Companies Are Firing Workers to Fund AI That Isn’t Working Yet
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Cloudflare cut 1,100 workers in May and called it an AI strategy. That same day, the company reported record quarterly revenue of $639.8 million. Meta eliminated 8,000 roles while posting $26.8 billion in net income. Block slashed 40% of its workforce to “refocus on AI” while projecting $12 billion in gross profit.
These aren’t struggling companies making hard choices. They’re among the most profitable enterprises in the world, cutting workers at peak performance to fund artificial intelligence infrastructure that has not yet proven it can replace the people being let go.
More than 142,000 technology-sector jobs were eliminated in the first five months of 2026, putting the industry on pace for nearly 370,000 by year-end. Hyper-scalers like Amazon, Microsoft, Alphabet, and Meta have collectively committed roughly $700 billion to AI infrastructure this year, nearly double what they spent in 2025. The money has to come from somewhere. Right now, it’s coming from payroll.
The question that should concern every leader, worker, and investor in 2026 isn’t whether companies are investing in AI. It’s whether they’re cutting people based on what AI can actually do today, or based on what they hope it will do eventually.
The Growing Gap Between the AI Narrative and Business Results
The pattern is hard to miss. Cloudflare reported a 34% year-over-year revenue increase and announced 1,100 layoffs on the same earnings call. Meta’s Q1 net income of $26.8 billion didn’t prevent 8,000 cuts. Block eliminated 40% of its employees to “refocus on AI” while projecting nearly $12 billion in gross profit. In each case, the layoffs were not a response to poor performance. They were a strategic bet made from a position of financial strength.
But here’s what the data shows when you look past the announcements.
A May 2026 Gartner study surveyed 350 global executives at companies with at least $1 billion in annual revenue. Among organizations piloting or deploying AI, roughly 80% had reduced their workforce. The critical finding: none of it correlated with better returns. Companies that cut workers were getting the same financial outcomes as companies that didn’t.
The organizations actually seeing strong AI returns were doing something different. Instead of eliminating roles, they were redesigning them. Instead of cutting workers, they were training them and creating new positions specifically built to manage and oversee AI systems. Gartner calls this “people amplification,” and it consistently outperforms workforce reduction as an AI strategy.
There’s a practical reason for this. AI needs institutional knowledge to function properly. It needs people who understand the business, the customers, and the context behind decisions. When companies cut those people, they often discover that AI can’t operate without them. Some have already had to rehire for roles they previously eliminated after learning that automation could handle only part of the work.
Over the long term, Gartner projects AI will create more jobs than it replaces. But the transition won’t be clean. AI is expected to significantly transform 32 million jobs a year. The organizations that come out strongest will be the ones that invested in their people during the shift, not the ones that used the technology as a reason to cut them.
Is AI the Reason for These Layoffs, or the Excuse?
There’s a meaningful difference between a company restructuring because AI has genuinely changed how work gets done, and a company using AI as a socially acceptable cover for cost cuts it was already looking for reasons to make. Both are happening right now. And they’re getting harder to tell apart.
Most companies laying off workers are doing so based on AI’s potential, not its current performance. The jobs being eliminated aren’t going to AI systems that are already doing the work. They’re going to AI systems that companies believe will eventually do the work. That’s not transformation. That’s speculation.
A term has emerged for the gap between what companies claim and what’s actually happening: AI washing. Many organizations announcing AI-driven cuts don’t have mature, production-ready AI applications that can fill the roles being eliminated. The layoffs are less about AI replacing workers today and more about freeing up cash for the AI infrastructure companies are betting on tomorrow.
There’s a financial logic driving this. Wall Street rewards companies that signal AI readiness, even when the operational results aren’t there yet. Announcing layoffs alongside an AI investment sends two messages investors want to hear: the company is ahead of the curve technologically, and it’s becoming leaner. Both messages boost the stock price. Whether the AI actually delivers is a question for a later quarter.
But this framing has consequences beyond the balance sheet. When companies tell workers their roles are being replaced by AI, it shapes how those workers relate to the technology going forward. People who believe AI was used against them are less likely to adopt it, less likely to engage with it, and less likely to help their organizations get real value from it. If the goal is successful AI integration, telling your workforce that AI just took their colleague’s job is a counterproductive way to start.
The Damage Is Landing Hardest on Younger Workers
The impact of these cuts isn’t distributed evenly. Senior developers and experienced engineers have been largely insulated. Younger workers are absorbing a disproportionate share of the damage.
Stanford HAI’s 2026 AI Index found that employment among software developers aged 22 to 25 has fallen nearly 20% since 2024. Developers aged 30 and older at the same companies saw headcount grow during the same period. AI isn’t replacing software engineering as a discipline. It’s replacing the specific tasks junior developers were hired to do: boilerplate code, scripted testing, routine bug fixes, basic operations.
The consequences go beyond individual job losses. Entry-level roles have always been how workers build skills, gain experience, and develop the judgment that makes them valuable over time. When those roles disappear, the pipeline feeding mid-level and senior positions weakens. Companies cutting junior roles today are betting they can hire experienced talent later. That bet gets harder to win every year the pipeline stays closed.
Software development job postings have fallen 53% since the release of ChatGPT in late 2022. Unemployment among recent college graduates has climbed to nearly 6%, rising twice as fast as the rest of the workforce. According to Federal Reserve Bank of New York research, computer science majors now have more trouble finding jobs than humanities majors. That’s not a typo.
What’s happening is structural, not cyclical. Companies are squeezing more output from existing employees and simply not replacing the ones who leave. That looks efficient on a quarterly earnings call. But entry-level roles aren’t just jobs. They’re how organizations build the next generation of experienced workers: the team leads, senior engineers, and future managers that companies will need in three to five years. That talent has to come from somewhere. If you close the pipeline now, you’ll be competing to poach it from someone else later, at a much higher cost.