Regulating the Frontier: Model-Level Evidence from the EU AI Act

The European Union's Artificial Intelligence Act creates a binding, economy-wide transparency regime directed specifically at providers of general-purpose AI models. Whether such rules alter model-development practice, rather than merely producing legal documentation outside public view, is an empirical question. This article offers an early model-level assessment using a daily updated public database of 3,571 machine-learning models. The main analysis compares 604 language-based models that satisfy a transparent general-purpose-AI proxy with 401 narrow-task models released during an approximately two-year window from 2 August 2024 to 21 July 2026, when the Act's general-purpose-model obligations began to apply to newly marketed models. A seven-component Structured Disclosure Index records whether public sources report parameter count, training compute, training-data scale, training time, training hardware, training-code status, and model-access status. Organization and calendar-month fixed-effects estimates associate the post-application period with an 8.7 percentage-point increase in structured disclosure for the general-purpose group. The change is concentrated in training-compute reporting. The estimate remains positive in several within-provider specifications, although it becomes small and statistically indistinguishable from zero when organization fixed effects are replaced by country and organization-category controls. No corresponding change is detected in open-weight release, model parameter scale, or disclosed training compute. Only two post-application observations with disclosed compute exceed the Act's $10^{25}$ floating-point-operation presumption, making a threshold design premature. These findings are best read as pre-enforcement evidence of selective documentation adaptation, not a definitive causal estimate. The article contributes a reproducible measurement framework, identifies the distinction between structured and substantive transparency, and provides code for extending the analysis to versioned Hugging Face model cards, safety reports, and copyright disclosures.