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There are many paths AI evolution could take. On one end of the spectrum, AI is dismissed as a marginal fad, another bubble fueled by notoriety and misallocated capital. On the other end, it’s cast as a dystopian force, destined to eliminate jobs on a large scale and destabilize economies. Markets oscillate between skepticism and the fear of missing out, while the technology itself evolves quickly and investment dollars flow at a rate not seen in decades. 

All the while, many of today’s financial and economic thought leaders hold to the consensus that the financial landscape will stay the same as it has been for the last several years. Two years ago, Joseph Davis, global chief economist at Vanguard, and his team felt the same but wanted to develop their perspective on AI technology with a deeper foundation built on history and data. Based on a proprietary data set covering the last 130 years, Davis and his team developed a new framework, The Vanguard Megatrends Model, from research that suggested a more nuanced path than hype extremes: that AI has the potential to be a general purpose technology that lifts productivity, reshapes industries, and augments human work rather than displaces it. In short, AI will be neither marginal nor dystopian. 

“Our findings suggest that the continuation of the status quo, the basic expectation of most economists, is actually the least likely outcome,” Davis says. “We project that AI will have an even greater effect on productivity than the personal computer did. And we project that a scenario where AI transforms the economy is far more likely than one where AI disappoints and fiscal deficits dominate. The latter would likely lead to slower economic growth, higher inflation, and increased interest rates.”

Implications for business leaders and workers

Davis does not sugar-coat it, however. Although AI promises economic growth and productivity, it will be disruptive, especially for business leaders and workers in knowledge sectors. “AI is likely to be the most disruptive technology to alter the nature of our work since the personal computer,” says Davis. “Those of a certain age might recall how the broad availability of PCs remade many jobs. It didn’t eliminate jobs as much as it allowed people to focus on higher value activities.” 

The team’s framework allowed them to examine AI automation risks to over 800 different occupations. The research indicated that while the potential for job loss exists in upwards of 20% of occupations as a result of AI-driven automation, the majority of jobs—likely four out of five—will result in a mixture of innovation and automation. Workers’ time will increasingly shift to higher value and uniquely human tasks. 

This introduces the idea that AI could serve as a copilot to various roles, performing repetitive tasks and generally assisting with responsibilities. Davis argues that traditional economic models often underestimate the potential of AI because they fail to examine the deeper structural effects of technological change. “Most approaches for thinking about future growth, such as GDP, don’t adequately account for AI,” he explains. “They fail to link short-term variations in productivity with the three dimensions of technological change: automation, augmentation, and the emergence of new industries.” Automation enhances worker productivity by handling routine tasks; augmentation allows technology to act as a copilot, amplifying human skills; and the creation of new industries creates new sources of growth.

Implications for the economy 

Ironically, Davis’s research suggests that a reason for the relatively low productivity growth in recent years may be a lack of automation. Despite a decade of rapid innovation in digital and automation technologies, productivity growth has lagged since the 2008 financial crisis, hitting 50-year lows. This appears to support the view that AI’s impact will be marginal. But Davis believes that automation has been adopted in the wrong places. “What surprised me most was how little automation there has been in services like finance, health care, and education,” he says. “Outside of manufacturing, automation has been very limited. That’s been holding back growth for at least two decades.” The services sector accounts for more than 60% of US GDP and 80% of the workforce and has experienced some of the lowest productivity growth. It is here, Davis argues, that AI will make the biggest difference.

One of the biggest challenges facing the economy is demographics, as the Baby Boomer generation retires, immigration slows, and birth rates decline. These demographic headwinds reinforce the need for technological acceleration. “There are concerns about AI being dystopian and causing massive job loss, but we’ll soon have too few workers, not too many,” Davis says. “Economies like the US, Japan, China, and those across Europe will need to step up function in automation as their populations age.” 

For example, consider nursing, a profession in which empathy and human presence are irreplaceable. AI has already shown the potential to augment rather than automate in this field, streamlining data entry in electronic health records and helping nurses reclaim time for patient care. Davis estimates that these tools could increase nursing productivity by as much as 20% by 2035, a crucial gain as health-care systems adapt to ageing populations and rising demand. “In our most likely scenario, AI will offset demographic pressures. Within five to seven years, AI’s ability to automate portions of work will be roughly equivalent to adding 16 million to 17 million workers to the US labor force,” Davis says. “That’s essentially the same as if everyone turning 65 over the next five years decided not to retire.” He projects that more than 60% of occupations, including nurses, family physicians, high school teachers, pharmacists, human resource managers, and insurance sales agents, will benefit from AI as an augmentation tool. 

Implications for all investors 

As AI technology spreads, the strongest performers in the stock market won’t be its producers, but its users. “That makes sense, because general-purpose technologies enhance productivity, efficiency, and profitability across entire sectors,” says Davis. This adoption of AI is creating flexibility for investment options, which means diversifying beyond technology stocks might be appropriate as reflected in Vanguard’s Economic and Market Outlook for 2026. “As that happens, the benefits move beyond places like Silicon Valley or Boston and into industries that apply the technology in transformative ways.” And history shows that early adopters of new technologies reap the greatest productivity rewards. “We’re clearly in the experimentation phase of learning by doing,” says Davis. “Those companies that encourage and reward experimentation will capture the most value from AI.” 

Looking globally, Davis sees the United States and China as significantly ahead in the AI race. “It’s a virtual dead heat,” he says. “That tells me the competition between the two will remain intense.” But other economies, especially those with low automation rates and large service sectors, like Japan, Europe, and Canada, could also see significant benefits. “If AI is truly going to be transformative, three sectors stand out: health care, education, and finance,” says Davis. “For AI to live up to its potential, it must fundamentally reshape these industries, which face high costs and rising demand for better, faster, more personalized services.”

However, Davis says Vanguard is more bullish on AI’s potential to transform the economy than it was just a year ago. Especially since that transformation requires application beyond Silicon Valley. “When I speak to business leaders, I remind them that this transformation hasn’t happened yet,” says Davis. “It’s their investment and innovation that will determine whether it does.”

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.