Everyone on Wall Street is busy celebrating the AI supercycle — until you try the math. This week, IBM (NYSE: IBM) CEO Arvind Krishna dropped a number so large it could stop the AI party cold.
At today's costs, he told Decoder, it takes roughly $80 billion to build and fully equip a 1-gigawatt AI data center. And with nearly 100 gigawatts of hyperscale capacity already announced across the industry, that implies around $8 trillion in capital spending.
His conclusion was blunt: "There is no way you're going to get a return on that," arguing companies would need about $800 billion in profit just to service interest on that scale of investment.
AI Data Center Economics Look Broken
That warning lands right as Big Tech is flexing spending like price doesn't matter. Amazon.com Inc (NASDAQ: AMZN), Microsoft Corp (NASDAQ: MSFT), Alphabet Inc (NASDAQ: GOOG) (NASDAQ: GOOG) and Meta Platforms Inc (NASDAQ: META) are pouring tens of billions into compute, GPUs, land, power and cooling in what increasingly looks like an existential race to prove dominance in AI — not necessarily a profitable one. Nvidia Corp's (NASDAQ: NVDA) revenue projections assume that every hyperscaler keeps building non-stop; the market caps of chipmakers and equipment suppliers depend on that narrative holding.
What Krishna is suggesting is a far darker possibility: the economics simply don't support the ambition.
Hyperscaler Capex Is A Financial Time Bomb
If capex continues to balloon while monetization remains vague, someone is going to hit the brakes.
Enterprises haven't proven that generative AI can deliver ROI at scale, inference costs are exploding, and power shortages are already delaying deployments in multiple markets.
You don't spend $8 trillion because you want to; you spend it because you're terrified of losing the race.
Who Blinks First In The AI Build-Out Arms Race?
Right now, investor psychology is driven by FOMO, not fundamentals. The first hyperscaler to slow spending could trigger a wider rethink about AI infrastructure profitability — and expose how much of this build-out is narrative rather than economics.
But at some point, CFOs would start asking simple questions with ugly answers: How fast can AI revenue scale? Who pays for inference? What if enterprise adoption is slower than promised? What if power constraints halt deployment?
If Krishna is right, the AI supercycle ends not with a crash in demand, but with a financial choke point — where the first company to pause spending triggers a broader reassessment of what all this infrastructure is really worth.
The AI revolution may be real. But IBM's math suggests the capital model may not be. And the market hasn't priced in the risk that the AI gold rush hits a wall long before returns arrive.