General-Purpose AI Models: New Obligations Under the EU AI Act

The rise of foundation models like GPT-4, Claude, and Gemini has fundamentally changed the AI landscape. These general-purpose AI models (GPAIs) power thousands of applications, from customer service chatbots to medical diagnostic tools. But with great capability comes great regulatory responsibility. The EU AI Act introduces specific obligations for GPAI providers that go beyond traditional AI system requirements.

If you're developing, fine-tuning, or providing access to foundation models, August 2025 brings a new compliance reality. Let's explore what this means for your organization and how to prepare for these unprecedented requirements.

Understanding What Makes Your Model "General-Purpose"

The term "general-purpose AI" might seem obvious when we think about ChatGPT or similar models, but the legal definition has nuances that matter for compliance.

A GPAI model under the EU AI Act is characterized by three key features:

Significant Generality: The model can competently perform a wide range of distinct tasks. This isn't about being good at variations of one task (like different types of image classification), but about genuine versatility across domains – language, reasoning, creativity, analysis, and more.

Multiple Purpose Capability: The same model can be used for customer service, content creation, code generation, data analysis, and countless other applications without fundamental retraining. This flexibility is what makes these models so valuable – and so regulated.

Integration Flexibility: The model can be incorporated into various downstream systems and applications. Whether through APIs, fine-tuning, or embedding, these models serve as building blocks for other AI systems.

Common examples include large language models (LLMs) like GPT-4, Claude, and Gemini; multimodal models like DALL-E and Stable Diffusion; and increasingly, domain-adapted foundation models that retain general capabilities while excelling in specific areas.

But here's where it gets interesting: even models that started as general-purpose but were fine-tuned for specific applications might still count as GPAI if they retain significant general capabilities. The test isn't what you call it or market it as – it's what the model can actually do.

The Two-Tier Compliance Framework

The EU AI Act creates a clever two-tier system for GPAI regulation, recognizing that not all foundation models pose the same level of risk.

Tier 1: Standard GPAI Models

All GPAI providers, regardless of model size or capability, must meet baseline requirements by August 2025. These aren't suggestions – they're mandatory obligations that apply whether you're OpenAI or a startup with a fine-tuned open-source model.

Tier 2: Systemic Risk Models

Models that exceed 10^25 floating-point operations (FLOPs) in training are presumed to pose systemic risks. To put this in perspective, GPT-4 is estimated to have required around 2×10^25 FLOPs for training. These models face additional obligations recognizing their potential for widespread societal impact.

The FLOPs threshold isn't arbitrary – it's a proxy for models that have emergent capabilities, broad adoption potential, and the ability to influence multiple sectors simultaneously. If your model crosses this threshold, you're not just building AI; you're managing infrastructure that could affect millions of users.

Core Obligations for All GPAI Providers

Let's dive into what every GPAI provider must implement by August 2025.

Technical Documentation: Beyond the Basics

The technical documentation requirement for GPAI goes well beyond typical software documentation. Regulators want to understand not just what your model does, but how it learned to do it.

Your documentation package must include:

Architecture Deep Dive: Not just "we used transformers," but detailed specifications of your model architecture. This includes layer configurations, attention mechanisms, parameter counts by component, and any novel architectural innovations. The goal is for a competent team to understand your design choices and their implications.

Training Process Transparency: Document your entire training pipeline – from data preprocessing through final evaluation. This includes optimization algorithms, hyperparameter selections, curriculum learning strategies if used, and convergence criteria. Explain not just what you did, but why you made specific choices.

Computational Footprint: Detailed accounting of computational resources used in training. This isn't just about FLOPs – include hardware specifications, training duration, energy consumption, and carbon footprint. As models grow larger, this environmental accounting becomes increasingly important.

Capability Assessment: Honest documentation of what your model can and cannot do. This includes benchmark performances across standard tests, but also qualitative assessments of capabilities like reasoning, creativity, and factual accuracy. Don't oversell, but don't undersell either – accuracy matters.

Information for Downstream Providers

If others will build on your model, you have obligations to them too. This isn't just good customer service – it's legal requirement.

Integration Documentation: Provide clear, comprehensive guidance on how to safely and effectively integrate your model. This includes API documentation, of course, but also guidance on prompt engineering, safety filtering, and output validation. Think of it as a safety manual for a powerful tool.

Use Case Boundaries: Explicitly document intended uses and known limitations. If your model shouldn't be used for medical diagnosis, say so clearly. If it performs poorly on certain languages or domains, document that. Downstream providers need this information to ensure their own compliance.

Performance Characteristics: Provide detailed performance metrics that downstream providers can rely on for their own risk assessments. This includes not just accuracy metrics but also latency characteristics, throughput limitations, and degradation patterns under various conditions.

Copyright Compliance: The New Frontier

The AI Act takes copyright seriously, and GPAI providers are on the front lines.

Policy Documentation: You must have and publish a detailed policy on copyright compliance. This isn't just "we respect copyright" – it needs to explain your approach to training data selection, how you handle copyrighted content, and your response to infringement claims.

Training Data Transparency: Publish a sufficiently detailed summary of training data content. This doesn't mean revealing every URL, but providing enough information for copyright holders to understand if their content might have been included. Think categories, sources, and selection criteria.

Opt-Out Mechanisms: If you're relying on legitimate interests for training data use, you must respect opt-out requests from copyright holders. This means having systems to track and implement these requests effectively.

Additional Requirements for Systemic Risk Models

If your model exceeds 10^25 FLOPs, buckle up – the requirements get significantly more stringent.

Model Evaluation and Adversarial Testing

This isn't your standard QA process. Systemic risk models require rigorous evaluation that goes beyond performance metrics.

Capability Evaluation: Systematic assessment of model capabilities, including emergent abilities that might not have been explicitly trained. This includes testing for reasoning, planning, tool use, and other advanced capabilities that could pose risks if misused.

Safety Testing: Comprehensive evaluation of safety measures, including robustness to adversarial inputs, resistance to harmful instruction following, and stability under edge cases. This isn't just about preventing bad outputs – it's about understanding failure modes.

Red Team Exercises: Regular adversarial testing by experts trying to make your model fail or behave harmfully. This includes attempts to bypass safety measures, extract training data, or use the model for prohibited purposes. Document these exercises and your responses to findings.

Serious Incident Tracking and Reporting

When things go wrong with systemic risk models, regulators want to know immediately.

Incident Detection Systems: Implement comprehensive monitoring to detect when your model is involved in serious incidents. This includes not just technical failures but also misuse cases and unexpected harmful outputs that come to your attention.

Rapid Reporting Protocols: Establish procedures to report serious incidents to authorities within prescribed timeframes. This includes having clear escalation paths, designated responsible persons, and pre-drafted reporting templates.

Corrective Measures: Document how you respond to incidents, including immediate mitigation, root cause analysis, and long-term preventive measures. Show that you learn from incidents and improve continuously.

Cybersecurity Protections

Systemic risk models are attractive targets for malicious actors. Your cybersecurity must match the threat level.

Model Security: Protect against model extraction, inversion attacks, and unauthorized access. This includes securing model weights, protecting inference endpoints, and monitoring for suspicious access patterns.

Supply Chain Security: Ensure security throughout your ML pipeline, from training data storage through model deployment. This includes securing compute resources, protecting intermediate checkpoints, and validating all components.

Incident Response: Have specific cybersecurity incident response procedures for AI-related threats. This includes procedures for model poisoning, data extraction attempts, and adversarial attacks.

Energy Efficiency Reporting

Large models consume massive amounts of energy. Transparency is now mandatory.

Training Energy Consumption: Document total energy used in training, including failed runs and experiments. Break this down by phase and provide context about energy sources and efficiency measures.

Inference Energy Profile: Measure and report ongoing inference energy consumption. This includes average energy per query, peak consumption patterns, and efficiency optimization efforts.

Optimization Efforts: Document what you're doing to reduce energy consumption, from model compression techniques to hardware optimization. Show that you're taking environmental impact seriously.

Practical Implementation Strategies

Knowing the requirements is one thing; implementing them is another. Here's how successful organizations are approaching GPAI compliance.

Start with Gap Analysis

Don't assume you know where you stand. Conduct a thorough gap analysis comparing current practices with AI Act requirements. You might be surprised – some organizations find they're further along than expected, while others discover critical gaps they hadn't considered.

Focus particularly on documentation gaps. Many organizations have the technical capabilities but lack the documentation to prove it. Starting documentation now is easier than retrofitting later.

Build Compliance into Development

The most successful approach integrates compliance into the development lifecycle rather than treating it as an add-on.

Documentation-First Development: Start documenting before you start training. Document your design decisions, data selection criteria, and safety considerations in real-time. This creates better documentation and often leads to better models.

Continuous Compliance Monitoring: Implement systems to continuously monitor compliance status. This includes automated checks for documentation completeness, performance degradation detection, and incident monitoring systems.

Version Control Everything: Not just code and models, but documentation, policies, and compliance artifacts. You need to show what you knew and when you knew it.

Prepare for the Open Source Question

If you're working with open source models, the compliance landscape gets complex.

Open source models have some exemptions from documentation requirements, but these are limited. If you're fine-tuning an open source model for commercial use, you likely inherit provider obligations. If you're contributing to open source models that exceed systemic risk thresholds, you still face those heightened obligations.

The key is understanding that "open source" isn't a free pass. The obligations follow the capability and use, not the license.

Establish Downstream Communication Channels

If others build on your model, you need robust communication channels.

Developer Portals: Create comprehensive portals where downstream users can access documentation, updates, and support. This isn't just good practice – it's becoming legally necessary.

Update Protocols: Establish how you'll communicate changes, especially those affecting safety or compliance. Downstream providers need time to adapt to your changes.

Feedback Mechanisms: Create ways for downstream providers to report issues and concerns. You're responsible for your model even when others are using it.

The Strategic View: Compliance as Competitive Advantage

Leading organizations are discovering that GPAI compliance, done right, creates competitive advantages.

Trust Premium: Organizations that can demonstrate robust compliance command premium prices. Enterprise customers increasingly require AI Act compliance as a procurement condition.

Market Access: Compliance isn't just for the EU market. Many global organizations are adopting EU AI Act standards globally, making compliance a gateway to international opportunities.

Innovation Framework: Clear compliance requirements actually enable innovation by removing uncertainty. Knowing the rules lets you innovate within them confidently.

Operational Excellence: The processes required for compliance – documentation, monitoring, incident response – make your AI operations better regardless of regulatory requirements.

Your GPAI Compliance Timeline

With August 2025 approaching, here's a practical timeline for achieving compliance:

Q4 2024 - Q1 2025: Foundation Building

• Complete gap analysis
• Establish documentation frameworks
• Begin technical documentation creation
• Design monitoring systems

Q2 2025: Implementation Sprint

• Complete core documentation
• Implement incident tracking systems
• Establish downstream communication channels
• Begin testing and validation

Q3 2025: Validation and Refinement

• Complete all documentation
• Test incident response procedures
• Validate compliance measures
• Prepare for regulatory review

August 2025 and Beyond: Operational Compliance

• Submit required documentation
• Activate monitoring systems
• Begin regular reporting
• Continuous improvement

The Bottom Line

General-purpose AI models are the engines of the AI revolution, but with that power comes regulatory responsibility. The EU AI Act's GPAI requirements aren't just bureaucracy – they're about ensuring these powerful systems are developed and deployed responsibly.

Whether you're building the next GPT or fine-tuning an existing model, understanding and implementing these requirements is crucial. The August 2025 deadline might seem distant, but the scope of required changes means starting now is essential.

The organizations succeeding with GPAI compliance are those that see it not as a burden but as a framework for building better, more trustworthy AI systems. They're documenting not just for regulators but for their own understanding. They're implementing monitoring not just for compliance but for operational excellence.

The era of unregulated foundation models is ending. The era of responsible, transparent, and trustworthy GPAI is beginning. The question isn't whether you'll comply – it's whether you'll lead or follow in this new landscape.

Start your GPAI compliance journey today. August 2025 will arrive faster than you think, and the organizations ready for it will shape the future of AI.