General Motors has cut over 10% of its IT department, equating to about 600 salaried employees, as part of a strategic shift towards hiring individuals with AI-focused skill sets. GM confirmed these layoffs, initially reported by Bloomberg News, to TechCrunch. The automaker described the layoffs as a move to better prepare for the future without giving detailed specifics. According to a source familiar with the layoffs, while there have been cuts, the company is actively recruiting for different roles in its IT department. Desired skills include AI-native development, data engineering and analytics, cloud-based engineering, agent and model development, prompt engineering, and new AI workflows. Essentially, GM seeks individuals skilled in building AI systems, designing models, and creating pipelines from scratch.
Over the past 18 months, GM has laid off white-collar staff from multiple departments, focusing resources on high-priority areas like AI. In August 2024, GM reduced its workforce by about 1,000 software positions. The software team has seen significant changes since Sterling Anderson, co-founder of the autonomous trucking startup Aurora, joined as chief product officer in May 2025. Last November, three key executives departed GM’s software team as Anderson aimed to merge the company’s varied technology divisions. These included 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 VP at Cisco who served briefly as GM’s chief AI officer.
GM has since addressed these gaps with AI-focused hires, including Behrad Toghi as AI lead, previously from Apple, and Rashed Haq as vice president of autonomous vehicles. Haq had a five-year tenure at Cruise, GM’s former self-driving vehicle subsidiary, as head of AI and robotics. GM’s restructuring highlights the real-world dynamics of enterprise AI adoption, emphasizing the need to rebuild the workforce from scratch rather than merely incorporating AI tools into existing teams. The specific skills being sought — such as agent development, model engineering, and AI-native workflows — reflect the future direction of large enterprise demand.