The AI Revolution Through an Economic Lens

Today on the AI Breakdown, we're reading and discussing a piece on the economics of the AI Revolution. The AI Breakdown is a daily podcast and video about the most important news and discussions in AI. Go to breakdown.network for more information about our YouTube, our Discord and our newsletter. Hello friends, Today we are reading a piece that was first published by the Mises Institute. The bitcoiners among you in the audience will of course know that name as it is one of the bastions of Austrian economics and overall it's a very individual, liberty and freedom focused type of philosophy. The author of the piece that we're reading today is Per Byland. He's a Senior Fellow of the Mises Institute and Associate professor of Entrepreneurship at the Speer School of Business at Oklahoma State University. This piece is called the Economics of the AI Revolution, and rather than give it a bunch of preamble, let's just read it and then we can discuss, byland writes. In a recent article, we briefly summarized what is what we would today call artificial intelligence. Whereas these technologies are certainly impressive and may even pass the Turing test, they are not beings and have no consciousness. Thus, this is neither the time nor the place to discuss philosophical issues of how to define a true or full AI, an artificial general intelligence, and whether we should recognize AI software legally as a person. Economically speaking, AI as technology, whether it is used for entertainment or in production, is a good. As Carl Menger taught what makes something a good is that it, whatever it may be, has the ability to satisfy a human need, that it must be recognized as such, and that a person, the consumer, has or can gain command over it to satisfy those actual needs. In other words, it must be scarce. There is less of it than we can use to satisfy wants and understood as valuable because we believe it can satisfy wants. AI certainly fits the criteria. Section AI as a Consumption Good when people entertain themselves by discussing with AI or generating quirky images using Dall E, it is a good of the lowest order, a consumption good. As such, the economic consequences are limited to the effect this has on consumer behavior, but this may in turn have a significant impact on production. Some consumption goods revolutionized the economy and society. Examples of such goods include the automobile from the introduction of Ford's Model T, and the smartphone, starting with Apple's iPhone. The former disrupted transportation and infrastructure and facilitated, just in time, manufacturing and urban sprawl, just to mention a few effects. The latter changed everything from how we bank to how we travel. Point here is that as consumer behavior changes, the production structure follows along for example, with the broad adoption of the smartphone paper map, production has all but disappeared, whereas digital location services and intelligent logistics have seen enormous growth and development. And change leads to more change because entrepreneurs build on, add to and challenge the new discoveries. AI has the potential to change consumer behavior well beyond its design functionality. Exactly how and in what ways remains to be seen, but it is safe to say that it has potential. On the other hand, many goods have had potential to disrupt but didn't leave a mark. For example, we may see people produce their own stories, songs, images, and even movies. So perhaps instead of relying on television or Netflix and Hollywood producers, we'll make movie night into a make a movie night where we watch content we have generated. And that fits us perfectly. AI as a higher order good as a tool, and thus a good of a higher order. AI has already had an effect and promises to disrupt several trades because it is very effective at producing and presenting content, including translating and editing text. Content related professions are threatened by AI. This includes journalists and copy editors, as AI programs can write and edit faster than humans. After all, anyone can ask AI to produce or edit a text. Students already use AI to spice up or improve their papers or let AI write them from scratch. Is similarly affecting photographers and illustrators. It only takes a minute to have Dall? E produce a new image exactly as directed, or to have an AI algorithm remove or add things in a picture you snapped, whereas having an illustrator create something takes much longer. Not to mention the cost. Programmers and system developers are also seeing the effects of AI, which has no problem both generating new code without bugs or checking already written code. Legacy software written in dated and ineffective programming languages can be run through an AI to make the coding more efficient and converted into a modern language. AI is also affecting academia. Why have an instructor tell students about some subject matter instead of letting AI do it? After all, the AI can easily present content in a way that the student prefers. For example, make a movie to explain, say, biology or chemistry in an entertaining way. And it can answer all kinds of questions without ever getting bothered or cranky. And it has nowhere else to be in research. AI can analyze data more effectively and run thousands of different regressions on data to find something that is significant and important. It can write up the paper too, with citations and everything in just seconds. AI as Production Capital all of this means AI can and will be used in production. In fact, it already is, and we have only started to see the effects. AI is best categorized as capital, which is used to make labor more productive, more value output per hour of labor invested through facilitating more roundabout but more effective production structures. Capital goods in general have one or both of two functions. It makes existing production processes more effective by increasing productivity or it makes possible types of production that were not previously possible. AI checks both boxes we have already seen how people working in several types of content based professions can easily be made more productive or replaced entirely by AI. It can also do things that people may have been unable to do or never thought of doing. This of course can cause so called technological unemployment as people lose their jobs because AI can do them better and cheaper. But this is a dystopian way of describing something quite normal and highly useful that we relieve people with all their ingenuity from comparatively simple tasks so that they can create much more value elsewhere. It is of course problematic for any person losing their source of income, but it is highly beneficial to consumers and therefore society at large that these and other professions are creatively destroyed. The economic point of employment is not to provide people with an income so they can pay taxes, but to produce goods that can satisfy consumer wants to make our lives better. Just like there are very few stable boys or buggy whip producers since the automobile revolution, the future will see fewer people doing news reporting, copy editing or coding. Note also that this revolution is not nearly as sudden and disruptive as it might first seem. The news media, for example, have for many years reduced the number of journalists doing reporting. Most outlets nowadays merely republishing standard articles from the AP are Reuters and Software development already uses increasingly effective development environment that correct and predict commands, allowing for what you see is what you get in drag and drop development and can debug code and suggest solutions to bugs. AI is only another step in this process, but the threat is greatly exaggerated. We tend to overestimate the impact of technology in the short term, but underestimate it in the long term. Limitations to overcome There is a problem however, and it has to do with how large language models work and what responses they generate. When used in a setting that is strictly rules based, such as in computer programming, the AI understanding of code can greatly improve the productivity of coders or replace them. AI will not introduce bugs in software unless the specifications are incomplete or contradictory and it will not make errors. The same is true for AI's language generation. It draws from large troves of text data and has a good quote unquote understanding for how humans use language. But there are no rules based ways in which it can distinguish fact from fiction. Instead, AI draws from what statistically is more likely to be a human sounding response. For this reason, it can produce content that can be entirely wrong. For example, I asked AI to summarize the content of my 2022 economics primer, how to Think about the Economy. Since it has access to the text, it did a pretty good job summarizing what is in the book. But it also added comments on content that is typically in economic books, but that is not part of the primer. The AI is correct that economics books typically discuss such things, and thus it is statistically possible that my primer would do the same. But it doesn't, so that's where the piece ends. It goes on to another part in a series called Separating Information from Disinformation that's also worth a read. But I want to talk a little bit about this one, and specifically I want to hone in on the section about creative destruction. Let's hold aside for a moment the question of runaway AI and AI X Risk, and let's just talk about the impact to society of technological disruption. Broadly speaking, there are two categories of thought when it comes to AI, and obviously this is wildly reductive, but I'm essentializing here for the sake of our conversation. On the one hand are those who see a wrecking ball coming for professions which have long been a part of a fabric of society and which AI seems poised to basically wipe out. This perspective is exhibit in every study, like the IMF's recent argument that 60% of jobs in developed countries are going to be impacted by AI, including half of those being wiped out entirely. The other perspective, at least in essence, is that yeah, but on the other side of all that change that creative destruction, whatever you want to call it, the jobs will be much better. AI will have enabled new types of things, perhaps less work. Remember this week we saw Bernie Sanders introduce a 32 hour workweek bill because of the productivity of AI. But even more than any of that, it's just a core belief that in general technology creates more opportunities than it destroys, even if that's hard to see in real time. I tend to be in the camp that humans don't have some sort of basal or max state for how much we want to consume, how many experiences we want, how many goods we want. We are basically endless want machines. In fact, in many ways the more that we have, the more that we want. So when I think about the ability for coders with AI to do the work of 10 or even a hundred coders before, do I think that means we'll have 1/10 or 1/100 of the coders? No, I think it means we'll have 10 times or 100 times the amount of code. This is extremely hard to imagine what that could possibly look like. The closest we have, I think, is some vague conception of UR personalization in which everything can be completely customized to each of us because of that increased capacity to create. Same with entertainment, art, creativity. I think we'll just have more of it now. More is obviously not necessarily better when it comes to art, creativity and entertainment, but I think that's the natural trajectory. Think about how many options you have today for what entertainment you consume versus what you had 20 years ago, to say nothing of 40 years ago in the 80s when you were just watching whatever was on TV. My optimism then says that all evidence points me to the idea that people being able to do more means that they will do more and that'll create some really interesting and cool and valuable things. However, the part of the conversation that seems to often get glossed over between these two perspectives is the transition, the period of change. Even if one believes that better futures will come, that more stuff will be created, there's still an incredible amount of disruption that's going to happen along the way. How we think about that, how we help people navigate that, are going to be some of the most important questions, even if they are temporary questions. From where I'm sitting, I'd love to see us spend a little bit less time on this oversimplified binary of whether AI will be job destroying or job creating, and instead assume that it's both and figuring out how we get from one to the other. Lots more to share on some ideas I have for that in the future, but for now, that will do it for today's AI breakdown. Until next time, peace.