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I feel like this is going to end up being like cookie laws. It sounds good, I don't know how any one benefits from it.

Currently I can recognize AI text because I read thousands of ai generated text. I know that 110% of yahoo finance news is generated. I don't want to read an AI generated personal blog, but if I do what's the problem really? Other than the companies distinguishing AI text for getting better training data, how do people benefit from watermarked text exactly?

This feature is born out of the same government that created the cookie laws, so it makes sense to me that it would follow similar politically-valuable but practically-questionable set of beliefs.

Really, Anthropic (and the other labs that follow) are just trying to satisfy the requirements of the law so they can continue to serve the EU. Wether it's actually effective is something else entirely.

There are stories of students literally copy-pasting LLM answers to school assignments with prompts and all.

This will most likely bring a sift end to at least the low hanging fruit.

You can't think of the benefit of having LLM output watermarked? So many lazy students are going to get caught by this.
>I feel like this is going to end up being like cookie laws. It sounds good, I don't know how any one benefits from it.

Does it sound good?

Either anthropic does not tell anyone the signal and only they can verify the text. Or they share the signal and everyone can verify it, which means everyone can bypass it and its just an inconvenience.

> I feel like this is going to end up being like cookie laws

Companies will implement it in the worst way possible? Nowhere in the cookie laws does it say you need to add a banner, you just can't spy on users without their consent.

I would income education institutions might benefit from being able to distinguish between AI and human generated text. The people who benefit, in the long run, are the students.
None of this is science or evidence based, it's political theater. The labs do this because the government has made it clear it will intervene if they don't say the line. I suspect most of the people at the labs think AI watermarking is a retarded approach.

The people that make the laws and the people complaining about AI don't know or care about the reality of the situation nearly as much as they care about being re-elected and feeling good about their social posture.

I for one value the transparency of being able to tell human-written content apart, especially when moving into a time where we’re suddenly questioning an AI agent's motivations. If that is ingrained so deeply into these systems they cannot rip it out without us noticing, there’s at least one more safeguard. I know how ludicrous this sounds, but you can’t deny AI development is speeding up to scary levels.

Additionally, think of cases like paying a lawyer or an expert for an extensive report or opinion on something. Wouldn’t you want to know if that is actually their carefully assembled professional assessment rather than the output of an LLM prompt?

The article mentions you can always simply use a smaller, local, un-watermarked LLM to rephrase the original watermarked text. Which is true, sure.

But if we're talking about deterministically taking some watermarked LLM output and having a function removeWatermark(text), it won't necessarily be "trivial" to remove, because the watermark function itself need not be public. Only the API that tests for the watermark need be public, right?

Anthropic's magic watermark could be, like the article mentions, something like "every 7th semicolon has a N% chance to be a comma where N is the sum of the last X characters mod Y, and every character in the bit range q1...q2 has a Z% chance to..." etc etc etc. And if Anthropic controls those variables, it would be very difficult to determine the rule, even with some pretty advanced analysis (I would assume). And keep in mind, that example rule I mentioned is pretty naive, too. I expect the actual rule would be way more advanced and not so straightforward as "swap every <charX> for a <charY>"

There exist methods to detect what kind of watermarking tool that someone is using, and most of the big tools have specific signatures that you can look for.

For Anthropic, it’s highly likely that the watermark is a SynthID type mark similar to the one that Sean is talking about (I actually ran the analysis here https://johnjwang.com/post/2026/08/12/how-claude-watermarkin...). When we get confirmation of whether all models actually are watermarked, I think we’ll be even more confident.

Of course it’s always possible that Anthropic has come up with a proprietary scheme, but I think it’s definitely harder to implement.

I think the game will be a cat and mouse game similar to LinkedIn and other websites trying to block scrapers: each iteration makes it harder for someone to figure out the watermarking scheme, but likely not impossible

If you can submit the text to determine whether it's watermarked, you just have to progressively alter the content more and more until it passes.
>Anthropic's magic watermark could be, like the article mentions, something like "every 7th semicolon has a N% chance to be a comma where N is the sum of the last X characters mod Y, and every character in the bit range q1...q2 has a Z% chance to..." etc etc etc.

Doesnt this and probably all techniques require the validator to know which portion of the text to validate?

If its not all generated together then how could it reliably carry the mark? Sure, run it against the full text. But what if the full text was not one-shot by the llm?

In other words, in order to reliably detect if the text is ai you need to first determine which part of the text was generated together by ai.

I don't know if it's possible to achieve a watermark that is undetectable, has a low false positive rate, and survives a wholesale rephrasing. Can you make a statistical measure that reliably survives 95+% of the words being different and the sentences reordered? Of course the more of the content you replace, the lower the quality, but in many cases you probably care more about the meaning of the text than the exact choice of words.
To remove the watermark you'd just need to paraphrase the text with another model that wasn't adding the statistical signature to it. You wouldn't need to know what the original statistical signature was.
It could be, but common, we all know that Anthropic's watermark is the using "load bearing", "genuine", and "seam" 1000x more in the same paragraph than any human in history.
Yeah, but it's better than nothing.

People underestimate the value of rules that only take malice and a little knowledge to break.

And they tend to exaggerate that underestimation if they... don't like the rule.

I even see the Gemini diamond watermark on so many fake social media profile pictures. You could ask an LLM to find a github project that removes/inpaints those watermarks and be done with it. Or you could just use the API, which doesn't stamp the visible diamond on it (just the invisible synthID).

Most people are generally lazy. They upload text to LinkedIn full of "genuine", "honest" and "load-bearing".

It's worse than nothing because it gives people the false impression that there exists a system they can trust to tell them what information is authentic and what isn't. The incentive for governments and other interests to undermine that system to push false narratives and disinformation is just too great.
How is this "better than nothing" considering that it's not even needed in the first place?
Yes! How many people have been caught committing fraud because they used a modern word processor and fonts to forge old documents? How many leaks have occurred because people failed to redact documents correctly, despite there being easy to use tools for this very purpose? How many people neglected to strip sensitive EXIF information from images they share (before websites started doing it for them)? How many people flat out post evidence of their crimes on social media?

Yes, this watermark will be easy to strip. It is still valuable for the vast majority of times where people just don't.

Exactly. If this works on pull requests, for instance, it'd be really useful for projects trying to do a first-pass filter to close slop spam.
The goal of the AI act is not to determine if an "oh yeah!" comment was AI generated. The target is long papers that falsely claim human review and can have real significant consequences.

E.g. research paper, law makers, lawyers, state policies, notaries,...

These are much longer content and thus statistically they will disclose a better guess at AI generated content.

Asking another AI to paraphrase will not erase the mark (which they are unaware about) but rather cumulatively add their own mark and make it easier to detect.

The problem is not to use AI, but to endorse the responsibility of the content you (as a human) deliver and somehow make sure that fake-news, biased content or unverified output is detected as early as possible.

You'd think that the humans being paid to review these things are actually reviewing them. Journals, laywers etc. are expensive. With the addition of AI, it should be easier than ever to review things on their merits.

Maybe some part of it is that the deluge of slop is uncovering how poorly/sloppily these social institutions were working in the first place.

I don’t understand how asking another AI to paraphrase causes both watermarks to remain
https://declaude.org/watermarking/ did a good job in explaining how SynthID works. As per their blog, it feels like it will be difficult to remove watermarking on bigger text and the checking for watermarking is also not complex
There’s some healthy discussion on the submission of that link: https://news.ycombinator.com/item?id=49292932
For SynthID and similar solutions, there is much I don't understand ...

Here's what I grasp: The AI system scores each token and then selects tokens based on those scores. If we encode something in the token selection routine ('in order choose the 1st, 3rd, 1st, 5th, 2nd, then 1st highest scored tokens'), we can identify AI-generated text by comparing sample text (ST) to the expected text (ET) for that prompt.

1) How do we score the tokens for the ET without the original prompt? Even a Markov-like process needs to start somewhere.

2) To recreate ET don't we need to maintain, until the end of time, the AI state - entire model and code - at the time of ST output?

3) Doesn't #2 require maintaining all states for all AIs? Often you won't know when and from which AI system the ST might have been generated. What happens when an AI vendor goes out of business?

4) To recreate ET, don't we effectively have to rerun the prompt? Won't rerunning it for every verification increase most costs of AI output by an order of magnitude? Most of what AI vendors do would be ST validation.

Would OpenPangram's LoRa fine-tuning training be able to pick up on the patterns of steganography left behind by LLM vendors? https://huggingface.co/pangram/editlens_Llama-3.2-3B

(pardon for reposting just now from off-front-page thread on https://declaude.org/watermarking/, just thought of question)

>watermarks

Whatever happened to just delivering the best product or service? Why must tech be full of ninnying nannies that act against their users, "for their 'safety'‽"

I've been working in the media and model IP space for quite many years. What this article misses a bit is the threat model for watermarking in general.

Watermarking and fingerprinting have inherently weak security guarantees -- they rely a lot on security through obscurity, weak assumed adversaries to deliver.

There are clear trade offs between true positives, false positives and maintaining the quality of the media. It's true for audio-visual media, models and their outputs alike.

As much as I like to take shots at poor technical choices by corps and govs, this one is unjustified. Sure, inserting glyphs is bad but biased sampling is as good as it gets in 2026.

EDIT: grammar

Isnt this absurd? Say im brainstorming a resume bulet point. Its 15 words. I like it but want to condense it to a single line. I give it to the ai and tell it how much overflows and now it gives me back a simplified sentence and 11 words and some extra stenography constraint? What kind of rule could possibly not effect the quality of that output?

Ok say i do that on 30% of bullet points. Karen the hiring manager is vehemently anti AI. She gives my resume to her AI scanner and what does she find? This not a rhetorical question. Will it treat the text as a whole and not find it? Does it scan every combination of contiguous terms? It could scan bullet points but i could generate in pairs of 2. What about novels?

I don't really see how this would work anyway.

Even if you find a way to 100% watermark any text, couldn't you just use a non-watermark model? I have a hard time believing every AI company on heart would comply.

Hell, even if every AI company on earth decide to somehow apply watermarking to their next model, they would also need to apply it to all the previous version that they commercialize. Given that anybody could make a copy of an open source model right now and would be safe forever, this seems quite the lost cause to me.

I don't think this is a good argument. There exist digital watermarking techniques for image and video that are imperceptible to humans but still survive cropping, rotation, resizing, recompression, or an analog round trip (photographing the image or pointing a camera at the video). These watermarks aren't, like, hiding in the low-bits of color information, they're spread among many perceptible details.

It's not clear to me that it's impossible, or even especially difficult, to make something that survives a casual LLM paraphrase. Remember, all you need to encode is a single bit of info. There's a lot of space to redundantly encode that signal.

"What would it even look like to sign ChatGPT outputs? There’s no artifact to pass around."

It would look like a lot of little signatures on little bits of text, and then larger signatures on a collection of those chunks once the larger chunk exists. It's not that hard. It's just a lot of signatures.

But this scheme could only ever prove that this bit of text was made by a given AI, and validate anything else ever included in the signature hasn't been tampered with. It's not hard to work up a scheme that proves (within reason) a text was generated no earlier than some date by incorporating some sort of information that could only have been known at that date so that could be validated. But this isn't even a step in the direction of proving that something was made by a human. And that's assuming the private keys stay private, which is its own tricky problem. If a private key ever leaks anything signed with it becomes invalidated.

AI watermarks feel like they're approaching the problem from the wrong side - no matter what it will be possible to remove the watermark (Via manual rewriting, local LLLMs, etc). Instead it seems like we need "proof of human creation". And the only way I see that being possible is hardware-attested proof of keypresses. Which obviously has huge privacy implications, but how else would you actually know that a piece of content was produced by a human pressing keys on a keyboard? The proof would also need to include timestamps for the keypresses, so tell if someone is just copying from another window.
“Everyone will simply do fraud” - it is a bizarrely immoral world that the LLMs seem to have unleashed on us. Tech was kinda heading that way anyway, but our friends the magic robots really seem to have turbocharged it.
I done a C2PA implementation for raw text, if that can ease someone’s life: https://github.com/dualeai/c2patxt
I have a feeling this is really aimed at coding comments. I've noticed that Opus and Fable have started writing huge block comments all of a sudden.
Anthropic the other day announced future models "launched in the EU" will include watermarking https://support.claude.com/en/articles/16266773-how-claude-m...

I was assuming it was something like SynthID rather than just sneaky invisible unicode but it's hard to tell from the description.

Maybe this is an information theory thing but can there exist a n“ I am human” shibboleth or is this concept fundamentally impossible?

It feel that today you can generally convince someone with text alone that you’re human. Beepity boopity zip zap zoopity today’s AIs aren’t this loose and derpy. Here’s a fTypo and my secret stash of dashes ——-–.

But one day even this won’t do, right?

here‘s what i don‘t get about this whole discussion. AI companies already store all prompts and responses for future training.

just make an API that returns the string distance between a previously generated paragraph and the query?

that would sidestep this whole problem class.

regulators could even specify how that has to work.

what am i missing?

Society can overcome this problem by changing the way we think about text. Raw plain text should be banned, all text is cryptographically signed by the editor, gui element, or tool that created it.
Is there a way to not only watermark that it was AI, but also which user generated it? Can it be used to fingerprint?
If everyone is using ai (per reported revenues) and nobody like the outcome (banned here, dismissed there), then what is the future of AI?
Good. Tracking and surveillance have no place in the modern world.
Yeah but it's like saying "Masterlocks will always be easy to pop off with a hammer". Of course, but by doing so you are actively engaging in fraud, which then puts the onus on you and whoever you are attempting to deceive.