Funding the AI revolution: evidence from euro area sectors

• THE ECB BLOG

6 October 2026

By Ana Sofia Cianfarani, Paola Di Casola, José Ramos and Dennis Zander

AI has the potential to reshape the euro area economy. This ECB Blog post explores how AI investments are being financed, focusing on the current financing mix of the most AI-intensive sectors and the consequences for monetary policy.

Euro area firms are taking part in the artificial intelligence (AI) revolution. And, as discussed in a previous ECB Blog post, those operating in the most AI-intensive sectors often rely more on internal funds. Firm balance sheet data reveal that AI-intensive firms have recently started to turn more towards market-based financing as a source of debt financing, rather than relying on traditional bank lending. A deeper and more integrated European capital market could support this development and help euro area firms to harness the full benefits of the AI revolution.

Adopting AI versus developing AI

Rapid advances in AI are prompting firms to invest more heavily in digital technologies in the euro area, and even more so in the United States.[1] Between 2023 and 2025 the share of euro area firms using at least one form of AI technology more than doubled. Technologies for generating and analysing written and spoken language and, more recently, images and videos have been driving this development. Firms engage with AI in different ways: some mainly adopt AI by using existing tools to enhance their operations, while others develop AI by building new models, applications or technologies, and protecting their innovations through patents. This distinction matters because adoption and development involve different types of investments, risks and financing needs. Adopting AI often requires investments in software, data systems and staff skills. By contrast, developing AI typically demands larger and riskier investments in research, computing capacity and specialised talent.

For our analysis, we classified euro area economic sectors according to their levels of AI adoption and development. We did this using survey data on firms’ use of AI technologies to measure AI adoption and AI patent data to measure AI development. The data shows that both adoption and development are unevenly distributed across sectors. Some sectors, such as media, telecommunications and IT services, are active both as adopters and developers. Other sectors, such as legal and accounting, mainly adopt AI developed elsewhere (Chart 1).

Chart 1

Euro area economic sectors: AI adopters and AI developers

Funding AI activities

In the euro area, AI-intensive firms tend to be those listed on the stock market or backed by private equity financing like venture capital.[2] So they may rely less on debt to fund their AI investments. Moreover, AI-related investments often differ from traditional capital spending as they focus more on intangible assets such as software, data, algorithms and organisational know-how. These assets can be harder to pledge as loan collateral than physical assets, such as machinery or buildings. As a result, firms may end up borrowing more money from financial markets rather than banks.

Firm balance sheet data at the sectoral level point in this direction. Firms in the most AI-intensive sectors, particularly AI developers, tend to be less leveraged than other sectors (Chart 2, panel a). This means that they rely less on borrowed funds and more on equity.[3] AI-intensive firms have recently turned more to market-based sources, such as debt securities, when raising debt funding. This shift is particularly pronounced among high AI adopters (Chart 2, panel b). From a monetary policy perspective, this development matters in two ways. First, debt securities usually have longer maturities, so these recent financing choices are less sensitive to interest rate hikes and more sensitive to changes affecting the long end of the yield curve. Second, since AI-intensive firms rely less on debt overall, their investment may be less sensitive to the cash-flow channel of monetary policy than that of other firms when interest rates rise.

Chart 2

Firm financing by AI activity

What is missing in Europe?

The euro area has seen substantial growth in AI patent activity over the last decade, though it still trails the levels seen in the United States (Chart 3, panel a). Euro area firms and researchers are active in many of the same technological fields as their US peers, including machine learning, data processing, communications and business process automation (Chart 3, panel b). When the economy experiences positive AI-related news, credit to firms and investment increase across both regions. However, the positive response of investment to AI-related news appears stronger in the United States than in the euro area (Chart 4).

But why is this the case? One possible explanation is that US firms can turn AI opportunities into funding and growth more easily. This is supported by their easier access to venture capital, deeper equity markets and larger unified market overall.[4] These same factors may also help explain why some European firms choose to relocate abroad – often to the United States – where access to capital and scale-up conditions are more favourable.[5] At the same time, investment fund flows reveal that euro area investors have been putting more and more money into US technology stocks, while investing less in European tech firms.

Chart 3

AI patent trends and field breakdown

Overall, the emergence of AI highlights the importance of completing the capital markets union. A deeper and more integrated European capital market could help AI-intensive firms find the funding they need at each stage of growth. Better capital markets could also steer European savings into European innovation. Together, such developments could support the euro area in realising its full potential and reaping the benefits of the AI revolution.

Chart 4

Peak effect of AI news shock on credit and investment

The views expressed in each blog entry are those of the author(s) and do not necessarily represent the views of the European Central Bank and the Eurosystem.

For topics relating to banking supervision, why not have a look at The Supervision Blog?

1. See Lane, P.R. (2026), “AI and the euro area economy” keynote speech at ECB-SAFE-RCEA International Conference on the Climate-Macro-Finance Interface, Frankfurt am Main, 23 March and Ferrando, A., Lamboglia, S., Rariga, J. and Schmidt, M. (2026), “Adopting and investing in AI: evidence from euro area firms in the SAFE”, Economic Bulletin, Issue 2, ECB.

2. See Ferrando, A. et al., op. cit. and Cianfarani, A.S., Ianiro, A., Nicoletti, G. and Zander, D. (2026), “Private markets and the euro area financial landscape”, The ECB Blog, ECB, forthcoming.

3. For an assessment of the recent credit recovery in the euro area and the possible factors behind it, see Di Casola, P., Mendicino, C., Nicoletti G. and Skoblar, A. (2026), “Mind the gap: credit dynamics in the euro area”, The ECB Blog, ECB, 26 February.

4. The crucial role of financing for the effect of technology news shocks on the US economy is also reported in Di Casola, P. and Sichlimiris, S. (2018), “Towards Technology-News-Driven Business Cycles”, Working Paper Series n.360, Sveriges Riksbank.

5. See Draghi, M. (2024), The future of European competitiveness – A competitiveness strategy for Europe, European Commission, September and European Investment Bank (2026), Drivers of relocation by innovative EU startups and scaleups, January.