At Web Summit Vancouver in late May 2026, Meta announced the termination of over 8,000 jobs, or about 10% of its staff, while tech executives spoke on stage about AI’s efficiency improvements. In the same week, Intuit eliminated 3,000 jobs, or roughly 17% of its worldwide workforce, with a clear justification of AI acceleration. The audience’s and the industry’s response was more akin to weary recognition than surprise: another week, another series of reorganizations presented as essential to the AI transition.
40% of workers now fear losing their jobs to artificial intelligence, according to the Mercer Global Talent Trends poll, which was released earlier this year and included responses from 12,000 workers and business leaders worldwide. Just two years earlier, that was only 28%. The percentage rises to 60% in the US in particular.
The trajectory is made tangible by the Challenger, Gray, and Christmas data, which shows that businesses directly blamed AI for around 55,000 job losses in 2025—a twelvefold rise from 2023. Employers used automation or artificial intelligence (AI) as the main justification for 21,490 scheduled layoffs in April 2026 alone. This is more than 25% of all April layoffs and the second consecutive month that AI was identified as the main justification for job eliminations.
The employment were mostly in the back office and in technology. Entry-level and junior professional positions seem to be the most vulnerable; Goldman Sachs predicted that AI would eliminate 16,000 U.S. employment every month in 2026, and Wall Street banks have projected cuts of about 200,000 back-office and entry-level positions over the next three to five years. These are not predictions of potential outcomes from think tanks. These are figures that are currently being reported from persons who have been laid off.
The quality of the evidence supporting these conclusions is what makes the issue especially challenging to handle. According to a January Harvard Business Review analysis, the majority of AI-related layoffs are motivated by expected AI effect rather than proven AI performance; businesses are making cuts based on what they predict the technology will accomplish rather than what it is already accomplishing.
Although 80% of the 350 global executives surveyed by Gartner had reduced their workforces, there was no discernible link between these reductions and increased return on investment. There are actual layoffs. It is currently really controversial whether they are economically justified by actual AI capabilities. The fear may become more rational as a result of this ambiguity.
Beneath it all is the financial strain that makes losing a job feel more than just inconvenient—it’s actually risky. In the year ending in April 2026, consumer energy costs increased by 17.9%. The cost of housing has not decreased. The cost of healthcare is still rising. Many white-collar industries’ wages have not kept up with these developments.
Many households have a smaller financial buffer than the employment data would imply due to the mix of stagnant or slightly increasing wages and rising expenditures. A worker with little funds who cannot afford to quit their job or who would be severely impacted financially by even a short layoff is one who is unable to bargain, resist, or be perceived as challenging. The current atmosphere is producing precisely such calculation.
As the number of quarterly restructuring announcements rises, there’s a sense that something less concrete and more difficult to quantify is being undermined in addition to employment: the fundamental working assumption that if you perform a competent job for a large firm, your position is quite secure. For decades, the presumption shaped a particular form of institutional allegiance and career planning. Whether anything will take its place is still up in the air.
