1. Introduction In response to rising geopolitical tensions, increasing Euro­pean sovereignty has moved to the forefront of the policy agenda. Under European Commission President Ursula von der Leyen, digital sovereignty became the centrepiece of the Commission’s digital agenda. As part of this push, efforts focused on cloud infrastructure, as the‘computational underbelly’ of the modern economy, were driven by con­cerns over remaining competitive and offering enhanced data protection. However, the heightened geopolitical risks associated with the second Trump administration have shift­ed the European policy window: from curtailing foreign actors through regulation towards building domestic capacity and alternative infrastructures. The emergence of the large-scale artificial intelligence(AI) paradigm following the release of ChatGPT in 2022 spurred corporate and political excitement. Combined with intensi­fying US-China technological competition, governments around the globe have reframed digital sovereignty concerns through the lens of AI technologies and their underlying infrastructure(Hawkins, Lehdonvirta& Wu 2025). Conse ­quently, the cloud infrastructure underpinning the AI revo­lution, – known as the AI cloud and defined by the OECD as“the specialised hardware and software stacks required to train and run AI models” – has emerged as a key spring­board for digital sovereignty discourses and policy initia­tives in recent years. This has led to new forms of AI industrial policy(Kak& Myers West 2024), including the procurement of chips required for AI development and deployment, 1 which are housed in data centres: physical warehouses that host the infrastructure required to operate and power AI chips (OECD 2025a). In the past few years, the European Com ­mission has launched several initiatives to build these capacities to protect European AI sovereignty. These include: support for AI Factories that upgrade the existing European High-Performance Computing(EuroHPC) capaci­ty; the building of several Gigafactories already announced, the EU AI Continent Action Plan and the upcoming EU Cloud and AI Development Act, with an objective being to “triple the data centre capacity in Europe”(European Com­mission 2026). This points to the evolving landscape of AI infrastructure in the European Union. Historically, European AI compute policy has focused almost exclusively on the training phase. While the EuroHPC and AI Factory initiatives primarily address the computational bottlenecks of large-scale training runs used to create the models, they neglect the ecosystem where these models are actually deployed( inference ), leading to what we term handover blindness in European AI compute policy. This is a notable omission. First, much of the value generation and extraction of AI models happens in the inference phase. Second, industry trends point almost exclusively to the increasing importance of inference services as the driver of future AI compute demand. Currently, this inference capac­ity is dominated by American hyperscalers. For European policymakers, this creates a potential paradox: a large AI model trained on a European public supercomputer will likely remain reliant on a US-controlled cloud for its daily operations, possibly negating the sovereignty gains of the training phase. Consequently, there is a substantial policy interest in find­ing alternatives to US hyperscalers for inference. We have identified three groups that are positioning themselves to capture the revenue resulting from the push towards sover­eign architectures due to shifting geopolitical tensions. One notable emerging group has been neoclouds: special­ised AI compute providers emerging in response to rising computational demands. There are also pivoting conven ­tional cloud operators who are expanding their portfolios to include AI compute offerings. Lastly, European telecom ­munications operators are looking to provide computa­tional capacity. In addition to hyperscalers adjusting their operations to include novel sovereignty guarantees, these form the set of potential providers of AI inference compute in the European Union. In this report, we focus primarily on neoclouds . While neo­clouds could be expected to serve as potential bastions of European digital sovereignty, their political economy remains poorly understood. There is a lack of data on their market strategies, supply-chain dependencies and actual capacity to compete. This policy brief addresses these gaps by ana­lysing the emerging interaction of political and economic forces in the EU’s AI cloud market and its implications for long-term EU technological autonomy. 1  By“AI chips” we refer to specialised parallel processing chips that are used to train and run large deep learning models. Examples include graphic processing units(GPUs, e. g. Nvidia and AMD), tensor processing units(TPUs, e. g. Google Ironwood), language processing units(LPUs, e. g. Groq) and wafer-scale engines(WSE, e. g. Cerebras). Currently, the vast majority of AI chips are GPU-variants, with recent signals on the emergence of TPUs as an alternative framework after notable models(Gemini 3) were successfully trained on those, and they are served to external parties as well. Introduction 5