You can’t automate what you don’t understand

Continuing the conversation on automation that was initiated by Eli Dourado, I love the way this conversation has turned to fear, history, and rhetoric. Any post that mentions Chaldeans is a winner in my book.

But I’m going to be a bit less high falutin’ than that and look at the technical issue of whether robots are replacing jobs (they are) and what that means for the future (nothing bad, IMO).

Basically, anything that can be automated is going to get taken over by robots. Even highly skills professions like lawyeringmedical diagnostics and anesthesiology are subject to robot take-over, not just middle-skill and low-skill jobs (as our economy has defined such things for the past couple centuries). It’s literally anything that can be reduced to an algorithm that’s in danger.

But where do these algorithms come from? Who tells the robot how to make a better hamburger? I’ll tell you one thing, it sure isn’t going to be a computer programmer who can’t make anything fancier than ramen noodles himself.

We can see an inkling of the future in a Toyota plant in Japan. In an article I read a couple months ago (unfortunately my Google Fu has failed me I cannot find the article now), Toyota described how they are returning in a limited way to human labor for building cars. Not just humans operating a stamping press, but actually hand-forging and hand-welding car bumpers together.

This artisanal “cars made by hand” strategy is not something that can scale, and Toyota has no intention of scaling it up. The number of people doing this is very limited. What’s going on is that Toyota ran into a limit to their ability to extract efficiency from robots and automation, and the limit was the personal knowledge and experience of the people programming the robots.

Within a few months of making car bumpers by hand however, the “car-artisans” (my term, not the article’s) were able to find significant efficiency improvements. The car-artisans turned their experience into an algorithm that improved efficiency by a biggish number I cannot recall now (20%? 40%? Anyway, it was a big bump, not a minor improvement).

This is how I see a big part of the future jobs playing out. It won’t be low skill or high skill as we understand it today, but an approach that more resembles a series of apprenticeships where you spend however long is necessary really learning and understanding a system, improving on it, teaching it to robots, and then moving on to something else.

This sort of system will be a major shift from our current economy. For one thing it will require long term investments in human capital as a person works their way deep into a system’s inner workings. It will emphasize deep learning and creative problem solving. And it will require that people make themselves obsolete and start all over again on a new problem fairly regularly.

But one thing I do not fear, just to anticipate an oft-seen complaint, is the possibility that large swaths of the human population are unsuited for this work. In fact, I think this could be the sort of work that humans are most suited for. Humans are curious, and if given a problem that interests them, very self-motivated. Agency costs are minimal when both the principal wants a problem solved and the agent is driven by his own curiosity to solve it. This is the lesson embedded in the success of the Montessori educational model, which benefits children of all cognitive ranges and interests.

Frankly, I’m looking forward to this future. From an enjoyment perspective, the drudgery of drafting a particular contract or diagnosing a particular cancer for the 1,000th time really isn’t any more fun than the drudgery of tilling a field of wheat. I’m happy to hand that off to a robot. Bring me the new.

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