131–140 of 557 posts
The tragedy is when it's all over one of the surviving passengers will go "See! I knew we were going to crash because of that knitter"
Earlier quoted context omitted.
Is that right? I think that you can serve tokens without training the next models. It would be bad strategy, but it would work. So it's an important question, are they covering their operating expenditure? If they are the business has legs (and it will be worth spending a lot to train the next models). If not, maybe not.
If a major model provider were to just halt progress on developing new and improved models, the open weight alternatives would catch up in a couple years. They would have a period of great margin, followed by possibly zero margin as enterprises move to free options. They would have to come up with a lot of great products around the inferior models to justify charging at that point.
It’s incredible how polarizing the AI rush is. I keep the perspective that the technology is an absolute step change but I have no idea where the cards will fall. I take a lot of issue with these style of articles. I get a sense that the authors are being overly defensive. The cost to serve tokens is absolutely profitable today and that’s been true for at least a year. What’s unclear is how R&D and capex fit into the…
> The cost to serve tokens is absolutely profitable Can you explain why you know better than the analyst at Cursor cited in this article?
It’s incredible how polarizing the AI rush is. I keep the perspective that the technology is an absolute step change but I have no idea where the cards will fall. I take a lot of issue with these style of articles. I get a sense that the authors are being overly defensive. The cost to serve tokens is absolutely profitable today and that’s been true for at least a year. What’s unclear is how R&D and capex fit into the…
> "decades of overinflated engineering salaries" 'Overinflated' relative to what? You make some good points but I don't accept this as a premise.
> nobody is sure if even their metered pricing is profitable This is most likely wrong. Lab executives insist that serving tokens is profitable. It's the cost of training next-gen models that requires them to keep raising ever larger rounds. More importantly, many independent providers price tokens of open-weight models at a fraction of Anthropic's prices.
So these companies will be profitable if training stops? Is that even a real possibility?
I suspect that once the models hit a point of “good enough” for certain use cases companies will start putting R&D focus in other areas that may be less expensive. Like figuring out how to run more efficiently, UI/UX conventions that help users get what they’re trying to accomplish in fewer steps, various kinds of caching of requests, etc. So the cost to serve tokens over time should only come down, and will probably start coming down more rapidly as the returns to model training slow down.
That’ll probably be a while though, because each successive model tends to be a lot better than the last.
HN is no longer a reliable place for the truth. Quite frankly, unless you are utterly self educated, you are terribly vulnerable to this place. At this rate, I’d almost prefer to talk on a private mailing list with vetted resumes.
> HN is no longer a reliable place for the truth. "No longer?" It never was. Especially with AI boosters being allowed to degrade the comments section and shilling their paid blogs and violating the HN guidelines.
This is an awful article. I don't know how it reached #1 on HN. Bottom line is that H100 prices are near 3 year highs, A100s are still profitable to run, B200 prices are increasing, no one has enough compute. Google, OpenAI, Anthropic, Meta, AWS, Azure are all compute constrained. Every single one of them said so publicly. Neo clouds are telling customers they're all sold out now and you even have to book compute in…
I am yet to see how a one-legged business model with just a single product (that is not crude oil), without a plan and money is going to become sustainable. Oh yeah, maybe they'll finally make money on those autonomous lethal weapons. That sounds the easiest.
Earlier quoted context omitted.
Training. But training costs are a smaller and smaller percentage of revenue as inference revenue grows faster than training costs.
Do you have any evidence that inference revenue is growing faster than training costs? RLVR is significantly less compute-efficient than token-prediction pretraining - especially as labs are trying to train models to achieve agentic tasks which take tens of minutes per rollout.
However, it seems to make a lot of sense. Anthropic literally added $6b ARR in February 2026 alone. I doubt training costs go up that fast.
This article tries to build upon a lot of half-truths or incorrect facts, like this: > OpenAI is struggling to monetize. They turned to showing ads in ChatGPT, The ads aren’t going into your paid plans (except maybe a highly discounted tier, depending on the market). The ads are a play to offer a free version. Having an ad-supported free tier isn’t new. The discussion about being unprofitable also repeats the reducti…
To be fair people aren't exactly bullish on the prospects of deepseek or z.ai either, it's just they're below radar so they don't get mentioned.