I would wager the majority of ML and data science work in the world aren’t frontier LLM development.
It's completely reasonable for the establishment to reject a request for an alcoholic drink, and suggest something alcohol-free instead.
It is not reasonable for them to say "sure, here's your alcoholic drink as you requested" and give them an alcohol-free substitute without telling them.
The fact that the patron broke the rules has nothing to do with it.
Your analogy doesn't work because: - they tell you the rules at the entrance of the bar - they totally tell you when they give you a substitute
The only issue is the bartender asking you for your money before serving you the drink really but again, this is known since day 1 by the customers.
"This is alcohol"
And
"Or maybe it isn't alcohol."
Or to rephrase it, "They tell you the rules at the entrance, they then tell you they don't follow those rules and they are totally serving alcohol even if they are not."
You can decide you are okay with that or not but they aren't dishonest. I wouldn't enter that bar personally but if you do you cannot really complain. It is like complaining because you haven't won at the casino.
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?
It's not really that hard to actually prove it with math.
It's a computer, so to produce the boolean result (safe or unsafe) there has to be a mathematical formula. This formula will inherently be extremely complex, but even a very simple formula has a huge problem. Suppose "unsafe" is true if X - Y > 0. Make X and Y themselves as simple or complicated as you like but even in the simplest version it's already impossible to calculate unless the model has perfect information.
You can't calculate "X - Y" if you don't know the value of X. And it's indisputable that there is information it doesn't have. Case in point, telling you about a vulnerability in some piece of code is safe (and indeed not telling you is unsafe) if you're the developer and you want to patch it or an administrator and want to mitigate it, but the opposite if you're the attacker and want to exploit it. The model does not know which one you are, therefore it cannot make the correct determination any more than it can solve one equation with two unknowns.
It's only the direction that has direct potential business impact they've decided to sabotage instead of reject.
(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.)
Not that I expect better from openai but at least they're not pretending to be good.