> The companies that are entirely AI-dependent may need to raise prices dramatically as AI prices go up.
It's not that clear. Sure, hardware prices are going up due to the extremely tight supply, but AI models are also improving quickly to the point where a cheap mid-level model today does what the frontier model did a year ago. For the very largest models, I think the latter effect dominates quite easily.
>> The companies that are entirely AI-dependent may need to raise prices dramatically as AI prices go up.
> It's not that clear. Sure, hardware prices are going up due to the extremely tight supply, but AI models are also improving quickly to the point where a cheap mid-level model today does what the frontier model did a year ago.
I agree; I got some coding value out of Qwen for $10/m (unlimited tokens); a nice harness (and some tight coding practices) lowers the distance between SOTA and 6mo second-tier models.
If I can get 80% of the way to Anthropic's or OpenAI's SOTA models using 10$/m with unlimited tokens, guess what I am going to do...
There's only so far engineers can optimise the underlying transformer technique, which is and always has been doing all the heavy lifting in the recent ai boom. It's going to take another genius to move this forward. We might see improvements here and there but the magnitudes of the data and vram requirements I don't think will change significantly
I’ve read and heard from Semi Analysis and other best-in-class analysts that the amount of software optimizations possible up and down the stack is staggering…
How do you explain that capabilities being equal, the cost per token is going down dramatically?
State space models are already being combined with transformers to form new hybrid models. The state-space part of the architecture is weaker in retrieving information from context (can't find a needle in the haystack as context gets longer, the details effectively get compressed away as everything has to fit in a fixed size) but computationally it's quite strong, O(N) not O(N^2).
Inference prices droped like 90 percent in that time (a combination of cheaper models, implicit caching, service levels, different providers and other optimizations).
Quality went up. Quantity of results went up. Speed went up.
Service level that we provide to our clients went up massively and justfied better deals. Headcount went down.
The decline of independent thoughts for one. As people become reliant on LLMs to do their thinking for them and solve all problems that they stumble upon, they become a shell of their previous self.
There is no decline. Human assets were always too expensive to process some additional information. We are simply processing lot more of low signal data.
Actually some of our analysts are empowered by the tools at their disposal. Their jobs are safe and necessary. Others were let go.
Clients are happy to get fuller picture of their universe, which drives more informed decissions . Everybody wins.
You also have to look at how exposed your vendors are to cost increases as well.
Your company may have the resources to effectively shift to cheaper models without service degradation, but your AI tooling vendors might not. If you pay for 5 different AI-driven tools, that's 5 different ways your upstream costs may increase that you'll need to pass on to customers as well.
It's not that clear. Sure, hardware prices are going up due to the extremely tight supply, but AI models are also improving quickly to the point where a cheap mid-level model today does what the frontier model did a year ago. For the very largest models, I think the latter effect dominates quite easily.