Is AI Causing Layoffs Or Simply Exposing Bad Hiring?
Every week there’s another headline. “Company cuts jobs because of AI.”
Yes, some organizations are explicitly linking workforce reductions to AI-driven productivity and there is data that backs that up. Challenger, Gray & Christmas reported over 54,000 announced layoff plans in 2025 where AI was cited as a factor. In January 2026 alone, AI was referenced in more than 7,600 job cuts.
But here’s the part that matters: since they began tracking this category in 2023, AI-related cuts represent roughly 3 percent of total announced layoffs. Three percent.
So while AI is increasingly part of the story, it is definitely not the whole story.
At Caerus, we see something else happening. When a company can suddenly remove thousands of roles because “AI made us more efficient,” that usually means one of the following was already true:
They hired for activity instead of outcomes.
They scaled headcount faster than they scaled clarity.
They duplicated work across teams.
They approved requisitions without redesigning the operating model.
AI did not create those issues. It simply made them impossible to justify.
Yes, companies like Block, HP, and Klarna have publicly tied efficiency gains from AI to headcount reduction. But if AI can remove that much capacity that quickly, the deeper question is why was that capacity there in the first place?
The uncomfortable truth is that many organizations over hired during growth cycles. Hiring became the default solution to pressure. Busy teams meant more people. Open budget meant approved headcount. Few leaders stopped to ask what the work should actually look like.
Now AI offers a narrative that feels forward-looking and innovative. It sounds strategic and it sounds modern.
But calling this an AI story risks masking what it often is, which is simply a workforce planning correction.
If productivity is genuinely increasing, the responsible response is not simply “fewer people.” It is smarter work design. Companies should be looking at what work should be automated, what skills need to be built and where can they redeploy instead of reduce.
AI is a capability shift but the real leadership test is this: are you using AI to evolve your operating model, or to clean up the consequences of poor hiring strategy?
One is transformation, whilst the other is damage control dressed up as innovation.
AI is an enabler. Leadership is the differentiator.