Watermarking: The Enshittification of Closed Models Has Begun
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Re: Watermarking: The Enshittification of Closed Models Has Begun
#2I think this is overblown, the watermarking is just enforcing a particular recognizable output text style.
The existing Claudisms like "smoking gun" and "it's not x it's y" evidently haven't impacted the model's agentic capabilities much, and i doubt the new ones will either.
Re: Watermarking: The Enshittification of Closed Models Has Begun
#3I think this is overblown, the watermarking is just enforcing a particular recognizable output text style. The existing Claudisms like "smoking gun" and "it's not x it's y" evidently haven't impacted the model's agentic capabilities much, and i doubt the new ones will either.
But it means we are optimizing for something else than the best outcome or not? It is like with mp3 vs flac: mp3 is fine and optimizes for amount of data to represent a certain waveform, but lossless is the real deal for audiophiles and the only one capturing the real thing at a certain resolution. Same might be true for complex code or the best solution to a compression algorithms. Can the question really be answered if optimizing for marking does still produce the best result? Is it not like an author wanting to say something one way, but being forced to use certain words to get there?
Re: Watermarking: The Enshittification of Closed Models Has Begun
#4So is k3 on openrouter the current meta?
Re: Watermarking: The Enshittification of Closed Models Has Begun
#5I think this is overblown, the watermarking is just enforcing a particular recognizable output text style. The existing Claudisms like "smoking gun" and "it's not x it's y" evidently haven't impacted the model's agentic capabilities much, and i doubt the new ones will either.
But it means we are optimizing for something else than the best outcome or not? It is like with mp3 vs flac: mp3 is fine and optimizes for amount of data to represent a certain waveform, but lossless is the real deal for audiophiles and the only one capturing the real thing at a certain resolution. Same might be true for complex code or the best solution to a compression algorithms. Can the question really be answere…
none of the large AI labs know what the best outcome is because lab science never fully predict large scale implementation.
We've had decades of grade A research on social improvement programs and it's clear to everyone that implementation cannot be simulated or easily predicted.
So the "optimizing" thing is a fallacy. It's also found in highly racially charged pseudo science and eugenics; selecting for trains on small scales do not equal large scale benefits. But people's mental model are predisposed to small scales while also being blissfully ignorant of cognitive biases to the predisposition.
Even the idea that we can just keep throwing compute, context, power is a fallacy when you see chinese models making smaller and just as capable models with limited resources.
so, optimizing is something we should be putting into a democratic process because anything else will be lopsided, much like when eugenics was tried.