AI is fundamentally learning the entire conditional probability distribution of our collective knowledge; but sampling it over and over is not going to fundamentally enhance it, except to, perhaps, reinforce a mean, or surface places we have insufficiently sampled. For me, even the deep research agents aren't the best when it comes to surfacing truth, because the nuance of that is lost on the distribution.

I think that if we're realistic with ourselves, AI will become exponentially more expensive to train, but without additional high quality data (not you, synthetic data), we're back to 1980s era AI (expert systems), just with enhanced fossil fuel usage to keep up with the TPUs. What's old is new again, I suppose!

I sincerely hope to be proven wrong, of course, but I think recent AI innovation has stagnated in terms of new things it can do. It's a great tool, when you use it to leverage that distribution (eg, semantic search), but it might not fundamentally be the approach to AGI (unless your goal is to replicate what we can, but less spikey)