Computing Power and the Governance of Artificial Intelligence
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.
65cda3264c7d24132a3a38e1
Girish Sastry
65cda3264c7d24132a3a38e2
Lennart Heim
65cda3264c7d24132a3a38e3
Haydn Belfield
65cda3264c7d24132a3a38e4
Markus Anderljung
65cda3264c7d24132a3a38e5
Miles Brundage
65cda3264c7d24132a3a38e6
Julian Hazell
65cda3264c7d24132a3a38e7
Cullen O'Keefe
65cda3264c7d24132a3a38e8
Gillian K. Hadfield
65cda3264c7d24132a3a38e9
Richard Ngo
65cda3264c7d24132a3a38ea
Konstantin Pilz
65cda3264c7d24132a3a38eb
George Gor
65cda3264c7d24132a3a38ec
Emma Bluemke
65cda3264c7d24132a3a38ed
Sarah Shoker
65cda3264c7d24132a3a38ee
Janet Egan
65cda3264c7d24132a3a38ef
Robert F. Trager
65cda3264c7d24132a3a38f0
Shahar Avin
65cda3264c7d24132a3a38f1
Adrian Weller
65cda3264c7d24132a3a38f2
Yoshua Bengio
65cda3264c7d24132a3a38f3
Diane Coyle
Computing Power and the Governance of Artificial Intelligence
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.