Raghu Raghuram

AWS is betting that AI demand is durable enough to justify $220 billion of 2026 capex, with no slowdown anticipated. Matt Garman argues the risk is tempered by AWS’s diversified customer base—individual-customer concentrations are in the single-digit percentages at the highest—and by production workloads already generating positive ROI. “There’s no bubble in which they stop spending on that.”

AWS is allocating scarce accelerators across the ecosystem rather than selling them all to frontier labs. Although AWS could sell every available chip to a few large labs, it reserves capacity for startups and eventually says yes in some form to roughly 60% of requests it receives, sometimes in another region or configuration. AWS plans to buy 2 million NVIDIA GPUs over the next couple of years, though Garman concedes, “Who knows if that’s enough?”

Agentic workloads push cloud architecture toward fast, potentially disposable, tightly permissioned resources alongside long-lived systems. Agents care about p99.9 latency, three-second database creation, compute sandboxes, gateways and time-boxed permissions rather than inheriting a person’s access. Garman’s core claim is categorical: “Agentic workflows tend to perform better on AWS than anywhere else.”

AWS is removing its traditional setup friction without forcing startups onto a simplified dead end. As the new flow rolls out, an account can be opened with Gmail, without a credit card, and become usable in under 30 seconds while VPC and IAM defaults are handled behind the scenes. Crucially, it remains “a real AWS account,” so customers can later expose the full security and organizational controls without migrating.

AWS’s custom-silicon path runs from Nitro and Graviton to Trainium. Graviton is described as 20% cheaper with 20% better performance, used by something like 90-plus percent of AWS’s top 100 customers; Trainium 3 capacity is sold out potentially through late next year, while Trainium 4 has been announced but not launched. Garman says Trainium may be “the best inference chip on the market right now” on absolute and cost performance.

Enterprise agent adoption is constrained less by model access than by workflow redesign, evaluation and trust. Garman rejects merely copying a human’s five-step process: agents can try 50 approaches in parallel, but enterprises need permissions, guardrails, labeled data, production measurement and drift testing before granting autonomy. AWS’s forward-deployed model aims to teach those skills in 45 days and leave, not create years of consultant dependence.

AWS is positioning Bedrock around enterprise data custody while keeping both proprietary and open-weight model paths available. Garman says prompts and data remain inside the customer’s VPC and never return to the model provider; customers with genuinely proprietary data may post-train or fine-tune open-weights models, potentially distilling them, for better performance at lower cost. The prerequisite is an evaluation system proving that advantage rather than assuming it.

Inside Amazon, agents are already compressing software, product and business workflows—and beginning to reshape team design. Garman says agent-first “frontier teams” manage agents that write all the code, while HR and finance employees build their own agents for planning, tax and compliance work. A capability that formerly required 10 people might now need three or four, creating a new problem: how to maintain products while moving small teams rapidly between projects.