Source Body Text

Opposition to AI datacenters has emerged as a primary theme in US politics, one that – surprisingly – doesn’t fall along party lines. We applaud people coming together for constructive debate on any issue, and agree that communities need to evaluate whether any economic benefits these datacenters bring is worth their costs. Still, we worry that a focus on datacenters obscures the larger impacts of AI on people’s lives: the concentration of power of AI companies, and their widespread political and financial influence. Local datacenter opposition is grounded in legitimate concerns about misallocation of land resources when housing is at a premium, pressures on already higher energy prices, and localized environmental impact. Unlike other resource-consuming and polluting industrial facilities, datacenters produce very few jobs. The fact that US opposition to datacenters seems to be most fierce among lower-income communities reflects righteous indignation with an inequitable bargain, where tech companies and developers profit from exploiting local resources but offer little in return. On a global scale, their carbon footprint could grow unsustainably if usage accelerates. And all this is in aid of a technology that many fear will propagate misinformation, take their jobs, or even cause existential risks for humanity. For some, datacenter opposition may feel like the only tangible mechanism for registering their concern, disapproval, or even anger about AI. The problem is that this may be exactly what the AI companies are banking on. They can overcome the protest when it matters to them, and live with a significant fraction of proposals being defeated. More importantly, focusing political opponents on the datacenter issue obscures the bigger prize they’re after. While there is a staggering three-quarters of $1tn being spent on datacenter infrastructure by US companies this year alone, this investment should be taken in perspective. The market for enterprise software, for example, is about twice this size. And it’s small compared with what these companies actually want. AI companies have their eyes set on capturing all the value created by entire industries. The technology has arguably already conquered customer service and consumer sales. But on the horizon are bigger targets, such as enterprise software development, creative design, management and even legal services. In AI companies and their allies’ vision of the future, AI replaces teachers and doctors. The companies would rather spend time fighting resistance to how fast they are building computing infrastructure than dealing with issues of how their products should be used in those fields, or how those fields should be protected from their products. And while datacenter opposition campaigns have been successful in building widespread appeal, their effectiveness in the US is mixed. They seem to be most successful when organizing against speculative, early-stage datacenter proposals that have a relatively low likelihood to ever see fruition. Meanwhile, advanced-stage, well-capitalized datacenter projects have proven to have the resources to overcome local opposition. An OpenAI- and Oracle-backed facility in Saline township, Michigan, is breaking ground on construction even after local officials voted to reject it. The developers sued the town of 3,000 and forced a settlement that involved their project going forward. Meanwhile, the Trump administration, a vigorous ally of corporate AI, has signalled its willingness to advance AI infrastructure development by overriding state objections and even using federal lands. Also consider that rampant datacenter development may be a momentary spike rather than a longstanding concern. Demand for the centralized computing that datacenters provide may well decline over time. The leading Chinese labs, such as Z.ai, are innovating in technical mechanisms to make frontier-class models smaller and cheaper to run. AI power users have become adept at miniaturizing open weight models, ones published free for anyone to download and use, to run locally on their own computers. Apple and Google both support infrastructure stacks for running AI models directly on mobile phones. It could be that the current mania for datacenters will look like the fiber optic cable bubble from the early 2000s, as demand shifts to smaller models and AI usage on people’s own devices. For those concerned primarily with affordability and environmental protection, singling out datacenter construction is misplaced. Energy rates and inflation today seem to be most visibly affected by the US-Iran war. The US is disinvesting in long-term energy security by ceding the renewable energy industry to China and actively cancelling climate commitments. Consider that 10% of global carbon emissions stem from heating buildings, which dwarfs energy use by AI and could be cut fivefold by using heat pumps powered by renewable energy. With respect to housing affordability, federal housing subsidies have changed little over three decades, in inflation-adjusted terms, even as housing costs have spiked and homeowners have enjoyed robust tax incentives. As for AI itself, the concentration of power and wealth in these tech companies is the greatest existential risk facing society today. This means we must limit corporate power, especially corporations’ ability to exploit the public and manipulate our political system. Opposing datacenters should be just a starting point. We can advocate for states to regulate AI, to reject irresponsible uses of the technology, and shape corporate behavior. We can fight for AI computation to be taxed, so that the public can capture some of the profit of AI use while also forcing AI companies to internalize more of the energy and environmental consequences associated with its use. And we all can join the global movement for Public AI, an alternative ecosystem for AI that is developed under public control with an incentive structure to create public benefit rather than private profit. The US midterm elections present ample opportunity for those seeking to control the AI political agenda. In the recent New York congressional Democratic primary, Pacs linked to the dueling AI companies Anthropic and OpenAI spent millions of dollars lobbying for or against “AI safety”, the idea that we must urgently monitor and prevent people from using AI to cause catastrophic harms. We’re already seeing a similar dynamic play out in races in Massachusetts and other states. Why would Anthropic and OpenAI – bitter industry rivals but fundamentally on the same side politically – support opposing viewpoints? Because they both ultimately profit from the mystique: the idea that their products are so powerful that controlling those products is the world’s most important challenge. Here’s the typical read on the dynamic. To one side (backed by OpenAI affiliates), “safety” comes from the appearance of US industry dominating AI innovation, under the slow-moving control of federal lawmakers (and without pesky state regulators in the way). To the other side (backed by Anthropic), “safety” means a heavier regulatory framework that plays to Anthropic’s posturing as the ethics- and compliance-focused AI vendor. In both cases, it’s more marketing than principled concern about safety. Political organizers should call out and reject the AI companies’ framing of the debate, and reorient campaign agendas around populist resistance to corporate concentration of wealth and power. When AI companies pump millions into legislative races, the result should not be hyperbolic discussion of AI superintelligence. And when a plot of land in a small town is pitched as a datacenter site, the debate should be about more than the local costs and benefits. It should include out-of-control money in politics, and Citizens United-proof solutions to limit corporate influence like public financing and state regulation. We all have a vested interest in what’s on the policy agenda, and what the outcomes are. Today, the greatest risk AI poses to society is the exacerbation of inequality and the concentration of wealth. The real problem is trillion-dollar AI companies and their trillionaire oligarchs cozying up to political power in Washington and governments worldwide, and using their money to enact their agenda over the popular will of the people. This is the issue we’d like to see put front and center, and it requires solutions much more extensive than slowing datacenter development. Bruce Schneier is a security technologist who teaches at the Harvard Kennedy School at Harvard University and University of Toronto’s Munk School Nathan E Sanders is a data scientist affiliated with the Berkman Klein Center of Harvard University and co-author, with Bruce Schneier, of the book Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship