AI is reopening a core question from the development of the web: how to preserve the freedom to learn while giving publishers meaningful control over how their works are made available.

In this episode, Matt Perault is joined by Derek Slater, cofounder of Proteus Strategies and an expert on information access, copyright, and free expression, to examine this question and explain why it matters for the future of AI.

The web worked because people could access, read, analyze, and build on lawfully available information. Standards like robots.txt helped manage the balance between openness and control at scale, giving publishers a way to express preferences without requiring every builder to negotiate permission website by website.

AI is now testing that equilibrium. Some proposals would restrict not only unlawful access, like when AI developers circumvent paywalls to get data, but also lawful learning from public information through expanded copyright theories, terms of service, technical barriers, or licensing requirements. Derek and Matt separate those issues, including the difference between training models on lawfully accessed data, producing infringing outputs, using AI tools to summarize content a user can already access, and breaking through access controls.

For Little Tech, this question is fundamental. Access to data operates as a form of startup capital, allowing new companies to develop products and compete. But if AI companies can’t learn without negotiating expensive licenses or if large pools of data are entirely off limits to AI learning, then only the biggest, most-resourced companies will be able to survive.

Topics covered:

00:00: Introduction

01:45: The freedom to learn and AI data access

04:16: What the early web can teach us about openness, control, and contested norms

08:48: How robots.txt helped publishers express preferences at scale

11:43: Why voluntary standards worked for search engines and publishers

13:50: How freedom to learn applies beyond technology and copyright debates

16:02: The publisher POV on traffic, monetization, and value exchange

18:36: Why AI agents are raising new questions about user control

20:36: The difference between protecting publishers and limiting lawful AI-assisted reading

21:42: How copyright law applies to AI training inputs and model outputs

28:08: How contracts and terms of service can attempt to restrict lawful learning

31:12: The limits of “learning” as a defense

33:53: How licensing markets are evolving around data access and AI outputs

37:12: Technical collaboration and the future of robots.txt-style standards for AI

39:43: Why data access is a Little Tech issue

42:25: Public policy guidance for preserving the freedom to learn

45:29: Closing thoughts

Disclosure: Derek Slater has previously provided consulting support to Andreessen Horowitz. The views expressed here are his own, and this conversation was not part of a paid engagement.

Resources:

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