Dan Fu

Assistant Professor, UC San Diego · 1 appearance on the record.

computed by AI from the episodes · how this works → · full disclaimer →

academicscientistengineerexecutive@realDanFu ↗danfu.org ↗

Dan Fu is an Assistant Professor of Computer Science and Engineering at UC San Diego and VP of Kernels at Together AI. He completed his Ph.D. at Stanford University and focuses on hardware-aware machine learning systems, efficient sequence architectures, and GPU performance engineering.

12statements → 8claims → 2claims resolved → 3.75/5average certainty → 2.42/5average debate potential → ≈4.5/5argument clarity, estimated →

1 supported 1 partly supported 0 contradicted 6 not checkable as stated how the 8 claims stand · each chip opens the sources

2 predictions · 6 assertions · 1 opinion · 3 insights · every statement was checked. The predictions and assertions are the 8 claims: statements the public record can support or contradict. 2 are resolved, and 6 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Dan argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

Their most notable supported claim

Assertion Supported

Poolside and Reflection are building clusters with massive B200 GPU deployments

“They're companies like Poolside. They're building out tens of thousands of B-two hundred, GB-two hundred chips. You know, there's other folks like Reflection who are who are building out. Tens of thousands of B 200 chips.”

Dan Fu Jan 22, 2026 ▶ 19:13 The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI)

Expressed certainty vs assessment result

none yet certainty 1

none yet certainty 2

50% certainty 3

100% certainty 4

none yet certainty 5

weighted support: a fully supported claim counts one, a partly supported claim counts half. Each filled bar is clickable and opens exactly those claims; "none yet" means nothing said at that certainty level has resolved yet

How they sound: speaking style how? →

263 words/min while actually speaking · 33.7 um and uh per 1k words

No argument clarity score for Dan Fu: only 7 usable question→answer exchanges on raw tape (a fair score needs 8+). We do not score a sample that small. Roundtable and news formats yield far fewer direct exchanges than interviews. Their coarse estimate from 7 raw tape exchanges is ≈4.5/5, shown at half point precision because the sample is small.

Measured by listening to the audio itself: 6,411 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

What they said12 Episodes1

Everything Dan Fu said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Opinion

Fu: Current LLMs meet the definition of AGI from 5-10 years ago

“By almost any definition anyone could have written down, let's say five years ago or 10 years ago, certainly when, you know, Tim, you and I started our PhD. We basically have the vision of AGI that, that we had back then. We have things that can write code. Th…”

Dan Fu Jan 22, 2026 ▶ 4:14 The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI)

Prediction Not checkable as stated

Fu: Next-generation models currently in training will achieve AGI

“You know, we maybe already have AGI or like some form of AGI. And if not, then certainly the next generation of models, the models that today are training already. If they're at all better than what we have today, then we're, we we've already hit something tha…”

Dan Fu Jan 22, 2026 ▶ 5:17 The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI)

Assertion Not checkable as stated

Fu: AI coding tools enable expert programmers to move 10x faster

“But if you give an expert programmer This set of tools, they can go 10, 10 times faster than they were able to go before.”

Dan Fu Jan 22, 2026 ▶ 34:41 The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI)

Assertion Partly supported

Dan Fu: DeepSeek-V3 was trained on ~2,000 H800s with 20% MFU

“If you look at the deep seek model, for instance, this is one of the best open source models we have out there today. It was trained at the end of 2024. On last generation, kind of nerfed GPUs, H 800 instead of H 100, the 800 is nerfed by all sorts of ways fro…”

Dan Fu Jan 22, 2026 ▶ 17:26 The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI)

Insight

Dan Fu: Deployed AI models lag cluster infrastructure by 1–2 years

“The models that we see today that we can play with today have been pre-trained on clusters that were built out a year or two ago. Because, you know, you need enough time to get the cluster running. You need enough time to do the large pre-training run. And the…”

Dan Fu Jan 22, 2026 ▶ 21:26 The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI)

Assertion Not checkable as stated

Fu: Hardware utilization during AI inference is under 5%

“At inference time, when the, when you have the model, when it's already been trained, already been post-trained, the hardware utilization is like less than five percent.”

Dan Fu Jan 22, 2026 ▶ 55:13 The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI)

Assertion Not checkable as stated

Dan Fu: Chinese AI labs take more architectural risks

“I think you see a lot more risk taking out of the Chinese labs where you're trying to differentiate the next model of your next open source model.”

Dan Fu Jan 22, 2026 ▶ 1:03:19 The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI)

Prediction Not checkable as stated

Fu predicts increasing hardware diversity, particularly for AI model inference

“I'm sure NVIDIA will still do great and still grow beyond their five trillion dollar company or whatever it is at the time of recording. But I think you're going to see a lot more diversity especially around, I think inference of the model.”

Dan Fu Jan 22, 2026 ▶ 31:39 The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI)

Insight

Dan Fu: Junior engineers using AI agents communicate better and level up faster

“When they are really gung ho about understanding and being able to use the AI agents, there's, they're able to communicate so much better than in the olden days. They're able to level up their level of understanding a lot faster.”

Dan Fu Jan 22, 2026 ▶ 49:34 The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI)

Insight

Dan Fu: Proper speculative decoding yields 2x to 3x model speedups

“So if you do the speculative decoding right, you can get, again, two X, three X speed ups over, over, you know, just running a vanilla model.”

Dan Fu Jan 22, 2026 ▶ 57:34 The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI)

Assertion Not checkable as stated

Dan Fu: Some top audio models use state space architectures

“So some of the best audio models in the world are at least partially based on state space models.”

Dan Fu Jan 22, 2026 ▶ 1:02:29 The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI)

Assertion Supported

Poolside and Reflection are building clusters with massive B200 GPU deployments

Dan Fu Jan 22, 2026 ▶ 19:13 The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI)

Appearances (1)

Episode	Date	Speaking time
The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI	Jan 22, 2026	28m

Dan Fu

The record, in short

Their most notable supported claim

Expressed certainty vs assessment result

How they sound: speaking style how? →

Appearances (1)

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