This year marks a clear shift in the European Commission’s approach to artificial intelligence - from a primary focus on regulation towards competitiveness and industrial policy. In April, the Commission published its AI Continent Action Plan, outlining how the European Union intends to close the gap in the global AI race.
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Built For Purpose? Demand-Led Scenarios for Europe's AI Gigafactories
The European Commission is shifting its AI approach from regulation to investment – most notably through five proposed AI Gigafactories. In this policy brief, we argue that the initiative should place more weight on demand, and we suggest two operational models for the AIGFs.
Dr.
Felix
Sieker
Project Manager
Content
Compute at the Core of the Plan
At the heart of the plan is a major expansion of compute infrastructure - the key ingredient for training and deploying AI models. The Commission proposes five AI Gigafactories (AIGFs), each hosting at least 100,000 advanced AI chips. The initiative would be partially funded by the Commission: a dedicated €20 billion fund under InvestAI - part of a broader €200 billion programme to scale AI development and adoption - would cover about one-third of each site’s capital expenditure. The explicit goal announced by the Commission is for these AIGFs to train and deploy frontier AI models.
Two Operating Models for the AIGFs
In partnership with Julia Christina Hess and the think tank interface, we have analysed the AIGF initiative in a policy brief and situated it within the broader context of AI compute build-outs. We find that the initiative places too much weight on Europe’s past compute limitations – insufficient supply - and overlooks a critical factor: demand.
Mapping existing data centre build-outs and the landscape of compute providers and users, we suggest two plausible operating models for the AIGFs:
- Anchor-customer model: Secure one or a few anchor customers with very high AI compute demand, as seen in the United States and China.
- Multi-client model: Serve a broader set of clients with low to moderate AI workloads.
Why a Multi-Client Model is Better Suited for Europe
We argue that the European Commission’s ambition to train and deploy frontier models aligns more closely with the anchor-customer model. However, leading AI labs are the only user group capable of generating high AI workloads and supporting the goal of training and deploying frontier AI models - and Europe currently hosts only one leading lab, Mistral. That makes the conventional anchor-customer model – in which a single high-demand lab guarantees utilisation – less feasible.
A multi-client AIGF model is likely better suited to Europe’s industrial structure. However, to compete with private providers such as neoclouds, AIGFs would need to offer more than raw compute – acting as a one-stop shop that bundles the ingredients required to train and deploy diverse AI applications: structured onboarding, maturity diagnostics, curated software stacks, and ongoing expert support. This approach could catalyse a dynamic ecosystem for SMEs, startups, and established companies, but it would require a recalibration of AIGFs’ current objectives.
Policy Recommendations for the AI Gigafactories Call for Proposals
With the official call for proposals expected by the end of this year, policymakers should prioritise three criteria when assessing AIGF bids:
Demand quantification: Require projected AI workloads and firm user commitments.
Realistic objectives: Align build-outs with EU AI industry dynamics, not just frontier ambitions.
Clear value proposition: Differentiate AIGFs from hyperscalers and neoclouds by offering integrated services - platform, software and support – in addition to compute.
