alt Hacker NewsTheir 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.
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It’s just impossible.
Look at real-life stuff like laws, company policies, or school rules. Humans have to enforce them, and we constantly see crazy cases in the news. There’s no way simple rules can ever make speech completely 'safe.' I can't prove it with math or logic yet, but I have a feeling that it’ll never happen. Even humans can't do it.
We can run a simple thought experiment here. Say Case A violates rule B, so we add rule C. Then Case D violates rule B but follows rule C, so we add an exception... and it just goes on and on like that forever. It never ends. In the end, you just get a massive pile of rules that makes it impossible to get anything done.
Ultimately, we will have to face the truth that knowledge is dangerous.
Giving knowledge directly to people who cannot actually understand it and allowing them to just use it blindly can be extremely unsafe.
To use a real-world analogy, the problem we are facing with weak AI right now is just like the debate over gun legalization. Do we want to risk the abuse of guns or knowledge just to protect the freedom to own them?
If it's a violation of ToS, just reject instead of silently downgrading.
They will give you s*t output, that’s how they deal with it. And say that less than 1% of the requests were affected. Think of this like a kind of shadow ban while you still pay top $.
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.)
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.