alt Hacker NewsIt 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.
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> 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.
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.
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.