When ChatGPT launched in November 2022, 100 million people signed up to use it within two months. Since then, this and other generative AI systems have begun to augment and replace human work in industries ranging from law and computer coding to graphic design and customer service. The prospect of even more intelligent and capable machines has generated fears that these devices might replace humans on vast scales and in a relatively short period of time while at the same time concentrating wealth in the hands of a tiny minority of people.⁵ Some jobs are already going this way. For example, machine-vision algorithms are currently upstaging radiologists in the task of assessing medical images. Translators are also being replaced by increasingly capable machine-translation algorithms. Robots are already replacing certain kinds of workers, particularly those performing relatively simple, repetitive tasks: certain kinds of machine operators and drivers.⁶

Future Horizons:

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5-yearhorizon

Machines perform low-skill work

Generative AI services such as Bard, ChatGPT, Midjourney and others become routine tools in the workflows of most industries, thanks to their rapid deployment via the global companies, such as Microsoft, Google and Facebook. Some human jobs quickly become obsolete but new classes of job also emerge. Nevertheless, the rapid rate of change causes governments to put policies in place that incentivise the employment of human labour and innovation with labour-augmenting technologies. This is supported by a change in taxation in favour of human labour and against capital, which smooths the transition.

10-yearhorizon

Governments tax automation

There is significant displacement of jobs because of the coupling of artificial intelligence and robots. Governments implement policies that ensure human capital is not wasted: education and retraining is common, preparing workers and rising generations for a changing workplace. A wide range of economies begin trialling universal basic income, paid for by the taxation of capital and automation.

25-yearhorizon

Machines alter the human experience

The workplace has changed substantially, with new jobs and tasks in place. People are working significantly less, thanks to the productiveness of machines. Universal basic income allows retraining or support of displaced workers, and allows governments to incentivise the development of technology that enhances human performance rather than replacing it, where appropriate. Policy measures place greater emphasis on the social obligations of companies towards their workers and the communities in which they operate to ensure relatively high levels of employment and to avoid the growth of social and economic inequalities.

Although it is unlikely that intelligent machines will replace humans in most jobs on the 25-year timescale, they are likely to lead to considerable changes in society.⁷ One report estimates that 80 per cent of the US workforce could have at least 10 per cent of their work tasks affected by current AI systems.⁸ The fraction of the workforce that becomes unemployed will need to be looked after and retrained where possible. Several countries have begun experimenting with universal basic incomes or UBIs, such as the UK, Kenya and India. If they are more widely adopted, UBIs will have to be paid for by governments, who will need to find new ways of gathering and redistributing the wealth generated by machines.⁹ Having historically raised revenue by taxing labour, governments will have to tax or redistribute capital to support future societies. This will also help to prevent the concentration of wealth in the hands of small group of machine owners. Radical economic innovations like new taxation models will need to be incentivised by regulators — a programme that will require collaborative economic, political and social action on global scales.

Automation and work - Anticipation Scores

The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:

The uncertainty related to future science breakthroughs in the field
The transformative effect anticipated breakthroughs may have on research and society
The scope for action in the present in relation to anticipated breakthroughs.

This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.