Our automation data analysis found a few surprising results. We found that labor demand is inversely correlated with education. As if, when facing a labor shortage for a particular kind of worker, employers respond in part by lowering education requirements. And even though more automation directly lowers demand for a job, it seems that labor demand changes, relative to labor supply changes, becomes a smaller factor for jobs where automaton rises more.

But the most interesting surprise, I think, is that while, over the last twenty years, we’ve seen no noticeable change in the factors that predict which jobs get more automated, we have seen job features change to become more suitable to automation. On average jobs have moved by about a third of a standard deviation, relative to the distribution of job automation across jobs. This is actually quite a lot. Why do jobs change this way?

Consider the example of a wave of human colonization moving over a big land area. Instead of all the land becoming colonized more densely at same rate everywhere, what you instead see is new colonization happening much more near old colonization. In the U.S., dense concentrations started in the east and slowly spread to the west. There was little point in clearing land to grow stuff if there weren’t enough other folks nearby to which to sell your crops, and from which to buy supplies.

If you looked at any particular plot of land and asked what factors predict if it will be colonized soon, you might see those factors stay pretty constant over time. But many of those factors would depend on what other land nearby had been colonized recently. In a spatial colonization wave, there can be growth without much change in the underlying tech. Instead, the key dynamic can be that there are big time delays to allow an initial tech potential to become realized via spreading across a large landscape. A colonization wave can be growth without much tech change.

Now think about the space of job tasks as a similar sort of landscape. Two tasks are adjacent to other tasks when the same person tends to do both, when info or objects are passed from one to the other, when they take place close in place and time, and when their details gain from being coordinated. The ease of automating each task depends on how regular and standardized are its inputs, how easy it is to formalize the info on which key choices depend, how easy it is to evaluate and judge outputs, and how simple, stable, and mild are the physical environments in which this task is done.

When the tasks near a particular task get more automated, those tasks tend more to happen in a more controlled stable environment, the relevant info tends to be more formalized, and related info and objects get simpler, more standardized, and more reliably available. And this all tends to make it easier to automate such tasks. Much like how land is easier to colonize when nearby land is more colonized.

Among the job features that predict automation in our analysis, the strongest is: Pace Determined By Speed Of Equipment. This feature clearly fits my story here; it says you coordinate your task closely with a task done by a machine. Many others fit as well; here is more from our paper:

Pace Determined By Speed Of Equipment picks out jobs that coordinate closely with machinery, while Importance of Repeating Same Tasks picks out jobs with many similar and independent small tasks. Variety picks out an opposite case of dissimilar tasks. The job features Wear Common Safety Equipment and Indoors Environmentally Controlled pick out tasks done in calm stable environments, where machines function better, while Hearing Sensitivity picks out less suitable complex subtle environments. In jobs with frequent Letters and Memos, such memos tend to be short and standardized. Jobs with more Advancement are “results oriented”, with more clearly measurable results. Simple machines tend to be bad at Thinking Creatively, Innovation and Mathematics. Physical Proximity picks out jobs done close to humans, usually because of needed human interactions, which tend to be complex, and where active machines could risk hurting them.

We have long been experiencing a wave of automation passing across the space of job tasks. Some of this increase in automation has been due to falling computer tech costs, improving algorithms and tools, etc. But much of it may simply be the general potential of this tech being realized via a slow steady process with a long delay: the automation of tasks near other recently automated tasks, slowly spreading across the landscape of tasks.