GM Cuts More Than 600 IT Jobs in a Deliberate AI Skills Swap

Five Hundred Jobs, One Meeting, Zero Warning

General Motors laid off between 500 and 600 employees in its global information technology division on May 11, eliminating more than 10% of its IT workforce in a single day. The cuts hit hardest in Warren, Michigan, and Austin, Texas, though the scope extended across GM's global operations. GM confirmed the layoffs after they were first reported by Bloomberg News.

Affected employees described the process as abrupt. An unusually short 15-minute virtual meeting invitation arrived with little context. HR delivered a scripted message. The call ended, and so did their employment. A data analyst who had worked at GM for more than a decade told CNBC there was 'no appreciation or empathy. No questions. Nothing.' Employees with one to four years at the company were offered two months of severance.

Still Hiring - Just for a Different Kind of Engineer

GM framed the cuts as a transformation rather than a contraction. The company had roughly 80 open IT positions posted on its careers page within 24 hours of the announcement, but they are not replacements in any conventional sense. The roles GM wants to fill require a fundamentally different skill profile.

According to TechCrunch, the capabilities GM is actively hiring for include AI-native development, data engineering and analytics, cloud-based engineering, agent and model development, prompt engineering, and new AI workflows. In practical terms, GM is looking for engineers who can build with AI from the ground up - designing systems, training models, and engineering pipelines - not workers who use AI as a productivity add-on to existing roles.

The Leadership Moves That Set This Up

The layoffs did not come out of nowhere. Chief product officer Sterling Anderson, a former Aurora co-founder who joined in May 2025, consolidated GM's technology businesses into a single organization. Three senior software leaders departed in November 2025 during that consolidation.

Since then, GM has made deliberate leadership bets on AI. The company hired Behrad Toghi, previously of Apple, as its AI lead, and Rashed Haq, formerly head of AI and robotics at Cruise, as vice president of autonomous vehicles. GM has also contracted with Nvidia to replace the Qualcomm Snapdragon platform with Nvidia's Drive Thor system, a next-generation architecture set to debut in 2028 and built specifically for software-defined vehicles. The May 11 layoffs follow a pattern: GM cut roughly 1,000 software employees in August 2024 as it concentrated resources on what it described as high-priority initiatives.

Cutting From a Position of Strength

The layoffs are not a sign of financial distress. GM reported $43.6 billion in revenue for Q1 2026, roughly flat year over year, with adjusted EBIT of $4.3 billion, up 22% from the prior year. Adjusted earnings per share came in at $3.70, beating Wall Street estimates by 40%. The company raised its full-year 2026 adjusted earnings guidance by $500 million to a range of $13.5 to $15.5 billion.

That financial stability gives GM room to absorb the disruption of restructuring. It also makes the workforce decisions harder to explain to affected employees. A data scientist who was laid off told CNBC they had spent months actively adopting AI tools, trying to anticipate exactly what GM wanted from their team.

The Skills Swap Playbook and What It Assumes

GM's approach - cutting workers with conventional IT skills and hiring workers with AI skills - is becoming a standard enterprise playbook across US industries. Meta, Amazon, Oracle, and Snap have all executed similar moves in the first half of 2026. What sets GM apart is the transparency. The company is not obscuring what it is doing: it is presenting the workforce replacement as strategy.

But the model rests on several assumptions that are not guaranteed to hold. GM is competing for AI-native engineers in one of the tightest talent markets in the country, going up against Google, Nvidia, Apple, and every other company running the same pivot simultaneously. Warren, Michigan, is not the obvious first choice for a machine learning engineer weighing multiple offers. One industry analyst raised a longer-term concern as well: AI systems frequently fail to perform as expected, which often leads companies to rebuild internal headcount to supervise and correct the very AI output they eliminated human oversight for in the first place.

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