Part of why the moratorium push is such a dead end is because the disparate right-left coalitions that have emerged around stopping data centers have different interests when it comes to other issues. It doesn’t follow that stopping data centers will lead us toward a clean energy build-out or the social policies needed to address job displacement, such as health care for all.
As policy advocate Nat Purser has already argued in Asterisk, a pause is not a substitute for actual AI governance, and attempting to tackle all the issues though a single move makes it less likely that they get addressed. Rather than gather progressive momentum for deep, multi-issue social reform, populist anger about data centers is likely to lead to a conspiratorial para-environmentalist politics rife with concerns about electromagnetic fields and cellular damage. Or it might lead to political violence and subsequent crackdowns on activists, as the recent attacks on Sam Altman’s residence warn of.
Rather than embrace apocalyptic rhetoric, we need to be clear-eyed about the real problems the data center build-out poses.
An epistemic environment shaped by alarmist claims carries real risks for people’s health and well-being. Rather than embrace apocalyptic rhetoric, we need to be clear-eyed about the real problems the data center build-out poses, because they are mounting. Making progress on the myriad issues packaged under “AI” is going to require separate work streams.
What about the climate? This is a decarbonization planning problem. We knew we needed clean power to decarbonize our cars, buildings, and factories. The emissions and energy draw are real issues, but they need to be placed within the bigger picture of our climate challenge. Of the United States’ six billion or so tons of greenhouse gas emissions per year, around 67 million tons is from data centers. Data centers for AI specifically are projected to account for roughly 24–44 million tons of CO2 by 2030, though this could be more if they end up relying on behind-the-meter gas turbines rather than connecting to the grid. The good news is that AI data centers are a lot easier to decarbonize than heavy industry. The fact that companies are desperate for power can potentially be leveraged to get them to finance some of the grid build-out that we need for decarbonization. And there is a proliferation of state legislation on data centers requiring clean energy — Minnesota’s HF 16, for example, has clean energy requirements. This is something that states can regulate.
What about the water use? This is a water resource management problem. For example, in 2024, Google’s global data center operations consumed 8.1 billion gallons — or as much as what it takes to irrigate fifty-four golf courses on average in the southwestern United States — though the majority of Google’s data centers are in places where water is abundant. In water-scarce regions, there are broader water management issues that data centers need to be contextualized within, such as trade-offs with irrigated lawns and agriculture. Where water is not scarce, states can oversee development and create water-permitting requirements. Or, they can mandate that companies use the most water-efficient technologies. Again, these are not pie-in-the-sky ideas, but concrete measures that legislative proposals are already exploring or enacting.
What about AI crashing the economy, either through a bubble or through labor displacement? This is where donors and organizations should be concentrating resources, at an emergency scale. The data center build-out is propping up the entire US economy right now. Hyperscalers are projected to spend an equivalent of 2.1 percent of US GDP on it this year, making it a larger capital outlay than railroad construction, the highway system, or the space program. At the same time, circular finance structures are leading to serious concerns about systemic risks. However, if AI companies do not crash out but successfully monetize their products, then there are the labor displacement issues.
The fact that OpenAI just launched a report on the need for industrial policy to address the social and economic disruptions from AI — framed in terms of “starting a conversation” (about a decade too late) — highlights how open the space for serious policy ideas still is. From public wealth funds to efficiency ideas, there are actually some good concepts in OpenAI’s report and in the wider policy debate, though OpenAI has a history of blocking many of these proposals.
Many of these challenges have some proposed legislation already drafted. But all of them need more specification, public deliberation, and progressive leadership. The people should be driving this discussion, not companies like OpenAI. The funders and organizers in environmental groups leading data center blocking efforts should put their attention toward a broader set of solutions — including public engagement and education on the technology, the stakes, and the policy options — and not be seduced by the simple lure of dead-end, inequitable data center moratoria.