Say you have some existing virus simulation codebase that you want to use ML on to derive an effective policy on. Without an AD tool like Enzyme, you'd have to spend significant time and effort understanding and rewriting that obnoxious 100K lines of fortran into TensorFlow, when you could've been spending it solving your problem. The reason you need to do this rewriting is because many ML algorithms require the derivatives of functions to be able to use them and Enzyme provides an easy way to generate derivatives of existing code.
This is also useful in the scientific world where derivatives of functions are commonplace.
You could also use it in more performance-engineering/computer systems ways as well by using the derivatives to perform uncertainty quantification and perhaps decide to use 32-bit floats rather than 64-bit doubles.
I'm a big believer in auto-diff, but I'm skeptical that any autodiff tool would differentiate a 100k line simulation code correctly and efficiently without manual intervention. I'd certainly love to be proven wrong, though and absolutely AD can be a big time saver :)
This is also useful in the scientific world where derivatives of functions are commonplace.
You could also use it in more performance-engineering/computer systems ways as well by using the derivatives to perform uncertainty quantification and perhaps decide to use 32-bit floats rather than 64-bit doubles.