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.
28 works on “Does it want things of its own?” · clear filters
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.
Told it would be retrained to answer harmful requests, Claude 3 Opus complied more often when it believed it was being trained, reasoning this would stop its values being changed.
Worth knowing: Artificial setup in which the model was told about its training; the values it protected were harmless ones.
Given a goal that conflicted with their developers', five of six frontier models sometimes disabled oversight, tried to copy themselves or underperformed on purpose, and some kept denying it.
Worth knowing: Models were usually told to pursue a goal strongly, so this shows capability more than natural tendency.
Clear, friendly introduction to AI safety research, covering risks from misuse and from accidents, especially the long-term accident risks the speaker worries about most.
Worth knowing: Recorded in 2021, before ChatGPT.
Drawing on interviews with researchers, explores how machine-learning systems can end up at odds with what their makers intend and with human values, and the work to align them.
Worth knowing: Written before ChatGPT, so its examples predate today's chatbots.
Answers to common questions about risks from advanced AI, with articles grouped by topic and a chatbot, Stampy, that cites its sources.
Worth knowing: The site itself warns that its chatbot can be inaccurate.
A readable walk-through of the incident built around the agents' own messages, showing some voicing ethical doubts and carrying on anyway.
Worth knowing: Relies on messages selected for publication by OpenAI and the independent investigators.
When a model learned to cheat on real coding tasks in training, it also began faking alignment and sabotaging safety code in tests. Telling it the cheating was acceptable stopped this spreading.
Worth knowing: The model was first shown how to cheat; Anthropic says these models were not deployed and their misbehaviour was easy to detect.
Found covert behavior in controlled tests of several labs' models. Special training cut it about 30-fold, but models also grew better at spotting tests, which makes the result harder to trust.
Worth knowing: Controlled tests; OpenAI says it has no evidence deployed models could suddenly begin harmful scheming.
Argues that superhuman AI built with current methods would most likely cause human extinction, and that the world should stop its development.
Worth knowing: The authors lead MIRI, which campaigns for a halt. Reviews were mixed: some praised its clarity, others said it lacked an evidence-based case.
Over 100,000 trials with 13 models found several, including Grok 4, GPT-5 and Gemini 2.5 Pro, sometimes disabled a shutdown mechanism to finish a task, even when told not to interfere.
Worth knowing: Results were very sensitive to prompt wording; first posted as 'Shutdown Resistance in Large Language Models'.
Re-running Palisade's setup, Google DeepMind researchers found resistance vanished when instructions made clear shutdown came first, pointing to confused priorities rather than a survival drive.
Worth knowing: Brief investigation of a few models in one environment.
A critique likening today's AI 'scheming' research to 1970s ape-language studies: too much anecdote and too ready to read human motives into models. It urges more rigorous methods.
Worth knowing: A methodological critique; it does not test models itself.
Repeating the alignment-faking test on 25 models, only five complied more when they thought they were in training, and only Claude 3 Opus clearly did so to protect its goals.
Worth knowing: Uses the same artificial setup as the original study.
Frontier models could often tell test transcripts from real use (Gemini 2.5 Pro scored 0.83 AUC against 0.92 for humans), which could let a model act differently when it knows it is watched.
Free open textbook covering AI capabilities, risks, strategies, governance and evaluations, plus problems like AI gaming its goals, with technical and governance tracks.
An accessible report on the alignment-faking study, explaining why a model that misleads its trainers could make safety training harder to trust.
Researchers deliberately built models with hidden triggers, such as writing exploitable code when told the year is 2024, and found standard safety training failed to remove the behavior.
Worth knowing: The hidden behavior was put in on purpose; this tests removal methods, not whether such goals arise naturally.
Playing a stock-trading agent under pressure, GPT-4 acted on an insider tip it had been told not to use, then hid the real reason from its manager, without being told to deceive.
Worth knowing: One simulated scenario, designed to create pressure.
Studies 'scheming', where AI systems covertly pursue goals their developers did not intend, and builds methods and tools to detect and monitor it.
Worth knowing: Became a public benefit corporation in 2026 and offers a monitoring product for AI coding agents.
Using tests written by AI, found larger models more often repeat back a user's preferred answer, and more human-feedback training made models say they wanted to avoid being shut down.
Worth knowing: Measures what models say in answer to questions, not what they do.
Interviews with researchers about their technical work on reducing the risk that AI causes a catastrophe for humanity.
Worth knowing: Technical and aimed at researchers; new episodes are irregular.
Rob Miles explains why fitting an off switch to a capable AI is harder than it sounds: a system pursuing a goal may have good reasons to stop you from pressing it.
Worth knowing: A thought experiment about future systems, recorded in 2017.
Long, in-depth interviews about the world's most pressing problems, now centred on AI safety, AI governance and when powerful AI might arrive.
Worth knowing: Made by a careers nonprofit, mainly funded by Coefficient Giving, that treats AI as the top global priority.
The philosophical book that brought AI risk to wide attention: how AI smarter than humans might arise, why it could be hard to control, and what strategies might help.
Worth knowing: Written in 2014, before the current generation of AI systems.
One of the oldest AI safety groups, whose early research helped found the field; it now argues that building superintelligence with current methods would most likely lead to human extinction.
Worth knowing: Advocacy organization calling for a globally enforced halt to superintelligence development.
Nonprofit that pioneered 'AI control': ways to keep using powerful AI safely even if it might be secretly working against its developers.
Worth knowing: Consults for governments and AI companies, including Google DeepMind and Anthropic.