6 ms·

For the mainstream audience, the sentiment around local ai today is the same that they had around open source a few decades ago. For a few products, some paid s

For the mainstream audience, the sentiment around local ai today is the same that they had around open source a few decades ago. For a few products, some paid solutions were so much more advanced that open source were very often completely overlooked. Why bother ? And the like. Then we had captive SaaS and other plateforms and now it's obviously wrong for most of us. The dependency we have with anthropic and openai for coding for instance is insane. Most accept it because either they don't care, or they just hope chinese will never stop open weights. The business model of open weights is very new, include some power play between countries and labs, and move an absurd amount of money without any concrete oversight from most people. It's a very dangerous gamble. Today incredible value is available for nearly everyone. But it may stop without any warning, for reason outside our control.

oytis 5mo ago
What is the business model of open weight AI? I don't think there is any. At best it can serve as an advertisement for the more advanced models you sell. The huge difference to open source is that you can't just train an LLM with free time and motivation. You need lots of data and a lot of compute. I sure want to be wrong on that, I definitely like the open-weight version of the future more
- worldsayshi 5mo ago
  It should be feasible to crowd fund training runs right?
  - dmd 5mo ago
    A training run costs somewhere in the neighborhood of a billion dollars. That’s a thousand millions. How many crowdfunded projects do you know that have raised even one percent of that? Who’s going to be in charge of collecting that scale of money? Perhaps some sort of company formed for the benefit of humanity, which will promise to be a non-profit? Some sort of “Open” AI? Oh, wait.
    - iugtmkbdfil834 5mo ago
      << That’s a thousand millions. I can't say that you are lying and you are not exactly exaggerating either. It is true that a new SOTA model -- from literal scratch -- it would be expensive. But, and it is not a small but, is the starting point really zero?
    - derektank 5mo ago
      It’s well within the capabilities of governments in developed countries. If Mistral did not already exist, I would definitely expect the French government to invest in a national LLM, if only because of how defensive they are of the French language.
- PAndreew 5mo ago
  Perhaps you can create a compelling UX around it and sell it as a subscription. "Normies" will not be able/willing to build it. You can then patch the model/ship new features around it as it evolves. For example I have built an ambient todo list / health data extractor using Gemma 4 2EB and Whisper. Nothing to brag about but it does fairly decent job even in foreign languages.
- karussell 5mo ago
  > What is the business model of open weight AI? This is what I do not understand as well and advertising the knowledge and more advanced model is also the only thing that comes to my mind. Since a month I am using gemma4 locally successfully on a MBP M2 for many search queries (wikipedia style questions) and it is really good, fast enough (30-40t/s) and feels nice as it keeps these queries private. But I don't understand why Google does this and so I think "we" need to find a better solution where the entire pipeline is open and the compute somehow crowdfunded. Because there will be a time when these local models will get more closed like Android is closing down. One restriction they might enforce in the future could be that they cripple the models down for "sensitive" topics like cybersecurity or health topics. Or the government could even feel the need to force them to do so.
  - 2ndorderthought 5mo ago
    Why would you want to try to support all users simple queries on your ai data center if they could run it on their own computer? It builds good will also. it also shows research prowess. For China it's different. They need to show Americans who don't trust them at all because of propaganda that they have no tricks up their sleeve. It also doesn't hurt when Chinese companies drop models for free people can run at home that are about as good as sonnet. Serious mic drop.
    - karussell 5mo ago
      Indeed cost can be another factor. Maybe also the main reason why Chrome added an offline model.
      - 2ndorderthought 5mo ago
        That and it's lucrative for Android/chrome to have a text summarizer model embedded on your phone probably for government contracts and data exfil but we won't go through there.
    - TheJCDenton 5mo ago
      Very good point on using local ai to avoid data centers costs. Running AI models on local hardware was exploratory at first, and if it's so easy today it's thanks to open source. It's a little bit coincidental that we have this today, and that mainstream hardware have this capability. The fact that a phone can run very small models is exploratory or some kind of marketing opportunity at best. Why would hardware company ships cards with more AI capabilites (like more VRAM) in the foreseable future ? On what ground does the marketing for on device AI will keep generating interest ? For something as important, it's very uncertain. But above all, it should not depends on these brittle justifications. Showing good will in distribution and research prowess today is positive communication, but it can be exactly the oppositite if/when an attack using those small models will reach a high value target. For China the cultural difference is so huge, it's difficult to say. I would think they first and foremost need to show to evryone inside and outside of China that they match american models. Second, i would say that when americans prefer few very powerfull companies on the get go because they can leverage a lot of capital rapidly to industrialize, China will prefer leveraging a lot of smaller companies exploring a lot of things simultanously (so doing a lot of research), THEN creating legislation to let only the best (or a few) to survive effectively. In the end it's the same result (monopoly or oligopoly), but China may have a stronger core (research) and America may have stronger productive capital, that may be proved obsolete... In the long run, in either side it's a gamble, again.
- wood_spirit 5mo ago
  Meta released Llama just when OpenAI was so hot and its valuation was going through the roof. Speculating, but Meta probably thought the model not competitive enough to keep as a secret weapon but well good enough to commercially damage OpenAI who were a sudden competitor for most-valued-company? In the same way you can imagine the Chinese government pushing the release of deepseek etc to make sure no one thinks the US has “won” and to keep everyone aware that a foreign model might leapfrog in the short term future etc. At some point though if OpenAI/Antropic/Google plateau or go bust then the open source sponsorship becomes less likely, as making it open source was a weapon not a principle.
  - 2ndorderthought 5mo ago
    I disagree. I think deepseek, qwen, and kimi earn a lot of trust open sourcing their models. While still profiting. Effectively they are saying "yea don't crowd our data centers with small queries, go ahead and send your frontier questions to our frontier models. Oh btw those us models? You can run something about as good for free from us if you want hah." It's a power and marketing move. It's also insanely smart to keep up with it to remain sustainable as a brand. Especially given how small their investments into this are. Look at anthropics growing pains. Deepseek has other hosts spreading their brand for free while they grow. Brilliant honestly. In my opinion it makes anthropic and openai look clueless on a lot of levels. China is playing a different game here. To them this is commoditizing their compliment and building good will. The Chinese economy doesn't teter on the brink of collapse to deliver frontier grade LLMs. Nope, Alibaba just made qwen because it needs it. It needs efficient models. Similarly, in China they manufacture and automate so much more than the US ever could. LLMs to them are a topping not the whole meal like they are in the us.
    - mystraline 5mo ago
      Thats because the USA has really nothing big to export. Yay, designs. China? Im getting ready to watch the URKL (universal robot knockout league) go on. The USA is dicking around with failed robot dogs. The USA has been a failed country, coasting on massive inertia. But the tech avenues from a article I cant find showed the USA 8/64 areas excelling. China was 56/64 areas excelling.
- majormajor 5mo ago
  > What is the business model of open weight AI? I don't think there is any. At best it can serve as an advertisement for the more advanced models you sell. I don't think local will necessarily be open-weight. And then it's not that different from personal computing: you're giving up the big lucrative corporate mainframe, thin-client model for "sell copies to a ton of individuals." So it'd be someone else (an Apple, or the next-year equivalent of 1976 Apple) who'd start eating into that. There are a few on-device things today, but not for much heavy lifting. At first it's a toy, could maybe become more realized in a still-toy-like basis like a fully-local Alexa; in the future it grows until it eats 80-90% of the OpenAI/Anthropic use cases. Incumbents would always rather you pay a subscription or per-use forever, but if the market looks big enough, someone will try to disrupt it.
  - treis 5mo ago
    Compute has gone back and forth from mainframe/thin client to fat client a few times already. LLMs will probably follow at some point but I think it's going to take a long time. The cost to transmit text is basically free and instantaneous. The rent (i.e. a GPU in a data center) vs buy is going to favor rent until buy is a trivial expense. Like 50-100 range. Even then a LLM that just works is easier than dealing with your own
    - zozbot234 5mo ago
      Except that buy is a trivial expense because the hardware has been bought already. You've got a whole lot of iGPU and dGPU silicon that's currently sitting idle as part of consumer devices and could be working on local AI inference under the end user's control.
    - majormajor 5mo ago
      Storage has moved back and forth but I don't thnk compute has ever really gone back to thin client. Even Gmail, Google Docs, etc are running a buttload of javascript on the user device. Various attempts at avoiding that (remote .NET or JVM stuff on early "smart-ish" phones) crashed and burned. Video game streaming is the closest thing, and it's never really taken off. (And this, IMO, is a good comparison because it's a pretty similar magnitude up-front-cost, $500-$4000.) Once the local-AI-is-good-enough (Sonnet level for a lot of basic tasks, say) for a $1k up-front investment the appeal of having something that can chew on various tasks 24/7 w/o rate limits, API token budget charge concerns, etc, is going to unlock a lot of new approaches to problems. Essentially more fully-baked line-of-business OpenClaw-type things. Or the smart home automation bot of Siri's dreams. You can more easily make that all private and secure when all the compute is local: don't give any outside network access. Push data into the sandbox periodically via boring old scripts-on-cronjobs, vs giving any sort of "agentic" harness external access. Have extremely limited data structures for getting output/instructions back out. I'd never want to pass info about my personal finances into a third party remote model; but I'd let a local one crunch numbers on it. Even if you need Opus/Mythos/whatever level for certain tasks, if 95% of everything else you'd pay Anthropic or OpenAI for can now be done on things you own w/o third party risk... what does that do to the investment appeal of building better AI appliances to sell end users vs building better centralized models? I think "what if today's LLM performance, but running entirely under your control and your own hardware" opens up a LOT of interesting functionality. Crowdsource the whole world's creativity to figure out what to do with it, vs waiting for product managers and engineers at 3 individual companies to release features.
- fragmede 5mo ago
  The business model is the total lack of attention to Qwen and Kimi that would happen if their models weren't downloadable. Before releasing the weights, there was basically zero attention paid in the western hemisphere to them, for whatever reason. By releasing the weights, they're relevant in the western world. The business model is to get people in the West to pay to use their platform hosting their AI, that otherwise would never have heard of them. As you said, advertising/marketing, essentially.
  - codebje 5mo ago
    Baidu have a lot of services I've never heard of, that are highly successful in China. The lack of interest in expanding into Western audiences doesn't seem to matter there - what's different about inference?
    - fragmede 5mo ago
      Looking at Temu and Shien, there's a ton of interest in expanding into western audiences, the difference with inference is that they've found a way to make that happen. Vs, I don't have any use for, eg Baidu's equivalent to Google maps because I have, well, Google maps.
- js8 5mo ago
  What is the business model of Wikipedia? I don't think there is any. Not everything good in our society needs to have a "business model". People still work on it. It's FINE.
  - avidphantasm 5mo ago
    Ultimately, information is a public good: it is non-excludable (you can’t stop people from using it) and it is non-rival (we can all use it at the same time). Public goods are often very useful, and because they are non-excludable and non-rival, ultimately can’t have a market-based business model. I would class open-weights AI models as public goods, and would support government expenditure to produce them.
  - phainopepla2 5mo ago
    Training AI models is capital intensive, though. Unless there's some sort of mega-crowdfunding effort for open weight model training there needs to be a way to recoup that money on the other end. Either that or state sponsorship I guess
  - sroussey 5mo ago
    > What is the business model of Wikipedia? Donations. Have you donated lately? Wikipedia is cheap compared to creating and training models. I don’t think donations will suffice at all. As an example, we had millions of web developers download and install Firebug before browsers shipped their own dev tools. Donations over the course of multiple years would have paid my salary for a month if I were not a volunteer. But from the “it’s fine” point of view, models will be baked into your OS. Then later models will be embedded into hardware. Likely only OS makers models.
    - selcuka 5mo ago
      > Wikipedia is cheap compared to creating and training models. DeepSeek said it spent $5.6M [1] on training V3, which doesn't sound too much for a near-SOTA model. An open source entity can come up with a hybrid business model, such as requiring a small fee from those who want to host the model as a business for the first n months following the release of a new model, but making it fully free for individuals. [1] https://arxiv.org/pdf/2412.19437 https://arxiv.org/pdf/2412.19437
- dleslie 5mo ago
  This is where government funding can play a role. Sometimes there are things where the public good is best served with public expenditure.
  - CamperBob2 5mo ago
    "Government funding" these days would mean that Trump pays Elon Musk (or more likely vice versa) to make Grok 4.20 the only legal LLM for use by Americans.
    - dleslie 5mo ago
      Outside of the USA it would not look like a wealth transfer to an oligarch. Not every country is in a crypto-libertarian race to hoard power and wealth.
      - CamperBob2 5mo ago
        Not every country is in a crypto-libertarian race to hoard power and wealth. Meanwhile, in the EU, the model would be collectively financed, trained by a competent, neutral agency... and then completely lobotomized in the name of "the children," "safety," "IP rights," "correct speech," dozens of individual countries' legal and regulatory requirements, and any number of additional vocal, noncontributing NGOs. So no one would get rich off of the public model, but no one would get much of anything else out of it, either. As another reply suggests, there's a reason why things happen in the USA first. Even when they don't, the prime movers move here as soon as they can. Or at least they used to.
        dleslie 5mo ago
        European models are competitive, despite the concerns you raise. I don't need a model that can easily produce CSAM or reproduce copyrighted works verbatim in order to be productive.
        CamperBob2 5mo ago
- sumeno 5mo ago
  If a local model hits critical mass the business model is to use it to shape opinions in a way that is advantageous for the company/owners. Much like the current Twitter model, being able to put your thumb on the scale of "truth". Bake a stronger bias towards their preferred narrative directly into the model. Could be as "benign" as training it to prefer Azure over AWS. Could be much worse.
- try-working 5mo ago
  Open sourcing models is a marketing strategy. Chinese labs and small international labs have no awareness or distribution, so unless they become a hot topic for a while, nobody is going to bother trying out their models. Open source gets them that, and is essentially a tax on newcomers. When you start out you simply have no other option but to open source your models. So, the business model of open models is the same as closed models: Sell inference. Open source is marketing for that inference. https://try.works/#why-chinese-ai-labs-went-open-and-will-remain-open https://try.works/#why-chinese-ai-labs-went-open-and-will-re...
  - kranke155 5mo ago
    China’s long term goal might just be to own the chip layer alongside everything else, and outproduce the US in data centers. Frontier US labs could still have an advantage for a long time, but many use cases would start gravitating towards Chinese models if they 10x the data centers and provide similar quality inference for a third of the cost.
  - pabs3 5mo ago
    None of these models are open source, they are just public weights, with licensing that sometimes but usually doesn't meet the Open Source Definition. The Open Source AI Definition (OSAID) is quite ridiculous, I prefer the Debian ML policy for defining freedoms around AI. https://salsa.debian.org/deeplearning-team/ml-policy/ https://salsa.debian.org/deeplearning-team/ml-policy/
- thefounder 5mo ago
  Cloud providers have incentives to release open source models but for some reasons this happens only in China. Amazon, Azure, Google benefit from open source models because people run them on their hardware.
- jononor 5mo ago
  Hardware sales would be an excellent business model for open weights. Nvidia is already on it with their Nemotron models. Any new LLM/NPU hardware companies would want to so the same, if noone else does it for them (Chinese labs currently do). Selling managed self-hosting solutions would be another. That is the business of that recent American company. Selling fine-tuning services or similar adaptations is another. That is what Unsloth is going for, I believe. Most likely any sound business strategy is going to be of "commoditize your compliments" type. There are many complementary products to open-weight - some probably not invented/discovered yet.
aabhay 5mo ago
Disagree with this. When cost becomes an important factor or the free but worse option becomes compelling and accessible (i.e. on device agent via apple style UX), there has been significant user behavior towards local. Think about stuff like removing backgrounds from photos, OCR on PDFs, who uses paid services for casual usage of these things?
iLoveOncall 5mo ago
The mainstream audience does not have the faintest idea that "local AI" is even a thing.
- CamperBob2 5mo ago
  Just as their counterparts in 1975 had no idea that "personal computers" were even a thing. Read through a 1970s-era issue of Popular Electronics or Byte, and then spend some time surfing /r/LocalLlama. You'll get a sense of real-time deja vu, like you're watching history unfold again.
RataNova 5mo ago
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slicktux 5mo ago
I’m just waiting for the US Government to implement their own local AI. Which will eventually lead to them open sourcing it because it’s tax payer funded and being that the NSA has decades worth of internet data they can train on; open weights would be just as good as any companies…
- fragmede 5mo ago
  with this administration?
- riponcm 5mo ago
  How does it help?
apublicfrog 5mo ago
> It's a very dangerous gamble. Today incredible value is available for nearly everyone. But it may stop without any warning, for reason outside our control. What stops you from running the best open weighted LLMs currently available on consumer grade hardware for the rest of time? They're good enough for 95% of use cases, and they don't have a used by date. From what I can see, the "danger" is not having the next tier that comes out, but the impact of that is very low.
- giobox 5mo ago
  > they don't have a used by date For quite a lot of use cases, the current systems arguably do get worse over time if not continually updated. The knowledge cutoff date will start to hurt more and more as the weights age in a hypothetical scenario where you are stuck with them forever. Coding, one of the most popular usescases today, would not be great if it say only understood java to a version from years ago etc. https://en.wikipedia.org/wiki/Knowledge_cutoff https://en.wikipedia.org/wiki/Knowledge_cutoff
  - rrvsh 5mo ago
    Nobody is unaware of the knowledge cutoff, and sharing the Wikipedia article is not helping anyone. Your point is easily rebutted by taking whatever open weights/source model has an outdated cutoff and training or fine tuning it on more data, which is again always going to be viable given a modicum of compute
  - tcp_handshaker 5mo ago
    You could learn how to code...a whole generation did it before...
  - throwyawayyyy 5mo ago
    One solution is not to advance anything of course. I'm not even joking, is there going to be a successor to React? I suspect not, with the vast amount of training data for React now, it's going to look silly to move to something else with less support. What is the last new popular programming language, rust? Will there be another one? I suspect not. Same reasoning. The irony of all this AI acceleration talk is it'll work best if we don't accelerate the underlying tech at all.
irishcoffee 5mo ago
I own 2 5070TI cards in a rig I would gladly donate time to for a distributed training model effort. The kicker is the training data. I would want to gate the data to anything before 2022. I don’t know how to coordinate that, but I would really like to be involved in something like this. SETI, for LLMs.
- AlexCoventry 5mo ago
  Bandwidth is the killer, in distributed LLM training.
  - irishcoffee 5mo ago
    What’s the rush?
    - codebje 5mo ago
      It depends on the purpose for the model. AFAIK LLMs aren't particularly capable at researching answers, relying more on having 'truth' baked in to their weights, so if it takes 12 months to train up a crowd-trained LLM it'll be 12 months behind the times. How serious a risk is poisoned weights? Can we leverage the cryptobros into using LLM training as a proof of work?
      - MarsIronPI 5mo ago
        What? I use Qwen 3.5 35B-A3B and it definitely knows how and when to do web searches to fill in gaps in its knowledge.
        codebje 5mo ago
        Does Qwen3.5 know it needs to do this because the API in question has had loads of churn and much of its training data is on obsolete versions, or do you need to prompt it? How well does it handle having an API reference with sample code in its context window? Having an LLM use a web search tool isn't the same thing as researching a topic, IMO, because it's so ephemeral and needs constant reinforcement. LLMs aren't learning machines, they're static ones.
furyofantares 5mo ago
What's the gamble here exactly? What agency do we have in it right now?
michaelje 5mo ago
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ios-contractor 5mo ago
I don't think it should be local vs cloud AI. I think local AI should be treated as a separate product. local ai should do things that really don't need cloud AI, then cloud AI should be used as a fallback. That would reduce a lot of costs
digitaltrees 5mo ago
Exactly this. The assumption that your access will last is very risky. Or that Chinese companies will keep trying to erode the economic viability of American models by open sourcing the reversed engineered models for ever is naive.
beloch 5mo ago
Keep the Silicon Valley pattern in mind: 1. Innovate, create, and offer it all at sweetheart prices to the public while you rack up debt. 2. Shovel in more money and either buy out or outlast the competition. Become dominant. Lock in your users any which way you can. 3. Enshittify and cash in. The deals Anthropic, OpenAI, etc. offer won't stay this good much longer. Don't let them lock you in. Failing that, you should budget more for the same service. You're going to need it. Having an open alternative running on your own hardware offers non-negligible peace of mind.

oytis 5mo ago

What is the business model of open weight AI? I don't think there is any. At best it can serve as an advertisement for the more advanced models you sell. The huge difference to open source is that you can't just train an LLM with free time and motivation. You need lots of data and a lot of compute. I sure want to be wrong on that, I definitely like the open-weight version of the future more

worldsayshi 5mo ago
It should be feasible to crowd fund training runs right?
- dmd 5mo ago
  A training run costs somewhere in the neighborhood of a billion dollars. That’s a thousand millions. How many crowdfunded projects do you know that have raised even one percent of that? Who’s going to be in charge of collecting that scale of money? Perhaps some sort of company formed for the benefit of humanity, which will promise to be a non-profit? Some sort of “Open” AI? Oh, wait.
  - iugtmkbdfil834 5mo ago
    << That’s a thousand millions. I can't say that you are lying and you are not exactly exaggerating either. It is true that a new SOTA model -- from literal scratch -- it would be expensive. But, and it is not a small but, is the starting point really zero?
  - derektank 5mo ago
    It’s well within the capabilities of governments in developed countries. If Mistral did not already exist, I would definitely expect the French government to invest in a national LLM, if only because of how defensive they are of the French language.
PAndreew 5mo ago
Perhaps you can create a compelling UX around it and sell it as a subscription. "Normies" will not be able/willing to build it. You can then patch the model/ship new features around it as it evolves. For example I have built an ambient todo list / health data extractor using Gemma 4 2EB and Whisper. Nothing to brag about but it does fairly decent job even in foreign languages.
karussell 5mo ago
> What is the business model of open weight AI? This is what I do not understand as well and advertising the knowledge and more advanced model is also the only thing that comes to my mind. Since a month I am using gemma4 locally successfully on a MBP M2 for many search queries (wikipedia style questions) and it is really good, fast enough (30-40t/s) and feels nice as it keeps these queries private. But I don't understand why Google does this and so I think "we" need to find a better solution where the entire pipeline is open and the compute somehow crowdfunded. Because there will be a time when these local models will get more closed like Android is closing down. One restriction they might enforce in the future could be that they cripple the models down for "sensitive" topics like cybersecurity or health topics. Or the government could even feel the need to force them to do so.
- 2ndorderthought 5mo ago
  Why would you want to try to support all users simple queries on your ai data center if they could run it on their own computer? It builds good will also. it also shows research prowess. For China it's different. They need to show Americans who don't trust them at all because of propaganda that they have no tricks up their sleeve. It also doesn't hurt when Chinese companies drop models for free people can run at home that are about as good as sonnet. Serious mic drop.
  - karussell 5mo ago
    Indeed cost can be another factor. Maybe also the main reason why Chrome added an offline model.
    - 2ndorderthought 5mo ago
      That and it's lucrative for Android/chrome to have a text summarizer model embedded on your phone probably for government contracts and data exfil but we won't go through there.
  - TheJCDenton 5mo ago
    Very good point on using local ai to avoid data centers costs. Running AI models on local hardware was exploratory at first, and if it's so easy today it's thanks to open source. It's a little bit coincidental that we have this today, and that mainstream hardware have this capability. The fact that a phone can run very small models is exploratory or some kind of marketing opportunity at best. Why would hardware company ships cards with more AI capabilites (like more VRAM) in the foreseable future ? On what ground does the marketing for on device AI will keep generating interest ? For something as important, it's very uncertain. But above all, it should not depends on these brittle justifications. Showing good will in distribution and research prowess today is positive communication, but it can be exactly the oppositite if/when an attack using those small models will reach a high value target. For China the cultural difference is so huge, it's difficult to say. I would think they first and foremost need to show to evryone inside and outside of China that they match american models. Second, i would say that when americans prefer few very powerfull companies on the get go because they can leverage a lot of capital rapidly to industrialize, China will prefer leveraging a lot of smaller companies exploring a lot of things simultanously (so doing a lot of research), THEN creating legislation to let only the best (or a few) to survive effectively. In the end it's the same result (monopoly or oligopoly), but China may have a stronger core (research) and America may have stronger productive capital, that may be proved obsolete... In the long run, in either side it's a gamble, again.
wood_spirit 5mo ago
Meta released Llama just when OpenAI was so hot and its valuation was going through the roof. Speculating, but Meta probably thought the model not competitive enough to keep as a secret weapon but well good enough to commercially damage OpenAI who were a sudden competitor for most-valued-company? In the same way you can imagine the Chinese government pushing the release of deepseek etc to make sure no one thinks the US has “won” and to keep everyone aware that a foreign model might leapfrog in the short term future etc. At some point though if OpenAI/Antropic/Google plateau or go bust then the open source sponsorship becomes less likely, as making it open source was a weapon not a principle.
- 2ndorderthought 5mo ago
  I disagree. I think deepseek, qwen, and kimi earn a lot of trust open sourcing their models. While still profiting. Effectively they are saying "yea don't crowd our data centers with small queries, go ahead and send your frontier questions to our frontier models. Oh btw those us models? You can run something about as good for free from us if you want hah." It's a power and marketing move. It's also insanely smart to keep up with it to remain sustainable as a brand. Especially given how small their investments into this are. Look at anthropics growing pains. Deepseek has other hosts spreading their brand for free while they grow. Brilliant honestly. In my opinion it makes anthropic and openai look clueless on a lot of levels. China is playing a different game here. To them this is commoditizing their compliment and building good will. The Chinese economy doesn't teter on the brink of collapse to deliver frontier grade LLMs. Nope, Alibaba just made qwen because it needs it. It needs efficient models. Similarly, in China they manufacture and automate so much more than the US ever could. LLMs to them are a topping not the whole meal like they are in the us.
  - mystraline 5mo ago
    Thats because the USA has really nothing big to export. Yay, designs. China? Im getting ready to watch the URKL (universal robot knockout league) go on. The USA is dicking around with failed robot dogs. The USA has been a failed country, coasting on massive inertia. But the tech avenues from a article I cant find showed the USA 8/64 areas excelling. China was 56/64 areas excelling.
majormajor 5mo ago
> What is the business model of open weight AI? I don't think there is any. At best it can serve as an advertisement for the more advanced models you sell. I don't think local will necessarily be open-weight. And then it's not that different from personal computing: you're giving up the big lucrative corporate mainframe, thin-client model for "sell copies to a ton of individuals." So it'd be someone else (an Apple, or the next-year equivalent of 1976 Apple) who'd start eating into that. There are a few on-device things today, but not for much heavy lifting. At first it's a toy, could maybe become more realized in a still-toy-like basis like a fully-local Alexa; in the future it grows until it eats 80-90% of the OpenAI/Anthropic use cases. Incumbents would always rather you pay a subscription or per-use forever, but if the market looks big enough, someone will try to disrupt it.
- treis 5mo ago
  Compute has gone back and forth from mainframe/thin client to fat client a few times already. LLMs will probably follow at some point but I think it's going to take a long time. The cost to transmit text is basically free and instantaneous. The rent (i.e. a GPU in a data center) vs buy is going to favor rent until buy is a trivial expense. Like 50-100 range. Even then a LLM that just works is easier than dealing with your own
  - zozbot234 5mo ago
    Except that buy is a trivial expense because the hardware has been bought already. You've got a whole lot of iGPU and dGPU silicon that's currently sitting idle as part of consumer devices and could be working on local AI inference under the end user's control.
  - majormajor 5mo ago
    Storage has moved back and forth but I don't thnk compute has ever really gone back to thin client. Even Gmail, Google Docs, etc are running a buttload of javascript on the user device. Various attempts at avoiding that (remote .NET or JVM stuff on early "smart-ish" phones) crashed and burned. Video game streaming is the closest thing, and it's never really taken off. (And this, IMO, is a good comparison because it's a pretty similar magnitude up-front-cost, $500-$4000.) Once the local-AI-is-good-enough (Sonnet level for a lot of basic tasks, say) for a $1k up-front investment the appeal of having something that can chew on various tasks 24/7 w/o rate limits, API token budget charge concerns, etc, is going to unlock a lot of new approaches to problems. Essentially more fully-baked line-of-business OpenClaw-type things. Or the smart home automation bot of Siri's dreams. You can more easily make that all private and secure when all the compute is local: don't give any outside network access. Push data into the sandbox periodically via boring old scripts-on-cronjobs, vs giving any sort of "agentic" harness external access. Have extremely limited data structures for getting output/instructions back out. I'd never want to pass info about my personal finances into a third party remote model; but I'd let a local one crunch numbers on it. Even if you need Opus/Mythos/whatever level for certain tasks, if 95% of everything else you'd pay Anthropic or OpenAI for can now be done on things you own w/o third party risk... what does that do to the investment appeal of building better AI appliances to sell end users vs building better centralized models? I think "what if today's LLM performance, but running entirely under your control and your own hardware" opens up a LOT of interesting functionality. Crowdsource the whole world's creativity to figure out what to do with it, vs waiting for product managers and engineers at 3 individual companies to release features.
fragmede 5mo ago
The business model is the total lack of attention to Qwen and Kimi that would happen if their models weren't downloadable. Before releasing the weights, there was basically zero attention paid in the western hemisphere to them, for whatever reason. By releasing the weights, they're relevant in the western world. The business model is to get people in the West to pay to use their platform hosting their AI, that otherwise would never have heard of them. As you said, advertising/marketing, essentially.
- codebje 5mo ago
  Baidu have a lot of services I've never heard of, that are highly successful in China. The lack of interest in expanding into Western audiences doesn't seem to matter there - what's different about inference?
  - fragmede 5mo ago
    Looking at Temu and Shien, there's a ton of interest in expanding into western audiences, the difference with inference is that they've found a way to make that happen. Vs, I don't have any use for, eg Baidu's equivalent to Google maps because I have, well, Google maps.
js8 5mo ago
What is the business model of Wikipedia? I don't think there is any. Not everything good in our society needs to have a "business model". People still work on it. It's FINE.
- avidphantasm 5mo ago
  Ultimately, information is a public good: it is non-excludable (you can’t stop people from using it) and it is non-rival (we can all use it at the same time). Public goods are often very useful, and because they are non-excludable and non-rival, ultimately can’t have a market-based business model. I would class open-weights AI models as public goods, and would support government expenditure to produce them.
- phainopepla2 5mo ago
  Training AI models is capital intensive, though. Unless there's some sort of mega-crowdfunding effort for open weight model training there needs to be a way to recoup that money on the other end. Either that or state sponsorship I guess
- sroussey 5mo ago
  > What is the business model of Wikipedia? Donations. Have you donated lately? Wikipedia is cheap compared to creating and training models. I don’t think donations will suffice at all. As an example, we had millions of web developers download and install Firebug before browsers shipped their own dev tools. Donations over the course of multiple years would have paid my salary for a month if I were not a volunteer. But from the “it’s fine” point of view, models will be baked into your OS. Then later models will be embedded into hardware. Likely only OS makers models.
  - selcuka 5mo ago
    > Wikipedia is cheap compared to creating and training models. DeepSeek said it spent $5.6M [1] on training V3, which doesn't sound too much for a near-SOTA model. An open source entity can come up with a hybrid business model, such as requiring a small fee from those who want to host the model as a business for the first n months following the release of a new model, but making it fully free for individuals. [1] https://arxiv.org/pdf/2412.19437 https://arxiv.org/pdf/2412.19437
dleslie 5mo ago
This is where government funding can play a role. Sometimes there are things where the public good is best served with public expenditure.
- CamperBob2 5mo ago
  "Government funding" these days would mean that Trump pays Elon Musk (or more likely vice versa) to make Grok 4.20 the only legal LLM for use by Americans.
  - dleslie 5mo ago
    Outside of the USA it would not look like a wealth transfer to an oligarch. Not every country is in a crypto-libertarian race to hoard power and wealth.
    - CamperBob2 5mo ago
      Not every country is in a crypto-libertarian race to hoard power and wealth. Meanwhile, in the EU, the model would be collectively financed, trained by a competent, neutral agency... and then completely lobotomized in the name of "the children," "safety," "IP rights," "correct speech," dozens of individual countries' legal and regulatory requirements, and any number of additional vocal, noncontributing NGOs. So no one would get rich off of the public model, but no one would get much of anything else out of it, either. As another reply suggests, there's a reason why things happen in the USA first. Even when they don't, the prime movers move here as soon as they can. Or at least they used to.
      - dleslie 5mo ago
        European models are competitive, despite the concerns you raise. I don't need a model that can easily produce CSAM or reproduce copyrighted works verbatim in order to be productive.
        CamperBob2 5mo ago
sumeno 5mo ago
If a local model hits critical mass the business model is to use it to shape opinions in a way that is advantageous for the company/owners. Much like the current Twitter model, being able to put your thumb on the scale of "truth". Bake a stronger bias towards their preferred narrative directly into the model. Could be as "benign" as training it to prefer Azure over AWS. Could be much worse.
try-working 5mo ago
Open sourcing models is a marketing strategy. Chinese labs and small international labs have no awareness or distribution, so unless they become a hot topic for a while, nobody is going to bother trying out their models. Open source gets them that, and is essentially a tax on newcomers. When you start out you simply have no other option but to open source your models. So, the business model of open models is the same as closed models: Sell inference. Open source is marketing for that inference. https://try.works/#why-chinese-ai-labs-went-open-and-will-remain-open https://try.works/#why-chinese-ai-labs-went-open-and-will-re...
- kranke155 5mo ago
  China’s long term goal might just be to own the chip layer alongside everything else, and outproduce the US in data centers. Frontier US labs could still have an advantage for a long time, but many use cases would start gravitating towards Chinese models if they 10x the data centers and provide similar quality inference for a third of the cost.
- pabs3 5mo ago
  None of these models are open source, they are just public weights, with licensing that sometimes but usually doesn't meet the Open Source Definition. The Open Source AI Definition (OSAID) is quite ridiculous, I prefer the Debian ML policy for defining freedoms around AI. https://salsa.debian.org/deeplearning-team/ml-policy/ https://salsa.debian.org/deeplearning-team/ml-policy/
thefounder 5mo ago
Cloud providers have incentives to release open source models but for some reasons this happens only in China. Amazon, Azure, Google benefit from open source models because people run them on their hardware.
jononor 5mo ago
Hardware sales would be an excellent business model for open weights. Nvidia is already on it with their Nemotron models. Any new LLM/NPU hardware companies would want to so the same, if noone else does it for them (Chinese labs currently do). Selling managed self-hosting solutions would be another. That is the business of that recent American company. Selling fine-tuning services or similar adaptations is another. That is what Unsloth is going for, I believe. Most likely any sound business strategy is going to be of "commoditize your compliments" type. There are many complementary products to open-weight - some probably not invented/discovered yet.

aabhay 5mo ago

Disagree with this. When cost becomes an important factor or the free but worse option becomes compelling and accessible (i.e. on device agent via apple style UX), there has been significant user behavior towards local. Think about stuff like removing backgrounds from photos, OCR on PDFs, who uses paid services for casual usage of these things?

iLoveOncall 5mo ago

The mainstream audience does not have the faintest idea that "local AI" is even a thing.

CamperBob2 5mo ago
Just as their counterparts in 1975 had no idea that "personal computers" were even a thing. Read through a 1970s-era issue of Popular Electronics or Byte, and then spend some time surfing /r/LocalLlama. You'll get a sense of real-time deja vu, like you're watching history unfold again.

RataNova 5mo ago

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slicktux 5mo ago

I’m just waiting for the US Government to implement their own local AI. Which will eventually lead to them open sourcing it because it’s tax payer funded and being that the NSA has decades worth of internet data they can train on; open weights would be just as good as any companies…

fragmede 5mo ago
with this administration?
riponcm 5mo ago
How does it help?

apublicfrog 5mo ago

> It's a very dangerous gamble. Today incredible value is available for nearly everyone. But it may stop without any warning, for reason outside our control. What stops you from running the best open weighted LLMs currently available on consumer grade hardware for the rest of time? They're good enough for 95% of use cases, and they don't have a used by date. From what I can see, the "danger" is not having the next tier that comes out, but the impact of that is very low.

giobox 5mo ago
> they don't have a used by date For quite a lot of use cases, the current systems arguably do get worse over time if not continually updated. The knowledge cutoff date will start to hurt more and more as the weights age in a hypothetical scenario where you are stuck with them forever. Coding, one of the most popular usescases today, would not be great if it say only understood java to a version from years ago etc. https://en.wikipedia.org/wiki/Knowledge_cutoff https://en.wikipedia.org/wiki/Knowledge_cutoff
- rrvsh 5mo ago
  Nobody is unaware of the knowledge cutoff, and sharing the Wikipedia article is not helping anyone. Your point is easily rebutted by taking whatever open weights/source model has an outdated cutoff and training or fine tuning it on more data, which is again always going to be viable given a modicum of compute
- tcp_handshaker 5mo ago
  You could learn how to code...a whole generation did it before...
- throwyawayyyy 5mo ago
  One solution is not to advance anything of course. I'm not even joking, is there going to be a successor to React? I suspect not, with the vast amount of training data for React now, it's going to look silly to move to something else with less support. What is the last new popular programming language, rust? Will there be another one? I suspect not. Same reasoning. The irony of all this AI acceleration talk is it'll work best if we don't accelerate the underlying tech at all.

irishcoffee 5mo ago

I own 2 5070TI cards in a rig I would gladly donate time to for a distributed training model effort. The kicker is the training data. I would want to gate the data to anything before 2022. I don’t know how to coordinate that, but I would really like to be involved in something like this. SETI, for LLMs.

AlexCoventry 5mo ago
Bandwidth is the killer, in distributed LLM training.
- irishcoffee 5mo ago
  What’s the rush?
  - codebje 5mo ago
    It depends on the purpose for the model. AFAIK LLMs aren't particularly capable at researching answers, relying more on having 'truth' baked in to their weights, so if it takes 12 months to train up a crowd-trained LLM it'll be 12 months behind the times. How serious a risk is poisoned weights? Can we leverage the cryptobros into using LLM training as a proof of work?
    - MarsIronPI 5mo ago
      What? I use Qwen 3.5 35B-A3B and it definitely knows how and when to do web searches to fill in gaps in its knowledge.
      - codebje 5mo ago
        Does Qwen3.5 know it needs to do this because the API in question has had loads of churn and much of its training data is on obsolete versions, or do you need to prompt it? How well does it handle having an API reference with sample code in its context window? Having an LLM use a web search tool isn't the same thing as researching a topic, IMO, because it's so ephemeral and needs constant reinforcement. LLMs aren't learning machines, they're static ones.

furyofantares 5mo ago

What's the gamble here exactly? What agency do we have in it right now?

michaelje 5mo ago

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ios-contractor 5mo ago

I don't think it should be local vs cloud AI. I think local AI should be treated as a separate product. local ai should do things that really don't need cloud AI, then cloud AI should be used as a fallback. That would reduce a lot of costs

digitaltrees 5mo ago

Exactly this. The assumption that your access will last is very risky. Or that Chinese companies will keep trying to erode the economic viability of American models by open sourcing the reversed engineered models for ever is naive.

beloch 5mo ago

Keep the Silicon Valley pattern in mind: 1. Innovate, create, and offer it all at sweetheart prices to the public while you rack up debt. 2. Shovel in more money and either buy out or outlast the competition. Become dominant. Lock in your users any which way you can. 3. Enshittify and cash in. The deals Anthropic, OpenAI, etc. offer won't stay this good much longer. Don't let them lock you in. Failing that, you should budget more for the same service. You're going to need it. Having an open alternative running on your own hardware offers non-negligible peace of mind.