AI has a path dependence problem.
It doesn’t matter whether you are Anthropic, OpenAI, Google, or xAI. Every frontier lab and hyperscaler is now committed to spending billions on infrastructure, data, and talent to build and scale large language models. This gamble is propping up the US economy. It is also being sold as the path to solving the world’s hardest problems. No matter where these labs started, they are now on the same road, and they are taking us with them.
I’ve been thinking about this while reading Sebastian Mallaby’s The Infinity Machine, his biography of Demis Hassabis and history of DeepMind. The book is also a snapshot of the current AI moment.
Hassabis is an extraordinary figure: chess prodigy, game developer, neuroscientist, now head of Google DeepMind. AlphaGo beat the world’s best Go player. AlphaFold solved protein folding and won him a Nobel Prize. Mallaby paints a sympathetic picture. Hassabis sees himself as Turing’s champion – using inductive reasoning to solve the world’s hardest problems.
And yet Hassabis and his peers are all running the same race. Altman, Amodei, Musk, each with his own higher calling: saving humanity, transcending the body, understanding the universe. In practice they are all spending billions to replicate each other’s work. The race has a winner-take-all logic, and that logic allows only one strategy: get there first.
So we get Dario Amodei warning about the destruction of white-collar work while Anthropic ships Claude Design, a tool aimed straight at automating design work. We get Hassabis talking about hadron colliders in space while pushing Gemini to catch OpenAI.
This is what path dependence looks like. Mallaby’s book shows that even a figure as sympathetic as Hassabis has a pragmatic, competitive side that will do what it takes to win.
I find Hassabis genuinely inspiring, and that is what makes the book unsettling. If the most thoughtful figure at the frontier cannot escape the spiral, then the spiral is the story, not the people inside it.