Readable, high-signal books on how AI is built, deployed, governed, overhyped, and absorbed into work and culture.
Karen Hao · 2025
A deeply reported account of modern generative AI through OpenAI, labor, infrastructure, extraction, and institutional power.
Ethan Mollick · 2024
A practical general-reader guide to working with AI as collaborator, coach, and cognitive tool.
Christopher Summerfield · 2024
A cognitive-science lens on how LLMs learned to talk and what that does and does not imply about machine intelligence.
Melanie Mitchell · 2019
Still one of the best sober explanations of what AI systems can do, where they fail, and why intelligence is hard.
Kate Crawford · 2020
A critical map of AI as material infrastructure: data, labor, minerals, classification, and power.
Brian Christian · 2020
A still-relevant narrative account of bias, reward design, robustness, and the problem of aligning machine learning with human values.