Recommended AI books
Thirteen books, chosen with judgment: three can be read legally for FREE (their authors publish them openly), three at $0 with Kindle Unlimited and seven paid ones covering practice, business, history and criticism. None is filler.
🆓 Free (start here)
All the essential deep learning in ~160 pocket-sized pages, written by a University of Geneva professor and published for free. It takes some math, but it's the shortest honest route to truly understanding what's inside a neural network.
The academic "bible" of deep learning, with a Turing Award winner among its authors, and legally free on the web. It's aged, but the foundations it teaches — the ones holding up ChatGPT and friends — haven't expired.
An interactive book-course: every concept ships with runnable code (PyTorch included) and it's continuously updated. Hundreds of universities use it. If you learn by doing, it's the best free option to go from theory to training your own models.
📚 $0 with Kindle Unlimited
How to future-proof your career and multiply your professional value as AI collapses the cost of cognitive work: the "Great Bifurcation" between those who wield it with judgment and those who don't. Practical and direct: a roadmap by professional profile, not theory.
The same bifurcation seen from the boardroom: strategy, execution and defensive moats to reorganize a business around AI before your competitors do. For executives who want a plan, not a speech.
A macroeconomic, sector-by-sector X-ray of how AI redraws the global chessboard over the coming decade, with the human factor at the center. It closes the trilogy by zooming out from your career (Vol. 1) and your company (Vol. 2) to the whole board.
💳 Paid (and worth it)
The best starting book if you're not technical: a Wharton professor explains how to work WITH AI — when to delegate, when to supervise, when to keep it away — using real experiments instead of opinions. Practical, skeptical and optimistic at once.
The reference manual for building products on top of AI models: prompts, RAG, fine-tuning, agents, evaluation and deployment. It became O'Reilly's most-read book for a reason: if you build with AI, this is the one that stays on your desk.
The definitive piece of investigative journalism on OpenAI: 260+ interviews, the clash between safety and speed, and the attempted firing of Sam Altman told from inside. Critical and at times uncomfortable — exactly why it should be read alongside the enthusiasts.
The story of NVIDIA and Jensen Huang: how a video-game card company ended up making the most coveted resource on the planet. Essential for understanding AI's physical economy — chips, fabs and power — that underpins everything else.
The memoir of the creator of ImageNet, the scientist who made the deep learning revolution in vision possible. Science told as a life: immigration, the lab, and the case for human-centered AI. The human side missing from most technical books.
AI as the latest chapter in the history of information networks, from gossip to the printing press to the algorithm. Debatable in several theses — which is the point: it's the book that brings the AI conversation to readers who don't come from tech.
THE AI textbook: used by 1,500+ universities. Not couch reading and not cheap, but if you want the complete map of the field — from classical search to deep learning — with academic rigor, there is no substitute.