- Jonathan Koomey, Koomey Analytics
- Eric Masanet, University of California, Santa Barbara
Many recent assessments of the effects of artificial intelligence (AI) systems lack rigor. The electricity use and emissions of AI operations are often viewed as the most salient issues, but use of AI systems can have important effects when they are deployed, and such deployments can lead to complicated systemic interactions between AI systems, the broader energy system, and the economy as a whole.
All effects of AI deployment are subject to deep uncertainty, but analyzing the effects of AI operations is usually the most feasible. Human understanding of the effects of AI deployments on specific domains and on interactions with the broader economy is in its infancy, but we know that these effects could either increase societal energy use (e.g., by making fossil fuel or geothermal extraction cheaper, or fueling increased consumer consumption by more targeted advertising) or decrease societal energy use (e.g., by enabling deployment of batteries to increase renewable energy adoption, which is more efficient than thermal plants on a primary energy basis, or improving efficiency throughout the broader economy). It is impossible to know in advance the sign of the net effect over the long term.
For these less well understood effects, researchers should design consistent test cases, focusing on measuring economic, energetic, and environmental parameters before and after the deployment of new AI systems. For testing interactions, new kinds of largescale economic models may be needed, as current models do not represent the effects of technology changes in a sufficiently detailed and systematic way.
