In this model the probabilities were measured by an ensemble of LLM's. We give them context and ask them to follow a specific methodology for making a forecast, and then aggregate their forecasts into a "crowd" forecast. We can get input from humans as well to compare/combine them further, but in this particular instance I just used our AI Forecaster.

You're right that a human analyst - especially a very experienced one - is going to be able to still do a better job of analysis most of the time, but the AI is useful for having loops to re-examine the question, consume and curate new information, etc. We've built the system with that assumption: let humans do what they're best at, let AI do what it's best at...