A boom in generative artificial intelligence and pandemic-induced workplace shifts will unleash a new era of faster productivity growth in the rich world, economists say, though it could take a decade or more for advanced economies to reap the full benefits.
After rising during the early stages of the pandemic, The Conference Board, a global business research organization, said this month that it expected productivity will hardly grow this year in developed economies. The board believes this weakness will continue for the next decade, citing rising capital costs and continued economic and geopolitical uncertainty.
The forecasts highlight the challenges facing advanced economies, where the struggle to increase productivity since the 2008 financial crisis has held back output and wage growth.
However, economists believe that the massive increase in investment in AI – plus several workplace trends that took off during the pandemic – will eventually lead to compelling results.
Chad Syverson, a professor at the Chicago Booth School of Business, said there is now a “data-driven cause for optimism” about productivity, with AI, new business formation and people changing jobs all poised to deliver results. to deliver.
While productivity growth remained sluggish on paper, he believed the results of recent changes in workplace practices — plus the ultimate benefits of AI — would take some time to sink into the numbers.
“Very little of this stuff is plug-and-play. . . companies have to invest a lot of resources to reconfigure their business model for this new thing,” Syverson said. for many reasons… productivity is falling.”
John Haltiwanger, a professor at the University of Maryland, agreed that AI breakthroughs with major language models would ultimately boost the economy. The US, he said, was now going through a transition similar to that of the late 1980s, when economist John Solow said, “You can see the computer age everywhere except in the productivity statistics.”
The radical shifts generative AI is driving could end what John Van Reenen, a professor at the London School of Economics, described as “a lot of drudgery” in workplace practices, improving efficiency and growth in the process .
However, it has taken decades for previous technological leaps to yield meaningful productivity gains.
“It takes an enormous amount of time for companies to change,” says Nick Bloom, a professor at Stanford University, citing the example of the invention of electric motors at a time when most industrial buildings were configured for water or steam power .
There are already big claims for the transformational effects of generative AI on productivity. a recent paper published by the Brookings Institution – written using the GPT4 model – cites evidence that it can help coders work twice as fast, cut the time it takes to complete certain writing tasks in half, and make call centers 14 percent more productive.
Meanwhile, investment banks are encouraging clients to buy into generative AI. Morgan Stanley researchers say U.S. productivity is “on the cusp of a recovery,” in part because demographic trends, combined with government “friendshoring” policies, will make it more difficult for multinationals to maintain a global pool of tap cheap labor and force them to automate.
An AI-focused “productivity revolution” could be broader than that following the introduction of personal computers, they suggested in a recent note, with sectors such as retail and manufacturing “ready to invest”.
Haltiwanger pointed to an increase in new business creation, largely driven by the shift from city centers to suburban work-from-home hotspots.
Provided these young companies can weather the rise in US interest rates and any turbulence at regional banks, the rewards should follow. “Anytime you have a change in the way you do business, both spatially and in parts of the economy, there’s productivity growth down the line,” he said.
Van Reenen was more skeptical that labor shortages would stimulate innovation. While a smaller pool of workers could change the direction of technological change – as in Japan, where an aging workforce has spurred investment in robotics – it probably also meant fewer new ideas.
The Conference Board also tried to temper the “excitement” around technological breakthroughs.
Bloom, meanwhile, warned that it was difficult to predict when the big turning points in productivity would come. “The development of the steam engine, electric motor, personal computer and internet has had no measurable impact on productivity within five years. So it’s hard to imagine what will happen. I involve (generative) AI in that.”
