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TBPN · 4 min · Jul 23, 2026

Different workloads need different kinds of compute

Lisa Su argues that no single chip fits every workload, so AMD builds across CPUs, GPUs and other systems. She also sees local AI and accessible hardware as ways to broaden adoption.

Lisa Su

Lisa Su is chair and CEO of AMD.

  • John Coogan, Host · Co-host, TBPN

    Co-founded Soylent and Lucy; OpenAI bought his show TBPN in April 2026.

  • Jordi Hays, Host · Co-host, TBPN

    Founded Branded Native; co-founded fintech Capital, which Rho bought.

About this segment

Lisa Su argues that no single chip fits every workload, so AMD builds across CPUs, GPUs and other systems. She also sees local AI and accessible hardware as ways to broaden adoption.

From “AMD CEO Lisa Su Live on TBPN”, Jul 23, 2026. Watch the full episode

“we fundamentally believe that there is no one size fits all. Like, the world is a heterogeneous world.”
Lisa Suat 1:41:36 · TBPN1:41:36

Takeaways

  1. AMD sees computing as heterogeneous, not one-size-fits-all.
  2. Its CPU, GPU and FPGA portfolio supports different workloads.
  3. Local AI and low-barrier hardware help more people experiment.

In this episode

Full episode
  1. 39:05Most AI compute is lost to poor scheduling and low utilization3:54
  2. 1:37:06Claude is helping AMD accelerate hardware development3:59

  3. 1:40:58Different workloads need different kinds of compute4:23

  4. 5 more moments in the full episode
Up next · Lex Fridman PodcastEmperors stayed honest because nearly half were overthrown5:39

Transcript

From the transcript TBPN publishes with the episode.

  1. 1:41:02

    Well, I you know, I think the the interesting thing is, you know, every so often, you hear, like, this is a killer chip. Yeah. Right? You guys have heard, like, GPUs on the model side too. GPUs are gonna take over the world. Yep. Like Everything's gonna be CPUs are dead. Yeah. Like, we're gonna move every single workload Yeah. Yeah. Over. And the world just doesn't work like that. Yeah. And so our thought process has always been, you know, people asked me very early on, hey, Lisa, why don't you just focus? Like, you decide. Like, whether it's CPUs or GPUs, why do you need both? And, you know, I I think about each one of these questions deeply and, you know, we fundamentally believe that there is no one size fits all. Like, the world is a heterogeneous world.

  2. 1:41:46

    Mhmm. Like, you are different than I am. We have different needs. We have different workloads. Our businesses are different. You're gonna need different compute. And I think the portfolio that we have, you know, CPUs, GPUs, we acquired Xilinx, so we brought the physical AI component in there. We have a rich, you know, PC, you know, ecosystem. I think allows you to truly pick the right compute. Mhmm.

  3. 1:42:11

    Now, it's a little bit harder. Right? We have a tremendous number of r and d priorities. But I think we've done it in a very, know, kind of, I wanna say smart way in the sense that each part of our product portfolio builds on each other. Mhmm. Like, we talk about, you know, triplets being a big thing that we do. We do that across a portfolio. So we do that across our CPU portfolio, our GPU portfolio, and it it just is an example of how we're able to leverage getting the right compute for the right workload.

  4. 1:42:45

    Yeah. Talk about, the other slice of the business, the other segmentation between, consumer, prosumer, enterprise. How valuable how important is it to, have a clear chain of products? In particular with, talent, it feels like, a lot of folks will start on a consumer rig, they might build some small model, or they might be doing CGI work or something else, and they're using a Threadripper to render something locally. And then eventually, they move to the cloud, and eventually, they start working in an enterprise.

  5. 1:43:23

    How how did how is that going to evolve? I think it is extremely important Mhmm. Like you said, because people access technology in different ways. Yeah. You know, not everyone is going to a, you know, big cloud instance Yeah. To experience AI. And so, you know, the fact that we do have a consumer road map, a prosumer road map, you know, Ryzen, our Radeon, you know, frankly, we're putting a lot more emphasis on, you know, some of these other ways to access, you know, AI. So we made a big investment in AI PCs. I still believe that local AI is going to be one of, you know, the key enablers to truly get all of the tokens that you need in different places. So our Rise and AI Max portfolio, I think physical AI is another place where, you know, we just launched these, you know, small robotics form factors so that people can can experiment with it. So I I I do think it's quite important

  6. 1:44:24

    Yeah. To have, let's call it, low barrier of entry. Like, I want people to experience AMD Yeah. And have a wonderful experience. And Yeah. Hey, they may end up, you know, being in, you know, the largest frontier model companies or, you know, frankly, the amount of startups that are doing just amazing technology is is fantastic, and and we wanna support those as well.

  7. 1:44:46

    What? How do you on that, how do you think about long shot r and d? You have your existing roadmap, but we talk to new chip companies very frequently on the It feels like every week there's somebody new that has a billion dollars and they've they're onto something and they're and I can't imagine you haven't thought of it, or at least the AMD team hasn't at least thought of it. And so, how do you think about how do you think about some of these investments in r and d that that are maybe like higher risk, but high potential over the long run?