Block recently slashed 40% of its workforce. Snap cut 16%. Amazon shed 30,000 jobs in a matter of months. Leading AI CEOs from Anthropic’s Dario Amodei to OpenAI’s Sam Altman to NVIDIA’s Jensen Huang have warned that AI will eliminate many jobs.
But look beyond the layoff theater and a different picture emerges. Companies over-hired during the pandemic and are now correcting. As one executive told The Wall Street Journal, AI has provided “air cover” for cuts that were coming anyway. AI is the convenient villain in a story that was already being written.
AI displacement, however, is not entirely fiction. Goldman Sachs finds that workers displaced from technology-disrupted roles take longer to find new jobs and accept earnings losses. Anthropic reports measurable slowing in hiring for AI-exposed occupations, particularly among workers aged 22 to 25. The pain is real.
Still, reading past the headlines reveals the precise pattern: the labor market is absorbing AI through task-level substitution, not wholesale job elimination.
The gap between that reality and the panic comes down to how we experience AI versus how economies actually absorb it.
I teach AI for Business to MBA students at the University of Illinois Gies College of Business, and the confusion often starts with experience. Former Tesla AI chief Andrej Karpathy recently observed that two groups are talking past each other. One group tries basic tools, sees them fail, and concludes AI is overhyped. The other uses frontier models in professional settings and is stunned by their capabilities. Both groups are right, but both are missing the full picture.
AI systems are remarkably powerful, but mostly in domains where success can be clearly measured. Did the code compile? Did the test pass? Yet much of our work, including persuasion, leadership, strategic judgment, and navigating ambiguity, doesn't offer that kind of clarity. This is not a temporary limitation. It is core to being human.
Venture firm Andreessen Horowitz finds that coding dominates enterprise AI adoption by an order of magnitude over any other use case—one of the few domains with clean, verifiable outputs.
But basic economics suggests that even where AI is effective, it won't necessarily reduce work. When technology makes a task cheaper, we tend to do more of it, not less. Economists have understood this dynamic, Jevons' paradox, since the 19th century. Spreadsheets didn't eliminate accountants; they expanded the scope of financial analysis.
Now we see this with AI. Engineers at Meta burned through 281 billion tokens in 30 days on an internal leaderboard before it was taken down. Visa employees consume nearly two trillion tokens per month, reflecting millions of AI-assisted interactions across their workforce. Even Anthropic is hiring video editors up to $250,000. These are not stories about workers being replaced. They are stories about workers using dramatically more AI to extend what they already do.
So where is the disconnect? There are three major constraints that are missing from the headlines.
The first is scale. Running advanced AI systems requires enormous energy and infrastructure. U.S. data centers already consume about 4.4% of the nation's electricity, potentially rising to 7–12% by 2028.
But this expansion is meeting resistance. Last year, 25 data center projects were canceled following local opposition. Amazon, Microsoft, and Google have all abandoned multibillion-dollar builds. In Illinois, recent polling found that 80% of likely voters either want strict regulation or oppose new data centers entirely.
Yet, even as new capacity comes online, demand quickly absorbs it. Technology analyst Benedict Evans notes that similar infrastructure constraints in mobile networks took multiple decades to resolve.
That is not a software challenge. It is a physical one.
The second constraint is pricing. The AI industry struggles to assign value to what it sells. Outcome-based pricing works when value is easy to measure, payment processing, for example. But in hiring, management, or organizational decision-making, AI's contribution is real but difficult to isolate. Someone still has to take responsibility for the decision. That accountability can't be automated.
The third is specialization. The principle of comparative advantage has held for two centuries: specialization persists even when one party is better at everything, because what matters is opportunity cost. Compute is expensive and finite. Firms will allocate AI to their highest-value problems, leaving other tasks to humans. Not because humans are superior, but because the AI's time is more valuable elsewhere.
Together, these constraints provide our guarantee.
My students aren't just worried about their own careers. They are asking what this means for their children, whether college still makes sense, whether traditional career paths will exist a decade from now.
My answer is the same thing I said five years ago: pursue your passions, build expertise, stay curious. The economics haven't changed that advice, they've reinforced it.
I don't minimize what's coming. Workers in routine, AI-exposed roles will face real disruption. But the broader trajectory is clearer than the headlines suggest.
AI capability remains narrow. Demand expands as costs fall. Infrastructure limits the pace of deployment. Pricing reveals the continued centrality of human judgment. And as long as compute is costly, human roles will persist.
We are not heading toward a world without work. We are heading toward a world with different work. The sooner we understand that, the better prepared we will be.
Robert Brunner is a professor of accountancy and Chief Disruption Officer at Gies College of Business