What signs point to any sort of acceleration besides in spending?
Most major companies have released new versions in the past year, but if people were asked to blindly determine whether they were using the newer or older version, I suspect the results would be close to random. It seems to me that the difference between versions is sharply decreasing in a way that seems to be asymptotic, similar to what happens in literally every other domain with neural networks.
I also think it's clear that the difference between the various choices is also diminishing. Aside from certain manually designed idiosyncrasies (like ChatGPT's obsequiousness), I think people assessing which model they're using would also be mostly random. Somewhat surprisingly, even in the 'LLM arena' [1], where you get to compare output side by side, the difference between models is approaching statistical 0!
Mainly growing energy demand of AI deployments leading to capacity increases becoming the bottleneck. The key challenge is going to be scaling up energy production in the US.
Most major companies have released new versions in the past year, but if people were asked to blindly determine whether they were using the newer or older version, I suspect the results would be close to random. It seems to me that the difference between versions is sharply decreasing in a way that seems to be asymptotic, similar to what happens in literally every other domain with neural networks.
I also think it's clear that the difference between the various choices is also diminishing. Aside from certain manually designed idiosyncrasies (like ChatGPT's obsequiousness), I think people assessing which model they're using would also be mostly random. Somewhat surprisingly, even in the 'LLM arena' [1], where you get to compare output side by side, the difference between models is approaching statistical 0!
[1] - https://huggingface.co/spaces/lmarena-ai/lmarena-leaderboard