alt Hacker NewsThe point that the major labs don’t seem to get is that the vast majority of use cases simply don’t need models that have 50 PhDs and can speak 12 languages. Most use cases are defined within constraints where costs matter a lot.
As open weight models and cheap fine tuning services become the norm the whole economic framework of these mega models the labs are in an arms race building just completely crumbles. As does the economic picture that justified the massive infrastructure building that’s now broadly funded by a complex network of debt.
This is what makes open weight models so threatening to them. The political and “it’s China” angle is mostly just a cover for the real reasons why they’re freaked out.
The fact that models are now a pure commodity is bad enough for the big labs. If small open weight models become the norm the big labs are toast.
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As someone who worked at multiple startups, I am pretty sure they get it but it doesn’t fit their goals.
They want to moat where they don’t need to compete with other companies because they have something that other companies can’t have.
In my opinion this is a short-sighted and greedy worldview. Haven’t seen it work personally. It is a different version of the month-to-month salary guy thinking he will be a billionaire and having that thrash “mindset”.
The reality is that practically none of those companies will amount to anything and they would be better off weighing the usefulness aspect of their output more. Instead they are imagining they will be Google.
Anthropic and openai ofc are the pinnacle of this greed culture and they correspond to FTX from the crypto trash hype so I don’t think they fit into the scale of sensibility.
Coming from this perspective, it is pretty easy to see what they are.
seems that, like software engineering and other areas before, they also have to rediscover that one single monolithic solution that handles everything is too inflexible and not maintainable, it's just a bad approach. people don't need the models they use to generate their codebase to also be able to translate Shakespeare into gen-z slang
Some specialized models may end solving themselves by helping fit the problem with the appropriate regular algorithms/formulas, which are a million times more efficient. So they are probably less attractive to dump money on. As of currently they still benefit from expressing lots of patterns that nobody bothered formalizing.
> The point that the major labs don’t seem to get is that the vast majority of use cases simply don’t need models that have 50 PhDs and can speak 12 languages
I think they understand it but they think they can get away with it because they own the narrative. As long as they can make people believe they need such a model, it doesn't matter if it's true or not.
They are playing the cloud playbook, it didn't matter that most companies didn't need 99,999% uptime and instantaneous horizontal scaling, as long as people believed they did they are happily paying 10-100x the cost to AWS instead.
the major labs want to advance science. current business use cases are a happy accident
Fine timing takes time and data, though. If smart enough models get cheap enough, then most people have lots of use cases that are cost insensitive enough that it's not worth the effort.
Of course, "smart enough" is a low enough threshold for most uses that this is still a problem for the frontier labs.
But at the same time, a truly smart enough closed model could also potentially command almost whatever they'd care to charge for it.
Whether they can actually get to that level remains to be seen, but I can definitely see a situation where most people are perfectly happy with cheap middle of the tree models while large corporations pay magnitudes more than current API pricing for access to models never even marketed as a mass market product and keep the labs afloat.
It's of course be a lot easier for them to find the path towards that of they didn't need to compete with open models in the meantime.