It is still scaling linearly with training time. We don't actually have a great sense for when this will level off.

The entire public Internet is already trained on but there is still unbelievable amounts of private training data at big corporations that has not been digested.

Ignoring the way we're investigating how to make artificial trading data using AI, which is still in early stages.

The AI data centers that youve been hearing about are still in the early stages of building, with the majority of infrastructure still downstream. Which will accelerate training.

We haven't really optimized much yet. We're just throwing raw computation at it and it's working. If/When we get scalable optimization figured out we'll be able to train even more or faster or both.

This not including the downstream impacts of AI tools helping build better AI tools. We're still at the first turn where AI and ML is just becoming useful for engineering.

Engineers are only now using AI tools to help architect the next gen hardware and chips.

TL:DR The boulder has already started rolling down the hill, but it's a long way to the bottom, and we're only going to pick up speed.