Artificial Intelligence: Supply-Chain Chokepoints and the Reach of Industrial Policy

Abstract

Artificial intelligence depends on a stack of inputs, models on compute, compute on chips, and chips on electricity and refined minerals. This paper measures the concentration of each layer on one scale, the Herfindahl-Hirschman Index (HHI), from cited and reproducible data. Three findings follow. First, concentration forms a clear gradient. It is modest downstream, where public and regulatory attention is heaviest and model usage and cloud fall below the 1{,}800 mark United States agencies treat as highly concentrated. It rises steeply upstream, where public discourse is sparse. There advanced packaging scores 8{,}100, leading-edge lithography reaches the ceiling of 10{,}000, and one country dominates the production or refining of several critical minerals, gallium near that maximum. Second, these upstream layers are chokepoints and a strategic vulnerability for the whole AI economy. Antitrust cannot reach them, because they lie in foreign or state hands. Contesting them falls to export controls and domestic industrial policy. Third, placing reserves beside refining shows the concentration is built rather than geological, a variable industrial policy can move. A rise in an upstream input's price barely changes the cost of the product built from it, because the input is only a small share of that cost. A chokepoint's threat is therefore the loss of the input itself rather than a higher price.