No matter how good AI gets, it won’t beat markets.
Whenever we see big leaps in computation, proponents of central planning come out of the woodwork, claiming this finally makes it possible to organize the economy better than markets do.
According to them, centralized computation could optimize tax rates, produce enough to meet our needs, and allocate resources in a way that maximizes well-being for all.
Such arguments gained prominence in the early 20th century, with Taylorism. They saw a resurgence later with the advent of modern computing, and most recently with advances in artificial intelligence.
Unfortunately for its proponents, this line of thinking rests on a false premise: that our economy is nothing more than a computational problem to be solved with the right equations, the right data, and enough processing power.
As I wrote in a recent paper published by the Montreal Economic Institute, in part, this error was understood as far back as the 18th century, when Adam Smith observed that even the production of simple goods necessitates the cooperation of so many different hands that the full network of exchanges would “exceed all computation.” Even the making of a woolen coat, for instance, required farmers, spinners, dyers, merchants, shippers, etc., just to get from raw materials to market.
Now, clearly, such complexity isn’t sufficient to stop the coat from being produced. The point, rather, is that there is no single mind directing every step of production, from raising the sheep to selling you a brand-new peacoat. Instead, it is through the cooperation of the many hands and many minds that make up the “invisible hand” of the market that such production is possible.
In the late 19th century, Italian economist Vilfredo Pareto expanded upon this, observing that if one were to attempt to coordinate even a modest economy and match resources to uses and preferences, the number of equations to be solved would quickly explode.
But this increase in complexity misses a key detail, allowing proponents of centralized planning to imagine that with the right amount of data, we could solve the economy as a series of equations.
That’s where Nobel laureate economist Friedrich Hayek comes in to explain that the problem is actually far greater, and indeed, unsolvable by any central computer.
It is not merely that the relevant knowledge is decentralized—spread out across millions of individuals—but that it is often tacit. Local shopkeepers’ understanding of their customers’ buying habits cannot be translated into one data point to feed into an AI model. Nor can we predict the emergence of an entrepreneur dreaming of a product that did not exist before.
Most importantly, there’s the issue of prices—indispensable signals that guide our decision-making process. Prices are neither set in stone nor arbitrarily fixed.
Instead, prices emerge from real exchanges. When the price of wheat rises, it is because buyers and sellers are competing for a limited supply. This price increase signals something about relative scarcity. It also provides an incentive to adjust consumption and conserve the resource, to look for a substitute, to increase production, and to innovate.
In short, prices are not lying around in the wild, waiting to be harvested and fed into an algorithm. Rather, they are the results of a constantly evolving discovery process. Without this process, the knowledge embedded in a price simply doesn’t come into existence.
Hayek called the price system a “marvel” due to its ability to generate knowledge in the market. He described competition as a “discovery procedure,” as it does much more than allocate resources.
When entrepreneurs bring new products to market, for instance, they make informed bets. If they’re wrong, they bear the cost. If they’re right, they reap the rewards. Through this process, we all learn a little more about what is possible, what is valued, and what works.
As for artificial intelligence, it has the ability to process vast quantities of historical data to detect patterns, forecast trends, and optimize within given parameters. But it can only look backward to find data, whereas our economic life is forward-looking and creative.
The growth of the social media influencer market, for instance, could hardly have been predicted by an algorithm 20 years ago. In the same way, the algorithms of today can’t accurately predict how much we’ll consume of what resources, since much of what will matter tomorrow just hasn’t been imagined yet.
As powerful and helpful as artificial intelligence can be to improve logistics, better manage inventories, and analyze markets today, it remains a tool. It can help us gain a better understanding of markets, but it can’t predict the results of the billions of voluntary exchanges between individuals that take place every day.
