Inside AI’s $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z скачать в хорошем качестве

Inside AI’s $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z

From pioneering software-defined networking to backing many of the most aggressive AI model companies of this cycle, Martin Casado and Sarah Wang sit at the center of the capital, compute, and talent arms race reshaping the tech industry. As partners at a16z investing across infrastructure and growth, they’ve watched venture and growth blur, model labs turn dollars into capability at unprecedented speed, and startups raise nine-figure rounds before monetization. Martin and Sarah join us to unpack the new financing playbook for AI: why today’s rounds are really compute contracts in disguise, how the “raise → train → ship → raise bigger” flywheel works, and whether foundation model companies can outspend the entire app ecosystem built on top of them. They also share what’s underhyped (boring enterprise software), what’s overheated (talent wars and compensation spirals), and the two radically different futures they see for AI’s market structure. We discuss: • Martin’s “two futures” fork: infinite fragmentation and new software categories vs. a small oligopoly of general models that consume everything above them • The capital flywheel: how model labs translate funding directly into capability gains, then into revenue growth measured in weeks, not years • Why venture and growth have merged: $100M-$1B hybrid rounds, strategic investors, compute negotiations, and complex deal structures • The AGI vs. product tension: allocating scarce GPUs between long-term research and near-term revenue flywheels • Whether frontier labs can out-raise and outspend the entire app ecosystem built on top of their APIs • Why today’s talent wars ($10M+ comp packages, $B acqui-hires) are breaking early-stage founder math • Cursor as a case study: building up from the app layer while training down into your own models • Why “boring” enterprise software may be the most underinvested opportunity in the AI mania • Hardware and robotics: why the ChatGPT moment hasn’t yet arrived for robots and what would need to change • World Labs and generative 3D: bringing the marginal cost of 3D scene creation down by orders of magnitude • Why public AI discourse is often wildly disconnected from boardroom reality and how founders should navigate the noise Substack Article w/Show Notes: https://www.latent.space/p/a16z — Martin Casado • LinkedIn:   / martincasado   • X: https://x.com/martin_casado Sarah Wang • LinkedIn:   / sarah-wang-59b96a7   • X: https://x.com/sarahdingwang a16z • https://a16z.com/ Timestamps 00:00:00 – Intro: Live from a16z 00:01:20 – The New AI Funding Model: Venture + Growth Collide 00:03:19 – Circular Funding, Demand & “No Dark GPUs” 00:05:24 – Infrastructure vs Apps: The Lines Blur 00:06:24 – The Capital Flywheel: Raise → Train → Ship → Raise Bigger 00:09:39 – Can Frontier Labs Outspend the Entire App Ecosystem? 00:11:24 – Character AI & The AGI vs Product Dilemma 00:14:39 – Talent Wars, $10M Engineers & Founder Anxiety 00:17:33 – What’s Underinvested? The Case for “Boring” Software 00:19:29 – Robotics, Hardware & Why It’s Hard to Win 00:22:42 – Custom ASICs & The $1B Training Run Economics 00:24:23 – American Dynamism, Geography & AI Power Centers 00:26:48 – How AI Is Changing the Investor Workflow (Claude Cowork) 00:29:12 – Two Futures of AI: Infinite Expansion or Oligopoly? 00:32:48 – If You Can Raise More Than Your Ecosystem, You Win 00:34:27 – Are All Tasks AGI-Complete? Coding as the Test Case 00:38:55 – Cursor & The Power of the App Layer 00:44:05 – World Labs, Spatial Intelligence & 3D Foundation Models 00:47:20 – Thinking Machines, Founder Drama & Media Narratives 00:52:30 – Where Long-Term Power Accrues in the AI Stack