From 2019 to Q1 2026, the equity wealth directly held by American households surged from approximately $29 trillion to about $55 trillion, nearly doubling. The period of rapid expansion was concentrated after 2023, coinciding with the AI-driven stock price rally.
The impact of AI (Artificial Intelligence) on income distribution is gradually becoming apparent.
On July 27, a Quarterly Macro Policy Report (Q2 2026) released by the China Finance 40 Forum (CF40) indicated that over the past few years, AI technology has accelerated the growth of the wealth pie while also exacerbating income inequality.
From 2019 to Q1 2026, the equity wealth directly held by American households surged from approximately $29 trillion to about $55 trillion, nearly doubling. The period of rapid expansion was concentrated after 2023, coinciding with the AI-driven stock price rally.
The report further analyzed that between 2022 and Q1 2026, the equity wealth directly held by American households increased by about $21 trillion. The top 10% of households captured approximately 88% ($18.5 trillion) of this wealth, while the bottom 50% received only about 1% ($0.2 trillion).
The shift in American household wealth preliminarily reflects the income distribution imbalance brought about by AI technology: wealthier families have taken a larger share of the pie.
At the report release event, Huang Yiping, a CF40 member and Dean of the National School of Development at Peking University, shared academic research findings showing that every major technological advancement in history has profoundly influenced the structure of income distribution.
According to research by economic historian Robert Allen, the period known as the "Engels' Pause" occurred during the First Industrial Revolution, where for several decades after its onset, worker output per capita increased significantly, but real wages did not show clear growth. In terms of distribution share, it took about 100 years after the First Industrial Revolution began for labor's share of total economic output to start rising.
This raises a question: Will the early stages of AI technological progress also see a period of "wage stagnation, rising capital returns, and widening inequality," similar to the "Engels' Pause"?
Huang Yiping believes that AI, as a general-purpose technology, is a historic opportunity for China's economic development. Simultaneously, AI technological progress may affect income distribution through four major mechanisms, potentially leading to further imbalance in the income distribution structure in the near future.
"We should attach great importance to the issue of income distribution and prepare for potential challenges in advance," Huang Yiping stated. Income distribution relates to aggregate social demand. If the current trend continues, the pattern of strong supply and weak demand may be difficult to reverse in the short term and is likely to intensify further, thereby affecting the sustainability of economic growth.
Consequently, Huang Yiping suggested adhering to the "people-oriented" principle and building a systematic policy mix around the "investing in people" strategy. This approach aims to balance short-term social stability with long-term economic efficiency, achieving inclusive sharing of technological dividends.
Four Mechanisms Affecting Income Distribution
While AI triggers waves of excitement in capital markets, the specter of layoffs and job displacement continues to loom over workers.
The CF40 report shows that since the advent of AI, capital expenditure substituting for labor hiring is indeed happening.
US market data reveals that from 2024 to 2025, employment growth rates in industries with higher AI application levels—such as information technology, professional services, finance, and insurance—were significantly lower than their average growth rates from 2010 to 2019. Meanwhile, AI has driven a substantial rise in US stock market value and very uneven growth in household wealth, with the top 10% capturing about 88% and the bottom 50% receiving only about 1%.
This has led to a divergence within household disposable income. In 2025, the share of disposable income for the top 10% of households rose to 36%, significantly higher than the 2010-2019 average of 34.3%. The share for the bottom 50% of middle- and low-income households was 11.5%, slightly lower than the 2010-2019 average of 11.8%.
Drastic changes in income distribution due to technological progress are not unprecedented. Robert Allen's research shows the First Industrial Revolution began in the 1760s, but labor's share of economic output continued to decline until the 1870s before it started to recover. In other words, it took about 100 years after the First Industrial Revolution dramatically boosted human productivity for the share of the pie going to laborers to begin expanding.
Huang Yiping noted that every industrial revolution in history has profoundly impacted income distribution. The First Industrial Revolution saw the inequality-worsening "Engels' Pause," where capitalists took a larger share of wealth. The Second Industrial Revolution gave an advantage in distribution to investments in intangible assets like technical knowledge and organizational capital. The Third Industrial Revolution led to employment polarization and wage inequality, with rapid growth in high-paying, high-skilled income and job opportunities, relative stability in low-end positions, shrinkage of middle-tier jobs, and stagnant wages for medium-skilled workers.
As an acknowledged general-purpose technology, AI will undoubtedly bring another leap in human productivity. This time, how will technological progress affect income distribution?
Huang Yiping stated that AI technological progress may influence income distribution through four mechanisms.
First, the capital-biased mechanism, which will manifest as a sustained decline in labor's income share. Firms continuously increase capital investment to substitute for traditional labor factors, altering the logic of production factor allocation and the pattern of income distribution. In other words, AI empowerment can increase output per worker, but worker income does not grow.
Second, the task-sorting mechanism, whose manifestation resembles the hollowing out of the middle class and "K-shaped" divergence seen during the Third Industrial Revolution.
"After AI technology is implemented, will you be replaced or empowered? If you are empowered by AI, then you will have more opportunities in the future, and your income will trend upwards along the 'K' shape; if you are easily replaced by AI technology, your future income or returns may trend downwards," Huang Yiping said.
Third, skill differentiation and the digital divide, which may hinder social mobility. Differences in individuals' technical skills directly translate into income gaps and inequality in development opportunities, causing industry and regional divergence and a winner-takes-all effect. "Simply put, unless you own a platform or possess specific skills, the development of new technologies may not be particularly favorable to you."
Fourth, the wealth distribution amplification mechanism, primarily manifested as the decoupling of capital returns from labor income. Specifically, through asset appreciation, cost shifting, and intergenerational resource transfer, wealth inequality is amplified from the capital side, leading to "the rich getting richer." "This is a general phenomenon, not strongly correlated with AI," Huang Yiping added.
Breaking the Monopoly on AI Dividends
Does productivity increase necessarily lead to faster economic growth?
Zhang Bin, the lead author of the aforementioned report, a CF40 senior fellow, and Deputy Director of the Institute of World Economics and Politics at the Chinese Academy of Social Sciences, suggested the answer might be negative, because AI could cause the demand side to grow even slower than before, and economic growth rates are often determined by the weaker side between supply and demand.
The reason AI's impact on income distribution receives such attention is not only because it concerns individual and family fortunes but also due to its potential profound impact on aggregate social demand.
"Worker groups primarily rely on salary income, and their marginal propensity to consume is significantly higher than that of capital owners who depend on asset appreciation. When AI shifts wealth from labor to capital owners, society as a whole will face severe insufficiency of aggregate consumption demand," the CF40 report cited a 2020 academic paper as stating.
In recent years, the Chinese economy has persistently exhibited characteristics of "strong supply, weak demand."
In the first half of 2026, China's GDP (Gross Domestic Product) real growth rate reached 4.7%. Within this, total retail sales of consumer goods grew by 1.3% year-on-year, and national fixed asset investment (excluding rural households) decreased by 5.7% year-on-year, indicating overall weak domestic demand.
Huang Yiping noted that AI's pull on the supply side is already evident, but if aggregate demand does not pick up and supply is too strong, the economy is also unsustainable. "If the trend continues as it is now, a scenario could likely develop where the pattern of strong supply and weak demand not only cannot be reversed in the short term but is also likely to intensify further," Huang Yiping said. "We need to consider how to continuously boost aggregate demand, keeping it relatively balanced with supply."
In Huang Yiping's view, AI is a historic opportunity for China's economic development, while we should also attach great importance to the issue of income distribution, preparing in advance for the possibility of further imbalance in income distribution in the coming period.
In response, Huang Yiping proposed a three-pronged coping strategy.
First, Defense: By strengthening the social safety net to buffer the unemployment shock brought by AI, and simultaneously curbing the excess monopoly profits capital may gain through algorithms, safeguarding the basic foundation of social fairness.
Specific measures include: improving unemployment insurance and basic livelihood guarantee systems; strengthening anti-monopoly enforcement in the platform economy and AI fields; establishing ethical review and restriction mechanisms for purely substitutive AI applications.
"In the short term, what is relatively certain is that AI technological innovation will definitely have a significant impact on certain occupations; some people will lose their jobs. This effect occurred in every past industrial revolution. This is a normal phenomenon, but the key issue is how to achieve a smooth transition," Huang Yiping suggested. He recommended adhering to the "employment priority principle," encouraging more AI technological innovation that empowers labor rather than substitutes for it, while ensuring no major social problems arise by reinforcing basic social security.
Second, Empowerment: Reshaping the human capital structure, promoting a shift for workers from "being replaced by AI" to "mastering AI innovation," achieving synergistic coexistence between humans and intelligent technology through capability upgrading.
Specific measures include: reforming the education system, integrating AI literacy and creative thinking cultivation; establishing a lifelong learning vocational skills training system; promoting "AI + profession" skills certification and employment support.
"In the future, the relative importance of academic diplomas for young people seeking jobs and career development may decline, while the importance of composite skills will rise," Huang Yiping said. Our government has proposed "investing in people," a very important aspect of which is cultivating the ability for humans to collaborate with AI.
Third, Rebalancing: Restructuring the distribution mechanism for production factors, breaking the monopoly of capital and technology over AI dividends, and using institutional design to allow the value created by data and algorithms to benefit broader social groups.
Specific measures include: exploring the imposition of adjustment taxes on AI's excess returns; clarifying data property rights, promoting socialized sharing of public data; establishing a public-sharing AI dividend distribution fund.
"How to allow the whole society to share the value created by technology and algorithms is a high demand placed on public policy," Huang Yiping acknowledged frankly. Considering a public-sharing AI dividend distribution fund might still be too early now, but perhaps some life or income support for low-income groups could be considered.
"From the current situation, overall, the inequality problem in income distribution is quite prominent and may become even more pronounced in the future, while insufficient demand is our most prominent problem at present," Huang Yiping stated. Making the "investing in people" strategy into a comprehensive set of policy solutions might help alleviate the current problem of strong supply and weak demand and improve income distribution.
(The author is a reporter from Caijing.)
This article is from the WeChat public account "Caijing MayFlower" (ID: Caijing-MayFlower), author: Tang Jun, editor: Zhang Wei
