In subsequent research, they and other scholars found that people most disposed to creative breakthroughs in art, science, or any endeavor tend to be problem finders. These people sort through vast amounts of information and inputs, often from multiple disciplines; experiment with a variety of different approaches; are willing to switch directions in the course of a project; and often take longer than their counterparts to complete their work.

The researchers discovered that participants based their decisions on two factors: utility and curiosity.

Professor Sheena Iyengar from the Columbia Business School is a psycho- economist who specializes in decision making. Her famous “jam study” was done using specialty jams in a grocery store. One week, the researchers set up a table in the store showing off six different specialty jams (with snazzy flavors like kiwi-orange, strawberry-lavender...you get the idea). Then they watched how the shoppers behaved—who stopped to look and, of those who stopped, who actually bought some jam. The first week, with six jams on display, 40 percent of the shoppers stopped to check out the six jams and about a third of them bought one—about 13 percent of the shoppers.

A few weeks later, in the same store, with the same time frame, the researchers came back with twenty-four jams. This time, 60 percent of the shoppers in the store stopped by—a 50 percent increase over the six-jam display! But with twenty-four jams on display, only 3 percent of the shoppers bought one.

Brilliant field studies by the MIT behavioural economist Sendhil Mullainathan and the Princeton cognitive psychologist Eldar Shafir, described in their book Scarcity, show that the scarcities of poverty (like those of time) can so focus one’s attention on immediate demands that little bandwidth is left for planning, learning about options, doing homework, and building relationships and cultural capital. The urgency of his sister’s having to rummage through garbage to find enough cans that his family could have milk and cereal at the same breakfast narrows Tyler’s bandwidth for school.

At a time when freewheeling corporate research was still permitted, Microsoft anthropologist Mary L. Gray and computational social scientist Siddharth Suri were among the first to show the world the plight of workers like those in Kenya who build their livelihoods around an essential piece of the AI supply chain.

In 2019, they published their book Ghost Work, based on five years of extensive fieldwork, revealing a hidden web of piecemeal labor and digital exploitation that propped up Silicon Valley. Tech giants and unicorns were building their extravagant valuations not just with engineers paid six-figure salaries in trendy offices. Essential, too, were workers, often in the Global South, being paid pennies to carefully annotate reams of data. Take self-driving cars. A self-driving car needs to drive in the correct lane, respond to erratic driving behavior, and pause at a safe distance for schoolchildren crossing the road. To do this, the software system controlling the car uses an amalgamation of several deep learning models, including those dedicated to recognizing objects on the road: lane markings, road signs, traffic lights, vehicles, trees, pedestrians.