Advanced Micro Devices Goldman Sachs Communacopia + Technology Conference 2026

Thank you, Jim. Thank you.

I can jump with that. Look, it's a great question, and I think that when I think about our journey, it's very similar to a number of enterprises. Our team started out, I think it's a multilayer approach to the infrastructure, right? We started out first and foremost with the data layer and optimized that. We talked to a lot of enterprise customers and this is often overlooked, is how you structure your data such that you can actually employ agents effectively. We actually open sourced our solution. It's called Optima. We started there, and then we've been on this journey about four agents driving what I would call automation for efficiency, and that's gone very well. Now, where I would say is we're really in the domain-specific type applications, right? If you think about it, for us, domain-specific is EDA.

We're seeing a tremendous amount of upside across coding, debug, and those two key areas along with kernel development and just software development in general. Very strong returns there. Then, of course, across all of the businesses, we're seeing very strong automation and efficiencies across each of the business. I would say that, and it's interesting because we were in New York City last week and I was with our CIO, and we had a roundtable with a number of top enterprise customers in New York City, and he started out and just walked them through the journey. It was a very good conversation about where each one of them are on this journey. I would say that we're advanced in this area. I would say that we took it on very aggressively.

We're also looking at how you balance token costs with the value, and we're really doing some advanced things across that too. So overall, very strong adoption. You got to look at us as both a provider and a major adopter of AI.

Yeah. Jim, the only thing I would add there is you guys saw us work with a big framework that we put together with Anthropic about obviously them buying up to 2 gigawatts worth of MI450. But there's also a lot of work of not just the OpenAI tools, but the Anthropic Claude tools being adopted across our engineering organizations and unlocking a much faster flywheel of software development, debug, time to production of chip programs, optimizing where our software people are spending their time. We have a huge software organization and trying to figure out what they need to be working on, where can they use tools to accelerate that flywheel versus doing anything manual. My boss, Gene, our CFO, has benchmarked us versus a whole bunch of leading semis and tech companies, and I think we're on the bleeding edge of AI adoption internally.

It's come with an increased token cost, but it's come with an even a much greater productivity gain across the organization, and it's allowed us. I think you'll see it allow us to bring hardware and software products to market much more quickly as we go forward.

Yeah. Actually, just one last point. I want to just emphasize that, right? So you've got domain specific, and then you've got what I would call general IT automation. One's for efficiency but when you can drive a faster time to market, that's where the real rubber hits the road. That's what we're after. As we go to external enterprises, our goal is to get them time to value very quickly, right? With ROCm and with some of our solutions. So again, we always say we eat our own dog food, right? Everything we build is deployed in our data centers first. It's going very well in terms of driving our time to market with our engineering teams.

You want to start or I can?

Yeah. I think, Jim, the answer is yes. It's not a very helpful answer, but it actually is the answer. Our goal is to make sure that our combination of CPU and GPU roadmaps are very differentiated in terms of driving tokens for dollar outcomes, regardless of whether it's open-weight models, frontier models for our largest customers. I think those obviously the industry is evolving quickly around what are the right use cases. How should we say this? How to apply the right tokens to the right problem relative to the cost of the token versus the return of the token. That's a very large continuum.

I think our goal is to make sure that on the GPU side, our hardware and software are driving the right efficiencies, regardless of whether it's open-weight models or our closed-weight models or our Frontier models, and that Dan's business is the right CPU to run agents to drive all of those models, regardless of where they come from.