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Pushing back on AI job loss fears, AWS CEO Matt Garman says Amazon plans to hire 11,000 software engineering interns in 2026, a figure in line with recent years

Business Insider Ben Shimkus

Context & Ripple Effects

Amazon has previously kept AWS hiring and data-center buildout moving even while other parts of the company faced hiring freezes and cuts. Earlier coverage also showed engineering talent being steered toward fast-growing businesses including AWS and Alexa.

The 2026 intern plan therefore matters less as a hiring spike than as a signal of continuity: AWS is maintaining a large early-career engineering pipeline while its chief executive discusses AI-driven changes to entry-level work and Amazon pursues major AI investment.

First-order effects

  • Amazon preserves 11,000 software-engineering internship slots for 2026, keeping a substantial near-term entry route into its engineering organization.
  • The announcement directly counters the idea that AI has already led Amazon to materially reduce this particular early-career hiring channel.

Second-order effects

  • Intern candidates retain a major large-company destination despite uncertainty around how AI coding tools may reshape junior engineering work; Amazon still competes for that talent while redefining the work it does.
  • For AWS, maintaining the pipeline supports staffing needs around its continued infrastructure expansion and AI product development, rather than relying solely on experienced hires.

Third-order effects

  • If large cloud providers continue pairing AI capital investment with steady early-career recruiting, AI's labor effect may be job redesign and changed skill requirements before it is a simple collapse in engineering intake.
  • The durable shift is toward engineering organizations that invest simultaneously in compute capacity and in talent able to build, operate, and govern AI-enabled systems; the balance between those inputs remains uncertain.

The trend: This is one data point in AI industrialization: cloud platforms are scaling infrastructure and AI capabilities while adapting—not necessarily eliminating—their early-career technical talent pipelines.