GM just cut about 600 IT workers and plans to fill those seats with AI-specialized engineers. It sounds like a sharp strategic pivot. But nearly every company that's tried this kind of swap has come to regret it.
Last week, GM laid off about 600 salaried employees, more than 10% of its IT department. The company simultaneously posted new roles in AI-native development, data engineering, and cloud infrastructure. A Vehicle AI engineering role in Mountain View is listed at up to $290,000 and requires experience in reinforcement learning and multi-agent systems.
Laid-off employees described the process to CNBC as an ominous meeting with little warning. GM President Mark Reuss framed the move differently, saying customers "are expecting more from our vehicles than ever before" and the company needs tighter integration between software and hardware. The automaker recently hired Aurora co-founder Sterling Anderson as chief product officer and is building teams around autonomous driving and what it calls "physical AI."
Earlier this week, we looked at tech companies cutting managers for AI. GM is a cousin of that trend, applied to IT workers at a legacy automaker. And the data on this kind of workforce swap is not encouraging.
Gartner surveyed 350 business leaders at companies with more than $1 billion in annual revenue that had already deployed AI. Eighty percent had implemented workforce reductions, with some cutting headcount by up to 20%. The companies that cut the most didn't see any better results than those that cut less.
"There's no connection or correlation between people who are achieving ROI and layoffs," Helen Poitevin, Distinguished VP Analyst at Gartner, told Fortune. "Labor reduction is not the best ROI metric."
The numbers get worse from there. According to an outplacement industry survey, 75% of companies that made AI-related layoffs ended up spending more than they saved. More than half said the cuts weren't worth it because they ended up "babysitting the technology," with AI tools demanding far more human oversight than anyone expected. The survey comes from CareerMinds, an outplacement firm, so the source has a commercial interest in the finding — but the pattern is consistent with what Gartner and others have reported independently.
Poitevin's sharper point is about which companies actually see returns. "Those who only look to the workforce tend to be the 'laggards,'" she said, "because they're not going after the broader set of value that they can get to."
Across industries, the pattern holds. In Singapore, forty-six percent of firms skip job redesign entirely when deploying AI, and they consistently underperform. As one manufacturing COO put it in the MLQ.ai report: "The hype on LinkedIn says everything has changed, but in our operations, nothing fundamental has shifted."
GM has a case that building autonomous vehicles genuinely requires different engineering talent. But the data is unforgiving on one point: companies that swap headcount without rethinking the work consistently end up worse off. If GM's new $290,000 AI engineers are dropped into the same org structure, the same meetings, and the same planning cycles, the result won't be transformation. It'll be expensive churn with better resumes.
