Regarding whether human evaluations are a good target to optimise for, no, I certainly don't think so. That's not very different than calculating BLEU scores, except that instead of comparing a machine generated translation with one reference text, it's compared with peoples' subjective criteria, which actually serves to muddle the waters even more -because who knows why different people thought the same translation was good or bad? Are they all using the same criteria? Doubtful! But if they're not, then what have we learned? That a bunch of humans agreed that some translation was good, or bad, each for their own reasons. So what? It doesn't make any difference when the human evaluators are blinded, you can make the same experiment with human translations only and you'll still have not learned anything about the quality of the translation- just the subjective opinions of a particular group of humans about it.
See, the problem is not just with machine translation. Evaluating human translaion results is also very hard to do because translation itself is a very poorly characterised task. The question "what is a good translation is very difficult to answer. We don't have, say, a science of translation to tell us how a text should be translated between two languages. So in machine translation people try to approximate not only the task of translation, but also its evaluation- without understanding either. That's a very bad spot to be in.
In fact, a "science of translation" would be a useful goal for AI research, but it's the kind of thing that I complain is not done anymore, having been replaced with beating meaningless benchmarks.
Regarding the fact that neural machine translation "generates value", you mean that it's useful because it's deployed in production and people use it? Well, "even a broken clock is right twice a day" so that's really not a good criterion of quality at all. In fact, as a criterion for an AI approach it's very disappointing. Look at the promise of AI: "machines that think like humans!". Look at the reality: "We're generating value!". OK. Or we could make an app that adds bunny ears and cat noses to peoples' selfies (another application of AI). People buy the app- so it's "generating value". Or we can generate value by selling canned sardines. Or selling footballs. Or selling foot massages. Or in a myriad other ways. So why do we need AI? It's just another useless trinket that is sold and bought while remaining completely useless. And that for me is a big shame.