Why Do Superforecasters Keep Getting AI Wrong?

Ethan Mollick says superforecasters keep getting AI wrong because an exponential is hard to predict. The labs have been right. Development has stayed on an exponential curve with a fast doubling time. He thought slowdowns were a real possibility a few years ago and no longer thinks they are.

The market has grown faster than the labs expected. The technology has surprised him, while the lab leaders are on target for where they think they are going. He is not sure they were surprised by the barriers they have overcome. Reasoning is something that they were not expecting to work as well as it has, and reasoning has been core to the math breakthroughs. People expected AI to be as good at math as it is now five or 10 years from now, but it is already solving millennium problems.

AI was starting to hit a wall of development. It was unable to do math, and it was unable to get the R in the word strawberry. OpenAI developed reasoners, a preview came out, and the pace became even faster than before. People still fail to forecast the exponential curve, so they keep making plans for what is happening today: vibe coding, token maxing, and lowering token costs.