It seems like the author isn't really defending against the bulk of the criticisms, which revolve around correctness and reliability, and instead highlights 'cool' language features. If you can't trust gradients or probability distributions / sampling to be correct, that's a pretty damning verdict for a language aimed at numerical calculations. The fact that it has some traction in academia (where the link from incorrect computation results to real-world consequences is extremely long-winded and hard to attribute) is worrying from that perspective. Imagine Boeing were to use Julia for its computations (not unlikely given that it has a great repuation for differential equation solvers).

Additional note, we recently did a overhaul of SciMLSensitivity (https://sensitivity.sciml.ai/dev/, as part of the new https://docs.sciml.ai/dev/) and setup a system which amounts to 15 hours of direct unit tests doing a combinatoric check of arguments with 4 hours of downstream testing (https://github.com/SciML/SciMLSensitivity.jl/actions/runs/25...). What that identified is that any remaining issues that can arise are due to the implicit parameters mechanism in Zygote (Zygote.params). To counteract this upstream issue, we (a) try to default to never default to Zygote VJPs whenever we can avoid it (hence defaulting to Enzyme and ReverseDiff first as previously mentioned), and (b) put in a mechanism for early error throwing if Zygote hits any not implemented derivative case with an explicit error message (https://github.com/SciML/SciMLSensitivity.jl/blob/v7.0.1/src...). We have alerted the devs of the machine learning libraries, and from this there has been a lot of movement. In particular, a globals-free machine learning library, Lux.jl, was created with fully explicit parameters https://lux.csail.mit.edu/dev/, and thus by design it cannot have this issue. In addition, the Flux.jl library itself is looking to do a redesign that eliminates implicit parameters (https://github.com/FluxML/Flux.jl/issues/1986). Which design will be the one in the end, that's uncertain right now, but it's clear that no matter what the future designs of the deep learning libraries will fully cut out that part of Zygote.jl. And additionally, the other AD libraries (Enzyme and Diffractor for example) do not have this "feature", so it's an issue that can only arise from a specific (not recommended) way of using Zygote (which now throws explicit error messages early and often if used anywhere near SciML because I don't tolerate it). tl;dr: don't use Zygote.params, the ML AD devs know this its usage is being removed ASAP.

So from this, SciML should be rather safe and if not, please share some details and I'd be happy to dig in.