The presentation argues that declarative languages are structurally better suited in an age where AI generates software.

It will point out which factors contribute to that potential strength, touching on formal semantics and structural schema information, and how documentation and curated examples help compete with imperative coding that builds on a much wider base of training data.

The argument will be made using Fore as an example, and which steps were taken to train AI on delivering accurate results.

Finally, there will be a comparison between React and Fore, with early results on time-to-solution, token usage, and accuracy of solution, and what those numbers suggest so far.