Prediction Machines: The Simple Economics of Artificial Intelligence

by Ajay Agrawal

Being so close to so many applications of AI forced us to focus on how this technology affects business strategy. As we’ll explain, AI is a prediction technology, predictions are inputs to decision making, and economics provides a perfect framework for understanding the trade-offs underlying any decision.

Our first key insight is that the new wave of artificial intelligence does not actually bring us intelligence but instead a critical component of intelligence—prediction.

Prediction Machines is not a recipe for success in the AI economy. Instead, we emphasize trade-offs. More data means less privacy. More speed means less accuracy. More autonomy means less control.

The rise of the internet was a drop in the cost of distribution, communication, and search.

When search became cheap, companies that made money selling search through other means (e.g., the Yellow Pages, travel agents, classifieds) found themselves in a competitive crisis. At the same time, companies that relied on people finding them (for example, self-publishing authors, sellers of obscure collectibles, homegrown moviemakers) prospered.

When the price of something fundamental drops drastically, the whole world can change.