Why do we need to jump to ML-based programming again? I'm confident that as we build simpler interfaces and workflows for replacing the most modular components in our programs with AI, we'll begin to see which components are the next lowest-hanging fruit and what concrete ML problems need to be solved to model them.

We imagine a future where AI becomes a dominant paradigm for "writing" software -- I think that will be the case, but not in the way everyone suspects. I think 80% of the new value of software in the future will be derived from AI components, but that 80% of the production costs will still go into the structural glue code that supports the value-powerhouse models. Thus, most of the software written in the future will look similar to most of the code written today.

I also suspect that as the complexity of AI models increases, the structural code required to support these models will keep pace in complexity. For this reason, I see a future where the unit cost of high-quality software does not see any big drop, but where the value of this software continues to increase exponentially. A corollary to this is that valuable software will be no easier to program in the future than it is today.