The speaker expects iterative model improvement to move from roughly quarterly updates to weekly, daily, and hourly updates, using deployment traces to train later models, until the process effectively solves the task continuously.

“like weight updates continuously and like in practice i think it's much more likely to be like the cartridges and stuff allow you specialized in deployment then you generate traces you put that in your model like three months later you come up with a model which is better at this stuff you specialize it again you like consolidate it again and then eventually we'll just like make this loop faster and faster so instead of like every three months we release a model now it's like every week and then every like day and then every hour at which point we basically have always solved it”