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May 11, 2026 • Kirsten Korosec

GM Lays Off 600 IT Workers to Hire AI-Native Engineers

GM Lays Off 600 IT Workers to Hire AI-Native Engineers GM Lays Off 600 IT Workers to Hire AI-Native Engineers GM Lays Off 600 IT Workers to Hire AI-Native Engineers

GM just laid off hundreds of IT workers to hire those with stronger AI skills

Strategic Workforce Restructuring

General Motors has laid off more than 600 salaried employees (over 10% of its IT department) in what the company frames as a deliberate skills swap: clearing out workers whose expertise no longer fits and making room for AI-focused talent.

Key Details

  • Not Just Layoffs: These are not all permanent headcount reductions. GM is simultaneously hiring for new IT roles, but with dramatically different skill requirements.

  • AI-First Skills in Demand: The most sought-after capabilities include:

    • AI-native development
    • Data engineering and analytics
    • Cloud-based engineering
    • Agent and model development
    • Prompt engineering
    • New AI workflows

  • Building from the Ground Up: GM is specifically looking for people who know how to build with AI from the ground up — designing the systems, training the models, and engineering the pipelines — not just using AI as a productivity tool.

Context: Ongoing Transformation

Recent Leadership Changes

  • Sterling Anderson (Aurora co-founder, AV industry veteran) hired as Chief Product Officer in May 2025
  • November 2025: Three top executives left the software team as Anderson pushed to consolidate GM's disparate technology businesses
  • New AI Hires: Behrad Toghi (ex-Apple) as AI lead; Rashed Haq (ex-Cruise) as VP of autonomous vehicles

Previous Layoffs

  • August 2024: Cut approximately 1,000 software workers
  • Past 18 months: Multiple rounds of white-collar layoffs across departments as GM focuses resources on high-priority initiatives, including AI

Industry Signal

What This Means for Enterprise AI: GM's restructuring demonstrates what enterprise AI adoption actually looks like in practice:

  • Not just adding AI tools on top of existing teams
  • Deliberately rebuilding the workforce from the ground up
  • Specific capabilities (agent development, model engineering, AI-native workflows) point to where large-enterprise demand is heading

This is a clear example of skills becoming the new job security — and legacy expertise becoming a liability when the technology stack fundamentally shifts.