AI Is Changing Who Gets Paid: What the Decline of the Labour Share Means for Europe
- Prof Emanuele Bracco
- 3 hours ago
- 5 min read
AI is shifting income from labour to capital across European regions. Evidence shows that AI-intensive areas experience declines in the labour share, with medium- and high-skilled workers facing wage compression. The findings highlight rising inequality risks and the need for policy responses to ensure more inclusive gains from AI.
Capital and labour constitute the two pillars on which the economy is built and that reap the benefits of the income produced. Historically a good rule of thumb has been that two thirds of national income is acquired by workers through wages and the remaining third goes to reward the capital through profits, dividends and royalties from intellectual property. In more recent times, economists have tracked a quiet but consequential shift in advanced economies: a growing share of national income is flowing to capital, and a shrinking share to labour. This trend, known as the decline in the labour share, predates artificial intelligence, but new evidence suggests that AI may be accelerating it — and doing so in ways that challenge our assumptions about who wins and who loses from technological change.
Since the 1980s, that stability of the labour share of income has eroded. Across advanced economies, labour’s share has declined, while capital’s share has risen. Globalisation, weaker union power, the falling price of investment goods, and capital-intensive technologies have all been accused of this shift. The latest of the alleged culprits is artificial intelligence, which appears to be reinforcing this shift.
A recent article by Antonio Minniti and coauthors explores exactly this phenomenon studying 238 European regions between 2000 and 2017 and finds a robust negative relationship between AI-related innovation and the labour share of income.
Regions that specialise more heavily in AI patenting tend to experience sharper declines in the share of income going to workers. A doubling of AI patent intensity is associated with a reduction in the labour share of between 0.5% and 1.6%. That may sound modest, but aggregated across regions and over time, it is economically meaningful.
As one can see Figure 1, this pattern is quite striking. The left panel shows regional AI intensity across Europe, measured by technological specialisation in AI patents. The right panel shows cumulative changes in the labour share between 2000 and 2017. The darker the region in AI intensity, the more likely it is to have seen a decline in labour’s share. The geographic overlap is hard to ignore.
Why would AI shift income away from labour? AI can be defined as a capital-biased innovation, i.e. an innovation that disproportionately reward capital as opposed to labour. More precisely, technological change can be labour-augmenting (raising workers’ productivity and therefore wages), capital-augmenting (raising the productivity of capital, therefore profits), or labour-replacing (substituting machines for workers). AI appears to combine capital-augmenting and labour-replacing features.
Unlike earlier waves of automation that focused on routine manual tasks, AI increasingly performs cognitive functions: prediction, classification, optimisation, and even elements of decision-making. These were once considered reserved to skilled professionals. As AI systems increase their place in companies, firms can generate more output with fewer human inputs — or at least without raising wages proportionately. If productivity gains accrue disproportionately to owners of algorithms, data, and intellectual property, the capital share rises.
The European evidence provided by Minniti et al. (2025) supports this mechanism. The authors construct a region-level dataset, combining OECD patent data with Eurostat regional accounts. Using detailed regional data on patents, wages, employment, capital stocks, and productivity, the authors estimate dynamic panel regressions that link AI patent stocks per worker to changes in the labour share. Importantly, they control for other forms of innovation (including ICT and Fourth Industrial Revolution technologies), fixed capital accumulation, R&D spending, productivity growth, industrial structure, and also demographic factors and institutional quality. The negative effect of AI persists across specifications even after controlling for all these confounders.
AI innovation is measured through specifically identified AI-related patent classes (e.g., machine learning, neural networks, natural language processing). Capital stocks are built using perpetual inventory methods. The econometric model is quite sophisticated, as it is able to disentangle short-run fluctuations from long-run relationships and account for spatial (geographic) dependence, i.e. the possibility that neighbouring regions influence one another and test the robustness of results to alternative depreciation rates, alternative productivity measures, and different transformations of the patent data. Across these exercises, the core finding remains: AI innovation is associated with a decline in labour’s share.
But the story becomes even more intriguing when the labour share is decomposed by skill level. Conventional wisdom about technological change has emphasised “skill-biased” innovation: new technologies complement high-skilled workers while displacing low-skilled, routine labour. That pattern helps explain rising wage inequality in the late 20th century. AI appears different, as it threatens medium and high-skill workers. The study finds that the income shares of medium- and high-skilled workers decline more strongly in AI-intensive regions. Crucially, this effect is driven largely by wage compression rather than employment loss. Employment shares of higher-skilled workers do not collapse; instead, their relative wages stagnate or decline. For low-skilled workers, employment expands slightly, partially offsetting wage declines. This suggests a form of skill compression rather than traditional polarisation. AI does not simply hollow out the bottom of the labour market. It also encroaches on cognitive, white-collar domains. Tasks once shielded by education credentials are increasingly automated or assisted by algorithms.
If this pattern continues, the long-term implications could be profound. The decline of the labour share does not merely redistribute income between workers of different skill levels; it shifts income from labour as a whole toward capital owners. And capital ownership is typically far more concentrated than labour income. That amplifies wealth inequality.
Moreover, AI development is geographically concentrated, with AI specialisation clustered in specific European regions. If those regions capture innovation rents while others lag, regional inequality may widen, compounding with increased inequality between labourers and capital-owners.
Investments in human capital such as AI-complementary skills can shape how workers adapt. Fiscal policy can respond to shifting income bases. If capital captures a larger share of income, tax systems may need to rebalance. Equally important is diffusion. If AI productivity gains remain confined to a handful of “superstar” firms and innovation hubs, inequality will intensify. Broader adoption across sectors and regions could spread the benefits more evenly.
Technological revolutions have always reshaped the distribution of income. Industrialism exploded with the diffusion of the steam engine. Electrification reorganised production. The digital revolution rewarded intellectual property and increased high-skill workers’ productivity. AI may opening a door to a world with algorithms, data, and platform capital capturing an expanding share of value.
References:
Minniti, A., Prettner, K. and Venturini, F. (2025) ‘AI innovation and the labor share in European regions’, European Economic Review, 177, 105043. doi:10.1016/j.euroecorev.2025.105043.
