AI revolution and wealth distribution following job market changes

[Student IDEAS] by Tommaso Vercelli - Master in Management at ESSEC Business School

Abstract

The article explores AI's transformative impact on the job market and wealth distribution, tracing its evolution from early symbolic AI to today's advanced machine learning. Unlike previous industrial revolutions, AI's capital-intensive nature may increase economic inequality. While AI promises significant productivity gains, its benefits could be unevenly distributed, highlighting the need for policies promoting equitable access and global cooperation to address disparities.

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What history taught us

Artificial intelligence: where is it coming from and where is it going

Artificial Intelligence (AI) is not the monolithic or one-dimensional entity often depicted in science fiction. It's not about sentient machines taking over the world or an all-knowing oracle. AI, in its true form, encompasses computational advancements that enable machines to perform tasks that would require intelligence if done by humans, such as learning, problem-solving, and decision-making. This definition underscores that AI is a constantly evolving frontier, stretching beyond static algorithms into adaptive learning and reasoning.

Historically, AI's journey began in the mid-20th century with the "Golden Age of AI," marked by the development of symbolic AI—machines designed to mimic human intelligence through logic and search algorithms. However, this approach eventually hit a roadblock known as combinatorial explosion, where the number of computational possibilities outstripped the capacity of computers to solve them in a reasonable time. This led to the first AI winter, a period of reduced funding and interest in AI research due to unmet expectations.

The resurgence came with the advent of knowledge-based AI, which focused on specific problem domains and relied on knowledge representation and inference rules, marking significant strides but also encountering limitations with the inherent "fuzziness" of the real world. These challenges pushed AI into the next evolutionary phase, marked by the emergence of machine learning and deep learning. Unlike their predecessors, these systems learn directly from data, leading to achievements such as natural language processing and object recognition. This sets the stage for more nuanced and complex tasks, including generative AI, which thrives on its ability to process vast amounts of unstructured data across multiple modalities. 

In particular, Generative AI refers to a subclass of artificial intelligence technologies that can generate new content based on patterns and data it has learned from.  The capabilities of generative AI have grown significantly in recent years, expanding the scope of AI's potential applications. For instance, AI programs like ChatGPT can write essays, compose emails, or code software, while others can create realistic images or compose music. The 'generative' aspect of this AI comes from its ability to take initial input and generate corresponding outputs that are coherent, contextually relevant, and often indistinguishable from human-created content. This features drastically increased speed of investment, development and adoption, projecting AI as the driver for a fourth industrial revolution. However, it will deeply differ from the previous three in speed and labour market consequences.

Why AI revolution differs from the 3 previous industrial revolutions?

The First Industrial Revolution (1760-1840) marked a pivotal moment in history, transitioning from manual labor to mechanized production. The advent of the steam engine and mechanization in textile production catalyzed the transformation of agricultural societies into industrialized ones. This shift not only spurred urbanization as populations migrated to cities for factory jobs but also ignited a surge in economic productivity. However, it also introduced significant labor challenges and environmental issues. Job displacement occurred as many workers, particularly those in rural and agricultural settings, had to adapt to new factory roles, often under harsh conditions.

Similarly, the Second Industrial Revolution (1870-1914), or the Technological Revolution, was characterized by mass production and notable technological advancements, including electricity, the internal combustion engine, and the assembly line. The expansion of railroads and telegraph lines facilitated rapid communication and transport, laying the groundwork for globalization. This era of industrial expansion brought economic prosperity and an improved standard of living, but it also stirred societal unrest and deepened class divisions. Indeed, traditional craftsmen and small-scale industries were replaced by mass production methods, requiring workers to adjust to new industrial environments.

The Digital Revolution, or the Third Industrial Revolution (1950-2010), heralded the rise of electronics, telecommunications, and computer technologies, marking the transition from analog to digital. The introduction of personal computers and the internet transformed information sharing and processing, propelling societies towards service and information-based economies. While this revolution led to job displacement due to automation, it required a workforce skilled in information technology, representing a shift towards a newly trained job market. Even if big players like Google, Microsoft etc. had the opportunity to create monopolies during that period, the Digital Revolution allowed for widespread technology ownership and access, enabling individuals to create and profit from internet websites with minimal investment. The third industrial revolution characterized itself as the most democratic revolution among the three, in contrast to the capital-intensive nature of the previous two.

On the other hand, the Fourth Industrial Revolution (2010-current), driven by advancements in artificial intelligence (AI), mirrors the capital-intensive characteristics of the first two revolutions more than the democratized nature of the third. Even if the access is available to a vast amount of people, the price is significantly higher than the one for internet; a monthly subscription to chat GPT-4 comes at $20 per month. Above all, it is only access that is widespread while ownership will more likely remain under the control of a minority. In fact, despite AI's foundation on digital technologies, its development demands substantial computational power, vast datasets, and significant capital investment. While in the internet revolution everybody had the opportunity to profit by owning his own website, the same will not happen with artificial intelligence. 

However, although AI development is currently resource-intensive, ongoing advancements in technology and methodology are likely to make it more accessible and cost-effective over time. However, whether these changes will fully democratize AI development or maintain its capital-intensive nature remains to be seen. Moreover, the development trajectory of AI could mirror that of telecommunications (and similar) in its initial capital intensity and gradual shift towards democratized access through service-based models. As AI technology continues to evolve, it may follow a similar path of increased regulation and widespread usage, making advanced AI capabilities accessible to a larger population.

Moreover, while the initial industrial revolutions primarily affected physical and manual labor (Exhibit 2 and 3), AI has the potential to transform all sectors of the labor market, including both manual (in conjunction with robotization) and knowledge work. This revolution will  bring unprecedented changes to high-skilled job tasks, underscoring a significant evolution in the nature of work and the structure of the labor market.

Exhibit 2

Exhibit 3

AI’s impact overview

Value and drivers

A report by McKinsey¹ underscores the transformative potential of Generative AI, estimating its impact could enhance the global economy by $2.6 to $4.6 trillion. Similarly, Goldman Sachs² forecasts a 7% growth in global GDP over the next decade, translating to an infusion of nearly $7 trillion. The pivotal question then becomes: what mechanisms will drive this remarkable surge in revenue?

The primary conduit for this economic uplift is the boost in output production efficiency. By elevating the productivity of cognitive workers—who play a vital role in the production process—the overall output level of the economy increases. Economic theory, particularly Hulten's theorem, suggests that the effect of a productivity enhancement within a specific sector is directly tied to the extent of the productivity increase and the sector's contribution to the economy. Hulten's theorem³ states that the aggregate productivity impact of a sector is proportional to its size within the economy and the magnitude of its productivity improvement. Therefore, if  generative AI amplifies the productivity of cognitive workers by 30% over one to two decades, and these workers constitute about 60% of the economy's value-added, we could anticipate an notablesurge in overall productivity. Specifically, multiplying the 30% productivity increase by the 60% economic contribution suggests an 18% surge in overall productivity and output during the same timeframe and output during the same timeframe.

The second, perhaps more vital, driver is the acceleration of innovation, which promises sustained productivity growth. Cognitive workers are instrumental not only in contributing to current output levels but also in pioneering new products, discoveries, and technological advancements that promise to elevate future productivity. This includes research and development efforts led by scientists and the crucial role of executives in integrating these innovations into broader production processes. A boost in the efficiency of cognitive workers can hasten technological advancement, thereby perpetually increasing the rate of productivity growth. For example, if the productivity growth rate stands at 2% and the efficiency of cognitive labor driving this growth improves by 20%, the productivity growth rate could ascend to 2.4%.

While the annual impact of such advancements may initially seem modest and can be overshadowed by cyclical economic fluctuations, the cumulative benefits of productivity growth are significant. A seemingly slight increase in productivity growth can lead to the economy expanding by 5% over a decade, with the effects magnifying progressively over time.

Pace

McKinsey's analysis suggests that AI has the capacity to automate tasks that occupy 60% to 70% of employees' time. However, the timeline for this transformation is subject to various factors. The adoption of new technologies on a large scale is a gradual process; for example, foundational technologies such as electricity and early computers took decades to significantly influence productivity. Additionally, the economic viability of adopting AI in the short term may not be immediately realized if the costs surpass those associated with human labor. Recent projections indicate that up to half of the work activities currently performed could be automated between 2030 and 2060, with a midpoint projection around 2045. It's worth noting that earlier forecasts made in 2016 anticipated this automation could occur between 2035 and 2070, with a midpoint in 2053. These timelines suggest a fast-shifting landscape, with the possibility that newer predictions could soon supplant existing ones. 

In fact, the 2024 AI Index Report from Stanford⁴ notes that AI investments continue to grow, with substantial funding directed towards generative AI despite a broader decline in AI private investments. This indicates a strong belief in the transformative potential of generative AI across various industries. Moreover, the Microsoft and LinkedIn 2024 Work Trend Index⁵ revealed that a significant portion of knowledge workers are already using AI tools to improve efficiency and creativity, with many employees bringing their own AI tools to work. Finally, The MIT Technology Review⁶ highlighted that organizations are rapidly integrating AI into their processes, with a significant increase in the number of functions deploying generative AI expected in 2024.

Industries and jobs 

The advent of generative AI promises significant productivity enhancements but also raises concerns about the equitable distribution of benefits across the workforce. It is anticipated that AI could automate a substantial number of tasks in various job sectors, notably impacting high-wage, cognitive roles. This development introduces uncertainty regarding future job demand and the adaptability of workers with skills that may become obsolete. Unlike previous technological shifts that primarily affected physical or routine tasks, generative AI targets cognitive functions, placing high-paying positions at risk and potentially resulting in considerable income losses for individuals with specialized skills that are no longer needed.

Generative AI's capacity for understanding and utilizing natural language is expected to maximize its impact in roles requiring communication, supervision, documentation, and interaction (Exhibit 4). Industries such as banking, high technology, and retail are poised for profound transformations (Exhibit 5). In banking, tasks like low-value risk management activities, reporting, monitoring, and collection are highly automatable. The high-tech sector may see dramatic improvements in software development speed and efficiency, while retail could benefit from more personalized marketing and sales approaches. For instance, Stitch Fix leverages algorithms for style recommendations, experimenting with DALL·E for product visualizations based on customer preferences. Similarly, Morgan Stanley is developing an AI assistant with GPT-4 to aid wealth managers in rapidly accessing and synthesizing information from extensive internal databases.

Although the risk of job displacement and adverse impacts on certain worker segments is real, evidence suggests that these technologies can also boost productivity, create demand for new skills, and enhance job quality. This situation highlights the importance for companies to invest in training their workforce for the AI era, focusing on developing both technical skills, such as AI engineering and enterprise architecture, and the ability for employees to work alongside AI-enhanced processes. Businesses must reconsider current roles, breaking them down into task bundles to pinpoint where AI can improve efficiency. Additionally, the rise of new positions, including linguistics experts, AI quality controllers, AI editors, and prompt engineers, is expected as AI becomes more widespread.

Exhibit 4

Exhibit 5

Let’s take a step back

Research indicates that technological advancements, particularly in automation, play a significant role in driving income and wealth inequality. As highlighted in a study by Benjamin Moll and colleagues⁷, the benefits of new technologies accrue not only to high-skilled labor but also to capital owners, leading to increased capital incomes and thus, heightened economic inequality. The study introduces a theoretical framework to explain how automation exacerbates inequality by shifting demand from labor to capital, where the supply of capital is generally upward sloping. This shift increases the returns to wealth, resulting in greater concentration of the latter and income inequality. Additionally, the paper suggests that automation may cause wages to stagnate or decrease, especially for workers whose skills are more prone to automation, as capital owners capture some of the productivity gains. 

Moreover, the preferential tax treatment of capital gains and corporate profits relative to income tax rates for manual labor plays a pivotal role in accelerating the adoption of AI and automation technologies. In many jurisdictions, the tax policies are structured in such a way that investments in technology, particularly in automation and AI, are more financially attractive than the costs associated with hiring and maintaining a human workforce. This is because corporate profits, often enhanced by the efficiencies brought about by AI, are taxed at lower rates compared to the taxes levied on wages. As a result, firms find a strong financial incentive to invest in technology that can perform tasks traditionally done by humans, even if the initial productivity and output remain unchanged.

Additionally, the increasing capability of AI systems to improve productivity over time further tilts the scale in favor of automation. Empirical evidence suggests that AI can lead to significant gains in efficiency and output, making the financial case for automation even stronger. The tax policy thus not only supports existing preferences for automation but actively encourages companies to replace manual labor with technological alternatives, potentially leading to job displacement and wider economic disparities within societies.

However, the issue of income inequality extends beyond national borders, affecting the global landscape. An analysis by the International Monetary Fund (IMF⁸) suggests that AI could impact nearly 40% of global employment, with advanced economies facing higher risks due to their reliance on high-skilled jobs susceptible to AI (Exhibit 6). Conversely, emerging markets and developing countries might not fully benefit from AI advancements due to infrastructural and workforce constraints, potentially exacerbating global inequality. The IMF's AI Preparedness Index measures countries' readiness for AI adoption, with wealthier nations like Singapore, the United States, and Denmark scoring high (Exhibit 7). 

Exhibit 6

Exhibit 7

However, this advantage starkly contrasts with the situation in emerging markets and developing countries. This disparity is not merely a technical or economic challenge but a fundamental issue of equity and access. The AI revolution, therefore, mirrors the capital-intensive characteristics of the first and second industrial revolutions more than the democratized nature of the third. While the digital revolution allowed for broad participation through relatively low barriers to entry, such as the internet, the AI revolution requires substantial capital, sophisticated technologies, and complex datasets that are not as universally accessible.

The concentration of AI technology and expertise in a few nations exacerbates global inequalities and underscores the non-democratic nature of this technological advancement. Unlike the democratizing spread of digital tools, AI technologies are likely to remain under the control of a minority, comprising primarily those who can afford the hefty investments in the necessary computational resources. This concentration of knowledge and capability highlights a critical challenge: the potential for AI to enhance global disparities unless concerted efforts are made to democratize its benefits.

To address these challenges and forge a more equitable path, it is crucial for global policymakers to not only foster AI innovation within their borders but also support initiatives that enhance AI readiness and capabilities in less developed economies. 

The transformative potential of AI across various industrial revolutions highlights both its capacity to drive innovation and its profound implications for the labor market and wealth distribution. As we stand on the brink of what may be the most transformative era yet—the Fourth Industrial Revolution—it is crucial to recognize that the benefits and challenges of AI are not distributed equally. The ongoing revolution has the potential to enhance global productivity and economic output significantly, yet it also poses risks of exacerbating existing inequalities and introducing new forms of economic disparity.

To navigate this transformative period, a balanced approach involving stakeholders from across the societal spectrum is essential. Policymakers, business leaders, and the workforce must engage collaboratively to ensure that the gains from AI are leveraged to promote inclusive growth and to mitigate the risks associated with job displacement and income inequality. This includes investing in education and training programs that equip workers with the skills needed to thrive in an AI-enhanced job market, as well as designing and implementing robust social safety nets to support those affected by the transition. 

However, it is important to recognize that such measures, while beneficial at a national or regional level, may not significantly alter international disparities. Countries with advanced technological infrastructures and robust economies are better positioned to leverage AI for economic growth, potentially widening the gap between developed and developing nations. Developing countries may struggle to keep pace due to limitations in resources, infrastructure, and access to high-quality education and training programs.

To address these global disparities, international cooperation and support mechanisms are needed. This could involve technology transfer, investment in digital infrastructure in developing countries, and global initiatives aimed at ensuring equitable access to AI advancements. Without such efforts, the benefits of AI could exacerbate existing inequalities on an international scale.

Furthermore, the global nature of AI's impact requires international cooperation to ensure that both developed and developing countries can benefit from AI technologies. Policies must be crafted not only to foster innovation and streamline the integration of AI into economic infrastructures but also to ensure that these advances lead to broad-based benefits. The Global Partnership on Artificial Intelligence (GPAI),  aimed at guiding the responsible development and use of AI, grounded in human rights, inclusion, diversity, innovation, and economic growth, has been created in 2020 in that direction. The partnership includes both developed and developing countries, encouraging the sharing of knowledge and resources to ensure that AI benefits are more equitably distributed globally​. Another example is the Digital Public Goods Alliance (DPGA), which aims to accelerate the attainment of Sustainable Development Goals (SDGs) in low- and middle-income countries by facilitating the development and deployment of digital public goods. These include open-source software, open data, open AI models, and standards that can be used to improve digital infrastructure and services globally.

Ultimately, the future of AI and its influence on our economic landscape will depend significantly on the choices we make today. By prioritizing equitable growth and proactive governance, we can harness the full potential of AI to create a future that reflects our collective aspirations for a fair and prosperous world for all.

References

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2. Goldman Sachs, Generative AI could raise global GDP by 7% (2023). https://www.goldmansachs.com/intelligence/pages/generative-ai-could-raise-global-gdp-by-7-percent.html
3. Charles R. Hulten. Growth Accounting with Intermediate Inputs. The Review of Economic Studies, Volume 45, Issue 3 (October 1978), Pages 511–518. https://doi.org/10.2307/2297252
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