The plan for the large public access data centres, unveiled by European Commission President Ursula von der Leyen last month, will face challenges ranging from obtaining chips to finding suitable sites and electricity.

"Even if we would build such a big computing factory in Europe, and even if we would train a model on that infrastructure, once it's ready, what do we do with it?," said Bertin Martens, of economic think tank Bruegel.

It's a chicken and egg problem. The hope is that new local firms such as France's Nvidia-backed Mistral start-up will grow and use them to create AI models that operate in line with EU AI safety and data protection rules, which are stricter than those in the U.S. or China.

But in the absence of large European cloud services businesses like Google and Amazon, or firms with millions of paying customers, like ChatGPT maker OpenAI, building hardware on this scale is a risky venture.

EUROPE'S ANSWER TO STARGATE

The gigafactory plan is part of Europe's response to the Draghi report on competitiveness, which advised bold investments and a more active industrial policy. Von der Leyen released details for the first time at the February 11 AI summit in Paris as part of InvestAI, Europe's 200 billion euro ($216.92 billion) answer to the $500 billion U.S. Stargate plan.

She described gigafactories as a "public-private partnership ... (that) will enable all our scientists and companies – not just the biggest - to develop the most advanced very large models needed to make Europe an AI continent."

They are to be financed via a new 20 billion-euro fund, with money being drawn from existing EU programmes, and from member states. The European Investment Bank will participate.

Von der Leyen said gigafactories will contain 100,000 "cutting-edge" chips each -- making them more than four times larger than the biggest supercomputer currently under construction in the EU, the Jupiter project in Germany. U.S. chipmaker Nvidia sells the cutting-edge GPU chips needed to train AI for around $40,000 each -- implying a price tag of several billion euros per gigafactory.

While that's big, it still trails projects announced by U.S. firms. Facebook owner Meta is spending $10 billion to build a 1.3 million GPU facility in Louisiana powered by 1.5 gigawatts of electricity.

Kevin Restivo, of data centre consultancy CBRE, said that gigafactories would face the same problems facing private projects in Europe: difficulty obtaining Nvidia chips and a lack of electricity on the scale required.

HURDLES TO ACCESSING CHIPS?

The U.S. government, under former President Joe Biden, capped access to AI chips to prevent gigafactories from being built in many European countries, though it is not clear if the Trump administration will uphold that.

"There's nothing to say that the government can't get its hands on those chips or that ... great projects can't come from it, but it's unlikely to happen in the short term," Restivo said.

Martens of Bruegel said it does not make sense to spend public money entering an AI spending race. "The lifetime of such factories, before you have to write it off and buy new Nvidia chips, is about ... a year and a half," he said.

Meanwhile, the breakthrough of Chinese AI model Deepseek raised questions about whether AI models can be trained with less computing power, and whether spending should instead be focused on applications, which require different kinds of chips.

Europe's previous major support plan for technology infrastructure, the 2023 Chips Act, failed to meet goals of bringing cutting-edge chip manufacturing to Europe or reaching 20% of global production, though it did lead to investment in new factories needed to make automotive chips.

Alongside the gigafactory plan, the Commission is also upgrading 12 scientific supercomputer centres to turn them into AI factories.

Kimmo Koski, managing director of Finland's LUMI supercomputer, said it is not yet clear how AI gigafactories will differ other than in size.

"In my understanding, it relates to pushing industry use further," he said. That would be "an innovation in Europe, a very welcome event of course."

He said supercomputers are already used for machine learning projects, alongside scientific uses such as in climate modelling. He pointed to Silo AI, a Finnish firm that used LUMI to help develop large language AI models before being snapped up in July last year by U.S. chipmaker AMD for $665 million.

Potential beneficiaries of the supercomputing expansion include European chipmakers that make non-GPU chips, still useful in data centres, including Germany's Infineon and ST Microelectronics of France, as well as startups including France's SiPearl and AxeleraAI of the Netherlands.

($1 = 0.9220 euros)