OECD warns: AI productivity dividends are not a "get-out-of-jail-free card" and won't fill the massive debt holes in developed countries

If AI leads to a leap in productivity, it may buy time for highly indebted developed economies, but it is not enough to reverse the upward trend in debt ratios on its own. Fiscal consolidation still depends on demographic structure, tax policies, and spending choices.

According to a preliminary estimate shared with Reuters by the OECD and three economists on February 27, if AI boosts labor productivity and employment in the long term, it will somewhat ease the relative debt burden of OECD countries compared to baseline forecasts, but the effect will be limited.

Several experts have noted that AI has a “counteracting effect” on both fiscal revenue and expenditure. Distribution issues may lead to tax revenue declines, while rising average wages could increase social security spending.

For markets, the growth brought by AI may temporarily ease the pressure on bond investors’ scrutiny of fiscal health, but rating agencies and multiple respondents emphasize that uncertainty remains high. If a recession occurs before productivity dividends materialize, rising financing costs could bring debt issues back into focus more quickly.

Debt pressure has become a “hard constraint”; AI is more about delaying rather than reversing it.

Reuters cites economists’ views that if AI productivity prosperity materializes, it could indeed help major economies better manage public finances and somewhat shield against fiscal expansion penalties, but it cannot eliminate the impact entirely.

Most wealthy economies already have debt exceeding 100% of GDP and face multiple upward pressures: aging population costs, interest expenses, and pressures on defense and climate-related spending.

Meanwhile, in an environment where developed economies’ government bond yields have risen significantly post-pandemic, bond investors’ tolerance for fiscal “generosity” is lower.

U.S. Scenario Divergence: “Slower deterioration” in the best case, but missteps could accelerate decline

In the U.S., two economists interviewed expect that in the “best case,” debt ratios could rise more slowly over the next decade from around 100% to about 120%. Another economist believes the change will be minimal.

Idanna Appio, fund manager at First Eagle Investment Management, said, “Productivity is like magic; it can significantly improve fiscal dynamics,” but she also emphasized, “Our fiscal problems go far beyond what productivity can fix.”

Kevin Khang, head of global economic research at Vanguard, sees demographic structure as the root of debt issues, stating that the “root” of debt lies in aging and the associated welfare commitments. AI is “just buying us time.”

According to her estimates, higher growth and tax revenues could slow the U.S. debt increase, bringing the debt-to-GDP ratio to about 120% in the late 2030s; but if AI disappoints, growth slows, and market pressures push borrowing costs higher, the debt ratio could rise to around 180%.

OECD’s Core Variables: Employment, Wage Transmission, and Government Spending Management

Unsal emphasizes that AI’s impact on debt trajectories depends on whether several key factors can occur simultaneously: whether job losses from automation can be offset by subsequent job creation; whether rising corporate profits translate into wage increases for workers; and whether governments can control overall spending.

Her scenario analysis shows that even if AI productivity gains reduce the debt ratio by “10 percentage points,” debt levels will still be significantly higher than current levels, meaning AI is more like “buying time” rather than an automatic tool to restore fiscal sustainability.

Both sides of fiscal policy—taxes and spending—have “counteracting effects,” and interest rates and recession risks remain critical.

From a revenue perspective, increased productivity theoretically broadens the tax base, but respondents warn that if AI reduces employment or weakens competition, more benefits may accrue to profits and capital, which are typically taxed at lower rates than labor, so fiscal revenue improvements may fall short of expectations.

On the expenditure side, efficiency gains in the public sector could reduce costs, but there is also a risk of “growth-driven” spending increases. Kent Smetters, head of the Penn Wharton Budget Model at the University of Pennsylvania, estimates that AI’s impact on U.S. debt over the next decade could be “very small.”

He notes that even if growth exceeds current expectations, it will have limited help in curbing social security expenditures because benefits are linked to average wages, and if productivity boosts wages in the private sector, other labor costs covered by the government could also rise. Unsal also emphasizes the need to observe whether wages increase and states that if AI does not create jobs, wages are more likely to rise.

Additionally, economists believe that debt costs also depend on whether productivity pushes real interest rates higher, a discussion already occurring within the Federal Reserve. Christian Keller, head of global economic research at Barclays, warns of the “possibility of recession,” stating, “AI prosperity may come too slowly.”

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