Is it, though? Most of OpenAIs customers are free users and outside of the coding sphere there isn’t a major usecase for LLMs. When the bubble pops and people will have to pay a lot more for their token usage, a lot of them will just turn away. The free users will never convert to a paid model as well.
Nobody is going to buy that stuff in bulk just to start desoldering chips.
Don’t underestimate Chinese vendors doing absolutely everything to turn a profit.
It’s the code generation and automation uses that are growing. I don’t know about casual use or using AI for office tasks.
AI providers that offer API access don’t have enough hardware to support the demand. They keep increasing prices (the roundabout way by slowing down requests, reducing the amount of included tokens in tiers, and replacing models with new ones that use more tokens) and that’s still not enough to reduce demand.
Using AI to code and automate stuff is probably its most effective use. But most people don’t see it. Is all business/enterprise stuff happening behind the scenes.
The hardware shortage is self-induced by the LLM providers, though - it doesn’t reflect actual demand. They’re shooting themselves in the foot by making their models bigger and throwing more and more data and compute (and energy and water) at it, for increasingly diminishing returns.
In my opinion, that’s not sustainable in the longterm and after the bubble pops, even the code generation ecosystem will change a lot. It’s gonna be interesting to see which way of working will survive.
Is it, though? Most of OpenAIs customers are free users and outside of the coding sphere there isn’t a major usecase for LLMs. When the bubble pops and people will have to pay a lot more for their token usage, a lot of them will just turn away. The free users will never convert to a paid model as well.
Don’t underestimate Chinese vendors doing absolutely everything to turn a profit.
It’s the code generation and automation uses that are growing. I don’t know about casual use or using AI for office tasks.
AI providers that offer API access don’t have enough hardware to support the demand. They keep increasing prices (the roundabout way by slowing down requests, reducing the amount of included tokens in tiers, and replacing models with new ones that use more tokens) and that’s still not enough to reduce demand.
Using AI to code and automate stuff is probably its most effective use. But most people don’t see it. Is all business/enterprise stuff happening behind the scenes.
The hardware shortage is self-induced by the LLM providers, though - it doesn’t reflect actual demand. They’re shooting themselves in the foot by making their models bigger and throwing more and more data and compute (and energy and water) at it, for increasingly diminishing returns.
In my opinion, that’s not sustainable in the longterm and after the bubble pops, even the code generation ecosystem will change a lot. It’s gonna be interesting to see which way of working will survive.