How Nvidia may capture the alternative AI compute market through aggregator strategy AI company € € € Hyperscaler’s cloud Hyperscalers Figure 9 uses to train EUROHPC Cluster Model € Public support € GPUs deployed in € DGX Lepton Handover blindness € GPU manufacturers EU companies € Alternative Cloud Alternative clouds Source: Authors, based on Arun 2025 Telekom 2025) and Siemens(Nvidia 2026), to generate demand; backstopping new European AI start-ups 22 ; and partnering and investing with asset managers to drive the adoption of Nvidia-specific architectures for new data centre buildouts(Butler 2026; Brookfield 2025b). It is only a slight exaggeration to note that the pursuit of EU AI sovereignty is largely shifting the nature of existing depend­encies from established American cloud service providers to the chip giant. This concentration and entrenched market position is a response to the tight competition in chip markets. In late 2025, the capacity gap of the Nvidia chips seemed to be nar ­rowing, with world-leading LLMs being trained with alterna­tive chips, such as Google’s own TPUs(Bradshaw 2025). Other hyperscalers, such as Amazon, are also advancing in the development of their in-house chips, providing them for use by some leading AI labs(Clark 2025). AMD, a lead ­ing alternative chip designer, is also catching up with its newest chips(Chen et al. 2025). By integrating itself into the infrastructural, computational fabric of European sover­eign AI, facilitating partner ecosystems(Siemens 2026), diversifying markets and de facto buying prospective com­petitors(Groq 2025; McKenna 2025), Nvidia is reinvesting profits into strategies that secure a sustainable market position for itself and technical ramp that locks in revenue if and when the current large-scale, LLM-based AI cycle will slow down. This protects the company from existing com­petitors and limits the chances of alternative chip design­ers breaking into the market. 23 Dependence on Nvidia comes with strategic risks for alternative AI cloud providers, specifically neoclouds. First, the interests of Nvidia’s product roadmap and neo­clouds’ business models are not aligned. Nvidia seeks to launch a new product every 12–18 months to sustain reve ­nue growth. For neoclouds, this creates a conundrum, as the value of their existing chips will depreciate with accel­erated schedules that come with new product launches. This has led to tensions in the market, with neoclouds struggling to guarantee enough demand for their chips to recoup their investment(Butler 2025). 24 Second, Nvidia is increasingly capturing the market of ­alternative providers. Providing‘bare compute’ leads to a ‘commodity trap’, which is highly contingent on the supply 22  One notable is example is Domyn in Italy, building a‘sovereign AI cluster’. See https://www.domyn.com/compute and https://www.youtube.com/watch?v=bnVwzjtkc04&­feature=youtu.be. 23  See, for instance, Axelera AI, which has received substantial direct equity funding in a€63m Series B from the European Innovation Council and the Dutch National Develop­ment Bank Invest-NL;see https://eic.ec.europa.eu/news/first-companies-put-forward-major-investments-under-eic-step-scale-scheme-2025-04-03_en and https://www.invest-nl.nl/ nl/impact/ons-portfolio/axelera-ai(last accessed on 23 April 2026). 24  The new leasing arrangements could be a Nvidia response to manage the outstanding stock of old GPUs, see Efrati 2025. Challenges for the EU AI Cloud 21