Open weight models do not require great investment. In fact I can run them on my 400 EUR computer.
Also why you want to regulate text output from machines in the name of "public good"? That's insanity.
They also said many other things that never happened. And they never showed it. I bet $100 they do not have a semi-resilient method with 99.9% accuracy, especially with all the evolving issues around "human vs computer" made content.
I bet you also the `semi-` in the beginning leaves a lot of room for interpretation and they are not releasing this for more reasons than "our model is too good".
If it's not possible to watermark, then just ban LLMs.
Tech people have this weird self-serving assumption that the tech must be developed and must used, and if it causes harms that can't be mitigated then we must accept the harm and live with it. It's really an anti-humanist, tech-first POV.
As for the rationality of watermarking itself, firstly I'd like to reiterate, no spam wave of this magnitude and undetectability has ever happened in the history of the web. A word processor cannot write a petabyte of propaganda on its own. A Markov chain can't generate anything convincing enough to fool a human. Transformer-based LLMs are the first of their kind and should be treated as such. There is no quick analogy or a rule of thumb to point to.
If statistical watermarking is proven to have sufficient recall and error, there'll be nothing to lose in implementing it. A demand already exists for detecting AI slop; half-working BERT classifiers and prejudiced human sniff tests already provide for it, with little incentive to reduce false positives. With watermarks, there'll be a less painful, more certain way to catch the worst offenders. Do you really think the same operations that produce papers with titles like "Sorry, as an AI model..." or papers with pieces of ChatGPT UI text will care to roundtrip translate or rewrite entire paragraphs?
We already had this exact dilemma back when email spammers tried Bayesian poisoning [0]. Turns out, it actually creates an identifiable pattern, if not for the system, then for the user on the other side. People will train themselves to look for oddly phrased sentences or the outright nonsense roundtripping produces, abrupt shifts in writing style, and other heuristics, and once the large enough corpus is there, we can talk about training a new classifier, this time on a much more stable pattern with less type-I errors.