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Under the EU AI Act, “high-risk,” “foundation” and “generative” describe different things: a system’s legally significant use, a model’s broad reuse across tasks, and the ability to create content. A system is not high-risk simply because it uses a large or generative model. Classification depends on intended purpose and deployment context, while separate obligations can apply to the model that supports the system and to content or interactions it produces.
What do “high-risk,” “foundation” and “generative” AI mean?
They are not three mutually exclusive categories. The first is a legal classification for certain AI systems and uses; the second is common technical terminology for a broadly reusable model; the third describes a system’s content-generation capability. One AI product can fit more than one description, and different applications built on the same model can have different classifications.
| Term | What it describes | What triggers it | What to keep in mind |
|---|---|---|---|
| High-risk AI | An AI system in a legally designated use or regulated-product route | Intended purpose and context, including whether a listed route applies | It brings specific system-level requirements; size or content-generation capability alone is not the test. |
| Foundation model | A technical kind of broadly trained model designed for reuse across tasks | Broad capability and reusability | It is common technical language, not the Act’s operative legal label. |
| General-purpose AI (GPAI) model | The Act’s model-level category for general-purpose models | General-purpose capability, assessed under the Act and Commission guidance | GPAI obligations can apply to the model provider independently of a downstream application’s risk classification. |
| Generative AI | A system or model that produces content such as text, images, audio, video or code | Content-generation capability | Generation may create transparency duties, but does not itself make an application high-risk. |
The European Commission describes GPAI models as “the foundation for many different AI systems.” That explains why “foundation model” and “GPAI” are often discussed together, but the terms are not interchangeable in every technical or legal context: foundation model is the familiar technical phrase, while GPAI is the Act’s operative category.
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What does high-risk AI mean under the EU AI Act?
High-risk is a legal status tied to what an AI system is intended to do and the context in which it is used. The key question is not “How powerful is the model?” but “Does this system’s intended purpose fall within a high-risk route under the Act?”
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The Act uses a risk-based structure. Some AI practices are prohibited; specified high-risk systems face detailed controls; certain systems have transparency duties; and GPAI models have their own model-level rules. These categories answer different compliance questions, and more than one can matter for the same product.
What duties come with a high-risk classification?
The Commission describes a range of provider requirements for high-risk systems. They cover the system’s design and lifecycle, the information passed to those using it, and controls after it is placed on the market.
- Risk management: assess risks and take steps to mitigate them.
- Data quality: use high-quality data for the relevant requirements.
- Records and documentation: maintain activity logging and technical documentation.
- Information for deployers: provide clear information to the organizations or people who deploy the system.
- Human oversight: enable appropriate human oversight of the system.
- System performance and security: address accuracy, robustness and cybersecurity.
- Organizational controls: establish quality management and complete the applicable conformity assessment.
- Registration and follow-up: meet public-registration requirements and monitor the system after it is placed on the market.
These are not merely model-training obligations. A high-risk assessment concerns the AI system and its intended use, and obligations can involve both the provider and the deployer. The precise role and applicable duties should be mapped to the actual product and deployment arrangement rather than assumed from a model’s brand, size or advertised capabilities.
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Is a foundation model the same as generative AI?
No. A foundation model is characterized by broad reuse across tasks; generative AI is characterized by producing content. A model can be both, but neither term logically guarantees the other.
For example, a broad model may be adapted or used in a particular application. That application’s use determines whether a high-risk route applies. Separately, if the product generates content or interacts with people as a chatbot, transparency requirements may also be relevant. The model category, downstream use and content behavior should therefore be evaluated as separate layers.
What are GPAI models, and what rules apply to them?
GPAI is the EU AI Act’s legal term for general-purpose AI models. Its obligations attach to providers of these models, rather than automatically making every application built on one a high-risk system.
Baseline obligations for GPAI providers
According to the European Commission, all GPAI model providers must:
- draw up technical documentation;
- implement a policy to comply with copyright law; and
- publish a summary of the content used to train the model.
Additional obligations for systemic-risk models
Providers of GPAI models with systemic risk face additional requirements: risk assessment and mitigation, incident reporting, and cybersecurity protections. This is a further model-level distinction, separate from whether a particular downstream system qualifies as high-risk because of its use.
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The Commission’s 2025 guidance names two compute thresholds. It identifies 1023 FLOP for language-capable models considered GPAI under that guidance, and 1025 FLOP as the presumption threshold for systemic risk. The Commission says the systemic-risk threshold is under review. These figures should be read with their qualifications: they are guidance thresholds, not a shortcut that replaces checking the applicable legal criteria or current Commission material.
Does generative AI automatically count as high-risk?
No. “Generative” describes what a system can produce, not a single EU AI Act risk tier. A generative system may be a GPAI model, may trigger transparency duties, and may support an application that is high-risk—but those conclusions arise from different tests.
The Commission says AI-generated content must be identifiable. The Act’s transparency rules also cover telling people when they are interacting with a machine in covered chatbot situations, and visible labelling for deepfakes and certain text of public interest. These disclosure duties should not be confused with the high-risk requirements: a content label does not by itself classify an application as high-risk, and a high-risk classification does not answer every content-transparency question.
When do the EU AI Act rules take effect?
The Act is phased in. As of 25 October 2026, the dates listed below mean that the first two milestones and the broad 2026 milestone have passed; the later milestones remain upcoming. Transitional rules and amendments can affect which deadline applies to a particular system, so verify the current Commission timeline before relying on a date for compliance planning.
| Date | Application milestone | Status as of 25 October 2026 |
|---|---|---|
| 2 February 2025 | Prohibitions, definitions and AI-literacy provisions apply. | Passed |
| 2 August 2025 | GPAI obligations and governance provisions apply. | Passed |
| 2 August 2026 | Most rules, transparency obligations and applicable enforcement begin. | Passed |
| 2 December 2027 | Annex III stand-alone high-risk rules apply under the current amended timeline. | Upcoming |
| 2 August 2028 | High-risk AI embedded in regulated products applies. | Upcoming |
These milestones are not interchangeable. A provider or deployer should identify the system’s legal route and any applicable transition before treating one calendar date as the deadline for every obligation. The Commission’s timeline is the appropriate point of reference for checking changes.
How do I classify an AI system for compliance?
Start with the system’s real intended purpose and deployment—not a broad label such as “AI assistant,” “foundation model” or “generative tool.” Then assess the relevant legal routes, model obligations, transparency triggers, parties and timing.
- Describe the system and deployment context. Write down what the system does, who uses it, who is affected, and the purpose for which it is supplied and deployed. A generic model description is not a substitute for the use-case description.
- Check prohibited practices and high-risk routes. Determine whether the practice is prohibited and whether the intended use falls within a listed high-risk area or the regulated-product route. Do not infer the result from model size or whether it produces content.
- Assess the model separately. If you provide a general-purpose model, determine whether GPAI obligations apply and whether systemic-risk criteria are relevant. A provider of a model and a provider of a downstream system may have different responsibilities.
- Map the parties and their duties. Identify provider and deployer roles, then assign applicable tasks such as documentation, human oversight, quality management and monitoring. Make sure the information a deployer needs is actually available to it.
- Check transparency triggers. Review whether people must be told they are interacting with a machine and whether generated content, deepfakes or certain public-interest text require disclosure or labelling.
- Confirm dates and transitions. Check the current application timeline and any transitional rule for the system’s route before setting a compliance deadline.
The outcome may be layered rather than a single label. A GPAI model can have model-level duties; an application built on it may independently be high-risk; and a user-facing feature may have transparency obligations. Keep those assessments distinct in the compliance record so that satisfying one layer is not mistaken for satisfying the others.
What is the practical difference between model-level and system-level regulation?
Model-level obligations focus on a broadly usable model and its provider. System-level high-risk requirements focus on a system in a specified use and the conditions under which it is supplied or deployed. This difference matters when a single underlying model supports multiple products or workflows.
A model provider should not assume that every downstream use has the same risk classification. Conversely, a deployer cannot settle its system’s classification merely by pointing to a model provider’s general documentation. The relevant questions include the system’s purpose, the deployment context, who is acting as provider or deployer, and which transparency triggers apply.
For operational planning, maintain separate records for the model assessment and each materially different deployment. Record the intended purpose, the route considered, the accountable parties, the applicable duties and the date basis used. This makes it easier to spot when a change in use or context requires the classification to be revisited, without treating the model’s technical label as the legal answer.
Frequently Asked Questions
Does every AI system need to be registered under the EU AI Act?
The Commission’s description associates public registration with high-risk system requirements; it does not establish a universal registration duty for every AI system. Whether registration applies depends on the system’s legal classification and route.
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Can the same AI model be used in both high-risk and non-high-risk products?
Yes. The legal classification can differ across applications that use the same broad model because intended purpose and deployment context are central to the high-risk test. Each materially different use should be assessed on its own facts.
What does 10^25 FLOP mean for an AI provider?
It is the systemic-risk presumption threshold identified in the European Commission’s 2025 guidance, not a statement that any model below it is automatically exempt from every relevant obligation. The Commission says that threshold is under review.
Are the AI Act’s disclosure duties the same as high-risk compliance?
No. Disclosure duties concern specified interactions or content, while high-risk status is tied to intended use and context. A product can need transparency measures without being high-risk, or have high-risk obligations in addition to applicable transparency duties.
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