Amazon CEO Andy Jassy discussed Amazon’s chips business on the company’s recent quarterly earnings call. Here’s an excerpt:

Our chips business continues to grow rapidly and is larger than what a lot of folks thought. We saw nearly 40% quarter-over-quarter growth in Q1, and, our annual revenue run rate is now over $20 billion, and growing triple digit percentages year-over-year.

But, this somewhat masks the size. If our chips business was a stand-alone business, and sold chips produced this year to AWS and other third parties (as other leading chips companies do), our annual run rate would be ~$50 billion. As best as we can tell, our custom silicon business is now one of the top three data center chip businesses in the world. And, the speed at which we’ve gotten here is extraordinary.

And, we have momentum. For our custom AI silicon, we’ve recently shared very large, multi-year, multi-gigawatt Trainium commitments from the two leading AI Labs in the world in Anthropic and OpenAI, as well as an increasing number of companies like Uber betting on Trainium. And we now have over $225 billion in revenue commitments for Trainium. Our Trainium2 chip has about 30% better price-performance than comparable GPUs, and has largely sold out. Trainium3, which just started shipping at the start of 2026 and is 30-40% more price-performant than Trainium2, is nearly fully subscribed. And, much of Trainium4, which is still about 18 months from broad availability, has already been reserved. Amazon Bedrock, which is used expansively by over 125,000 customers, runs most of its inference on Trainium, and almost 80% of Fortune 100 companies are using Bedrock.

We also just announced that Meta has committed to using tens of millions of Graviton cores (Graviton is our industry-leading CPU chip), which allows Meta to run the CPU-intensive workloads behind agentic AI with the performance and efficiency they need at their scale.

AI is commonly seen as a GPU story, but the rise of agentic workloads—real-time reasoning, code generation, reinforcement learning, and multi-step task orchestration—is driving massive CPU demand as well. As AI systems shift from answering questions to taking actions, and as post-training and inference scale up, the compute required pulls heavily on CPUs. That’s why Meta chose Graviton, which delivers up to 40% better price-performance than other x86 processors, and is now used by 98% of the top 1,000 EC2 customers.

Nobody has a better set of chips across AI and CPU workloads than AWS with Trainium and Graviton, and we’re unusually well-positioned for this AI inflection we’re in the early stages of experiencing.