Jaden Schaefer

Reid Hoffman was recently writing and he said, AI lives at the workflow level.

The people closest to the work know where the friction is.

They're the ones who will discover what should be automated, compressed, or redesigned.

And I think that is sort of what humans is trying to do.

The vision is basically a system that's going to act as like the quote-unquote connective tissue across an organization or even maybe like a household, right?

If they're trying to do more consumers.

And it's trying to understand individual skills, preferences, motivations.

And then it's also trying to help balance all of that in service of different like shared goals.

So I think getting there is going to require a lot of rethinking of how models are trained.

According to them, they said, we're trying to train the model in a fundamentally different way.

That's Yuchen He, one of their co-founders and who's formerly an OpenAI researcher.

And they're planning to rely on long horizon reinforcement learning and multi-agent reinforcement learning.

So these are both techniques that are kind of designed to help models plan, revise, and follow through over extended periods.

And also across, you know, a lot of different participants.

So long horizon reinforcement learning, it basically is focusing on outcomes over time rather than just like one-off responses.

And multi-agent reinforcement learning trains a system to operate in environments where

There's multiple humans and AIs are all kind of interacting simultaneously, right?

So both of these approaches, I think, are getting a lot of traction in academic research.

The field is kind of pushing beyond just chatbots and kind of pushing towards systems that are capable of coordination and sustained action.

So if they can come out on the right side of a really powerful kind of like agent coordination tool, I think that they will build something that a lot of people want.