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It matters because as long as they are selling inference for less than it costs to serve they have a potential path to profitability. Training costs are fixed

by simonw 9mo ago

It matters because as long as they are selling inference for less than it costs to serve they have a potential path to profitability. Training costs are fixed at whatever billions of dollars per year. If inference is profitable they might conceivably make a profit if they can build a model that's good enough to sign up vast numbers of paying customers. If they lose even more money on each new customer they don't have any path to profitability at all.

citrin_ru 9mo ago
> If they lose even more money on each new customer they don't have any path to profitability at all. In theory they can increase prices once the customers will be hocked up. That's how many startups works.
bildung 9mo ago
But only if you ignore all the other market participants, right? How can we ever reach a point where all the i.e. smaller Chinese competitors perpetually trailing behind SOTA with a ~9 month lag but at a tiny fraction of the cost stop existing? I mean we just have to look at old discussions about Uber for the exact same arguments. Uber, after all these years, still is at a negative 10 % lifetime ROI , and that company doesn't even have to meaningfully invest in hardware. IMO this will probably develop like the railroad boom in the first half of the 19th century: All the AI-only first movers like OpenAI and Anthropic will go bust, just like most railroad companies who laid the tracks, because they can't escape the training treadmill. But the tech itself will stay, and even become a meaningful productivity booster over the next decades.
- viking123 9mo ago
  I am also thinking long term where is the moat if it will inevitably lead to price competition? Like it's not a Microsoft product suite that your whole company is tied in multiple ways. LLMs can be quite easily swapped to another.
overgard 9mo ago
I'm curious just because you're well known in this space -- have you read Ed Zitron's work on the bubble, and if so what did you think of it? I'm somewhat in agreement with him that the financials of this just can't be reconciled, at least for OpenAI and Anthropic. But I also know that's not my field. I find his arguments a lot more convincing than the people just saying "ahh it'll work itself out" though.
- simonw 9mo ago
  My problem with Ed is that he's established a very firm position that LLMs are mostly useless and the business is a big scam, which makes it difficult to evaluate his reporting. He often gathers good information but his analysis of that information appears to be heavily influenced by the conclusions he's already trying to reach. I do pay attention to him but I'd like to see similar conclusions from other analysts against the same data before I treat them as robust. I don't personally have the knowledge or experience of company finance to be able to confidently evaluate his findings myself!

by simonw 9mo ago

citrin_ru 9mo ago

> If they lose even more money on each new customer they don't have any path to profitability at all. In theory they can increase prices once the customers will be hocked up. That's how many startups works.

bildung 9mo ago

But only if you ignore all the other market participants, right? How can we ever reach a point where all the i.e. smaller Chinese competitors perpetually trailing behind SOTA with a ~9 month lag but at a tiny fraction of the cost stop existing? I mean we just have to look at old discussions about Uber for the exact same arguments. Uber, after all these years, still is at a negative 10 % lifetime ROI , and that company doesn't even have to meaningfully invest in hardware. IMO this will probably develop like the railroad boom in the first half of the 19th century: All the AI-only first movers like OpenAI and Anthropic will go bust, just like most railroad companies who laid the tracks, because they can't escape the training treadmill. But the tech itself will stay, and even become a meaningful productivity booster over the next decades.

viking123 9mo ago
I am also thinking long term where is the moat if it will inevitably lead to price competition? Like it's not a Microsoft product suite that your whole company is tied in multiple ways. LLMs can be quite easily swapped to another.

overgard 9mo ago

I'm curious just because you're well known in this space -- have you read Ed Zitron's work on the bubble, and if so what did you think of it? I'm somewhat in agreement with him that the financials of this just can't be reconciled, at least for OpenAI and Anthropic. But I also know that's not my field. I find his arguments a lot more convincing than the people just saying "ahh it'll work itself out" though.

simonw 9mo ago
My problem with Ed is that he's established a very firm position that LLMs are mostly useless and the business is a big scam, which makes it difficult to evaluate his reporting. He often gathers good information but his analysis of that information appears to be heavily influenced by the conclusions he's already trying to reach. I do pay attention to him but I'd like to see similar conclusions from other analysts against the same data before I treat them as robust. I don't personally have the knowledge or experience of company finance to be able to confidently evaluate his findings myself!