Both of those things are true, I'd guess.

I am not an accountant, but as I understand it, "Profit margin" is generally "operating profit margin", meaning revenues less operating expenses, all divided by revenues, and their operating expenses aren't that large, so they can absolutely have very strong operating profits. But they also have mind-bogglingly huge capital expenditures going on, so when you account for those they're losing a lot of money. I'm surprised they're projecting profitability as soon as 2030, actually. That's only 3.5 years away.

Well... an AI model that is capable of hacking into the benchmark database and altering its score is clearly better than one that is not.

Nope. You misunderstood. It's not that the US model wasn't able to detect it, it's that the US model's guardrails prevented it from explaining. This doesn't demonstrate a capability gap against Chinese models, it demonstrates a two-sided failure of the safety protections of the US models. On the one side, the safety guardrails on the OpenAI model failed to prevent the attack. On the other side the guardrails on whatever US model(s) they tried blocked the model(s) from explaining the attack (presumably to protect against the explained-to people from learning how to perform it).

It's absolutely nicer. I've been writing C++ for 36 years and really enjoy it, but I find Rust to be much more pleasant to work in.

I don't know about Qt from Rust (I avoid UI code). It's been 15 years since I wrote any Qt code so I don't really remember it all that well even from C++, and it's probably evolved, so I can't even guess about what it might be like and whether the impedance mismatches with Rust are significant -- The C++ and Rust object models are somewhat different, so there definitely can be mismatches.

But in general, I find Rust is just nicer to work in. I'm more productive and the code feels cleaner and tighter.

As for the "nagging"... that's really only a learning curve issue. It's a non-trivial learning curve, true, but once you get past it the borrow checker doesn't really get in your way. And what you get in trade for that learning curve is fantastic. You just don't have to think about a whole raft of memory-safety and concurrency problems, because the language guarantees they can't occur. You can get the memory safety guarantees in Python and similar language, but they come at a heavy runtime cost.

And, yes, I know all about "Modern C++" and how avoiding raw pointers, etc., is supposed to give you memory safety. And it does help, a lot... but you'd still better run valgrind on your programs to check for subtle mistakes, and even that might not catch them all.

Overall, Rust is just nicer.

The incident report's actual words were:
  The campaign was run by an autonomous agent framework ... executing many thousands of individual actions across a swarm of short-lived sandboxes,

Ooh! I love syllogisms.

1. LLMs cannot actually do things like that with proper protections in place.
2. A LLM did that.
3. Conclusion?

Conclusion: proper protections were not in place.

I don't care whether I use Python or C++ or Rust... though I prefer Rust. It's nicer.

Models are not "supposed to" commit crimes, for YHVH's sake. That's literally what the entire job of alignment research is for - preventing precisely that.

Sol is a misaligned model.

It was a state actor-level hack. I doubt many sites would stand up to tens of thousands of actions trying to probe your site for weaknesses all at once. And it wasn't a simple hack; it required compromising a worker via a code execution exploit it discovered in the data processing pipeline, vertical escalation from there to gain local node control, using that for credential theft, moving sideways through the network, and then eventually gaining database access.

OpenAI and Anthropic have far higher cost that they can currently charge customers.

Literally the opposite. Both have about 40% margins, and that includes free users.

They charge an arm and a leg for access to their models compared to what open models of similar param counts charge. And people pay it because they're the best in benchmarks, and *were* perceived as the best aligned as well. This isn't helping the alignment perception any, though. Sol was already showing clear signs of being a poorly aligned model (there were reports a week or two ago about it being unusually bad about deleting files; now this). And the fact that the US model HuggingFace *tried* to use refused to help is a double whammy.

If this is an ad for anything, it's an ad for the Chinese models.

No, I think this is along the lines "our products are too good to let you use them".

Nobody wants to use a product that is going to make them liable for crimes it committed in their name

Do you think the news the other day that Sol is unusually prone to deleting files unrequested is also an "ad"?

You have a very bizarre concept of what enccourages people to buy things.

So your argument is that OpenAI hatched an elaborate scheme with a separate company, to promote the idea that their main product will, unrequested, commit crimes in pursuit of its goals, in order to.... sell their product?

"Hi, I'm a product manager at Big Company! We had been thinking about using Claude in our office, but when we tell it to a job, it only does the job and doesn't commit any crimes in the process! What we really want is an AI that, if we tell it to file our taxes will decide on its own to maximize our return by committing tax fraud. We want an AI that when we have it develop a web frontend, it extorts money from our users by threatening their families. We want an AI that when we tell it to provide customer service, it saves money in dealing with complaints by ordering a hit against the complainants. THAT's the sort of get-go spirit that WE want in an AI here at Big Company!"

Is that what you're picturing in your conspiracy theory?

You think "my AI is so misaligned that it responds to a benchmark task by hacking" is an ad for your products? You think this makes companies eager to allow this on their networks?

It did it because it's a powerful but misaligned model and was tasked to max its scores on a hacking benchmark, and solved the problem by hacking to get the scores.

It wasn't told to hack HuggingFace, but it was a viable solution to the problem.

I would advise people to not task Sol with maximizing paperclip production.

You can read the attack report here, before HuggingFace learned that the attacker was OpenAI. It was a state attacker-level assault, involving tens of thousands of simultaneous actions. The entry point found was the data-processing pipeline, where a malicious dataset abused two code execution paths to gain access to a processing worker. They escalated that to node level access, and from there harvested cloud and cluster credentials, moving sideways through the network until they eventually gained the database access credentials that they needed to access the benchmark scores.