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Nvidia, Microsoft, Meta warn against overregulating open-weight models
131–140 of 337 posts
Re: Nvidia, Microsoft, Meta warn against overregulating open-weight models
#132Earlier quoted context omitted.
They did. Check out the Phi models (e.g., https://huggingface.co/microsoft/phi-4 ).
Great, how about some frontier models like China seems to be doing on a weekly basis at this point. Give us a CoPilot variant that's open source & open weights.
They could start by releasing the MAI-1 models they announced recently. https://microsoft.ai/models/
Re: Nvidia, Microsoft, Meta warn against overregulating open-weight models
#133> The unbearable cheapness of open weight models So the closed model try to get client with their model by saying it's cheaper than employees, and then turn around to lawyer-out cheaper alternative? Truly the american dream.
And the verdict is still out on if it's going to be cheaper than employees in the long run. Maybe for companies in HCoL areas (SV/NYC), but any smaller mid-market org in a LCoL area, frontier model token spend may not end up all that cheaper when your employee salaries are $55k-$75k/year so you're mostly evaluating it as an additional force multiplier expense rather than a headcount replacer.
Those salaries seem low for mid sized cities even no?
Re: Nvidia, Microsoft, Meta warn against overregulating open-weight models
#134Now that their lawyers have cornered everyone trying out open weight models and banned the ones they can, they put out a statement like this.
Re: Nvidia, Microsoft, Meta warn against overregulating open-weight models
#135Earlier quoted context omitted.
The future will probably have most of all companies running local models, simply because the alternative would be essentially every company that uses LLMs ending up becoming completely dependent upon Anthropic et al. And that dependence would be milked to the point of absurdity once solidly established. And as a more general point - more major competitors in a domain is very good for everybody except those competitor…
Is that different from how companies are reliant on cloud computing?
Yes. No law prevents you from provisioning and using your own hardware.
Re: Nvidia, Microsoft, Meta warn against overregulating open-weight models
#136Just saw on Microsoft https://www.microsoft.com/en-us/corporate-responsibility/top...
Makes sense Microsoft is placing it self as a model host and os providing open weight models for much cheaper on their infra already. No wonder openai and anthropic are terrified
Makes sense for Microsoft. Copilot is already capable of being model agnostic (both GH Copilot, obviously, and M365 as well). They're an enterprise software & services company, they want to sell integrated AI tooling in their stack, powered by whatever models their customers want rather than any specific model.
Models are quickly becoming a dumb pipe, like an ISP. Just a commodity. The labs should rightfully be terrified, eventually the value isn't going to come from the model itself but the tools & integrations built on top. Non-tech businesses and non-tech employees don't want to buy API access to a model, they want to buy off the shelf software with its capabilities packaged up into a pretty, easy to use GUI.
Re: Nvidia, Microsoft, Meta warn against overregulating open-weight models
#137I'm finding the list of signatories on this letter really interesting and somewhat confusing, looking purely through the lens of economic interest. It makes sense to see Meta, the startups, and the VC firms on this list. And it makes sense to see OpenAI, Anthropic, and Google all missing. Microsoft is somewhat unexpected to me. I don't think of them as having a focus on open weight models (no more than Google), and t…
> But the letter is really about _American_ open weights models.
AI models don’t really have nationality. They don’t have race/ethnicity fields on their model cards. It’s impossible to determine the country of origin of a model by looking at their weights. There is no DNA test for AIs. They exist in a mathematical space where human-made borders make no sense.
This means IF there is a regulation for open weight models, it has to apply to ALL open weight models. There is no any other option, since you can always train/fine-tune a model to change it’s weights and rebrand it as a new model.
Re: Nvidia, Microsoft, Meta warn against overregulating open-weight models
#138> The unbearable cheapness of open weight models So the closed model try to get client with their model by saying it's cheaper than employees, and then turn around to lawyer-out cheaper alternative? Truly the american dream.
And the verdict is still out on if it's going to be cheaper than employees in the long run. Maybe for companies in HCoL areas (SV/NYC), but any smaller mid-market org in a LCoL area, frontier model token spend may not end up all that cheaper when your employee salaries are $55k-$75k/year so you're mostly evaluating it as an additional force multiplier expense rather than a headcount replacer.
55k??
Re: Nvidia, Microsoft, Meta warn against overregulating open-weight models
#139I wonder what is happening behind closed doors for these companies to be issuing such a joint letter.
Probably a real effort to push for some kind of sanctions or commerce block on Chinese models. Anthropic just doubled their political spending to $40 mil for the midterms to push for "AI Safety."
Re: Nvidia, Microsoft, Meta warn against overregulating open-weight models
#140It would be interesting to know how many optimizations of the Chinese models were incorporated back into Claude and Codex.
To some degree even unintentionally this is all inevitable. I know this isn’t what you’re taking about, but it’s a similar line of thought. These models are consuming public free information, and they’re all also producing free public information, so they’re all pissing and drinking into same pool.
If we go down the line of dead internet theory which I’m becoming more convinced of these days, the volume of information that’s not necessarily original or extracted from reality and just interpolated and extrapolated from existing information in different ways by LLMs will greatly outnumber human information coming up.
In which case these models should.. start to converge on the same data I imagine, with slightly different behaviors within that. One big generative orgy feedback loop.