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.
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
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.
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".
"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.
> 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.
We don't appear to be talking about cryptographic signing here. (That would never work for the problem because everyone expects unsigned text anyway.) We're talking about:
> It’s basically a text steganography problem (concealing a secret code), made more difficult because the plaintext cannot be arbitrarily manipulated.
As in, trying to force ChatGPT output to contain intentionally crafted ChatGPT-specific LLMisms that a human is unlikely to imitate, even one who reads a lot of ChatGPT output.
> 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 how is this implemented? It's a few lines of code to implement a basic "identify and strip/replace any non printable ascii, unicode ..." or whatever.
A screenshot/OCR will also do this.
SO at the end of the day you're left with some dumb rules like "you used `load-bearing` more than once per 500 words, that's AI!"
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.
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.
> AI companies already store all prompts and responses for future training.
They claim to only do this when you agree to this in your personal settings. Though Google does say they will train on it, unless you disable history and only use ephemeral chats. Anthropic has a setting for it and claims not to train by default.
Also it would be vulnerable to attacks and privacy problems. You could search for substrings about some suspected information, like "John Smith's medical records show advanced cancer" etc. Of course you'd have to guess the phrasing but still.
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.
Key phrase. And I'm not saying fraud in the legal liability sense. If you're not trying to hide the fact that something was LLM generated, then you have no reason to remove it. If you are trying to hide it, then there's probably a reason, i.e. you would face consequences for doing so, therefore it is fraud.
Or, you simply edit the text the LLM generated ruining the hidden message. That's not even remotely fraud. There are legitimate reasons to edit text. There are fewer legit reasons to bash off someone else's lock with a hammer.
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.
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.
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.
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.
Yeah, but it's better than nothing.
People underestimate the value of rules that only take malice and a little knowledge to break.
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".
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.
"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.
> 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.
We don't appear to be talking about cryptographic signing here. (That would never work for the problem because everyone expects unsigned text anyway.) We're talking about:
> It’s basically a text steganography problem (concealing a secret code), made more difficult because the plaintext cannot be arbitrarily manipulated.
As in, trying to force ChatGPT output to contain intentionally crafted ChatGPT-specific LLMisms that a human is unlikely to imitate, even one who reads a lot of ChatGPT output.
> 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 how is this implemented? It's a few lines of code to implement a basic "identify and strip/replace any non printable ascii, unicode ..." or whatever. A screenshot/OCR will also do this.
SO at the end of the day you're left with some dumb rules like "you used `load-bearing` more than once per 500 words, that's AI!"
I done a C2PA implementation for raw text, if that can ease someone’s life: https://github.com/dualeai/c2patxt
Good. Tracking and surveillance have no place in the modern world.
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.
You can ask an AI agent to type into Microsoft Word via computer use.
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.
1. score by the past paragraph (this is also how the verifier checks without needing a GPU, it takes the previous length of text)
2. same answer
3. no, it just needs the previous text, private key, and the matrix math (CPU is fine)
4. no, see above.
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?
> AI companies already store all prompts and responses for future training.
They claim to only do this when you agree to this in your personal settings. Though Google does say they will train on it, unless you disable history and only use ephemeral chats. Anthropic has a setting for it and claims not to train by default.
Also it would be vulnerable to attacks and privacy problems. You could search for substrings about some suspected information, like "John Smith's medical records show advanced cancer" etc. Of course you'd have to guess the phrasing but still.
> AI companies already store all prompts and responses for future training.
They store some prompts and responses, not all, that's what you're missing.
Local models?
Local models enable:
- watermark-free generation
- the stripping of watermarking from the output of SAAS models
Any discussion of watermarking is dead in the water in a world where we are permitted to have these things. I fear for the future.
Chill my dude. This is just a sane default which will catch normies copy pasting stuff from claude and chatgpt. It's good enough.
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.
Except this isn't like saying that at all. This isn't fraud, because it's legal in almost every circumstance.
>almost every circumstance
Key phrase. And I'm not saying fraud in the legal liability sense. If you're not trying to hide the fact that something was LLM generated, then you have no reason to remove it. If you are trying to hide it, then there's probably a reason, i.e. you would face consequences for doing so, therefore it is fraud.
Or, you simply edit the text the LLM generated ruining the hidden message. That's not even remotely fraud. There are legitimate reasons to edit text. There are fewer legit reasons to bash off someone else's lock with a hammer.