An Interview with OpenAI CEO Sam Altman and AWS CEO Matt Garman About Bedrock Managed Agents

Listen to this post: Good morning, As I noted yesterday, today’s Stratechery Interview is early in terms of my timing — Tuesday instead of Thursday — and late in terms of delivery — 1pm Eastern instead of 6am — because the topic was embargoed. That embargo created a bit of a weird situation for me over the last several days: - Last Friday I conducted the following interview with OpenAI CEO Sam Altman and AWS CEO Matt Garman about Bedrock Managed Agents, powered by OpenAI; naturally, one of my questions was about how this fit in with OpenAI’s deal with Microsoft giving Azure exclusive access to OpenAI models. - Late Sunday I heard through the grapevine that Microsoft would announce something Monday morning; I wondered if it might be a preemptive lawsuit! - On Monday Microsoft and OpenAI announced they had amended their agreement, allowing OpenAI to serve its products on other cloud providers, including AWS. So here we are. I think the Microsoft-OpenAI deal makes a lot of sense for both sides. Here are the bullet points of the new arrangement from Microsoft’s post: - Microsoft remains OpenAI’s primary cloud partner, and OpenAI products will ship first on Azure, unless Microsoft cannot and chooses not to support the necessary capabilities. OpenAI can now serve all its products to customers across any cloud provider. - Microsoft will continue to have a license to OpenAI IP for models and products through 2032. Microsoft’s license will now be non-exclusive. - Microsoft will no longer pay a revenue share to OpenAI. - Revenue share payments from OpenAI to Microsoft continue through 2030, independent of OpenAI’s technology progress, at the same percentage but subject to a total cap. - Microsoft continues to participate directly in OpenAI’s growth as a major shareholder. I think the most important point is the last one. Azure had a real competitive advantage thanks to being the only hyperscaler able to offer OpenAI models, but this also hindered OpenAI, particularly once it became clear that many enterprises cared first and foremost about accessing models on their current cloud of choice; I’ve been noting for a while that this was a real competitive advantage for Anthropic. In other words, Azure’s exclusivity was actively damaging Microsoft’s investment in OpenAI, and given Anthropic’s rapid growth this year, Microsoft needed to tend to their investment, even if it diminished Azure’s differentiation. OpenAI, meanwhile, clearly sees AWS as a massive opportunity — so much so that they are forgoing Azure-related revenue for the next few years (which, per the previous point, will help Azure management feel better about losing their exclusivity; their PnL is going to look a lot better without paying a revenue share to OpenAI). OpenAI is also releasing Microsoft from the AGI clause; now the agreement between the two companies will run through 2032 no matter what. What does seem clear is that OpenAI’s focus is going to be on AWS, and the greatest evidence in that regard is the topic of this interview: Bedrock Managed Agents, powered by OpenAI. The easiest way to think about this offering is Codex in AWS; a lot of what makes Codex work is the fact that it is local, which gives you a lot of complexity, particularly in terms of security, for free. It’s another thing entirely to figure out how to make agents work across an organization, and the goal of this offering is to make these workflows much more accessible for organizations who already have most of their data in AWS. To that end, in this interview, we discuss how AWS created the entire cloud category, and the impact it had on startups, and how AI is both similar and different to that previous paradigm shift. Then we discuss Bedrock Managed Agents, what it is, and how it differs from Amazon’s existing AgentCore offering. We also touch on Trainium and why chips won’t matter to most AI users, and why partnering makes sense relative to Google’s focus on full integration. As a reminder, all Stratechery content, including interviews, is available as a podcast; click the link at the top of this email to add Stratechery to your podcast player. On to the Interview: This interview is lightly edited for clarity. Topics: AWS and Startups | Bedrock Managed Agents | Local vs. Cloud | AgentCore vs. Managed Agents | Trainium | Customer Demand | Building the AI StackMatt Garman and Sam Altman — well Matt, welcome to Stratechery — and Sam, welcome back [I previously interviewed Altman in October 2025, March 2025, and February 2023]. Sam Altman: Thank you. Matt Garman: Thank you, thanks for having me. So Matt, this is your first time on Stratechery. Alas, I think that Sam’s presence is going to preclude the usual getting to know you section. Besides, he doesn’t want to hear us reminisce about our times at Kellogg Business School, but it is good to have a fellow alumnus on the podcast. MG: Yeah, I’m happy to be here. I’ll come back another time and we can do a little deeper dive. That’d be great. You’ve been working on AWS since you were an intern, and you’re now in charge of the entire organization during this AI wave. What aspects of building the AI business are the same as building the original commodity compute business, for lack of a better term, and what aspects are really different? MG: I think that the parts that are the same are that I see that same excitement and builders out there being able to do things that they were never able to do before, and one of the cool things is when we first started AWS, is developers all of a sudden could get their hands on infrastructure that was only available to the largest companies who had millions of dollars to go build data centers. With a credit card and a couple of dollars, they could spin up applications and it really exploded what was possible for people building out there on the Internet. We kind of took the idea that people could build whatever they want and we weren’t going to presuppose what they should do and that the creativity of the world out there was, if we could put powerful tools in front of them, they’d build interesting and amazing things. I think this is as much, if not more, transformational to what it’s enabling builders out there to do. As you think about what’s possible, you don’t have to have gone to school and learned for 10 years to code in order to go build an application, you don’t have to have huge teams of hundreds of people and months and months and months of time to go build things. You can build things with small teams, you can build it fast and you can iterate quickly, and AI is unlocking all sorts of innovation across every different aspect of the world. I think in many ways that’s very similar, and it’s super exciting to see what it’s enabling from the customer base out there. There was a bit, though, when AWS came along, you were the only one, so you get all the upsides and downsides and everything sort of for free. Is there a bit where it felt like in the AWS era, there’s a lot about commodity compute, making it fungible, elastic, cheap — in AI, particularly in training, it feels like the winning abstraction was more about these really vertically integrated super clusters, really advanced networking, and really tight linkages between software and hardware. Was that sort of a surprise for you, where you’re coming at it now — instead of fresh, “We’re the only ones here, we had a particular way of looking at large-scale compute”, and at least for the first few years of AI, it maybe didn’t perfectly align? MG: I don’t know that it was different for us. I think for what was different though, is just the incredible rapid scale of adoption, and I think that that’s probably surprised everybody. Sam, you can weigh in different if you disagree, but just the speed of adoption and how fast people have grabbed onto the capabilities there, I think has surprised everyone. It’s different if you go to the, when we started cloud computing, it took us a really long…