The handover problem in the European AI compute ecosystem AI company € € € Hyperscaler’s cloud Figure 1 Hyperscalers uses EuroHPC Cluster to train Model € Public support € GPUs deployed in € Alternative cloud € EU companies Handover blindness GPU Manufacturers Source: Authors, based on Arun 2025 1.1 From traditional to AI cloud To start with the basics: what is“the cloud” ? Contrary to the imagined seamlessness of the digital cloud(Hu 2016), cloud computing infrastructure forms the definite physical backbone of the contemporary digital economy. It compris­es the software and hardware required to store and process data, including the development and deployment of AI models and downstream applications. This infrastructure includes computer chips, which are accessed remotely via the internet but stored in data centres(physical, warehouse-­like facilities that house the software, hardware and net­working equipment required for data storage and process­ing)(OECD 2025a). Computational resources are hosted in data centres and either accessed directly by companies (e. g. Google directly accesses computations via a data ­centre) or as a shared on-demand service that individual customers access via a cloud platform(e. g. a developer accesses computation via the Google Cloud Platform)(Leh­donvirta, Wu& Hawkins 2024). Cloud can be further broken down into access to infrastructure to run arbitrary software (Infrastructure-as-a-Service), access to an environment to develop applications via the cloud(Platform-as-a-Service) or ready-to-use software accessible via the cloud(Software-­as-a-Service)(OECD 2025b). When discussing digital sovereignty, it is vital to distin­guish between the conventional cloud , referring to the broad market supporting general data processing(health­care, finance, etc.), and the specialised AI cloud used for running AI workloads. Both conventional cloud companies and AI cloud companies own the computational resources (central processing units, or CPUs) required to store and process data, held in specialist infrastructure. Cloud com­panies in Europe, of which 70 % are US hyperscalers and 15 % are European companies such as OVHcloud, Hetzner and IONOS Cloud(Verbovikov 2026), support many indus ­tries, such as healthcare, financing and manufacturing, by enabling data storage and processing. However, the AI cloud requires data centres with higher-­capacity AI chips that run specific computational opera­tions for AI. These chips for AI workloads are extremely expensive and require a higher rack density(amount of energy consumed by a single server rack) compared with traditional CPU and storage servers required for general purpose computing: traditional data centres require rack densities of 12–15 kW per rack compared with 100– 150 kW per rack for AI data centres, with newest models expected to need up to 600 or 900 kW(Patel, Nishball & Eliahou Ontiveros 2024; Walton 2025). AI chips also require specialised supporting infrastructures with cool­ing technology (e. g. direct-to-chip or immersion cooling) to keep racks cool, compared with traditional air cooling methods (Computer Room Air Conditioning, or CRAC) used in ­traditional data centres. We have summarised the key differences between the conventional and AI cloud in Table 1. 6 Friedrich-Ebert-Stiftung e.V.