📖 AI Books
10 books actually worth reading, from first contact to building it yourself — no affiliate links.
Start here
Ethan Mollick — How to actually live and work with AI — the best non-technical starting point.
Anil Ananthaswamy — The elegant math behind ML, told as a story — rigorous but readable.
Practitioner
Chip Huyen — Building applications on foundation models: evals, RAG, agents, deployment.
Aurélien Géron — The standard practical ML text — scikit-learn to deep nets, all code.
Chip Huyen — Production ML end-to-end: data, deployment, monitoring, iteration.
Deep dive
Sebastian Raschka — Implement GPT yourself, line by line — the best way to truly get it.
Goodfellow, Bengio & Courville — The theory bible — free online. Math-heavy, foundational.
Big picture
Mustafa Suleyman — A lab founder on AI proliferation and what containment would take.
Brian Christian — The definitive narrative of AI safety research and why it is hard.
Cade Metz — The people and rivalries behind deep learning — the origin story.