AI & Trust

Using AI well requires more than getting an answer quickly. It requires deciding what deserves trust, which facts need checking, and where human judgment still changes the outcome. This collection brings together public work on confidence, reliability, documents, and the practical limits of automation. The starting point is not that AI is always right or always dangerous. It is that a fluent answer and a dependable answer are different things.

The episodes below explore that distinction in everyday professional work. The research links consider the structures through which information becomes usable, verifiable, and retrievable. Read the original paper before relying on a research proposition, and distinguish an episode's public notes from a full transcript. These materials offer ways to frame questions and examine assumptions; they are not evidence that a particular product has passed an independent evaluation.

Key questions

• What makes a confident answer reliable?
• Which facts should be checked before acting?
• When is the document, rather than the model, the bottleneck?

Start here: Difficult Problems

• AI Won’t Replace You. Someone Who Knows How to Use It Might.
• Don’t Let AI Make You Fast at Being Wrong
• Are Documents the Real AI Bottleneck
• The Biggest AI Mistake Smart People Make

Original research

• The Objectivity Premium in Generative Retrieval: How Factual Copy Outranks Promotional Copy When AI Systems Choose What to Cite
• No One Can Own the Language of Medicine: The Legal and Economic Case for an Open, AI-native Standard
• The $812 Billion Illusion: Replacing PDF with Block-Semantic Trust Architectures

Related books

• Don't Fear the Machine