Beyond Solow: Rethinking growth in the age of AI
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(Disclaimer: The opinions expressed in this column are that of the writer. The facts and opinions expressed here do not reflect the views of www.economictimes.com .)
Long-run economic growth hinges on technological progress, a core insight of Robert Solow 's renowned Growth Model. The model argues that once an economy reaches a "steady state," growth can't be sustained through capital or labour alone. Instead, ongoing technological advancements are essential for higher output. A key assumption in this model is that technology enhances labour productivity without replacing workers. However, the rise of artificial intelligence challenges this assumption, potentially reshaping our understanding of economic growth.The Solow Model was developed in the 20th century, long before the emergence of advanced large language models. At that time, it was reasonable to assume that technological progress would boost productivity by enhancing rather than replacing human labour. This assumption matched the realities of that era. However, as artificial intelligence evolves, the idea that it might replace rather than simply support human labour is no longer speculative. It is becoming a visible trend. Leading economists have already begun to acknowledge this shift. In a 2019 study, Daron Acemoglu and Pascual Restrepo pointed to the rising wave of automation that could displace workers instead of making them more productive.Daniel Susskind, in his 2020 book A World Without Work, examined how machines might render large parts of the workforce unnecessary. Futurist Martin Ford made a similar case in his 2021 book Rule of the Robots, where he predicted that AI would transform nearly every aspect of life. Clearly, economists and thinkers are increasingly warning of a future shaped by AI, where new jobs may not appear quickly enough to replace those lost, and the transition could be long and difficult. While some still hope for mostly positive outcomes, that seems less likely as AI becomes more capable and less limited to repetitive tasks. In this new environment, the assumption that technology only augments labour, as embedded in the Solow Model, may no longer hold.Whether AI functions as a labour-augmenting or labour-replacing technology largely depends on the context and era in which it is deployed. If social and economic constraints make large-scale implementation of AI more costly than the economic benefits of replacing labour, then even highly capable AI, comparable to the average worker, may end up serving primarily as a tool to augment human labour. This would be a blessing in disguise for many workers whose jobs are otherwise at risk of automation. However, if the scalability of AI improves to the point where its labour-replacing benefits outweigh implementation costs, then the foundational assumption of the Solow Model begins to collapse. In such a scenario, the production function would continue to shift upward, signalling higher output, but with reduced labour input. As a result, we would need broader measures of prosperity beyond indicators like GDP per capita to accurately assess our economic well-being, especially as a growing share of output will get concentrated in the hands of a small elite made primarily of business owners and top-tier technical specialists. At this stage, governments and societies may find themselves at a crossroads. Technological progress is irreversible, and businesses will inevitably adopt AI to remain competitive. Yet this path could lead to a troubling outcome, one where machines generate ever-increasing wealth, but human participation in economic production shrinks dramatically.Ultimately, the larger question is: where do these dynamics leave India? What kind of future should we realistically anticipate? If we take a step back and consider the broader implications, India could find itself at a complex and uncertain crossroads. On one hand, it is an economic, social, and political imperative to foster an environment that supports AI adoption to remain globally competitive. On the other hand, this path comes with significant costs. As AI becomes more capable, labour input is likely to decline. A small minority of highly paid technical specialists could come to dominate the already prestigious IT industry. While output may increase due to AI's capabilities, the gains are likely to accumulate in the hands of top-tier investors and business elites thereby increasing inequality to unprecedented levels.This makes collaboration between the government and the private sector crucial. First, we must collectively recognize that the global AI landscape is currently dominated by Western nations. Even if AI improves productivity in Indian firms, a significant portion of the value created could end up flowing abroad. To safeguard economic gains, the government must foster an environment that encourages private investors in India to develop their own large language models and AI infrastructure. Second, India should identify the sectors most vulnerable to AI-driven disruption. The country is still far from deploying AI at scale, particularly in labour-intensive industries such as agriculture and construction. These, along with manufacturing and textiles, remain relatively insulated for now and must be central to job creation strategies. However, according to the 2023–24 Economic Survey, agriculture employs 45% of the workforce, services 28%, construction 13%, and manufacturing 11% which is in sharp contrast to China, where industrial employment remains around 30%. Compounding this is the fact that India's capital-to-labour ratio has doubled between 1994–2002 and 2003–2017, reflecting a growing tendency among firms to favour capital investments over labour. This trend strengthens the economic incentive to adopt AI, further raising the risk of labour displacement. The imbalance is troubling because more young Indians are entering IT, finance, and consulting which are sectors highly exposed to automation. If AI adoption leads to widespread job losses here, India could face a severe employment crisis, with limited fallback options.Finally, we need a new paradigm of economic growth, one that moves beyond the Solow model's assumption of labour-augmenting technology. Emerging models, such as modern extensions of Romer's endogenous growth theory and Aghion and Howitt's Schumpeterian framework, begin to account for labour-replacing technologies. Though still evolving, these models offer a necessary foundation for deeper debates on India's economic future in the age of AI. Ultimately, India must tread carefully in its transition to AI. Non-IT sectors, long overlooked, may offer a crucial fallback for the country's youth. However, their prolonged neglect could undermine our economic ambitions at the very moment we need them most.(Amit Kapoor is Chair and Mohammad Saad is a Researcher at the Institute for Competitiveness).
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Beyond Solow: Rethinking growth in the age of AI
Tired of too many ads? Remove Ads Tired of too many ads? Remove Ads (Disclaimer: The opinions expressed in this column are that of the writer. The facts and opinions expressed here do not reflect the views of .) Long-run economic growth hinges on technological progress, a core insight of Robert Solow 's renowned Growth Model. The model argues that once an economy reaches a "steady state," growth can't be sustained through capital or labour alone. Instead, ongoing technological advancements are essential for higher output. A key assumption in this model is that technology enhances labour productivity without replacing workers. However, the rise of artificial intelligence challenges this assumption, potentially reshaping our understanding of economic Solow Model was developed in the 20th century, long before the emergence of advanced large language models. At that time, it was reasonable to assume that technological progress would boost productivity by enhancing rather than replacing human labour. This assumption matched the realities of that era. However, as artificial intelligence evolves, the idea that it might replace rather than simply support human labour is no longer speculative. It is becoming a visible trend. Leading economists have already begun to acknowledge this shift. In a 2019 study, Daron Acemoglu and Pascual Restrepo pointed to the rising wave of automation that could displace workers instead of making them more Susskind, in his 2020 book A World Without Work, examined how machines might render large parts of the workforce unnecessary. Futurist Martin Ford made a similar case in his 2021 book Rule of the Robots, where he predicted that AI would transform nearly every aspect of life. Clearly, economists and thinkers are increasingly warning of a future shaped by AI, where new jobs may not appear quickly enough to replace those lost, and the transition could be long and difficult. While some still hope for mostly positive outcomes, that seems less likely as AI becomes more capable and less limited to repetitive tasks. In this new environment, the assumption that technology only augments labour, as embedded in the Solow Model, may no longer AI functions as a labour-augmenting or labour-replacing technology largely depends on the context and era in which it is deployed. If social and economic constraints make large-scale implementation of AI more costly than the economic benefits of replacing labour, then even highly capable AI, comparable to the average worker, may end up serving primarily as a tool to augment human labour. This would be a blessing in disguise for many workers whose jobs are otherwise at risk of automation. However, if the scalability of AI improves to the point where its labour-replacing benefits outweigh implementation costs, then the foundational assumption of the Solow Model begins to collapse. In such a scenario, the production function would continue to shift upward, signalling higher output, but with reduced labour input. As a result, we would need broader measures of prosperity beyond indicators like GDP per capita to accurately assess our economic well-being, especially as a growing share of output will get concentrated in the hands of a small elite made primarily of business owners and top-tier technical specialists. At this stage, governments and societies may find themselves at a crossroads. Technological progress is irreversible, and businesses will inevitably adopt AI to remain competitive. Yet this path could lead to a troubling outcome, one where machines generate ever-increasing wealth, but human participation in economic production shrinks the larger question is: where do these dynamics leave India? What kind of future should we realistically anticipate? If we take a step back and consider the broader implications, India could find itself at a complex and uncertain crossroads. On one hand, it is an economic, social, and political imperative to foster an environment that supports AI adoption to remain globally competitive. On the other hand, this path comes with significant costs. As AI becomes more capable, labour input is likely to decline. A small minority of highly paid technical specialists could come to dominate the already prestigious IT industry. While output may increase due to AI's capabilities, the gains are likely to accumulate in the hands of top-tier investors and business elites thereby increasing inequality to unprecedented makes collaboration between the government and the private sector crucial. First, we must collectively recognize that the global AI landscape is currently dominated by Western nations. Even if AI improves productivity in Indian firms, a significant portion of the value created could end up flowing abroad. To safeguard economic gains, the government must foster an environment that encourages private investors in India to develop their own large language models and AI infrastructure. Second, India should identify the sectors most vulnerable to AI-driven disruption. The country is still far from deploying AI at scale, particularly in labour-intensive industries such as agriculture and construction. These, along with manufacturing and textiles, remain relatively insulated for now and must be central to job creation strategies. However, according to the 2023–24 Economic Survey, agriculture employs 45% of the workforce, services 28%, construction 13%, and manufacturing 11% which is in sharp contrast to China, where industrial employment remains around 30%. Compounding this is the fact that India's capital-to-labour ratio has doubled between 1994–2002 and 2003–2017, reflecting a growing tendency among firms to favour capital investments over labour. This trend strengthens the economic incentive to adopt AI, further raising the risk of labour displacement. The imbalance is troubling because more young Indians are entering IT, finance, and consulting which are sectors highly exposed to automation. 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Long-run economic growth hinges on technological progress, a core insight of Robert Solow 's renowned Growth Model. The model argues that once an economy reaches a "steady state," growth can't be sustained through capital or labour alone. Instead, ongoing technological advancements are essential for higher output. A key assumption in this model is that technology enhances labour productivity without replacing workers. However, the rise of artificial intelligence challenges this assumption, potentially reshaping our understanding of economic growth. The Solow Model was developed in the 20th century, long before the emergence of advanced large language models. At that time, it was reasonable to assume that technological progress would boost productivity by enhancing rather than replacing human labour. This assumption matched the realities of that era. However, as artificial intelligence evolves, the idea that it might replace rather than simply support human labour is no longer speculative. It is becoming a visible trend. Leading economists have already begun to acknowledge this shift. In a 2019 study, Daron Acemoglu and Pascual Restrepo pointed to the rising wave of automation that could displace workers instead of making them more productive. Daniel Susskind, in his 2020 book A World Without Work, examined how machines might render large parts of the workforce unnecessary. Futurist Martin Ford made a similar case in his 2021 book Rule of the Robots, where he predicted that AI would transform nearly every aspect of life. Clearly, economists and thinkers are increasingly warning of a future shaped by AI, where new jobs may not appear quickly enough to replace those lost, and the transition could be long and difficult. While some still hope for mostly positive outcomes, that seems less likely as AI becomes more capable and less limited to repetitive tasks. In this new environment, the assumption that technology only augments labour, as embedded in the Solow Model, may no longer hold. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Play War Thunder now for free War Thunder Play Now Undo Whether AI functions as a labour-augmenting or labour-replacing technology largely depends on the context and era in which it is deployed. If social and economic constraints make large-scale implementation of AI more costly than the economic benefits of replacing labour, then even highly capable AI, comparable to the average worker, may end up serving primarily as a tool to augment human labour. This would be a blessing in disguise for many workers whose jobs are otherwise at risk of automation. However, if the scalability of AI improves to the point where its labour-replacing benefits outweigh implementation costs, then the foundational assumption of the Solow Model begins to collapse. In such a scenario, the production function would continue to shift upward, signalling higher output, but with reduced labour input. As a result, we would need broader measures of prosperity beyond indicators like GDP per capita to accurately assess our economic well-being, especially as a growing share of output will get concentrated in the hands of a small elite made primarily of business owners and top-tier technical specialists. At this stage, governments and societies may find themselves at a crossroads. Technological progress is irreversible, and businesses will inevitably adopt AI to remain competitive. Yet this path could lead to a troubling outcome, one where machines generate ever-increasing wealth, but human participation in economic production shrinks dramatically. Ultimately, the larger question is: where do these dynamics leave India? What kind of future should we realistically anticipate? If we take a step back and consider the broader implications, India could find itself at a complex and uncertain crossroads. On one hand, it is an economic, social, and political imperative to foster an environment that supports AI adoption to remain globally competitive. On the other hand, this path comes with significant costs. As AI becomes more capable, labour input is likely to decline. A small minority of highly paid technical specialists could come to dominate the already prestigious IT industry. While output may increase due to AI's capabilities, the gains are likely to accumulate in the hands of top-tier investors and business elites thereby increasing inequality to unprecedented levels. Live Events This makes collaboration between the government and the private sector crucial. First, we must collectively recognize that the global AI landscape is currently dominated by Western nations. Even if AI improves productivity in Indian firms, a significant portion of the value created could end up flowing abroad. To safeguard economic gains, the government must foster an environment that encourages private investors in India to develop their own large language models and AI infrastructure. Second, India should identify the sectors most vulnerable to AI-driven disruption. The country is still far from deploying AI at scale, particularly in labour-intensive industries such as agriculture and construction. These, along with manufacturing and textiles, remain relatively insulated for now and must be central to job creation strategies. However, according to the 2023–24 Economic Survey, agriculture employs 45% of the workforce, services 28%, construction 13%, and manufacturing 11% which is in sharp contrast to China, where industrial employment remains around 30%. Compounding this is the fact that India's capital-to-labour ratio has doubled between 1994–2002 and 2003–2017, reflecting a growing tendency among firms to favour capital investments over labour. This trend strengthens the economic incentive to adopt AI, further raising the risk of labour displacement. The imbalance is troubling because more young Indians are entering IT, finance, and consulting which are sectors highly exposed to automation. If AI adoption leads to widespread job losses here, India could face a severe employment crisis, with limited fallback options. Finally, we need a new paradigm of economic growth, one that moves beyond the Solow model's assumption of labour-augmenting technology. Emerging models, such as modern extensions of Romer's endogenous growth theory and Aghion and Howitt's Schumpeterian framework, begin to account for labour-replacing technologies. Though still evolving, these models offer a necessary foundation for deeper debates on India's economic future in the age of AI. Ultimately, India must tread carefully in its transition to AI. Non-IT sectors, long overlooked, may offer a crucial fallback for the country's youth. However, their prolonged neglect could undermine our economic ambitions at the very moment we need them most. (Amit Kapoor is Chair and Mohammad Saad is a Researcher at the Institute for Competitiveness).
