How AI Watermarks for Text Balance Clarity and Control

On 11 August, Anthropic announced that all future Claude models will generate text that contains a watermark that identifies its results as AI generated. The company is not alone. Google has its own text watermark (which Anthropic’s is based on) it uses on the output of its Gemini models. OpenAI has yet to introduce a text watermark but it plans to do so.

The rapid spread of watermarking is in part a response to the European Union’s AI Act, which mandates watermarks for AI models released after 2 August, 2026, along with other planned and proposed regulations aimed at curbing the spread of deceptive or manipulative AI-generated content. But the new rules may come at a cost for AI users who simply want the best possible results.

AI watermarks can apply to many forms of content: The EU AI Act also requires them for images, audio, and video. Such media watermarks have been in use for years, and while their effectiveness as a holistic solution to marking AI remains up for debate, they can achieve detection rates above 99 percent. Image and video watermarks are already deployed by OpenAI, Google, and Meta, among others. (Anthropic doesn’t provide an image generation model.)

Text watermarks have been less frequently deployed, however, and not everyone is convinced that text watermarking can work without compromising the quality of an AI model’s response. John Gruber, a prolific technology writer and co-creator of the Markdown language, calls the watermark a “perversion of writing” and disputes Anthropic’s assertion that a watermark doesn’t change the meaning or quality of text. Images consist of millions of pixels, he notes, whereas text responses often span just dozens or hundreds of words. Text seems to provide far less space to alter AI output in a way that is detectable yet not disruptive.

John Kirchenbauer, postdoctoral fellow at the Vector Institute and co-author of a 2023 paper which was among the first to describe a text watermarking method, disagrees. “[A watermark] wouldn’t be detectable if there wasn’t a change. This is a very fundamental point,” he says. “The question is, do you care if it’s not the exact original distribution if, for all intents and purposes, it doesn’t change the utility to you?”

Realistically, the issue comes down to that word, “utility.” Does watermarking AI-generated text meaningfully degrade the experience of the person using it? The answer is still under dispute.

How AI Text Watermarks Work

The term “watermark” is so familiar that it can cause confusion about how the technology works when applied to AI. A text watermark is not metadata or invisible characters; it is something much more subtle. The exact details vary between methods, but text watermarks are generally impossible for a human (and, in many cases, even a computer) to detect without access to the specific key used to detect a specific watermark. Understanding why requires an understanding of how LLMs work.

An LLM…