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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This is basically the entire argument by Richard Sutton happening in real time:
https://www.youtube.com/watch?v=21EYKqUsPfg
Right now, we keep creating new models with larger and larger weights and throwing hardware at it. His argument is that this is a dead end and ultimately we'll eventually go back to purpose fit algorithms like we've always done in the history of AI development.
Very true. In fact, there's a lot more under the surface that will come to light once more efficient tiny models come out.
P.S. Building something that proves that you don't need those many params even.
Going by the chart in the article, if your total workload is 1k cataloged items and your quality threshold is 70%, why wouldn't you just pay $19 to gemini API instead of $500 + time to make a custom fine tune?
>As does the economic picture that justified the massive infrastructure building that’s now broadly funded by a complex network of debt.
I have a genuine question and I'd like to hear people's good faith thoughts on this.
There's a fair case that open models are threatening to institutions who spent a lot on training proprietary SOTA models.
But to my understanding, the massive investment spend (much of it debt backed as you note) is on data centers, chips, physical infra.
Yes, this infra is needed to train the models, but it is also needed to serve inference.
Perhaps the costs associated with training SOTA models is ultimately a "bust" given open models eroding the SOTA closed model performance advantage.
But demand for inference is skyrocketing and there seems to be no end in sight.
The physical hardware underpinning inference is in fact a scarce good (currently, and this seems sustainable at least over mid-term). And inference is a scarce service as such.
I know that cost of inference constantly goes down as models, technical infrastructure, and applied AI techniques become more efficient (specialized SLMs etc). So this puts downward pressure on prices.
But still... demand for inference is just growing like crazy regardless. Putting upwards pressure on prices.
Doesn't this mean that all the spending on AI infra is much better positioned to get positive ROI regardless of the type of model being served?
Put another way, models seem to be commoditizing, but physical hardware is not (currently).
The vast majority of the AI boom spend is on hardware to my understanding (even training capex can be repurposed for inference).
Doesn't this suggest that the economics for the "railroads level of build-out spending" are healthier than they might seem at first glance?
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.
But then can't the case be made that narrower and stricter-defined use cases are better served by more conventional ML? If/Wherever efficiency is a concern, that is.
I mostly agree, but at the same time I wouldn't blindly ignore the "it's China" angle at all. These are models you cannot open up and do a thorough audit of. I do think local compute will eventually be good enough that most people will just use local models. Microsoft might be the ultimate winner of that if they can stop ruining Windows and focus on making an OS similar to how straightforward and simple macOS is, no ads literally everywhere for Microsoft Office. Just make a good OS Microsoft, is that so much to ask? You HAD a really solid OS and you ruined it.
Yeah and these latest and greatest models universally suck hard for any use case outside of the few 'blessed' ones. Like I'm sure their ability to write prose and generally sound like a human being has regressed quite a bit, but even if not. Opus 5 is barely above GPT4 when it comes to stuff like home improvement advice.
I’m not sure it will go this direction. Using a generic (near) frontier model is often cheap enough that you need to talk really big volumes before it pays off to fine tune.
My example: we were doing single digit millions of automated call summaries a few years back at a major bank with GPT-4o. Smaller model gave more rejected summaries (compliance not happy), so we briefly looked at fine tuning a smaller model and basically concluded that even at that scale the effort of data collection, management, fine tuning, hosting the model, etc didn’t have a sufficient business case vs picking up other projects.
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.
I actually prefer using less powerful models most of the time, I might use a stronger model initially and then switch to weaker models as I fine tune the output.
Offloading too much of your task to models eliminates the human ownership, without ownership you can't move forward. Context sizes can't keep up with large codebases and markdown files with instructions and guidelines only take the LLM so far in the ownership aspect.
My gut feeling is that to start offloading ownership to the LLM we would need to see at least two order of magnitude increases on context size.
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.
There is no such thing as extracting "task specific intelligence" into a cheaper model.
the major labs want to advance science. current business use cases are a happy accident
> 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.
Most generic use cases do benefit from a model that has been trained broadly. When you don’t know the specific use case ahead of time, you have to have world knowledge ready to go. Even when coding it’s helpful to have all that knowledge on tap so the model can understand product intent and use cases for the product you’re building.
> 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
The real expensive part of fine tuning is gathering a good data set. That’s been the hard part of training anything for a long time. If you’re lucky enough to have a neatly organized and clean data set then you can attempt it, but you need to be in a position to run evals and measure quality.
I’ve done it, but there are so many use cases where the engineering, data labeling, and ongoing quality review hours cost so much that it would be cheaper to continue using a frontier lab model that just works from the start.