DEV Community

Cover image for The Demographic Dividend of Digitalization: Productivity Despite Shrinking Workforces
Dirk Röthig
Dirk Röthig

Posted on • Edited on

The Demographic Dividend of Digitalization: Productivity Despite Shrinking Workforces

#productivity #digitalization #demographics #ai

The Demographic Dividend of Digitalization: Productivity Despite Shrinking Workforces

By Dirk Röthig | CEO, VERDANTIS Impact Capital | March 11, 2026

Europe will lose approximately 35 million workers by 2050. Yet the equation "fewer workers equals less economic output" is too simplistic. Digitalization — led by generative AI — can not only offset the productivity loss but generate a demographic dividend: more output per capita, despite fewer people. Dirk Röthig analyzes the historical parallels, the current productivity data, and the conditions under which the digital dividend succeeds.

Tags: Productivity, Digitalization, Demographics, AI, Economic Growth

The Paradox: Fewer People, More Prosperity?

The conventional logic is compellingly simple: fewer workers mean less production, fewer tax revenues, and less prosperity. Yet economic history shows that this equation does not hold. Japan's workforce has been shrinking since 1995 — yet GDP per capita grew by 36 percent between 1995 and 2024 (OECD, 2025). Germany increased its hourly labor productivity by 14 percent between 2010 and 2024, despite the workforce growing only marginally during this period — driven by immigration, not natural population growth (Statistisches Bundesamt, 2025).

The key lies in productivity. As long as per-worker productivity growth exceeds the decline in the number of workers, economic output grows — despite a shrinking workforce. Dirk Röthig, CEO of VERDANTIS Impact Capital, formulates the central thesis: "Demographic change is a productivity problem, not a population problem. And productivity problems can be solved with technology — if done correctly."

The European Commission estimates that the EU workforce will shrink by 35 million people between 2025 and 2050 — from 202 million to 167 million (European Commission, 2024). To maintain the current GDP level, labor productivity per capita would need to rise by an average of 0.8 percent per year. To sustain the usual economic growth rate of 1.5 percent, Europe would need a productivity increase of 2.3 percent annually. For comparison: actual productivity growth in the EU averaged 0.7 percent per year over the last decade (Eurostat, 2025).

The gap is therefore 1.6 percentage points. Can digitalization close this gap?

Historical Parallels: How Technology Has Compensated for Demographic Shocks

History records several episodes in which technological progress compensated for the loss of workers — in some cases with dramatic productivity gains.

The Black Death (1347–1353). The plague killed one-third of Europe's population. The immediate consequence was a massive labor shortage in agriculture. The long-term consequence was a wave of technological innovation: improvement of plow technology, spread of three-field crop rotation, mechanization of textile production. Real wages in England rose by 100 percent between 1350 and 1450 — more than in the preceding 200 years (Pamuk, 2007). The historian Slicher van Bath spoke of the "golden age of the worker."

The Industrial Revolution. Between 1760 and 1840, England transformed from an agrarian society into an industrial nation. Mechanization — the spinning machine, the steam engine, the mechanical loom — replaced human labor with machine work. Labor productivity in textile production increased by a factor of 200 (Allen, 2009). A single worker at a mechanical loom produced as much fabric as 200 hand weavers had before.

The Green Revolution (1960–1980). In Asia, agricultural productivity rose by 200 to 300 percent through high-yield varieties, irrigation technology, and fertilizers — with stable or even declining agricultural employment (Pingali, 2012). The Green Revolution fed billions of additional people with fewer farm workers.

Dirk Röthig draws the parallel to the present: "Every major technological revolution has had the same effect: it has massively increased output per worker and thereby more than compensated for the loss of workers — whether through plague, migration, or demographic change. The question with AI is not whether this effect will occur, but how quickly."

AI as a Productivity Driver: What the Data Says

The empirical evidence for AI-driven productivity gains is consolidating. Several large-scale studies from 2024 and 2025 provide robust figures for the first time.

Macroeconomic Estimates

McKinsey Global Institute (2024) estimates the global value-creation potential of generative AI at 2.6 to 4.4 trillion dollars annually — equivalent to the combined economic output of Germany and France (McKinsey, 2024). For the EU-27, this means a potential of 600 to 1,000 billion euros in additional value creation per year.

Goldman Sachs (2024) forecasts that generative AI could increase global labor productivity by a cumulative 7 percent over a ten-year period — an effect comparable in magnitude to electrification (Goldman Sachs, 2024).

Acemoglu and Restrepo (2024), the most prominent labor economists of the present, are more cautious: in an MIT study, they estimate the realistic AI productivity effect at 0.5 to 0.9 percent GDP growth per year over the next ten years — a significant but not transformative effect (Acemoglu and Restrepo, 2024). For Europe, even the lower end of this estimate would be sufficient to almost entirely close the demographically driven productivity gap of 0.8 percent.

Microeconomic Evidence: What Is Happening in Companies?

The most convincing data comes from corporate experiments:

Stanford/MIT Customer Service Study (2024). In a randomized controlled trial with 5,179 customer service workers, the use of an AI assistant increased processing speed by 14 percent with simultaneously higher customer satisfaction. The effect was greatest among the least experienced workers — 35 percent productivity increase — suggesting that AI primarily raises the productivity floor (Brynjolfsson et al., 2024).

Harvard Study on Management Consultants (2024). 758 consultants at a top management consulting firm completed typical consulting tasks with and without AI support. Result: the AI group was 25 percent faster, produced 40 percent more output, and achieved 12 percent higher quality ratings (Dell'Acqua et al., 2024).

GitHub Copilot (2024). In a controlled study with 950 software developers, developers with AI coding assistants completed programming tasks 55 percent faster than without AI support (GitHub, 2024).

Röthig summarizes: "The microeconomic evidence is convincing: 14 to 55 percent productivity gains, depending on the task. Even if only half of that materializes in the broader economy, it fully closes the demographic productivity gap."

The Prerequisites of the Demographic Dividend

Productivity gains from digitalization and AI do not fall from the sky. Historical experience shows: between the invention of a technology and its full economic impact lie decades — the so-called Solow Paradox. Electricity was commercialized in the 1880s, but its full productivity effect unfolded only in the 1920s, when factories reorganized their entire production logic around electric drives (David, 1990).

For the AI-driven demographic dividend, four prerequisites must be met:

1. Infrastructure: Broadband and Cloud for All

AI applications require high-performance digital infrastructure. In Germany, 15 percent of households and 22 percent of businesses lack access to fiber-optic internet (BMDV, 2025). In rural regions — where demographic change has the strongest effect — the fiber-optic coverage rate is below 30 percent. Dirk Röthig warns: "AI without broadband is like a steam engine without coal. As long as one-fifth of businesses are offline, the demographic dividend will only materialize in metropolitan areas."

2. Education: AI Competency as a Basic Skill

The greatest productivity gains occur among workers who can competently use AI tools. In Germany, according to an OECD study, only 38 percent of adults assess their digital skills as sufficient for using AI in the workplace (OECD, 2025). The federal government launched the "AI Competency Offensive" in 2024, aiming to train one million workers in AI application by 2027 (BMAS, 2025). Whether that is sufficient is questionable — the gap encompasses millions of workers.

3. Organizational Transformation: Redesigning Processes Around AI

As electrification demonstrates: the productivity impact of a technology depends on whether companies fundamentally redesign their processes. A factory that replaced a steam engine with an electric motor but maintained the same production logic gained little. Only the reorganization of the factory around decentralized electric drives brought the breakthrough (David, 1990).

For AI, this means: companies that layer ChatGPT onto existing email communication gain five percent productivity. Companies that reorganize their entire knowledge work around AI-native workflows gain 30 to 40 percent. Röthig observes the difference in practice: "The AI adoption speed in German companies is encouragingly high — 63 percent already use AI tools. But the organizational transformation that unlocks the full productivity effect is still in its early stages for most."

4. Investment: Capital for the Digital Transformation

The digital transformation requires massive investment. The digital industry association Bitkom estimates the annual investment need of the German economy in digitalization at 125 billion euros — actually invested are 84 billion euros (Bitkom, 2025). The investment gap of 41 billion euros per year must be closed if the demographic dividend is to be realized.

Sectoral Analysis: Where the Dividend Is Greatest

Not every economic sector benefits equally from the digital productivity dividend:

Knowledge-intensive services (finance, consulting, IT, media, public administration): Productivity potential through AI: 25–40%. This is where the greatest and most quickly realizable lever lies. These sectors employ approximately 12 million people in Germany (Statistisches Bundesamt, 2025).

Manufacturing (automotive, mechanical engineering, chemicals, pharmaceuticals): Productivity potential through AI and automation: 15–25%. German industry is already highly automated; AI delivers incremental but cumulative improvements in quality, maintenance, and process optimization.

Retail and logistics: Productivity potential: 15–20%. Autonomous warehouses, AI-driven inventory planning, and predictive supply chain optimization reduce personnel needs while increasing service quality.

Care, education, skilled trades: Productivity potential: 5–15%. In these sectors with a high proportion of human interaction, the AI lever remains limited — but not zero. Documentation automation, assistive systems, and process optimization deliver tangible relief.

Dirk Röthig derives a clear recommendation from this analysis: "The greatest demographic dividend arises where knowledge-intensive work is augmented by AI. Germany must concentrate its digitalization investments on precisely these sectors — not with a scattergun approach but strategically."

International Comparisons: Who Is Already Realizing the Dividend

South Korea — with a birth rate of 0.72, the country with the world's lowest fertility rate — compensates for demographic decline through the world's highest robot density (1,012 robots per 10,000 manufacturing employees) and massive AI investments. The result: despite a shrinking workforce, GDP per capita grew by 2.1 percent in 2024 (OECD, 2025).

Estonia has almost entirely digitalized its public administration. 99 percent of public services are available online, and administrative costs per inhabitant are 40 percent below the EU average (e-Estonia, 2025). In a country with 1.3 million inhabitants and a shrinking population, this is not a luxury but a survival strategy.

Singapore invests 1.5 percent of GDP annually in AI research and application — three times as much as Germany. The national AI strategy "Smart Nation" systematically integrates AI into healthcare, administration, logistics, and education. Labor productivity is growing at 3.2 percent per year — twice as fast as the EU average (Singapore Government, 2025).

The Risks: When the Dividend Fails to Materialize

The demographic dividend of digitalization is not automatic. Three risk scenarios deserve attention:

Scenario 1: Digital divide. If only large corporations and metropolitan areas benefit from AI while SMEs and rural regions fall behind, no aggregate economic dividend emerges — only a productivity divide. In Germany, SMEs employ 60 percent of all workers — if they miss the AI connection, the dividend remains theory.

Scenario 2: Missing complementary investments. AI without broadband, without training, without organizational transformation yields no productivity gains. The Solow Paradox — "computers everywhere, except in the productivity statistics" — could repeat itself with AI.

Scenario 3: Regulatory overreach. The EU AI Act creates legal certainty but carries the risk of slowing AI adoption in Europe through excessive compliance requirements — while China and the USA scale faster.

Conclusion: The Greatest Opportunity Since Industrialization

Demographic change is inevitable — its economic consequences are not. Digitalization, led by generative AI, offers Europe the opportunity to turn demographic decline into a productivity revolution. The historical parallels — from the Black Death through industrialization to the Green Revolution — show: labor shortages have historically been the strongest driver of innovation.

Dirk Röthig sums up the opportunity: "Europe faces a choice: either we use AI and digitalization to achieve more with fewer people — or we accept relative economic decline. The technology is mature. The question is whether politics, businesses, and society are ready to deploy it decisively."

The demographic dividend of digitalization is not utopia. It is an achievable goal — if Europe builds the infrastructure, creates the competencies, transforms the processes, and mobilizes the capital. The alternative — a Europe that ages and shrinks without becoming more productive — is not an option.

More Articles by Dirk Röthig

References

  1. OECD (2025): GDP per capita — Japan 1995–2024. OECD Data. Available at: https://data.oecd.org/gdp/gross-domestic-product-gdp.htm
  2. Statistisches Bundesamt (2025): Labor Productivity per Hour Worked 2010–2024. Available at: https://www.destatis.de/DE/Themen/Wirtschaft/Volkswirtschaftliche-Gesamtrechnungen-Inlandsprodukt/
  3. European Commission (2024): 2024 Ageing Report — Economic and Budgetary Projections for the EU Member States. Available at: https://economy-finance.ec.europa.eu/publications/2024-ageing-report_en
  4. Eurostat (2025): Labour Productivity per Person Employed — EU-27. Available at: https://ec.europa.eu/eurostat/databrowser/view/tesem160/default/table
  5. Pamuk, S. (2007): The Black Death and the origins of the 'Great Divergence' across Europe, 1300–1600. European Review of Economic History, 11(3), 289–317. DOI: 10.1017/S1361491607002031
  6. Allen, R.C. (2009): The British Industrial Revolution in Global Perspective. Cambridge University Press.
  7. Pingali, P. (2012): Green Revolution: Impacts, limits, and the path ahead. Proceedings of the National Academy of Sciences, 109(31), 12302–12308. DOI: 10.1073/pnas.0912953109
  8. McKinsey & Company (2024): The Economic Potential of Generative AI — The Next Productivity Frontier. McKinsey Global Institute. Available at: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai
  9. Goldman Sachs (2024): Generative AI: Global Macroeconomic Effects. Goldman Sachs Economic Research. Available at: https://www.goldmansachs.com/intelligence/pages/generative-ai-could-raise-global-gdp-by-7-percent.html
  10. Acemoglu, D. and Restrepo, P. (2024): The Simple Macroeconomics of AI. NBER Working Paper 32487. Available at: https://www.nber.org/papers/w32487
  11. Brynjolfsson, E., Li, D. and Raymond, L.R. (2024): Generative AI at Work. NBER Working Paper 31161. Available at: https://www.nber.org/papers/w31161
  12. Dell'Acqua, F. et al. (2024): Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. Harvard Business School Working Paper 24-013. Available at: https://www.hbs.edu/ris/Publication%20Files/24-013_d9b45b68.pdf
  13. GitHub (2024): Research: Quantifying GitHub Copilot's impact on developer productivity. Available at: https://github.blog/2024-06-27-research-quantifying-github-copilots-impact/
  14. David, P.A. (1990): The Dynamo and the Computer: An Historical Perspective on the Modern Productivity Paradox. American Economic Review, 80(2), 355–361.
  15. BMDV — Federal Ministry for Digital and Transport (2025): Broadband Atlas Germany 2025. Available at: https://www.bmvi.de/DE/Themen/Digitales/Breitbandausbau/breitbandausbau.html
  16. OECD (2025): Skills for the Digital Transition — Survey of Adult Skills. Available at: https://www.oecd.org/skills/piaac/
  17. BMAS (2025): AI Competency Offensive — Program Description. Available at: https://www.bmas.de/DE/Arbeit/Digitalisierung-der-Arbeitswelt/KI-Kompetenzoffensive
  18. Bitkom (2025): Digitalization of the German Economy — Investments and Potentials. Available at: https://www.bitkom.org/Presse/Presseinformation/Digitalisierung-Investitionen
  19. e-Estonia (2025): e-Estonia Factsheet. Available at: https://e-estonia.com/wp-content/uploads/factsheet.pdf
  20. Singapore Government (2025): National AI Strategy 2.0. Smart Nation and Digital Government Office. Available at: https://www.smartnation.gov.sg/initiatives/artificial-intelligence/

About the Author: Dirk Röthig is CEO of VERDANTIS Impact Capital, headquartered in Zug, Switzerland. As an entrepreneur and impact investor, he analyzes the intersections of demographics, technology, and sustainable economic development. Contact and more articles: verdantiscapital.com | LinkedIn | dirkdirk2424@gmail.com

Top comments (0)