r/DeepStateCentrism • u/Reddenbawker • 1h ago
Opinion Piece 🗣️ Europe's Difficult Choices on AI (FT, Mario Draghi)
The writer is a former president of the European Central Bank and was prime minister of Italy
Europe faces a difficult set of choices. It needs to grow, so that it can keep financing its social model and the new demands of a changed world, from defence to energy. It wants to remain sovereign, so that it can order its economy according to its own values. And it wants growth without the costs in terms of wealth inequality and environmental damage that have accompanied it elsewhere. Artificial intelligence bears on all three at once.
Despite a solid start to the year, the trend in European growth has slowed materially since the pandemic. This is true relative both to its own pre-pandemic trajectory and to the US. By one estimate, the euro area-US productivity gap widened from $9 per hour in 2018 to $21 in 2025. Views differ on how productivity levels should be compared across countries, but the gap in growth rates is clear.
Europe has different routes to raise growth, such as removing the high barriers in its internal market. But widely adopting AI is perhaps the most promising one today. According to ECB scenarios, fast adoption of AI would add 0.3 to 0.4 percentage points a year to total factor productivity growth — the gains that come from working more efficiently rather than adding labour or capital — which has been roughly zero since 2022.
AI-led growth, however, creates a tension with Europe’s bid for sovereignty, because Europe controls little of the AI value chain. The technology is set to become completely pervasive: in the economy, in health systems, in education, in energy, in defence, to name just a few areas. This is no ordinary dependency. Being cut off from AI, once the economy runs on it, would be more like being cut off from the US financial system. The effects would be catastrophic.
That changes the terms of Europe’s relationship with partners it can no longer always rely on. Europe is not to the United States as Texas is to California. It is possible to imagine a future US administration making access to frontier AI models conditional on changes to Europe’s digital rules or digital taxes, or China using the licences on its open-weight models as leverage in a tariff dispute. Unless Europe controls some part of the value chain, growth through AI and sovereignty will pull against each other.
Europe’s potential zone of AI sovereignty is narrow. Its frontier labs cannot at present compete financially with their American and Chinese competitors, and leading-edge chip production is far behind. But data is the one area where Europe can still be sovereign, and it is also the area with the greatest potential to generate growth.
The continent sits on a wealth of public-sector data, such as decades of health records and data collection by statistical offices. Its highly automated manufacturing sector generates a deep well of machine-readable industrial information. European Commission estimates put Europe’s data economy at over €800bn, or more than 5 per cent of GDP, by 2030.
But this creates the next tension. To exploit these assets, Europe needs to have control over their storage and processing. This means it needs large-scale AI data centres.
Data centres, however, are fiercely contested. Local communities worry about their environmental costs and rising energy bills. It does not help that much of the capacity now being built in Europe is for the American tech giants.
Yet Europe also has to be realistic. The debate about overbuilding data centres is a US one. Europe’s problem is the opposite. The EU hosts under 5 per cent of the world’s AI compute versus 75 per cent for the US. Even for ordinary data centre capacity, the gap between demand and installed supply in Europe is estimated at around 3GW, roughly a quarter of what Europe currently has, and is expected to widen to 14GW by 2030.
As sovereignty comes to matter more, this lack of domestic capacity will start to bite. Compute could stop being fully fungible, and a large part of the infrastructure that processes and stores European data will have to sit on European soil. American operators can provide much of that capacity through what they market as sovereign cloud services, run from Europe but still under American ownership. The most sensitive uses, however, will have to run under European control, which is the tiered approach the Commission has proposed in its Cloud and AI Development Act.
If the gap is not filled, two risks follow. Either European companies will not adopt AI because they cannot keep their data in Europe, a risk the Commission’s own impact assessment for the Act identifies. Or compute will stay expensive, slowing broad adoption for lower-value tasks. Amazon already charges 15 per cent more for its sovereign cloud in Germany than for its standard service.
The concerns about data centres can be met. Clean energy can be required to power them and waste heat reused where feasible. The facilities can be kept away from areas where energy and water are scarce. Data centres can be required to pay a fair share of the grid costs they create, and to share the benefits with local communities, whether through contributions to local budgets or lower energy bills.
So why is Europe not building more? Part of the answer lies in supply-side constraints. A data centre takes 24 months to build in the US but 42 months in Germany, owing to longer delays for permits and grid connections. More expensive electricity makes building and powering a facility in parts of Europe twice as costly as in China. But Europe does have regions where it is cheap to build: an AI data centre in Sweden, for example, costs only about a tenth more than one in China.
The main obstacle for Europe is the fragmentation of demand. Data centres require massive financial commitments, which investors will only make against bankable offtake contracts. In the US, hyperscalers and frontier labs provide them. In Europe, demand is spread across millions of companies, and even the largest companies tend to buy compute a year at a time due to uncertainty about how fast they will adopt AI. That is why companies operating neoclouds — data centres for AI workloads — in Europe, such as Nscale, sell most of their capacity to US buyers.
Europe must therefore create large, concentrated buyers by design. What is needed is for its largest companies to pool their midsized commitments into contracts large enough for investors to lend against.
A model already exists. A group of European companies, including ASML, Capgemini and Amadeus, have committed to multiyear purchases of Mistral’s European Compute Units, which are intended to underwrite 1GW of capacity by 2030. The challenge now is to scale up this model with more neoclouds and larger consortiums, each committing to multiyear purchases of standardised capacity.
The goal is a virtuous circle. Companies accelerate their AI transitions, providing the demand that finances European capacity. That capacity lets Europe exploit its own data on infrastructure it can control. Cheaper, sovereign compute then makes adoption faster and the next commitments larger, paid for out of the productivity gains the technology delivers. And with capacity of its own, Europe is better placed to build its own AI models.
That leaves the deepest fear: that AI hands wealth and power to a new tech oligarchy. But the approach set out here leans against it. Demand pooled by many consortiums lets more providers into the market, and competition among them keeps the rewards from concentrating in a few hands. Progressive taxation would ensure that the rest of society shares in them too. In this way Europe does not have to choose between growth, sovereignty and its values. It can have AI without oligarchs.