Author: John Sloman

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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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The second-hand market and GDP

This Christmas, more people are considering giving second-hand (or ‘pre-loved’) goods as presents. This allows them to afford better-quality presents and to save money at a time when a large proportion of the population are finding that their finances are stretched. This continues a trend towards buying second-hand products – a trend driven by the rise of various online retailers, such as Vinted and Preloved, and a growing online presence of charity shops, as well as extensive use of established platforms, such as Facebook Marketplace, eBay, Depop, Gumtree and Nextdoor.

Clearly, people gain from buying and selling second-hand items – part of the ‘circular economy’. But what are the implications for gross domestic product (GDP)? After all, GDP is one of the main indicators of the size of an economy, and growth in GDP is probably the most widely-used measure of economic progress. Are second-hand transactions captured in GDP?

If you directly sell your own second-hand items, this does not count towards GDP. There is no new product being made. The items are only counted when they are first produced. Any service you provide to the purchaser (and to yourself) is in a similar category to housework, childcare, DIY and other services that people provide to themselves, household members and friends. But like such services, there is a strong argument that they should be.

Likewise, the environmental benefits (positive externalities) of recycling products, rather than throwing them away or hoarding them, are not counted. In fact, if reusing products causes fewer new products to be made, this would be counted as subtracting from GDP.

If, however, you set up a business by buying and selling second-hand items, the service you provide would contribute towards GDP. What would be counted would the value added to the product – captured through the difference in the purchase and selling prices. In fact, HMRC has warned people that buying and selling second-hand items is taxable, as it counts as self-employment for tax purposes. But it is only this value added that counts. If you buy an item on Vinted, only the value added by Vinted counts towards GDP.

As no production takes place, the purchase of second-hand items adds either nothing to GDP or just the service of a retailer. It is effectively just a transfer of goods and money. If buying second-hand items means that you buy fewer new ones, then that would cause GDP to fall if the response of firms is to produce fewer newer items. However, the person selling the second-hand items will gain revenue, which could be used to buy new items. If that increased production, that would boost GDP. The net effect on GDP of this transfer of goods and money in the second-hand market will be pretty small.

Yet, clearly, the second-hand market provides a welfare gain to both sellers and purchasers – a gain that is likely to grow as the use of second-hand markets increases. At Christmas time, it provides a timely warning of the limitations of using GDP to measure wellbeing.

Articles

Questions

  1. What other items or activities affecting human wellbeing are not counted in GDP?
  2. Name some goods and services that are produced, and hence are included in GDP, but which can be classed as ‘bads’.
  3. For what reasons might a country have a high GDP per capita but a poor average level of wellbeing?
  4. How might GDP figures be adjusted for international comparison purposes?
  5. Would it be possible to adjust GDP figures to take account of externalities in production (negative and positive)? If so, how?
  6. Production involves human costs. To what extent does GDP take this into account?
  7. What is meant by the circular economy? How might you have a ‘circular’ Christmas?
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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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How to increase productivity – the key to economic growth

The productivity gap between the UK and its main competitors is significant. In 2024, compared to the UK, output per hour worked was 10.0% higher in France, 19.8% higher in Germany and 41.1% higher in the USA. These percentages are in purchasing-power parity terms: in other words, they reflect the purchasing power of the respective currencies – the pound, the euro and the US dollar.

GDP per hour worked (in PPP terms) is normally regarded as the best measure of labour productivity. An alternative measure is GDP per worker, but this does not take into account the length of the working year. Using this measure, the gap with the USA is even higher as workers in the USA work longer hours and have fewer days holiday per year than in the UK.

The productivity gap is not a new phenomenon. It has been substantial and growing over the past 20 years. (The exception was in 2020 during lockdowns when many of the least productive sectors, such as hospitality, were forced to close temporarily.)

The productivity gap is shown in the two figures. Both figures show labour productivity for the UK, France, Germany and the USA from 1995 to 2024.

Figure 1 shows output (GDP) per hour, measured in US dollars in PPP terms.

Figure 2 shows output (GDP) per hour relative to the UK, with the UK set at 100. The gap narrowed somewhat up to the early 2000s, but since then has widened.

Low UK productivity has been a source of concern for UK governments and business for many years. Not only does it constrain the growth in living standards, it also make the UK less attractive as a source of inward investment and less competitive internationally.

Part of the reason for low UK productivity compared to that in other countries is a low level of investment. As a proportion of GDP, the UK has persistently had the lowest, or almost the lowest, level of investment of its major competitors. This is illustrated in Table 1.

It is generally recognised by government, business and economists that if the economy is to be successful, the productivity gap must be closed. But there is no ‘quick fix’. The policies necessary to achieve increased productivity are long term. There is also a recognition that the productivity problem is a multi-faceted one and that to deal with it requires policy initiatives on a broad front: initiatives that encompass institutional changes as well as adjustments in policy.

So what can be done to improve productivity and how can this be achieved at the micro as well as the macro level?

Improving productivity: things that government can do

Encouraging investment. Over the years, UK governments have increased investment allowances, enabling firms to offset the cost of investment against pre-tax profit, thereby reducing their tax liability. For example, in the UK, companies can offset a multiple of research and development costs against corporation tax. The rate of relief for small and medium-sized enterprises (SMEs) allows companies that work in science and technology to deduct an extra 86% of their qualifying expenditure from their trading profit in addition to the normal 100% deduction: i.e. a total of 186% deduction. Meanwhile, since April 2016, larger companies have been able to claim a R&D expenditure credit, initially worth 11 per cent of R&D expenditures, then 12 per cent from 2018 and 13 per cent from 2020. This was then raised to 20 per cent from 2023.

Strengthening competition. A number of studies have revealed that, with increasing market share, business productivity growth slows. As a result, government policy sought to strengthen competition policy. The Competition Act 1998, which came into force in March 2000, and the Enterprise Act of 2002, enhanced the powers of the Office of Fair Trading (OFT) (a predecessor to the Competition and Markets Authority) in respect to dealing with anti-competitive practices. It was given the ability to impose large fines on firms which had been found guilty of exploiting a dominant market position. Today, one of the strategic goals of the Competition and Markets Authority (CMA) is the aim of ‘extending competition frontiers’ in order to improve the way competition works.

Encouraging an enterprise culture. The creation of an enterprise culture is seen as a crucial factor not only to encourage innovation but also to stimulate technological progress. Innovation and technological progress are crucial to sustaining growth and raising living standards. The UK government launched the Small Business Service in April 2000, later renamed Business and Industry. Its role is to co-ordinate small-business policy within government and liaise with business, providing advice and information. However, according to the OECD, there remains considerable scope for increasing the level of government support for entrepreneurship in the UK.

Improving productivity: things that organisations can do

In the podcast from the BBC’s The Bottom Line series, titled ‘Productivity: How Can British Business Work Smarter’ (see link below), Evan Davis and guests discuss what productivity really looks like in practice – from offices and factories, to call centres and operating theatres.’ The episode identifies a number of ways in which labour productivity can be improved. These include:

  • People could work harder;
  • Workers could be better trained and more skilled and thus able to produce more per hour;
  • Capital could be increased so that workers have more equipment or tools to enable them to produce more, or there could be greater automation, releasing labour to work on other tasks;
  • Workplaces could be arranged more efficiently so that less time is spent moving from task to task;
  • Systems could put in place to ensure that tasks are done correctly the first time and that time is not wasted having to repeat them or put them right;
  • Workers could be better incentivised to work efficiently, whether through direct pay or promotion prospects, or by increasing job satisfaction or by management being better attuned to what motivates workers and makes them feel valued;
  • Firms could move to higher-value products, so that workers produce a greater value of output per hour.

The three contributors to the programme discuss various initiatives in their organisations (an electronics manufacturer, NHS foundation trusts and a provider of office services to other organisations).

They also discuss the role that AI plays, or could play, in doing otherwise time-consuming tasks, such as recording and paying invoices and record keeping in offices; writing grants or producing policy documents; analysing X-ray results in hospitals and performing preliminary diagnoses when patients present with various symptoms; recording conversations/consultations and then sorting, summarising and transcribing them; building AI capabilities into machines or robots to enable them to respond to different specifications or circumstances; software development where AI writes the code. Often, there is a shortage of time for workers to do more creative things. AI can help release more time by doing a lot of the mundane tasks or allowing people to do them much quicker.

There are huge possibilities for increasing labour productivity at an organisational level. The successful organisations will be those that can grasp these possibilities – and in many cases they will be incentivised to so so as it will improve their profitability or other outcomes.

Podcast

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Data

Questions

  1. In what different ways can productivity be measured? What is the most appropriate measure for assessing the effect of productivity on (a) GDP and (b) human welfare generally?
  2. Why has the UK had a lower level of labour productivity than France, Germany and the USA for many years? What can UK governments do to help close this gap?
  3. Find out how Japanese labour productivity has compared with that in the UK over the past 30 years and explain your findings.
  4. Research an organisation of your choice to find out ways in which labour productivity could be increased.
  5. Identify various ways in which AI can improve productivity. Will organisations be incentivised to adopt them?
  6. Has Brexit affected UK labour productivity and, if so, how and why?