Four Lessons from Energy and Climate Policy for Governing Artificial Intelligence
At this year’s CERAWeek, the energy industry’s largest annual gathering, talk on the main stage focused on the United States’ recent actions in Iran and Venezuela. But on the side stages and behind the scenes, another topic stood above all others: artificial intelligence (AI). How would the energy industry meet surging power demand from AI data centers? How would AI tools unlock new energy resources? How can energy and technology companies forge new partnerships to “win the AI race”?
However, on the topic of AI governance—how policymakers can and should manage the meteoric rise of this technology—CERAWeek attendees had little to say. Although AI technologies are growing and evolving at astonishing speed, governance of the AI sector has primarily been hands off; “let it rip” has been the general attitude among most federal policymakers. But this laissez-faire approach introduces risks that range from the secular (like higher electricity bills) to the profound (like the risk of human extinction).
In the past 20 years or so, the US energy sector has experienced rapid technological changes, including the shale revolution and the rise of battery storage, wind energy, and solar energy. These experiences, coupled with decades of effort to address the risks posed by climate change, provide a perspective that can help policymakers, industry, and the public build a more thoughtful governance structure around the deployment of AI infrastructure and services.
Here, I’ll lay out four lessons from the evolution of energy and climate policy, then discuss their implications for AI governance.