In early 2026, Block cut 40% of its workforce and told shareholders that the decision reflected how intelligence tools have changed what it means to build and run a company. Its stock rose 24% that day. Coinbase followed weeks later, cutting 700 jobs and telling employees the company needed to become AI-native; its stock gained too, according to CNBC’s coverage of the announcement.
Neither company was in financial distress. Both described themselves as profitable and growing. Framing the cuts around AI, rather than around softening demand or a correction to pandemic-era hiring, was a choice. It’s a choice more companies made in 2026 than in any year before it, whether or not AI was the actual reason the work still got done with fewer people.
That choice looks rational if the goal is a favorable market reaction rather than a verifiable one. For IT leaders heading into 2027 budget planning, the gap between the narrative markets currently pay for and the operational reality is becoming one of the more consequential accountability problems in enterprise technology.
The pattern behind 2026’s AI layoffs
Block and Coinbase are not outliers. Meta cut roughly 8,000 roles in May, which was about 10% of its workforce, in the same quarter it disclosed plans to spend $125-$145 billion on AI infrastructure. They reported quarterly revenue was up 33% year after year. None of these companies were in financial distress when they announced cuts, and all of them named AI as the reason.
Gartner’s own research complicates that explanation. A survey of 350 executives found that 80% of large enterprises piloting AI reported workforce reductions, but researcher Helen Poitevin found no correlation between those cuts and measurable ROI. Her analysis is clear: Workforce reductions can create budget room, but they do not create a return on their own
SHRM has a name for the space between the AI story a company tells and the AI outcome it can actually demonstrate: AI-washing, the practice of overstating AI’s role in a business decision. Outplacement firm Challenger, Gray & Christmas has tracked AI as the single most-cited reason for layoffs for four consecutive months in 2026.
What the market is actually paying for
Why would a market reward a story its own research firms can’t validate? Because a layoff framed around AI reads to investors as evidence of technological ambition rather than an admission of overhiring or soft demand, and markets have historically responded better to the former. Jason Schloetzer, a faculty affiliate at Georgetown University’s McDonough School of Business, offered SHRM a more direct explanation. Executives often cite AI when the real driver is that they lack the cash flow to fund AI investment without freeing up capital elsewhere. For investors, he noted, reduced expenditure reads as improved profit, regardless of the reason behind it.
Kenny Pyle, an HR technology analyst at SHRM, put the incentive plainly. Invoking AI lets a company send two positive signals in place of a negative one and implies it is technologically ahead of its peers, and that it is willing to make hard calls. Neither signal actually requires that an AI investment should replace anyone’s job.
What the AI efficiency story leaves out
On June 25, 2026, Apple raised prices across its Mac, iPad, Apple TV, HomePod, and Vision Pro lines up to$300 a unit, pointing to a global memory chip shortage that CEO Tim Cook called a hundred-year flood. Apple’s stock fell 6% that day, its steepest single-session drop since April 2025, erasing roughly $275 billion in market value. The increase was a plain admission that components now cost more, not a claim about headcount or productivity. The market punished that honest admission about as readily as it rewards the flattering layoff story.
The shortage behind Apple’s price increases is real, and it isn’t specific to Apple. AI is also making IT budgets more expensive industry-wide, for reasons that have nothing to do with any single company’s headcount decisions. IDC described the shortage as an unprecedented inflection point, severe enough that IDC expects it to persist well into 2027. Samsung, SK Hynix, and Micron, which together produce nearly all the world’s DRAM, have redirected manufacturing capacity toward the high-bandwidth memory used in AI data centers, because those chips carry substantially higher margins than the commodity memory used in laptops, phones, and enterprise servers. Deutsche Bank analysts, quoted in Fortune’s coverage of the shortage, called memory production a zero-sum game: Every wafer devoted to AI infrastructure is a wafer that does not become the RAM inside a piece of enterprise hardware.
That is the part of the AI cost story a headcount reduction cannot offset. If server and endpoint hardware costs are climbing because of a supply-side shock affecting the entire industry, cutting staff and calling it AI efficiency doesn’t make that shock disappear; it just changes which line item absorbs the pressure. IT leaders working through IT cost optimization frameworks are about to find memory and compute pricing behaving less like a controllable expense and more like a commodity, subject to swings no procurement strategy can fully insulate against.
Where the reckoning eventually lands
The stock market reward for an AI-framed layoff is captured immediately in the boardroom on the day of the announcement. The operational proof that AI actually delivered the value that was promised takes considerably longer to arrive, if at all, and it lands somewhere else entirely: It lands on the lean IT organization while absorbing rising infrastructure costs and delivering on commitments made in an earnings call.
Gartner has already put a number on how much of that promise won’t hold up. The firm expects more than 40% of agentic AI projects to be cancelled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. “Most agentic AI projects right now are early-stage experiments or proof of concepts that are mostly driven by hype,” said Anushree Verma, the Gartner analyst who led the research.
The same Gartner study that found no correlation between AI layoffs and AI ROI also identified what does correlate with it: Organizations seeing real returns are the ones investing in what Poitevin calls people amplification, training employees to build and run their own automations rather than treating headcount reduction as the source of value.
That’s the actual choice sitting in front of most IT leaders right now: Invest in people to get better at AI, or cut people and call it an AI outcome. The data already shows which one tends to work.
