The EU Artificial Intelligence Act now requires providers to mark generated text so its origin can be checked. In regulated industries the duty is sharper: a marker must never flag third-party writing, survive routine review edits, and work on outputs often a sentence long. We call these the regulated-provenance triad and prove that no bounded-distortion watermark perturbing a model’s token distribution can satisfy all three below a critical length, the provenance horizon. The proof combines the Le Cam two-point method with a strong data-processing inequality for the edit channel; a matching achievability bound for the green-list family under a memoryless resampling channel makes the scaling order-tight. The barrier rises at negligible-distortion budgets, not at deployed ones. The contraction factor entering the bound is a proven upper bound for the memoryless resampling channel and a calibrated estimate for realistic paraphrase, under which we estimate most sentence-length regulated outputs fall below the horizon. Relocating provenance to a tamper-evident log read through a conformally calibrated retrieval test sidesteps the barrier: under exchangeable calibration its false-positive rate is at or below the target at every length. Coverage carries no guarantee and degrades under an adaptive editor.