding World Labs and launching Marble (a "prompt-to-worlds" generative 3D model), and reflects on her career philosophy of intellectual fearlessness.

Grant Lee, CEO of Gamma, tells the story of building a profitable M ARR AI startup with ~30 people in a category investors called "the dumbest idea I've ever heard." The conversation covers how Gamma rebuilt onboarding to deliver magic in the first 30 seconds (triggering word-of-mouth that grew signups from hundreds to 20,000/day), how Grant personally onboarded micro-influencers, and how they found pricing by studying churn patterns.

Jen Abel delivers a step-by-step enterprise sales playbook for M to M ARR through founder-led sales. She explains why this phase is about "selling the alpha" — landing design partners who co-build with you — and walks through cold outreach (counterintuitive hook-based messaging), discovery calls (talk under 50%), multi-threading deals, and closing through real scarcity.

Melanie Perkins tells the story of building Canva to $42B after 100+ investor rejections, explaining how she treated each rejection as a free consulting session. The conversation covers her core operating philosophy: start from the wildly ambitious future ('Column B'), build a 'ladder to the moon' with tiny concrete rungs, set Crazy Big Goals with visible milestones, and sustain the company through painful multi-year dark tunnels — including a two-year front-end rewrite that halted all product shipping.

Dhanji R. Prasanna, CTO of Block, describes how Block with 4,000+ engineers became one of the most AI-native large enterprises — reporting 8-10 hours saved per week per engineer on AI-forward teams. He shares the story of Goose, Block's open-source AI coding agent that monitors Slack and proactively opens PRs, and Block's framework for AI adoption: make it opt-out, measure outcomes not keystrokes.

Author of 'AI Engineering' gives a ground-up explanation of how AI systems actually work — pre-training, post-training, fine-tuning, RAG, reinforcement learning — and why most companies that try to build AI products fail because they don't understand the problem they're solving. She argues the industry is in an 'idea crisis' where people are enamored with tools but have lost sight of user problems.

Nicole Forsgren, creator of the DORA and SPACE frameworks, discusses how to measure developer productivity in the AI era — arguing the biggest challenge is not choosing metrics but failing to define what problem you're solving. She covers how AI changes inner-loop versus outer-loop work, why eliminating all friction is wrong, and how measurement programs fail when they become surveillance rather than improvement tools.

Robby Stein, VP of Product at Google Search, explains the strategy behind AI Overviews and the new AI Mode, how Google navigates the tension between ads, user trust, and answer engines, and why AI is "expansionary" — more questions get asked when AI can actually answer them. He shares his "relentless improvement" product philosophy and lessons from both his biggest successes and failures.

Jason Droege, CEO of Scale AI, gives an inside view on where frontier AI is heading — arguing the transition is from "models knowing things" to "models doing things," and that enterprise AI deployment takes 6-12 months to make a single process robust. He discusses Scale's pivot from data labeling toward AI evaluation and red-teaming infrastructure, and shares lessons from building Uber Eats from zero to B.

Growth leader who has worked across Duolingo, Grammarly, and Chess.com discusses how to find hidden monetization and growth opportunities by deeply understanding user behavior. He shares how at Grammarly, interspersing paid suggestions into the free tier nearly doubled upgrade rates, and explains frameworks for knowing when to explore versus exploit and building AI-powered SQL bots to make companies more data-informed.

Ravi Mehta, former CPO of Tinder and EIR at Reforge, discusses his framework for product leadership and building AI prototypes — specifically why he starts with JSON data structures before design, to test logic before visual polish. He covers a five-layer strategy stack, a leadership matrix distinguishing scalable leadership from selective micromanagement, and a PM competency model built around 12 skills.

Hamel Husain and Shreya Shankar walk through a complete live demonstration of building AI evals using a real property management AI assistant as the example — covering open coding error analysis, axial coding with LLMs, and building an LLM-as-judge. They address major misconceptions and explain why this social-science-inspired process is the highest-ROI activity for AI product builders.

CEO of Mercor — which grew from $1M to $400M annual revenue run rate in 16 months — discusses how they identified the opportunity to be the marketplace connecting expert professionals to AI labs that need high-quality human evaluation data. The core insight: model improvement is bottlenecked by humans who can measure model capabilities, and frontier labs will pay whatever it takes for domain experts to write evaluations.

Ethan Smith, CEO of Graphite, delivers a comprehensive breakdown of Answer Engine Optimization (AEO) — how to get your product recommended by ChatGPT, Claude, and other LLMs. He draws on original research showing 100% AI-generated content doesn't rank in either Google or LLMs, while AI-assisted human content does. He covers citation mechanics, AEO differences across B2B and commerce, and why controlled experiments are essential.

a16z co-founder covers why the hardest part of leadership is making confident decisions under genuine uncertainty when all options are bad, drawing on stories from running Loudcloud/Opsware (including taking the company public with only $2M trailing revenue to avoid bankruptcy). He also shares lessons from unlikely cultural role models — Haitian revolutionary Toussaint Louverture and a reformed prison gang leader — on building trust from zero in low-trust environments.

Scott Wu, CEO of Cognition and creator of Devin, explains how his 15-engineer team uses Devin to merge several hundred PRs per month and demonstrates it live on the podcast. His central thesis: AI shifts engineers from "bricklayers" to "architects" — the valuable skill is precisely defining problems and specifying architecture, while 90% of rote implementation becomes delegable.

Howie Liu, CEO of Airtable, discusses restructuring his entire product team for the AI era — including moving toward "ICCO" (individual contributor CEO/operators), collapsing the EPD triangle, and the counterintuitive lesson that "step back from the details as you scale" was wrong. He shares a framework for which companies and roles will win in AI: those who become full-stack and break down role silos.

CVP of AI Platform at Microsoft discusses the shift from 'product as artifact' to 'product as organism' — products that continuously learn from interactions. She argues the org chart is becoming the 'work chart' as agents eliminate layers, and makes a strong case that post-training and reinforcement learning will become more strategically important than pre-training for most companies building on foundation models.

Garrett Lord, CEO of Handshake, tells the story of building a M ARR AI business in under a year inside his existing M ARR company — by recognizing that Handshake's 18 million students and PhDs gave them a zero-CAC expert network exactly when AI labs shifted from needing generalist data labelers to domain experts. He covers product and org design for internal startups, total separation, and "leave nothing to chance" intensity.

Eoghan McCabe, founder of Intercom, tells how the company nearly hit zero net new ARR as ChatGPT launched, his decision to go "wartime," fire a third of the company, rewrite the values, and bet everything on Fin — an AI customer service agent that crossed M ARR in under three quarters. He discusses the personal transformation (therapy, ego dissolution, burnout) that enabled the turnaround.

Matt LeMay lays out a three-step framework for escaping the "low-impact PM death spiral" — where product teams do work that feels productive but doesn't connect to company-level outcomes. The framework: set team goals no more than one step from company goals, keep impact first at every execution step, and connect every prioritization decision to the same unit of measure as your goals.

Nick Turley, Head of Product at ChatGPT, gives an inside account of building the fastest-growing product in history — from a hackathon project called "Chat with GPT-3.5" to 700 million weekly active users. He discusses OpenAI's product philosophy (ship fast to discover what to polish), the decision to keep health use cases enabled, and ChatGPT's emerging role as a traffic and discovery engine.

Founder of the Modern Elder Academy and Brian Chesky's longtime mentor at Airbnb shares what it was like to join at 52 as a 'modern elder' — simultaneously mentoring the CEO and reporting to him. He saved Airbnb from going mobile-only by pointing out older hosts couldn't manage listings on a phone. He also covers the world's first midlife wisdom school, how nine near-death experiences changed his relationship with purpose, and why curiosity matters more than wisdom as you age.

Bret Taylor — inventor of Google Maps and the Like button, former co-CEO of Salesforce, and chairman of OpenAI — traces the arc from his first product failure (Google Local) to his conviction that agents and outcomes-based pricing are the inevitable future of software. He explains why agents are fundamentally different: they accomplish jobs autonomously rather than helping someone do a job, making productivity gains measurable and enabling entirely new business models.

Madhavan Ramanujam, author of "Monetizing Innovation," delivers a deep dive into pricing strategy — arguing pricing should be treated as a measure of value and the first conversation in product development, not the last. He covers willingness-to-pay research during discovery, good-better-best packaging, the four types of products, outcome-based pricing for AI, and behavioral tactics like the "Panini effect."

Anthropic co-founder and former OpenAI researcher discusses why he left OpenAI over safety concerns, estimating a 50th percentile chance of superintelligence by 2028. He explains Anthropic's Constitutional AI approach, the difference between alignment today versus post-superintelligence, and why spreading safety awareness through networks is the most important thing anyone can do right now.

Dan Shipper, CEO of Every (a media company with four AI apps and seven-figure revenue, all run by 15 people with 100% AI-written code), shares the operating playbook for an AI-native company. Key practices include a dedicated "head of AI operations" role, "compounding engineering" (every unit of work should make the next easier), and how Claude Opus 4's ability to genuinely judge writing quality unlocked new content automation products.

Co-founder of Tiny, who has built and acquired 75+ businesses, argues that most entrepreneurs choose the wrong ideas by pursuing glamorous, crowded markets instead of boring niches. He walks through Charlie Munger's 'fish where the fish are' principle, explains why he bootstrapped to nearly $300M in revenue without VC, and shares how he built an elaborate AI agent system using Lindy to manage his inbox, calendar, and decisions.