General Motors has laid off about 600 salaried IT employees — more than 10% of its IT department — and is rehiring for AI-focused engineering roles, in one of the clearest enterprise examples yet of a workforce being rebuilt around AI rather than augmented by it. The automaker confirmed the cuts after they were first reported by Bloomberg News. "GM is transforming its Information Technology organization to better position the company for the future," the company said in a statement.
The cuts are not a permanent headcount reduction. A person familiar with the layoffs told TechCrunch that GM is still hiring inside its IT organization, but for a different mix of skills: AI-native development, data engineering and analytics, cloud-based engineering, and agent and model development, including prompt engineering and new AI workflows.
In practical terms, GM wants engineers who can build with AI from the ground up — designing systems, training models, and engineering data pipelines — rather than staff who plug AI tools into existing processes. That is a different job description from the one that defined most enterprise IT departments even two years ago.
Key facts
- 01GM laid off about 600 salaried IT employees, more than 10% of its IT department, on May 11, 2026.
- 02The cuts are a skills swap — GM is still hiring for AI-native development, data engineering, cloud engineering, and agent and model development.
- 03GM cut roughly 1,000 software workers in August 2024 in an earlier round of white-collar layoffs.
- 04Sterling Anderson, co-founder of Aurora, joined as chief product officer in May 2025 and has been consolidating GM's software teams.
- 05Three top software executives exited in November 2024, including Barak Turovsky, who spent nine months as chief AI officer.
The May 11, 2026 round is the latest in roughly 18 months of white-collar reductions at GM as it concentrates resources on a smaller set of high-priority initiatives, AI among them. In August 2024, the automaker cut about 1,000 software workers in a separate restructuring of its software organization.
“GM cut about 600 salaried IT staff — more than 10% of the department — and is rehiring for AI-native development, model engineering, and prompt design instead of permanently shrinking headcount.”— Jaeden Schafer
The software workforce has been in flux since Sterling Anderson, co-founder of autonomous trucking startup Aurora, was hired as chief product officer in May 2025. Anderson has pushed to consolidate GM's disparate technology businesses into a single organization, a reorganization that has produced visible churn at the top.
Three senior software leaders left in November 2024 as that consolidation accelerated: Baris Cetinok, senior vice president of software and services product management; Dave Richardson, senior vice president of software and services engineering; and Barak Turovsky, a former Cisco vice president who spent just nine months as GM's chief AI officer.
GM has moved to refill the senior bench with AI-specific hires. Behrad Toghi, previously at Apple, joined as AI lead in October 2025. Rashed Haq came in as vice president of autonomous vehicles after five years at Cruise, the self-driving subsidiary GM acquired and later shut down, where he ran AI and robotics.
The pattern at the executive level mirrors the pattern in the rank-and-file: out with leaders and engineers built for the previous generation of automotive software, in with people whose résumés are anchored in autonomy, large models, and AI-native infrastructure. GM is not shrinking its technology organization so much as swapping its composition.
What is harder to evaluate is how cleanly the swap actually works. AI-native engineering talent is among the most expensive and most contested labor pools in tech, with the largest AI labs and hyperscalers setting compensation benchmarks that traditional automakers have historically struggled to match. GM has not disclosed how many of the 600 cut roles it intends to refill, on what timeline, or at what cost per hire.
There is also the question of institutional knowledge. The IT staff being cut included people who maintained the systems that actually run a global automaker — ERP, dealer networks, manufacturing systems, vehicle data. Replacing them with engineers fluent in agents and model development does not automatically replace the operational expertise that kept those systems running.
GM's restructuring is one of the more honest pictures of what enterprise AI adoption looks like once the pilot phase ends. The companies most committed to AI are not just buying licenses and bolting copilots onto existing teams; they are rewriting the job descriptions underneath. For workers, the implication is that "using AI tools" is no longer the skill that protects a role — building with them is. For the AI vendor market, the demand signal is specific: agent frameworks, model tooling, and the data infrastructure that feeds both.
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