a plain language map · in progress

People arguing about AI risk are usually arguing about different things.

Four walkthroughs that take the vocabulary apart, show where the real disagreements are, and separate what has been measured from what is argued. Every claim is tagged and sourced.

no predictions sources on every claim work in progress
Belle looking curious
why this exists

I build things with these systems every day and I wanted to understand the argument about them properly, rather than by absorbing headlines. So I did what I do with anything complicated: took it apart and drew it. This is that, in public, with the uncertainty left visible instead of smoothed out.

Start anywhere

How to read the flags

Every substantive claim on this site carries one of three marks. They are the most important thing here, because the usual failure of writing on this subject is letting the three blur together.

measured someone's estimate argument

Measured means a study, survey or evaluation actually counted something. An estimate means a named person said it, which makes the saying a fact and the belief still a belief. An argument is philosophical and cannot be settled by data.

On sources

Every page here ends with its sources, linked to the primary document rather than to coverage of it. Where a person is quoted, the link goes to the place they actually said it. Where the evidence points both ways, as it does on biological risk uplift, both sides are listed rather than one being picked.

borrowed, not invented

None of the frameworks used here are mine. The two axis grid comes from Bostrom and Cirkovic, the causal categories from Zwetsloot and Dafoe and from the International AI Safety Report, the training pipeline from the published method papers. What I have done is arrange them and draw them. Each page says which parts it borrowed and from whom.