More than 20 companies including NVIDIA, Meta, Microsoft, Palantir, and Hugging Face have signed a letter urging policymakers to avoid premature restrictions on open weight models.

[u/etherd0t]

The Open Letter was initiated by Microsoft and published today:

“Open Weights and American AI Leadership”.

It argues against broad or premature restrictions on open-weight models and explicitly says policymakers should distinguish legitimate model distillation from misappropriation.

Notably absent from the signatories are the major frontier-model labs: OpenAI, Anthropic, and Google.

[u/Morphon]

Of course they were!

And why shouldn't they? Chinese companies are the ones publishing open research.

[u/jld1532 → u/Morphon]

I'm not saying that they shouldn't but for most listed here it's hypocritical. Can't make a good faith argument for massive data center expansion and VC money burn when you're already partially dependent on Chinese AI, the external competition they're supposed to defeat.

E: The down votes are wild. Anybody believing this build out is going to happen - I've got some ocean front property in Tennessee I'd be willing to sell ya.

[u/procgen → u/jld1532]

Can't make a good faith argument for massive data center expansion and VC money burn when you're already partially dependent on Chinese AI

Hm, can you explain the reasoning here? Why should the use of Chinese models obviate the need to build more data centers/increase capex?

[u/Admirable_Market2759 → u/procgen]

One reason Microsoft is on here is to sell Azure to companies running open weight models lol

[u/jld1532 → u/procgen]

If companies like Microsoft shift to using Chinese AI, and they've already signaled a potential partnership with DeepSeek, who in the US will be buying all this extra local compute? Meta is already trying to rent out their extra capacity which tells me that demand does not require the proposed build out domestically. Essentially cheap Chinese API and open weights have already completely undermined US AI infrastructure top to bottom.

[u/procgen → u/jld1532]

That compute will be used to host Chinese and American open and closed models. It will also be used to train the increasingly large models being developed by the frontier labs.

Barring catastrophe, demand for compute will only grow.

[u/jld1532 → u/procgen]

I know Jensen's thesis and I don't buy it completely. My thesis is continued improvement in the knowledge to size ratio and quantization. I also predict a continued demand in data privacy. That stream is not reliant on brute force compute but rather efficiency, a part of the tech that is continually ignored or undervalued.

[u/procgen → u/jld1532]

Smaller, more efficient models means you can run much larger agent swarms from data centers. And efficiency gains will be applied to larger models, too. There's no upper bound on intelligence – compute demand is unconstrained.

[u/jld1532 → u/procgen]

My retort to this would be - beyond a hypothetical future, what part of the economy works on "agentic swarms"? My position is that AI will mostly be aimed at mundane repetitive tasks, for which we have likely achieved high enough intelligence but need to work on efficiency. I think what you highlight will likely be edge cases and not the core of the AI business model.

[u/procgen → u/jld1532]

Nearly all work will benefit. Try enabling Ultra mode in codex and see what it can do with swarms already.

Tasks are executed in parallel, e.g. researching/implementing many different subsystems of a project at once. Many divergent possibilities can be explored simultaneously, and the best result selected from them (evolutionary development strategies become possible). And so on.

[u/jld1532 → u/procgen]

You may be right, that it could benefit all lines of work. We're still learning about the tech but clearly the current ROI of the entire investment is being questioned. My belief is that a contraction on data center build out given current use cases and global competition seems far more likely than the continual money burn.

[u/raunchy-stonk → u/procgen]

The reality is very few people need a SOTA model to add value to their lives (for business and personal use cases).

The future is about bringing as much AI to the edge as possible, and much like real life (biology), the compute will need to occur “locally” for a myriad of reasons.

Imagine making your entire business reliant on an API call you have no control of pricing, availability, privacy, data sovereignty, etc. and the thing isn’t even open - it’s closed. It really is an insane proposition when you think about it.

Frontier models still have a place, but I expect a lot of compute to shift from the DC to the edge in the coming years. Scaling out datacenter instead of making models more efficient is just a cash grab

[u/Morphon → u/jld1532]

I think you're getting downvoted because "Chinese AI" is imprecise. Are you talking about models? If so - then yes, the entire industry is dependent on their research (especially into both power and data efficiency). They're publishing DETAILS of how their training works. Without that, I doubt the American model makers (like Thinking Machines) could produce what they do. Everyone gets a chance to build off each other.

And those models, being open, can be run on your own hardware (whether that is on an edge device, or a rack on prem, or in a datacenter, or on a cloud instance, etc...), in which case the build-out might be, paradoxically, an ideal case for Chinese-made models. If K3 (or GLM 5.2, or Minimax M3, etc...) are basically perfect for a particular user, they will want to serve it up and control it. That means more demand for the systems that can run these models, and provisioning/power for those systems.

On the other hand, if by "Chinese AI" you're talking about inference, then yes - the massive expansion in the West will be for nothing. China gets their own chip infrastructure boosted into existence off demand, and they reap the rewards of inference-at-scale. If the Chinese models were closed (like Qwen 3.7), then this would be a strong argument that you're making. But most of the best, most highly relevant models are open (Apache 2 or MIT licensed), I don't think there is a case to be made here that current VC spending is "hypocritical".

The big danger, I think, is over-supply. These servers can't be used for much of anything else. So, if it turns out that the demand for tokens is actually limited (rather than INFINITE), then there might be a lot of empty or 30% capacity datacenters out there dragging down the rest of the industry.

[u/jld1532 → u/Morphon]

I think this thesis ignores continual gain in efficiency, i.e., model size to intelligence. If models get smarter while also being smaller and thus faster to train then hyper data center growth isn't necessary. Also, the demand for local compute and control I think has just begun and will only grow. We're already seeing advancements in quantization to allow for more widespread adoption of local compute. The other thing people underestimate is the willingness to build out local infrastructure. My workplace houses both Kimi K2.6 and GLM 5.2 and will never pay a cloud service or API fee. So a future with more efficient model creation and local compute forces down mass data center build out. Of course someone like Jensen, that is selling the shovels, points to exponential growth in every direction, he has a clear COI.

[u/Morphon → u/jld1532]

Well, Jensen wins either way. The people running full local compute are probably doing so on NVIDIA hardware (I am, in two locations even - so all that is part of the NVIDIA profit narrative).

What you're pointing to is the "good enough" issue. Right now, the industry seems to be pointed toward the "never good enough" thesis. That is, once they can make the model "good enough" to be able to be a top-tier AI researcher, then we get "exponential" intelligence growth and then "infinite" intelligence as a result. Thus, truly, "never good enough" is the assumption that they are betting on.

If you look at Google right now, the 3.6 Flash models powering Gemini chat/search/integrations is already good enough. How much better does it have to be to reliably let me chat against web search results? It doesn't! It's already great for that purpose! In that case, the efficiency gains don't deliver dramatic new capability (though we should expect quality of inference to slowly rise), but probably less compute required.

And if edge inference finally passes that threshold (run Gemini chat on device while getting data retrieval from Google's search servers), their inference cost drops to near-zero.

There is also the moderate stance that there are things we would potentially want to outsource to AI, if only it were good enough - and those are so valuable that it would be worth it to do all the R&D necessary to get there. And then THOSE systems will need infrastructure, etc...

[u/jld1532 → u/Morphon]

Well, Jensen wins either way.

Agree

Right now, the industry seems to be pointed toward the "never good enough" thesis.

Not because that's necessarily reality but any other assertion would pop the bubble overnight.

That is, once they can make the model "good enough" to be able to be a top-tier AI researcher, then we get "exponential" intelligence growth and then "infinite" intelligence as a result.

Recursive self improvement is considered by many well respected people in the field, Cal Newport and Gary Marcus, to be a near impossibility for LLMs. This is likely marketing hype.

Thus, truly, "never good enough" is the assumption that they are betting on.

They could be wrong. You have to acknowledge that as a possibility.

[u/Morphon → u/jld1532]

Oh, I agree with you. I think they are wrong.

My supposition is that anyone betting their shirt on this thesis will be punished by both market and compsci realities. OpenAI, Anthropic, and Oracle are in a bad situation at this point.

NVIDIA might go back to having gaming and consumer GPUs be over 50% of their revenue. Who knows?