Really enjoyed this episode. Thanks to @dwarkesh_sp and @pawtrammell for the conversation. What I hope that I was able to convey that it is incredibly difficult to make predictions when there is so much uncertainty: there is not just uncertainty around the parameters, but even what model to use in the first place.

In my view, the best application of economics to our current moment is not trying to individually forecast scenarios 5 or 10 years out (though aggregate forecasts are useful). There is way too much uncertainty at every level of the exercise. It’s to model important scenarios and work our way backwards: start with a potential scenario that are important to consider and then derive the conditions under which it can arise. This not only allows you to potentially rule out a very intuitive-sounding scenarios because the conditions required are implausible.

It also points to data that you need to track which you were not considering before. Eg latent demand for human involvement, substitution between AI and human interaction, task bundling inside jobs, AI bottlenecks, and whether AI looks more like electricity or social media. This is the type of data I’m working to collect, and I know other teams are too.

The last point is particularly important. To quote Demis Hassabis, we are potentially at the foothills of the singular. As economists we have the responsibility to guide that transition with both humility and the best information we can gather.