One of the most important issues for investors and policymakers alike is understanding the potential impact that generative artificial intelligence models will have on the economy and labor market.  

The term describes algorithms that can be used to create new content including audio, code, images, text, simulations and videos. For lay people, they are often thought of as machine-learning techniques.

The task is daunting considering the complexity and sophistication of the models deployed, the wide range of applications they serve and the inherent uncertainty about how they will evolve.  

Amid this, one thing is clear. Namely, the launch of Chat GPT more than a year ago has captured the attention of technology experts. 

Rob Thomas, IBM’s chief commercial officer, has called it a “Netscape Moment” referring to the introduction of the firm’s browser in 1994 that “brought the internet alive.”  

Kai-Fu Lee, CEO of 01.AI, likens it to the advent of Gutenberg’s printing press, which made it possible for ideas to spread around the world at previously unimaginable speeds, creating huge gains for mankind.

The development of generative AI has also resonated with equity investors. The so-called “magnificent seven” stocks, posted outsized gains last year, with those in the AI space leading the way. They include semiconductor giant Nvidia and Meta Platforms, which were up by nearly 240 percent and 195 percent, respectively.

Some observers, however, caution that the AI revolution may not come as fast as optimists think or be as transformative as proponents contend.

Steve Lohr, who covers technology for the New York Times, observes that there has always been a lag between the invention of new technologies and their adoption across industries and the economy. He also cites the example of JPMorgan Chase, which has told workers not to put any bank information into the chatbot or other Gen AI tools. The reason: Management is cognizant of the risks of leaking confidential data, and it questions how the data will be used and how accurate the answers will be.

Governments, moreover, will have an important say in determining regulatory policies for artificial intelligence. The European Union announced a landmark deal on AI regulation in December that the EU’s internal market commissioner proclaimed is: “much more than a rulebook — it’s the launch pad for EU startups and researchers to lead the global race for trustworthy AI.”  

According to the Economist, however, the launch of ChatGPT is making it more difficult for the U.S., Britain and the EU to agree on a common risk-based approach to regulate AI.  

So, what is generative AI’s potential to boost productivity?

One consideration that differentiates it from previous technologies is the much wider specter of jobs it can impact. The list includes professional workers in high-end, high-pay jobs, as well as people working in low-wage, repetitive jobs and in back offices.

Using a database that lists about 900 occupations, Goldman Sachs’ economists estimate that roughly two-thirds of U.S. occupations are exposed to some degree of automation by AI. They further estimate that of those occupations that are exposed, roughly one-quarter to one-half of the workload could be replaced.  

All told, Goldman’s researchers conclude: “They could drive a 7 percent (or almost $7 trillion) increase in global GDP and lift productivity growth by 15 percentage points over a 10-year period.” If so, annual productivity growth for the U.S. would double from about 1.5 percent to 3.0 percent, which would be an unprecedented achievement.

Daron Acemoglu and Simon Johnson of MIT, however, are skeptical of this assessment. They see it at odds with the historical record in which new technologies that expand the set of tasks performed by machines and algorithms often wind up displacing workers and boosting corporate profits, but do not lead to shared prosperity.   

A National Bureau of Economic Research working paper by David Autor and Anna Salomons helps shed light on this issue. Using four decades of cross-country and industry data, they find that automation typically displaces employment and reduces labor’s share of value added in the industries in which it originates. However, these job losses are often reversed by indirect gains in consumer-oriented industries and by increases in aggregate demand.  

Overall, they find that “technological progress is broadly employment-augmenting in the aggregate.” Indeed, the report observes that 60 percent of today’s workers are employed in occupations that did not exist in 1940.

A Brookings report by Martin Neal Bailey, Erik Brynjolfsson and Anton Korinek spells out the case for an AI-powered productivity boom. The authors observe that large language models such as ChatGPT are powerful tools “that not only make workers more productive but also increase the rate of innovation, laying the foundation for a significant acceleration of economic growth.”  The authors produce a simulation that shows that the difference in output between estimates based on future technology versus current technology results in nearly a doubling of output over 20 years.

At the same time, Erik Brynjolfsson, who directs Stanford’s Digital Economy Lab, acknowledges that the increase in productivity is unlikely to be straight-line. In fact, there is typically a period where productivity declines and there is a lull. The reason is that technological change often forces organizations to reinvent themselves and to develop new processes that take time. 

His view is that the current slump in productivity growth is nearing a bottom and about to turn up. However, there will be considerable differences between the top 10-15 percent of the firms that are doing the most to benefit from new technologies by investing in them and training their employees versus the remainder who are lagging.

Weighing these considerations, what is clear is the rapid speed with which generative AI has developed and the potential for it to become a game-changer. But there is also considerable uncertainty about diffusion lags and the pace of technological change as well as regulatory changes that render forecasts of productivity growth tenuous at best.  

Nicholas Sargen, Ph.D. is an economic consultant for Fort Washington Investment Advisors and is also affiliated with the University of Virginia’s Darden School of Business.  He has written three books including “Global Shocks: An Investment Guide for Turbulent Markets.”