Open-Weight AI is Having Its Kubernetes Moment: Let's Not Ruin It
Open-weight AI is having its Kubernetes moment. Let's not ruin it
Drawing from my experience with Kubernetes, I see open-weight AI models becoming the industry's center of gravity. Banning Chinese models would isolate US developers from a thriving global ecosystem. Instead, American labs should release frontier-grade models, use government procurement to drive open standards, and build the supporting stack to compete on merit rather than retreat behind walls.
America should not respond to open Chinese models by building a wall around its own developers.
- ozgung
Everyone is talking about banning Chinese models but nobody talks how it is feasible to ban them. I think it’s impossible simply because technically there is no such thing as a “Chinese model”. There is no way to tell apart an “American” model from a “Chinese” one by looking at their weights. Weights are just numbers and you can’t assign country of origin to numbers. One can find very easy workarounds to any naive attempt to ban them by origin.
So, any solution to this “problem” must include ALL open-weight models. As far as I understand this is exactly what they intend to do. Axios article linked in the post mentions that. As in this quote:
“The source described leading AI labs or their allies approaching the administration every 3-5 months with an idea to ban open-source models.”
It doesn’t say “Chinese” open-source models. Because they already know that it’s not feasible. Any regulation must cover all the models.
Now there are solutions for that latter problem. But they are all ugly and restrictive. Making a DRM-like license protection system mandatory can be a solution. If a company wants to run an open model in their own servers, they can only use approved and certified pure “American” models. This of course creates a monopoly for the big labs who are authorized to train and distribute such “open” models. A company can fine-tune the model for its own needs but of course can’t distribute the derivative model.
I’m sure there are other solutions but all of them would be equ […]
- firasd
One of the strangest things in the AI industry is 'tokenomics'. It's not very clear why using GPT-4 in early 2023 was so expensive and then six months later 20 bucks could get you a fair amount of GPT-4 inference. This pattern has continued across various labs/providers for years--there is a continuous see-saw of pricing that doesn't seem related to anything.
So what open weight models do is at least provide a baseline of inference cost to add some sanity to the price markers. And of course predictability too--if you really want Kimi K2 instead of K3 you can still use it.
So the competitive pressure and predictability offered by open models is helpful for users
- pianopatrick
Eventually I think to truly be like Kubernetes, you would need an AI model that has public training data and that a lot of companies collaborate on.
Might make sense eventually. Same logic as companies working on Linux. "An AI model is a business necessity. But making an AI model is so expensive we should not make our own. So let's just use the open one, and contribute the stuff that we need."
- drnick1
> American labs need to release frontier-grade open-weight models under licenses that startups can actually build on.
To be fair, OpenAI has released a couple of (then very good) OSS models. I run the 20B version at home and it is excellent for reviewing text and common tasks like drafting bash scripts. There is a larger 120B that you can't realistically run on consumer hardware at reasonable tok/s too. I wish OpenAI updated these models more frequently though.
- curious_cat_163
> The government should use procurement to create demand for portable, interoperable systems rather than permanent dependence on one API vendor.
Now, here is an idea that I have not heard before... and I think there is some merit to this. This is also the sort of thing that a state (looking at you CA, CO, IL, NY) could do, instead of just the federal government.
- amazingamazing
Sadly until china scales production of hardware it really isn’t economical to run this stuff yourself. It is good it exists though to put pressure against the labs.
Honestly imo this is just proof apple will win in the end. Eventually a phone will be able to run a model good enough to do most things and it then is game over.
- thih9
Is anyone using open weight models for agentic coding?
What is your stack (harness, model) and how much do you pay per month?
How would you compare your experience to a typical subsidized plan like Claude Code + Pro plan?
I’m asking because i keep hearing that open weight models are cheap and efficient - is that really the case in practice?
- jitbit
You can contribute to an OSS platform. To accellerate innovation, adoption etc
You CANNOT contribute anything to a model.
- chasd00
FTFA: American labs need to release frontier-grade open-weight models under licenses that startups can actually build on.
oh now i see, the Chinese government is funding the training and release of their best models to pressure OpenAI, Anthropic, and others to do the same for competition's sake. I don't buy it, this seems more like a way to get SOTA models RL'd to comply with Chinese government approved information distribution. If I have to trust a black box of answers to questions i would trust one from a US for-profit publicly traded company subject to market forces over one approved, and heavily subsidized, by the Chinese government.
- netdur
why would any software want to have Kubernetes moment? can't count how devop I know that is confused by it