Short version: Nvidia's only moat is in software; AMD already makes flatly superior hardware priced far lower, and Google probably does too but doesn't publicly sell it. And if AI undergoes smooth takeoff on current trajectory, then ~all software moats will evaporate early.
Long version: Nvidia is pretty obviously in a hype-driven bubble right now. However, it is sometimes the case that (a) an asset is in a hype-driven bubble, and (b) it's still a good long-run bet at the current price, because the company will in fact be worth that much. Think Amazon during the dot-com bubble. I've heard people make that argument about Nvidia lately, on the basis that it will be ridiculously valuable if AI undergoes smooth takeoff on the current apparent trajectory.
My core claim here is that Nvidia will not actually be worth much, compared to other companies, if AI undergoes smooth takeoff on the current apparent trajectory.
Other companies already make ML hardware flatly superior to Nvidia's (in flops, memory, whatever), and priced much lower. AMD's MI300x is the most obvious direct comparison. Google's TPUs are probably another example, though they're not sold publicly so harder to know for sure.
So why is Nvidia still the market leader? No secret there: it's the CUDA libraries. Lots of (third-party) software is built on top of CUDA, and if you use non-Nvidia hardware then you can't use any of that software.
That's exactly the sort of moat which will disappear rapidly if AI automates most-or-all software engineering, and on current trajectory software engineering would be one of the earlier areas to see massive AI acceleration. In that world, it will be easy to move any application-level program to run on any lower-level stack, just by asking an LLM to port it over.
So in worlds where AI automates software engineering to a very large extent, Nvidia's moat is gone, and their competition has an already-better product at already-lower price.
Apparently there already exists a CUDA-alternative for non-Nvidia hardware. The open source project ZLUDA. As far as I can tell its less performant than CUDA, and it has the same challenges as firefox does when competing with chromium based browsers, which will only get worse as it gets more popular. But its something to track at least.
Transpiling assembly code written for one OS/kernel to assembly code for another OS/kernel while taking advantage the full speed of the processor, is a completely different task from transpiling say, java code into python.
Also, the hardware/software abstraction might break. A python developer can say hardware failures are not my problem. An assembly developer working at an AGI lab needs to consider hardware failures as lost wallclock time in their company’s race to AGI, and will try to write code so that hardware failures don’t cause the company to lose time.
GPT4 definitely can’t do this type of work and I’ll bet a lot of money GPT5 can’t do it either. ASI can do it but there’s bigger considerations than whether Nvidia makes money there, such as whether we’re still alive and whether markets and democracy continue to exist. Making a guess of N for which GPT-N can get this done requires evaluating how hard of a software task this actually is, and your comment contains no discussion of this.
Have you looked at tinygrad’s codebase or spoken to George Hotz about this?
4Josh You
AI that can rewrite CUDA is a ways off. It's possible that it won't be that far away in calendar time, but it is far away in terms of AI market growth and hype cycles. If GPT-5 does well, Nvidia will reap the gains more than AMD or Google.