Expert takes Sep 5, 2026

Trend watch · originally published August 18, 2026

Whether a watermark can tag your output is not settled by policy. It is settled by how much randomness your output has.

Arvind Narayanan is a professor of computer science at Princeton and a co-author of AI Snake Oil. Across two long posts he explained the mechanism behind text watermarking and filled in the piece this argument had been missing: "The watermark doesn't introduce new randomness; it hides in the randomness that already existed in the distribution of responses to a given prompt. If the output distribution for a given prompt doesn't have enough variation, there will simply be no detectable watermark." He also corrects a widespread misreading: this is not a question of length but of how much randomness there is; length is merely correlated with it. (Arvind Narayanan, 2026-08-18) One example he endorses makes the consequence plain. Ask a model for JSON in a strict format and almost no variation remains to sample from, so the watermark has nothing to attach itself to. That is where the inference for compliance and engineering leaders sits. Coverage sounds like something regulation and company policy decide. By the mechanism it behaves more like a physical boundary: consumer long-form text is naturally high-variance and is where watermarking works best, while strictly formatted production output sits at the far end and structurally cannot carry a mark. The same rule may bind those two groups an order of magnitude differently. ⚠️ This mechanical explanation carries no experimental figures and touches no vendor's actual implementation. The one anchor we hold with a number is a Stanford team's 2023 measurement: for responses to typical user instructions, at a median length of around 100 tokens, only about 25% were detectable (Kuditipudi et al., arXiv 2307.15593). A token is the unit a model counts text in, usually smaller than a word. ⚠️ And "most production output is low-variance" has no measurement behind it. We inferred it from how production systems generally work. Change the question from "does this vendor's model watermark" to "how much variation does our own output carry" — the second is an engineering question you can measure yourself and act on.