Category: Economics for Business: Ch 01

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Will AI make the world less equal?

With businesses increasing their use of AI, this is likely to have significant effects on employment. But how will this affect the distribution of income, both within countries and between countries?

In some ways, AI is likely to increase inequality within countries as it displaces low-skilled workers and enhances the productivity of higher-skilled workers. In other ways, it could reduce inequality by allowing lower-skilled workers to increase their productivity, while displacing some higher-skilled workers and managers through the increased adoption of automated processes.

The effect of AI on the distribution of income between countries will depend crucially on its accessibility. If it is widely available to low-income countries, it could significantly enhance the productivity of small businesses and workers in such countries and help to reduce the income gap with the richer world. If the gains in such countries, however, are largely experienced by multinational companies, whether in mines and plantations, or in labour-intensive industries, such as garment production, few of the gains may accrue to workers and global inequality may increase.

Redistribution within a country

The deployment of AI may result in labour displacement. AI is likely to replace both manual and white-collar jobs that involve straightforward and repetitive tasks. These include: routine clerical work, such as data entry, filing and scheduling; paralegal work, contract drafting and legal research; consulting, business research and market analysis; accounting and bookkeeping; financial trading; proofreading, copy mark-up and translation; graphic design; machine operation; warehouse work, where AI-enabled warehouse robots do many receiving, sorting, stacking, retrieval, carrying and loading tasks (e.g. Amazon’s Sequoia robotic system); basic coding or document sifting; market research and advertising design; call-centre work, such as enquiry handling, sales, telemarketing and customer service; hospitality reception; sales cashiers in supermarkets and stores; analysis of health data and diagnosis. Such jobs can all be performed by AI assistants, AI assisted robots or chat bots.

Women are likely to be disproportionately affected because they perform a higher share of the administrative and service roles most exposed to AI.

Workers displaced by AI may find that they can find employment only in lower-paid jobs. Examples include direct customer-facing roles, such as bar staff, shop assistants, hairdressers and nail and beauty consultants.

Such job displacement by AI is likely to redistribute income from relatively low-skilled labour to capital: a redistribution from wages to profits. This will tend to lead to greater inequality.

AI is also likely to lead to a redistribution of income towards certain types of high-skilled labour that are difficult to replace with AI but which could be enhanced by it. Take the case of skilled traders, such as plumbers, electricians and carpenters. They might be able to use AI in their work to enhance their productivity, through diagnosis, planning, problem-solving, measurement, etc. but the AI would not displace them. Instead, it could increase their incomes by allowing them to do their work more efficiently or effectively and thus increase their output per hour and enhance their hourly reward. Another example is architecture, where AI can automate repetitive tasks and open up new design possibilities, allowing architects to focus on creativity, flexibility, aesthetics, empathy with clients and ethical decision-making.

An important distinction is between disembodied and embodied AI investment. Disembodied AI investment could include AI ‘assistants’, such as ChatGPT and other software that can be used in existing jobs to enhance productivity. Such investment can usually be rolled out relatively quickly. Although the extra productivity may allow some reduction in the number of workers, disembodied AI investment is likely to be less disruptive than embodied AI investment. The latter includes robotics and automation, where workers are replaced by machines. This would require more investment and may be slower to be adopted.

Then there are jobs that will be created by AI. These include prompt engineers, who develop questions and prompt techniques to optimise AI output; health tech experts, who help organisations implement new medical AI products; AI educators, who train people in the uses of AI in the workplace; ethics advisors, who help companies ensure that their uses of AI are aligned with their values, responsibilities and goals; and cybersecurity experts who put systems in place to prevent AI stealing sensitive information. Such jobs may be relatively highly paid.

In other cases, the gains from AI in employment are likely to accrue mainly to the consumer, with probably little change in the incomes of the workers themselves. This is particularly the case in parts of the public sector where wages/salaries are only very loosely related to productivity and where a large part of the work involves providing a personal service. For example, health professionals’ productivity could be enhanced by AI, which could allow faster and more accurate diagnosis, more efficient monitoring and greater accuracy in surgery. The main gainers would be the patients, with probably little change in the incomes of the health professionals themselves. Teachers’ productivity could be improved by allowing more rapid and efficient marking, preparation of materials and record keeping, allowing more time to be spent with students. Again, the main gainers would be the students, with little change in teachers’ incomes. Other jobs in this category include social workers, therapists, solicitors and barristers, HR specialists, senior managers and musicians.

Thus there is likely to be a distribution away from lower-skilled workers to both capital and higher-skilled workers who can use AI, to people who work in new jobs created by AI and to the consumers of certain services.

AI will accelerate productivity growth and, with it, GDP growth, but will probably displace workers faster than new roles emerge. This is likely to increase inequality and be a major challenge for society. Can the labour market adapt? Could the effects be modified if people moved to a four- or three-day week? Will governments introduce statutory limits to weekly working hours? Will training and education adapt to the new demands of employers?

Redistribution between countries

AI threatens to widen the global rich–poor divide. It will give wealthier nations a productivity and innovation edge, which could displace low-skilled jobs in low-income nations. Labour-intensive production could be replaced by automated production, with the capital owned by the multinational companies of just a few countries, such as the USA and China, which between them account for 40% of global corporate AI R&D spending. For some companies, it would make sense to relocate production to rich countries, or certain wealthier developing countries, with better digital infrastructure, advanced data systems and more reliable power supply.

For other companies, however, production might still be based in low-income countries to take advantage of low-cost local materials. But there would still be a redistribution from wages in such countries to the profits of multinationals.

But it is not just in manufacturing where low-income countries are vulnerable to the integration of AI. Several countries, such as India, the Philippines, Mexico and Egypt have seen considerable investment in call centres and IT services for business process outsourcing and customer services. AI now poses a threat to employment in this industry as it has the potential to replace large numbers of workers.

AI-related job losses could exacerbate unemployment and deepen poverty in poorer countries, which, with limited resources, limited training and underdeveloped social protection systems, are less equipped to absorb economic and social shocks. This will further widen the global divide. In the case of embodied AI investment, it may only be possible in low-income countries through multinational investment and could displace many traditional jobs, with much of the benefit going in additional multinational profit.

But it is not all bad news for low-income countries. AI-driven innovations in healthcare, education, and agriculture, if adopted in poor countries, can make a significant contribution to raising living standards and can slow, or even reverse, the widening gap between rich and poor nations. Some of the greatest potential is in small-scale agriculture. Smallholders can boost crop yields though precision farming powered by AI; AI tools can help farmers buy seeds, fertilisers and animals and sell their produce at optimum times and prices; AI-enabled education tools can help farmers learn new techniques.

Articles

Questions

  1. What types of job are most vulnerable to AI?
  2. How will AI change the comparative advantage of low-income countries and what effect will it be likely to have on the pattern of global trade?
  3. Assess alternative policies that governments in high-income countries can adopt to offset the growth in inequality caused by the increasing use of AI.
  4. What policies can governments in low-income countries or aid agencies adopt to offset the growth in inequality within low-income countries and between high- and low-income countries?
  5. How might the growth of AI affect your own approach to career development?
  6. Is AI likely to increase or decrease economic power? Explain.
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AI stock: still under priced or a speculative bubble?

The share prices of various AI-related companies have soared in this past year. Recently, however, they have fallen – in some cases dramatically. Is this a classic case of a bubble that is bursting, or at least deflating?

Take the case of NVIDIA, the world’s most valuable company, with a market capitalisation of around $4.2 trillion (at current share prices). It designs and produces graphics cards and is a major player in AI. From a low of $86.62 April this year, its share price rose to a peak of $212.19 on 29 October. But then began falling as talk grew of an AI bubble. Despite news on 19 November that its 2025 Q3 earnings were up 62% to $57.0bn, beating estimates by 4%, its share price, after a temporary rise, began falling again. By 21 November, it was trading at around $180.

Other AI-related stocks have seen much bigger rises and falls. One of the biggest requirements for an AI revolution is data processing, which uses huge amounts of electricity. Massive data centres are being set up around the world. Several AI-related companies have been building such data centres. Some were initially focused largely on ‘mining’ bitcoin and other cryptocurrencies (see the blog, Trump and the market for crypto). But many are now changing focus to providing processing power for AI.

Take the case of the Canadian company, Bitfarms Ltd. As it says on its site: ‘With access to multiple energy sources and strategic locations, our U.S. data centers support both mining and high-performance computing growth opportunities’. Bitfarms’ share price was around CAD1.78 in early August this year. By 15 October, it had reached CAD9.27 – a 421% increase. It then began falling and by 24 November was CAD3.42 – a decline of over 63%.

Data centres do have huge profit potential as the demand for AI increases. Many analysts are arguing that the current share price of data centres undervalues their potential. But current profits of such companies are still relatively low, or they are currently loss making. This then raises the question of how much the demand for shares, and hence their price, depends on current profits or future potential. And a lot here depends on sentiment.

If people are optimistic, they will buy and this will lead to speculation that drives up the share price. If sentiment then turns and people believe that the share price is overvalued, with future profits too uncertain or less than previously thought, or if they simply believe that the share price has overshot the value that reflects a realistic profit potential, they will sell and this will lead to speculation that drives down the share price

The dot.com bubble of the late 1990s/early 2000s is a case in point. There was a stock market bubble from roughly 1995 to 2001, where speculative investment in internet-based companies caused their stock values to surge, peaking in late 1999/early 2000. There was then a dramatic crash. But then years later, many of these companies’ share prices had risen well above their peak in 2000.

Take the case of Amazon. In June 1997, its share price was $0.08. By mid-December 1999, it had reached $5.65. It then fell, bottoming out at $0.30 in September 2001. The dot-com bubble had burst.

But the potential foreseen in many of these new internet companies was not wrong. After 2001, Amazon’s share price began rising once more. Today, Amazon’s shares are trading at over $200 – the precise value again being driven largely by the company’s performance and potential and by sentiment.

So is the boom in AI-related stock a bubble? Given that the demand for AI is likely to continue growing rapidly, it is likely that the share price of companies providing components and infrastructure for AI is likely to continue growing in the long term. But just how far their share prices will fall in the short term is hard to call. Sentiment is a fickle thing.

Articles

Questions

  1. Using a supply and demand diagram, illustrate how speculation can drive up the share price of a company and then result in it falling.
  2. What is meant by overshooting in a market? What is the role of speculation in this process?
  3. Does a rapid rise in the price of an asset always indicate a bubble? Explain.
  4. What are the arguments for suggesting that markets are/are not experiencing an AI share price bubble? Does it depend of what part of the AI market is being considered?
  5. What is meant by the market capitalisation of a company? Is it a good basis for deciding whether or not a company’s share price is a true reflection of the company’s worth? What other information would you require?
  6. Find out what has been happening to the price of Bitcoin. What factors determine the price of Bitcoin? Do these factors make the price inherently unstable?
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Rising global uncertainty and its effects

In a blog in October 2024, we looked at global uncertainty and how it can be captured in a World Uncertainty Index. The blog stated that ‘We continue to live through incredibly turbulent times. In the past decade or so we have experienced a global financial crisis, a global health emergency, seen the UK’s departure from the European Union, and witnessed increasing levels of geopolitical tension and conflict’.

Since then, Donald Trump has been elected for a second term and has introduced sweeping tariffs. What is more, the tariffs announced on so-called ‘Liberation Day‘ have not remained fixed, but have fluctuated with negotiations and threatened retaliation. The resulting uncertainty makes it very hard for businesses to plan and many have been unwilling to commit to investment decisions. The uncertainty has been compounded by geopolitical events, such as the continuing war in Ukraine, the war in Gaza and the June 13 Israeli attack on Iran.

The World Uncertainty Index (WUI) tracks uncertainty around the world by applying a form of text mining known as ‘term frequency’ to the country reports produced by the Economist Intelligence Unit (EIU). The words searched for are ‘uncertain’, ‘uncertainty’ and ‘uncertainties’ and the number of times they occur as percentage of the total words is recorded. To produce the WUI this figure is then multiplied by 1m. A higher WUI number indicates a greater level of uncertainty.

The monthly global average WUI is shown in Chart 1 (click here for a PowerPoint). It is based on 71 countries. Since 2008 the WUI has averaged a little over 23 000: i.e. 2.3 per cent of the text in EIU reports contains the word ‘uncertainty’ or a close variant. In May 2025, it was almost 79 000 – the highest since the index was first complied in 2008. The previous highest was in March 2020, at the start of the COVID-19 outbreak, when the index rose to just over 56 000.

The second chart shows the World Trade Uncertainty Index (WTUI), published on the same site as the WUI (click here for a PowerPoint). The method adopted in its construction therefore mirrors that for the WUI but counts the number of times in EIU country reports ‘uncertainty’ is mentioned within proximity to a word related to trade, such as ‘protectionism’, ‘NAFTA’, ‘tariff’, ‘trade’, ‘UNCTAD’ or ‘WTO.’

The chart shows that in May 2025, the WTUI had risen to just over 23 000 – the second highest since December 2019, when President Trump imposed a new round of tariffs on Chinese imports and announced that he would restore steel tariffs on Brazil and Argentina. Since 2008, the WTUI has averaged just 2228.

It remains to be seen whether more stability in trade relations and geopolitics will allow WUI and WUTI to decline once more, or whether greater instability will simply lead to greater uncertainty, with damaging consequences for investment and also for consumption and employment.

Articles

Uncertainty Indices

Questions

  1. Explain what is meant by ‘text mining’. What are its strengths and weaknesses in assessing business, consumer and trade uncertainty?
  2. Explain how the UK Monthly EPU Index is derived.
  3. Why has uncertainty increased so dramatically since the start of 2025?
  4. Compare indices based on text mining with confidence indices.
  5. Plot consumer and business/industry confidence indicators for the past 24 months, using EC data. Do they correspond with the WUI?
  6. How may uncertainty affect consumers’ decisions?
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Hacking capitalism

The development of open-source software and blockchain technology has enabled people to ‘hack’ capitalism – to present and provide alternatives to traditional modes of production, consumption and exchange. This has enabled more effective markets in second-hand products, new environmentally-friendly technologies and by-products that otherwise would have been negative externalities. Cryptocurrencies are increasingly providing the medium of exchange in such markets.

In a BBC podcast, Hacking Capitalism, Leo Johnson, head of PwC’s Disruption Practice and younger brother of Boris Johnson, argues that various changes to the way capitalism operates can make it much more effective in improving the lives of everyone, including those left behind in the current world. The changes can help address the failings of capitalism, such as climate change, environmental destruction, poverty and inequality, corruption, a reinforcement of economic and political power and the lack of general access to capital. And these changes are already taking place around the world and could lead to a new ‘golden age’ for capitalism.

The changes are built on new attitudes and new technologies. New attitudes include regarding nature and the land as living resources that need respect. This would involve moving away from monocultures and deforestation and, with appropriate technologies (old and new), could lead to greater output, greater equality within agriculture and increased carbon absorption. The podcast gives examples from the developing and developed world of successful moves towards smaller-scale and more diversified agriculture that are much more sustainable. The rise in farmers’ markets provides an important mechanism to drive both demand and supply.

In the current model of capitalism there are many barriers to prevent the poor from benefiting from the system. As the podcast states, there are some 2 billion people across the world with no access to finance, 2.6 billion without access to sanitation, 1.2 billion without access to power – a set of barriers that stops capitalism from unlocking the skills and productivity of the many.

These problems were made worse by the response to the financial crisis of 2007–8, when governments chose to save the existing model of capitalism by propping up financial markets through quantitative easing, which massively inflated asset prices and aggravated the problem of inequality. They missed the opportunity of creating money to invest in alternative technologies and infrastructure.

New technology is the key to developing this new fairer, more sustainable model of capitalism. Such technologies could be developed (and are being in many cases) by co-operative, open-source methods. Many people, through these methods, could contribute to the development of products and their adaptation to meet different needs. The barriers of intellectual property rights are by-passed.

New technologies that allow easy rental or sharing of equipment (such as tractors) by poor farmers can transform lives and massively increase productivity. So too can the development of cryptocurrencies to allow access to finance for small farmers and businesses. This is particularly important in countries where access to traditional finance is restricted and/or where the currency is not stable with high inflation rates.

Blockchain technology can also help to drive second-hand markets by providing greater transparency and thereby cut waste. Manufacturers could take a stake in such markets through a process of certification or transfer.

A final hack is one that can directly tackle the problem of externalities – one of the greatest weaknesses of conventional capitalism. New technologies can support ways of rewarding people for reducing external costs, such as paying indigenous people for protecting the land or forests. Carbon markets have been developed in recent years. Perhaps the best example is the European Emissions Trading Scheme (EMS). But so far they have been developed in isolation. If the revenues generated could go directly to those involved in environmental protection, this would help further to internalise the externalities. The podcasts gives an example of a technology used in the Amazon to identify the environmental benefits of protecting rain forests that can then be used to allow reliable payments to the indigenous people though blockchain currencies.

Podcast

Questions

  1. What are the main reasons why capitalism has led to such great inequality?
  2. What do you understand by ‘hacking’ capitalism?
  3. How is open-source software relevant to the development of technology that can have broad benefits across society?
  4. Does the current model of capitalism encourage a self-centred approach to life?
  5. How might blockchain technology help in the development of a more inclusive and fairer form of capitalism?
  6. How might farmers’ co-operatives encourage rural development?
  7. What are the political obstacles to the developments considered in the podcast?
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Is capitalism changing?

The coronavirus pandemic and the climate emergency have highlighted the weaknesses of free-market capitalism.

Governments around the world have intervened massively to provide economic support to people and businesses affected by the pandemic through grants and furlough schemes. They have also stressed the importance of collective responsibility in abiding by lockdowns, social distancing and receiving vaccinations.

The pandemic has also highlighted the huge inequalities around the world. The rich countries have been able to offer much more support to their people than poor countries and they have had much greater access to vaccines. Inequality has also been growing within many countries as rich people have gained from rising asset prices, while many people find themselves stuck in low-paid jobs, suffering from poor educational opportunities and low economic and social mobility.

The increased use of working from home and online shopping has accelerated the rise of big tech companies, such as Amazon and Google. Their command of the market makes it difficult for small companies to compete – and competition is vital if capitalism is to benefit societies. There have been growing calls for increased regulation of powerful companies and measures to stimulate competition. The problem has been recognised by governments, central banks and international agencies, such as the IMF and the OECD.

At the same time as the world has been grappling with the pandemic, global warming has contributed to extreme heat and wildfires in various parts of the world, such as western North America, the eastern Mediterranean and Siberia, and major flooding in areas such as western Europe and China. Governments again have intervened by providing support to people whose property and livelihoods have been affected. Also there is a growing urgency to tackle global warming, with some movement, albeit often limited, in implementing policies to achieve net zero carbon emissions by some specified point in the future. Expectations are rising for concerted action to be agreed at the international COP26 climate meeting in Glasgow in November this year.

An evolving capitalism

So are we seeing a new variant of capitalism, with a greater recognition of social responsibility and greater government intervention?

Western governments seem more committed to spending on socially desirable projects, such as transport, communications and green energy infrastructure, education, science and health. They are beginning to pursue more active industrial and regional policies. They are also taking measures to tax multinationals (see the blog The G7 agrees on measures to stop corporate tax avoidance). Many governments are publicly recognising the need to tackle inequality and to ‘level up’ society. Active fiscal policy, a central plank of Keynesian economics, has now come back into fashion, with a greater willingness to fund expenditure by borrowing and, over the longer term, to use higher taxes to fund increased government expenditure.

But there is also a growing movement among capitalists themselves to move away from profits being their sole objective. A more inclusive ‘stakeholder capitalism’ is being advocated by many companies, where they take into account the interests of a range of stakeholders, from customers, to workers, to local communities, to society in general and to the environment. For example, the Council for Inclusive Capitalism, which is a joint initiative of the Vatican and several world business and public-sector leaders, seeks to make ‘the world fairer, more inclusive, and sustainable’.

If there is to be a true transformation of capitalism from the low-tax free-market capitalism of neoclassical economists and libertarian policymakers to a more interventionist mixed market capitalism, where capitalists pursue a broader set of objectives, then words have to be matched by action. Talk is easy; long-term plans are easy; taking action now is what matters.

Articles and videos

Questions

  1. How similar is the economic response of Western governments to the pandemic to their response to the financial crisis of 2007–8?
  2. What do you understand by ‘inclusive capitalism’? How can stakeholders hold companies to account?
  3. What indicators are there of market power? Why have these been on the rise?
  4. How can entrepreneurs contribute to ‘closing the inequality gap for a more sustainable and inclusive form of society’?
  5. What can be done to hold governments to account for meeting various social and environmental objectives? How successful is this likely to be?
  6. Can inequality be tackled without redistributing income and wealth from the rich to the poor?