The Ledger, every show

“You don't talk about the field failure rate of your products, which are massive. Rather you sort of keep talking about the future in an effort to convince people to wait, to make a good decision rather than go with technology that's, that that's better and pre…”

“And after we did it Elon tried Dojo and they failed.”

“In inference, it's not real at all. There's no CUDA lock-in in inference. None. Well, you can move from OpenAI on an NVIDIA GPU to Cerebrus to Fireworks Service on something else to Together to perplexity with 10 keystrokes. I mean, anybody who actually uses A…”

“I think two years ago every state of the art model was trained in a Cuda flow. And right now, Gemini is trained without Cuda. Anthropical is trained without Cuda. Open AI as strange as could. So in a one or two year period, they lost 70% share. Of training mod…”

“They had available capacity because the Grok model wasn't used very much. So they had these GPUs that were sitting around and that's a bad idea. And so they sold a whole block of them or released a whole block of them to Anthropic.”

“I think to date, most of the layoffs were AI washed. They were, because we did boneheaded hiring, During COVID. It is actually because a great deal of productivity gains has been, have occurred over the years that we're just now harvesting. The ability to gath…”

“No company ever went bankrupt by paying extraordinary people too much.”

“No, we're faster on both, but the software challenges In training are real.”

“Really focused on performance, both for training and for inference. You think 20 times faster than Nvidia B 200 GPUs and it's been an amazing run.”

“And that had never been done in a computer before, and that's at the heart of our architecture. That allowed us to yield and deliver whole wafers. Nobody had ever been able to do that in the seventy-year history of our industry.”

“You, ah, sought to limit Chinese access to EDA tools to delay the growth of a Chinese chip market. And so U.S. Venture capitalists backed tons of Chinese companies in Shenzhen to build EDA tools, right?”

“And we were the fastest at it, and the largest, and, you know, our sales were more than 10 times the Grox, and they paid twenty billion dollars for the number two collector.”

“And so, as we do more and more AI work, and more and more agentic work, we're making more and more calls to CPUs, and therefore the demand for CPUs is through the roof.”

“And so the speed of moving waits to compute is about two and a half thousand times faster here than on a Wilben GP.”

“Obviously, what follows that Is video, because a video is just a collection of images. But that takes an enormous amount of compute right now. And that's one of the reasons it's been sort of set aside by the leading labs. So unbelievably computation intensive.”

“And we're the fastest, not by a little bit, but by 20 x.”

“Look, I think that we have a chance for our children or the next generation, not only to not die from cancer, but to not know anybody who died from cancer. I think that is a real achievable goal in 25 years.”

“What we're talking about now are data centers that are in the next several years going to use more power than the previous 50 years on earth took.”

“And my view is in the next 18 months will be way over two X.”

“There's a shot that our children, none of them, nor their people they love will die of cancer.”

“So when OpenAI uses us, we're 15 or 18 times faster than a GPU.”

“Micron is producing numbers where they have 80, 85% gross margins. I mean, they're getting software gross margins on making memory.”

“Now, we are 15 x faster because of architectural reasons.”

“Why do we believe that inference will be any different? There'll be zero marking for slowing them.”

“If we get, as we get more productive, we do more things, we're gonna hire more engineers. We're not gonna hire less engineers.”

“This is made doubly worse by some of the best open source models were made by Chinese companies. And they are exceptionally good models. Kimi Ketu, Deep Seek. When the GLM, these are extraordinarily good models. They're not quite as good as the closed source m…”

“But what we did know was that we had a chance to be the first and only AI peer play in the entire market. There's only one, and that's us.”

“And right now we're the fastest at inference, not by a little bit, but by a lot. 1518, 20 X faster than GPUs.”

“How big is the market for slow search? It's zero. How big is the market for dial-up internet? It's zero. That's how big the market for slow inference will be.”

“We would be the first and only, for a period of time, AI pure play. We are the only company that, that you can, a hundred percent of the revenue, this exact market. There's no gaming, there's no graphics, there's no PC, that, this is it.”

“For a, 20 plus billion dollar deal to do it in four and a half weeks was exceptional.”

“I think it's both the obviously the right thing to do and probably not the source of data centers for the next three or four years.”

“I think it's in the eight to 10 year category, not in the three to five.”

“The GPU, for example, has a lot of capacity of memory, but it's really slow. All right, and that's a huge bottleneck in inference. It's why they can't be fast.”

“I think on the other side, I think they have pushed ahead in the open model category. The open source model is an area where they've pushed ahead.”

“I think because they're a top down economy, they were able to Make decisions like we're going to bring a huge amount of power onto our grid. We're going to modernize our grid. So they were able to bring on huge amounts of power, and that's something that we're…”

“However is it possible that our economy looks very different? In five years. Is it possible that, ah, the things we value, ah, are very different? That we have reorganized around AI. We've seen a major bump in, in labor productivity. We have benefited dramatic…”

“I think people are clearly still getting value from H-one hundreds, and that's more than two years, right? So you know If you say it's a two-year depreciation, you're empirically wrong, right? I mean, they are and I think people are still getting value from A-…”

“I think the reason I'm a hundred percent sure that, that we are underestimating the market is because of that premise.”

“People often say we don't have enough power in the US, and this is strictly wrong. We have plenty of power. It's in the wrong places, right? It's not where we have people or where we have fiber optic cable.”

“Well, because they want to get rid of the 78% gross margin that NVIDIA is charging. That's why it makes sense. Just like that. It might be, on the high-end chips, 85%.”

“They backstop their venture groups, right? So if you lost money in an AI company, the government would make you whole.”

“Samsung built a fab in Texas, and they had to change the design of a fab because they had local fire ordinance. And the U.S. Government worked for years to get deployment of billions of dollars in taxes, and a local fire ordinance set them back eight months, 1…”

“AlphaFold solved one of the hardest problems in in chemistry. A problem that had been open for years. Name a drug that's resulted from it. Not one.”

“And we have 2625 times more memory bandwidth than the GPU does.”

“I think also just go up and look at artificial analysis. Wherever we are, we're the fastest not by a little bit, but by a lot.”

“I think for sort of AI companies like Cognition, like all your competitors, like AlphaSense, like dozens of others, they are trying to replace closed source models with very, very fast open source models, and they're trying to drive the open source Accuracy dr…”

“In a GPU, most of the time it's doing inference, it's five or seven percent utilized. That means it's 95 or 93% wasted.”