Amazon’s Decade-Long AI Journey: How Infrastructure-First Became the Winning Strategy

Amazon’s AI reorg didn’t happen overnight. It’s the culmination of a decade-long journey: AWS (2006) to Annapurna Labs (2015) to Inferentia (2018) to Trainium (2020) to Bedrock (2023) to Nova (2024). Each layer built the foundation for the next.

The Infrastructure-First Ascent

Amazon’s pattern is distinctive: they built infrastructure first, then moved up the stack.

2006: AWS launches – Cloud infrastructure becomes Amazon’s second business

2015: Annapurna Labs acquired – Custom silicon capability comes in-house

2018: Inferentia launches – First inference chip for ML workloads

2020: Trainium announced – Training chips to compete with NVIDIA

2023: Bedrock launches – Model marketplace, platform play

2024: Nova revealed – Proprietary frontier models

2025: DeSantis unification – Full stack under one leader

Why Sequence Matters

Most AI companies started with models and are now scrambling for infrastructure. Amazon did the opposite: infrastructure first, then inference chips, then training chips, then model marketplace, then proprietary models.

This sequence provides: lower costs due to scale, better latency and performance, existing enterprise relationships, and no infrastructure build costs when launching new capabilities.

The Compounding Effect

Each layer creates advantages for the next. Trainium is optimized for Nova. Nova is optimized for AWS. AWS has the enterprise relationships. The whole stack reinforces itself in ways competitors can’t replicate by acquiring pieces.

Key Takeaway

As Enterprise AI transforms from software to substrate, Amazon’s decade of infrastructure investment becomes their moat. You can’t compress ten years of compounding into a sprint.

Source: Amazon’s AI Superstructure on The Business Engineer