Estimates that around 80 percent of US workers have at least 10 percent of tasks exposed to LLMs.
Why read it. The most-quoted employment number in the debate. Read what exposure actually means before quoting it.
The clearinghouse
The best navigable map of the argument
Not a catalogue of scary AI stories, and not a reading list for optimists. Both cases are given the same seriousness, because a person deciding what to think needs the strongest version of each.
Proposes evaluating models for dangerous capabilities and for alignment, before deployment rather than after.
Why read it. The technical foundation under most frontier-safety policies now in force.
A method for training model behaviour against a written set of principles rather than case-by-case human labels.
Why read it. One of the few concrete answers to 'so what would you actually do about it?'
The foundational statement of the present-harms critique: environmental cost, data provenance, and the illusion of understanding.
Why read it. The most-cited counterweight to existential-risk framing, and it predates the current wave.
Turned abstract worry into five engineering problems: side effects, reward hacking, scalable oversight, safe exploration, distributional shift.
Why read it. Where safety stopped being philosophy and became a research agenda.