Fei-Fei Li is a Stanford professor, co-director of Stanford Institute for Human-Centered Artificial Intelligence, and co-founder of World Labs. She created ImageNet, the dataset that sparked the deep learning revolution. 

Justin Johnson is her former PhD student, ex-professor at Michigan, ex-Meta researcher, and now co-founder of World Labs.

Together, they just launched Marble—the first model that generates explorable 3D worlds from text or images.

In this episode Fei-Fei and Justin explore why spatial intelligence is fundamentally different from language, what's missing from current world models (hint: physics), and the architectural insight that transformers are actually set models, not sequence models.

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Recently, a16z General Partner Anish Acharya joined Ollie Forsyth on NEW ECONOMIES. They talked about why consumer tech is surging again, how AI is enabling 100M-user products at unprecedented speed, and what founders need to understand heading into 2026 — from distribution shifts to founder mindset to the mechanics behind the fastest product cycle in tech history.

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a16z General Partners David Haber, Alex Rampell, and Erik Torenberg discuss why 19 out of 20 AI startups building the same thing will die - and why the survivor might charge $20,000 for what used to cost $20.

They expose the "janitorial services paradox" (why the most boring software is most defensible), explain why OpenAI won't compete with your orthodontic clinic software despite having 800 million weekly users, and reveal how non-lawyers are building the most successful legal AI companies. Plus: the brutal truth about why momentum isn't a moat, but without it, you're already dead.

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A16Z co-founder Ben Horowitz joins Shaan Puri and Sam Parr on My First Million to talk about how to be a great leader.

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Russ Fradin sold his first company for $300M. He’s back in the arena with Larridin, helping companies measure just how successful their AI actually is.

In this episode, Russ sits down with a16z General Partner Alex Rampell to reveal why the measurement infrastructure that unlocked internet advertising's trillion-dollar boom is exactly what's missing from AI, why your most productive employees are hiding their AI usage from management, and the uncomfortable truth that companies desperately buying AI tools have no idea whether anyone's actually using them. 

The same playbook that built comScore into a billion-dollar measurement empire now determines which AI companies survive the coming shakeout.

Timecodes: 

0:00 — Introduction 

2:15 — Early Career, Ad Tech, and Web 1.0

3:09 — Attribution Problems in Ad Tech & AI

4:30 — Building Measurement Infrastructure

6:49 — Software Eating Labor: Productivity Shifts

8:51 — The Challenge of Measuring AI ROI

14:54 — The Productivity Baseline Problem

18:46 — Defining and Measuring Productivity

21:27 — Goodhart’s Law & the Pitfalls of Metrics

22:41 — The Harvey Example: Usage vs. Value

25:18 — Surveys vs. Behavioral Data

28:38 — Interdepartmental Responsiveness & Real-World Metrics

31:00 — Enterprise AI Adoption: What the Data Shows

33:59 — Employee Anxiety & Training Gaps

38:31 — The Nexus Product & Safe AI Usage

42:08 — The Future of Work: Job Loss or Job Creation?

44:40 — The Competitive Advantage of AI

53:45 — The Product Marketing Problem in AI

55:00 — The Importance of Specific Use Cases

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In this episode, a16z GP Martin Casado sits down with Sherwin Wu, Head of Engineering for the OpenAI Platform, to break down how OpenAI organizes its platform across models, pricing, and infrastructure, and how it is shifting from a single general-purpose model to a portfolio of specialized systems, custom fine-tuning options, and node-based agent workflows.

They get into why developers tend to stick with a trusted model family, what builds that trust, and why the industry moved past the idea of one model that can do everything. Sherwin also explains the evolution from prompt engineering to context design and how companies use OpenAI’s fine-tuning and RFT APIs to shape model behavior with their own data.

Highlights from the conversation include: 

• How OpenAI balances a horizontal API platform with vertical products like ChatGPT
• The evolution from Codex to the Composer model
• Why usage-based pricing works and where outcome-based pricing breaks
• What the Harmonic Labs and Rockset acquisitions added to OpenAI’s agent work
• Why the new agent builder is deterministic, node based, and not free roaming

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Ben Horowitz reveals why the US already lost the AI culture war to China—and it wasn't the technology that failed. While Biden's team played Manhattan Project with closed models, Chinese developers quietly captured the open-source heartbeat of global AI through DeepSeek, now running inside every major US company and university lab. The kicker: Google and OpenAI employ so many Chinese nationals that keeping secrets was always a delusion, but the policy locked American innovation behind walls while handing cultural dominance to Beijing's weights—the encoded values that will shape how billions of devices interpret everything from Tiananmen Square to free speech.

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Marc Andreessen and Ben Horowitz sit down with Margit Wennmachers—the woman who turned two unknown entrepreneurs with $300 million and zero investing track record into the most talked-about firm in venture capital. She unpacks how they weaponized transparency in an industry built on secrecy, why Fortune's cover story triggered a cartel meltdown, and the exact moment a casual lunch conversation became "Software Is Eating the World." This is the origin story of how A16Z broke every unwritten rule, made enemies of every top-tier firm, and permanently rewired what it means to build companies in public.

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Half a billion people can access the world’s best AI on their phone. So why are most using it to write emails while only some are using it to build empires?

In this conversation with Mark Halperin from Next Up, Marc Andreessen reveals why small bakeries are beating Fortune 500 companies at AI adoption, how to turn ChatGPT into your personal board of directors, and why Silicon Valley just reversed five years of geographic dispersion overnight. He also shares the questions that unlock AI's real power—including one of his favorite prompts: "What questions should I be asking?"

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Epoch AI researchers reveal why Anthropic might beat everyone to the first gigawatt datacenter, why AI could solve the Riemann hypothesis in 5 years, and what 30% GDP growth actually looks like. They explain why "energy bottlenecks" are just companies complaining about paying 2x for power instead of getting it cheap, why 10% of current jobs will vanish this decade, and the most data-driven take on whether we're racing toward superintelligence or headed for history's biggest bubble.

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