# Jeremy Howard

Data scientist and educator who proposed llms.txt, extending the agent-readable web standards from crawling to inference.

**Jeremy Howard** is an Australian data scientist and educator, co-founder of the research lab Answer.AI and of fast.ai. In 2024 he proposed [llms.txt](/docs/llms-txt.md), a convention for a curated, machine-readable manifest that tells a language model what to read at inference time.

## Significance

Howard's proposal extends the [agent-readable web standards](/wiki/agent-readable-web-standards.md) lineage — [robots.txt](/docs/robots-txt.md), [sitemap.xml](/docs/sitemap-xml.md) — from *crawling* to *inference*, inheriting the Sitemaps pattern of publishing an operator-curated manifest. It is part of the protocol surface of the Independent Internet, and a current example of how operators reclaim control over how machines read their sites.

---

## Backlinks

Pages that link here:

- [The Governance of Agent-Readable Standards](/wiki/governance-of-agent-readable-standards.md) — The five agent-readable web standards differ sharply in who governs them — and that trajectory is the real story.
- [llms.txt](/wiki/llms-txt.md) — A proposed standard for a curated, LLM-shaped manifest at a site's root — telling language models what to read at inference time.