Oly Sourbut

So this can take hours or even days.

And so that's unfortunate if this thing has already gone around, you know, there's the classic saying, the lie makes its way around the world before the truth has got its boots on.

So one thing that we're supporting there is can we accelerate that

through tools which support note makers and note graders.

And one way that you can accelerate that is actually have AI perform some of the research or even drafting of notes, but of course, maintaining the gold standard of the voting and the rating and this kind of thing.

And there's an interesting kind of design tension there.

So that's one direction we're supporting.

And that comes under the kind of umbrella that we're calling collective epistemics.

It's a sort of platform-focused intervention there.

We could talk about other kinds of interventions there.

As another quick example, another thing we're supporting is various initiatives in scenario planning.

You may have come across deep research.

This is a kind of form factor that various AI companies have put their products into where...

Language models have been trained to do not only kind of surface level web search, but also kind of jumping a little bit deeper, doing something like a literature review and then putting that together into some kind of context.

So you can see that there's potential there for these quite deeper sort of agentic workflows using LLMs in sort of structured ways with appropriate scaffolding and tuning and so on.

And they're able to support these kind of exploratory investigative processes.

They're not replacing them.

It's not like you would just fire and forget or trust the output entirely, but it's able to inform an analyst or someone like that.

And so for scenario planning and this kind of other sort of institutional decision making, and perhaps outside of institutions as well, we think there's a lot of potential there.

So we've got a couple of people that we're supporting working on that kind of thing as well.