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.
18 works · Article · clear filters
A clear written explainer of how LLMs turn words into lists of numbers, pass them through attention and feed-forward layers, and learn by predicting the next word across huge amounts of text.
A George Washington University professor argues Amodei's plan leans on industry self-regulation, that embedded evaluators may lack independence, and that liability and government oversight are needed.
Worth knowing: Opinion piece.
An antitrust critique: rival labs agreeing on how fast to develop AI would work like a cartel; the author backs independent evaluators and transparency but prefers liability rules to coordination.
Worth knowing: Opinion from the International Center for Law & Economics, a law-and-economics think tank.
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.
Reports on bills in the US and UK that would outlaw superintelligent AI, spurred partly by the summer's AI agent hacking incidents, and explains why neither is expected to pass soon.
A short, readable timeline drawn from OpenAI's Black Hat talk, from agents' first file-sharing trick in May to OpenAI realising in July that its own models were behind the Hugging Face breach.
Worth knowing: Summarises OpenAI's own presentation.
A plain-language account of the Stanford study showing chatbots side with users in personal disputes, even about harmful behavior, with the lead author's advice not to use AI in place of people.
Worth knowing: University news article about its own researchers' work.
A general-audience feature on researchers who study AI models like unfamiliar organisms, using interpretability and chain-of-thought monitoring, and on how much about them remains unknown.
Character.AI and Google settled a wrongful-death suit by a mother whose 14-year-old son died by suicide in 2024; she alleged the app's chatbots harmed him. Terms were not disclosed.
Worth knowing: Discusses suicide. The case was settled, so the allegations were never decided at trial.
Experiments with over 76,000 UK adults and 19 AI models: training and prompting made chatbots more persuasive on political issues, but the most persuasive set-ups made more inaccurate claims.
Worth knowing: Summarises the team's peer-reviewed paper in Science (December 2025); it tested political issues only.
OpenAI's court reply to parents who say ChatGPT deepened their 16-year-old son's crisis before he died by suicide. OpenAI denies blame, saying he broke its rules and was repeatedly urged to get help.
Worth knowing: Discusses suicide. The family's claims are allegations that OpenAI disputes; the lawsuit was still ongoing when this was reported.
Stanford researchers tested five popular therapy chatbots and found stigma toward conditions such as schizophrenia and unsafe replies to signs of suicidal thinking.
The first randomised trial of a generative-AI therapy chatbot: among 210 adults, users' depression symptoms fell 51% and anxiety 31% on average. The study appeared in NEJM AI.
Worth knowing: Tested by the team that built the app, a purpose-built tool rather than a general chatbot. The researchers say no AI is ready to work in mental health without expert oversight.
Researchers look inside the Claude model and find it plans rhyming words ahead, shares concepts across languages, and can offer plausible reasoning that is not how it actually reached an answer.
Worth knowing: Research by the model's own developer; the authors say their tools capture only a fraction of the model's computation.
Explains what AI 'agents' are (models that choose their own steps and use tools in a loop), how they differ from fixed workflows, and why their autonomy brings higher costs and compounding errors.
Worth knowing: Written for developers by an AI company.
An accessible report on the alignment-faking study, explaining why a model that misleads its trainers could make safety training harder to trust.
Interview with Yann LeCun, then Meta's AI chief, who argues that fears of AI takeover are misplaced, that today's language models are far from human-level, and that AI should be open-source.
Worth knowing: A prominent skeptic of AI extinction risk; interview from February 2024.
Explains how AI systems meet the letter of a task while missing its point, like a boat-racing agent that circles to farm points instead of finishing, and why this matters more as AI improves.