00:27:35 · Ronak Malde
[00:27:35] S0: So I think there's a couple of things. The first thing is continual learning as a paradigm is is the next learning paradigm. Any massive company that you can think of of the previous age, so let's take Google or Amazon or Facebook, they were all doing continual learning, basically. If you think about like recommender systems or ads, the idea is taking all of these preferences from the real world and then creating online systems that can learn and get smarter the more they're used. And I think for agents that hasn't existed yet, and that's exactly the place, that Trajectory I want to be in. We're starting with just V1 of the product, which is a very easy way to train models, but in aggregate, I want it to be a massive recommender system of every single agent should be attuned to what companies want and what every single user eventually wants. I And think that's going to be a fundamentally exciting world to get to. There's a lot of work that needs to happen, and I also think Trajectory is not the only company in the ecosystem that needs to work really well to make this happen as well. You need, like two blocks down from us is Modal at our office that works on sandboxes, and that powers a lot of the infrastructure that we have, and two blocks down from that is Thinking Machines also in SF, and I think there's like all these sort of companies that need to be built to basically power all the infrastructure to make continual learning work, but I think the the product interface is where a lot of the exciting work can happen.
00:29:02 · Pat Grady
[00:29:02] S2: Yeah. One of the things you said when we were first getting to know you many, many months ago was this idea that the world right now has plentiful intelligence thanks to the Foundation Model Labs and plentiful experience thanks to all the people who do hard work in lots of different domains every day, but there's this big gap between the two And in some ways, continual learning is kind of the thing that bridges that gap, and it's kind of the thing that honors all of this earned experience that people have and, like, makes it legible to the intelligence coming out of the foundation models. And I thought that was, like, a very kinda elegant and beautiful way to think about what it is you guys are doing. And then the question that that leads to is what what is your most optimistic view of the future in terms of where continual learning can take us?
00:29:54 · Ronak Malde, Pat Grady
[00:29:54] S0: I love that question. A good example of this is Adam Smith's Wealth of Nations, where we started out in the 1700s in this world where everyone was kind of doing the same thing, right? You would do this end to end workflow, you could call it, of an artisan. And what he theorized basically is a world of abundance needs specialization, where you want someone to basically not be context switching all of the time and work on the thing that they've built up experience about. And it seems like in AI right now, we're in this pre industrial revolution society, or even a hunter gatherer sort of society where everyone is just doing the same work over and over again. We're consulting one oracle of technology. I think what needs to happen is basically get to a world where it's no longer just one manufacturer of a car, one manufacturer of like every single unit, but we get to a world of specialization where everyone can basically customize every single part of the harness, the model, and that's where we get to a world of abundance. And there's a reason why only 1% of enterprises actually have been adopted by AI, and there's so many workflows out there that are super manual and could be automated and actually increase intelligence based on all of these agents that could be in production. And so my goal is to basically take that 1% and turn that into 99% so that we are actually learning from all of this built up experience that lives in the world right now. [00:31:25] S2: Love it. Ronak, thanks for being here. [00:31:27] S0: Yeah, thanks so much for having me.