AI Spending Is Shifting Budgets and Jobs
There are multiple forces pushing jobs out of the workforce as companies invest in AI: some roles are being automated, others are cut as firms replace many positions at once, and a third force is capital displacement where spending shifts from labor to infrastructure. That reallocation can weaken companies if those big bets on AI infrastructure fail. In Texas, FERMI—touted as one of the largest AI projects—has struggled to secure an anchor tenant and recently saw its CEO and CFO step down.
The usual story treats displacement as a task-level event: a machine learns a task and the specific job vanishes. But a different pattern shows up in earnings calls, SEC filings, and restructuring notes: companies are explicitly reallocating payroll dollars into AI capital expenditures. That means layoffs or hiring freezes can be driven by budget choices rather than immediate technical substitution.
Corporate leaders have sometimes put this plainly: “Companies are shifting budgets toward AI investments at the expense of jobs,” said Andy Challenger of Challenger, Gray & Christmas. In March 2026, AI was the lead reason cited for U.S. job cuts, with 15,341 layoffs attributed to the technology, representing 25% of planned cuts that month. Through Q1 2026, AI was cited in 27,645 cuts, about 13% of total announced layoffs, and March’s spike suggested an acceleration.
Daniel Keum of Columbia Business School framed the dynamic as resource reallocation: some workers lose jobs not because a role has been automated but because companies are directing money to AI and away from other expenditures. That distinction matters for how quickly white-collar roles shrink and for what policy responses make sense. If paylines are shifted, the work may still be needed but left undone or outsourced.
Major tech firms are backing the math with massive capex. The five largest U.S. cloud and AI infrastructure providers have collectively committed to roughly $660 billion to $690 billion in capital spending in 2026, while capital expenditures among big tech more than doubled in two years to $427 billion in 2025. That spending goes toward data centers, GPUs, and networking, assets that are capital-intensive but not particularly labor-intensive compared with other growth investments.
Examples are straightforward. Dell cut about 11,000 employees in fiscal 2026, bringing headcount to 97,000 and marking a roughly 10% year-over-year reduction as part of what the company called business modernization. The cuts coincided with a push into AI infrastructure, where Dell’s Infrastructure Solutions Group revenue rose 40% in fiscal 2026 and the company expects AI-optimized server revenue to double in fiscal 2027. Cisco’s leadership framed a 2024 reduction similarly as reallocating “hundreds of millions of dollars” into AI and growth areas and urged investors to view the moves as reallocation rather than pure cost cutting.
Meta’s numbers show the tradeoff starkly: the company reported $201 billion in revenue for 2025 and guided 2026 capital spending to $115 billion–$135 billion, nearly double its 2025 outlay of $72 billion. To support that capex without crushing margins, the company redirected funds previously used for salaries and eliminated roughly 25,000 positions across rounds of layoffs since 2022. That sequence illustrates how strong revenue need not prevent cuts when capital priorities change.
Bank of America described a quieter version of the same dynamic, noting the ability to “just make decisions not to hire and let the headcount drift down.” The bank’s CFO said adopting AI “saves us about 2,000 people” who would otherwise write code. A March 2026 survey of 866 U.S. business leaders found 54% have or will cut compensation to free capital for AI spending, and 88% of those leaders said a weak job market makes it easier to reduce pay without losing staff.
Block cut about 4,000 employees in February 2026 and explicitly cited AI involvement in reducing its workforce by roughly 40%. Jack Dorsey wrote that the reductions were not driven by business failure and added he would “rather get there honestly and on our own terms than be forced into it reactively.” A Morgan Stanley analysis found roughly one quarter of S&P 500 companies mentioned at least one quantifiable AI impact in the first three months of 2026, up from 13% in the same period a year earlier.
The practical difference between automation and appropriation is important. If a job is gone because an algorithm can do it, retraining and task-specific policy responses are relevant. If a job was cut so capital could be spent on AI infrastructure, the role may still be necessary but deprioritized by corporate strategy. For workers and policymakers, the mechanism behind job losses shapes what remedies or protections are most appropriate.