Packy McCormick challenges the prevailing narrative that AI data centers are merely wasteful energy sinks, arguing instead that they are the unlikely engines of American reindustrialization. In a piece that reframes a controversial infrastructure boom as a providential catalyst for hard tech, McCormick suggests that the insatiable demand of these facilities is doing what government procurement once did: funding technologies before they are ready, forcing them down the learning curve, and making them viable for the rest of the world.
The Meta-Alpha Product
The core of McCormick's argument rests on a historical pattern: new technologies often fail because they are too expensive to compete with incumbents until a specific, high-value customer forces them to scale. He identifies the data center not just as a consumer of chips, but as a unique economic actor. "Data Centers are increasingly serving as Buyers of Capabilities, acting as something between a government and a commercial buyer," McCormick writes. This distinction is crucial; unlike a typical commercial buyer who shops for the lowest price, these facilities need capability now, regardless of cost, to meet the demands of AI development.
McCormick draws a parallel to the "Alpha Products" of the past—like the Sony Handycam for lithium-ion batteries or the calculator for microcontrollers—but notes that data centers operate on a vastly larger scale. "If you can sell them something they need, fast, they have an almost bottomless bid," he observes. This creates a rare environment where companies building advanced nuclear reactors, enhanced geothermal systems, or high-voltage direct current grids can secure revenue without waiting for the market to mature naturally. The author posits that this dynamic is "a commercial analog operating on DoD-style procurement logic but commercial timescales."
This framing is compelling because it shifts the focus from the controversial output (AI models) to the necessary inputs (physical infrastructure). It suggests that even if the AI bubble bursts, the physical advancements in energy and construction funded by this boom will remain. "Data Centers are funding the future where no one else will," McCormick asserts, a line that captures the urgency of the situation. However, critics might note that this optimism assumes the capital is being directed toward genuinely novel technologies rather than simply inflating the cost of existing, inefficient solutions. The risk is that the "bottomless bid" could sustain a bubble of overcapacity rather than true innovation.
Far from being the villains they are painted as, Data Centers may be the greatest accelerant of American Reindustrialization and a built-world future that benefits all people that we've ever seen.
The Apollo Parallel
To contextualize the public backlash against these massive energy consumers, McCormick reaches back to the Apollo program. He reminds readers that the space race was deeply unpopular at the time, with polls showing a majority of Americans opposed to the cost and prioritization of lunar missions while problems on Earth remained unsolved. "President John F. Kennedy gave his canonical 'We choose to go to the Moon' speech not because it was popular, but because it was unpopular and he needed to rally support," McCormick notes. The historical record shows that even a decade after the moon landing, public support for the program's costs was still divided.
The author uses this history to illustrate a recurring theme: the immediate costs of ambitious projects are visible and painful, while the long-term technological dividends are invisible until they arrive. Just as the Apollo program accelerated the development of integrated circuits, fireproof fabrics, and water filtration systems, the current data center boom is doing the heavy lifting for technologies like solid-state transformers and modular construction. McCormick points out that "the absurdity of the task's ambition coupled with the bottomlessness of its budget... were exactly the conditions needed to create terrestrially-useful innovations that would otherwise have taken much longer, or never been invented at all."
This historical lens adds significant weight to the argument, suggesting that the current friction is a necessary growing pain. The comparison to the "Mother of All Demos" and the funding of early AI labs by the Defense Advanced Research Projects Agency (DARPA) further cements the idea that high-stakes, high-cost procurement has always been the engine of general-purpose technologies. Yet, the analogy is not perfect; unlike the centralized, mission-driven nature of the Cold War space race, the current data center boom is driven by private competition and profit motives, which may lead to different outcomes in terms of equity and distribution.
The Learning Curve Advantage
Perhaps the most distinct contribution of the piece is the focus on the "learning curve." McCormick explains that many superior technologies, such as advanced nuclear reactors, are currently too expensive because they lack scale. "The challenge with these technologies, in normal times, is that there is little economic incentive for the buyers who would enable the scale to stick their necks out," he writes. Natural gas is cheap and abundant, creating a stalemate where the superior technology cannot break through.
Data centers break this stalemate by providing the initial demand needed to drive costs down. "They offer dilution-free capital (real revenue on a negative working capital cycle) to fund the big vision, and more importantly, the opportunity to get to scale and down the learning curve years earlier than would otherwise have been possible," McCormick argues. This transforms the data center from a passive consumer of electricity into an active investor in the industrial base. The author suggests that this mechanism "makes companies that might otherwise have died in the Valley of Death viable."
The argument holds up well against the backdrop of current supply chain constraints, where developers are indeed willing to pay premiums for speed and reliability. However, the piece glosses over the potential for resource misallocation. If the demand for data centers is temporary or if the technology stack shifts rapidly, the specialized infrastructure built for them could become stranded assets. The assumption that the "learning curve" will inevitably lead to cheaper, better technologies for the broader market is a strong bet, but it relies on the continuity of this specific, high-cost demand.
In five years, this could all fall apart, and the world will be much better off.
Bottom Line
McCormick's strongest move is reframing the data center not as a symptom of AI excess, but as the primary vehicle for reindustrializing the physical world, effectively bypassing the need for a traditional government industrial policy. The argument's biggest vulnerability lies in its assumption that private capital will consistently prioritize long-term technological advancement over short-term profit, and that the resulting infrastructure will be adaptable enough to serve needs beyond AI. Readers should watch to see if the "bottomless bid" of data centers actually de-risks hard tech or merely inflates a speculative bubble in the energy sector.