The University and the AI Gold Rush
On the Promises and Pitfalls of MIT’s New Generative AI Impact Consortium (and Others Like It)
As I prepare to defend my dissertation, I’ve been reflecting on MIT’s expanding influence in shaping the future of AI. This short commentary considers how the Institute’s newly announced Generative AI Impact Consortium fits into a longer history of corporate alignment, extractive development, and the restructuring of public institutions for private benefit. I hope it prompts reflection on the deeper politics of our digital future and the evolving role of the university amid growing threats to public research funding.
On February 3, 2025, MIT announced the launch of its Generative AI Impact Consortium, a multi-department initiative (including my own) that promises to shape AI’s role in society. The announcement makes many optimistic claims (and vague allusions)—AI will “transform industries,” “enhance human flourishing,” and adds a small note that its aims will also “mitigate negative consequences.”
We’ve heard this kind of techno-utopian rhetoric before—that new technology will make the world better, that progress is inevitable, that risks will be responsibly managed. Yet time and again, corporate profits and global competition have an outsized influence on how technologies are actually used. It’s not the aims of the consortium that I am skeptical of, but the aims of its founding corporate members which include Analog Devices, Coca-Coal, OpenAI, SK Telecom, Tata, and TWG Global.
So what does it mean when a university with deep corporate and military ties sets out to define AI's "impact"? MIT has long shaped—and been shaped by—the U.S. military-industrial complex, from Cold War missile guidance to contemporary defense partnerships. Today, firms like OpenAI—included in the consortium—are partnering with defense contractors like Palantir and Anduril. MIT’s convening role in this moment raises pressing questions about whether its academic values can meaningfully counterbalance the interests of national security and commercial expansion.
Meanwhile, AI's material demands are growing. Data centers require massive energy and water. Infrastructure must be built, land acquired, supply chains expanded. As Karen Hao reported in a 2019 MIT Technology Review article training a single AI model can emit as much carbon as five cars in their lifetimes. Thus, it’s not necessarily the complexity of the software, but the large-scale infrastructural investments of AI in which uneven power dynamics are being reflected and entrenched. These infrastructural and environmental realities shape who gets to do AI research as the increasing resource intensity makes it harder for academic labs to compete with industry-funded counterparts. It makes one wonder how these incentives may play out within the university-corporate partnership.
MIT also seems eager to reclaim its dominance in digital innovation having fallen in stature thanks to Stanford and Silicon Valley. The Technology Licensing Office, for example, proudly promotes tech transfer and commercialization. As of this writing, 167 AI and machine learning technologies have been licensed. Commercialization isn’t inherently bad, but in an age of digital monopolies, we must ask: whose interests are being advanced? What happens when knowledge production is governed more by market logic than public mission?
To its credit, MIT does have researchers critically examining AI’s environmental and social implications. But how much weight can such work carry in a broader institutional environment structured around economic competitiveness and geopolitical rivalry? Recent federal shifts, such as the rollback of AI safety orders and the declaration of an energy emergency to prioritize data center growth, signal a troubling convergence of AI development with fossil fuel interests and deregulation.
A recent MIT article has likened today’s AI boom to a gold rush—a feverish pursuit of opportunity and fortune. Yet, gold rushes were also shaped by state-backed land grabs, environmental devastation, and corporate consolidation. More similar to the gold rush than it may first appear, the World Economic Forum has called AI the steam engine of the Fourth Industrial Revolution. Both metaphors suggest unstoppable progress. But history tells a more complicated story: these revolutions weren’t just entrepreneurship and discovery, they were about land, labor, and infrastructure—and they were often violently extractive.
The steam engine didn’t just revolutionize transportation. It enabled colonial expansion, displaced communities, and laid the groundwork for monopolies. Railroads, backed by state power, carved through Indigenous territories, accelerated land speculation, and reshaped the economy. MIT itself was founded in 1865 using land grant funds tied to these histories. Over 366,000 acres of land, taken from Native nations, helped finance the university’s creation.
These histories aren’t just background—they are blueprints. Each major technological revolution has involved struggles over territory, resource control, and governance. AI is no exception. The same rail corridors that enabled land grant university later became the backbone of the modern digital economy, as much of today’s long-haul internet infrastructure was laid along railroad rights-of-way. Today’s data infrastructure—data centers, transmission corridors, rare earth mining—rests on the same patterns of extraction and displacement. Yet public conversations about AI often ignore this. Instead, we’re offered the fantasy of “multi-stakeholder regulation,” as if monopolies, state security interests, and market consolidation can be tempered by dialogue alone.
MIT’s Generative AI Impact Consortium presents itself as a force for progress, a responsible steward of AI’s future. But without serious engagement with AI’s environmental, geopolitical, and economic impacts, it risks becoming just another vehicle for corporate consolidation under the guise of innovation.
Regulation is not merely lagging behind—it is being shaped by the very industries profiting from expansion. MIT’s own climate-focused researchers acknowledge that AI’s growth has “outpaced global regulatory efforts, leading to varied and insufficient oversight”—but they stop short of asking why. The answer isn’t complicated. Regulation isn’t just lagging behind—it is being shaped by the very industries profiting from AI’s unchecked expansion.
Universities are also not passive observers in this story. They help set the terms of technological legitimacy. They shape narratives of innovation. They convene industry and government. And increasingly, they offer their credibility to help justify extractive systems as inevitable or beneficial.
So as MIT positions itself to define AI’s societal impact, we should ask: Whose vision of the future is being advanced? Who decides what counts as ethical, responsible, or beneficial? And what is being left out of frame?
Still, I hope this moment might be different. MIT is full of students and researchers deeply committed to justice, sustainability, and accountability. The tools we are building are powerful. So is the opportunity to reshape how power and infrastructure operate in our digital future. But that requires more than (even cautious) optimism and vague commitments. It requires confronting the deeper politics of technological development—and choosing differently.
🚀 Techno-Statecraft will continue exploring these issues, unpacking the evolving power struggles over digital infrastructure and the landscapes it transforms.
Excellent commentary!
Like this piece, like the analogy to other technological revolutions. I feel you could have said the same with fewer words and more impact. Some questions you pose are valid but a bit repetitive. Look forward to reading more, all the same!