The Economics of Artificial Intelligence [Archive.org URL]

When looking at artificial intelligence from the perspective of economics, we ask the same, single question that we ask with any technology: What does it reduce the cost of? Economists are good at taking the fun and wizardry out of technology and leaving us with this dry but illuminating question. The answer reveals why AI is so important relative to many other exciting technologies. AI can be recast as causing a drop in the cost of a first-order input into many activities in business and our lives—prediction.

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As the cost of prediction continues to drop, we’ll use more of it for traditional prediction problems such as inventory management because we can predict faster, cheaper, and better. At the same time, we’ll start using prediction to solve problems that we haven’t historically thought of as prediction problems.

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As in the case of arithmetic, when the price of prediction drops, the value of its substitutes will go down and the value of its complements will go up. The main substitute for machine prediction is human prediction. As humans, we make all kinds of predictions in our business and daily lives. However, we’re pretty noisy thinkers, and we have all kinds of well-documented cognitive biases, so we’re quite poor at prediction. AI will become a much better predictor than humans are, and as the quality of AI prediction goes up, the value of human prediction will fall.

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But, at the same time, the value of prediction’s complements will go up. The complement that’s been covered in the press most is data, with people using phrases such as “data is the new oil.” That’s absolutely true—data is an important complement to prediction, so as the cost of prediction falls, the value of a company’s data goes up.

But there are other complements to prediction that have been discussed a lot less frequently. One is human judgment. We use both prediction and judgment to make decisions. We’ve never really unbundled those aspects of decision making before—we usually think of human decision making as a single step. Now we’re unbundling decision making. The machine’s doing the prediction, making the distinct role of judgment in decision making clearer. So as the value of human prediction falls, the value of human judgment goes up because AI doesn’t do judgment—it can only make predictions and then hand them off to a human to use his or her judgment to determine what to do with those predictions.

Another complement to prediction is action. Predictions are valuable only in the context of some action that they lead to.