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
- Using a supply and demand diagram, illustrate how speculation can drive up the share price of a company and then result in it falling.
- What is meant by overshooting in a market? What is the role of speculation in this process?
- Does a rapid rise in the price of an asset always indicate a bubble? Explain.
- 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?
- 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?
- 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?
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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Questions
- 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?
- 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?
- Find out how Japanese labour productivity has compared with that in the UK over the past 30 years and explain your findings.
- Research an organisation of your choice to find out ways in which labour productivity could be increased.
- Identify various ways in which AI can improve productivity. Will organisations be incentivised to adopt them?
- Has Brexit affected UK labour productivity and, if so, how and why?
According to Ofcom’s November 2024 Online Nation report (see report linked below), UK adults are falling out of love with dating apps. Use of the top three platforms in the UK (Tinder, Hinge, and Bumble) is declining, even though most users are juggling multiple apps at once. So, what’s going on? Economics may have some valuable insights to help explain the decline.
Too much choice
First, dating platforms don’t function like typical commodity markets, where prices adjust until supply and demand balance. Instead, dating can be seen as what economists call a ‘matching market’, where success depends on mutual interest, not on a specific price. So even with thousands of potential matches, forming actual connections remains difficult, and more choice doesn’t necessarily translate into better outcomes.
In fact, more choice can backfire. The paradox of choice, a behavioural economics concept, suggests that too many options can lead to choice paralysis. Instead of feeling empowered by an abundance of potential partners, users can feel overwhelmed, unsure, and often less satisfied with whatever choice they end up making (if they make one at all).
So, while we often think of dating apps, like many other platforms, benefiting from positive network effects, where more users increase the platform’s value by offering more potential matches, this can also have negative effects. Swiping through endless profiles and repeating the same small talk, can turn dating into a chore rather than an exciting opportunity.
Adverse selection
What makes this even harder is that users can’t easily distinguish between who’s genuinely looking for the same thing you are, and who’s just there to pass the time. This information asymmetry leads to the adverse selection problem – a concept famously explored by economist George Akerlof in his 1970 paper ‘The Market for Lemons’ (see link below). He showed how lack of information about product quality can cause high-quality sellers to exit, resulting in market failure where the market becomes dominated by low-quality goods (i.e. ‘lemons’).
A similar dynamic can play out on dating apps. If users believe most profiles are unserious or not genuine, they become less willing to engage, or even stay on the platform. Meanwhile, the most genuine users may give up altogether, worsening the quality of the pool and discouraging others.
In economics, there are some well-known ways in which the problem of adverse selection could be overcome. One such possibility is through signalling, where the more informed person tries to reveal important information to the uninformed person. Indeed, platforms have experimented with signalling mechanisms, like verification tools for example. Paid subscriptions have also been implemented, which could help to some extent (assuming that those who are willing to pay are those who are genuine and serious about finding a match). But these solutions only go so far, and with fewer users paying to signal intent, the problem persists.
Lack of innovation
This ties into the wider revenue model of dating apps. Unlike many apps that rely on revenue from advertising on one side of the market to offer the app free to consumers on the other side, dating platforms often rely more on revenue through monthly subscriptions and paid upgrades. But with fewer users willing to pay, these platforms may be under pressure. This financial pressure may also affect their ability to innovate or improve the service.
In fact, in the dating app world, there is another reason why platforms may not be innovating as much as they should, aside from simply trying to convince their users to pay for a better service. While it seems like there’s endless choice in the dating app world, much of the market is controlled by a single company, InterActiveCorp (IAC), which owns Tinder, Hinge, Match.com and more. With limited competition, there’s less incentive to compete on quality.
Worse still, dating apps face a unique business problem: if their service works too well, users leave and delete the app. So, there may be a built-in tension between helping users succeed and keeping them swiping.
The outlook for dating apps
So, is the decline in dating app use just temporary, or the start of something bigger? Time will tell. However, from an economics perspective, there is a noticeable shift in demand towards substitutes, such as organised in-person social events and activities, which encourages more and more of these opportunities to emerge. This shift may reflect changing preferences and the costs (in terms of time and emotional energy) that users are willing to invest in online dating.
At the same time, AI already plays a key role in dating apps, and new possibilities seem to be emerging. For example, we could see a bigger rollout of AI-driven chatbots that facilitate conversations or even interact on behalf of users. This could make it easier to connect with potential matches and might help in addressing some of the other issues discussed above.
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Questions
- How might ‘signalling’ and ‘screening’ be used to create new features or services that could help overcome the adverse selection problem in this market?
- Can you think of any other ways in which the adverse selection problem could be overcome in this context?
- Draw a diagram to illustrate the two-sided nature of the dating app market, making clear where there may be positive or negative network effects.
- How else might dating app platforms be making revenue that allows them to offer the app to users at no charge?
- Is the dating app market competitive? You might consider factors such as the availability of substitutes, barriers to entry and innovation.
In a blog from March 2023 (reproduced below), we saw how there has been growing pressure around the world for employers to move to a four-day week. Increasing numbers of companies have adopted the model of 80% of the hours for 100% of the pay.
As we see below, the model adopted has varied across companies, depending on what was seen as most suitable for them. Some give everyone Friday off; others let staff choose which day to have off; others let staff work 80% of the hours on a flexible basis. Firms adopting the model have generally found that productivity and revenue have increased, as has employee well-being. To date, over 200 employers in the UK, employing more than 5000 people, have adopted a permanent four-day week.
This concept of 100-80-100, namely 100% of pay for 80% of hours, but 100% of output, has been trialled in several countries. In Germany, after trials over 2024, 73% of the companies involved plan to continue with the new model, with the remaining 27% either making minor tweaks or yet to decide. Generally hourly productivity rose, and in many cases total output also rose. As the fourth article below states:
The primary causal factor for this intriguing revelation was simple – efficiency became the priority. Reports from the trial showed that the frequency and duration of meetings was reduced by 60%, which makes sense to anyone who works in an office – many meetings could have been a simple email. 25% of companies tested introduced new digitised ways of managing their workflow to optimise efficiency.
Original post
In two previous posts, one at the end of 2019 and one in July 2021, we looked at moves around the world to introduce a four-day working week, with no increase in hours on the days worked and no reduction in weekly pay. Firms would gain if increased worker energy and motivation resulted in a gain in output. They would also gain if fewer hours resulted in lower costs.
Workers would be likely to gain from less stress and burnout and a better work–life balance. What is more, firms’ and workers’ carbon footprint could be reduced as less time was spent at work and in commuting.
If the same output could be produced with fewer hours worked, this would represent an increase in labour productivity measured in output per hour.
The UK’s poor productivity record since 2008
Since the financial crisis of 2007–8, the growth in UK productivity has been sluggish. This is illustrated in the chart, which looks at the production industries: i.e. it excludes services, where average productivity growth tends to be slower. The chart has been updated to 2024 Q2 – the latest data available. (Click here for a PowerPoint of the chart.)
Prior to the crisis, from 1998 to 2006, UK productivity in the production industries grew at an annual rate of 6.9%. From 2007 to the start of the pandemic in 2020, the average annual productivity growth rate in these industries was a mere 0.2%.
It grew rapidly for a short time at the start of the pandemic, but this was because many businesses temporarily shut down or went to part-time working, and many of these temporary job cuts were low-wage/low productivity jobs. If you take services, the effect was even stronger as sectors such as hospitality, leisure and retail were particularly affected and labour productivity in these sectors tends to be low. As industries opened up and took on more workers, so average productivity rapidly fell back. Since then productivity has flatlined.
If you project the average productivity growth rate from 1998 to 2007 of 6.9% forwards (see grey dashed line), then by 2024 Q3, output per hour in the production industries would have been 3.26 times higher than it actually was: a gap of 226%. This is a huge productivity gap.
Productivity in the UK is lower than in many other competitor countries. According to the ONS, output per hour in the UK in 2021 was $59.14 in the UK. This compares with an average of $64.93 for the G7 countries, $66.75 in France, £68.30 in Germany, $74.84 in the USA, $84.46 in Norway and $128.21 in Ireland. It is lower, however, in Italy ($54.59), Canada ($53.97) and Japan ($47.28).
As we saw in the blog, The UK’s poor productivity record, low UK productivity is caused by a number of factors, not least the lack of investment in physical capital, both by private companies and in public infrastructure, and the lack of investment in training. Other factors include short-termist attitudes of both politicians and management and generally poor management practices. But one cause is the poor motivation of many workers and the feeling of being overworked. One solution to this is the four-day week.
Latest evidence on the four-day week
Results have just been released of a pilot programme involving 61 companies and non-profit organisations in the UK and nearly 3000 workers. They took part in a six-month trial of a four-day week, with no increase in hours on the days worked and no loss in pay for employees – in other words, 100% of the pay for 80% of the time. The trial was a success, with 91% of organisations planning to continue with the four-day week and a further 4% leaning towards doing so.
The model adopted varied across companies, depending on what was seen as most suitable for them. Some gave everyone Friday off; others let staff choose which day to have off; others let staff work 80% of the hours on a flexible basis.
There was little difference in outcomes across different types of businesses. Compared with the same period last year, revenues rose by an average of 35%; sick days fell by two-thirds and 57% fewer staff left the firms. There were significant increases in well-being, with 39% saying they were less stressed, 40% that they were sleeping better; 75% that they had reduced levels of burnout and 54% that it was easier to achieve a good work–life balance. There were also positive environmental outcomes, with average commuting time falling by half an hour per week.
There is growing pressure around the world for employers to move to a four-day week and this pilot provides evidence that it significantly increases productivity and well-being.
Additional articles
Original set of articles
- Results from world’s largest 4 day week trial bring good news for the future of work
4 Day Week Global, Charlotte Lockhart (21/2/23)
- Four-day week: ‘major breakthrough’ as most UK firms in trial extend changes
The Guardian, Heather Stewart (21/2/23)
- Senedd committee backs four-day working week trial in Wales
The Guardian, Steven Morris (24/1/23)
- ‘Major breakthrough’: Most firms say they’ll stick with a four-day working week after successful trial
Sky News, Alice Porter (21/2/23)
- Major four-day week trial shows most companies see massive staff mental health benefits and profit increase
Independent, Anna Wise (21/2/23)
- Four-day week: Which countries have embraced it and how’s it going so far?
euronews, Josephine Joly and Luke Hurst (23/2/23)
- Firms stick to four-day week after trial ends
BBC News, Simon Read, Lucy Hooker & Emma Simpson (21/2/23)
- The climate benefits of a four-day workweek
BBC Future Planet, Giada Ferraglioni and Sergio Colombo (21/2/23)
- Four-day working week: why UK businesses and workers will continue with new work pattern, plus pros and cons
National World, Rochelle Barrand (22/2/23)
- Most companies in UK four-day week trial to continue with flexible working
Financial Times, Daniel Thomas and Emma Jacobs (21/2/23)
- The pros and cons of a four-day working week
Financial Times, Editorial (13/2/23)
- Explaining the UK’s productivity slowdown: Views of leading economists
VoxEU, Ethan Ilzetzki (11/3/20)
- Why the promised fourth industrial revolution hasn’t happened yet
The Conversation, Richard Markoff and Ralf Seifert (27/2/23)
Questions
- What are the possible advantages of moving to a four-day week?
- What are the possible disadvantages of moving to a four-day week?
- What types of companies or organisations are (a) most likely, (b) least likely to gain from a four-day week?
- Why has the UK’s productivity growth been lower than that of many of its major competitors?
- Why, if you use a log scale on the vertical axis, is a constant rate of growth shown as a straight line? What would a constant rate of growth line look like if you used a normal arithmetical scale for the vertical axis?
- Find out what is meant by the ‘fourth industrial revolution’. Does this hold out the hope of significant productivity improvements in the near future? (See, for example, last link above.)
Artificial Intelligence (AI) is transforming the way we live and work, with many of us knowingly or unknowingly using some form of AI daily. Businesses are also adopting AI in increasingly innovative ways. One example of this is the use of pricing algorithms, which use large datasets on market conditions to set prices.
While these tools can drive innovation and efficiency, they can also raise significant competition concerns. Subsequently, competition authorities around the world are dedicating efforts to understanding how businesses are using AI and, importantly, the potential risks its use may pose to competition.
How AI pricing tools can enhance competition
The use of AI pricing tools offers some clear potential efficiencies for firms, with the potential to reduce costs that can potentially translate into lower prices for consumers.
Take, for instance, industries with highly fluctuating demand, such as airlines or hotels. Algorithms can enable businesses to monitor demand and supply in real time and respond more quickly, which could help firms to respond more effectively to changing consumer preferences. Similarly, in industries which have extensive product ranges, like supermarkets, algorithms can significantly reduce costs and save resources that are usually required to manage pricing strategies across a large range of products.
Furthermore, as pricing algorithms can monitor competitors’ prices, firms can more quickly respond to their rivals. This could promote competition by helping prices to reach the competitive level more quickly, to the benefit of consumers.
How AI pricing tools can undermine competition
However, some of the very features that make algorithms effective can also facilitate anti-competitive behaviour that can harm consumers. In economic terms, collusion occurs when firms co-ordinate their actions to reduce competition, often leading to higher prices. This can happen both explicitly or implicitly. Explicit collusion, commonly referred to as illegal cartels, involves firms agreeing to co-ordinate their prices instead of competing. On the other hand, tacit collusion occurs when firms’ pricing strategies are aligned without a formal agreement.
The ability for these algorithms to monitor competitors’ prices and react to changes quickly could work to facilitate collusion, by learning to avoid price wars to maximise long-term profits. This could result in harm to consumers through sustained higher prices.
Furthermore, there may be additional risks if competitors use the same algorithmic software to set prices. This can facilitate the sharing of confidential information (such as pricing strategies) and, as the algorithms may be able to predict the response of their competitors, can facilitate co-ordination to achieve higher prices to the detriment of consumers.
This situation may resemble what is known as a ‘hub and spoke’ cartel, in which competing firms (the ‘spokes’) use the assistance of another firm at a different level of the supply chain (e.g. a buyer or supplier that acts as a ‘hub’) to help them co-ordinate their actions. In this case, a shared artificial pricing tool can act as the ‘hub’ to enable co-ordination amongst the firms, even without any direct communication between the firms.
In 2015 the CMA investigated a cartel involving two companies, Trod Limited and GB Eye Limited, which were selling posters and frames through Amazon (see linked CMA Press release below). These firms used pricing algorithms, similar to those described above, to monitor and adjust their prices, ensuring that neither undercut the other. In this case, there was also an explicit agreement between the two firms to carry out this strategy.
What does this mean for competition policy?
Detecting collusion has always been a significant challenge for the competition authorities, especially when no formal agreement exists between firms. The adoption of algorithmic pricing adds another layer of complexity to detection of cartels and could raise questions about accountability when algorithms inadvertently facilitate collusion.
In the posters and frames case, the CMA was able to act because one of the firms involved reported the cartel itself. Authorities like the CMA depend heavily on the firms involved to ‘whistle blow’ and report cartel involvement. They incentivise firms to do this through leniency policies that can offer firms reduced penalties or even complete immunity if they provide evidence and co-operate with the investigation. For example, GB eye reported the cartel to the CMA and therefore, under the CMA’s leniency policy, was not fined.
But it’s not all doom and gloom for competition authorities. Developments in Artificial Intelligence could also open doors to improved detection tools, which may have come a long way since the discussion in a blog on this topic several years ago. Competition Authorities around the world are working diligently to expand their understanding of AI and develop effective regulations for these rapidly evolving markets.
Articles
Questions
- In what types of markets might it be more likely that artificial intelligence can facilitate collusion?
- How could AI pricing tools impact the factors that make collusion more or less sustainable in a market?
- What can competition authorities do to prevent AI-assisted collusion taking place?
The world’s biggest company just told everyone to chill out