My interpretation is that yes in the long haul, lower energy/hardware requirements might increase demand rather than decrease it. But right now, DeepSeek has demonstrated that the current bottleneck to progress is _not_ compute, which decreases the near term pressure on buying GPUs at any cost, which decreases NVIDIA's stock price.
Short term, I 100% agree, but remains to be seen what "short" means. According to at least some benchmarks, Deepseek is two full orders of magnitude cheaper for comparable performance. Massive. But that opens the door for much more elaborate "architectures" (chain of thought, architect/editor, multiple choice) etc, since it's possible to run it over and over to get better results, so raw speed & latency will still matter.
I think it's worth carefully pulling apart _what_ DeepSeek is cheaper at. It's somewhat cheaper at inference (0.3 OOM), and about 1-1.5 OOM cheaper for training (Inference costs: https://www.latent.space/p/reasoning-price-war)
It's also worth keeping in mind that depending on benchmark, these values change (and can shrink quite a bit)
And it's also worth keeping in mind that the drastic drop in training cost(if reproducible) will mean that training is suddenly affordable for a much larger number of organizations.
I'm not sure the impact on GPU demand will be as big as people assume.
