You are assuming that a 10x bigger model will be 10x better or will bring us close to AGI. It might be too unweildy to do inference on. Or the gain in performance maybe minor and more scientific thought needs to go into the model before it can reap the reward with more training. Scientific breakthroughts sometimes take time.

I’m not assuming 10x bigger will yield 10x better. We have scaling laws that can tell you more.

But I find it bizarre that you made the conclusion that AI has stopped scaling because DeepSeek optimized the heck out of the sanctioned GPUs they had. Weird.

I have not said that. I simply said that you now know that you can get more juice for the amount you spend. If you’ve just learnt this you would now first ask your engineers to improve your model to scale it rather than place any further orders with nvidia to scale it. Only once you think you have got the most out of the existing GPUs you would buy more. DeepSeek have made people wonder if their engineers have missed some more stuff and maybe they should just pause spending to make sure before sinking in more billions. It breaks the hegemony of the spend more to dominate attitude that was gripping the industry e.g $500 billion planned spend by openAI consortium etc

It doesn’t break the attitude. The number one problem DeepSeek’s CEO stated in an interview is they don’t have access to more advanced GPUs. They’re GPU starved.

There’s no reason why American companies can’t use DeepSeek’s techniques to improve their efficiency but continue the GPU arms race to AGI.

DeepSeek’s impact does not change any attitude.