The future of research is a shepherd-ship paradigm where humans steer distributed, multi-threaded AI systems that run experiments autonomously.

35:53a lot about sort of this chat assistant interface as the way how we interact with LLMs, but it's most of the times inherently single threaded, right? So we're sitting in front of the computer, we're interacting with the chat. We're seeing sort of changes as they occur in the editor, we accept them and so on. But I think this is sort of also just a stepping stone towards sort of a more

36:15let's say distributed way about thinking about research, optimization and so on. So I like to sort of think of vibe coding, vibe chatting. And on the other hand we have sort of vibe optimization and vibe researching where sort of my ideal future scenario is one in which um you as a researcher sort of during the day cool work with like a system like Shinka or the AI scientist

36:38you um sort of steer the ship like a shepherd in some sense and then during the night you you you you press play and you go to bed and in the in the background you've multiple experiments running and automatically new ones being proposed by LLMs, evidence being accumulated and then in the morning you come back and sort of you have an uh multi-threaded sort of system running in

36:59parallel and you're more like the shepherd of the ship than the the person actually executing experiments and analyzing. Oh yeah, you're still analyzing but you're not executing. This is happening sort of by the system itself. >> Yes, and increasingly this might be semi-supervised or even proactive. I mean, you know, there's that new product from OpenAI where it knows what you're interested in and while you sleep it's

37:21going off and you know, find your pulse. That's right. And you know, we're in the situation now where we're reasonably technical people. So, you know, MATLAB and Mathematica they're they're supremely powerful but you need to know how to express problems precisely. Whereas I can imagine a future where we um express problems just in natural language or maybe just based on our interactions with language models the platform knows what we're interested in

37:48and it can just go and find things on our behalf because this is about democratizing this technology to people who perhaps don't know exactly what they're looking for. >> I think one of the bigger problems there is sort of this verification aspect to it, right? In the sense that oftentimes it's easier to generate a lot of solutions than in to actually like hard verify them, right? Language models are