At OpenAI, figuring out how artificial intelligence will transform the labor market has become a moving target, with the researchers studying AI’s economic impact forced to adapt almost as quickly as the technology itself.

Ronnie Chatterji, OpenAI’s chief economist, leads a team of about a dozen economists, data scientists, business professionals, former teachers and former government workers examining how AI is changing workers, companies and the broader economy.

But even the questions they are trying to answer are changing.

“The job description is changing a lot,” Chatterji told Business Insider.

He cited recursive self-improvement, the idea that AI systems could improve their own capabilities, as an example. It was not a major focus of his team’s work a year ago but has since emerged as an area the researchers are studying. That illustrates one of the central difficulties of researching AI’s economic effects: the underlying technology is evolving faster than many traditional economic models and assumptions can accommodate.

Chatterji joined OpenAI in 2024 after a career in government and academia. He served in the Biden White House as coordinator of the $52 billion CHIPS program and as acting deputy director of the National Economic Council. He was previously chief economist at the Commerce Department and remains a professor of business and public policy at Duke University.

OpenAI was not initially part of his plans.

Chatterji had been preparing to write a book about his government experience when a former colleague who had joined OpenAI contacted him. Their initial discussions centered on supply chains and semiconductors, areas that were increasingly important to OpenAI as the company considered the enormous computing infrastructure required to train and operate advanced AI systems.

The conversations eventually expanded into a broader question: how should a company building increasingly capable AI understand its economic consequences?

“This is a whole emerging category, and that’s when the chief economist role got created,” Chatterji said.

He reports to OpenAI’s chief financial officer, Sarah Friar.

Chatterji’s team is organized around three broad questions: what AI is doing to work now, how companies are adopting the technology and reorganizing around it, and what increasingly capable AI could mean for the economy in the future.

“We built the team around three sets of questions: How AI is changing work today; how businesses are adopting and reorganizing around AI; and what increasingly capable AI could mean for the economy tomorrow,” he said.

The three areas are closely connected.

The first involves measuring changes that are already taking place in employment, wages, productivity and the tasks workers perform. The second examines how companies are incorporating AI into their operations and whether the technology changes organizational structures, staffing needs and business models.

The third is considerably harder because it requires economists to reason about technologies that may not yet exist in mature form.

That uncertainty is shaping the type of people Chatterji wants to hire.

“We need people who can bring rigorous economic thinking to what’s happening today, but who are also comfortable tackling questions where we don’t have all the answers yet,” he said. “You have to be comfortable with being uncomfortable.”

That represents a significant departure from conventional economic research, where researchers can spend years studying relatively stable datasets and established relationships.

At OpenAI, the underlying technology can change between the beginning and end of a research project.

Chatterji described the pace of innovation inside the company as “a little insane,” noting that a study using data through June could already be viewed by some as outdated by August.

That creates a methodological problem for economists attempting to measure AI’s impact. If AI capabilities, adoption rates and business practices are changing rapidly, conclusions based on historical data can become obsolete before they are published. It also means researchers must combine traditional economic analysis with real-time data, industry research and close engagement with companies, governments and universities.

Collaboration is therefore another central part of the job.

“We get a lot of questions from our colleagues about economics,” Chatterji said, while noting that his team also works with external organizations.

OpenAI has argued that no single company, government or academic institution has enough information or resources to fully understand AI’s economic effects. The scale of the changes being considered makes cooperation relevant, particularly for questions involving employment, productivity, taxation and economic inequality.

Chatterji’s own career illustrates the breadth of issues now falling under the chief economist’s remit.

His academic research focused on innovation and entrepreneurship. At OpenAI, his work extends into labor markets, corporate adoption, industrial policy and the potential effects of increasingly capable AI systems.

That breadth reflects how difficult it is to separate AI’s technological impact from its economic consequences.

A more capable model can alter the economics of software development. Greater automation can change hiring decisions. Lower costs for certain forms of knowledge work can create new businesses while reducing demand for some existing tasks. At the same time, entirely new categories of work may emerge around technologies that did not previously exist.

For Chatterji’s team, the challenge is to distinguish between these competing effects rather than assume that AI will simply eliminate jobs or, alternatively, make workers uniformly more productive.

The need for that analysis is becoming more urgent as companies move from experimenting with AI to incorporating it into everyday operations. Businesses are increasingly using AI for coding, customer service, research, marketing, administration and other knowledge-intensive tasks. The economic consequences could depend less on whether AI can perform a particular task and more on how companies reorganize work around those capabilities.

That is why Chatterji says members of his team must have a high degree of independence.

Researchers cannot simply wait for a fixed assignment. They need to identify which questions matter, determine who needs the answers, and adjust their work as the technology changes.

The approach also shows that OpenAI is no longer simply developing AI models and measuring their technical performance. It is now building an internal research capability aimed at understanding how those models affect the economy in which OpenAI operates. That could become more relevant for a company whose technology is being adopted across industries.

OpenAI needs to understand not only whether its models are becoming more capable, but what those capabilities mean for customers, workers and businesses. The answers could influence product development, enterprise strategy and the company’s engagement with governments as policymakers debate how AI should be regulated.

The research also has an unusual feedback loop. OpenAI is studying the economic effects of the technology while simultaneously building the technology that could cause those effects. That gives Chatterji’s team access to an unusually close view of AI adoption, but it also creates a need for rigorous analysis to separate evidence from assumptions about what the technology may eventually achieve.

For now, Chatterji says the uncertainty is part of what makes the work compelling.

“If you’re an economist at a cocktail party or on the sidelines of your kid’s soccer game, usually you’re not very popular,” he said.

That has changed.

“What’s AI going to do in the job market?” is now a question that economists, business leaders, workers and governments increasingly want answered.

OpenAI’s decision to employ a dedicated team to study that question reflects how central the economic consequences of AI have become to the company’s own strategy. But the team’s biggest challenge may be that by the time it answers one question, the technology may have created several new ones.

A further rise in long-term U.S. Treasury yields toward 5% could trigger a sharp de-risking across equities and send the S&P 500 down as much as 20%, according to Phillip Colmar, a partner at market research firm MRB Partners.

The warning comes after a turbulent week in bond markets, with investors increasingly focused on the combination of elevated inflation, strong economic growth expectations and rising U.S. government debt.

The benchmark 10-year Treasury yield climbed above 4.7% this week, while the 30-year yield moved above 5.2%, increasing pressure on equity valuations and raising concerns about how much higher borrowing costs could affect corporate investment and profits.

Colmar said stocks could be “on the brink” of a de-risking episode if long-term yields continue to rise.

“If it looks like it’s going up and it’s not going to be stopped, you could end up with a de-risking event that starts below 5%,” Colmar told Business Insider, referring to the 10-year Treasury yield.

He estimated that a move toward 5% could ultimately result in a 15% to 20% decline in the S&P 500, although he did not make a specific forecast for where Treasury yields will settle.

The warning comes at a particularly sensitive point for equity markets. Investors have spent much of the past year placing large bets on artificial intelligence, expecting rapid productivity gains and strong earnings growth from companies building AI infrastructure and applications.

Higher long-term yields threaten that trade by changing the relative attractiveness of stocks. Treasury securities are generally viewed as the benchmark risk-free asset, so when their yields rise, investors can demand higher expected returns from equities to compensate for taking additional risk.

The effect weighs largely on growth stocks, whose valuations depend heavily on earnings expected several years into the future. Higher discount rates reduce the present value of those future earnings, putting pressure on companies with high valuations and long-duration growth expectations.

The AI sector is especially exposed because the industry’s expansion requires enormous amounts of capital.

Technology companies are spending heavily on data centers, computing infrastructure, chips, and electricity capacity to support sophisticated AI systems. Much of that investment is expected to generate returns over several years rather than immediately.

Higher financing costs could therefore reduce returns on invested capital and make it more difficult for companies to justify the scale of their spending.

Colmar said the combination of elevated expectations and rising borrowing costs could create an “air pocket” in the market.

“You just end up with an air pocket between what might be a decent theme, but expectations were just too high and they can’t be met now,” he said.

That dynamic could be especially damaging if investors begin lowering earnings forecasts for AI companies at the same time that they demand greater discipline over capital spending.

The concern is not necessarily that the AI investment cycle will collapse. Rather, higher rates could force investors to reassess how quickly companies can turn enormous infrastructure spending into revenue and profits.

The Treasury’s response to rising long-term yields could also become a source of market uncertainty.

Treasury Secretary Scott Bessent said this week that the department would increase its bond buybacks in an effort to reduce the supply of longer-dated securities available in the market. Lower supply can support bond prices and, in turn, put downward pressure on yields.

Colmar warned, however, that the policy could have the opposite psychological effect if investors interpret the move as an attempt to suppress yields without addressing the underlying reasons for the increase.

He also pointed to the lack of coordination with the Federal Reserve, which has been allowing Treasury securities to mature and roll off its balance sheet as part of its quantitative-tightening process.

If investors conclude that the Treasury is becoming increasingly concerned about the level of long-term yields, the intervention could undermine confidence rather than restore it.

“The market sniffs out the panic,” Colmar said.

The underlying drivers of higher yields are considered important. Rising government borrowing needs increase the amount of debt that investors must absorb, while expectations for stronger economic growth can push yields higher by reducing expectations for monetary easing.

Persistent inflation creates another challenge because investors demand greater compensation for holding long-duration bonds when they believe purchasing power will erode more rapidly.

The U.S.-Iran war has added another inflationary dimension by increasing uncertainty around energy prices and supply chains, further complicating the outlook for inflation and interest rates.

A sustained rise in long-term yields would therefore create a difficult environment for both bonds and equities. Higher yields can attract capital away from stocks while simultaneously increasing the discount rate used to value companies and raising financing costs across the economy.

Colmar remains positioned for economic growth rather than an immediate recession, but he recommends investors reduce exposure to areas most vulnerable to higher rates if Treasury yields continue to rise.

One strategy is to trim exposure to AI stocks and increase allocations to defensive sectors, with healthcare among his preferred areas because of its relatively lower sensitivity to interest rates. Financial stocks could also benefit from a higher-rate environment, depending on the shape of the yield curve and the effect of borrowing costs on banks’ net interest margins and credit quality.

Exchange-traded funds such as the Vanguard Health Care ETF and Financial Select Sector SPDR Fund provide exposure to those sectors.

The broader message from the bond market is that the next major risk to equities may not come from an abrupt deterioration in economic growth. It could instead come from a gradual repricing of the cost of capital.

That matters for investors because a strong economy can coexist with falling stocks if Treasury yields rise quickly enough. In such an environment, corporate earnings may continue to grow while equity valuations contract as investors demand greater returns to compensate for higher interest rates.

The AI sector has benefited from expectations of enormous future earnings, but those expectations are increasingly being tested against the cost of building the infrastructure needed to produce them.

If the 10-year Treasury yield approaches 5%, investors may begin asking whether projected AI returns are large enough, and arrive quickly enough, to justify current valuations and capital spending. That could turn higher bond yields from a macroeconomic concern into a direct threat to one of the market’s most crowded investment themes.