Patrick O'Shaughnessy

What's your model for thinking about that today?

What most interests you?

I'll say tokenomics 20 more times.

If you could snap your fingers and change a dial somehow that would most unlock and unleash more development, is it just inference latency?

Because then we could do bigger models and serve them much faster in a way that consumers would enjoy.

Is that the main like bottleneck to be attacked?

Just because it's a better experience.

And as a result, we're just going to probably have to wait a little bit longer to see what the bigger models are in practice to see what consumers actually do with them because it's just going to be too hard.

The click of understanding, yeah.

Giving it environments, specific environments to learn it and then fold back in.

In those two, like in pure raw internet pre-training world and in this new like environments world, what inning are we in in each of those, would you say?

Like how far into the potential benefits

Maybe late innings on text, mid innings on pre-training non-text.

let's say we fast forward or we're in the seventh inning of that or something like this.

What do you think the way that the average person will most feel that difference in terms of the utility of the model?

But you're talking about order me this vitamin and just like, it's just done.

Before asking even more holistically your view on where we're going, there's a third category, which is the reasoning part of the equation.

So we've got pre-training, we've got RL and environments post-training.

What about raw time spent reasoning and where that going as its own independent part of the overall scaling law?

On the topic of embodiment, continuing with the human analogy, how do you think about things like short and long-term memory in a human versus raw model capacity or something?