As per the link, the words in the green and red groups are calculated dynamically, so it's not like the model is going to be told "use 'unique' over 'unusual'" and suddenly writing from the model will contain the word 'unique' far more often than 'unusual'. So I'm not sure it's clear that this has an impact on creativity as such?

That being said, I do question how this will apply to code as opposed to prose. Even data dense text (ie, if you ask Claude to evaluate what running shoe to buy, and it spits back a list of options with reviews and prices) may struggle.

What it probably will work well at it flagging the current tsunami of entirely AI generated novels on Amazon/Kindle, which is...honestly not without value.

> Identifying code will be paramount in training future models

True, but note that this strictly allows providers to identify text generated by their own models. If Anthropic wants to filter out GPT generated text in their training data, they'll need to feed it through an OpenAI API, which is implausible. So it might help on the margins, but I don't think it solves the problem of model collapse.

The model is not given a list of words. The model has a seed (key) which at inference splits all possible next token into list A and B, and nudges (has a bias) for list A. How many possible options the next word has depends on what came before, some will have a few hundred, some will have hundreds of thousands of possibilities.

The model essentially is just doing what it always does which is predict the next token, but the next token now is split into two and nudged towards one side more often than the other which is how over a body of text identifies if the text was in-fact AI or not. Which is also why the shorter the text the harder it is to identify.

As for the second half, I predict that almost all American, Japanese and European labs will use watermarking at some point (as well as SynthID for other generative AI). It doesn't matter if they all have unique internal seeds because they'll give people the tools to ID AI, be it by selling it (unlikely for most use cases , but likely gonna happen for academic where they'll provide some value app that bulk checks student works), or more likely make it free to check like SynthID where you simply ask Gemini if the image has SynthID. At which point they can just pay each or use each other's tools to check.

The wildcard are the Chinese models, but those will likely force some sort of watermarking as well, if not for the global market, for the CCP's benefit.