by Martin Ford
By looking at all this, we have concluded that on a task level in the US economy, roughly about 50% of activities—not jobs, but tasks, and it’s important to emphasize this—that people do now are, in principle, automatable.
MARTIN FORD: You’re saying that half of what workers do could conceivably be automated right now, based on technology we already have?
JAMES MANYIKA: Right now, it is technically feasible to automate 50% of activities based on currently demonstrated technologies. But there are also separate questions, like how do those automatable activities then map into whole occupations?
So, when we then map back into occupations, we actually find that only about 10% of occupations have more than 90% of their constituent tasks automatable. Remember this is a task number, not a jobs number. We also find that something like 60% of occupations have about a third of their constituent activities automatable—this mix of course varies by occupation. This 60-30 already tells you that many more occupations will be complemented or augmented by technologies than will be replaced. This leads to the “jobs changed” phenomena I mentioned earlier.
MARTIN FORD: I recall that when your report was published, the press put a very positive spin on it—suggesting that since only a portion of most jobs will be impacted, we don’t need to worry about job losses. But if you had three workers, and a third of each of their work was automated, couldn’t that lead to consolidation, where those three workers become two workers?
JAMES MANYIKA: Absolutely, that’s where I was going to go next. This is a task composition argument. It might give you modest numbers initially, but then you start to realize that work could be reconfigured in lots of interesting ways.
For instance, you can combine and consolidate. Maybe the tipping point is not that you need all of the tasks in an occupation to be automatable; rather, maybe when you get close to say, 70% of the tasks being automatable, you may then say, “Let’s just consolidate and reorganize the work and workflow altogether.” So, the initial math may begin with modest numbers, but when you reorganize and consolidate the work, the number of impacted jobs start to get bigger.
However, there is yet another set of considerations that we’ve looked at in our research at MGI which we think have been missing in some of the other assessments on the automation question. Everything that we have described so far is simply asking the technical feasibility question, which gives you those 50% numbers, but that is really only the first of about five questions you need to ask.
The second question is around the cost of developing and deploying those technologies. Obviously, just because something’s technically feasible, doesn’t mean it will happen.
Look at electric cars. It’s been demonstrated we could build electric cars, and in fact that was a feasible thing to do more than 50 years ago, but when did they actually show up? When the costs of buying it, maintaining it, charging it, etc., became reasonable enough that consumers wanted to buy them and companies want to deploy them. That’s only happened very recently.
So, the cost of deployment is clearly an important consideration and will vary a lot, depending on whether you’re talking about systems that are replacing physical work, versus systems that are replacing cognitive work. Typically, when you’re replacing cognitive work, it’s mostly software and a standard computing platform, so the marginal cost economics can come down pretty fast, so that doesn’t cost very much.
If you’re replacing physical work, on the other hand, then you need to build a physical machine with moving parts; and the economics of those things, while they’ll come down, they’re not going to come down as fast as where things are just software. So, the cost of deployment is the second important consideration, which then starts to slow down deployment rates that might initially be suggested by simply looking at technical feasibility.
The third consideration is labor-market demand dynamics, taking into account labor quality and quantity, as well as the wages associated with that. Let me illustrate this by thinking in terms of two different kinds of jobs. We’ll look at an accountant, and we’ll look at a gardener. First let’s see how these considerations could play out in these occupations.
First, it is technically easier to automate large portions of what the accountant does, mostly data analysis, data gathering, and so forth, whereas it’s still technically harder to automate what a gardener does, which is mostly physical work in a highly unstructured environment. Things in these kinds of environments aren’t quite lined up exactly where you want them to be—as they would be in a factory, for example, and there’s unforeseen obstacles that can be in the way. So, the degree of technical difficulty of automating those tasks, our first question, is already far higher than your accountant.
Then we get to the second consideration: the cost of deploying the system, which goes back to the argument I just made. In the case of the accountant, this requires software with near zero-marginal cost economics running on a standard computing platform. With the gardener, it’s a physical machine with many moving parts. The cost economics of deploying a physical machine is always going to be—even as costs come down, and they are coming down for robotic machines—more expensive than the software to automate an accountant.
Now to our third key consideration, that is the quantity and quality of labor, and the wage dynamics. Here again it favors an automating the accountant, rather than automating the gardener. Why? Because we pay a gardener, on average in the United States, something like $8 an hour; whereas we pay an accountant something like $30 an hour. The incentive to automate the accountant is already far higher than the incentive to automate the gardener. As we work our way through this, we start to realize that it may very well be that some of these low-wage jobs may actually be harder to automate, from both a technical and economic perspective.
MARTIN FORD: This sounds like really bad news for university graduates.
JAMES MANYIKA: Not so fast. Often the distinction that’s made is high wage versus low wage; or high skill versus low skill. But I really don’t know if that’s a useful distinction.
The point I want to make is that the activities likely to be automated don’t line up neatly with traditional conceptions of wages structures or skills requirements. If the work that’s being done looks like mostly data collection, data analysis, or physical work in a highly structured environment, then much of that work is likely to be automated, whether it’s traditionally been high wage or low wage, high skill or low skill. On the other hand, activities that are very difficult to automate also cut across wage structures and skills requirements, including tasks that require judgment or managing people, or physical work in highly unstructured and unexpected environments. So many traditionally low wage and high wage jobs are exposed to automation, depending on the activities, but also many other traditionally low wage and high wages jobs may be protected from automation.
I want to make sure we cover all the different factors at play here, as well. The fourth key consideration has to do with benefits including and beyond labor substitution. There are going to be some areas where you’re automating, but it’s not because you’re trying to save money on labor, it is because you’re actually getting a better result or even a superhuman outcome. Those are places where you’re getting better perception or predictions that you couldn’t get with human capabilities. Eventually, autonomous vehicles will likely be an example of this, once they reach the point where they are safer and commit fewer errors than humans driving. When you start to go beyond human capabilities and see performance improvements, that can really speed up the business case for deployment and adoption.
The fifth consideration could be called societal norms, which is a broad term for the potential regulatory factors and societal acceptance factors we may encounter. A great example of this can be seen in driverless vehicles. Today, we already fully accept the fact that most commercial planes are only being piloted by an actual pilot less than 7% of the time. The rest of the time, the plane is flying itself
. The reason no-one really cares about the pilot situation, even if it goes down to 1%, is because no-one can see inside the cockpit. The door is closed, and we’re sitting on a plane. We know there’s a pilot in there, but whether we know that they’re flying or not doesn’t matter because we can’t see. Whereas with a driverless car, what often freaks people out is the fact that you can actually look in the driver’s seat and there’s no-one there; the car’s moving on its own.
There’s a lot of research going on now looking at people’s social acceptance or comfort with interacting with machines. Places like MIT are looking at social acceptance across different age groups, across different social settings, and across different countries. For example, in places like Japan, having a physical machine in a social environment is a bit more acceptable than in some other countries. We also know that, for example, different age groups are more or less accepting of machines, and it can vary depending on different environments or settings. If we move to a medical setting, with a doctor who goes into the back room to use a machine, out of view, and then just comes back with your diagnosis—is that okay? Most of us would accept that situation, because we don’t actually know what happened in the back room with the doctor. But if a screen wheels into your room and a diagnosis just pops up without a human there to talk you through it, would we be comfortable with that? Most of us probably wouldn’t be. So, we know that social settings affect social acceptance, and that this is going to also affect where see these technologies adopted and applied in the future.
MARTIN FORD: But at the end of the day, what does this mean for jobs across the board?
JAMES MANYIKA: Well, the point is that as you work your way through these five key considerations, you start to realize that the pace and extent of automation, and indeed the scope of the jobs that are going to decline, is actually a more deeply nuanced picture that’s likely to vary from occupation to occupation and place to place.
In our last report at MGI, which considered the factors I just described, and in particular considered wages, costs and feasibility, we developed a number of scenarios. Our midpoint scenario suggests that as many as 400 million jobs could be lost globally by 2030. This is an alarmingly large number, but as a share of the global labor force that is about 15%. It will be higher, though, in advanced countries than in developing countries, given labor-market dynamics, especially wages, that we’ve been discussing.
However, all these scenarios are obviously contingent on whether the technology accelerates even faster, which it could. If it did, then our assumption about “currently demonstrated technology” would be out of the window. Further, if the costs of deploying come down even faster than we anticipate, that would also change things. That’s why we’ve got these wide ranges in the scenarios that we’ve actually built for how many jobs would be lost.
MARTIN FORD: What about the “jobs gained” aspect?
JAMES MANYIKA: The “jobs gained” side of things is interesting because we know that whenever there’s a growing and dynamic economy, there will be growth in jobs and demand for work. This has been the history of economic growth for the last 200 years, where you’ve got vibrant, growing economies with a dynamic private sector.
If we look ahead to the next 20 years or so, there are some relatively assured drivers of demand for work. One of them is rising global prosperity as more people around the work enter the consuming class and demand products and services. Another is aging; and we know that aging is going to create a lot of demand for certain kinds of work that will lead to growth in a whole host of jobs and occupations. Now there’s a separate question as to whether those will turn into well-paying jobs or not, but we know that the demand for care work and other things is going to go up.
At MGI we’ve also looked at other catalysts, like whether we’re going to ramp up adaptation for climate change, retrofitting our systems and our infrastructure—which could drive demand for work above and beyond current course and speed. We also know that if societies like the United States and others finally get their act together to look at infrastructure growth, and make investments in infrastructure, then that’s also going to drive demand for work. So, one place where work’s going to come from is a growing economy and these specific drivers of demand for work.
Another whole set of jobs are going to come from the fact that we’re actually going to invent new occupations that didn’t previously exist. One of the fun analyses we did at MGI—and this was prompted by one of our academic advisors, Dick Cooper at Harvard—was to look at the Bureau of Labor Statistics. This typically tracks about 800 occupations, and there’s always a line at the bottom called “Other.” This bucket of occupations called “Other” typically reflects occupations that in the current measurement period have not yet been defined and didn’t exist, so the Bureau doesn’t have a category for them. Now, if you had looked at the Labor Statistics list in 1995, a web designer would have been in the “Other” category because it hadn’t been imagined previously, and so it hadn’t been classified. What’s interesting is that the “Other” category is the fastest-growing occupational category because we’re constantly inventing occupations that didn’t exist before.
MARTIN FORD: This is an argument that I hear pretty often. For example, just 10-years ago, jobs that involve social media did not exist.
JAMES MANYIKA: Exactly! If you look at 10-year periods in the United States, at least 8% to 9% of jobs are jobs that didn’t exist in the prior period—because we’ve created them and invented them. That’s going to be another source of jobs, and we can’t even imagine what those will be, but we know they’ll be there. Some people have speculated that category will include new types of designers, and people who trouble shoot and manage machines and robots. This undefined, new set of jobs will be another driver of work.
When we’ve looked at the kind of the jobs gained, and considered these different dynamics, then unless the economy tanks and there’s massive stagnation, the numbers of jobs gained are large enough to more than make up for the jobs lost. Unless, of course, some of variables change catastrophically underneath us, such as a significant acceleration in the development and adoption of these technologies, or we end up with massive economic stagnation. Any combination of those things and then yes, we’ll end up with more jobs lost than jobs gained.
MARTIN FORD: Ok, but if you look at the employment statistics, aren’t most workers employed in pretty traditional areas, such as cashiers, truck drivers, nurses, teachers, doctors or office workers? These are all job categories that were here 100 years ago, and that’s still where the vast majority of the workforce is employed.
JAMES MANYIKA: Yes, the economy is still made up of a large chunk of those occupations. While some of these will decline, few will disappear entirely and certainly not as quickly as some are predicting. Actually, one of the things that we’ve looked at is where, over the last 200 years, we’ve seen the most massive job declines. For example, we studied what happened to manufacturing in the United States, and the shift from agriculture to industrialization. We looked at 20 different massive job declines in different countries and what happened in each, compared to the range of scenarios for job declines due to automation and AI. It turned out the ranges that we anticipate now are not out of the norm, at least in the next 20 years anyway. Now beyond that, who knows? Even with some very extreme assumptions, we’re still well within the ranges of shifts that we have seen historically.
The big question, at least in the next 20 or so years, is whether there will be enough work for everybody. As we discussed, at MGI we conclude there will be enough work for everybody, unless we get to those very extreme assumptions. The other important question we must ask ourselves is how big are the scale of transitions that we’ll see between those occupations that are declining, and those occupations that are be growing? What level of movement will we see from one occupation to another, and how much will the workplace need to adjust and adapt to machines complementing people as opposed to peop
le losing their jobs?
Based on our research, we’re not convinced that on our current course and speed we’re well set up to manage those transitions in terms of skilling, educating, and on-the-job training. We actually worry more about that question of transition than about the “Will there be enough work?” question.
MARTIN FORD: So, there really is the potential for a severe skill mismatch scenario going forward?
JAMES MANYIKA: Yes, skill mismatches is a big one. Sectoral and occupation changes, where people have to move from one occupation to another, and adapt to higher or lower skill, or just different skills.
When you look at the transition in terms of sectoral and geographic locational questions, say in the United States, there will be enough work, but then you go down to the next level to look at the likely locations for that work, and you see the potential for geographic locational mismatches, where some places look like they’ll be more in a hole than other places. These kinds of transitions are quite substantial, and it’s not quite clear if we’re ready for them.
The impact on wages is another important question. If you look at the likely occupational shifts, so many of the occupations that are likely to decline have tended to be the middle-wage occupations like accountants. Many well-paying occupations have involved data analysis in one form or another. They have also involved physical work in highly structured environments, like manufacturing. And so that’s where many of the occupations that are going to decline sit on the wage spectrum. Whereas many of the occupations that are going to grow—like the care work we just talked about—are occupations that, at today’s current wage structures, don’t pay as well. These occupational mix shifts will likely cause a serious wage issue. We will need to either change the market mechanisms for how these wage dynamics work or develop some other mechanisms that shape the way these wages are structured.
