Reading the link, it seems that Jeremy Howard only tried out Swift because Swift for Tensorflow existed. I got the impression from your comment that using Swift for this purpose was somehow "in-the-air" at the time, and many people independently considered it, but I don't think this is true. My impression is quite the contrary - when they announced Tensorflow support for Swift, I think many people were surprised.
In his lecture, he specifically said he interviewed both the Swift and Julia teams and got a view inside what happening at Google... and the the Swift teams work, development, and vision was well beyond what the Julia team had even envisioned.
Interesting since the manifesto hasn't mentioned something that's not already completed in Julia. Is their main project or motivation something that is not being shared? I find it quite odd that this manifesto doesn't mention anything that actually requires differentiable programming (old adjoint equations for their applications have existed since the 90's), which makes it a very odd justification for this work.
I suspect his comments are about the vision for the whole solution and environment, not just auto-diff, which is just a piece.
Auto-diff extends back 40 years. But the relative obscurity of its usage is that it is a "add-on", meta-programming step, or/and a limited sub-set of a language.
The Swift approach of 1st class compiler support is fairly unique. But also supports a future view of computing. If many problems can be solved by differentiable programming, not just the typical but somewhat niche NN/ML problems, then it needs to built into the whole language, not a sub-set.
But more importantly the future requirements, and death of Moore's Law means a fast, deeply statically analyzable language will be required to make the most of a heterogeneous computing environment.
