(I ran a large forecasting project for a while) I think one criticism that the linked post misses but the OP article touches on is that most forecasts (whether

(I ran a large forecasting project for a while) I think one criticism that the linked post misses but the OP article touches on is that most forecasts (whether they be prediction markets or super-forecasting style) is that they often predict the wrong things. > We asked questions of the type “Will Google integrate LLMs into Gmail by Spring 2023?” and “How many parameters will the next LaMDA model have?” Yet what executives would have wanted to know was “Will Microsoft integrate LLMs into Outlook by Spring 2023?” and “How many parameters will the next GPT model have?” It's really hard to build a prediction market for these things and I'm not sure "forecasting" is the right way of thinking about them. (Following some links led to https://www.lesswrong.com/posts/uGkRcHqatmPkvpGLq/contra-papers-claiming-superhuman-ai-forecasting https://www.lesswrong.com/posts/uGkRcHqatmPkvpGLq/contra-pap... which has some interesting points too)