Is the new data that models are more useful for coding than they once were?
Imagine being in 2003 and saying compute costs won’t go down. That’s Ed lol.
EDIT: Some quick research on this so you guys have actual numbers: https://gist.github.com/dwaltrip/a037be938d2b5ecc8b8b238736e....
There's multiple separate angles that all contribute to token-costs going down: chip improvements, engineering improvements for running inference in general, AI architecture and training advances that give similar intelligence in a smaller model, improvements in the quality of the training data, data center design / economies of scale, networking and rack-level improvements that are multiplicative with chip advancements, and so on...
If you analyze the situation for 5 minutes, it's blindingly obvious that price-per-token will continue to improve. And there's a very similar case for intelligence-per-token as well.
And don't get me wrong -- I have many concerns about how this is all unfolding and how it will impact society. But let's get our basic facts straight.
But if it helps, no, the data being discussed is surrounding the economics of running inference and R&D, nothing to do with the utility of models for coding.