Open-weight AI is having its Kubernetes moment(tobi.knaup.me)
tobi.knaup.me
Open-weight AI is having its Kubernetes moment
https://tobi.knaup.me/2026-07-25-open-weight-ai-is-having-its-kubernetes-moment/
316 comments
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
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
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."
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."
> 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.
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.
> 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.
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.
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.
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.
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?
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?
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.
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.
why would any software want to have Kubernetes moment? can't count how devop I know that is confused by it
Open-weight and OSS are wildly different and the article makes a poor comparison.
What's the incentive for the Chinese labs to continue releasing weights 5 years from now? It's not a stable equilibrium and cannot last.
- The lab spending large sums on research and training does not get the inference revenue to fund those efforts.
- Unlike OSS where a single volunteer can keep a project going, training costs run into the $billions.
- OSS is often a two way street where features and integrations are built that the original author benefits from. Open weight models are largely a one way street because the marginal benefit is so much less than training costs.
In the short term, it means Chinese labs can attract talent and, I suspect, funding from their gov. Similar to every other industry the CCP subsidized to take over.
What's the incentive for the Chinese labs to continue releasing weights 5 years from now? It's not a stable equilibrium and cannot last.
- The lab spending large sums on research and training does not get the inference revenue to fund those efforts.
- Unlike OSS where a single volunteer can keep a project going, training costs run into the $billions.
- OSS is often a two way street where features and integrations are built that the original author benefits from. Open weight models are largely a one way street because the marginal benefit is so much less than training costs.
In the short term, it means Chinese labs can attract talent and, I suspect, funding from their gov. Similar to every other industry the CCP subsidized to take over.
Yes, and yes, again the only way to compete is to build the best not hide in a corner and once again the rest of the world will go on in AI without the United States if we flub it. Circling the wagons, isn’t the long range answer.
Everyone just keeps assuming, as if it were the law of gravity, that China will continue in perpetuity to deliver the weights of its 'frontier' models to Hugging Face. Its Mythos moment is a few months away and there is plenty of reporting suggesting their response will be similar, which is anyway obvious.
It baffles me that anyone can seriously believe that China is going to put its Mythos successor on Hugging Face and let us all strip the guardrails etc.
Things just didn't turn out the way OpenAI and Anthropic thought; nor did they turn out the way China thought.
It baffles me that anyone can seriously believe that China is going to put its Mythos successor on Hugging Face and let us all strip the guardrails etc.
Things just didn't turn out the way OpenAI and Anthropic thought; nor did they turn out the way China thought.
> Kubernetes did not win simply because its repository was public.
And here I am still using docker with compose because it's easier and works. Always saying I might be looking at swarm when needed, but then never really need it.
Just because you are using it doesn't mean everyone is and doesn't mean it has won. On the other hand yeah I want open models and I want them local as well.
And here I am still using docker with compose because it's easier and works. Always saying I might be looking at swarm when needed, but then never really need it.
Just because you are using it doesn't mean everyone is and doesn't mean it has won. On the other hand yeah I want open models and I want them local as well.
I worry about the Chinese models sending data back to China. How do we know that isn't the case?
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You can contribute to an OSS platform. To accellerate innovation, adoption etc
You CANNOT contribute anything to a model.
You CANNOT contribute anything to a model.
> ... once an open platform that people can customize becomes the industry’s center of gravity, no single vendor can match the combined rate of innovation around it
That assertion is sublime, and if not true, it should be and can be.
Thanks for the thought (and the optimism hit).
That assertion is sublime, and if not true, it should be and can be.
Thanks for the thought (and the optimism hit).
Kubernetes is a system/infrastructure orchestration tool. I completely fail to see how it is comparable to open weight neural nets. In either application or function.
I'm sorry to do that hn comment thing where we all just race to contradict or talk in opposition of whatever was said before. I'm aware. But really guys, was this article really not just a miss?
I'm sorry to do that hn comment thing where we all just race to contradict or talk in opposition of whatever was said before. I'm aware. But really guys, was this article really not just a miss?
I don’t know, which of
You have a problem, you decide to solve it with a {open_weights,proprietary} LLM. Now you have two problems.
Drat, I was hoping this article would be about outlawing Kubernetes.
Meanwhile America is having its glorious McCarthy moment....
It's going to be so complicated that nobody quite understands how it works?
In fact more countries should have government funded models. There are some obvious issues in China completely dominating open weights space. Kimi had funding of just $2B and could literally create national security threat. A lot of countries could fund something in the range of few billion for something so important. At the very least US and EU could fund few companies.
I respect Knaup's opinion but disagree with the claim he makes here.
Kubernetes took off because everyone could run it on pretty much anything. Bigger hardware meant bigger clusters, but developers could spin up a cluster on their laptops and deploy their apps into it. More importantly, companies could repurpose their decommissioned servers as k8s clusters, a massive unlock seeing how huge companies had heaps of these in their racks.
This isn't possible with open weights models. Not in the same way.
First, you're out of the game if you don't have a data center class GPU (or it's sort of prosumer equivalent). Model servers support CPU inference, but you might as well watch paint dry as you wait for results...and you'll still have to run super quantized low-parameter models that aren't as good.
Realistically, companies will need to purchase millions of dollars of nVIDIA gear (through suppliers) to serve agentic-capable models at scale, an activity that is being made more expensive and complicated by the day as the hyperscaleds slurp up the demand.
It also really is a huge problem that all of the open weights models are coming out of one country that also happens to be a superpower. I don't think this can be handwaved away, and it's concerning to see so many folks here minimize this.
American companies running on Chinese intelligence. As a country that prides itself on being the knowledge capital of the world, the optics manifested by this are horrible, not to mention the absolutely massive supply chain risks (the counterfeit Cisco devices comes to mind, except worse because the rangers are in the weights and might not be possible to distill out).
This is a threat even if you focus solely on the individual developer. Recall how AWS became...AWS. They "fanatically" focused on the developer experience. They designated this as key to their growth strategy, and rightfully so. How people talk about using Qwen or Kimi and the like on here feels like that (ignoring how these models are ALSO from huge for-profits).
The only solution here is for the big labs to make some of their most capable models open-weights. There is a lot of secret sauce around routing, inference, hosting and other stuff that makes them work as well as they do, but the community can figure that out. Of course, this is basically a death sentence to those companies, but that's what they get for playing with monkey paws, I guess.
Kubernetes took off because everyone could run it on pretty much anything. Bigger hardware meant bigger clusters, but developers could spin up a cluster on their laptops and deploy their apps into it. More importantly, companies could repurpose their decommissioned servers as k8s clusters, a massive unlock seeing how huge companies had heaps of these in their racks.
This isn't possible with open weights models. Not in the same way.
First, you're out of the game if you don't have a data center class GPU (or it's sort of prosumer equivalent). Model servers support CPU inference, but you might as well watch paint dry as you wait for results...and you'll still have to run super quantized low-parameter models that aren't as good.
Realistically, companies will need to purchase millions of dollars of nVIDIA gear (through suppliers) to serve agentic-capable models at scale, an activity that is being made more expensive and complicated by the day as the hyperscaleds slurp up the demand.
It also really is a huge problem that all of the open weights models are coming out of one country that also happens to be a superpower. I don't think this can be handwaved away, and it's concerning to see so many folks here minimize this.
American companies running on Chinese intelligence. As a country that prides itself on being the knowledge capital of the world, the optics manifested by this are horrible, not to mention the absolutely massive supply chain risks (the counterfeit Cisco devices comes to mind, except worse because the rangers are in the weights and might not be possible to distill out).
This is a threat even if you focus solely on the individual developer. Recall how AWS became...AWS. They "fanatically" focused on the developer experience. They designated this as key to their growth strategy, and rightfully so. How people talk about using Qwen or Kimi and the like on here feels like that (ignoring how these models are ALSO from huge for-profits).
The only solution here is for the big labs to make some of their most capable models open-weights. There is a lot of secret sauce around routing, inference, hosting and other stuff that makes them work as well as they do, but the community can figure that out. Of course, this is basically a death sentence to those companies, but that's what they get for playing with monkey paws, I guess.
Dario is a FUD-spreading douche
Way over complicated for what most users need? Huh?
What’s interesting is no one is talking about political censorship in models and how DeepSeek, Kimi, and the rest have to abide by CCP rules. It’s a big opportunity for China to control information.
This seems like a pretty balanced and thoughtful idea. I’m HN will hate it
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 equally ugly. Also these regulations can’t be enforced to other countries easily so only Americans will be restricted.