tangled

I cancelled my subscription. I tolerate advertising on free products, but find it pretty distasteful when I'm already paying $15/month. This reeks of trying to extract every last piece of value from the customer.

Yes, customers are sticky, but every customer has their limit.

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.

Each to their own, and the market will decide in the end? If a company is going to (implicitly or explicitly) incentivize everyone to directly ship Claude’s output straight to production, and prioritize features above everything else, that’s a choice. Providing that I have a long enough runway, I’d love to have this company as my competitor? Yeah, year 1 will suck, because they’ll pull ahead, but if I can stay in the game until years 2 and 3…

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.

What am I missing here? I think of fireworks as an inference provider serving open weights model. The value that they primarily provide to customers is that (i) they improve reliability by balancing across a bunch of clouds/neoclouds, (ii) they get better pricing by buying capacity in bulk, and (iii) they reduce operational costs. So far so good.

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?

As others have said: this was not a good faith discussion. Huang was using the interview as an opportunity to “talk his book”, and Klein was using it to sell his own Serious Interviewer brand. Both were getting something out of this.

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.

Europe absolutely needs a home-grown AI lab, especially with Pax Americana looking increasingly shaky.

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.

My default position is that making money takes precedence over everything else. Yes, some people inside a company may say “we care about doing the right thing” and they might even mean it, but if that comes into conflict with making money, then they tend to lose. Maybe not totally, or immediately, but in the end. The only effective way to prevent (this that I’ve seen) is to have legislation with teeth. It’s probably not a coincidence that after Mark Zuckerberg had to start personally signing off on adherence to the privacy program mandated under the 2020 FTC consent decree, privacy started to become Very Important.

Having previously worked for several years at a Big Tech company, I have seen many humans precisely tailor their work to maximize their scores during performance review. The evaluation criteria are written down, with examples, so... that's what people work at maximizing, almost entirely ignoring everything else. These really are human "paperclip maximizers". And, at first, it's shocking to see.

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.