Nvidia’s moat comes from a few things. As you pointed out, they have CUDA, which is a proprietary set of APIs for running parallelised math operations. But they also have the best performing chips on the market by a long way. This is not merely a function of having strong optimisation on the software side (possibly replicable by o3 but I would need to see more evidence to be convinced that an LLM would be good at optimisation), or on the hardware side (much, MUCH trickier for an LLM given that a lot of the hardware has to operate on nanometre scale, which can be hard to simulate), but also because having the most money and a strong track record & relationship means they can get preferential access to next-gen fabs at TSMC.

It is also true that the recent boom has increased investment into running CUDA code on other GPUs. The SCALE project is one such example. This implies (a) the bottleneck is not about replicating CUDA’s functionality (which it does), but more about replicating its performance (they might have gains to make there) and/or (b) that the actual moat really does lie in the hardware. Again, probably a mix of both.

However, this hasn’t stopped other companies from making progress here. I think it’s indicative that Deepseek v3 was allegedly trained for less than $10m. If this is true, it suggests to me that:

1. Frontier labs might be currently using their hardware very inefficiently, and if these efficiencies were to be capitalised on, demand for Nvidia hardware would reduce (both by using less of their GPUs, but also because you wouldn’t need the best of the best to do well)

2. If it turns out to be cheap to train good LLMs, captured value might shift back to frontier labs, or even to downstream applications. This would reduce Nvidia’s pricing power.

Also, it looks like the competition is catching up anyway. It seems like it’s very reasonable to do inference on Apple or Google chips (Apple Intelligence runs on M2-series chips, these also have top TSMC node access; Google run a lot of inference on their own TPUs). I was particularly impressed that you can run a 600B+ parameter model on 8 Mac Minis, not even running Apple’s best chips. Even if it’s only inference, that’s a huge chunk of the market that might fall to competitors soon.

So I’m not exactly counting on Nvidia to hold, but I think it will be for other reasons than automation. Even if you are very AI-pilled, we still live in the world where market dynamics are much stronger than labour automation effects. For now :)

Dylan Patel of Semianalysis (one of the leading semiconductor research firms) did a good recent episode on the Bg2 podcast where he covers this question and others:

Youtube link

Dexa summary