You need those staggering amounts of compute to run an inference service, but you don't need it to run a powerful model.
It doesn't cost a company that much to buy a box capable of running the Qwen3.8-Flash-Next model in the GP comment and never pay Anthropic again. The cost will be even more reasonable once the supply issue is solved for that hardware.
AI-as-a-service is a unique product because when the interface is natural language, there is no lock in. VS code has a hotkey to swap between claude, openai and a local model. Compare that to the difficulty of swapping from macOS to Windows.
I’m not convinced that being able to serve open models makes the closed labs business model non-viable. I think there is overlap but I also think these are distinct value propositions. I wouldn’t dismiss the ability for one organization to point lots of resources at a particular problem, for example.
I don’t have any stake in this and I hate the idea of concentrated wealth and power, wherever it is: government or private businesses. But would I bet against it? Probably not.
I wouldn't be so sure about capital in future. I think there might be real look for return on any new capital they are getting. Be it equity or debt. And that alone will not be great for them.
How long they will have access to staggering amounts of compute is uncertain and not guaranteed, especially if they get a junk or near-junk credit status when they go public.
the idea of a moat is that it's an inexpensive way to keep the invaders at bay.
If you need to buy staggering amounts of compute, spend staggering amounts in utility costs and lobbying, and keep spending that as your hardware amortizes and your operations go on, what you have isn't a moat, it's a standing army. It's extremely expensive, and the day you start having issues, mercenaries will cut your throat before you know it.
The current compute they are buying is NOT state of the art (check this year's academic research on inference hardware). There is 10x-100x inference-per-watt improvements in existing prototypes (in the industry, not just academia). Not sure what I am talking about? Ask your friendly AI agent of choice, for the details.
Capital only means anything if you can spend it to buy resources like compute and turn those resources into additional value.
Hardware's ridiculously expensive. Then, there's no good place to put it, because the new breed of tech bro managed to propose the data center buildout in the most exploitative, alienating, and electorate-angering way possible. So compute's hard to buy.
You need those staggering amounts of compute to run an inference service, but you don't need it to run a powerful model.
It doesn't cost a company that much to buy a box capable of running the Qwen3.8-Flash-Next model in the GP comment and never pay Anthropic again. The cost will be even more reasonable once the supply issue is solved for that hardware.
AI-as-a-service is a unique product because when the interface is natural language, there is no lock in. VS code has a hotkey to swap between claude, openai and a local model. Compare that to the difficulty of swapping from macOS to Windows.
I’m not convinced that being able to serve open models makes the closed labs business model non-viable. I think there is overlap but I also think these are distinct value propositions. I wouldn’t dismiss the ability for one organization to point lots of resources at a particular problem, for example.
I don’t have any stake in this and I hate the idea of concentrated wealth and power, wherever it is: government or private businesses. But would I bet against it? Probably not.
Sure, but anyone with money could do that.
We're the only people that can spend this money is not a moat.
I wouldn't be so sure about capital in future. I think there might be real look for return on any new capital they are getting. Be it equity or debt. And that alone will not be great for them.
How long they will have access to staggering amounts of compute is uncertain and not guaranteed, especially if they get a junk or near-junk credit status when they go public.
The capital is running out and the compute is a massive liability on their balance sheet. Your definition of a moat is rather odd.
How much of their compute is leased/rented? Last I read it was quite a lot of it.
the idea of a moat is that it's an inexpensive way to keep the invaders at bay.
If you need to buy staggering amounts of compute, spend staggering amounts in utility costs and lobbying, and keep spending that as your hardware amortizes and your operations go on, what you have isn't a moat, it's a standing army. It's extremely expensive, and the day you start having issues, mercenaries will cut your throat before you know it.
That argument seemed to have legs in 2022 when OpenAI was the only game in town.
But the fact that were even talking about Anthropic like this only a few short years later demonstrates that that was only kind of a kind of moat.
The current compute they are buying is NOT state of the art (check this year's academic research on inference hardware). There is 10x-100x inference-per-watt improvements in existing prototypes (in the industry, not just academia). Not sure what I am talking about? Ask your friendly AI agent of choice, for the details.
Check out mythic.ai for one example.
compute and debt*
Banks are going to want that "capital" to materialize into gains at some point. The water in that moat is evaporating.
Capital only means anything if you can spend it to buy resources like compute and turn those resources into additional value.
Hardware's ridiculously expensive. Then, there's no good place to put it, because the new breed of tech bro managed to propose the data center buildout in the most exploitative, alienating, and electorate-angering way possible. So compute's hard to buy.