What makes compute usage regulation promising for AI governance?

Recent AI progress has relied on the “AI triad”: algorithms, data, and compute. Regulating these key inputs is a way to govern the capabilities of AI. Sastry et al. list some properties that make compute especially viable as a target for regulation:

• Detectability: Compute consists of physical hardware that can be observed, unlike data or algorithms. Data centers are up to several football fields in size and require extensive infrastructure for cooling and power. The process to construct them is complex and expensive. All this makes data centers easy to see.¹

• Excludability: Whoever controls a computer can prevent others from using it. This is in contrast to data and algorithms, which can be copied easily as soon as they’re public, in a way that’s much harder for governments to prevent.

• Quantifiability: Compute can be clearly quantified in terms of how many chips of a given kind are being used, or how many FLOP of computation they’re doing. Other associated infrastructure, for networking and power and cooling, is also quantifiable.

• Supply chain concentration: The global supply chain for the most advanced AI chips is highly concentrated. These chips are mostly designed by a single company (Nvidia) and manufactured by a single company (TSMC) using lithography machines produced by a single company (ASML), in an extremely complex process that relies on tacit knowledge and is hard for others to replicate. Many of the chips are then bought by a few cloud computing providers.

(Image source: Sastry et al.)

Some of these properties (like excludability) are intrinsic to compute, but others (like supply chain concentration) are a contingent feature of today’s technological landscape, and could change in the future. Still, despite the limitations of compute governance, it has the potential to reduce existential risk from AI.