Their goal is to downgrade people who are violating their TOS, so I think they'd have some argument there. I have no idea how they'll deal with inevitable false positives, especially given how oversensitive most of the other triggers are.

The challenge is the examples they’ve mentioned (distributed training infra? ML acceleration techniques?) go beyond what’s prohibited by their ToS and is like a catch net.

I would wager the majority of ML and data science work in the world aren’t frontier LLM development.

You know, I'm not saying I don't understand what they are doing from a business perspective, but I'm just saying: DeepSeek V4 doesn't silently sabotage you because it thinks you are trying to violate a ToS. Anthropic's clawing back a bit of a moat perhaps, with Fable being an actual improvement of sorts, but now with torching user trust they are really banking on open weight models not catching up to where they are now. I wonder if they have a good reason to believe that they won't, or are hoping for something entirely different to save them.

(P.S. Yes of course I know about model censorship, a different problem, but all of the models are censored to some degree. It happens to be less of a problem for open weight models anyhow, but I figured I'd just preempt this since it's inevitable.)

I actually kinda like DSv4 over Opus 4.7 for some tasks, although I have not figured out what the deciding factor is. (Opus 4.8 so far has not worked very well for me at all, no idea why.)

If it's a violation of ToS, just reject instead of silently downgrading.