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TL;DR
A preprint based on an online survey of 8,234 working-age adults in the UK, US, Denmark and India examines what workers value beyond pay as AI reshapes jobs. About one in five respondents said they would not quit even for guaranteed income equal to 120% of their salary, but the findings are self-reported and do not establish how workers would behave during actual job losses.
A new arXiv preprint based on a survey of 8,234 working-age adults finds that many respondents value purpose, colleagues and recognition alongside pay—and about one in five would not leave a job even for guaranteed income equal to 120% of their salary. The study, by Julian Jacobs of the University of Oxford and Google DeepMind, explores what work provides as AI raises concerns about job replacement; it is not peer-reviewed and measures stated preferences, not observed behavior.
The survey was conducted online in June and July 2026 by polling organization More in Common. It included respondents in the UK, US, Denmark and India, with national quotas for age, gender, education and region. The paper reports that respondents were offered guaranteed income at successive levels from 60% to 120% of current salary, while keeping healthcare, pension and paid leave, and asked whether they would quit.
At 80% of salary, a slight majority said they would leave; at 100%, about three in four said they would. At the top offer, roughly 20% remained unwilling to quit. Among people who accepted an offer, the median point at which they said they would leave was 70% of salary. The paper describes this as a hypothetical choice, not a test of what people would do if their jobs or income actually changed.
In the study’s classification of work values, about 33% were mainly motivated by the work itself, such as purpose, learning or contribution; 23% by colleagues and belonging; 17% by status and recognition; and 26% by pay and benefits. The paper also reports that 40% of white-collar respondents considered AI at least somewhat likely to replace their job within ten years, compared with 29% of manual and service workers. These figures reflect respondents’ views, not forecasts of actual job losses.
WHAT WORK IS FOR, ACCORDING TO WORKERS
Three in four put something other than pay first
The paper sorts workers by what they rate most important in their job. In its own figure, 73.7% of classified workers put a non-money reason first.
The work itself: purpose, learning, contributing to society.
Pay and benefits come first.
Belonging, colleagues, meeting people.
Recognition and prestige.
Pay-first share, by country
Share of classified workers whose top value is pay and benefits
Meaning is not a luxury
Share driven by the work itself (intrinsic), by household income third
Also about a third in craft and trade jobs (33%) and in services and sales (31%).
Shares of the 6,224 working respondents the paper could classify; 2,010 respondents (non-working, or no clear top value) were left out. 2,000 tied answers were assigned by a tie-break rule. Satisfied with their job: 81%.
Source: Jacobs, What is Work For?, arXiv:2610.39375v1, 29 Oct 2026 · preprint, not peer-reviewed · survey of 8,234 working-age adults in the UK, US, Denmark and India by More in Common, June–July 2026, supported by Google DeepMind.
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What Workers Value Beyond Pay
The findings speak to a practical question for employers and policymakers: if AI reduces demand for some kinds of labor, would replacing wages with income support address the loss workers experience? The survey suggests that some people associate work with purpose, learning and social connection, not only a paycheck. That may matter when considering job redesign, retraining or support for workers facing disruption.
But the paper does not show that a basic income or other payment would fail, nor does it measure the effects of real displacement. Its evidence is about answers to a hypothetical question. The estimated difference in the quit threshold between people primarily motivated by purpose and those primarily motivated by pay is also modest: after statistical controls, the paper reports differences of about 1.9 to 5.8 percentage points of salary, depending on the method.
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How the Survey Tested Work Choices
The paper, What is Work For? Labor, Politics, and Meaning in the Age of AI, is a 39-page, single-author preprint posted to arXiv on 29 October 2026 in General Economics. It uses a nine-item set of questions about job values to group respondents into four profiles, alongside questions about guaranteed income, how they would spend their time, perceived AI threats and politics.
The four country samples were Denmark (2,032 respondents), India (2,072), the UK (2,052) and the US (2,078). The paper says the survey was supported by Google DeepMind, Jacobs’s employer, and says its views do not necessarily reflect those of Oxford or DeepMind. The online sample in India was younger, more urban and more educated than the country’s population overall, a limitation the source material flags when discussing country comparisons.
The study uses cross-sectional statistical models with controls including income, occupation, age, gender, education, part-time work and country. It reports associations among respondents’ work values, AI concerns and other answers. Its own description says the analysis is “purely relational, not causal.”
““may indeed be necessary””
— Julian Jacobs, author of the preprint
Limits of the Preprint Evidence
The paper has not been peer-reviewed, and its results may change in later versions. The survey records what people said they would do under a hypothetical guaranteed-income offer; it does not observe decisions made after automation, unemployment or a real change in pay and benefits.
It is also unclear from the supplied material how well the findings would generalize beyond the four online samples, particularly in countries where the sample profile differs from the wider population. The paper’s political analysis is limited: although it reports more pessimistic views among some purpose- and community-oriented respondents who expect AI to hurt jobs in their industry, its tables do not show significant differences in populist vote intention between the work-value groups. The results do not establish that AI will replace a particular share of jobs or that any policy will offset its effects.
Peer Review and Further Evidence
The next milestone is whether the preprint receives peer review or is revised with additional analysis. Later versions could clarify the survey design, the robustness of the work-value categories and how the results vary across countries and occupations.
For readers tracking AI’s labor effects, the key evidence still to come is whether stated concerns and hypothetical choices align with observed workplace changes: job losses, task redesign, pay changes and workers’ decisions when alternatives are real. The paper itself does not provide that follow-up, and no specific publication or replication timetable is stated in the source material.
Key Questions
What did the survey find about workers quitting for guaranteed income?
About three in four respondents said they would leave at 100% of their current salary, while roughly one in five said they would still not quit at 120%. These were answers to a hypothetical offer that also preserved healthcare, pension and paid leave.
How many workers took part, and where were they from?
The online survey included 8,234 working-age adults: 2,032 in Denmark, 2,072 in India, 2,052 in the UK and 2,078 in the US. It was fielded in June and July 2026 by More in Common.
Does the paper prove that AI will replace workers’ jobs?
No. It reports respondents’ expectations about possible job replacement, including that 40% of white-collar respondents considered it at least somewhat likely within ten years. Those answers are perceptions, not a forecast of actual job losses.
Has the study been peer-reviewed?
No. It was posted to arXiv on 29 October 2026 as a preprint. The source material says the paper is not peer-reviewed and its numbers could change in later versions.
Who funded the survey?
The paper says the survey was supported by Google DeepMind, where the author works. It also says the views do not necessarily reflect those of DeepMind or the University of Oxford.
Source: ThorstenMeyerAI.com
