AI is changing how households spend their time, from managing finances and accessing public services to education and menu planning. Diane Coyle, Mihai Codreanu and Arthur Turrell argue that understanding AI’s impact on productivity and the wider economy requires recognising households as both consumers and producers – and measuring what happens to productivity growth in both paid work and domestic activity.
Economists have appreciated the importance of the allocation of time to different activities for decades, at least since Nobel-winner Gary Becker highlighted in a classic 1965 paper the choices people make among paid work, household work, volunteering, and leisure. Central to this is the “third-party criterion”: if an activity could in principle be outsourced to someone else (or, perhaps, to an algorithm), it is production rather than leisure. Yet, although decisions are jointly made about paid market activities and unpaid household activities, the household side of the boundary is often a second-class citizen in economic analysis.
The main reason for this omission is that statistical agencies collect far less data on unpaid activity than on market-traded activities. However, available estimates suggest the scale of home production – if valued at an equivalent market wage – is a substantial fraction of a nation’s Gross Domestic Product (GDP). What’s more, productivity data needs to be interpreted with care when technology shifts activities across the boundary between the market and household sectors. Innovations may even appear to reduce productivity in official economic statistics when they shift activity into the household, simply because only the market sector is adequately measured.
AI, like earlier digital services, is expanding what can be done at home. And, in all advanced economies, a growing share of marketed activities consists of services that are often substitutable with household production.
Official data for the US show that eight headline categories of such work (including childcare, home healthcare, and food services) have expanded as a share of total non-farm employment from about 11% in 1990 to 15.7% in 2025. Other market services such as coaching, interior design, or filing government forms are small in number but expanding in scope. So the scope of potential substitutions people can make from market to household production is greater than in the past and the technological possibilities for substitution have expanded.
The American Time Use Survey (ATUS) provides the most regular and detailed statistics on how household time is allocated in the US. The statistics shift slowly over time. For example, looking at the changes in the two decades prior to 2024, most granular activities shift by around five minutes (with rising time spent on computer use for leisure, pet care, and physical fitness, alongside declines in social communication, and some routine chores).
However, the longer-term trends can be significant. Think of the role of household durables (such as the washing machine, vacuum cleaner, and refrigerator) enabled by the general purpose technology of electricity. These enabled (mainly) women to enjoy more leisure in the home and to work outside the home. The impact on the labour market was consequential; and indeed the trend for more women to work in paid jobs (sometimes also paying other people to do their household work) helped boost measured productivity growth in the 1970s and 1980s. Technology-use timelines are much shorter with digital and AI, compared to the decades-long shifts previously.
In terms of household productivity, using AI could either enable people to carry out some activities faster and/or better (for example, it could draw up your weekly grocery order; and it can plan the week’s menus with healthier ingredients and recipes provided). What’s more, the use of AI within households is likely to affect both the formal labour market and economy (as it might lead to substitutions from market to household activities) as well as the composition of household activities. To the extent AI use helps people cut costs – say by making it easier to shop around – it will also alter their combined choice of how to spend time and money.
In sum, as AI use in the household continues to increase, it will lead to changes in activity on both sides of the production boundary between the ‘official’ economy and the domestic economy. As households are simultaneously both consumers and producers, these changes will depend on the extent to which AI lifts their combined time and money budget constraint (by saving time in absolute terms), and on the substitutability of different activities for each other in the household’s demand function. Neither a standard production function applied to the household nor models of household consumption demand alone can capture the decision problem fully. Moreover, the act of production is often also identical with the act of consumption, especially in areas of online services where AI seems to be increasingly used.
ATLAS, Google’s new research data promises to offer a timely and detailed way to monitor such changes in activities and demand. The results already offer useful insights. For example, as can be seen in Figure 1, compared to the ATUS, Gemini is being used more for activities such as education, accessing services (such as banking or public services) and household management; and less for physical tasks such as preparing food and housework. As can be seen in Figure 2, AI is also used disproportionately for high friction tasks, such as government and civic obligations, at a rate several orders of magnitudes higher than human time use.
Such insights are not only important in themselves – because what happens in the household is as valuable as what happens in paid work – but also for helping track the impact of AI on the conventionally-measured economy. Making sense of trends in the labour market and measured productivity requires taking account of both sides of the production boundary. These are separated in most economic analysis, but not in the choices individuals are making; and AI is affecting household time as much as it is measured working time.
Figure 1. AI usage concentrates in cognitive household tasks, rather than physical chores
Notes: This figure compares Gemini AI conversation shares against ATUS human time allocation across the five most over-represented and most under-represented intermediate (Tier 2) activity domains (excluding work and sleep). Full methodology can be found in Iscenko et al. (2026).
Figure 2: Conversational AI is used across a wide set of productive household tasks and is particularly common in selected professional, government services and civic obligations
Notes: This figure compares US AI conversation shares with ATUS human time allocation across two sets of economic activities using dumbbell plots. All shares are calculated out of total non-work active time (excluding work and sleep). Panel A (Productive Household Tasks) displays top activities meeting the economic “third-person criterion”, tasks you could theoretically pay a third party to perform, such as cooking, cleaning, home maintenance, shopping, vehicles services and childcare and Panel B focuses specifically on Professional, Government Services and Civic Obligations. Full methodology can be found in Iscenko et al. (2026).
The views and opinions expressed in this post are those of the author(s) and not necessarily those of the Bennett School of Public Policy.
