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Listen to Eric Schmidt explaining why Nvidia is dominating GPU market, the CUDA moat that everyone completely missed, and the impact of GenAI that no one understands yet. $NVDA

The AI Investor

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Join our AI investing community for in-depth analysis on $NVDA, $TSLA, and other top AI stocks.

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The VLM open sourced libraries are only optimized to CUDA and very hard to divert by a competitor. So it’s gonna take insanely more than just “hey Amazon is working on some chips” to cut Nvidia market share and margins.

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All fair but he's overstating how overlooked it is. Everyone knows CUDA is the moat. Nvidia has put thousands of engineers to work on this over 30 years; that's why it's so hard to replicate. The sheer scale of the project. Not enough engineers to go around.

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No i think he meant the software layers on top of CUDA, highly optimized for it, not the CUDA itself is being missed.

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CUDA is the moat. How long it lasts, is another question.

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The biggest moat of all time

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whats the 2nd? ios ?

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Since AI can now program nearly by itself what is stopping AI to write its own AI optimized coding language to optimize itself for even better, faster more accurate training? CUDA now… AI self code in the very near future. That’s where we’re going right? You see it right? 🤯

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Ben Thompson explains how LLMs greatly diminished Nvidia's CUDA moat even as they sent the stock to the moon "So the weird thing about large language models is they were obviously incredible for Nvidia. That's why their stock went to the moon." "They have been on and off the most valuable company in the world." "It was also very bad for Nvidia. And the reason it was bad for Nvidia is that the play with CUDA is to build a developer ecosystem on top of CUDA." "But CUDA only works on Nvidia GPUs. So you get CUDA for free. It's easier to use, and it's a tremendous investment. Nvidia almost went under trying to build CUDA at a time when no one understood what they were doing or why they were wasting money on it." "And that's why Jensen Huang will get bristly, particularly when people question their rent-seeking or profit, whatever. It's like, no, they earned their spot fair and square." "Absolutely. It shouldn't be forgotten. They have earned every dollar they've gotten through 25 years of taking massive risks." "It bottomed out in October 2022. I wrote an article like three weeks before ChatGPT came out, tracing their bottoming-out history and their search for what was next." "'Nvidia in the Valley.' So, go back to this GTC. So I wrote an article at the time called 'Nvidia Waves and Moats'." "And what was interesting about that GTC was, number one, it was very boring. All the cool stuff kind of got scrubbed out." "Now, Jensen Huang has brought that stuff back, so the last few GTCs he's more talking about other things. Now it comes across as, oh, you're still looking for something beyond the LLM." "Because the problem with the LLM is it shifts the developer platform far above where Nvidia sits. All the activity is happening on top of LLMs. And so no one who's writing an AI application today is using CUDA." "Now, some people are, if you're training your own model and you're doing some low-level things or non-LLM things." "But the vast majority of the energy and all the money and the ecosystem is far removed from CUDA." "They have no idea and don't need to know or care what chips their application is running on. They're just on the OpenAI API, or the Anthropic API, or using Bedrock on Amazon, and it's sitting on Trainium, and they're using a Chinese open-source model. It's totally abstracted away, and this is why LLMs were bad for Nvidia." "Now, again, all the money they made along the way is worth it, but their moat has been tremendously diminished." "CUDA is still a moat if you need to do stuff that requires CUDA. But the vast majority of stuff, in energy, doesn't require CUDA, like in a post-LLM world."

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