SOURCE · [002] · READING LIST
a16z AI Canon
This source supports 10 books recommended by a16z, including Deep Learning, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again, Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World, and 7 more.
- Recommended books
- 10 books
- Recommenders
- 1 person
- Source host
- a16z.com
Andreessen Horowitz's curated canon of the books and papers behind modern AI.
Original source
Visit source: a16z.com/ai-canon/ ↗Recommended by a16z
On this list
10 BOOKs-
Deep Learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville
The definitive textbook underpinning every technical section of the AI Canon; foundational reference for the transformer, diffusion, and fine-tuning papers listed.
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Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again
Explicitly listed in the a16z AI Canon under market analysis as a book-length treatment of AI in healthcare, accompanied by an a16z podcast episode.
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Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World
Narrative history of the deep learning era; the journalist's account of the researchers and organizations behind the landmark papers the AI Canon cites.
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Power and Prediction: The Disruptive Economics of Artificial Intelligence
Ajay Agrawal, Joshua Gans, Avi Goldfarb
Provides the economic theory behind the AI Canon's market analysis section—where value accrues, how power shifts, and what AI does to industry structure.
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Prediction Machines: The Simple Economics of Artificial Intelligence
Ajay Agrawal, Joshua Gans, Avi Goldfarb
Standard economics primer on AI; frames AI as cheap prediction and is the entry-level companion to the AI Canon for business and enterprise audiences.
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The Alignment Problem: Machine Learning and Human Values
Best accessible treatment of AI alignment and safety research; complements the canon's technical papers section on RLHF and Constitutional AI.
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The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma
Core AI Canon-adjacent reading on AI containment and governance, representing the safety and policy dimension of the canon's scope.
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The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
Best non-technical primer on machine learning paradigms; aligns with the AI Canon's 'gentle introduction' and 'foundational learning' sections.
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Human Compatible
Russell's technical and philosophical case for preference-uncertain AI; the serious computer scientist's companion to the AI Canon's safety-adjacent papers.
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Superintelligence
Foundational text of the AI safety field; defines the vocabulary used in the AI Canon's references to alignment and Constitutional AI research.
