The Smoking Checkbox: How We Know AI Is Being Used as Cover for Layoffs

Here is a story about a checkbox. In March 2025, New York became the first state in the country to require employers to disclose on their WARN Act filings whether AI or automation played a role in mass layoffs. It was a simple addition: check a box, name the technology. Nearly a year later, according to reporting by Bloomberg Law¹ and TechBuzz², 162 companies had filed WARN notices affecting 28,300 workers in New York. The number that checked the AI box? Zero. Not one.

Among those 162 filers were Amazon, which cut 660 New York jobs, and Goldman Sachs, which shed 4,100 roles. Both had publicly linked their workforce reductions to AI-driven efficiency gains. Both marked 'economic' reasons on their state filings. As one employment attorney quoted by Bloomberg Law put it, companies are engaged in "AI-washing in reverse" — publicly crediting AI for efficiency gains while privately avoiding linking AI to layoffs³ on legal documents.

I think this single data point — zero compliance on a mandatory disclosure form — tells us more about the relationship between AI and layoffs than any earnings call ever could. And it is part of a much larger pattern that points to a clear conclusion: AI is functioning primarily as a narrative convenience for workforce reductions that are financially motivated.

Let me walk through the evidence. The most rigorous survey I've found comes from Harvard Business Review⁴, published in January 2026. Researchers Thomas Davenport and Laks Srinivasan surveyed 1,006 global executives and found that 60% had already reduced headcount in anticipation of AI's future impact. But only 2% said large layoffs were tied to actual AI implementation. Read that gap again. Sixty percent cutting for potential. Two percent cutting for performance. Companies are not eliminating jobs because AI is doing the work. They are eliminating jobs because AI makes a better press release than "we overhired during the pandemic and need to fix our margins."

This finding is reinforced by what's happening at the operational level. MIT's Project NANDA published "The GenAI Divide"⁵ in mid-2025, a comprehensive study analyzing over 300 enterprise AI deployments. Their headline finding: 95% of generative AI pilot projects delivered no measurable financial return. Despite $30-40 billion in collective enterprise AI investment, only 5% of integrated systems created significant value. The tools work well enough for individual productivity — writing drafts, brainstorming — but they do not yet transform core business workflows. As the MIT researchers noted, the problem isn't model quality; it's that most enterprise AI systems "do not retain feedback, adapt to context, or improve over time."

So we have a situation where companies are firing people in the name of a technology that, by the most rigorous available measures, is not yet delivering the results that would justify those firings. Forrester Research's Predictions 2026 report⁸ quantified the inevitable consequence: 55% of employers who made AI-attributed layoffs now regret it. A Fortune 500 CHRO admitted at an IBM-hosted dinner that "we didn't have a lot of strategic intent when [our] layoffs were done," according to WebProNews⁹.

The Klarna case is the most instructive parable here. In February 2024, the Swedish fintech proudly announced that its OpenAI-powered AI assistant was "doing the equivalent work of 700 full-time agents," handling two-thirds of all customer service chats according to OpenAI's own case study⁶. This was held up as the single best example of genuine AI-driven restructuring. Then reality intervened. By May 2025, Klarna reversed course⁷, CEO Sebastian Siemiatkowski admitting that "cost unfortunately seems to have been a too predominant evaluation factor" and that the company was rehiring human customer service workers. The company that was supposed to prove AI could replace humans ended up proving the opposite.

Now, I want to be precise about what I'm arguing. I am not saying AI has zero impact on employment. It does. Goldman Sachs research¹¹ found that just above 15% of layoffs discussed during Q3 2025 earnings calls were attributed to AI, a figure that had been growing through the year. There are genuine pockets of AI-driven displacement, concentrated in customer service, routine data processing, and certain coding tasks. A Goldman Sachs survey of investment bankers¹² found 11% of US companies "actively reducing headcount due to AI," rising to 31% for tech, media and telecom firms. Real displacement is happening at the edges.

But the gap between the real impact (narrow, concentrated, often reversible) and the claimed impact (sweeping, inevitable, permanent) is where the deception lives. According to Oxford Economics data cited by Metaintro¹³, AI-related displacement accounted for approximately 4.5% of total U.S. layoffs in 2025. Economic conditions drove roughly four times more job losses. Yet nearly 6 in 10 hiring managers admitted they used AI as the stated reason for layoffs that were actually driven by budget pressures, revenue uncertainty, or past overhiring.

The market itself has caught on. Goldman Sachs published research in December 2025 showing that stocks now drop about 2% on average¹⁰ after AI-attributed layoff announcements. That's the opposite of what happened in 2023, when Meta's stock soared after its "Year of Efficiency" cuts. Companies that can't show real AI-driven productivity gains alongside cuts get punished even harder. Wall Street, for all its faults, has learned to distinguish between genuine restructuring and narrative management.

The strongest counterargument I've encountered is that even if the initial 2022-2023 cuts were pandemic-correction layoffs dressed in AI language, the technology is catching up to the narrative. Companies that restructure now around AI capabilities will be better positioned for genuine automation gains in 2027-2028. This is plausible! But it is also exactly what executives said about "digital transformation" after 2008, and about "cloud restructuring" in 2015-2016. The pattern — cut now for financial reasons, justify it with the technology of the moment, and hope the technology catches up before anyone notices — is old enough to have its own academic literature. What's different this time is we have the New York checkbox data showing the gap between public claims and legal disclosures in real time.

The consequences of accepting the AI narrative at face value are concrete. Workers lose negotiating leverage because you cannot collectively bargain against an algorithm that doesn't exist yet. Regulators defer because intervening in "technological progress" is politically costly. And executives who overhired during zero-interest-rate mania and then corrected with mass layoffs get to rebrand their capital misallocation as visionary leadership.

Here is what I'll be watching. Gartner predicts that 50% of companies that attributed headcount reductions to AI will rehire staff by 2027¹⁴ to perform similar functions. Forrester expects the same. If those predictions prove correct, it will be the single most damning piece of evidence that the AI layoff wave of 2024-2026 was primarily a rebranding exercise for cost-cutting. Track the rehiring. That's where the truth will be.