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Globe and Mail
3 days ago
- Business
- Globe and Mail
AI will eventually lead to a more extreme society of haves and have-nots
The discussion these days is all about whether artificial intelligence will lead to a surge in productivity, lower inflation, higher economic growth, better living standards and value creation. The stock market seems to think so, as the shares of many AI-related companies have surged. This is leading many to believe that a bubble is forming. Such bubbles are not atypical. A 2018 paper published in Marketing Science titled Two Centuries of Innovations and Stock Market Bubbles shows that groundbreaking innovation tends to be linked to bubbles in the stock prices of companies commercializing innovation. Those investors who remain overallocated to an innovative company after the bubble has ended suffer the effects of long-term reversals. My sense is that AI will not improve productivity as much as markets expect. I tend to agree with Nobel Prize winner Daron Acemoglu, who believes that the AI-related frenzy will eventually lead to a tech crash that will leave everyone disillusioned with the technology. Historically, new technologies have been disappointing in terms of increasing productivity. There is no clear link between technological innovation and productivity growth, as defined by gross domestic product per worker. A recent example: Nobel Prize winner Robert Solow has written that the computer age was everywhere except in productivity statistics. Editorial: A real reform mandate for the first federal AI minister Here are three points to counter too much optimism about AI. First, it is hard to see the big societal problem that AI will solve. Second, AI may lower inflation but at the same time will increase demand for capital because of the huge investments and funding it will require in its early stages. This will push real interest rates up, leaving nominal interest rates little changed. Finally, AI may be hurting more than helping society. For example, researchers at Microsoft published a paper recently arguing that while AI may improve efficiency, it can also reduce human critical thinking capabilities and diminish independent problem solving. 'Used improperly, technologies can and do result in the deterioration of cognitive faculties that ought to be preserved,' it found. Despite all this, I do believe that AI needs to be embedded in our day-to-day lives, as it is not going away. Whether we like it or not, we are headed full speed toward an AI-powered future. The key question is, will AI benefit and reach all people? Historically, that hasn't been the case when it comes to new technology, which is usually controlled by a few people. And it may be worse this time. In my opinion, AI will most likely create a more rigid class-based society: the upper class, which will include people who are on top of AI knowledge and applications, and the lower class – those who are not, and who will be left behind without embedding AI into their everyday life. A recent article in The Globe and Mail hit the nail on the head. Author Don Tapscott, who is co-founder of Blockchain Research Institute, said, 'AI will become a new social fault line. Those with intelligent agents [i.e., large language model-powered systems] will be superpowered; those without them will fall behind. A small class of enhanced individuals could dominate productivity, creativity and influence.' An AI-powered future and an AI class-based society reminds me of the landlord-serf relationship in medieval Europe, which was a central feature of the feudal system and could end up being a central feature of the AI-powered future. 'Landlords' were typically those who controlled large estates, while 'serfs' were peasants bound to the land, obligated to work for the landlord and lacking many freedoms. The land cultivated by serfs was owned by a landlord. A large portion of what serfs produced had to be given to their landlord. Serfs lacked freedom of movement; they could not permanently leave their village, marry, change occupation, or dispose of their property without their landlord's permission. In the future, these landlords will be those in control of AI, and the serfs will be those without much knowledge about AI. This looks like a scary dystopian future that should force us all to become sufficiently prepared ahead of time in AI and be in full command of AI agents, irrespective of whether we believe the technology will solve the society's productivity problems or not. George Athanassakos is a professor of finance and holds the Ben Graham Chair in Value Investing at the Ivey Business School, Western University. His latest book is Value Investing: From Theory to Practice.
Economic Times
17-05-2025
- Business
- Economic Times
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. If AI adoption leads to widespread job losses here, India could face a severe employment crisis, with limited fallback 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).
Time of India
17-05-2025
- Business
- Time of India
Beyond Solow: Rethinking growth in the age of AI
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).
New York Times
07-04-2025
- Business
- New York Times
The Trump White House Cited My Research to Justify Tariffs. They Got It All Wrong.
My first question, when the White House unveiled its tariff regime, was, 'How on earth did they calculate such huge rates?' Reciprocal tariffs, after all, are supposed to treat other countries the way they treat us, and foreign tariffs on American goods are nowhere near these levels. The next day it got personal. The Office of the U.S. Trade Representative released its methodology and cited an academic paper produced by four economists, including me, seemingly in support of their numbers. But they got it wrong. Very wrong. I disagree fundamentally with the government's trade policy and approach. But even taking it at face value, our findings suggest the calculated tariffs should be dramatically smaller — perhaps one-fourth as large. Let's start with the biggest mistake. The office said it calculated its reciprocal tariffs at a level that would theoretically eliminate trade deficits with 'each of our trading partners,' one by one. Is that a reasonable goal? It is not. Trade imbalances between two countries can emerge for many reasons that have nothing to do with protectionism. Americans spend more on clothing made in Sri Lanka than Sri Lankans spend on American pharmaceuticals and gas turbines. So what? That pattern reflects differences in natural resources, comparative advantage and development levels. The deficit numbers don't suggest, let alone prove, unfair competition. There are some reasonable arguments in favor of reducing the overall trade deficit, such as to reduce risks from our debt. But these arguments don't apply country by country. The Nobel laureate Robert Solow explained why when he quipped, 'I have a chronic deficit with my barber, who doesn't buy a darned thing from me.' Mr. Solow also surely ran a chronic surplus with his students, and these imbalances reveal nothing about trade barriers in hair care or higher education, nor would they speak to his financial health. For the sake of argument, let's grant President Trump his goal of eliminating all trade deficits, no matter how destructive that would be. Could these reciprocal tariffs succeed? Again, no. The administration's tariff formula assumes that a tariff placed on one country won't affect imports from any others and ignores any implications for exports. These assumptions may work for an action against one small trade partner, but not for the broad salvo announced last week. A large tariff on Japanese auto parts could cause an increase in demand for imports from Mexico and vice versa. And the tariffs clearly invite retaliation and may over time increase the dollar's value, both factors that would most likely depress U.S. exports. Let's keep going. Not only will we grant the government its goal, but we will also ignore flaws in its tariff formula. Do the computed tariffs then look right? Guess what? They do not. The government's formula uses four different numbers to calculate tariffs, including imports and exports for each trading partner. The part that directly relates to our research is an estimate of how much import prices change in response to the additional costs imposed by tariffs. The value of that term, known as the rate of pass-through, isn't obvious and depends on how companies behave. If foreign exporters cut prices to fully offset the tariffs, leaving import prices unchanged, the pass-through would be zero. Alternatively, it could equal 100 percent if exporters don't budge, which means import prices would rise in step with the tariffs. Alberto Cavallo, Gita Gopinath, Jenny Tang and I studied the tariffs placed on Chinese exports in 2018 and 2019. (This is the 'Cavallo et al.' reference in the government's methodology.) We found that tariffs of, say, 20 percent caused domestic importers to pay nearly 19 percent more. This represents a pass-through into import prices of about 95 percent, which is the value I would have plugged into the government's tariff formula. In simple terms, that implies that the price paid for U.S. imports would rise almost as much as the tariff rate. The administration's trade office cites our work, but mentions a different result from the paper, which found a low pass-through rate to the listed prices at two retailers. The Trump administration then plugs a rate of 25 percent into its formula. Where does 25 percent come from? Is it related to our work? I don't know. The reciprocal tariffs have enormous implications for workers, firms, consumers and stock markets around the globe. But the methodology note offers shockingly few details. Had the trade office instead used a value closer to the 95 percent number from our work, as I believe it should have done, the computed tariffs would have been as little as one-fourth of what they are. As a result of these and other methodological choices, Wednesday's reciprocal tariffs will bring average tariff rates to their highest level in over 100 years. Their breadth is striking, hitting large economies such as China and Europe, and also small developing and emerging-market countries including Jordan and Zambia. And despite being billed as a 'do unto others' trade policy, they are not calculated in line with the Bible's golden rule. I would strongly prefer that the policy and methodology be scrapped entirely. But barring that, the administration should divide its results by four.
Arab News
02-03-2025
- Business
- Arab News
AI will not supercharge GDP growth
Everyone knows that artificial intelligence is a hugely powerful technology with immense economic implications. US equity prices reflect not only confidence in the prospects of technology companies but also a belief that AI will fuel a broader boom. The growth-obsessed UK government views AI development as a top priority and everyone at the World Economic Forum in Davos in January wanted to hear from the world's AI leaders. We have been here before. In the 1960s, computers were too enormous and expensive to be used by anyone but the largest government agencies and businesses. Yet so great were concerns about 'automation' that US President Lyndon Johnson launched an inquiry into the danger that computer-based technologies might 'eliminate all but a few jobs.' It was not to be. By the 1970s, there was no sign of a productivity surge and fears of mass technological unemployment subsided. Personal and business computer use then soared in the 1980s; but by 1990, as the economist Robert Solow famously observed, information technology was 'everywhere but in the productivity statistics.' With mobile phones, the internet, ever-expanding hardware capacity and growing software capabilities promising a new connectivity-based productivity revolution, everyone at the World Economic Forum in 2000 wanted to hear from the leaders of 'information and communications technology.' Cisco CEO John Chambers predicted that ICT would enable the US economy to grow by 5 percent per year for the foreseeable future and that 'the internet will form half of gross domestic product by 2010.' Then there was 'big data,' 'the digital economy,' 'machine learning' and now artificial intelligence. None, so far, has had any measurable impact on medium-term growth rates. A case can be made that generative AI, owing to its self-learning capability, represents more than just another stage of technological development. But there are still two reasons why it, too, may not show up in growth data. First, a large and probably growing share of economic activity involves a zero-sum struggle for competitive advantage with no positive impact on either measured growth or human welfare. Using basic internet searches, and now sophisticated large language models, lawyers are increasingly able to analyze every possible precedent before presenting their arguments. But if the opposing law firm can do the same, the result is an arms race in which neither party has a durable advantage. For at least two decades, experts have warned that after the steady decline of manufacturing jobs, professional services such as the law would be next in line for automation. But employment and pay in the field of commercial law continue to grow. AI will have massive potential to exacerbate the harms that previous generations of ICT have already produced. Adair Turner Similarly, marketing departments can use AI to produce ever more targeted and effective communications to influence consumer choice. If their competitors are doing the same, however, there is no benefit to end consumers, and no boost to GDP. Conversely, AI will almost certainly deliver huge human welfare benefits almost for free. The late Martin Feldstein, writing in 2017, correctly observed this phenomenon at work in the previous three decades of remarkable IT and ICT progress. By then, smartphones boasted many thousands of times more processing power and memory than the biggest computers of the 1960s, enabling vastly more communication, data storage, video and image sharing, and so forth. Yet the share of GDP accounted for by the telecoms sector had hardly changed, leading Feldstein to conclude that 'low growth estimates fail to reflect the remarkable innovations in everything from healthcare to internet services to video entertainment that have made life better during these years.' Likewise, Google DeepMind's AlphaFold Protein Structure Database (which predicts a protein structure from its amino acid sequence) is set to accelerate drug discovery while slashing the cost of research. But once drugs come off patent, their prices fall toward their marginal cost of production and their contribution to measured GDP collapses. If, by 2070, an AI-enabled acceleration of knowledge acquisition has furnished us with a wonder drug that gives everyone a 100-year life of perfect health, and which is produced in wholly automated factories powered by cheap nuclear fusion, it will count for almost nothing in global GDP. The more powerful a technology, the more rapidly it disappears from measured GDP. At the same time, AI will have massive potential to exacerbate the harms to human welfare that previous generations of ICT have already produced. Deepfake capabilities are already driving an explosion of online scams and AI-powered social media algorithms are deepening political polarization and probably contributing to what social psychologist Jonathan Haidt sees as an epidemic of mental illness among young people. Yet none of these negatives show up in measured GDP either. For good or ill — or merely as an enabler of ever more intense zero-sum competition — AI will have a pervasive and perhaps transformative impact on society. But the hope that it will unleash a sustained increase in measured productivity and GDP growth is probably a delusion.
