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Comment by jtraffic | Hacker News Reader

> Plus, modern factories rely more on automation than ever, so even if production comes back, it might be done by robots.

I feel this is the most important sentence in the article. The important question is not about individual products for end-users, but about intermediate means of manufacturing automation.

I really don't know which countries are most advanced in this area, but it makes the question of human labor less important. Or, at least it moves it up the skills ladder, from "making a glove" to "designing a robot".

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Disagree. This argument is a huge straw man. We are still a long way off from 100% lights-out factories being the norm for most industries. Even in IT manufacturing which is highly-automated, Foxconn employs hundreds of thousands of people in China. Most automotive factories employ several thousand workers.

That's fair. But I guess I think of it as a somewhat continuous transition from human to machine manufacturing. Yes, currently manufacturing uses lots of humans, but that is changing, and the pace of change has not, AFAIK, reached any plateau.

What I think matters, is where we are going, not where we are. You give China as evidence of the human dominated state of manufacturing. I give it as evidence of the shift toward robot dominated manufacturing: https://www.technologyreview.com/s/601215/china-is-building-...

Many years ago there was a startup which did web-storefront on-demand laser-cut custom-fit khakis. They did the largely-manual stitching in the US, rather than deal with the cost and delay of shipping further.

My fuzzy recollection is they sold out to Levi's, which mismanaged it into nonexistence. The patents prevented replacement. And the stitching factory used eventually died.

So at least some partially-automated traditionally-low-margin industry is/was possible in the US. But the usual issues of losing needed industry infrastructure still apply.

Machine learning has improved by leaps and bounds in the last half decade. Much of that progress has yet to make it out of the lab. But that's typical for new technologies, and will happen eventually.

It's not a huge technological leap from "robot learns to play Dota" to "robot learns to stitch baseball gloves". Machine vision and reinforcement learning were always the technologies holding back interesting applications of robotics. And now they are basically solved problems.

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