Artificial Intelligence

Why machines smarter than humans pose a control problem to solve before they arrive.

Why AI must be redesigned from the ground up to keep superhuman machines in check.

The struggle to make machine learning systems do what people actually intend.

The five schools of machine learning, and the search for one algorithm to unite them.

How AI could change crime, war, justice, jobs and what it means to be human.

AI as a fall in the cost of prediction, and what that does to decisions and jobs.

How Hinton, LeCun and Bengio kept neural networks alive until deep learning took off.

How chips became the key to economic and military power, made in a few chokepoints.

Jensen Huang and how Nvidia came to power the move to artificial intelligence.

A reported history of OpenAI and the industry it created.

The founding of AI research, from interviews with McCarthy, Minsky and others.

AI research from Dartmouth in 1956 to the funding cuts, told by the field's leaders.

Work is splitting between those who work well with smart machines and the rest.

Why China may match the US in AI now that applying it matters more than research.

Autonomy from the incumbent and Waymo side.

The self-driving contest Tesla bet its valuation on.

Why AI and synthetic biology will spread fast and cheaply, and how to contain them.

The founder scene around OpenAI, reported from inside the funding rounds.

Traces AI to its physical costs: lithium mines, data-centre labour, military money.

Iansiti and Lakhani examined AI-native firms — Ant Financial, Ocado, Amazon — and documented how AI changes firm architecture, not just processes. The shift from traditional operating models to AI-driven ones turns out to restructure competitive dynamics in ways that standard strategy frameworks miss.

Harari's concern — that humans may cede decision-making to opaque systems through preference rather than coercion — reframes the alignment problem as a cultural and political challenge, not only a technical one. Less rigorous than Bostrom or Russell, but more widely read, making it a useful lens for understanding public discourse.

Agrawal, Gans, and Goldfarb apply economic logic to AI's real contribution: cheap prediction. The framework — AI as prediction machine that changes the relative value of judgment and data — is more useful for understanding where AI creates and destroys value than any amount of case studies.

Yudkowsky's collected LessWrong essays, free online and in compiled book form, develop the epistemic foundations that underpin his AI doom arguments. The reasoning about cognitive biases, Bayesian updating, and decision theory is independently useful; the AI safety arguments that follow are the most influential non-peer-reviewed work in the field.

How Google and others turn behaviour into data to predict and steer what people do.

Ord, a philosopher at Oxford's Future of Humanity Institute, places AI risk within a broader taxonomy of existential threats and attempts probability estimates for each. His calibrated approach — acknowledging uncertainty while still arguing for prioritization — offers a useful epistemological model for evaluating AI timeline and risk claims.

Brynjolfsson and McAfee provide the economic context: AI as a general-purpose technology that decouples productivity from employment. The alignment problem doesn't exist in a vacuum — it plays out in economies where AI is already restructuring labor markets in ways that are hard to reverse.

Kurzweil's optimistic counterpoint to the safety literature. His law of accelerating returns and the vision of intelligence explosion as fundamentally benign is the premise that alignment researchers are most directly arguing against. Reading it gives the other side of the debate its strongest formulation.

Cathy O'Neil catalogues algorithmic systems deployed in hiring, lending, policing, and education that reinforce rather than correct existing inequities. The book's value is empirical: these are real systems with documented effects, not speculative risks.

Predictions of computers passing the human brain and merging with human life.

How a game designer built artificial life from simple rules of biology.

Popular account of algorithms taking over trading and other trades.

How machine learning and robots will displace work faster than institutions adapt.

The textbook on deep learning: how machines learn layered concepts from data.

The rationalists around LessWrong and their fear of artificial intelligence risk.

Subtitled 'Amplifying Our Humanity Through AI'; co-written with GPT-4

The OpenAI-DeepMind race that set the shape of Nvidia's customer base.

Fifteen years of AI through Demis Hassabis and DeepMind, from AlphaGo to AlphaFold.

An argument that consciousness is not algorithmic and rests on quantum mechanics.

Japan's drive to build fifth-generation AI computers, and its stakes for America.