General Motors just fired more than 600 IT employees, about 10% of the department, in a deliberate skills swap aimed at rebuilding the team around AI. According to TechCrunch AI, the automaker confirmed the cuts after Bloomberg first reported them, framing the move as transforming its IT organization “to better position the company for the future.”
This isn’t a pure cost-cutting headcount reduction. A person familiar with the layoffs told TechCrunch AI that GM is still hiring inside IT, but it’s looking for a fundamentally different profile.
What GM Actually Wants
The automaker is recruiting for capabilities that barely existed on org charts five years ago:
- AI-native development
- Data engineering and analytics
- Cloud-based engineering
- Agent and model development
- Prompt engineering and new AI workflows
What stands out here is the bar GM is setting. The company isn’t looking for engineers who use Copilot to ship faster. It wants people who can design AI systems from scratch, train the models, and engineer the pipelines underneath. Using AI as a productivity tool is now table stakes. Building with AI from the ground up is the actual job.
The Bigger Pattern at GM
This is the latest in an 18-month sweep through GM’s white-collar ranks. Back in August 2024, the company cut roughly 1,000 software workers. The software team has been in motion ever since Sterling Anderson, co-founder of autonomous trucking startup Aurora, joined as chief product officer in May 2025.
Last November, three senior software leaders exited as Anderson consolidated GM’s scattered tech units into one organization: Baris Cetinok (SVP of software and services product management), Dave Richardson (SVP of software and services engineering), and Barak Turovsky, the former Cisco VP who lasted nine months as GM’s chief AI officer.
The replacements tell you where GM is aiming:
- Behrad Toghi, ex-Apple, hired in October as AI lead
- Rashed Haq, former head of AI and robotics at Cruise (GM’s shuttered self-driving unit), now VP of autonomous vehicles
Why This Matters
GM’s restructuring is one of the clearest enterprise signals yet about what AI adoption looks like inside a Fortune 50 company. Companies aren’t bolting AI onto existing teams. They’re rebuilding the workforce around it.
For practitioners, the message is direct. The roles GM is paying for, agent development, model engineering, AI-native workflows, are exactly where large-enterprise demand is moving. “AI-curious” engineers who plug ChatGPT into their workflow aren’t the target. Engineers who can architect autonomous systems, ship production-grade agents, and run data pipelines at scale are.
For IT veterans whose careers were built on enterprise systems integration, traditional cloud migrations, or business-application support, GM’s move is a warning shot. The skill stack is being rewritten in real time, and the companies doing the rewriting aren’t waiting for staff to retrain on their own timelines.
Expect more announcements like this from legacy industrials over the next 12 months. The combination of expiring software headcount, pressure to ship AI features, and a tight market for AI-native talent means swaps, not additions, will be the default playbook.
Full details at TechCrunch AI.