Future Claude models will generate text that contains a watermark. This is a way of determining the likelihood that Claude was involved in writing the text, and we, along with several other major AI providers, are implementing this change to comply with the EU AI Act. In this article, we share answers to some of the questions we’ve received about how our chosen watermarking method works, whether it affects Claude’s outputs, and why we’re making this change.
Alex Cui on Twitter / X
Claude's watermark probably doesn't work how you think. As the CTO of GPTZero, I'll explain how Anthropic, Google and OpenAI are building text watermarking in this brief explainer and whether it can be defeated.Almost all forms of watermarking that are fast and cheap enough for… https://t.co/3Ghcz3PTwJ— Alex Cui (@alexcdot) August 11, 2026
How Claude marks AI-generated content | Claude Help Center
Anthropic has signed the EU AI Act's Article 50(2) Code of Practice on Transparency of AI-Generated Content, as a provider of both generative AI models and generative AI systems. This article describes how we’re planning to put those commitments into practice, how marking works, and what its limitations are. We’ll update this article and publish more detailed technical guidance as it becomes available.
Watermarks aren’t the silver bullet for AI misinformation
Digital watermarks are easily broken and abused.
Identifying AI-generated images with SynthID
Today, in partnership with Google Cloud, we’re beta launching SynthID, a new tool for watermarking and identifying AI-generated images. It’s being released to a limited number of Vertex AI customers using Imagen, one of our latest text-to-image models that uses input text to create photorealistic images. This technology embeds a digital watermark directly into the pixels of an image, making it imperceptible to the human eye, but detectable for identification. While generative AI can unlock huge creative potential, it also presents new risks, like creators spreading false information — both intentionally or unintentionally. Being able to identify AI-generated content is critical to empowering people with knowledge of when they’re interacting with generated media, and for helping prevent the spread of misinformation.
AI-text detection tools are really easy to fool
A recent crop of AI systems claiming to detect AI-generated text perform poorly—and it doesn’t take much to get past them.
Google's SynthID AI watermarking tech is being adopted by OpenAI, Nvidia, and more
AI content is getting good, but SynthID might be able to help tell truth from fiction.
Google joins AI watermarking coalition as deepfakes hit mainstream tech platforms
Google said it will look to use a project from Adobe called Content Credentials, which adds metadata that indicates AI editing and allows viewers to verify images, videos, audio and documents.
Google’s invisible AI watermark will help identify generative text and video
New AI tools to generate videos and detect them as well.
Scalable watermarking for identifying large language model outputs
Large language models (LLMs) have enabled the generation of high-quality synthetic text, often indistinguishable from human-written content, at a scale that can markedly affect the nature of the information ecosystem1–3. Watermarking can help identify synthetic text and limit accidental or deliberate misuse4, but has not been adopted in production systems owing to stringent quality, detectability and computational efficiency requirements. Here we describe SynthID-Text, a production-ready text watermarking scheme that preserves text quality and enables high detection accuracy, with minimal latency overhead. SynthID-Text does not affect LLM training and modifies only the sampling procedure; watermark detection is computationally efficient, without using the underlying LLM. To enable watermarking at scale, we develop an algorithm integrating watermarking with speculative sampling, an efficiency technique frequently used in production systems5. Evaluations across multiple LLMs empirically show that SynthID-Text provides improved detectability over comparable methods, and standard benchmarks and human side-by-side ratings indicate no change in LLM capabilities. To demonstrate the feasibility of watermarking in large-scale-production systems, we conducted a live experiment that assessed feedback from nearly 20 million Gemini6 responses, again confirming the preservation of text quality. We hope that the availability of SynthID-Text7 will facilitate further development of watermarking and responsible use of LLM systems.
The Impact of AI-Generated Text on the Internet
The proliferation of AI-generated and AI-assisted text on the internet is feared to contribute to a degradation in semantic and stylistic diversity, factual accuracy, and other negative...
The Impact of AI-Generated Text on the Internet
The proliferation of AI-generated and AI-assisted text on the internet is feared to contribute to a degradation in semantic and stylistic diversity, factual accuracy, and other negative...
Claude for Chrome | Claude
Bring Claude's AI assistance to your browser. Ask questions, analyze data, automate tasks, and navigate sites in Chrome. Works with Claude Code and Desktop.
