Prediction Machines: The Simple Economics of Artificial Intelligence

by Ajay Agrawal

The rise of the internet was a drop in the cost of distribution, communication, and search. Reframing a technological advance as a shift from expensive to cheap or from scarce to abundant is invaluable for thinking about how it will affect your business.

Heralded as the first computer programmer, Ada Lovelace was the first to see this potential.

she wrote the earliest recorded program to compute a series of numbers (called Bernoulli numbers) on a still-theoretical computer that Charles Babbage designed. Babbage was also an economist, and as we will see in this book, that was not the only time economics and computer science intersected.

Cheaper prediction will mean more predictions. This is simple economics: when the cost of something falls, we do more of it.

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.

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.

Humans have three types of data that machines don’t. First, human senses are powerful. In many ways, human eyes, ears, nose, and skin still surpass machine capabilities. Second, humans are the ultimate arbiters of our own preferences. Consumer data is extremely valuable because it gives prediction machines data about these preferences. Grocery stores provide discounts to consumers who use loyalty cards in order to obtain data on their behavior. Stores pay consumers to reveal their preferences. Google, Facebook, and others provide free services in exchange for data that they can use in other contexts to target advertising. Third, privacy concerns restrict the data available to machines. As long as enough people keep their sexual activity, financial situation, mental health status, and repugnant thoughts to themselves, the prediction machines will have insufficient data to predict many types of behavior. In the absence of good data, our understanding of other humans will provide a role for our judgment skills that machines cannot learn to predict.

Like classical computing, AI is a general-purpose technology. It has the potential to affect every decision, because prediction is a key input to decision making. Hence, no manager is going to achieve large gains in productivity by just “throwing some AI” at a problem or into an existing process. Instead, AI is the type of technology that requires rethinking processes

AI tools can change work flows in two ways. First, they can render tasks obsolete and therefore remove them from work flows. Second, they can add new tasks. This may be different for every business and every work flow.

As Steve Jobs once remarked, “One of the things that really separates us from the high primates is that we’re tool builders.” He used the example of the bicycle as a tool that had given people superpowers in locomotion above every other animal.

“What a computer is to me is it’s the most remarkable tool that we’ve ever come up with, and it’s the equivalent of a bicycle for our minds.”

Today, AI tools predict the intention of speech (Amazon’s Echo), predict command context (Apple’s Siri), predict what you want to buy (Amazon’s recommendations), predict which links will connect you to the information you want to find (Google search), predict when to apply the brakes to avoid danger (Tesla’s Autopilot), and predict the news you will want to read (Facebook’s newsfeed). None of these AI tools are performing an entire work ...

But the point is that automation that eliminates a human from a task does not necessarily eliminate them from a job.

A job is a collection of tasks. When breaking down a work flow and employing AI tools, some tasks previously performed by humans may be automated, the ordering and emphasis of remaining tasks may change, and new tasks may be created. Thus, the collection of tasks that make up a job can change.

C-suite leadership must not fully delegate AI strategy to their IT department because powerful AI tools may go beyond enhancing the productivity of tasks performed in the service of executing against the organization’s strategy and instead lead to changing the strategy itself.

AI can lead to strategic change if three factors are present: (1) there is a core trade-off in the business model (e.g., shop-then-ship versus ship-then-shop); (2) the trade-off is influenced by uncertainty (e.g., higher sales from ship-then-shop are outweighed by higher costs from returned items due to uncertainty about what customers will buy); and (3) an AI tool that reduces uncertainty tips the scales of the trade-off so that the optimal strategy changes from one side of the trade to the other (e.g., an AI that reduces uncertainty by predicting what a customer will buy tips the scale such that the returns from a ship-then-shop model outweigh those from the traditional model).

According to the Bureau of Labor Statistics, tellers were not being automated out of a job (see figure 16-1). However, they were automated out of the bank-telling task. Tellers ended up becoming the marketing and customer service agents for bank products beyond the collection and dispensing of cash.

The boundary between the startup and the doctor is the boundary where the AI ceases to be strategic and instead is simply an input to a different process.

The innovator’s dilemma occurs because, when they first appear, innovations might not be good enough to serve the customers of the established companies in an industry, but they may be good enough to provide a new startup with enough customers in some niche area to build a product. Over time, the startup gains experience. Eventually, the startup has learned enough to create a strong product that takes away its larger rival’s customers. By that point, the larger company is too far behind, and the startup eventually dominates.

In the case of Siri, Alexa, or Google Inbox, a mistake means a lower-quality user experience. In the case of autonomous vehicles, a mistake means putting lives at risk. That experience can be scary.

Just as in agriculture, homogeneity improves results at the individual level at the expense of multiplying the likelihood of system-wide failure.

The key policy question isn’t about whether AI will bring benefits but about how those benefits will be distributed. Because AI tools can be used to replace “high” skills—namely, brainpower—many worry that even though jobs exist, they won’t come with high wages.