THE TECHNOLOGY INTERVIEW
T hree years ago, AWS made decisions about power procurement that will determine what the company can offer customers in 2027. Not about which features to build or which markets to enter, but about megawatts: how many to procure, where to locate them and how much computing infrastructure those megawatts could support. Get it wrong, and AWS turns away customers. Get it right, and the company maintains its lead in the cloud computing market.
“ We had to make decisions three years ago about the power we’ re going to have in’ 26 and‘ 27,” says David Brown, who runs all compute and machine learning services at AWS.“ Amazon. com is a supply chain company, and we brought a lot of that into our DNA.”
David’ s responsibilities span EC2, containers, Lambda, Bedrock and SageMaker – the foundational layer that determines whether AI workloads run efficiently or expensively. During re: Invent week in Las Vegas, where AWS announced both its Trainium3 accelerator and Graviton5 processor, Technology Magazine spoke with David on chip development timelines, the physics constraints of distributing training clusters and why controlling the full stack from chip design through data centre deployment creates advantages that merchant silicon alone cannot deliver.
From announcement to deployment in 10 months Project Rainier shows how quickly AWS can move custom silicon from announcement to production. The company revealed the project in December 2024 at re: Invent. By October 2025, the infrastructure was running. The facility now houses more than 500,000 Trainium 2 chips and will hit one million by year end.
David says execution speed matters as much as scale.“ This is very different to some of the other announcements you’ ve seen where it’ s much longer term.”
28 January 2026