The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI)
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What is the episode’s introduction and the main question about AGI?
We have maxed out the features.
How do Tim Dettmers and Dan Fu’s essays present opposing views on whether AGI will happen?
We have maxed out the hardware. That's what we get.
By almost any definition, anyone could have written down, let's say, five years ago or 10 years ago.
Why does Tim argue that physical limits and memory‑movement will halt GPU scaling?
We basically have the vision of AGI that we had back then.
How does Dan counter Tim’s claim by pointing to utilization headroom and new hardware?
Everything that grows exponential will level off because if you need resources, the resources will be exhausted. You can see up to two orders of magnitude more compute available, 100x more compute.
If you don't know how to use agents well, you will be left
behind. How far can you push it? I think it's never been a more exciting time to work in AI.
Hi, I'm Matt Turk. Welcome to the Matt Podcast. Today, we have a special reality check on AGI with two guests who are very close to the computational reality of AI, Tim Detmers of AI2 and Dan Fu of Together AI. In this episode, Tim argues that we are hitting diminishing returns and running into hard physical constraints, while Dan argues that we are still leaving huge performance on the table. and that today's models are lagging indicators of hardware progress. Then we shift into a fun, practical discussion on how to use agents and what to expect from AI in 2026. Please enjoy this fun conversation with Tim and Dan. Tim and Dan, welcome. Thanks for
having
us. So Tim, a few weeks ago, you wrote a great provocative blog post entitled, Why AGI Will Not Happen. And then Dan, a few days later, you replied with your own blog post, equally fascinating, entitled, Yes, AGI Will Happen. I'd love to go into your backgrounds. You both have the very interesting characteristic of having a foot in industry and a foot in academia. So Tim, if you want to start with yours.
I'm an assistant professor at Carnegie Mellon University, machine learning and computer science department, and also research scientist at the Allen Institute for AI. My past research has been mostly on efficient deep learning, quantization, that means model compression. Take large models, compress them down from like 16-bit to something like 4-bit. Key research has been there. Eulora, for example, it's a very efficient fine-tuning, compressed to 4-bit, use adapters on the model, and then use up to 16 times less memory than if you have dense . And now I'm working on coding agents. And then we have a very exciting release in about two weeks. I'm set of the out agents. You can quickly specialize to private data, get strong performance on any code base that you like.
And yeah, that's very exciting.
Okay.
Dan? Hey, so I'm an assistant professor at UC San Diego and also my total VP of kernels at Together AI. So in industry, I focus a lot on basically making models go fast. So GPU kernels are the things that actually translate the models to how they run on the GPU. You can think of them as basically specialized GPU programs. A lot of my research, my PhD in my lab focused on that. So I developed things like flash attack which was a efficient kernel for one of the core operations of a lot of the language models that we use today. I also did research on sort of alternative architectures to transformers, things like state-to-state models and things like that. And together, I'm really focused on how do you make the best language models that we have today, how do we make them go faster?
I think, you know, as of this morning's recording, we actually just released a blog post with Cursor about how we accelerated a bunch of their models and helped them launch Composer 2.0 on NVIDIA's Blackwell GPU. So that's a bit of a flavor of what I do.
So let's get into this AGI discussion. And then in the second part of this conversation, we'll talk about agents and coding agents and your thoughts there, because I want to make sure we cover that. AGI, obviously, it's a term that everybody uses. And I think we can all agree that nobody really knows what that means. But for purposes of this discussion, what is a useful definition of AGI from your perspective?
Sure. Yeah, I think so. One of the things that we kind of discuss back and forth in this set of blog posts is sort of what AGI means.
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Chapters
8 chapters
1
What is the episode’s introduction and the main question about AGI?
0:00–0:01
2
How do Tim Dettmers and Dan Fu’s essays present opposing views on whether AGI will happen?
0:01–0:08
3
Why does Tim argue that physical limits and memory‑movement will halt GPU scaling?
0:08–0:11
4
How does Dan counter Tim’s claim by pointing to utilization headroom and new hardware?
0:11–33:19
5
Did agents already cross the “software singularity” threshold for writing GPU kernels?
33:19–39:10
6
What practical steps can non‑coders take to automate their work with AI agents?
39:10–58:40
7
What are the key predictions for 2026 regarding hardware diversification, small specialized models, and inference efficiency?
58:40–1:01:46
8
How will AI architectures evolve beyond transformers, such as state‑space and other emerging models?
1:01:46–1:04:06
Speakers
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