General Motors is reshaping its white-collar workforce, cutting hundreds of traditional IT roles while racing to rebuild its tech backbone around artificial intelligence.
Early restructuring and the May 2026 layoffs
Over the past 18 months, GM has repeatedly trimmed salaried staff across several departments as it prioritized software and AI initiatives.1 The latest and most visible step came in May 2026, when the company cut more than 10% of its IT department — about 600 salaried workers — in what insiders described as a “deliberate skills swap.”1
GM confirmed the layoffs and said it is “transforming its Information Technology organization to better position the company for the future,” while continuing to hire into IT, but for different skills.1 New roles emphasize “AI-native development, data engineering and analytics, cloud-based engineering, and agent and model development, prompt engineering, and new AI workflows,” signaling a shift toward people who can build systems and models from the ground up rather than simply deploy AI tools.1
A broader automotive AI skills arms race
By mid-May, industry analysts were framing GM’s move as part of a wider pattern across transportation, where “AI is creating jobs for some at the loss of others.”2 The same analysis noted that GM’s restructuring is unlikely to be a one-for-one trade, warning of a “net-negative job loss” even as the company insists it is hiring AI-focused IT talent.2
CNBC figures cited in that coverage show Ford, GM, and Stellantis together have eliminated more than 20,000 U.S. salaried jobs — about 19% of their combined white-collar workforce — this decade, with many cuts linked to technological shifts including AI.2
Competing visions of the AI future
Supporters inside the industry argue that companies must rebuild their technology stacks and human capital around AI to remain competitive, pointing to GM’s hunt for specialists who can “design the systems, train the models, and engineer the pipelines.”2 Critics, including displaced workers and labor advocates, see an uneven transition in which a relatively small cohort of highly skilled AI engineers replaces a much larger pool of traditional IT staff, amplifying concerns about long-term job erosion as automakers join an “AI skills arms race.”2