Compute für alle

Unleashing a European AI buildout will require national supply-side policy, not EU funds.

The European Commission wants more European compute—the infrastructure for training and running AI. They are therefore funding their own data centers. But the planned facilities, called “AI Gigafactories”, are 50 times smaller than those planned abroad, thinly spread across Europe, and not breaking ground. In short: the food is bad, and the portions are too small.

Compare this with the United States. The scale of its privately-funded data center buildout is staggering. Amazon’s Project Rainier has already brought online 750 MW, with plans to scale to over 1200 MW by June this year. Meta’s Prometheus is at 700 MW, scaling to 1275 MW by October. xAI’s new Colossus 2 data center will likely come online in May with 1500 MW. In sum, Prometheus, Colossus 2, and Project Rainier would consume all power generated by the UK’s Hinkley Point C nuclear power plant, planned to come online in 2030.

Together, these three US projects absorb around 70 billion dollars—three times as much as the European Union wants to put toward AI Gigafactories (which are yet to be announced). On current trajectories, the United States is projected to have around 24 GW of capacity by 2029. The UAE adds another 1.5 GW. Ignoring projects whose fate is uncertain, the share of European capacity will be a fraction of America’s.

The Commission’s goal of having more data center capacity in Europe is a good one. But data center construction is a poor fit for governments, and a particularly bad fit for the European Commission: data centers aren’t a commodity like oil, which you can purchase and store for local use. Instead, they are incredibly complex infrastructure projects and depend tightly on private investments and subsequent private demand to be financially viable. To go ahead in the first place, they often need financial backstops from trillion-dollar companies such as Google or Amazon.

The demand for more compute is only increasing. The price for renting high-end AI chips is rising quickly, because of the many individuals who want to access ever-improving American AI models. To alleviate this shortage, AI companies continue to scout for locations where they can transform hundreds of billions of dollars into compute.

This shortage could, in part, be addressed by European-American collaborations that build new data centers in Europe. Such buildouts are possible. Nscale, a British company, is building a 230MW facility in Norway for use by OpenAI, which might be expanded by an additional 290MW. A 720MW project in Northumberland, UK, funded with £13 billion by Blackstone, is currently undergoing site preparation.

Europe would benefit from such investment, even if data centers on European ground will first run American AI models. Yes, European citizens and companies could also use AI hosted in the US. But even if financed by American companies, building AI infrastructure in Europe creates the pre-conditions for new sources of European growth: Inventive Bavarians could launch robot manufacturing companies by accessing low-latency frontier AI. The French could develop new nuclear power plants that power data centers over the long haul. Domestic expertise in building large data centers could allow the construction of clusters used by researchers at ETH or the Max-Planck Institutes. Ultimately, the interests of European nations and (most) American AI companies align in important respects.

Europe could also reject such investment. But in my mind, there is no clear alternative—the domestic funding for new data centers simply isn’t there. And I worry that the Commission will not realize this anytime soon, as it is invested in its own data center projects.

Compared to Brussels, capitals like Berlin, Paris, and Oslo are more likely to take the kinds of high-upside, unilateral decisions that enable privately-run compute buildouts: turning power plants back on, prioritizing grid connections, and providing expedited permitting. Ultimately they could even lobby the Commission to help in the build-out by removing barriers to data center construction¹. Engaging with them, not Brussels, is the key to compute in Europe.

The perils of compute poverty

AI infrastructure projects resemble a game of Factorio—a highly addictive infrastructure game where one smart strategy involves generating power with hundreds of steam engines. In this vein, one Utah AI infrastructure project has ordered 700 Caterpillar G3520 engines: Weighing 19 tons, each of these piston-driven, gas-fueled generators produces 2.5 MW, yielding 1.75 GW in total. Using a small number of large-scale gas turbines would be far more efficient—but those are sold out for years.

No project of similar size is undergoing construction in Europe. Though there is talk of a 1GW project in France, one of the involved companies—Fluidstack—recently pulled out of the deal. As of now, Mistral (the leading European company) is targeting a mere 200 MW by 2027. If no other projects begin construction soon, the continent will end up with a minuscule share of global compute by 2030.

European compute poverty could be harmful. For instance, German companies might be able to build technologies complementary to AI, such as advanced robotics. Tom Davidson has previously laid out how such complements could help non-US nations experience AI-enabled growth:

“The US will have abundant cognitive labour. That cognitive labour will be strongly complementary with other economic inputs like human manual labour, physical capital, and raw materials. Those other physical inputs are very much distributed worldwide. So getting the most economic value out of AGI will involve significant trading with other countries. [...]

In practice, US-built superintelligent AI systems will instruct human workers in (e.g.) Germany on how to use existing German factories and machines to build new and improved physical technologies like robots. If there’s just one US AI company, they might sell cognitive labour at monopoly prices and extract most of the gains from trade. But if there’s multiple competing AI companies, the German companies providing the physical inputs might capture most of the gains from trade. Either way, the manufacturing will take place on German soil.”

This is one small but positive example of how economies without frontier AI companies can capture AI-driven growth.

But if German companies want to focus on robotics, they might need access to very low latency compute. Agents interacting with the physical world will need to perform continuous, seamless inference calls. If those calls need to run across the Atlantic seabed, German robotic systems’ performance might be low. Compare this to China: They are capable of rapidly building large amounts of energy and data centers, the resulting efficiency of which might compensate for having slightly worse AI systems. China might thus outcompete the West’s industrial sector in one of this century’s most important industries.

German robotics is of course just one example of how Europe could profit from AI adoption. The larger point is simply that local compute, even if initially used for American AI systems, will improve European productivity. Such gains from trade alone might motivate American companies to place more compute in Europe. But enabling rapid data center buildouts will require action by European capitals.

A German example

Working on tech policy in Europe can make one frustrated with the European Union’s many regulatory barriers. But all too often, member states are to blame too.

A prime example is the EU’s “Energy Efficiency Directive”. Because it’s a directive, the precise implementation is left up to national governments. For this directive, Germany’s previous government engaged in severe gold-plating—the practice of adding things on top of European regulation. The original directive only contained reporting requirements, but Germany’s law (Energieeffizienzgesetz) went further.

First, Germany’s efficiency law captured more facilities, moving the cutoff from 500 kW down to 300 kW. They also added binding power usage effectiveness (PUE) rules, i.e., mandating facilities to spend little energy on anything beyond their IT equipment. Finally, they added obligations on waste heat reuse e.g., via municipal heating networks. None of this the EU asked for.

Many other European nations have committed similar mistakes in the past. But with targeted policy engagement, they might be fixable. Reviewing the German government’s current work, there are plenty of valuable policy windows one could use to improve things.

Right now the new German coalition is rewriting the Energieeffizienzgesetz. They already propose loosening the data center power effectiveness requirements so that they always exempt AI data centers². They will also amend the heat reuse obligations to make them not apply to AI data centers in the first place.

Additionally, the government has recently passed its data center strategy. It is not yet ambitious enough: they plan on quadrupling AI capacity by 2030, which would amount to adding 1.5GW—as much as the aforementioned 700-piece fleet of Caterpillar engines in Utah will produce. Still, the strategy contains smart proposals: instead of taxing data centers by the number of people that work there (of which there are few), they might tie taxation to the amount of capital deployed, or revenues created. Attaching tax income to the actual economic value of data centers will make municipalities far more likely to welcome their construction.

Data center realism

The politics of enabling foreign data center investments remains very tricky. It runs counter to many political instincts, particularly in Brussels. But the German government’s approach points at some partial solutions. As you get closer and closer to the economic activity generated by data center investments—foreign or domestic—the likelier you are to get buy-in from political stakeholders. Governments are interested in hosting hundreds of well-paid AI engineers. A municipality might welcome funding with which they can improve local roads and schools. Domestic companies would be eager to partner with the world’s best AI companies. Hence, it is at the national and state level where the path to Europe-based compute is easiest.

Right now, European governments are not close to the AI ambitions of the United States. In part, this can be explained by policymakers not having heard good explanations of the strategic importance of AI. But that’s exactly why nation-based policy engagement is so valuable. Germany and other governments are already moving in the right direction, they just need to be pushed to act more ambitiously. One such policy priority will involve attracting and expediting data center investment.

Most likely, a successful AI infrastructure buildout will be a precondition for staying relevant in the 21st century. But if you want to make this buildout happen, don’t focus on the Commission. Nation states are far more likely to appreciate that only the private sector is ambitious, pragmatic, and risk-tolerant enough to take on infrastructure projects requiring tens of billions of capital. It is on us to argue why they should enable the private sector’s ambition.

1. E.g., the EU could reduce carbon pricing on gas power plants or ease environmental rules that hamper infrastructure development.

2. The current EnEfG requires new data centers to achieve a PUE of ≤1.2. The draft revision loosens this to ≤1.3. Hyperscalers like Google already operate at PUE 1.1–1.2, making it easy to pass the new requirement.