The EU’s Real AI Leverage Is Making Compliance the Path of Least Resistance

Sometime this spring, a compliance team at a frontier AI lab will sit down to prepare the first Safety and Security Model Report required under the EU’s GPAI Code of Practice. They will document their evaluation methodology, disclose the conditions under which red-teaming was conducted, specify how they assessed systemic risks, and describe their incident reporting procedures. GPAI obligations took effect in August 2025; the AI Office can begin enforcement from August 2026. That team faces a practical choice: build a compliance package tailored to Europe alone, or build one that regulators in other jurisdictions can also interpret and accept.

This piece argues that Europe’s interest, and the interest of AI safety institutes, standards practitioners, and allied regulators worldwide, lies in making the second option the obvious one. The EU’s 450 million consumers, procurement budgets, and regulatory capacity give it significant weight. But that weight does not translate through headline prohibitions. It translates through whether the evaluation, documentation, and incident reporting requirements now taking shape become standardized, interoperable, and affordable enough that firms worldwide adopt them as the path of least resistance for demonstrating trustworthiness.

If meeting EU expectations costs less than maintaining separate assurance regimes for every jurisdiction, the pathway diffuses through economics, regardless of formal adoption elsewhere. This logic applies most directly to jurisdictions that are importers of AI capability rather than builders of it; countries with industrial AI ambitions may prefer domestic assurance frameworks as a tool for building local evaluation capacity, even at higher cost. The four recommendations at the end of this piece are designed to be actionable this year by the AI Office, CEN-CENELEC, and the International Network of AI safety institutes.

The pipeline, not the parts

The AI Act’s GPAI provisions create three interlocking obligations for providers of models that present systemic risk. First, model evaluations: providers must conduct and document adversarial testing and risk assessments, producing evidence about what a model can and cannot safely do. Second, structured documentation: technical files, safety frameworks, and model reports that make those evaluation results auditable and transferable to downstream providers and regulators. Third, serious incident tracking and reporting to the AI Office and national authorities, with staggered deadlines by severity.

These obligations are typically discussed as separate compliance tasks. Their real value lies in their pipeline. Evaluations produce risk claims. Documentation makes those claims portable and auditable across borders. Incident reporting reveals where evaluations and documentation fell short, generating the feedback needed to recalibrate baselines over time. The Commission’s draft guidance on incident reporting already signals this feedback logic, explicitly seeking alignment with the OECD’s AI Incidents Monitor and Common Reporting Framework. A reporting system only generates useful signals if it is compatible across borders.

For the pipeline to function as reusable infrastructure, each element must be specified clearly enough that a third-party evaluator in one country can produce results interpretable by a regulator in another.

Where the friction is

Much of the demand for this work already exists. AI safety institutes in multiple jurisdictions are coordinating on evaluation methodology through the International Network for Advanced AI Measurement, Evaluation and Science, whose members include Australia, Canada, the EU, France, Japan, Kenya, South Korea, Singapore, the UK, and the US. MLCommons has released the AILuminate benchmark for standardized safety testing across twelve hazard categories. Frontier labs have asked for clearer compliance guidance. The demand side is not the bottleneck.

The friction is on the supply side. Three gaps stand out.

The first is the evaluation methodology. The Code of Practice requires systemic risk evaluations, but evaluation methods are advancing faster than the formal standards needed to make results comparable and reusable across jurisdictions. Researchers at RAND have proposed a dedicated EU GPAI Evaluation Standards Task Force organized around four desiderata: internal validity, external validity, reproducibility, and portability. The AI Evaluator Forum’s AEF-1 standard specifies minimum operating conditions for third-party evaluators, covering independence, access depth, and transparency requirements. But the pool of qualified evaluators remains thin (a constraint the RAND task force proposal explicitly identifies), and without agreed methodology standards, an evaluation report produced for the AI Office cannot be straightforwardly reused by a regulator in Ottawa or Singapore. The AI Office’s own GPAI guidelines acknowledge this gap. It should act on it soon.

The second is harmonized technical standards. CEN-CENELEC JTC 21 is developing standards for the AI Act’s high-risk provisions, including quality management, risk management, and conformity assessment. This work is primarily aimed at high-risk AI systems rather than GPAI models, but the broader standards bottleneck affects the credibility and resourcing of the assurance ecosystem that the AI Office will rely on. Exceptional acceleration measures adopted in October 2025 aim for publication by late 2026. Until harmonized standards are published, the Code of Practice fills the gap, but it is a bridge, not a permanent foundation.

The third is the incident reporting feedback loop. The Commission has published a standardized reporting template for GPAI incidents, but the criteria for what constitutes a serious incident remain partially unspecified. The Code of Practice envisions signatories incorporating incident intelligence into their safety frameworks (Measure 1.2), but the operational infrastructure for systematically connecting aggregated incident data back to evaluation baselines across the ecosystem does not yet exist. In aviation and pharmaceutical safety, incident databases inform recalibration of testing protocols. The AI Act should aim for the same closed loop.

Frontier evaluations: what portability looks like in practice

The GPAI systemic risk regime is the sharpest test of whether this pipeline can travel. Under the Code of Practice, providers of models trained at 1025 FLOPs or more—a threshold the AI Act uses as a proxy for capability, on the assumption that models requiring the most compute are likeliest to pose systemic risks—must produce a Safety and Security Framework, conduct pre-release evaluations, prepare Model Reports, and report incidents on staggered timelines. The Signatory Taskforce, chaired by the AI Office, is responsible for facilitating a coherent application.

What would portability actually mean here? If the AI Office required that every Model Report include a standardized evaluation summary specifying the model version tested, the access conditions granted to evaluators (black-box, grey-box, or white-box), the elicitation methods used, the statistical tests applied, and the hazard categories assessed, then a regulator in Tokyo or Canberra could read that summary and decide whether the evaluation meets their own threshold, without commissioning a duplicate exercise. If the AI Office instead accepts free-form reports with no common structure, every jurisdiction builds its own template, and the compliance team from the opening paragraph ends up preparing five different packages.

Methodological diversity has value—different approaches to evaluation can surface risks that a single framework might miss. But a common reporting format is compatible with diverse evaluation methods. The goal is not to homogenize how evaluations are conducted, but to standardize how results are documented and communicated, so that regulators can interpret them without requiring duplication.

The same logic applies to red-teaming disclosure. A minimum disclosure standard for adversarial testing conditions, covering who tested, under what constraints, for how long, and with what access, would let external parties assess evaluation quality without requiring full replication. The International Network’s NeurIPS 2025 workshops surfaced growing consensus on these principles, while identifying open questions around report flexibility and system-level assessment. The UK, coordinating the Network in 2026, has committed to producing best-practice documentation this year. If the EU framework develops in dialogue with that work, the cost of multi-jurisdictional compliance drops, and the incentive to treat EU assurance as the default baseline grows.

What should happen in the next 12 months

With enforcement approaching and standards still in development, choices made this year will determine whether EU assurance becomes reusable infrastructure or jurisdiction-specific paperwork.

First, the AI Office should publish, before enforcement begins in August, a shared evaluation reporting schema for systemic risk assessments: a structured template specifying the minimum contents of an evaluation summary, including model identifiers, evaluator credentials and access conditions, elicitation methods, statistical reporting, hazard coverage, and known limitations. This schema should draw on the RAND task force proposal and align with the International Network’s emerging consensus. Alongside it, the Commission should endorse minimum operating conditions for third-party evaluators, building on AEF-1, and begin defining a lightweight accreditation pathway that a new evaluator could complete within six months.

Second, CEN-CENELEC should ensure that harmonized standards in the final drafting phase, expected to be completed by late 2026, explicitly map to international equivalents. Where possible, final texts should use shared technical vocabulary and reference the NIST AI Risk Management Framework and equivalent national frameworks by name, rather than defaulting to European-only terminology where international equivalents exist.

Third, the AI Office should, before the end of 2026, publish a public incident taxonomy aligned with the OECD’s Common Reporting Framework and establish a process by which aggregated incident data informs revisions to evaluation baselines and risk-tier frameworks within the Code of Practice. The two-year review cycle already envisioned for the Code provides a natural anchor.

Fourth, allied regulators and safety institutes should agree, by the International Network’s next directors-level meeting, on a “minimum contents” standard for evaluation documentation that allows results to be recognized across jurisdictions without full mutual recognition. This does not require harmonizing substantive safety standards. It requires agreeing on what information an evaluation report must contain to be interpretable by another regulator. That is a lower bar, and one that the Network is well placed to coordinate within the year.

The AI Act’s most lasting contribution may not be its risk categories or its prohibitions. It may be the assurance infrastructure it builds: a pipeline of evaluation, documentation, and incident reporting rigorous enough to be trusted, affordable enough to be adopted, and, because each stage feeds back into the others, adaptive enough to keep pace with a technology that will not wait for regulators to finish their work. The next twelve months will determine whether Europe’s regulatory investment becomes reusable infrastructure for the world or an expensive island.