GPAI (general-purpose AI)
A category under the EU AI Act referring to AI models trained on broad data and usable across a wide range of tasks (large language models are the most prominent example). GPAI providers face specific transparency and documentation duties under the Act, in force since August 2, 2025, with a GPAI Code of Practice published July 2025 setting out how providers can demonstrate compliance.
Defined in 4 GAGE programs, which carry 10 distinct definitions of it. The wording above is taught in The AI Lobbyist: Certified AI Policy Strategist.
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
A category under the EU AI Act referring to AI models trained on broad data and usable across a wide range of tasks (large language models are the most prominent example). GPAI providers face specific transparency and documentation duties under the Act, in force since August 2, 2025, with a GPAI Code of Practice published July 2025 setting out how providers can demonstrate compliance.
AI models built for broad, general use rather than a single task. The EU AI Act places obligations on GPAI model providers, and the Commission's enforcement powers over them were scheduled to begin August 2, 2026 (not delayed by the Omnibus).
An AI model, under EU AI Act terminology, capable of competently performing a wide range of distinct tasks, subject to specific obligations including public training-data summary disclosure under Article 53(1)(d).
The EU AI Act's cross-cutting regulatory tier for foundation-model providers, applying regardless of the downstream application the model is used for. Obligations became enforceable 2 August 2025.
A category of AI model, defined under the EU AI Act, capable of performing a wide range of tasks; GPAI providers carry specific obligations including publishing a training data summary.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Why the data person just became the most consequential role in the building · Taking the Data Chair, Certified AI Data Governance Professional (CADGP)
- Membership inference and extraction: what a model reveals about its training data · Poison, Leaks, and the Adversary, Certified AI Data Governance Professional (CADGP)
- Why the AI Point Person Wins the Next Decade · The Point Person Mandate, The AI Lobbyist: Certified AI Policy Strategist
- Translating Corporate AI Risk into Policy Talking Points · Business and Industry Representation, The AI Lobbyist: Certified AI Policy Strategist
- The EU Machine: Parliament, Commission, and the AI Office · Government and Policy Navigation, The AI Lobbyist: Certified AI Policy Strategist
- EU AI Act Operational Reality: The Timeline After the Digital Omnibus · AI Law in Practice: The Compliance-to-Persuasion Toolkit, The AI Lobbyist: Certified AI Policy Strategist
- Stakeholder Persona Simulation: Modeling Positions, Concerns, and Objections · Prompt Engineering for Strategic Persuasion, The AI Lobbyist: Certified AI Policy Strategist
- The Stakeholder Map and Engagement Strategy · Stakeholder Architecture and Relationship Building, The AI Lobbyist: Certified AI Policy Strategist
- Transforming Across the Regulatory Map · Lab: Regional and Geopolitical AI, Certified AI Transformation Professional (CATP)
- China vs the EU AI Act: Mapping the GenAI Measures Against Europe's GPAI Regime · China, EU, US: The Comparative Playbook, Certified China AI Regulatory Professional (CCARP)
Where it shows up at work
A definition is where GPAI (general-purpose AI) starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Convenes partners across industry, civil society and government, keeps a stakeholder map current, and gets many parties to one position.
2 lessons on this page teach it as a primary outcome.
Turns technical uncertainty into a one-page decision: material risks, trends, exceptions, remediation, and what the board is being asked to accept.
One lesson on this page teach it as a primary outcome.
Compares the EU AI Act, NIST AI RMF, ISO/IEC 42001 and sector rules by intent and control objective, and says where they do not overlap.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- Senior AI Compliance Analystneeds it at depth
- Chief AI Officerneeds it at depth
- Chief Audit Executive, AI audit editionneeds it at depth
- Chief Risk Officer, AI risk editionneeds it at depth
- VP of AI Governance and Ethicsneeds it at depth
- AI Policy Analystneeds working knowledge · 7 open postings this week
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: EU AI Act; NIST AI RMF; ISO/IEC 42001.
One interview question about it
Who are the stakeholders in an AI policy fight, and how do you build a coalition among people who disagree on everything else?
Asked of candidates for coalition building and stakeholder mapping. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Policy, legislative and government affairs staff carries it as a claim to back up: Coalition and stakeholder work is the day job.
- A public policy, political science or international relations degree carries it as a claim to back up: Stakeholder mapping is a standard exercise.
- Internal audit and IT audit carries it as a claim to back up: Audit committee reporting is part of senior audit roles.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.