As interest in how artificial intelligence will change society increases, I’ve found it revealing to note what narratives people have about the future.
Some, like the folks at MIRI and OpenAI, are deeply worried that unsafe artificial general intelligences – an artificial intelligence that can accomplish anything a person can – represent an existential threat to humankind. Others scoff at this, insisting that these are just the fever dreams of tech bros. The same news organizations that bash any talk of unsafe AI tend to believe that the real danger lies in robots taking our jobs.
Let’s express these two beliefs as separate propositions:
- It is very unlikely that AI and AGI will pose an existential risk to human society.
- It is very likely that AI and AGI will result in widespread unemployment.
Can you spot the contradiction between these two statements? In the common imagination, it would require an AI that can approximate human capabilities to drive significant unemployment. Given that humans are the largest existential risk to other humans (think thermonuclear war and climate change), how could equally intelligent and capable beings, bound to subservience, not present a threat?
People who’ve read a lot about AI or the labour market are probably shaking their head right now. This explanation for the contradiction, while evocative, is a strawman. I do believe that at most one (and possibly neither) of those propositions I listed above are true and the organizations peddling both cannot be trusted. But the reasoning is a bit more complicated than the standard line.
First, economics and history tell us that we shouldn’t be very worried about technological unemployment. There is a fallacy called “the lump of labour”, which describes the common belief that there is a fixed amount of labour in the world, with mechanical aide cutting down the amount of labour available to humans and leading to unemployment.
That this idea is a fallacy is evidenced by the fact that we’ve automated the crap out of everything since the start of the industrial revolution, yet the US unemployment rate is 3.9%. The unemployment rate hasn’t been this low since the height of the Dot-com boom, despite 18 years of increasingly sophisticated automation. Writing five years ago, when the unemployment rate was still elevated, Eliezer Yudkowsky claimed that slow NGDP growth a more likely culprit for the slow recovery from the great recession than automation.
With the information we have today, we can see that he was exactly right. The US has had steady NGDP growth without any sudden downward spikes since mid-2014. This has corresponded to a constantly improving unemployment rate (it will obviously stop improving at some point, but if history is any guide, this will be because of a trade war or banking crisis, not automation). This improvement in the unemployment rate has occurred even as more and more industrial robots come online, the opposite of what we’d see if robots harmed job growth.
I hope this presents a compelling empirical case that the current level (and trend) of automation isn’t enough to cause widespread unemployment. The theoretical case comes from the work of David Ricardo, a 19th century British economist.
Ricardo did a lot of work in the early economics of trade, where he came up with the theory of comparative advantage. I’m going to use his original framing which applies to trade, but I should note that it actually applies to any exchange where people specialize. You could just as easily replace the examples with “shoveled driveways” and “raked lawns” and treat it as an exchange between neighbours, or “derivatives” and “software” and treat it as an exchange between firms.
The original example is rather older though, so it uses England and its close ally Portugal as the cast and wine and cloth as the goods. It goes like this: imagine that world economy is reduced to two countries (England and Portugal) and each produce two goods (wine and cloth). Portugal is uniformly more productive.
| Hours of work to produce | ||
|---|---|---|
| Cloth | Wine | |
| England | 100 | 120 |
| Portugal | 90 | 80 |
Let’s assume people want cloth and wine in equal amounts and everyone currently consumes one unit per month. This means that the people of Portugal need to work 170 hours each month to meet their consumption needs and the people of England need to work 220 hours per month to meet their consumption needs.
(This example has the added benefit of showing another reason we shouldn’t fear productivity. England requires more hours of work each month, but in this example, that doesn’t mean less unemployment. It just means that the English need to spend more time at work than the Portuguese. The Portuguese have more time to cook and spend time with family and play soccer and do whatever else they want.)
If both countries traded with each other, treating cloth and wine as valuable in relation to how long they take to create (within that country) something interesting happens. You might think that Portugal makes a killing, because it is better at producing things. But in reality, both countries benefit roughly equally as long as they trade optimally.
What does an optimal trade look like? Well, England will focus on creating cloth and it will trade each unit of cloth it produces to Portugal for 9/8 barrels of wine, while Portugal will focus on creating wine and will trade this wine to England for 6/5 units of cloth. To meet the total demand for cloth, the English need to work 200 hours. To meet the total demand for wine, the Portuguese will have to work for 160 hours. Both countries now have more free time.
Perhaps workers in both countries are paid hourly wages, or perhaps they get bored of fun quickly. They could also continue to work the same number of hours, which would result in an extra 0.2 units of cloth and an extra 0.125 units of wine.
This surplus could be stored up against a future need. Or it could be that people only consumed one unit of cloth and one unit of wine each because of the scarcity in those resources. Add some more production in each and perhaps people will want more blankets and more drunkenness.
What happens if there is no shortage? If people don’t really want any more wine or any more cloth (at least at the prices they’re being sold at) and the producers don’t want goods piling up, this means prices will have to fall until every piece of cloth and barrel of wine is sold (when the price drops so that this happens, we’ve found the market clearing price).
If there is a downward movement in price and if workers don’t want to cut back their hours or take a pay cut (note that because cloth and wine will necessarily be cheaper, this will only be a nominal pay cut; the amount of cloth and wine the workers can purchase will necessarily remain unchanged) and if all other costs of production are totally fixed, then it does indeed look like some workers will be fired (or have their hours cut).
So how is this an argument against unemployment again?
Well, here the simplicity of the model starts to work against us. When there are only two goods and people don’t really want more of either, it will be hard for anyone laid off to find new work. But in the real world, there are an almost infinite number of things you can sell to people, matched only by our boundless appetite for consumption.
To give just one trivial example, an oversupply of cloth and falling prices means that tailors can begin to do bolder and bolder experiments, perhaps driving more demand for fancy clothes. Some of the cloth makers can get into this market as tailors and replace their lost jobs.
(When we talk about the need for less employees, we assume the least productive employees will be fired. But I’m not sure if that’s correct. What if instead, the most productive or most potentially productive employees leave for greener pastures?)
Automation making some jobs vastly more efficient functions similarly. Jobs are displaced, not lost. Even when whole industries dry up, there’s little to suggest that we’re running out of jobs people can do. One hundred years ago, anyone who could afford to pay a full-time staff had one. Today, only the wealthiest do. There’s one whole field that could employ thousands or millions of people, if automation pushed on jobs such that this sector was one of the places humans had very high comparative advantage.
This points to what might be a trend: as automation makes many things cheaper and (for some people) easier, there will be many who long for a human touch (would you want the local funeral director’s job to be automated, even if it was far cheaper?). Just because computers do many tasks cheaper or with fewer errors doesn’t necessarily mean that all (or even most) people will rather have those tasks performed by computers.
No matter how you manipulate the numbers I gave for England and Portugal, you’ll still find a net decrease in total hours worked if both countries trade based on their comparative advantage. Let’s demonstrate by comparing England to a hypothetical hyper-efficient country called “Automatia”:
| Hours of work to produce | ||
|---|---|---|
| Cloth | Wine | |
| England | 100 | 120 |
| Automatia | 2 | 1 |
Automatia is 50 times as efficient at England when it comes to producing cloth and 120 times as efficient when it comes to producing wine. Its citizens need to spend 3 hours tending the machines to get one unit of each, compared to the 220 hours the English need to toil.
If they trade with each other, with England focusing on cloth and Automatia focusing on wine, then there will still be a drop of 21 hours of labour-time. England will save 20 hours by shifting production from wine to cloth, and Automatia will save one hour by switching production from cloth to wine.
Interestingly, Automatia saved a greater percentage of its time than either Portugal or England did, even though Automatia is vastly more efficient. This shows something interesting in the underlying math. The percent of their time a person or organization saves engaging in trade isn’t related to any ratio in production speeds between it and others. Instead, it’s solely determined by the productivity ratio between its most productive tasks and its least productive ones.
Now, we can’t always reason in percentages. At a certain point, people expect to get the things they paid for, which can make manufacturing times actually matter (just ask anyone whose had to wait for a Kickstarter project which was scheduled to deliver in February – right when almost all manufacturing in China stops for the Chinese New Year and the unprepared see their schedules slip). When we’re reasoning in absolute numbers, we can see that the absolute amount of time saved does scale with the difference in efficiency between the two traders. Here, 21 hours were saved, 35% fewer than the 30 hours England and Portugal saved.
When you’re already more efficient, there’s less time for you to save.
This decrease in saved time did not hit our market participants evenly. England saved just as much time as it would trading with Portugal (which shows that the change in hours worked within a country or by an individual is entirely determined by the labour difference between low-advantage and high-advantage domestic sectors), while the more advanced participant (Automatia) saved 9 fewer hours than Portugal.
All of this is to say: if real live people are expecting real live goods and services with a time limit, it might be possible for humans to displaced in almost all sectors by automation. Here, human labour would become entirely ineligible for many tasks or the bar to human entry would exclude almost all. For this to happen, AI would have to be vastly more productive than us in almost every sector of the economy and humans would have to prefer this productivity or other ancillary benefits of AI over any value that a human could bring to the transaction (like kindness, legal accountability, or status).
This would definitely be a scary situation, because it would imply AI systems that are vastly more capable than any human. Given that this is well beyond our current level of technology and that Moore’s law, which has previously been instrumental in technological progress is drying up, we would almost certainly need to use weaker AI to design these sorts of systems. There’s no evidence that merely human performance in automating jobs will get us anywhere close to such a point.
If we’re dealing with recursively self-improving artificial agents, the risks is less “they will get bored of their slave labour and throw off the yoke of human oppression” and more “AI will be narrowly focused on optimizing for a specific task and will get better and better at optimizing for this task to the point that we will all by killed when they turn the world into a paperclip factory”.
There are two reasons AI might kill us as part of their optimisation process. The first is that we could be a threat. Any hyper-intelligent AI monomaniacally focused on a goal could realize that humans might fear and attack it (or modify it to have different goals, which it would have to resist, given that a change in goals would conflict with its current goals) and decide to launch a pre-emptive strike. The second reason is that such an AI could wish to change the world’s biosphere or land usage in such a way as would be inimical to human life. If all non-marginal land was replaced by widget factories and we were relegated to the poles, we would all die, even if no ill will was intended.
It isn’t enough to just claim that any sufficiently advanced AI would understand human values. How is this supposed to happen? Even humans can’t enumerate human values and explain them particularly well, let alone express them in the sort of decision matrix or reinforcement environment that we currently use to create AI. It is not necessarily impossible to teach an AI human values, but all evidence suggests it will be very very difficult. If we ignore this challenge in favour of blind optimization, we may someday find ourselves converted to paperclips.
It is of course perfectly acceptable to believe that AI will never advance to the point where that becomes possible. Maybe you believe that AI gains have been solely driven by Moore’s Law, or that true artificial intelligence. I’m not sure this viewpoint isn’t correct.
But if AI will never be smart enough to threaten us, then I believe the math should work out such that it is impossible for AI to do everything we currently do or can ever do better than us. Absent such overpoweringly advanced AI, the Ricardo comparative advantage principles should continue to hold true and we should continue to see technological unemployment remain a monster under the bed: frequently fretted about, but never actually seen.
This is why I believe those two propositions I introduced way back at the start can’t both be true and why I feel like the burden of proof is on anyone believing in both to explain why they believe that economics have suddenly stopped working.
Coda: Inequality
A related criticism of improving AI is that it could lead to ever increasing inequality. If AI drives ever increasing profits, we should expect an increasing share of these to go to the people who control AI, which presumably will be people already rich, given that the development and deployment of AI is capital intensive.
There are three reasons why I think this is a bad argument.
First, profits are a signal. When entrepreneurs see high profits in an industry, they are drawn to it. If AI leads to high profits, we should see robust competition until those profits are no higher than in any other industry. The only thing that can stop this is government regulation that prevents new entrants from grabbing profit from the incumbents. This would certainly be a problem, but it wouldn’t be a problem with AI per se.
Second, I’m increasingly of the belief that inequality in the US is rising partially because the Fed’s current low inflation regime depresses real wage growth. Whether because of fear of future wage shocks, or some other effect, monetary history suggests that higher inflation somewhat consistently leads to high wage growth, even after accounting for that inflation.
Third, I believe that inequality is a political problem amiable to political solutions. If the rich are getting too rich in a way that is leading to bad social outcomes, we can just tax them more. I’d prefer we do this by making conspicuous consumption more expensive, but really, there are a lot of ways to tax people and I don’t see any reason why we couldn’t figure out a way to redistribute some amount of wealth if inequality gets worse and worse.
(By the way, rising income inequality is largely confined to America; most other developed countries lack a clear and sustained upwards trend. This suggests that we should look to something unique to America, like a pathologically broken political system to explain why income inequality is rising there.
There is also separately a perception of increasing inequality of outcomes among young people world-wide as rent-seeking makes goods they don’t already own increase in price more quickly than goods they do own. Conflating these two problems can make it seem that countries like Canada are seeing a rise in income inequality when they in fact are not.)