Chemical engineering process design can become very complex and highly nonlinear (in fact, Nick Sahinidis' group in the chemical engineering department at CMU created BARON explicitly for solving these kinds of problems to global optimality — see http://archimedes.cheme.cmu.edu/?q=baron). Granted, Google's systems don't involve chemical reactions, but that doesn't automatically mean that designing these data centers is a simple task.

I work with a guy who has done ground-breaking linear algebra work, spent most of career at Shell. The systems have been too complex to model for a long time. I can imagine an AI instructed to optimize a parameter could be a huge gain.

Over-engineering? Possibly, but we don't know what was in place before.

Given the cost of data centers, I'm not sure why we should assume it was naive - or less than state-of-the-art, for that matter.

State-of-the-art, that is, until the AI solution :)

It's not just physics. It's also patterns of workload distribution, changing over time. If you predict them, you can distribute energy more efficiently.

+1

There can be hidden variables that the PID system failed to account for. Workload distribution over time, temperature at the time of day, heat leaking into the datacenter, etc. I'm not sure if a PID based controller would account for such things.

What remains to be seen, however, is whether these manipulations keep the machines working at similar efficiencies and if they fail at similar rates as before turning on the control system.