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General Motors Lays Off 600 IT Staff in Strategic Pivot to AI and Data Engineering Talent

GM Restructures IT Workforce: Lays Off Hundreds to Pivot Toward AI ExpertiseGeneral Motors (GM) has confirmed the layoff of APProximately 600 salaried...
GM Restructures IT Workforce: Lays Off Hundreds to Pivot Toward AI Expertise
General Motors (GM) has confirmed the layoff of APProximately 600 salaried employees, representing over 10% of its IT department. This strategic move marks a deliberate "Skills swap," where the automaker is reducing roles focused on legacy expertise to create openings for talent with specialized backgrounds in Artificial Intelligence.
While GM confirmed the layoffs first reported by Bloomberg News, the company emphasized that this is not a total headcount reduction. Instead, the automaker is ACTively hiring for its IT department, but with a shifted focus. The most sought-after capabilities now include AI-native development, data engineering, analytics, cloud-based engineering, and Agent and model development. In practical terms, GM is seeking professionals capable of building AI systems from the ground up—designing infrastructure, training models, and engineering pipelines—rather than simply utilizing AI as a productivity tool.
This restructuring is part of a broader transformation within GM's software division over the past 18 months. The shift accelerated following the May 2025 appointment of Sterling Anderson as Chief Product Officer. Anderson has moved to consolidate GM’s disparate Technology units, a transition that saw the departure of several top software executives, including the former Chief AI Officer, Barak Turovsky. To fill the leadership gap, GM has recruited high-profile AI talent, including Behrad Toghi (formerly of Apple) as AI Lead and Rashed Haq (ex-Cruise) as VP of autonomous vehicles.
For the wider industry, GM's strategy signals a mature phase of enterprise AI adoption. It dEMOnstrates that integrating advanced AI requires more than just adding tools to existing teams; it necessitates a fundamental rebuilding of the workforce to support AI-native workflows and model engineering.
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