The annual measurement volume: capability, investment, deployment, incidents, public opinion.
Why read it. Numbers, updated yearly, that let you check your own drift.
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.
Long-form primary interviews where researchers and executives say more than they mean to.
Why read it. A high proportion of the quotable positions in this observatory originate here.
The maximalist case, argued at book length: that there is no version of building superintelligence under current conditions that humanity survives.
Why read it. Read the strongest form of the argument you disagree with, from the people who hold it most firmly.
The closest thing the field has to an IPCC-style synthesis, commissioned after the Bletchley summit.
Why read it. Where the disagreements are documented rather than argued.
Separates predictive AI, which mostly does not work as advertised, from generative AI, which works differently than advertised.
Why read it. The best available inoculation against capability claims in press releases.
Takes the optimistic case seriously enough to ask the harder question: if the technical problems are solved, what is left for people to do?
Why read it. The rare treatment of the upside that is not marketing.
The first comprehensive statutory regime for AI, risk-tiered, with obligations phasing in through 2026 and 2027.
Why read it. Whatever anyone believes, this is now law, and it is the text that binds.
The compressed-21st-century argument: that powerful AI could deliver 50 to 100 years of biological progress in five to ten.
Why read it. The most specific, most falsifiable version of the abundance case, written by someone who also puts double-digit odds on catastrophe.
An extrapolation-driven argument that AGI by 2027 follows from straight lines on graphs, with national-security consequences.
Why read it. Whether or not the trendlines hold, this document shaped how a lot of capital and policy attention got allocated.
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.
A field study of 5,000 customer-support agents: productivity up about 14 percent, with gains concentrated among novices.
Why read it. Measured effects in a real workplace, not a benchmark.
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.
Called for a six-month moratorium on training systems more powerful than GPT-4. It did not happen.
Why read it. A resolved prediction about coordination, and a useful calibration on what open letters achieve.
Technology raises general prosperity only when institutions force it to. Otherwise the gains concentrate.
Why read it. Reframes the AI question from 'how capable' to 'who decides', with a thousand years of evidence.
An early, contested claim that GPT-4 showed general-intelligence characteristics.
Why read it. A case study in the evaluation problem: the disagreement was about what counts, not about what the model did.
One sentence: mitigating the risk of extinction from AI should be a global priority alongside pandemics and nuclear war.
Why read it. The signature list is the argument. Read who signed and who pointedly did not.
Argues containment of AI and synthetic biology is the defining problem, and that it may not be achievable.
Why read it. A frontier-lab leader making the containment argument from inside the industry.
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 clearest existing case of AI producing a genuine scientific result rather than a demonstration.
Why read it. When someone asks for evidence that AI accelerates science, this is the strongest single answer.
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.
Showed that loss falls predictably with compute, data and parameters, which turned capability into a budgeting exercise.
Why read it. The empirical basis for every timeline argument in either direction.
Traces alignment from present-day bias and reward hacking through to the long-horizon version of the problem.
Why read it. The best bridge between documented near-term harms and speculative long-term ones.
Puts explicit probabilities on existential risks by source and defends the practice of doing so.
Why read it. It is the reason people now answer the P(doom) question with a number instead of a mood.
Argues the failure is in the standard model itself: systems built to maximise a fixed objective will pursue it past the point anyone intended.
Why read it. The clearest technical statement of why the problem is structural rather than a bug.
Seven hundred words arguing that general methods leveraging computation beat human-designed structure, every time, eventually.
Why read it. The shortest item in this library and among the most consequential.
The transformer architecture. Everything in the current debate runs on top of this paper.
Why read it. Provenance: the whole argument has a technical origin, and this is it.
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.
The book that moved the control problem from science fiction into philosophy departments and, eventually, into boardrooms.
Why read it. Almost every later risk argument is either a development of this or a reply to it.