Summary Under the proposed Cloud and AI Development Act (CADA), Article 2(4) defines "frontier AI" as "AI models or AI systems built upon such models that can perform a wide variety of tasks and that approach, reach or exceed the current state of the art." This is an original CADA definition, and it deliberately sets no fixed compute threshold — it is framed around capability and state-of-the-art performance, which makes it a moving target. The term matters because it gates the frontier AI priority projects in Articles 8 and 9, which can unlock pooled computing resources. As CADA is a proposal, the definition could change before adoption.
Detail
If you are planning infrastructure or an AI roadmap against CADA, the definition of "frontier AI" is foundational: it is the trigger for the proposal's most significant supply-side support.
The definition: Article 2(4)
As proposed, Article 2(4) provides:
"'frontier AI' means AI models or AI systems built upon such models that can perform a wide variety of tasks and that approach, reach or exceed the current state of the art".
The definition is notable for what it leaves out. It is framed around capability ("a wide variety of tasks") and performance relative to the field ("the current state of the art"). It does not rely on parameter counts, training-compute figures (FLOPs) or dataset sizes. That makes it inherently dynamic: as the state of the art advances, so does the bar for what qualifies.
How it differs from the AI Act
It is worth distinguishing this term from the EU AI Act (Regulation (EU) 2024/1689). The AI Act is a product-safety and fundamental-rights regime: it classifies AI systems by risk and imposes specific obligations on providers of general-purpose AI (GPAI) models, including a systemic-risk tier that is triggered, in part, by a presumption based on the amount of compute used to train a model. CADA is not a risk regime at all — it is a development and deployment framework, and its goal is to foster frontier AI within the EU, not to restrict it.
The consequence is that the two definitions serve different purposes and do not line up. A model could meet CADA's capability-based "frontier AI" definition without crossing the AI Act's GPAI systemic-risk threshold, and vice versa. CADA's term is about eligibility for support; the AI Act's terms are about safety and market access. The reference point for any AI Act threshold is the AI Act and its delegated acts, not CADA.
Why it matters: Articles 8 and 9
Article 2(4) is the gateway to the support mechanisms in Title II. Article 8, as proposed, lets the Commission recognise "frontier AI priority projects" by decision — projects selected through open calls for expression of interest that support grand challenge 3 in Annex I — provided three criteria are met: (a) it is a pioneering project focused on the support and scaling-up of frontier AI technologies; (b) it is undertaken by a European Digital Infrastructure Consortium (EDIC) established under Decision (EU) 2022/2481, or another legal entity eligible for Union funding, and involves at least three Member States; and (c) the participating Member States pool computing time and other relevant resources.
Article 9 then provides the compute support. Article 9(1) requires the Union and Member States to ensure that sufficient AI computing resources from their compute capacities are allocated to support frontier AI priority projects meeting the Article 8 criteria, within the limits of available capacity. Article 9(2) adds a matching mechanism:
"The Union shall at least match the AI computing resources contributed by Member States to frontier AI priority projects to the extent that sufficient AI computing capacity is available within the Union's share of European high performance computing access time."
In practice, a project that meets the Article 2(4) definition and is structured to satisfy Article 8 may gain access to matched European high-performance computing (EuroHPC) capacity — directly addressing the compute bottleneck that constrains large-scale model development.
No fixed compute threshold
For architects, the absence of a hard technical threshold is the defining feature. Because qualification turns on being "at or beyond the current state of the art", a model is not frontier merely by being large; a more efficient model with superior reasoning or multimodal capability could qualify if it advances the state of the art, while a large but unremarkable model might not. The flip side is that the bar moves: a project must keep demonstrating that it is genuinely cutting-edge to stay eligible.
Strategic context
The proposal frames frontier AI as a strategic priority. In its recitals, CADA treats the development of frontier AI technologies as strategic assets — supporting pioneering projects to maintain a competitive edge in the global digital economy and to reduce dependence on third-country technologies. For SMEs, the practical route in is collaboration: a single SME may not train a frontier model alone, but participating in an EDIC or other eligible consortium under Article 8 lets smaller entities contribute to, and benefit from, priority projects.
What this means for you
1. Structure for funding. If you build large-scale models, assess whether they fit Article 2(4). If they do, consider structuring the work to meet Article 8 — a consortium across at least three Member States, focused on scaling frontier AI, with pooled compute — to access the matching support in Article 9.
2. Plan compute against the matching mechanism. The prospect of matched EuroHPC access is significant. Exact allocation will depend on secondary measures and EuroHPC access policies, but recognition under Article 8 is the privileged pathway to public compute.
3. Keep CADA status and AI Act compliance separate. Qualifying as frontier AI under CADA is about support, not safety. You still comply with the AI Act where your model falls within its scope. Coordinate the two workstreams; do not assume one answers the other.
Common misconceptions
"Frontier AI under CADA equals systemic-risk GPAI under the AI Act." No. The AI Act's systemic-risk tier rests partly on a compute-based presumption; CADA's frontier AI definition rests on capability and state-of-the-art performance. The two can diverge in both directions.
"Only hyperscalers can be involved." Article 8 requires broad participation across at least three Member States and contemplates consortia such as EDICs. SMEs can participate and share in the associated resources.
"The definition is static." Because it references "the current state of the art", it is inherently dynamic. What qualifies today may not in a couple of years; projects must keep proving they are at the frontier.
Official sources
Related
- Why does CADA's frontier AI definition have no fixed compute threshold?
- How is frontier AI defined differently from a general-purpose AI model under CADA?
- How does CADA's frontier AI definition compare to the AI Act's GPAI with systemic risk?
- Does my AI model qualify as frontier AI under CADA?
- Does an AI agent automatically count as frontier AI under CADA?
This is general information about a draft EU regulation, not legal advice.