But there's a more subtle point here which I don't see a lot of people talking about, maybe because they know more about this than me. Why wouldn't frontier model developers take DeepSeek R1 techniques and make their models even better or even larger? Or another way to ask: Are the DeepSeek R1 innovations only for making models cheaper (and slightly worse) or can the algorithms developed by the DeepSeek team be scaled up to make more powerful frontier models? Leading edge model developers don't just care about cost, they also want to release models with maximum capabilities. And as we've seen over the past few years, they're willing to pay almost anything to achieve this.
I think most AI researchers know there are still many things to explore in this space so disruptive innovations shouldn't be seen as a bubble burst but as more opportunity.
NVIDIA is in an extremely strong position right now. Even if someone has a major breakthrough on the hardware design side that dramatically lowers the cost of compute for AI workloads (which is highly unlikely), NVIDIA will just create their own implementation that will outperform the original since they have a stranglehold on an entire stack-up of technology: circuits, drivers, libraries and software.