Is the AI productivity dividend a "miracle cure" or a "delaying tactic"?

In February 2026, a discussion about whether artificial intelligence can save the public finances of developed countries continued to ferment within the global macro strategy circles. There is a widespread optimistic expectation: the productivity leap brought by AI will expand the overall economy and increase the tax base, providing governments with a relatively “painless” path to fiscal consolidation amid mounting debt. However, preliminary estimates shared with Reuters by the OECD and several former institutional economists challenge this narrative on a quantitative level.

Objectively, developed economies are facing the most severe fiscal constraints since World War II. The U.S. federal debt-to-GDP ratio has reached a historic high of around 100%, and most wealthy economies have debt exceeding 100% of GDP, while simultaneously contending with the “triple squeeze” of aging populations’ rigid welfare costs, increased defense spending, and climate transition investments. Against this backdrop, whether the productivity dividends promised by AI are a “miracle cure” that fundamentally repairs balance sheets or merely a “delaying tactic” that postpones structural reforms has become a key issue for macroeconomists and bond markets alike.

Background and Timeline: From Technological Breakthroughs to Fiscal Scrutiny

The penetration of AI into macroeconomics is shifting from a “micro-efficiency tool” to a “macro-growth variable.” Looking back, between 2023 and 2024, generative AI represented mainly by large language models was viewed as a tool for cost reduction and efficiency improvement for enterprises, with market focus on labor substitution and corporate profit margins. Starting in 2025, discussions gradually escalated to the level of national competitiveness, with institutions like Goldman Sachs releasing reports predicting that AI would significantly boost global GDP over the next decade.

By 2026, the scope of the discussion has again undergone structural shifts. In late February, OECD economists publicly shared their internal model projections, directly linking AI productivity gains to sovereign debt sustainability for the first time. Meanwhile, research firm Citrini Research published the “2028 Global AI Crisis” report, introducing the concept of “Ghost GDP,” warning that if AI benefits are overly concentrated in capital and consumption shrinks, it could erode the tax base and trigger fiscal crises. At this point, AI’s fiscal significance is no longer a theoretical proposition but an unavoidable variable for bond investors assessing national creditworthiness.

Data and Structural Analysis: Model Boundaries and Transmission Mechanisms

According to preliminary estimates presented to Reuters by OECD economist Filiz Unsal and her team, the positive fiscal impact of AI has clear quantitative limits. The model indicates that if AI can sustainably boost labor productivity and effectively promote employment, by 2036, debt burdens in OECD countries like the U.S., Germany, and Japan could decrease by about 10 percentage points relative to current baseline forecasts.

While this figure appears significant in absolute terms, it warrants cautious interpretation within the context of fiscal difficulties. A 10 percentage point improvement is insufficient to reverse the long-term upward trend of debt ratios; even under “best-case” scenarios, debt levels in most developed countries would remain substantially above current levels. Kevin Khang, head of global economic research at Vanguard, attributes the root of debt problems to demographic aging and the social welfare commitments tied to it, emphasizing that AI is merely “buying us time.”

Structurally, AI influences fiscal policy through two opposing pathways. The positive pathway depends on “productivity improvements—corporate profits and wages—tax base expansion—improved fiscal revenues.” Conversely, there are counteracting effects: automation could lead to net job losses, or productivity gains might flow more to capital with lower tax rates, limiting revenue improvements; additionally, if private sector wages rise due to productivity, government expenditures on employment and social security could increase simultaneously.

Public Opinion and Perspectives: Optimists, Cautious, and Reverse Scenarios

Current market views on this topic are markedly stratified.

Optimists emphasize the “magical” effects of productivity. First Eagle Investment Management fund manager Idanna Appio admits that productivity gains could significantly improve fiscal dynamics but also emphasizes a key caveat—“our fiscal problems far exceed what productivity can fix.” This statement effectively frames AI’s role as a “relief” rather than a “cure.”

Cautious analysts focus on uncertainties in the transmission mechanisms. Unsal from OECD stresses that AI’s actual impact on debt trajectories depends on three core conditions: whether displaced jobs can be absorbed by new ones; whether corporate profit increases can effectively translate into higher wages; and whether governments can restrain expenditure growth. Kent Smetters of the Penn Wharton Budget Model is more direct, estimating that AI’s impact on U.S. debt over the next decade could be “very small,” since rigid expenditures like social security are linked to wages, and productivity gains might even increase the government’s expenditure base.

Reverse scenario proponents extend their view to the risk of a “Ghost GDP.” Citrini Research warns that if AI agents massively replace white-collar workers, corporate output and GDP figures might continue to grow, but displaced workers would lose income and be unable to sustain their consumption, leading to demand collapse in the macroeconomic cycle. In this scenario, personal income tax and social security revenues tied to wages would be under pressure, while unemployment benefits and transition costs would rise, directly impacting sovereign creditworthiness.

Evaluating the Narrative’s Authenticity: Historical Lessons and Current Constraints

When assessing these viewpoints, it is essential to consider historical experiences of technological change. Citadel Securities’ recent macro strategy report notes that AI adoption follows a similar S-curve pattern to personal computers and the internet, rather than exponential leaps. Over the past century, technological revolutions have not rendered labor obsolete; instead, they have sustained long-term growth around approximately 2% annually.

This historical perspective provides an important anchor. The Information Technology and Innovation Foundation (ITIF) emphasizes that technological change has never eliminated net employment; jobs continue to evolve, tasks are continuously adjusted, and productivity improvements ultimately create new labor demand. Therefore, the current narrative of “AI ending the workforce” is more likely an overinterpretation of boundary cases rather than an accurate reflection of the actual trajectory.

However, it is also crucial to acknowledge that this wave of AI possesses the intrinsic ability to “replace cognitive labor,” a fundamental difference from past technological substitutions of physical work. If large-scale displacement occurs first in knowledge-intensive sectors like finance, law, and consulting—where high salaries are common—the pace of white-collar job compression could surpass expectations, exerting transmission pressure on credit markets built on these stable high-income expectations.

Industry Impact and Asset Revaluation in a Macro Shift

Whether AI productivity dividends can be realized, and how, is becoming a key variable for bond markets and sovereign credit ratings.

From a market pricing perspective, growth expectations driven by AI can temporarily ease concerns about fiscal sustainability. However, Christian Keller, head of global economics at Barclays, warns that if an economic downturn occurs before AI-driven prosperity materializes, markets may preemptively become wary of fiscal trajectories, and rising financing costs could bring debt issues into sharper focus. This indicates that the narrative effect of AI is time-sensitive—if dividends are delayed relative to cyclical pressures, market trust could fracture prematurely.

For the crypto asset markets, macro liquidity and sovereign credit conditions remain critical external factors. If AI-driven productivity gains can sustain real interest rates in the medium to long term, risk asset valuations could benefit; conversely, if the AI narrative unravels amid fiscal stress, triggering a new risk-off wave—including in crypto assets—liquidity could contract across all risk exposures.

Multi-Scenario Evolution

Combining existing models and viewpoints, the ultimate trajectory of high-debt countries’ fiscal dilemmas influenced by AI can be summarized into three scenarios:

Scenario 1: Best case—Time buys space (moderate probability)

AI productivity steadily improves and effectively transmits to employment and wages; economic growth expands the tax base, and debt ratios are controlled in their upward slope. The U.S. debt ratio might rise from about 100% to around 120% over the next decade, rather than reaching even higher levels in the baseline. In this scenario, AI plays the role of “buying time,” providing a buffer for governments to implement long-delayed structural reforms.

Scenario 2: Neutral—Inefficient transmission, limited effects (higher probability)

Productivity gains mainly accrue to corporate profits and capital returns, with wages growing slowly; fiscal revenue improvements are limited, and social security and public spending rigidly rise with prices. Debt ratios improve marginally, but long-term fiscal sustainability remains unresolved, requiring ongoing market discounting of sovereign credit.

Scenario 3: Reverse—Recession precedes dividends (lower but non-negligible probability)

Economic downturn occurs before AI productivity benefits materialize, leading to reduced investment, rising unemployment, and automatic stabilizers triggering fiscal contraction. If markets doubt fiscal trajectories at this point, financing costs could spike, and debt ratios might reach around 180% in the late 2030s. In this scenario, AI fails to rescue fiscal health and may even erode trust due to overhyped narratives.

Conclusion

Based on OECD models and multiple economists’ projections, the role of AI productivity gains in the current fiscal predicament is becoming clearer: it is neither a “panacea” capable of solving all problems nor a hollow narrative with no substance. A more accurate framing is that AI offers a limited but valuable “time window”—whether this window can be effectively used to address structural issues like aging populations and rigid welfare costs depends ultimately on policymakers’ choices.

For market participants, the key is not to believe or dismiss the macro narrative of AI but to distinguish “facts” from “opinions,” and to identify “speculation” versus “certainty.” The 10-percentage-point improvement revealed by the OECD model, together with Appio’s statement that it exceeds what productivity can fix, form the most authentic macro landscape of this era’s trading environment.