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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
263,815 Aufrufe • vor 1 Jahr •via X (Twitter)
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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.
No i think he meant the software layers on top of CUDA, highly optimized for it, not the CUDA itself is being missed.
CUDA is the moat. How long it lasts, is another question.
The biggest moat of all time
whats the 2nd? ios ?
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? 🤯
![Cerebras just had the biggest IPO of the year. Founder Andrew Feldman says the 3 most important things he had to convince investors of while doing the roadshow were that demand for inference is going to 1,000,000x, the GPU isn't the only way to do compute, and that the CUDA moat is overstated. What he said: "Jensen said some time ago on Brad Gerstner's podcast that the demand for inference will grow by a 1,000,000x, and nobody believed him. And at the same time, you saw Sam Altman displaying real vision and going out and trying to lock up huge amounts of compute, memory, data centers, and power, because he saw it too." "[We tried] to share what that means — what exponential demand means. And that we're still so early, and yet the demand for AI compute is overwhelming." "The other thing is that there are lots of ways to do this. The GPU isn't the only way. You've got TPUs, Trainium, and us. There are lots of different ways to build a solution here." "And finally — the notion that CUDA is this grand lock-in is overplayed. Gemini 3, which is an excellent model, was trained on TPUs with no CUDA. The Anthropic models were trained on Trainium with no CUDA. Some of the best models, some of the most interesting things are being done without CUDA. And that lock-in might be overplayed." $CBRS](https://image.24vids.com/tw-2055069621439590522/amplify_video_thumb/2055069555819790336/img/rMYCY-B2d4--txr_.jpg)
