551 karma · joined May 4, 2015
And of course, this is very much dependent on the switching costs. Which sucks, because tech companies are great at making this as high as possible; the EU is trying to do something about this with their data portability legislation.
Anecdote: I worked at a company providing infrastructure as service. We lost some bids the first time around, likely for a variety of reasons (price, features, ...). And, some customers came "back" to us after using our competitors' offerings and being burned by their reliability. Yes, switching costs were real, but the pain of losing their customers was even higher.
Aside: this reminds me of running a “negative split” in a long distance race, where you aim to run the second half faster than the first. It’s very hard to do this because you have to be willing to let everyone else in your pace group pull waaay ahead, and running above race pace in the beginning feels “free” with all of the adrenaline. But if you do manage to stay disciplined, it’s a fantastic feeling to reach half-way with gas in the tank, and then start to reel in all those runners who sped by in the beginning.
I can also see the argument for providing a post-training service from a customer acquisition perspective: "hey, we can fine-tune this open weights model, so it both gives better/more predictable results than OpenAI/Anthropic and also is cheaper. And btw, once we've won your business, please run this model on our infra."
But what I'm struggling to understand is fireworks spending a bunch of money (on salaries and compute) releasing a frontier model that is going to rapidly fall behind the frontier. Is this "just" advertising for them, both for customers and also for hiring? Or are they actually trying to stay on the frontier? If so, to what end?
Given this arrangement, Klein had a limited ability to push back (well, he could, but that would mean a bunch of future interviews wouldn’t happen). Instead, he left it up to the viewer to make their own inferences. And some people are going to see Klein’s lack of push-back as a legitimization of Huang’s views.
After watching the first twenty minutes or so (I couldn’t watch any more), I had a much lower opinion of Klein as an interviewer who is willing to ask the tough questions no matter who he’s talking to.
LLMs embody value systems, and American and European values are not the same (yes, there are overlaps, but also key differences).
More nefariously, I can also imagine LLMs that silently degrade their reasoning when used in a national security context of a non-US country.
So, Mistral may not be competitive with OpenAI and Anthropic, but in many contexts that doesn’t matter. And, perhaps this gap could be closed with more funding (the three billion funding figure is a rounding error next to US labs). I’m sort of surprised that the EU isn’t stepping in to support them.
Of course, there are some things that aren't exactly written down, but which you should either do just enough of, or else be able to plausibly deny doing (ignorance is a good cover for this), so that's what people do. For example, during oncall, you investigate just enough to clear the alert and show that you attempted to understand the problem. Of course, you don't really try to understand the problem, because that would take too much time away from your paperclip maximizing.
Which is all to say: I don't know anything about OpenAI culture, or why nobody stopped this sooner, but I have seen examples in other organizations of people not really wanting to understand too much.
“Despite the desperate humanitarian crisis, a survey conducted in May by the Institute for National Security Studies at Tel Aviv University found that 64.5 percent of the Israeli public was not at all, or not very, concerned about the humanitarian situation in Gaza.”
“About three-quarters of Israeli Jews thought that Israel's military planning should not take into account the suffering of the Palestinian civilian population in Gaza, or should do so only minimally, according to another recent survey by the Israel Democracy Institute, a nonpartisan research group in Jerusalem.”
https://www.nytimes.com/2025/07/28/world/middleeast/israel-d...
> The SU service fetches candidate accounts from various sources, such as your friends, accounts that you may be interested in, and popular accounts in your area. Then, a machine learning model blends them to produce a list of personalized account suggestions.
> This enabled us to reduce the number of instances of the Suggested Users service from 720 to 38.
They're practically bullet proof, although they can take a little longer to bite when coated in mud :)