More than 30 major companies cut jobs in 2026 citing AI, but the cuts aren't improving results

🕒 Published on Zendoric: October 24, 2026 · 00:20
✨ AI-generated · how it's made
A Reuters tally counts more than 30 major companies that have announced layoffs in 2026, from banking to logistics. But a Gartner survey suggests deeper cuts don't bring bigger returns. For now, AI may be more of a justification than a lever.
By Perú Retail (based on a Reuters report) · October 23, 2026.
More than 30 major companies have announced job cuts in 2026 with AI cited as a factor, according to a Reuters report updated on October 22. The cuts range from hundreds to tens of thousands of jobs at companies in the US, Europe, Australia and Brazil.
Banking tops the list, according to the compilation. Standard Chartered will eliminate more than 7,000 positions over four years under an AI-backed plan. Mizuho will cut up to 5,000 over a decade. Barclays plans to shed about 5,000 jobs (5% of its workforce) and save more than £1.1 billion in 2027; around 60% of the cuts will fall on support and development functions. Danske Bank (420) and DNB (about 400) round out the sector. Other coverage of the same trend adds American Express, with 3,500 positions.
In tech, according to Crunchbase News, Microsoft laid off 4,800 people in July and Meta made 8,000 cuts in May, plus more than 1,000 at Reality Labs in January and another 600 in October. Block cut more than 4,000 jobs in February, nearly half its workforce. Outside the sector, Dow eliminated 4,500 positions through process automation, Kroger about 3,000 corporate roles and British American Tobacco 5,500, with another 3,500 transferred to third parties.
The most striking figure is the trend. US tech layoffs totaled 94,046 between January and August, up 16.8% from 80,486 in the same period of 2025. According to the article, AI is cited as the reason for 33% of tech-sector layoffs in 2026, compared with 1% in 2024. The Center for AI Safety's Wage Loss Dashboard, cited by ZDNet, estimates more than 117,000 AI-attributed layoffs in the US this year, with an annual wage loss of $8.5 billion. These are third-party estimates using different methodologies, so they are best read as orders of magnitude rather than exact figures.
The counterpoint lies in the results. A Gartner survey of 350 executives at companies with revenues of at least $1 billion that use AI agents, cited by Tech Times, found that 80% had cut staff. But those that cut the most achieved returns almost identical to those that cut the least. On youth employment, the article cites Stanford data: employment of developers aged 22 to 25 has fallen nearly 20% since late 2022, while it is growing among those over 26.
Our take: AI has become the preferred explanation for cuts, and it is not always the cause. Going from 1% to 33% of layoffs attributed to AI in two years partly reflects a change in narrative. Saying “automation” sounds like strategy to investors; saying “cost-cutting” sounds like weakness. Block's case illustrates the problem: according to the article, its CEO denied in 2025 that layoffs were about replacing workers with AI, and in 2026 attributed some 4,000 positions to the technology. When the rationale changes from year to year, it is hard to separate real capability from messaging.
The Gartner finding is the most uncomfortable for that narrative. If cutting more doesn't pay off more, many companies are laying people off before measuring what AI actually does in their processes. That is a bet, not a transformation. The article's recommendation to startups is sensible: run a 60- to 90-day trial with time and cost metrics before touching headcount.
The short-term human cost is real and should not be sugarcoated. It is concentrated in back-office, support and junior development roles, exactly what our sector thesis anticipates: routine work is the most exposed, while judgment and relationships hold up. The collapse in entry-level employment is the most worrying symptom. Without junior roles, the seniors that companies later want to hire never get trained, and that is a long-term problem individual companies cannot solve on their own.
Overall, the way out is collective: safety nets, reskilling, and ensuring productivity gains are shared, not just captured. In the long run, AI that produces more with less human labor could bring abundance and free people to pursue what they are passionate about. But that transition won't happen on its own, and for now the cuts are arriving before the benefits.
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