Unpredictable Patterns #102: Demographics and AI in Europe

Policies and models to figure out how to use AI to deal with a vanishing Europe

Dear reader,

This week we return to the question of demographics that we touched on lightly in #49. Using demographics as a lens to understand policy issues remains one of the most important, and possibly undervalued ways in which we should think about public policy.

Thank you for the thoughts on randomness from last week’s note! As one reader noted, a key thing will be to balance randomness, since both too much and too little creates a brittleness that risks upending systems. This notion that life is lived at the edge of randomness and order is a deep one, and the subject of a few interesting observations that I hope to come back to.

European futures

If we were to pick a single chart that will have an outsized impact on the future of Europe, it would probably be something like this:

In the middle scenario the population of Europe is expected to fall from 740.6 million in 2023 to just 586.5 million people by 2100, and this coincides with the rest of the world experiencing continued population growth, making Europe’s relative population a smaller percentage of the whole. This comes with a lot of obvious impacts, like:

1. Aging Population
   With fewer births, the median age will rise, creating a larger older demographic relative to the working-age population.

2. Shrinking Workforce
   Fewer new entrants into the labor market can cause labor shortages in sectors like healthcare, manufacturing, and technology.

3. Pressure on Pension Systems
   More retirees per worker makes pay-as-you-go systems harder to sustain without reform (e.g., raising retirement ages or cutting benefits).

4. Higher Healthcare Demand
   An older population tends to require more medical care, taxing healthcare systems and budgets.

5. Slower Economic Growth
   A reduced labor force and lower consumer demand can dampen GDP expansion.

6. Regional Depopulation
   Rural areas may see further decline, as younger citizens cluster in urban centers or emigrate for opportunities.

7. Need for Immigration
   Governments may promote immigration to fill workforce gaps and fund social systems through tax revenue.

8. Shift in Consumer Markets
   Product demand changes as older consumers prioritize healthcare, senior housing, and leisure over childcare or education.

9. Infrastructure Realignment
   Schools, housing, and public transport networks might become underutilized in areas experiencing population loss.

10. Geopolitical Rebalancing
    Reduced population can lead to a weaker global influence, particularly relative to rapidly growing regions (e.g., Africa, Asia).

All of this is easy to see, even if it is sometimes discounted.

Policy implications

This in turn leads to a few fairly obvious policy predictions for AI:

1. Incentivize AI for Healthcare and Elder Care

• Invest in medical robotics, diagnostics, and telemedicine platforms, especially for remote rural regions with limited healthcare staff.

• Ensure there is a focus on startups targeting age-related conditions (e.g., dementia) and rehabilitation robotics.

2. Expand Workforce Upskilling and Lifelong Learning

• Fund programs that retrain displaced workers in AI-related skills (data and process curation, validation and audit - checking the work of the machine - will be important)¹. Deal with this early, rather than later - and try to forecast possible displacements before they happen.

• Create public–private partnerships for continuous education, mitigating labor shortages by transforming the existing workforce, and introducing demographically strategic automation.²

3. Develop Robust Regulatory Frameworks (Beyond the AI act)

• Balance innovation with oversight: ensure the EU’s AI Act sets clear risk-based guidelines without stifling competitiveness. Move away from “…by design” to “…by adaptation” as a key model for technology regulation.³

4. Support Specialized AI Infrastructure

• Invest in European supercomputing centers, data-sharing platforms, and secure cloud infrastructure to handle large-scale datasets and national data libraries. Reset the national data collection from zero and ask seriously what data we should collect, curate and use given that we now have these new tools.

5. Attract International AI-Talent

• Europe needs to use its data for attracting the best researchers, and ensure that it streamlines visa processes for AI researchers, data scientists, and engineers, offsetting skill shortages. If Europe can become the data focus for the next wave of applications for AI, it will gain enormously.

6. Focus on Robotics and Automation in Manufacturing

• Make robotics a key area for European strategic research.

• Use targeted subsidies or tax breaks to help small and medium enterprises (SMEs) adopt AI-driven automation.

• Mitigate labor shortages in key sectors—e.g., logistics, food processing—through advanced robotics.

7. Promote Cross-Border Data Collaboration

• Harmonize data standards and open up secure data pools across the EU, enabling AI-driven insights for healthcare, transport, and climate.

• Foster “data trusts” that allow for new data to be collected.

• Initiate a reform of the GDPR and review how it should be adapted to balance the social learning and discovery that AI enables.

8. Encourage AI Entrepreneurship Ecosystems

• Develop regional AI innovation hubs in areas with strong university–industry links (e.g., Munich, Barcelona, Tallinn).

• Offer targeted seed funding and mentorship programs to build globally competitive AI startups - but work with private sector capital to validate models and due diligence.

9. Align AI with Climate Goals

• Incentivize AI research on energy efficiency, resource optimization, and smart grids to address climate challenges alongside demographic concerns - since climate change challenges will hit an aging population harder.⁴

10. Build immigration systems that speed up social integration

• Use AI to build social integration programs that can scale and create personalized introductions for immigrants, create paths to ensure that skills and capabilities can be translated faster to new home countries (no doctors driving cabs) and focus on language integration through constant personal AI-tutoring.

None of this is rocket science - and when we use the demographic lens we gain the added advantage of a natural, more long term perspective.

In addition to these more obvious policy interventions, we may also want to ensure that we double down on ensuring equality in medicine - and that we understand more about how to extend and support older parents. Another stark chart is this:

In the 1980s, women started to postpone pregnancy for a number of reasons.⁵ This in itself is not a problem, but it requires that we think about how to support that decision, work to ensure that the right health care is offered and to make sure that postponement is not involuntarily transformed into a foreclosure of the option itself.

This also means that childhood will change - older, more financially fit parents will create another kind of childhood and spend more time and money on their kids - some of this in ways that will be helpful, some in ways that may continue the trend of extending childhood. But older parents will also mean that the overlap in life experiences will shrink, and that kids will lose their parents at a younger age - potentially breaking the grandparents function of taking care of the kids that exist in many societies. Thinking through what that means also requires modeling demographics closely.

Any serious policy review should at least try to also look at the issues at hand from a demographic lens.

Economic aspects

If, as some argue, productivity growth is, essentially, a function of population growth, the Europe is in deep trouble.⁶ Now, rather than accept this as a given, the task should be the opposite - to assume that we will see no population growth and then ask how we can build an engine of productivity growth with a shrinking population.

Productivity growth under population decline presents a complex problem to solve. Luckily there seem to be a lot of different possible solutions that Europe could explore, and in the wake of the Draghi report should look more closely at.⁷ In one sense, the key economic mission for Europe is to solve this problem in the coming decade, and it would not be too much of an exaggerated measure to have tasked on commissioner only with working on this.

So what?

The demographics of Europe present an important lens to view AI-policy through, and it is central enough to the future of Europe politically and economically to merit much more attention than we currently give it. The casual nod to “an aging Europe” hides the complexity and reality of this problem, and makes it seem like this is just a macro-trend that we cannot deal with.

It seems not impossible, however, that our chances to address demographic changes are at least an order of magnitude greater than our chances to fix, as Europe, climate change - and so we should approach demographics differently than we approach climate change issues (and today, I do not think we do).⁸

With a solid focus on demographics, Europe could address its challenges more effectively.

Thanks for reading!

Nicklas

1. We see this in coding.

2. Demographically strategic automation involves channeling AI and robotics toward areas where labor shortages will be most acute and societal needs are highest. Healthcare should be the top priority, with automation helping hospitals and care facilities manage routine and labor-intensive tasks. Telemedicine platforms, AI diagnostics, and robots for patient handling can free up skilled caregivers and improve service quality. Next, manufacturing and logistics stand to benefit from collaborative robotics that lighten heavy or repetitive tasks, enabling older workers to stay in the labor force longer and alleviating structural labor shortages. Meanwhile, digitizing government services — from administrative tasks to smart city infrastructure — can help local authorities maintain efficient operations and legitimacy despite having fewer employees.

Beyond those sectors, agriculture also deserves attention, since fewer young people are entering farm work and much of rural Europe faces population decline. This is is also important because of security concerns, and geopolitical pressures on supply chains. Precision farming tools, including drones and autonomous tractors, can boost yields while requiring fewer hands. These measures won’t just replace workers; they’ll complement them, making it easier for people to remain in the workforce longer and moving labor to value-added roles.

3. This is arguably controversial - but the notion of implementing policy goals by design is necessarily connected to teh complexity of the systems, and we have gone through a phase transition of systems complexity with AI, and that in turn shifts the regulatory focus - or should shift it - from ex ante to ex post. Such oversight should also be designed with artificial intelligence, automating a lot of the oversight. There is much more research needed on how to build robust and incorruptible AI oversight over AI.

4. Think heat-waves.

5. These included things like the following - and all of these should be welcomed rather than pushed back on, except for the last..

• Extended Education: More women pursued higher education in the 1970s and 1980s, often delaying marriage and parenthood until they completed their studies. Research by demographers like Ronald R. Rindfuss (see https://www.cpc.unc.edu/people/fellows/ronald-r-rindfuss/) points to the link between expanded college enrollment for women and later childbearing.

• Growing Career Opportunities for Women: The rise in female labor participation, influenced by wider socio-economic changes and movements for gender equality, encouraged many women to establish themselves in the workforce before starting a family. Thus, securing financial stability became a higher priority prior to motherhood.

• Shift in Social Norms: Cultural attitudes toward marriage and childbearing became more flexible. Cohabitation without marriage, acceptance of smaller families, and the normalization of waiting to have children all contributed to delaying first births.

• Improved Access to Contraception: The widespread availability of reliable birth control (notably the Pill) and better family planning resources allowed couples to time pregnancies more precisely. As a result, women who wanted to build careers or pursue studies could more easily postpone childbearing.

• Higher Living Costs: Rising housing costs and the broader expense of raising children often led couples to delay starting a family. Contemporary economic analysis shows that the 1980s was marked by changing labor markets and growing financial pressures, making early parenthood less feasible.

6. See Jones, C.I., 2023. The Outlook for Long-Term Economic Growth (No. w31648). National Bureau of Economic Research.

7. A first quick review gives us papers like Danila, A., Morozov. (2024). Demographic Decline and Economic Growth: Analysis of Causes and Successful Solutions in World Practice. Obŝestvo: politika, èkonomika, pravo, Available from: 10.24158/pep.2024.10.13, Theodore, P., Lianos., Anastasia, Pseiridis., Nicholas, Tsounis. (2023). Declining population and GDP growth. Humanities & social sciences communications, 10, 1-9. Available from: 10.1057/s41599-023-02223-7 and Carl-Johan, Dalgaard., Claus, Thustrup, Kreiner. (2001). Is Declining Productivity Inevitable. Journal of Economic Growth, 6(3), 187-203. Available from: 10.1023/A:1011343715594 — see also, for more on robotics: Juan, F., Jimeno., Juan, F., Jimeno. (2019). Fewer babies and more robots: economic growth in a new era of demographic and technological changes. Series, 10(2), 93-114. Available from: 10.1007/S13209-019-0190-Z.

8. Hopefully goes without saying that climate change also is important, of course. It is just in a different category of tractability right now. It is not a choice between the two problems, but a choice in how they are addressed that I am after.