> This is a fascinating shift in economics and suggests there could be a runaway power concentrating moment for AI system developers who have the largest number of paying customers. Those customers are footing the bill to create new high quality data … which improves the model … which becomes better and more preferred by users … you get the idea.
While I think this is an interesting hypothesis, I'm skeptical. You might be lowering the cost of your training corpus by a few million dollars, but I highly doubt you are getting novel, high quality data.
We are currently in a world where SOTA base model seems to be capped at around GPT4o levels. I have no doubt that in 2-3 years our base models will compete with o1 or even o3... just it remains to be seen what innovations/optimizations get us there.
The most promising idea is to use reasoning models to generate data, and then train our non-reasoning models with the reasoning-embedded data. But... it remains to be seen how much of the chain of thought reasoning you can really capture into model weights. I'm guessing some, but I wonder if there is a cap to multi-head attention architecture. If reasoning can be transferred from reasoning models to base models, OpenAI should have already trained a new model with o3 training data, right?
Another thought is maybe we don't need to improve our base models much. It's sufficient to have them be generalists, and to improve reasoning models (lowering price, improving quality) going forward.