This competition is playing out on four fronts: Intelligence: which countries develop the most capable AI models. Domestic adoption: which countries integrate AI most effectively across commercial and public sectors. Global distribution: which countries deploy the global AI stack on which the world economy runs. Resilience: which countries sustain political stability through the economic transition.

Bipartisan policy action has protected the US and allied innovation engine by restricting access to the US AI stack by PRC firms under the jurisdiction of the CCP. Our CEO has publicly commented on the importance of export controls, for example. These controls have curbed the sale of the highest-end AI chips and semiconductor manufacturing equipment (SME) to China over the last several years, constraining China’s frontier AI development even as Beijing has poured enormous state resources into the sector. Without action to limit China’s access to US compute, the CCP would have had all the ingredients to develop AI at par or superior to America’s.

Algorithmic improvements are both a function and a multiplier of compute, not a substitute for it, and discovering those advances is itself a compute-intensive process: more compute enables labs to run more experiments, which enables labs to discover more algorithmic improvements. As frontier models increasingly conduct AI R&D themselves, that loop will tighten further, and frontier models will help build their own successors. In short, compute advantage compounds into algorithmic advantage, and from there into a durable lead in AI itself.

What this predicts Imagine aliens landed on Earth. I would expect aliens embedded in the same physics to discover arithmetic, geometry, approximate objects, causal structure, and frame transformations. If they live at similar scales and act in similar environments, I would also expect them to use categories resembling solid objects, liquids, containers, paths, agents, tools, obstacles, boundaries, markets. If they study Earth or train on human data, I would expect more convergence: apples, chairs, money, smiles, promises. Now: alien minds actually have landed. And yes, even if you start with raw photographs and fairly universal neural network architecture, you get apples.

Natural-abstraction work often bundles two ideas: first, that the world supports low-dimensional summaries useful across many contexts; second, that many cognitive systems converge on those summaries. I want to separate these. CAH accepts the convergence phenomenon, but treats it as a fact about learning processes under shared pressures, not automatically as evidence that the abstraction is mostly situated in the structure of the world (abstraction correspond to low-dimensional summaries of information relevant far away from a subsystem)

The canonical motivating example for natural abstractions is gas, and state variables like temperature as the abstractions. The ambition of the research program is to have a principled way to identify them. The natural abstraction hypothesis has roughly physics aesthetics. In contrast, convergent abstraction makes mostly a process claim: given similar pressures and environments, similar abstractions emerge. The ambition is to understand basins of convergence, contingencies, and the ways how the structures in the world interact with the learners. The aesthetics is way closer to biology, and assumes messiness. It is less ambitious.