Andreas Stuhlmüller: belief

Transcript context

…Have you found any particular data structures, particularly if they're open source and something I could also incorporate into my own personal AI infrastructure that work well? I'm just doing very simple markdown wiki as of now. Is there a next level that I should be considering? Graph database or I have no idea what it would be, but. I think step one is just to use the representations people already find useful. And SQL databases are a pretty useful thing. So when I'm currently like building my model off the list of the company and step one is like ingest a lot of the information from Mixpanel and Addio and like various other types of systems and put it into a representation that the model can then operate on. And a lot of those representations are just SQL tables. Cool, interesting. Everybody's doing their own experiments in recursive self-improvement these days. That's what I'm noticing across the board is there's always this, not always, but it's been striking that in the last, I don't know, 6 to 8 weeks, it seems like everybody's tipping into this moment of, maybe we too can be an experiment in recursive self-improvement. So you're now. elicit as a test case for can we get elicit to build effective world models and then be able to from its own, use these detailed representations that it itself has constructed to inform its own analysis of what it itself should become in the future. The hall of mirrors there is deep and fascinating. This obviously relates to a blog post that you put out not too long ago, which is called Planning is Unsolved. And I think, obviously that's totally true. Or we could just ask the AIs to handle all this and retire to the beach as I think it was an Anthropic person once famously put it. I do wonder a little bit, and this is probably a cultural question as much as it is a technology question, but when I think about like my own company that I'm now just the AI advisor to and not running, And I think about our planning process that we developed before AI. Sometimes I'm like, maybe we should just scrap the whole thing. Maybe all these times that we like come together and sit around the table and talk about this or that and try to convince each other that if we do this, it'll be more successful than if we do that. I'm often like, man, what we should do is build it all, launch all these things and see what happens. We can like coding has gotten cheap. So maybe the future of planning is less about guessing and more about like fast iteration and actually like making more contact with reality. Obviously, again, there's not like a true strict binary there. But how are you guys thinking about that question. And how are your clients at, especially like pharma companies thinking about that question? There's also this, I think it's more of an aspiration than a trend at this point, but there's the notion of clinical trial abundance, which sounds great. I don't think we're, again, I don't think we're there, but you can imagine a very different vision for a pharmaceutical company where one is like, we're gonna use these world models, we're gonna make much better decisions, and we're gonna we're going to deploy these like scarce resources and these like precious few at bats we have at clinical trials in the best way possible. And then there's this other vision that's we'll do 10 times as many clinical trials and that'll be, or 100 times, who knows, and that'll be bigger on lock because we'll actually get the real answers in far more cases. Where do you want to be on that spectrum? Where do you think pharmaceutical companies should be on that spectrum? Maybe it's different. Maybe Waymark should be one place and pharma should be somewhere else.…

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