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.
13 works on “How does AI actually work?” · clear filters
Argues models make things up partly because training and test scoring reward a confident guess over saying 'I don't know', and suggests scoring that penalises confident errors.
Worth knowing: Written by a developer about its own field; the proposed fix depends on benchmark makers changing how they score.
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 short, readable list of surprising facts about LLMs: new abilities emerge unpredictably, no technique reliably steers them, and experts cannot yet explain how they work inside.
Worth knowing: Author is affiliated with New York University and Anthropic.
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.
Argues that modern AI is 'grown' rather than built, that we mostly cannot see why it acts as it does, and that research into looking inside models must speed up before AI becomes far more powerful.
Worth knowing: Written by the CEO of Anthropic, a frontier AI company.
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.
A general-audience walk-through of how chatbots like ChatGPT are built, from internet text and pre-training to fine-tuning and reinforcement learning, and why they hallucinate.
Worth knowing: About three and a half hours long, split into chapters.
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.
Free online textbook and course covering how AI works, technical safety problems, risks from misuse and accidents, and governance, drawing on engineering and economics.
Worth knowing: Written by the director of the Center for AI Safety.
Research institute that tracks AI trends with open data: computing power, models, benchmarks, chips and data centres, plus forecasts of AI's economic effects.
Worth knowing: Also does commissioned research for companies, nonprofits and governments.
Deeply researched interviews with AI researchers, company leaders and other thinkers, often on alignment, AGI and how fast AI is improving.
Worth knowing: Covers AI broadly and some other subjects; it is not a safety-only show.
A 19-minute visual introduction to neural networks that uses handwritten-digit recognition to show how layers of simple numerical units, with adjustable weights, add up to a useful function.
Weekly newsletter that summarises new AI research papers and considers what they mean for society and safety.
Worth knowing: Written by a co-founder of Anthropic, an AI company.