Computing Power and the Governance of Artificial Intelligence
Authors: name: Girish Sastry, name: Lennart Heim, name: Haydn Belfield, name: Markus Anderljung, name: Miles Brundage, name: Julian Hazell, name: Cullen O'Keefe, name: Gillian K. Hadfield, name: Richard Ngo, name: Konstantin Pilz, name: George Gor, name: Emma Bluemke, name: Sarah Shoker, name: Janet Egan, name: Robert F. Trager, name: Shahar Avin, name: Adrian Weller, name: Yoshua Bengio, name: Diane Coyle

Computing power, or "compute," is crucial for the development and deployment
of artificial intelligence (AI) capabilities. As a result, governments and
companies have started to leverage compute as a means to govern AI. For
example, governments are investing in domestic compute capacity, controlling
the flow of compute to competing countries, and subsidizing compute access to
certain sectors. However, these efforts only scratch the surface of how compute
can be used to govern AI development and deployment. Relative to other key
inputs to AI (data and algorithms), AI-relevant compute is a particularly
effective point of intervention: it is detectable, excludable, and
quantifiable, and is produced via an extremely concentrated supply chain. These
characteristics, alongside the singular importance of compute for cutting-edge
AI models, suggest that governing compute can contribute to achieving common
policy objectives, such as ensuring the safety and beneficial use of AI. More
precisely, policymakers could use compute to facilitate regulatory visibility
of AI, allocate resources to promote beneficial outcomes, and enforce
restrictions against irresponsible or malicious AI development and usage.
However, while compute-based policies and technologies have the potential to
assist in these areas, there is significant variation in their readiness for
implementation. Some ideas are currently being piloted, while others are
hindered by the need for fundamental research. Furthermore, naive or poorly
scoped approaches to compute governance carry significant risks in areas like
privacy, economic impacts, and centralization of power. We end by suggesting
guardrails to minimize these risks from compute governance.