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In brief:
Generative AI (GenAI) has emerged as an important catalyst for growth and innovation. In the previous articles of our series, we showed that the economic impact of prior technological revolution can be significant. We estimated that the GenAI revolution could lift global GDP by US$1.7t to US$3.4t1 over the next decade and meaningfully impact over half of the workforce globally.
Yet, GenAI risks and challenges could manifest in several ways, such as rising income inequality, increasing market concentration and “winner takes all” dynamics, and widening global disparities. These are important challenges that will need to be adequately tackled to harness the potential of GenAI in an inclusive way for households, businesses and economies throughout the world. “I am hopeful that GenAI can connect people as it interconnects data. CEOs that drive responsible, human-centric AI deployments will realize more sustainable impact for their business and shareholders.” says Dr. Khalid A. Khan, EY Americas Strategy and Transactions AI Leader.
Our key findings are:
Addressing these GenAI divides will require a multifaceted approach:
I am hopeful that GenAI can connect people as it interconnects data. CEOs that drive responsible, human-centric AI deployments will realize more sustainable impact for their business and shareholders.
Dr. Khalid A. Khan
EY Americas Strategy and Transactions AI Leader
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Chapter 1
How will GenAI risks related to distribution of income play out?
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Read moreAs GenAI continues to make large strides in adoption and diffusion, one of the most pressing concerns is that its benefits may not be shared equitably. GenAI could exacerbate two types of inequalities: 1) inequality in the distribution of national income between corporate profits and labor, and 2) inequality in the distribution of income across workers and households.
As GenAI technologies spur greater productivity gains, most of the increase in business output will be split between workers in the form of wages (labor share) and businesses in the form of profits (profit share). In advanced economies, this split has become increasingly unequal in recent decades, with the labor share declining markedly since the late 1990s. In the US, it reached a record low of 52% of GDP in 2022 as the profit share reached record-high levels of around 13%. This well-documented secular trend has been attributed to a combination of factors, including technological innovation and automation — with the International Monetary Fund (IMF) estimating that half of the decline in the labor share in advanced economies can be explained by technological change — and, more recently, industry concentration and market power.3
Like prior technological innovation, GenAI will likely exert downward pressure on the labor share. As organizations adopt and absorb the new technologies, they will most likely increasingly substitute capital for labor. This would likely lead to lower labor demand and wage growth amid reduced workers' bargaining power. Elevated market concentration, as the GenAI market becomes increasingly dominated by a small number of large businesses, will also tend to generate higher markups and result in a growing fraction of productivity gains going to corporations.
Rapid technological change, led by the digital revolution, has also been associated with a significant increase in income inequality across most major advanced economies over the past four decades. This widening wealth and income gap has been particularly salient in the US. Since 1980, the average income for US households in the bottom income quintile has quadrupled, while the average income for those in the top quintile has increased sixfold.4 GenAI has the potential to perpetuate and potentially exacerbate these longstanding inequality patterns if not implemented in an inclusive way.
As we have highlighted, the GenAI-induced capital investment and productivity gains are set to provide a substantial lift to the global economy worth between US$1.7t and US$3.4t.5 For the US economy, the real GDP boost could range between US$900b and US$1.7t6 over the next decade. With personal income accounting for 75% of US GDP, the lift to aggregate household income could range between US$675b and US$1.3t.7
If distributed equally among households, it would represent an average gain of US$5,135 per household. However, the boost to income from GenAI will likely be uneven. Indeed, US households in the lowest income quintile received only about 3% of all income in 2022, compared with 52% for the highest income quintile. Assuming this trend holds, GenAI could increase the income gap between the top and bottom earners by nearly US$33b annually in the next 10 years.8
The extent and nature of the labor market disruptions from GenAI will be key for the trajectory of inequality. Globally, we find that 59% of occupations have a high to moderate exposure, with 67% in advanced economies and 57% in emerging markets.9 We showed that 66% of jobs will be moderately to highly impacted by GenAI. The remaining 34% of occupations have low AI exposure but will still be affected by GenAI via some tasks.10 Unlike prior waves of technological innovation, which had the strongest impact on low- and middle-wage workers in routine jobs, higher-wage workers are the most exposed to GenAI augmentation.
Research from the IMF11 shows that when GenAI is highly complementary to labor, the complementarity effect more than offsets the displacement effect, particularly at the top of the income distribution, which leads to a smaller share of high-income workers facing potential job losses. With our GenAI augmentation scores generally stronger for occupations generating higher wages, earners at the top of the wage scale seeing stronger productivity gains could see a disproportionate increase in their labor income compared with low-wage earners who are less exposed to GenAI, which would lead to a rise in income inequality.
Chapter 2
GenAI may further widen the digital gap by pushing smaller businesses out.
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Read moreSince the 1980s, technological innovation has led to a marked increase in market concentration where a small number of large organizations capture a larger share of the profits and value added.
This has been especially apparent in the digital economy with the US high-tech digital sector dominated by a few big players, leaving little room for innovators to break in. GenAI has the potential to deepen the current divide between technological leaders and laggards.
Research from the Organisation for Economic Co-operation and Development (OECD)12 has shown that technology diffusion is a highly uneven process as the productivity gap between the most productive businesses — global frontier organizations — and the rest increased significantly during the 2000s. Between 2001 and 2009, labor productivity in OECD countries grew 35% among “frontier firms,” compared with only 5% for other businesses.13 This widening productivity gap has also been documented in more recent research14, showing that the gap between leading and laggard organizations has increased over time, with the greatest increase in the IT sector.
The risk is that the benefits from GenAI — including higher productivity levels and stronger profitability — could accrue to a handful of “superstar” businesses that have the resources to successfully deploy GenAI solutions and applications, and develop these capabilities via access to the vital building blocks of GenAI, including:
Chapter 3
Lack of GenAI readiness could put another wedge between the most and least advanced economies.
The deployment of GenAI is poised to provide a significant boost to the global economy. However, the benefits are likely to be unevenly distributed among economic regions and could widen global economic disparities, as some economies are better equipped for AI adoption than others.
Oxford Insights’ Government AI Readiness Index16, which assesses the capacity of governments to exploit the innovative potential of AI technologies, sheds light on these global gaps in AI preparedness. The index, which ranks 193 countries based on 39 indicators across three pillars (government, technology sector, and data and infrastructure) shows that there is a significant gap in AI readiness across countries and regions around the world, with low-income and developing economies with low GDP per capita levels generally lagging developed economies.
The Stanford Global AI Vibrancy Tool17, which assesses countries’ level of AI advancement across various metrics related to research and development and the economy, paints a similar picture with most advanced economies better positioned to reap the AI benefits. However, China and India stand out as emerging economies ranking in the top three economies for their AI vibrancy, with China leading in R&D and India in AI talent.
Overall, we find that three groups of countries stand out:
Such disparities in AI readiness and advancement could worsen inequalities between the most and least advanced economies. The risk is that the AI revolution could amplify the existing global digital divide — which refers to the disparity in access and use of information technologies such as the internet. Global cooperation aimed at reducing technological disparities by expanding access to AI technology and infrastructure and building digital skills will be critical to help bridge the global AI divide.
Figures in the content include those obtained from the US Bureau of Economic Analysis, the US Bureau of Labor Statistics and the US Census Bureau.
Summary
This is the fifth installment of the EY-Parthenon macroeconomic article series on the economic impact of AI. The series aims to provide insights on the economic potential of GenAI, including new developments and actionable insights to arm companies’ decision-makers. The fifth article in this series covers GenAI risks and challenges for the economy.
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Harness the productivity potential of GenAI
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