Scanning the headlines, it can be easy to get the impression that every investor, banker, and financial analyst is enamored with AI. Yet this simplified view obscures a more complicated story: the US economy isn’t where tech companies say it is.
By and large, businesses have gone bonkers over automation, lavishing $410 billion on AI in 2025 alone. To them, it’s a productivity miracle. AI should obviously make everybody work faster, reducing the need for human labor as it takes less staff to do more — saving companies gobs of cash in the long run.
At least, that’s the narrative in the corporate world. In banking, however, Goldman Sachs is spinning another yarn. After months of carefully-worded warnings about the dangers of over-investing on AI, Goldman has now dramatically escalated its rhetoric: the bank’s analysts now claim that AI has had zero impact on US economic growth over 2025.
The disconnect between AI investment and growth comes down to two structural issues. The first is geographic: when US companies buy chips from Taiwan, for example, that money boosts Taiwan’s economy, not the US. Second is productivity. AI might make some workers faster, sure, but that speed doesn’t automatically make supply chains more efficient — so far, those productivity gains are largely trapped inside company walls.
This pushback on AI’s economic impact marks a sharp break from even the most cynical analyses of 2025, in which even doomers credited the technology with single-handedly keeping US GDP growth afloat. Though the market more broadly has yet to see things Goldman’s way — investors are projected to spend $660 billion on AI across 2026 — a growing number of analysts are starting to cry foul.
Dario Perkins, head of macroeconomics at consulting firm TS Lombard, agrees that AI’s effects on productivity are nonexistent, even as massive layoffs have the workforce reeling. He was recently quoted in the Financial Times, arguing that “there is no evidence that AI deployment is either boosting productivity or damaging US employment.”
“While US productivity has been strong and hiring weak, our analysis finds that cyclical forces — not automation — are to blame,” Perkins concluded.
Meanwhile, former bank regulator at the New York Fed Brian Peters recently wrote that, while AI’s “capabilities are extraordinary” and the “capital deployment is unprecedented,” the “near-term economic payoff is, at best, debatable.”
At the National Bureau of Economic Research, economists studying the effects of AI on productivity recently published a working paper identifying a “productivity paradox,” where “perceived productivity gains are larger than measured productivity gains, likely reflecting a delay in revenue realizations.”
The implications of all this are stark. An investment boom measured in the hundreds of billions has, by Goldman’s accounting, generated essentially no measurable economic return for the US. The question facing us now in 2026 is whether $660 billion more of the same will produce anything other than an even bigger AI bubble.
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