Keeping Pace with Rapid Change

On the eve of the pandemic, work from home made up about 5 percent of Nicholas (Nick) Bloom's research agenda—just two papers tucked among roughly forty others on innovation, uncertainty, and management written over two decades. Within months, it was the only thing anyone wanted to ask him about. How, exactly, did he become the “work from home guy”?

Bloom, the William D. Eberle Professor of Economics at Stanford’s School of Humanities and Sciences, a Senior Fellow at the Stanford Institute for Economic Policy Research, and the Co-Director of the Productivity, Innovation, and Entrepreneurship Program at the National Bureau of Economic Research, describes his career as having three distinct throughlines, none of them planned. 

An Instinct to Follow the Threads

Innovation came first, stemming from an undergraduate summer job measuring patents for what was then the UK's Department of Trade and Industry. 

Uncertainty came next, becoming his main academic focus after a friend in graduate school told him excitedly about a new book on investment under uncertainty. Around the same time, his wife, who was working at the Bank of England, mentioned the Bank had data on implied volatility, a forward-looking measure of how uncertain markets expect the future to be. Bloom realized that combining this data with firm-level accounting data would let him measure uncertainty's effect on business decisions. This became the subject of his PhD.

Management came third. Seeking to take a break from economics, Bloom spent a year and a half at McKinsey. There, he says, he came to see how much a company's success depends on how well it's run—and how badly conventional economics, which typically assumes firms are profit-maximizing and well managed, misses that. He began studying management practices directly: not just monitoring and performance reviews, but what he calls "be-good-to-your-employees" practices—things like job sharing, flexible scheduling, parental leave, and yes, working from home. 

Back in the classroom a few years later, Bloom had the opportunity to grow his research on work from home in a real-world setting. A PhD student in one of his classes turned out to be James Liang, the founder of the travel company Trip.com (then Ctrip), who was willing to test Bloom's questions with call center employees at his company. The resulting study, which ran from 2010–2013, became one of the first credible pieces of evidence that remote work could benefit employees and employers alike.

The Advantage of Being Nimble

While the focus of his work may have shifted over time, Bloom's approach has been consistent: rather than following a fixed research agenda, he stays alert to unexpected openings—a data set, a conversation, a student with a large company—and moves on them quickly. How does he manage to stay so nimble? "It's kind of the big battleship versus the little speedboat," he says. "I'm like a speedboat. Most of the time in a war, the speedboat isn’t very useful. You want to rely on the battleship. But the one time you need to change direction fast, having a speedboat is really helpful." He runs no defined research lab, manages no large staff, courts no donors—nothing built to turn slowly. 

When the pandemic arrived, and with it the unexpected opportunity to study working from home on a global scale, Bloom could swivel toward it immediately, while larger, more heavily resourced research operations were still, as he puts it, turning the battleship. He quickly became the go-to guy for government officials and corporate executives—anyone looking for answers about how to manage their workforce in such an unprecedented situation. “In the land of the blind, the one-eyed person is king," Bloom says. In this case, he leveraged his sparse earlier research into practical guidance for an unprecedented moment of crisis.

Pivoting to AI

While his research on work from home continues, Bloom is applying the same capacity to pivot quickly to emerging questions around AI and productivity. He doesn't count himself among the field's more prolific researchers on the subject, but infrastructure from his previous work is helping him gain traction. Two CFO surveys he helped build a decade ago, the Survey of Business Uncertainty in the U.S. and the U.K.'s Decision Maker Panel, each polling roughly 1,000 to 2,000 executives a month, began asking about AI spending, employment, and productivity about a year ago. That data, as slim as it is, has become unusually valuable because almost nothing else like it exists. 

“AI is the 800-pound gorilla in the room for everyone—central banks, policymakers, c-suites,” says Bloom. It’s difficult to assess AI spending because most companies don’t carry a line item for it in their budget and the companies making and selling AI only know about their own customers, he explains. "It's like asking a beer company how many people drink beer," Bloom says. "They can tell you how many customers they have, but they don't know the answer about anyone else. The data is just really hard to get right now, but our speedboat-style questions to CFOs are at least giving us something." 

The monthly numbers are now available to the public. What the data shows so far is that AI's effect on net employment has been close to zero. Looking forward three years, employment remains roughly flat in the surveys, but the projected effect on productivity is now running toward three-quarters of a percent annually, a number Bloom frames in scale rather than hype. "During the Industrial Revolution, the entire productivity growth was one percent," he notes. Three-quarters of a percent, in other words, is a genuinely large number, but also a middle ground between predictions that AI will barely matter and claims, which he considers implausible, of 30 percent annual gains.

Investing in Adaptability

Bloom's ability to respond to opportunities and trends, rather than needing to commit to one fixed path, has made him a significant contributor to his field. It has also made him a fit for Stanford Impact Labs funding, which he has leveraged to build partnerships with entities, including the City of San Francisco, keen to study the impacts of remote and hybrid work on housing demand, commercial real estate, transportation, and municipal finances. At the same time, he has teamed up with central banks to look closely at what the rapid spread of AI means for output, quality of work, decision-making, productivity, and jobs. 

Continuing on his trajectory as a speedboat, Bloom is committed to advancing his uncertainty-focused research agenda at a time of heightened unpredictability in the economy. He briefs government officials, appears frequently on television, and consults widely with corporations—evidence, in his view, that a researcher can shape public and economic life in nontraditional ways, and build an academic career by following wherever the most pressing real-world questions lead.