Cory Doctorow cuts through the fog of artificial intelligence hype by asking a question almost everyone else is ignoring: why are we pouring trillions into a business model that loses money on every single transaction? In an era where tech narratives often demand blind faith in "the next industrial revolution," Doctorow offers a stark economic reality check, arguing that the entire AI boom is a speculative bubble fueled by unsustainable subsidies rather than genuine value creation.
The Gish Gallop of AI Governance
Doctorow identifies a critical failure in how we discuss technology: the industry's reliance on overwhelming opponents with so many contradictory claims that meaningful critique becomes impossible. He calls this the "Gish gallop," a debating tactic named after Creationist Duane Gish, who was notorious for making so many claims that "it's impossible to address them in the time available." Doctorow applies this directly to modern AI discourse, noting how he is frequently asked to condense complex issues of labor rights, data center siting, and existential risk into a mere 13-minute radio segment.
The author writes, "I think about the Gish Gallop whenever I'm asked to comment on AI." He illustrates this with a recent interaction where a producer wanted him to discuss everything from democratic oversight to whether AI will "wake up, becomes God, and turns us all into paperclips" in a single short window. This framing is powerful because it exposes the absurdity of the current media cycle; by forcing critics to address every wild prediction at once, the industry ensures that no specific policy or economic flaw can ever be properly examined.
The AI industry has made so many claims about its past, present and future that it's almost impossible to have a reasonable critical conversation about it.
Doctorow argues that this tactic often leads to what he terms "criti-hype," where critics accept the industry's grandiose premises—such as AI curing cancer or solving climate change—and then debate the implications rather than questioning the premises themselves. This is a crucial distinction. By accepting the premise that these outcomes are inevitable, we shift the conversation from "should we do this?" to "how do we manage the fallout?" As Doctorow points out, true criticism requires challenging the foundational claims of technological determinism before assessing their consequences.
The Economics of the Money Furnace
The core of Doctorow's argument rests on a brutal economic assessment: AI is currently the most money-losing venture in human history. He describes the sector as a "money-furnace" where companies are effectively selling $100 bills for five dollars, relying on massive subsidies to keep the lights on. The author writes, "Not only does AI have terrible unit economics, those unit economics are getting worse over time."
This analysis reframes the entire debate from one of capability to one of solvency. If the business model is fundamentally broken, then questions about whether AI can replace doctors or teachers become secondary to the question of who will fund these systems when the capital runs dry. Doctorow highlights a recent example where customers who previously praised AI's efficiency flew into a rage when companies tried to raise prices from $5 to $20 per unit, revealing that their enthusiasm was entirely dependent on artificial subsidies.
Critics might argue that early-stage technologies often operate at a loss before reaching economies of scale, pointing to the historical precedents of railroads or the internet. However, Doctorow counters this by noting that even selling the product at face value ($100 for $100) yields no profit, suggesting the issue is structural rather than temporary. He writes, "You can't keep a business afloat by selling $100 bills for $5, nor for $20."
AI is a money-furnace, and AI hustlers are clearly on the hunt for a way to force all of us to feed every dime we've got to it.
The author extends this economic critique to the environmental impact, asking what happens when these massive data centers go bankrupt before they can even be fully powered. He poses a provocative question: "How many laser-tag arenas do we actually need?" This rhetorical device forces readers to visualize the physical waste of a bubble burst, shifting the focus from abstract digital threats to concrete resource mismanagement.
The Opportunity Cost of Hype
Perhaps the most compelling part of Doctorow's commentary is his examination of opportunity cost. He argues that by shoveling unlimited capital into AI companies in hopes they might one day cure cancer or solve climate change, we are actively starving other fields that have promising solutions but lack funding. He writes, "There are plenty of cancer researchers who have promising approaches they haven't been able to pursue due to funding shortfalls."
This argument connects the speculative nature of AI to tangible human suffering and delayed progress in critical areas. The author suggests that the promise of an AI-driven future may actually be a distraction from immediate, actionable solutions. He notes, "Unless there's some way to estimate how much money we have to give to AI companies before they cure cancer, we should at least consider the possibility that the true sum is 'more money than exists now and that will ever exist.'"
Doctorow also touches on the irony of using energy-intensive technology to solve the climate crisis. He writes, "Likewise, it may be that the amount of CO2 that AI will generate atmosphere before it 'solves climate change' will render Earth permanently unfit for humans." This is a sobering reminder that the path to a technological savior might be paved with environmental destruction.
We should also consider that whatever benefits to cancer research that AI might deliver could come with a higher price-tag than the promising cancer research we're dropping because we can't find far more modest sums.
In his upcoming book, The Reverse Centaur's Guide to Life After AI, Doctorow aims to provide a framework for navigating this "messiness." He argues that the industry's disjointed claims require a response that is equally robust and grounded in reality, rather than the "13-minute segment" approach that dominates current discourse.
Bottom Line
Doctorow's most significant contribution here is stripping away the mystique of AI to reveal a fragile, loss-making business model propped up by hype and subsidies. His strongest argument lies in exposing the opportunity cost of this frenzy, suggesting we are sacrificing real-world progress on the altar of speculative fiction. The biggest vulnerability in his analysis is that it assumes a rational market correction will occur before catastrophic environmental or social damage happens; history shows that bubbles can persist far longer than logic suggests they should. Readers should watch for how regulators and investors respond to these unit economics as the subsidy era inevitably ends.