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.
8 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 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.
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.
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.
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.