[samhw]

I work on these systems, and if anything my only complaint about the field is the propensity to solve every optimisation problem with ML. I have seen people solve textbook-grade linear, and even differentiable, optimisation problems.

And the reason it happens despite the 'invisible hand' etc is because it still works, it just happens to be horrendously inefficient. I think that's the main area of inefficiency in the industry: not in getting the job done, nor even arguably in accuracy - at least not severely - but in overcomplicating the solution[0] because we've formed a cargo cult around one particular method of optimisation, beyond all nuance.

[0] I mean 'overcomplicating' in absolute terms. Of course the very crux of my point is that, from the data scientist's perspective, it's not overcomplicated - it's less complicated than using e.g. ILP precisely because we have made libraries like TensorFlow so incredibly easy and tempting to use.

[whimsicalism]

Fwiw, my org heavily relies on LP solvers in conjunction with ML to solve these problems