4. Policy implications : questions for EU AI policy The findings 32 put forward in this report highlight the challenges in implementing the planned European cloud buildout(European Commission 2026) that will have to be considered and addressed. The market incentives available to actors in the AI cloud ecosystem are shaped around the hyperscalers, making the latter the likely beneficiaries of horizontal policy interventions. The findings also raise sev­eral guiding questions in preparation for policy debates: what kind of cloud are we talking about? Where will it be hosted? What are the infrastructural requirements? Who will be the end users? And what use cases will there be? The findings also reveal some tricky political tradeoffs. Nvidia has been identified as the inherent facilitator of EU AI sovereignty. It bears noting that Nvidia’s own strategic business interests are not always aligned with the Europe­an public interest. One especially crucial point relates to Nvidia’s product roadmap. While updating hardware every 12-18 months and racing to build ever larger computing clusters is good for the company’s shareholders and its strategic objectives, this does not obviously benefit the European tax payer. The public sector ends up bankrolling Nvidia’s product roadmap by procuring chips for the EU´s Gigafactory buildout. Industrial policy is often justified on the grounds that it solves demand bottlenecks and provides predictability to domestic providers. But in this case, it also implicitly com­mits the EU to a specific paradigm of AI buildout that locks in particular technological choices. 33 The increase in supply also glosses over the most crucial problem: the lack of aggregate compute demand outside hyperscalers and few leading AI labs(Hess& Sieker 2025). If the public sector takes as its task to provide that anchor demand, this might lead to the forced adoption of AI technologies to provide support for the local companies instead of crafting a sover­eign technology policy in the public interest. In the case of Gigafactories, the obsolescence risk is also shifted to the European taxpayer. Asset manager Brook­field predicts that the H100 and H200 chips will be phased out by 2028. The EU Gigafactories that have been proposed are now aiming to rely on 100,000s of H100 chips, with the first operational centres built in 2027-2028(Brookfield 2025a, p.11). This points to a fundamental mistiming and potential strategic miscalculation at the heart of the Euro­pean data centre buildout. Policy considerations Policy should prioritise energy and grid investments before the acquisition of hardware . As the most durable element of AI infrastructure, power generation and trans­mission provide a‘no-regret’ foundation; even if AI demand fluctuates, these assets still benefit the wider European economy. Integrating energy considerations into the initial policy framework also ensures that the distributional impacts remain subject to public debate rather than being obscured. Energy is a necessary condition for data centre development, not a secondary concern. Addressing this early avoids a scenario where the implications of data ­centre buildout are presented as fait accompli after the fact, with costs falling disproportionally to the taxpayers. Moreover, before expanding the new data centre capacity with long-ranging implications, it would be useful to map out to what extent the current capacity is used efficiently, merely blocked or reserved for strategic purposes. 34 The public interest is not synonymous with an‘Nvidia arms race’. There is a danger of goal displacement, where the interests of private intermediaries in making profit and capturing emerging market opportunities are misaligned with the public interest in deploying technology sensibly and productively, with respect for societal boundaries. The success of efficiency-first models(such as DeepSeek) sug­gests that compute-constrained development can be more cost-effective and sustainable(Varoquaux, Luccioni& Whittaker 2024). Policymakers should be sceptical of industry claims that every new chip generation renders ­previous infrastructure obsolete and requires new invest­ments. They should also be wary of becoming systemati­cally entangled with corporate interests and providing a sovereign buffer to facilitate production decisions by pri­vate companies. A strategic choice would be procuring chips once the depreciation curve sets in. There will be 32  While we maintain concerns about AI concentration dynamics, this analysis focuses specifically on infrastructure policy implementation challenges. 33  This has been discussed also in previous work. See Warso 2026, as well as Renda& Kyosovska 2025. 34  Some recent research suggests that the data centre capacity in some member states might be operating“nowhere near the full capacity”, with estimates that for the Nether­lands, up to 2/3 of the available maximum capacity is reserved, but unused. See Schulze& van Veen 2025a. 26 Friedrich-Ebert-Stiftung e.V.