Who is on this subjectordered by the date of their first quote here
4 of 5 lanes rest on fewer than 5 quotes and are marked thin. Offsets are days from the middle first-quote date, 27 Aug 2024 — a date, and nothing else. It is not a claim about who reached a view first.
- Dylan Patel 20 2 Feb 2023 – 17 Aug 2026 -572 d
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Jensen Huang
1
thin
21 May 2024 – 21 May 2024
-98 d
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Gavin Baker
3
thin
27 Aug 2024 – 20 May 2026
+0 d
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Brad Gerstner
1
thin
25 Sep 2024 – 25 Sep 2024
+29 d
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David Friedberg
1
thin
14 Nov 2025 – 14 Nov 2025
+444 d
The chronologysourced and dated, oldest first
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Dylan Patel
Patel says NVIDIA GPU bandwidth increased less than 10x while FLOPS increased 100x from 2016 to 2023.
“The bandwidth has not even gone up one order of magnitude. Right? Whereas flops have gone up two orders of magnitude. So so less than one order of magnitude versus two orders of magnitude increase.”
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Dylan Patel
Patel states memory cost has quadrupled in NVIDIA GPUs while per-gigabyte pricing remained flat since 2016.
“And now with the h 100, they have 96 or 80 gigabytes of memory, but the cost per gigabyte is the same.”
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Dylan Patel
Patel says GPU orders take four to six months from placement to data center installation.
“call it four or five five, six months between, you know, when an order is placed and you can actually have it installed in your data center if it got worked on immediately.”
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Jensen Huang
Huang explains CUDA crushed NVIDIA's gross margins because it added cost without applications to justify premium pricing.
“And if people aren't willing to pay you for it but your cost went up, then your gross margins get crushed”
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Gavin Baker
Baker says GPUs got 50x faster while data centers only 4-5x faster, creating investment opportunity in infrastructure.
“GPUs have gotten 50 times faster, and the rest of the data center has only gotten four to five times faster.”
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Gavin Baker
Baker explains GPUs got 50x faster while data center infrastructure only got 4-5x faster, causing low utilization.
“GPUs have gotten 50 times faster, and the rest of the data center has only gotten four to five times faster. And that is why MFU is so low.”
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Brad Gerstner
Gerstner frames NVIDIA debate as 6 million GPUs versus bearish 4.5 million estimate for next year.
“We're kind of at 6,000,000 GPUs for next year. The bearish people are at like 4,500,000. The numbers will ultimately tell.”
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Dylan Patel
Patel suggests China could centralize all GPUs under one entity if Xi Jinping becomes scale-pilled, avoiding US data center constraints.
“If you're Xi Jinping and you're scale pilled, do you just say x y z company is now in charge Yeah. And every GPU goes to one place.”
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Dylan Patel
Patel argues export controls aim to limit AI usage scale in China, not prevent AGI development.
“And I think that is a much easier goal to achieve than trying to debate on what AGI is.”
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Dylan Patel
Patel says NVIDIA made 4 million GPUs last year and will produce 7 million this year.
“Nvidia made over 4,000,000 GPUs last year, they're making over 7,000,000 this year, right? High end data center GPUs.”
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Dylan Patel
Patel reports China imported one million H20 GPUs in recent quarters, enough for largest cluster.
“Just in Q3, Q4 and the early part of Q1 this year, they imported a million H20s, Right?”
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Dylan Patel
Patel explains AlexNet was the first recognizable AI model, trained on NVIDIA GPUs because CPUs were too slow.
“This is because, well, CPUs were really slow because they're really single focused on a single task. GPUs are focused on doing many tasks at once.”
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Dylan Patel
Patel reveals DeepSeek inference implementation requires 160 GPUs worth over $10 million of hardware per replica.
“That's over $10,000,000 of hardware, and then that's just one replica, then you'll have a lot of replicas and you share the caching servers between them.”
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Dylan Patel
Patel says OpenAI and Meta run NVIDIA GPUs at lower power to fit 10% more chips despite worse TCO.
“Even though it's terrible on a TCO basis, they were able to get, you know, 10% more GPUs in, and it's great. And Meta has done similar.”
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David Friedberg
Friedberg reports seven and eight year old TPUs and GPUs are still at 100% utilization in data centers.
“I've... I I made some calls and I checked around with some other friends, and everyone says the same thing, that these seven and eight year old TPUs and GPUs that are sitting in the data centers are still being used, and they're being used at a 100% utilization.”
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Dylan Patel
Patel says GPT-5 pre-training uses fewer than 100,000 GPUs despite OpenAI having over 500,000 GPUs for R&D.
“Right? Like like, g p d five, not even a 100 k GPU pre training scale.”
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Dylan Patel
Patel reports 10-15% of NVIDIA GPUs fail and require RMA within first two weeks of cluster deployment.
“When you first turn on the cluster, about ten to fifteen percent of them fail RMA in the first two weeks. Wow. And then that's fine. Like you have to receipt them, whatever.”
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Dylan Patel
Patel says 10-15% of NVIDIA GPUs fail and need RMA in the first two weeks after cluster deployment.
“When you first turn on the cluster, about ten to fifteen percent of them fail RMA in the first two weeks. Wow. And then that's fine.”
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“We try and track the entire supply chain from tools that manufacture chips, fabs, data centers, energy, industrials, and then AI models and who's using them, how much, and where.”
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Gavin Baker
Baker predicts disaggregation extends GPU lives to 10-15 years, lowering financing costs and saving private credit.
“This is going to be really good for the whole private credit industry. It's going to help finance the AI build out.”
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Dylan Patel
Patel reports Trainium rents for under $10 billion per gigawatt while GPUs cost $12-13 billion.
“Tranium sells at sub $10,000,000,000 per gigawatt rental rate to Anthropic and to OpenAI. GPUs, at least before the craziness of the last six months, usually went around 12 to $13,000,000,000 per gigawatt.”
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“AMD's announcing it in a few weeks and it's very exciting. It's 72 GPUs. It's got more memory than Vera Rubin. It's got more more, memory, flops.”
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“It's 72 GPUs. It's got more memory than Vera Rubin. It's got more more, memory, flops. It's got it's it's better than Vera Rubin in most every way. It's a little bit later.”
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Dylan Patel
Patel says AMD MI450X has 72 GPUs, more memory and flops than Vera Rubin, launching three to six months later.
“It's got more more, memory, flops. It's got it's it's better than Vera Rubin in most every way. It's a little bit later. I mean, three to six months after,”
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Dylan Patel
Patel reveals SemiAnalysis operates over $80 million in compute for daily automated benchmarking across all major AI chips.
“Every day it runs on an automated CI, and we run it on all the latest Chinese models from GLM, Zebu, Moonshot, Kimi, Alibaba, all these models we run.”
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Dylan Patel
Patel argues silicon supply can be optimized for either high throughput or high interactivity use cases.
“Supply of silicon can go many ways. You can either leverage it to high throughput things or high interactivity things.”
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