Illustration: Matija Medved for Bloomberg
For all the excitement, evidence of a boost to productivity is still thin on the ground
By David Wilcox and Tom Orlik, Bloomberg Economics | Updated on gen 31, 2025 at 05:16
For investors in artificial intelligence, the last week delivered a painful shock. The sudden appearance of DeepSeek — a Chinese AI firm boasting a world-class model developed at bargain-basement costs — triggered a massive selloff in Nvidia and other US tech champions.
What matters for the economy, though, is not the ups and downs of stock prices for the Magnificent Seven, but whether AI drives gains in productivity, and how those gains are divided up. For all the excitement, and the trillion-dollar valuations for AI firms, evidence of a boost to productivity remains thin on the ground.
This disconnect doesn’t exactly ring an alarm bell. From the electric motor to the personal computer, past technological revolutions took decades not years to show up in the productivity data. The inventor’s ‘eureka’ moment takes time to diffuse through the economy. In the end, though, the gap has to be closed.
There are three ways that could happen.
Optimists see AI as a driver of rising prosperity: investors win and so do workers.
Pessimists worry that chatbots are more parlor trick than paradigm shift, and the billions sunk into training models won’t ever generate a return.
There’s also a dystopian view, with AI making the algorithm-elite rich beyond imagining, and everyone else unemployed.
We’ll see how well DeepSeek’s claim of massive costs efficiencies — a leading-edge model developed for millions instead of billions of dollars — stands up to scrutiny. If AI is about to get much cheaper, the path to an answer on its economic impact is going to get shorter. For workers nervously wondering if large language models will make their skills redundant, a lot is riding on which camp is right.
Three Different Paths
Assessments of how big the productivity benefits of AI will be — and how quickly they will manifest — are all over the map.
In an optimistic scenario, AI lives up to the hype and spreads through the broader economy, fueling a surge in productivity that propels growth and wages. Goldman Sachs analysts estimate that by 2034 US GDP will be 2.3% bigger as a result of AI. McKinsey Global Institute goes further and expects a 5-13% boost by 2040.
Workers control the temperature by operating intelligent greenhouse systems at the Qinhu Smart Agricultural Park digital factory in Taizhou, China.
Source: CFOTO/Future Publishing/Getty Images
They’re by no means the most optimistic. In a recent paper , Anton Korinek of the University of Virginia and Donghyun Suh of the Bank of Korea outline a range of growth scenarios. At the more temperate end, annual growth is boosted by a full percentage point. At the more aggressive, the boost is a circuit-overloading 6 percentage points per year, on average, over the next 10 years.
That latter scenario assumes we are on the road to “the singularity” — a moment when machines become more intelligent than humans. It also assumes that thinking machines prove more solicitous of human wellbeing than, for example, Skynet, the malign intelligence of the Terminator movies.
In a pessimistic scenario, AI stumbles as it moves from lab to market — proving more of a damp squib than a rocket charger for productivity. MIT’s Daron Acemoglu , a 2024 Nobel laureate in economics, estimates that only 5% of tasks currently performed by humans will be taken over by AI in the next 10 years. He expects the contribution to GDP a decade from now to be around 1% .
In a third scenario, AI will be powerful in its application but dystopian in its impact. Elon Musk has warned that the technology could lead to the end of cognitive work, saying, “There will come a point where no job is needed.” Perhaps it’s not a coincidence that one of the first acts of the Trump administration where Musk wields outsize influence is to offer some 2 million Federal employees a buyout.
Elon Musk during Trump’s presidential inauguration in Washington, DC, on Jan. 20.
Photographer: Chip Somodevilla/Getty Images
If AI turns out to be better at replacing workers than bolstering their productivity, the result could be a wave of job losses — the white-collar version of the blue-collar redundancies that followed automation and offshoring of factory jobs. Growth will stay on trend, and may even speed up, but the benefits of that growth will accrue mainly to anyone early or smart enough to be on the right side of the revolution.
Acemoglu appears in this camp as well. In 2023, he and MIT colleague Simon Johnson — also a Nobel laureate — published “ Power and Progress ,” a grim review of technology’s impact on labor. In the grand sweep of history, advances in technology from the plow to the textile factory have improved prosperity for all. But in the span of decades in which lives are lived, Acemoglu and Johnson show workers often lose out.
A Solow Paradox for the Age of AI
For now, all three camps are patiently waiting to be proven right. They might have to wait a while. Technology is a major driver of productivity growth, but gains are not always quick to arrive.
“You can see the computer age everywhere but in the productivity statistics,” wrote Nobel Prize-winning economist Robert Solow back in 1987. Back then, a youthful Bill Gates was sprinting to bring personal computers onto desks around the world. It would be another decade before Fed Chair Alan Greenspan found evidence of the boom in the GDP numbers.
Likewise, it took decades for the electric motor to show up in the productivity statistics, as economic historian Paul David explained .
The personal computer took decades not years to show up in the productivity data.
Source: Photo Media/ClassicStock/Getty Images
Why so slow? Before electric motors could be widely used to power manufacturing, generating capacity had to be expanded, and the price of electricity had to come down. Factory owners, keen to get a return on their existing steam-powered machines, took their time electrifying. When they did decide to hit the switch, factories had to be rebuilt on a different design.
Fast forward to 2025, and Solow’s paradox is back, with the gap between AI buzz and missing-in-action productivity gains even wider than it was with PCs.
For the economy as a whole, evidence of an AI boom is, so far, hard to find. Productivity growth — delivering more output from the same amount of inputs — is a crucial measure of economic health. If productivity is rising, workers can get more pay, companies more profits, and government more tax revenue. If shares are divided more or less equally, everyone can be better off.
Since the eve of the Covid pandemic, output per hour for US workers is estimated to have increased at an annual rate of just 1.86%. That’s nowhere near the 3.3% pace that prevailed from the mid-1990s through the mid-2000s as the internet revolutionized the economy. Still, it’s up from a doleful 1.48% average over the 15 years preceding the pandemic.
Delving into the detail, evidence of an AI-driven surge remains hard to find. Energy demand is growing at a rapid clip, with the plan for a 2028 reopening of a nuclear reactor on Pennsylvania’s Three Mile Island a striking example. The Biden administration poured funds into construction of semiconductor fabs in Arizona, Idaho, New York and Texas.
But hiring and investment in IT and spending on research and development, areas that surged in the PC and internet revolutions, have showed little deviation from trend.
AI Adoption Has Been Much Faster Than PCs Or the Internet
If that were the end of the story, the skeptics would seem to have the upper hand over the bulls and the dystopians. However, by other measures AI adoption really is unprecedented.
A 2024 study by economic researchers Alexander Bick , Adam Blandin and David J. Deming found that just two years into the AI revolution, 40% of US adults had used it. By comparison, it wasn’t until 12 years after the introduction of the PC, and four years after the public launch of the internet, that adoption of those earlier technologies reached that level.
Source: “The Rapid Adoption of Generative AI,” by Alexander Bick Adam, Blandin and David J. Deming, NBER
Then there’s the fact that investors are betting big money that AI will deliver on its early promise.
Even after the DeepSeek shock, with markets panicked that lower-cost AI might not require so many high-end chips, Nvidia remains one of the world’s most valuable companies by market capitalization.
For seven of the companies positioned to benefit most from the AI revolution — including Nvidia, Microsoft, Google and Amazon — market cap has increased by 15% of US GDP since generative AI was unveiled to the public.
By comparison, the increase in market cap for internet champions topped out at a little higher than 10% of GDP. AI companies are still valued far higher than internet companies were in their early years.
Another reason for AI optimism: compelling case-study evidence of AI tools boosting worker productivity. Sida Peng, an economist at Microsoft, and his coauthors found that computer programmers with access to GitHub Copilot completed tasks 56% faster than those without.
In another study , ChatGPT helped participants complete writing assignments 40% faster, and with significant improvements in quality for lower-skilled writers. A third found that customer service agents with an AI assistant resolved 14% more issues per hour than those without, again with lower-skill agents improving more than the average.
Best of Times, Worst of Times
A doubling of US GDP, or no change in the trend? A white-collar wasteland with mass job losses and soaring inequality, or AI bringing opportunities to workers that lost out as a result of globalization and automation? Billion dollar development costs a barrier to market entry, or DeepSeek’s massive cost efficiencies allowing a thousand AI flowers to bloom?
The wide spectrum of imagined futures underscores how little we know.
To us, a middle path seems plausible, with some sectors enjoying efficiency gains and others largely unaffected. Some workers are relieved of tedious aspects of their jobs while others are forced to look for other lines of work. Overall productivity gains are visibly higher but don’t outpace those in the PC and internet revolution.
These are not purely questions of efficiency. If the early gains from AI aren’t divvied up fairly, questions of equity and social cohesion will come to the fore. Blue-collar workers who lost their jobs to automation played a part in making Trump a two-term president. If AI causes a swath of white-collar job losses, the political consequences could be similarly far-reaching.
This article was downloaded by calibre from https://www.bloomberg.com/news/articles/2025-01-31/will-ai-take-our-jobs-3-scenarios-for-how-it-could-impact-the-economy
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