What it is — Argues that information architecture, long underfunded because its failures were diffuse and hard to measure, now has a concrete business case because AI systems expose structural flaws as measurable token costs and hallucinations.

Key points

• Metadata, taxonomies, and content typing let AI systems tell authoritative information from anecdotal — without them, "a price, a policy, a deprecated note, and a customer quote can read as similar strings of text and mean opposite things."
• Humans forgive vague labels by using judgment; AI pattern-matches over messy structures and confidently reproduces errors at scale, turning isolated mistakes into systematic ones.
• IA can now be funded through measurable AI outcomes — retrieval accuracy, hallucination reduction, agent reliability — rather than abstract UX arguments.