Trenton Bricken

I think we got to drink the bitter lesson here.

And yeah, like there aren't infinite shortcuts.

Like you do just have to scale.

Something's going to have a bigger model and pay more inference for it.

And if you want AGI, then that's what you got to pay the price of.

And even in the last year, there's been much more of a factor of the inference cost, right?

So just explicitly, like, the bigger the model, the more expensive it is to do a forward pass and generate tokens.

And the calculus used to just be, should I allocate my flops to more training data or a bigger model?

And now another huge factor is, how much am I actually going to do forward passes on this model once it's trained?

And then even within inference, there's all this research on, well, what strategy should I use?

Should I sample 10 and take the best?

Do I do this sort of like branching search, et cetera, et cetera?

And so with RL, where you're sampling a whole lot of tokens, you also need to factor in the ability for the model to like actually generate those tokens and then learn and get feedback.

I would say some combination of like get rid of the sunk cost of your like previous workflows or expertise in order to evaluate what AI can do for you.

That's right.

And another way to put this, which is fun, is just like be lazier.

in so much as figure out the way that the agent can do the things that are toilsome.

Ultimately, you get to be lazier, but in the short run, you need to critically think about the things you're currently doing and what an AI could actually be better at doing, and then go and try it or explore it.

Because I think there's still just a lot of low-hanging fruit of people assuming and not writing the full prompt, giving a few examples, connecting the right tools.

for your work to be accelerated and automated.