In this episode, Jen Kha, Head of Investor Relations, and David George, General Partner, discuss how late-stage private markets are evolving as AI reshapes scale, capital intensity, and growth timelines. They explain why AI-driven companies are staying private longer, how infrastructure spending is changing return profiles, and what this moment means for durability, value creation, and long-term outcomes in private markets.

Timecodes:

0:00 — Introduction

04:21 — The Market Opportunity for AI

26:48 — Pricing, Monetization, and Cash Burn

43:15 — Companies Staying Private Longer

51:30 — Portfolio Composition and Construction

57:18 — Team Culture and Collaboration

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Not an offer or solicitation. None of the information herein should be taken as investment advice; Some of the companies mentioned are portfolio companies of a16z. Please see https://a16z.com/disclosures/ for more information. A list of investments made by a16z is available at https://a16z.com/portfolio.

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Mintlify is a documentation platform built by cofounders Han Wang and Hahnbee Lee to help teams create and maintain developer docs. In this episode, Andreessen Horowitz general partners Jennifer Li and Yoko Li speak with Han and Hahnbee about how coding agents are changing what “good docs” mean, shifting documentation from a human-only resource into infrastructure that powers AI tools, support agents, and internal knowledge workflows. They share Mintlify’s early journey, including eight pivots, the two-day prototype that landed their first customer, and the “do things that don’t scale” sales motion that helped them win early traction. The conversation also covers why docs go out of date, what “self-healing” documentation requires to actually work, and how serving fast-moving customers has shaped both their product priorities and their pace.

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Inferact is a new AI infrastructure company founded by the creators and core maintainers of vLLM. Its mission is to build a universal, open-source inference layer that makes large AI models faster, cheaper, and more reliable to run across any hardware, model architecture, or deployment environment. Together, they broke down how modern AI models are actually run in production, why “inference” has quietly become one of the hardest problems in AI infrastructure, and how the open-source project vLLM emerged to solve it. The conversation also looked at why the vLLM team started Inferact and their vision for a universal inference layer that can run any model, on any chip, efficiently.

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