Today we answer the question that’s been in the back of everyone’s mind since The Terminator came out – is Skynet taking over? The short answer is no, but let me explain…
The AI capable of decision-making and judgment (i.e., Skynet) is nowhere near being ready to go, and won’t be for at least another 30+ years. On the other hand, applied AI (i.e., mission-specific and code-driven) is great at completing tasks as long as it’s written into its program.
So AI that’s used as a copywriter or chatbot is already up and running, but the omnipotent robots that hunt us down and try to take over the world…ya, not happening anytime soon.
The biggest takeaway is that change is coming, but not in the scary revolution type of way that we’ve been hearing about for years.
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Hey Everybody. Peter Zeihan here. We’ve had a lot of questions come in about AI and what that means for the workforce moving forward. What sort of activity should we expect to be replaced? What does this mean for economics and labor and politics and are there any obvious winners either in terms of geography or sectors? What popular when it comes in is whether or not this is going to hit red states or blue states more. For example, the cop out answer is we don’t really know yet because we’re dealing with technologies that have yet to be invented. But there are a few general guidelines we have.
First of all, it’s not so much that jobs get created or destroyed, it’s that they change. And it’s pretty common when you’re dealing with an environment that has evolved because of technology. You know, jobs evolve, too. We’ve been talking about technology overwhelming the workforce really since the onset of the Industrial Revolution, and it obviously generates changes. We don’t all live on subsistence farms anymore. The trick is whether or not the technology evolves faster than our political ability to adapt to the changing workforce conditions. And I would argue that at least at the moment, we’re nowhere near that.
I mean, yes, we’re dealing with the information revolution. And, yes, there is the possibility that’s going to replace a lot of jobs, increase productivity. The point that a lot of people just don’t have anything to do. But that’s all theoretical. Our experience in the last five years is, if anything, it’s going to be the opposite. You see the sort of things that IT revolutions do is they make it, it’s in the name information, information tech. It manipulates information at a faster rate, but that is not where the uneducated people in our society are. Most of the uneducated society, people in our society are in lower class, blue collar jobs. That is not something that AI can help with at all. That’s something that the advances we’ve seen in productivity are almost irrelevant. AI instead is taking away those low to mid skilled white collar jobs, which is not normally what we think of when we think about the sort of jobs that can be destroyed.
So we’ve actually, in the last three years since the greatest increase in take home pay for low skilled blue collar workers in over a century. And that has actually helped in the case of United States, narrow economic inequality to a degree that we have not seen since before the World Wars. So if anything, the theory is proving itself wrong rather than right.
However, if you’re, say, a copy editor or a secretary, well, you might have some really big problems because I already has been able to deal with those jobs in a more efficient manner. You just don’t need as many people. In fact, the blockchain, which is one of the things that undergirds crypto. Crypto is something that could be very transformative in things like health care. If you think about any doctor’s office you’ve been in, there’s that huge forest of staff in the back who are basically on the phone with the insurance agents every day, all day. Well, the whole idea of blockchain is that anyone who controls half of the pieces can grant others access. Well, in your health care records, that would be you. And if everything from that can be digitized, then that entire flood of low skilled white collar workers in the back of every doctor’s office and hospital simply goes away. So it’s probably not going to hit where we think it is. And it’s probably not a red versus blue thing, and it’s probably not a coastal versus interior thing. It’s a mid-levels of education versus the edges.
If you’re highly educated or low educated, you look fine from this. Second, there’s the issue of time. Now, obviously, these technologies continue moving, but there’s two reasons to expect that we’re going to have a lot more time to make this adaptation that I think a lot of people give it credit for. Number one is, as the baby boomers are retiring, which is happening right now, they are liquidating all of their investments and going into really boring stuff like T-bills and cash.
That’s not what funds I.T. startups. That’s not even what funds the big IT companies in Silicon Valley. For that, you need venture capital. You need a high velocity of money. Retirees are no good for that. And the whole world is aging very rapidly. And baby boomers are not a phenomenon limited to the United States. So we’re going to see the amount of capital just kind of seize up in the entire space.
At the same time, most of the world is running out of the 20 and the 30 somethings that are necessary to do the research and develop these technologies in the first place. So overall, we should expect the pace of technological evolution in the world to slow quite a bit in the two decades to come, compared to the two decades we’ve just completed.
Second, we’re not close to a general, a breakthrough. Let me explain what I mean by that. Artificial intelligence kind of falls into two general buckets. General A.I. is, you know, Skynet. The idea that the machine can actually look at a situation thing, come up with a potential solution and act on it, or nowhere close to that. I don’t know anyone, even Elon Musk, who thinks we’re going to be there before 2050. And this is before you consider that the amount of capital and workers that are available to develop these sort of things is in the process of drying up. So probably we’re looking at 2060, 2070 or beyond. We’re just we’re not even close.
The other type of AI is applied A.I. or mission specific A.I. and it’s not so much artificial intelligence in the way that we kind of have it on our heads. But it is machine learning, but it’s really more like machine programing. So you put in dozens, hundreds of thousands of if then statements into a program for it to execute. And as long as the conditions that are presented to the machine fall within the rubric of what you programed, you’re okay. But if you see something even a little out of context, the whole thing tends to fall apart.
So an example, let’s say you’re developing an A.I. driving program and you tell it what a stop sign looks like. What if the stop sign has a bumper sticker on it or graffiti, or if it’s on the side of a building as part of an ad. Applied to a I can’t recognize those other conditions. And if you kind of widen your parameters to make it a rounding error, then it’s going to make a very real mistakes in the very real world that any four year old could hit.
So if you need A.I. to do calculus, yes, they’re light years ahead of what we as humans can do right now. If you need it to make a decision based on a judgment call, they are still completely and utterly incompetent.
Alright. But let’s assume that some of this happens anyway. And so we’re going to have to deal with an A.I. system that is making decisions. What does that mean for the job industry?
So historically speaking, this is not the first time we’ve dealt with this issue. In fact, for those of you who remember your 1800s of political economic theory, good old Karl Marx, his whole idea was that the future of the proletariat was to take over from the capitalists, that once the industrial plant was built, then you could get rid of the bourgeoisie and the proletariat could live and do very, very well for itself because the machines and the industrial plant would be there to provide for everyone.
Well, folks, he was wrong then. He’s still wrong. Now, universal basic income is the idea that we live in such a world of plenty that we don’t need to work. But as we have seen in the last three or five years, if anything, the opposite is true. The productivity has stalled in part because of tech, but moreover, because we’ve discovered that as populations evolve in industrialization, we live longer, we have fewer kids. And that means after we urbanize five, six, seven, eight decades after, we’re actually running out of young people to do a lot of the lower skilled work. So if anything, Marx was completely wrong because the part of the population that he thought that would benefit the most from industrialization is at the current moment, actually not doing all that well.
The middle class, it’s the lower classes that are cleaning up right now. There are very, very few places in the United States at this point where if you were earning $15 an hour before COVID, you’re still in that bracket today. You’ve been able to leverage the fact that there’s a sharp labor shortage to move up, and that means you have a vested interest in the system.
And it means if you decide not work, there is no one who is willing to pay you to not work because there are jobs, jobs, jobs everywhere. So in conclusion, is AI real yes, but we’ve been thinking about it completely wrong. And most of the assessments that I have seen from almost everywhere are drawing the wrong conclusions when it comes to sociological outcomes.
It’s going to be important. It’s going to change who we are. It’s going to change how we live and how we work. But the word here is change. It’s not a revolution. All right. That’s it for me. I want to go get a snow shovel. Take care.