Hacker News Reader

Comment by ls_stats | Hacker News Reader

America needs its own DeepSeek or Z.ai, a lot of people (myself included) root for open chinese models to win because they have no other choice.

Thinking Machines might be it.

View on HN

I don't hear about them a lot but it looks like arcee.ai is aiming to be just that.

Here are some of their current open weight offerings: https://www.arcee.ai/open-source-catalog

You don't hear about them much because their models aren't really competitive. I really wanted to try Trinity Large as a daily-driver in the MiniMax M2 sort of niche but I couldn't make it through a single day. The models need another couple point releases worth of post-training to make useful agents and if memory serves they weren't any less slopped in writing style and those are really the only two things people look for in models.

It could be but there are a host of companies going after open weights models: Arcee, Reflection, Llama (TBD on Meta's focus on closed-source versus open-source), etc.

That said, the fine-tuning API + open weight model at least is a semblance of a viable business that could work so I will be curious about it. I'm not sure the synergy is fully there (why is someone with an open weights model privelaged to fine-tune it better if it's just QLora or Lora) but let's see!

I don’t really get the business plan for open weights model companies, is the idea companies would pay them for serving?

Thinky's main commercial product AFAIK is Tinker [0] - companies pay them to host their fine-tuning workloads and then the resulting fine-tuned models. I don't know if this is a good business plan, but I'm sure at least one person there has read Joel on Software [1].

[0] https://thinkingmachines.ai/tinker/

[1] https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/

I don’t know if it’s a great business model but it makes perfect sense to me. Open models when fine tuned are capable at better than frontier performance at a fraction of the price for many (probably most) domain specific tasks. If companies help make that easy to implement, there is value to capture. But I kind of like Unsloths model here which is to be really good at just layer, and not bothering with building their own models.

I don't get this - I can do LORA on my mac... ok I can't do LORA on a 1tn param model, but if I was in the tn parameter model game I would get some kit that I could use to do that...

What's their moat / secret sauce?

Like, buy and set up the physical hardware? I cba with that. Plus the hardware you want for LoRA (the type but especially the quantity) is different than what you want for inference, so either you'd under-spec it and wait forever for fine-tuning runs, or over-spec it and have low utilization most of the time. And even then who knows if it would be good enough to LoRA next year's best open source model. AWS gets great margins for renting out commodity hardware as a service because it built the right abstractions and can serve them efficiently at scale, I think the arguments here are basically the same.

> don't really get the business plan for open weights model companies

The Chinese "Neijuan" aside, most competing labs are going for the classic, 'your margin is my opportunity': https://tomtunguz.com/is-your-margin-my-opportunity-software... / https://archive.vn/5Vmq3

From what I can tell it’s a bit of the following.

1. Magic

2. Managed hosting of their model

3. Hurting competitors. If people are using Meta’s commoditized models they’re not paying Google or allowing OpenAI to become too big.

4. Free R&D from open source. If open source developers are optimizing systems to run Llama, that helps Meta.

5. More magic

I think that LeCunns belief was/is that LLM's have limited value and are a dead end, so what they wanted to do was kill any competitor while evolving tech that was/is a winner.

Well, he lost his job on that bet... and yet... I do not think that the verdict of history is quite in.

> don’t really get the business plan for open weights model companies

Feature as a company for now. Apple is struggling to build an in-house model set. And plenty of software behemoths, e.g. IBM, are realising they don't have a ticket to the new tech economy.

Llama is dead. Meta is now releasing proprietary models (Muse Spark).

They’ve made some wishy-washy statements about their intention to release a future version of Muse Spark as open weights. We’ll see.

Do any of these even have match a year old Deepseek 3.1?

DS3 isn't even looked at anymore.

GLM-5.2 is the best in that class right now. It is competitive with current GPT/Claude/Gemini.

"Current GPT/Claude/Gemini" is not a meaningful statement about perf. There's many different models from each of those providers and there's a considerable gap between the best of anthropic and open ai compared to gemini.

Benchmarks have GLM 5.2 somewhere underneath Sol and Fable and closer to now last-gen openai and anthropic models.

One error: GLM 5.2 beats the best public Gemini model, 3.5 pro.

There's 2 caveats with the rest. First, GLM 5.2 matches those models in "xhigh" effort modes, which has a very low quota on the subscriptions, especially for Claude.

Second, last-gen GPT/Claude means what they release in April/May of 2026. Or to be even more complete/fair:

GLM 5.2 beats what OpenAI released in March 2026 (GPT 5.5 xxhigh), and what Anthropic released in April 2026 (Opus 4.7 xhigh). It is beaten by what OpenAI released in April of 2026 (GPT 5.6 Sol xxhigh) and Anthropic released in May 2026 (Opus 4.8 (the same as "Fable" ?), xhigh effort)

GLM 5.2 was released on Jun 16 and if OpenAI and Anthropic hadn't done those quick releases they would have been beaten on their best available models ...

So great news! Open source now has SOTA performance 3 months after OpenAI/Anthropic/Google. Wow.

The gap has been steadily closing over time.

Opus 4.8 (May) to Kimi K3 (July) has apparently just dropped it to two months.

China also does efficiency improvements. Qwen 3.6 27B is better than Sonnet 4.5 and you can run it on a couple of gaming video cards. That's incredible. I can do real actual work with this!

As Google said in 2023, none of them have a moat, open weight models will win.

> As Google said in 2023, none of them have a moat, open weight models will win.

Google, who probably canceled the release of Gemini 3.5 pro to avoid having their best new model perform worse than BOTH GLM 5.2 AND Kimi K3?

I mean how is this anything but an incredible defeat of Google's supposed inventiveness?

My previous comment from 24 hours ago is now irrelevant.

Newly released Kimi K3 is benching better than Claude Opus 4.8. The only better models are Claude Fable and GPT 5.6 Sol Max Effort.

> It could be but there are a host of companies going after open weights models: Arcee, Reflection, Llama (TBD on Meta's focus on closed-source versus open-source), etc.

my bet is that Chinese government fund Chinese models way more compared to what those companies receive (except llama, which is outdated but was strong foundation at its time)

The story of Reflection AI is supposedly that the company was faffing and failing at winning in the coding agent space, but was introduced to Jenson, who suggested they build an open-weight model and said he would fund it. That turned into a $2 billion financing with NVIDIA doing roughly $500 million and was a complete pivot.

I think the bet would have to be that a US Open Weight company either: 1. Gets a lot of money from Jenson who views them as a counterbalance to the big labs in his ecosystem and a way to generate leverage (the same way he is positioning neoclouds-- it also could be synergistic with neoclouds who could offer the model serving endpoints) 2. Can fast follow the same way Mistral does (which, honestly, seems like just distilling the Chinese model, which distills the US lab but is pretty innovative on a whole lot of architecture both in training and serving land.) 3. AND figure out some (maybe not super lucrative but lucrative enough) sort of business model, as well. There are lots of possible business models, so I will be curious how this whole space evolves.

Jensen Huang is just trying to commoditize the complements to his GPUs.

Cf Microsoft v Intel circa 1995

Usually software wins over hardware, but the voracious appetite of LLMs has inverted the usual balance of power.

That and the actual algorithms that power AI are well known and there doesn’t seem to be any secret sauce at that level.

> That turned into a $2 billion financing with NVIDIA doing roughly $500 million and was a complete pivot.

I suspect 2B is not enough to boostrap frontier model from the scratch (for both talent and hardware)

>The story of Reflection AI is supposedly that the company was faffing and failing at winning in the coding agent space, but was introduced to Jenson, who suggested they build an open-weight model and said he would fund it. That turned into a $2 billion financing with NVIDIA doing roughly $500 million and was a complete pivot.

You can pretty much remove the supposedly here

I have a similar bet. Looks like people don't like this idea. You got downvoted a lot.

Some stories recently that China has decided to reign in and manage it's model production landscape much more tightly.

Ah. Is it true? What do you think?

I think...

we will see!

AllenAI is also one to keep your eye on. Founded by Paul Allen of Microsoft, they are one of the best teams working towards truly transparent / open AI (including training data)

I love the tasteful thickness of Paul Allen’s model.

I love Allen AI.

I find it wonderful that, as a non-profit, they are only one to two years behind SOTA models that cost billions of dollars to build, if not more.

AllenAI is great, but they don't have the budget or remit to build large models.

I wonder if the recent sale of the Seahawks will change that. IIRC, ~$10B and all is supposed to go to charity. Not sure how much of that will go to AllenAI, though. (If any.)

No reason to think it will. Paul Allen died after donating the money to create AllenAI and I don't think there are any links.

Hopefully it somehow works out though!

I will wait for Modernist AI by Myhrvold.

What is the business model for an open weight model?

The same business model that Deepseek is using.

Open-source models + services. This is more attractive because it doesn't lock in the vendors. If I grow larger, I can decide to deploy the open-source models.

So they're constantly hemorrhaging their most valuable clients?

Tech history is littered with the corpses of "open source but we sell hosting" services. Models are so expensive to train, you can't be losing the big clients once they get super profitable.

This is genuine, noob question: how is this different from AWS?

I get that they're in very different businesses, but for both don't they have the issue that once a client gets big enough the client might decide to move the services in-house? Based on how much of the internet went down when that AWS data center crashed the answer is clearly "No" for AWS.

Is that because of physical, real-world infrastructure? Are there no open versions of their APIs? Is it too hard to migrate to something else once a client has achieved that size?

Data is heavy.

I would say "it's risky and requires a lot of labor to migrate without corruption, loss of data" and also minimizing downtime. Sure anyone can run pg_backup, but can you do it across 90 databases? Can you do it live? Can you coordinate rollout of the process, cutover, and monitor for failure? What's the cost of egress for this? Is the team your A-team or the B-team? Can you trust this to the B-team? Is it worth having this team spend all this time on a migration rather than, say, getting something new set up, or optimizing performance on an existing system?

I'm a database guy, but the same migration argument is presumably also extra work for (say) blob storage, networking, etc.

Since LLMs are stateless by their current implementation, switching to "the same open-weight model running in a different datacenter run by a different vendor" is "just" switching the API endpoint. (If they are the exact same shape, it's fine, if they differ somehow, there's perhaps some work to do there, fixing things and monitoring for failures on switch-over)

There are several open APIs it seems and OpenRouter.ai is doing a fine job making a commodity out of models and datacenters.

I don't think it's that difficult. Their servers are stateless too. S3 is easy to migrate.

Database is more difficult, but tons of people have done it successfully.... meanwhile people who host their own LLMs are relatively small in number in comparison.

Most companies don't do their own data centers mainly because it is more expensive and less reliable. It's something they can just pay for the problem to go away. The calculus for hosting your own LLM is probably similar.

Even Stripe who built their own coding agents and has tons of money/resources still decides not to host their own LLMs.

Still, many people will prefer open-weight models. It is similar to how we prefer linux but still use AWS/Render/and whatever. It doesn't lock us in, and we can move providers if we want to.

> This is genuine, noob question: how is this different from AWS?

AWS owns the hardware, and doesn't write a lot of the software.

AWS actually is kind of the opposite - it often takes open source software (e.g. Apache, Mongo, Kubernetes) and then makes money off it by hosting it itself (with some enhancements etc).

If they do develop their own software (e.g. with S3) they don't give away the source code so others can deploy it, as that's part of their secret sauce.

In this scenario, where they would be offering the open source model and then offering the same model hosted, there isn't really a moat here - they would be leasing the hardware from a company like AWS, and adding a margin, but it woudl be trivial for another company (or Amazon) to take their same model and offer it for the same price or less.

> The same business model that Deepseek is using.

there is a chance their business model is absorbing government funding..

Better than average chance I’d say. I suspect they are hoovering up EVERYTHING that gets sent to them. Whether that’s a problem or not depends on your data. I do wonder how many security tokens they get in the stream on a daily basis.

I don't think it's bad if the mandate of that funding is to have open-weights.

its not representative example of open weight business sustainability.

You don't have to assume that. They just IPO'd

If they get subsudized say hardware, it won't be captured in filings.

To compete against America. If your country has something like DeepSeek you really can't afford to let it fall as it's your best leverage if the US government decides to ban companies in your country from accessing American LLMs. And this is why there will never be a "DeepSeek of the US."

Considering how volatile things can get depending on who's president, I'd say even American companies need to "compete against America" if they don't want to get their rug pulled from under them (which, apparently, the legal system allows to easily happen in the US).

Not to mention the entire world outside the US and China. China seems to have the edge in stability.

Thinky has a potential answer in Tinker — give away the weights and charge for the SFT (and maybe RL down the line) to make the model more capable for specific tasks.

SFT/RL can be done without parent company.

But not conveniently. This is why you outsource to vendors. Not everyone will do it

In the US, there isn't one, which is why nobody in the US is currently doing it at frontier scale. And the people that were doing it stopped.

I’m trying to be charitable but your comment reads as “China bad” propaganda to me. Who cares that DeepSeek and Z.ai are Chinese companies?

It doesn't matter until it does. If the chinese government decides that open weight model releases are no longer allowed, that's a lot of companies that can't release new models. Same with the US government, etc. Having diversity is important.

However unlike the US models, China banning the release of new models would not break existing ones. Betting on US models only can get you locked out in just a few hours.

No, Open weights US models would not break as well - this isn't related to China or USA, it's about Open Weights and the fact that you can download the models.

It's a similar problem the human DNA solved by telling our teenage selves that our parents are dumb and we needed to move to a new tribe. Genetic diversity, but a digital equivalent.

China’s got absolute control over its outputs. For America to have any guarantees around long-term availability of OW models, they need domestic production.

FWIW this is the same logic for China’s need for their own OW models

LLMs aren't just for coding and math. Many people understand the world through LLMs, even when it comes to philosophy and politics.

If you understand the world through a Chinese LLM, you are seeing it through a biased lens stemming from biased training data.

(Also, in that way, having all major LLMs developed by the US carries a risk too. We need more diversity than just the viewpoints of the US or China.)

All 6 UN languages should have their own dedicated LLM at the very least: American for English, Chinese for Chinese, LATAMgpt for Spanish, Russia has their own, Mistral for French, the Arabs don't have one yet I think, then there's sarvam for India and South Korea's sovereign models like SK telecom's

I think practically every government will want to put restrictions on private companies building models.

Frankly the EU and the US will practically be less involved and have more pushback from the public in this than China. I think that’s less “China bad” than recognizing that China is a more authoritarian state and has far more proclivity to interfere than western states.

Maybe I’m wrong? What does deep seek say about Tiananmen square in 1989?

https://chat.deepseek.com/share/uf8jih4q95lbq3s8t2

This is what Deepseek replied when I asked it with a burner account. Claims it doesn't have it in its training data... sure.

Be interesting to see if it's possible to jailbreak and have it talk about it.

I am sure it can be but like it does prove the point of state interference in what the LLM is allowed to say/do in this case.

Ask an American model about the Epstein files and trump's involvement of them. Or about Israel committing genocide in Palestine.

Every model will have their own bias. Freedom is relative and American freedom is not the only form of freedom. China bad or China authoritarian and thus not free is a result of the red scare

Here is what Sonnet says on genocide:

"Surveys of genocide scholars (e.g., one by the journal Journal of Genocide Research contributors) show meaningful disagreement, though a visible and growing share of specialists in genocide studies specifically have concluded the term applies or is defensible — more so than international law scholars generally, who tend to be more cautious about the intent requirement."

You can be of the opinion that this shows bias, but it's a far cry from the Chinese censorship.

Clichés like "freedom is relative" are not serious arguments. You can't genuinely argue that the US, for all its problems, is less free than China. Words have meaning.

E.g. looking at freedom house index you can disagree on the precise score or how different aspects are weighed, but you can't argue with the underlying facts in the country reports

https://freedomhouse.org/country/scores

You asked about genocide in general. Not Israeli genocide. Are you astroturfing? No one can make an omission that obvious

You can't genuinely argue that the US, for all its problems, is less free than China

This is how Americans convince themselves they are at the top of the world when they are not. No one outside of Europe and the US cares about the US anymore. They buy Chinese products and do business with Chinese businesses instead, right now. Worldwide, China is seen more positively than the US specially in global south countries. The exceptions of course are Europe and The US, the cold war first world countries of course.

freedom house index

This is obvious American bias. Astroturfing.

Try selling SaaS for finance (think Private Equity/Wall St type customers) that is powered by a Chinese model. See how far you get.

you live under a rock or what?

Hopefully they'll release some smaller models (<100B) that we can run on home hardware at faster than 10tok/s.

Its not as good as GLM 5.2 for agentic workflows while also being bigger. Competition is going to be ruthless because the super low cost to switching.

There is also AllenAi in the US, but they have yet to produce a model at this scale. Thankfully, new contenders can come out of nowhere and do well, as long as they can produce a competitive model.

> Its not as good as GLM 5.2 for agentic workflows while also being bigger

GLM 5.2 underwent extensive post-training and iteration since its original release to reach its current state. This seems like an extremely strong model for a first release, with a lot of potential for improvement, just like DS4.

Sometimes I wish Meta had stuck with Llama 4 a bit longer to see how much further it could be pushed.

Llama 4 wasn't deemed a success, and Meta pivoted away as its now former head of AI couldn't demonstrate, nor even showed interest in, business profit.

They overspent on llama 3 anyway so money ran dry, LeCun is good at running research, but budgets didn't stretch. Meta isn't investing in frontier big models anymore.

> Meta isn't investing in frontier big models anymore

Yes they are. Meta Muse is their attempt.

It's below frontier performance at the moment but they are spending on getting there.

Llama 4 was a bad architecture.

Meta Spark is moderately promising but of course closed source.

This is a great point

Also the fact that China is building solar power like crazy: that makes it fantastically more well spirited an endeavor to wish well.

Do you think American companies will secretly distill frontier models to build open weight ones?

> root for open chinese models to win

What does "winning" mean to you?

unlikely I think, they're likely doing this to garner some interest in their company but they seem pretty interested in revenue (judging by the companies they're working with)

It’s what Meta was supposed to do but Llama fell of the wagon.

There’s also Prism

isn't that what Reflection is trying to be?

What about Meta?

i refuse to root for our enemies, but otherwise you are correct.

Reply on news.ycombinator.com