Further Reading

Attention Is All You Need — Vaswani et al. (2017)

The original Transformer paper that underpins virtually every modern large language model. Essential reading for understanding the architecture behind GPT, BERT, and their successors.

A Unified Theory of Adaptation in Human and Artificial Minds — Binz & Schulz (2023)

A compelling framework comparing how humans and AI systems adapt to new environments, bridging cognitive science and machine learning in productive ways.

Prospect Theory: An Analysis of Decision under Risk — Kahneman & Tversky (1979)

The paper that launched behavioral economics as a field. Shows systematically how humans evaluate losses and gains — and why we don't behave like the rational agents of classical economics.

The Alignment Problem — Stuart Russell (talk, 2019)

A clear statement of why building AI systems that reliably do what we want is harder than it sounds, and what a rigorous solution might look like.

Thinking, Fast and Slow — Daniel Kahneman

The definitive introduction to behavioral economics and the psychology of decision-making. Covers System 1 vs. System 2 thinking, cognitive biases, and why human judgment so often goes wrong.

The Alignment Problem — Brian Christian

A deeply reported account of how AI researchers are trying to build machines that reliably share human values. One of the most accessible serious books on AI safety.

How Minds Change — David McRaney

A rigorous, narrative-driven look at the cognitive science of belief change, deep canvassing, and persuasion. Surprisingly hopeful.

Predictably Irrational — Dan Ariely

A fun, accessible survey of how human behavior systematically deviates from rational choice theory — with clever experiments to back up every claim.

The Brain from Inside Out — György Buzsáki

A provocative challenge to the input/output model of the brain. Argues that the brain is fundamentally action-oriented, not reactive — with big implications for how we think about cognition.

Lex Fridman Podcast

Long-form conversations with AI researchers, neuroscientists, and thinkers. Best episodes for this site's topics: Yann LeCun, Geoffrey Hinton, Robert Sapolsky, and Daniel Kahneman.

Sean Carroll's Mindscape

Physics, complexity, and consciousness with rigorous thinkers. A favorite for cross-disciplinary thinking — Carroll is unusually good at connecting ideas across fields.

The Knowledge Project — Shane Parrish

Mental models and decision-making from leaders across industries. Grounded in the rationalist tradition; great companion to behavioral economics reading.

80,000 Hours Podcast

Long-form conversations about AI safety, global priorities, and how to think clearly about high-stakes problems. More rigorous than most.

Nobel laureate who pioneered the psychology of judgment and decision-making under uncertainty. The System 1 / System 2 framework is his.

Deep learning pioneer now focused on AI safety and the scientific understanding of intelligence. One of the most credible voices on what AI can and can't do.

Computational cognitive scientist studying how the brain learns and makes decisions using Bayesian and reinforcement learning models.

Behavioral economist studying scarcity, poverty, and the application of machine learning to social problems. His book Scarcity is essential.

Developmental psychologist and philosopher studying how children learn — and what that tells us about intelligence more broadly. A compelling critic of naive AI comparisons.