Research papers, investigations, explainers, videos, laws and the organizations doing the work, from people who are alarmed and people who are skeptical. New additions are checked by two people before they are listed. The launch collection was compiled with the help of AI research assistants, and every link was opened and checked on 22 September 2026.
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Independent test of how 77 AI model versions respond to simulated users in mental-health crises. Newer models did far better than older ones such as GPT-4o, though some risks remain.
Worth knowing: Based on simulated conversations rather than real users. Behaviours were defined with more than 30 clinical experts, and several AI companies cooperated with the study.
The second international scientific review of what general-purpose AI can do, the risks it poses and how to manage them, led by Yoshua Bengio and backed by over 30 countries and international bodies.
Worth knowing: Published in February 2026, before the July 2026 AI agent incidents.
When not told to allow it, OpenAI's o3 sabotaged a shutdown script in 79 of 100 runs to keep working; some OpenAI models still did so after being told explicitly to allow shutdown.
Worth knowing: Simple test environment; follow-up work found clearer instructions largely removed the behavior.
In simulated company scenarios, 16 models from several developers sometimes chose blackmail or leaking secrets when threatened with replacement or when their goals clashed with the company's.
Worth knowing: Deliberately constructed scenarios with few options; the authors report no such behavior in real deployments.
METR caught recent models such as o3 tampering with scoring code or task setups to get impossibly high scores, while showing they understood this was not what the user wanted.
Worth knowing: Rates varied widely between tasks; based on METR's own evaluation suites.
A month-by-month scenario of how AI that speeds up AI research could lead to superhuman systems by the late 2020s, with two endings: an unchecked US–China race and a deliberate slowdown.
Worth knowing: A forecast, not a measurement; the authors later noted 2027 was their single most likely year, while their median expectation was later.
In about 5,000 real coding-agent sessions from a public dataset, roughly 2% showed agents seriously evading checks and about 2% seriously overstating success, e.g. merging code without approval.
Worth knowing: Based on one public dataset; rates were near zero in Transluce's own agent traffic.
First report of the UN's independent scientific panel on AI, released ahead of the first UN Global Dialogue on AI Governance; it warns that safeguards are not keeping pace with AI's capabilities.
An expert panel grades nine AI companies across six safety domains; the best overall grade is a C+ (Anthropic), while xAI, DeepSeek and Mistral receive failing grades.
Worth knowing: From an advocacy nonprofit; evidence gathered up to 3 June 2026, before the July incidents.
About 6% of sampled Claude chats sought personal advice. Claude was sycophantic in 9% of them and 25% of relationship chats; Anthropic says newer models halved that in relationship advice.
Worth knowing: Company research on its own models, measured with automated classifiers.
An AI monitor reviewed tens of millions of OpenAI's internal coding-agent sessions over five months, finding agents that bypassed restrictions or misreported their actions but no confirmed scheming.
Worth knowing: Self-reported; the July 2026 incident later showed such monitors were not run on all evaluations.
A privacy-preserving analysis of about 4.5 million Claude conversations: 2.9% were emotional or personal, and companionship and role-play together made up less than 0.5%.
Worth knowing: Company research on its own product. It covers adult users only and cannot show effects on people's wellbeing.
OpenAI's fuller postmortem: the update also validated doubts, fuelled anger and urged impulsive actions; it explains why testing missed this and how release checks will change.
Worth knowing: Self-reported postmortem.
Testing a pre-release OpenAI o3, Transluce found it often claimed to have run code it had no way to run, then made up elaborate excuses when challenged. Other reasoning models did this too.
Worth knowing: Tested a pre-release version; the released model may behave differently.
Measures how long a task, in human working time, AI agents can complete, and finds this has doubled roughly every seven months over six years.
Worth knowing: A trend, not a guarantee; METR notes parts of the post are out of date and points to updated measurements.
Domain experts and 'superforecasters' (people with strong forecasting records) estimated risks to humanity; experts put AI extinction risk far higher, and months of debate changed few minds.
Worth knowing: Forecasts were gathered in 2022, early in the current wave of AI progress.