GPAI in the AI Act: what it means and what it means for you

Anyone digging into the European AI Act will quickly run into the abbreviation GPAI: general-purpose AI. It sounds like jargon for lawyers, but it covers exactly the tools you probably use every day. In this article we explain what the term means, who the rules mainly apply to, and which parts are actually relevant for you as a user in the workplace.

What does GPAI cover?

The AI Act distinguishes between AI systems built for one specific purpose and AI models that can be used broadly. The second category is GPAI: a model trained on large amounts of data that can handle a wide range of tasks, regardless of the application it ends up in.

The large language models behind well-known chatbots are the clearest example. The same underlying model can summarise emails, write code, translate, brainstorm and much more. Models that generate images or audio can fall under it too. The defining feature is always the breadth: not built for one task, but generally applicable, and often used as a building block inside other products.

That last point matters. A lot of the software you use at work now has AI features running on such a general model from a large provider. You may be using GPAI without ever having consciously opened a chatbot.

Why the rules mainly target providers

This is where workplace conversations often go wrong. People hear “rules are coming for AI models” and assume that they, as users, will have to meet all sorts of obligations. That is not how it works. The GPAI obligations in the AI Act are aimed almost entirely at the providers of those models: the companies that develop them and place them on the European market.

Among other things, those providers must draw up technical documentation, make information available to parties building on top of their model, have a copyright policy, and publish a summary of the training data used. For the very largest models, which the Act designates as models with systemic risk, extra obligations apply around risk assessment, incident reporting and security.

The logic is reasonable: the party building the model can assess and reduce risks at the source. You as an end user cannot inspect what a model was trained on; its maker can. So the Act places the burden where the knowledge and the influence are.

Organisations that use AI systems do have obligations under the AI Act as well, but those depend on the application and its risk level, not on the fact that a GPAI model happens to sit under the hood. Think of the general requirement that staff working with AI are sufficiently AI literate, and stricter requirements when AI is used in high-risk applications.

What you as a user should still know

Does this mean GPAI is irrelevant to you? Not quite. A few points matter precisely on the work floor.

Transparency: knowing when you are dealing with AI

The AI Act contains transparency obligations that boil down to this: people should not interact with AI without realising it. Chatbots must be recognisable as AI, and certain AI-generated content is subject to marking requirements. For you at work this mainly means: if your organisation uses AI in customer contact, it must be clear to the customer that they are talking to a system, not a person. That is not just a legal matter; it is also simply honest.

Limitations: broadly applicable is not the same as good at everything

The fact that a model can be used for anything creates the impression that it knows about everything. That is a misunderstanding. GPAI models can produce convincing text on topics where they are factually wrong, and they rarely tell you how confident they should be. The breadth is exactly why you need to stay alert: with a specialised tool you know what it is for, but with a general model you set the boundaries yourself, which means you also decide where it fails. Check output before you build on it, especially facts, figures and anything that goes out the door.

The chain: your tool is often a shell around someone else’s model

Many AI products are built on top of a GPAI model from another company. In practice this means your tool’s terms may not be the whole story: it is worth knowing which model sits underneath and what happens to the data you enter. Good vendors are open about this.

Being honest: this is still a moving target

The AI Act has been adopted and its obligations take effect in stages, but a lot around GPAI is still being worked out. Think of codes of practice and guidance that spell out how providers must meet their obligations in detail, and the role of the European AI Office overseeing the largest models. Practice will also have to bring clarity on the exact boundaries: when is a model “general” enough to count as GPAI?

So be wary of anyone who claims to tell you today, down to the last detail, exactly what is and is not allowed. Anyone stating confident specifics about matters still being worked out is running ahead of the facts. The broad lines are settled; the details will take shape over the coming years.

What does this mean for your organisation?

For most teams, the level-headed conclusion is this: you do not need to become a GPAI expert, and you do not need to worry that you will accidentally be subject to model obligations as a user. What organisations are expected to ensure is that people working with AI understand what they have in their hands. AI literacy, in other words: knowing what these systems can do, where they fail, and how to use them responsibly.

You can start on that today, without waiting for the rules to be fleshed out further. The AI literacy course covers the AI Act in plain language, focused on the main lines and everyday practice. Team licences are available via the for employers page, and you can always sample it first with the free module.