Prediction Machines: The Simple Economics of Artificial Intelligence

by Ajay Agrawal

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.

To understand how AI will affect your organization, you need to know precisely what price has changed and how that price change will cascade throughout the broader economy.

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

Technological change makes things cheap that were once expensive.

Such significant price drops create opportunities to do things we’ve never done; it can make the impossible possible.

What will new AI technologies make so cheap? Prediction. Therefore, as economics tells us, not only are we going to start using a lot more prediction, but we are going to see it emerge in surprising new places.

Prediction is the process of filling in missing information. Prediction takes information you have, often called “data,” and uses it to generate information you don’t have.

classification, clustering, regression, decision trees, Bayesian estimation, neural networks, topological data analysis, deep learning, reinforcement learning, deep reinforcement learning, capsule networks, and so on.

when the cost of something falls, we do more of it.

Integrate.ai,

When prediction is cheap, there will be more prediction and more complements to prediction.

Some AIs will affect the economics of a business so dramatically that they will no longer be used to simply enhance productivity in executing against the strategy; they will change the strategy itself.

The prediction becomes sufficiently accurate that it becomes more profitable for Amazon to ship you the goods that it predicts you will want rather than wait for you to order them.

Adopting too early could be costly, but adopting too late could be fatal.

What does this mean for strategy? First, you must invest in gathering intelligence on how fast and how far the dial on the prediction machines will turn for your sector and applications. Second, you must invest in developing a thesis about the strategic options created from turning the dial.

You need to build foundations before the strategic implications of prediction machines for your organization become apparent. That is precisely how we structured this book, building a pyramid from the ground up.

Prediction facilitates decisions by reducing uncertainty, while judgment assigns value. In economists’ parlance, judgment is the skill used to determine a payoff, utility, reward, or profit. The most significant implication of prediction machines is that they increase the value of judgment.

the AI canvas.

KEY POINTS

PREDICTION is the process of filling in missing information. Prediction takes information you have, often called “data,” and uses it to generate information you don’t have.

KEY POINTS

The first step in reducing churn is to identify at-risk customers. Companies can use prediction technologies to do that.

What does regression do?

Regression takes the data and tries to find the result that minimizes prediction mistakes, maximizing what is called “goodness of fit.”

“back propagation.”

Many problems have transformed from algorithmic problems (“what are the features of a cat?”) to prediction problems (“does this image with a missing label have the same features as the cats I have seen before?”). Machine learning uses probabilistic models to solve problems.

KEY POINTS

With AI, data plays three roles.

First is input data,

Second is traini...

Finally, there is feedback data,

To make the right data investment decisions, you must understand how prediction machines use data.

How Machines Learn from Data

Many commercial AI applications have this structure: use a combination of input data and outcome measures to create the prediction machine, and then use input data from a new situation to predict the outcome of that situation.

Decisions about Data

Economies of Scale

KEY POINTS

Prediction machines utilize three types of data: (1) training data for training the AI, (2) input data for predicting, and (3) feedback data for improving the prediction accuracy.

The New Division of Labor

Former Secretary of Defense Donald Rumsfeld once said:

“prediction by exception”

known knowns, known unknowns, unknown knowns, and unknown unknowns)

they take input data from the world that enables a prediction. That prediction is possible because training occurred about relationships between different types of data and which data is most closely associated with a situation. Combining the prediction with judgment on what matters, the decision maker can then choose an action. The action leads to an outcome (which has an associated reward or payoff). The outcome is a consequence of the decision. It is needed to provide a complete picture. The outcome may also provide feedback to help improve the next prediction.

Ada Support, a startup,

What would a human do?

X.ai, a startup focused on providing an assistant that can arrange meetings and put them into your calendar, is another example.

Humans are a resource, so simple economics suggest they will still do something.

human eyes, ears, nose, and skin still surpass machine capabilities.