Summary As proposed in Article 2, point (4), your model is "frontier AI" only if it meets two cumulative conditions: it can perform a wide variety of tasks, and it approaches, reaches or exceeds the current state of the art. Because "state of the art" shifts as the technology advances, qualifying is a moving target rather than a permanent label. Note that, as proposed, qualifying as frontier AI does not by itself make a project eligible for CADA's frontier AI priority projects β€” those have separate criteria in Article 8.

Detail

As proposed, the definition is in Article 2, point (4), which 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 a two-part, cumulative test. Both elements must be met. If your model fails either, it is not frontier AI under CADA, though it may still face obligations under the EU AI Act or under CADA's cloud framework when delivered as a service.

Criterion 1: a wide variety of tasks

The first requirement is functional versatility β€” the model must perform a "wide variety of tasks." This distinguishes frontier AI from narrow systems built for a single purpose (for example, a model trained only to detect payment fraud or to translate one language pair). General-purpose foundation models that span text, code, images and analysis across unrelated domains will tend to satisfy this element.

Criterion 2: approaching, reaching or exceeding the state of the art

The second requirement is performance-based: the model must "approach, reach or exceed the current state of the art." This is the dynamic part. "State of the art" is a technological benchmark, not a fixed legal standard, and it rises rapidly. A model that is state-of-the-art today may fall below the line within months as new architectures, training methods and datasets emerge. In practice, providers gauge this through benchmarking against recognised evaluations and independent assessments. Note that Article 2, point (4), does not set a numeric computational threshold for frontier AI.

The cumulative nature of the test

Both criteria must hold. A versatile but dated model that lags the state of the art does not qualify; nor does a highly specialised model that exceeds the state of the art in one narrow domain (such as protein-structure prediction) but cannot perform a wide variety of tasks.

What this means for you

For CSPs, data centre operators and AI developers, classification as frontier AI carries practical implications under CADA β€” but read the operative articles, not just the label.

1. Frontier AI priority projects (Articles 8 and 9)

As proposed, Article 8 lets the Commission recognise frontier AI priority projects. Crucially, qualifying as frontier AI is necessary context but not sufficient on its own: the project must be selected through an open call for expression of interest supporting grand challenge 3 in Annex I; be undertaken by a European digital infrastructure consortium (or another entity eligible for Union funding) involving at least three Member States; and the participating Member States must pool computing time and other resources. As proposed, Article 9 then provides that the Union and Member States shall allocate sufficient AI computing resources to such projects within available capacity, and that the Union shall at least match the AI computing resources contributed by Member States, to the extent capacity is available within the Union's share of European high-performance computing access time. So priority-project status is a route to compute, but it depends on the Article 8 consortium criteria, not on the frontier-AI label alone.

2. Sovereignty when delivered as a service (Title IV)

As proposed, the frontier-AI definition is technical, but deploying such a model as a cloud service to Union entities or public sector bodies engages the Union assurance levels (Article 16 and Annex II). Higher levels (3 and 4) impose strict criteria β€” EU establishment, EU-located infrastructure and data, Union-citizen personnel and freedom from third-country control β€” which can constrain where and how a frontier model is served to the public sector.

3. Ongoing re-assessment

Because "state of the art" moves, your classification is not static. Routine benchmarking against current standards is prudent; a model that drops below the state of the art may cease to be frontier AI. Where you rely on frontier-AI status in a process (for instance, an Article 8 application), expect to substantiate it through technical documentation.

4. Interaction with the AI Act

As proposed, CADA operates alongside the EU AI Act (Regulation (EU) 2024/1689). CADA uses "frontier AI" for capacity and infrastructure purposes; the AI Act separately regulates high-risk AI systems and general-purpose AI models, including systemic-risk obligations under its Articles 51–56. A model that is frontier AI under CADA may also be a general-purpose AI model with systemic risk under the AI Act where the AI Act's own criteria are met β€” in which case you would need to comply with both regimes.

Common misconceptions

"Frontier AI is the same as general-purpose AI (GPAI)." Not quite. As proposed, frontier AI adds a performance dimension: it must also approach, reach or exceed the state of the art. A capable but dated GPAI model may not be frontier AI.

"Once frontier AI, always frontier AI." No. As proposed, the standard is dynamic; the benchmark rises as more capable models appear.

"Only the largest models qualify." As proposed, the test is capability and performance, not size as such β€” though in practice today's state-of-the-art performance correlates strongly with scale and compute.

"Frontier AI status has no consequences." It can. As proposed, relying on the status in an Article 8 application means meeting the objective criteria of Article 2, point (4); misrepresenting capabilities to obtain public resources could raise compliance issues.

Official sources

Related

This is general information about a draft EU regulation, not legal advice.