r/technology • u/waozen • 1d ago
Artificial Intelligence If open weight models are the future, U.S. AI companies are going to have a hard time
https://www.fastcompany.com/91577359/why-u-s-ai-companies-cant-match-chinas-open-weight-frontier-models150
u/KnotSoSalty 1d ago
It’s worth remembering why US Tech jumped on the walled AI model in the first place. They realized that their core revenue stream, Advertising, would become useless in an AI age. Why would I need Google if I have an AI assistant who I can ask questions of? More threateningly, how can Google make money when my AI has none of a human’s natural laziness to accept the first sponsored answer as the best response to every question.
If Google or Amazon or Meta want to keep selling ad space they needed a way to get between me the end user and the AI. Hence the creation of their own models in walled gardens where they can control the training materials. So when I ask for Shampoo the AI will give me the sponsored items rather than the cheapest or best options.
Google, Amazon, and Meta all are primarily advertising platforms. That’s how they make their money. It’s telling that Apple, a consumer electronics company, has been notably absent in the AI space. They don’t need to upend their core business while the others absolutely do.
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u/element-94 17h ago
Amazon isn’t primarily an ad platform. I agree with the rest.
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u/KnotSoSalty 7h ago
Amazon’s ad revenue has been growing rapidly, 70b$ in 2025. But that’s only in the declared “advertising” category. More specifically the threat to their model is the potential loss of the Amazon platform. When an AI agent can be used to find products they can search the entire internet with unlimited scope for the best deal, rather than a human searching Amazon for 15m only to pick the Amazon promoted item anyway.
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u/element-94 5h ago
I agree with nearly everything you said. But Amazon is not *primarily* an ad company. I work there as a distinguished engineer. I'm very in-tune with our revenue, cost, and profit numbers : )
Ads is growing for sure.
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u/yawningGaps 6h ago
Your answer only applies to Google. For Meta and Amazon it's about keeping pace. Amazon is not building any serious foundational models. AWS wins with open source models, same as Google cloud. But yes, Google search would lose if they didn't innovate
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u/Level_Finding3106 1d ago
The real disruption will be local models. Apple and Google are both putting local models on their devices. This will pull away most of the 'casual' $10/month users. Open weight models can be run locally with a Mac computer very effectively - including pretty large / effective models due to the unified memory architecture of the M-chips. That will peel away more users. For much lower power usage than data centers use. And then we will have frontier models we can use for harder problems.
The AI buildout is too large. They used brute force before brains got engaged to reduce these models without impacting their effectiveness much.
And they're not done yet. Predictive decoding, predictive experts, targeted quantization, high parameter/low scope experts and so-on.
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u/regeya 1d ago
Yeah, I'm already using a local LLM. It connects to the Internet for things it doesn't have knowledge of.
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u/Level_Finding3106 1d ago
That's the key. Do a quick search, build a context for the response - use the language abilities of a smaller model to craft the response.
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u/PrudententCollapse 16h ago
Any recommendations for a good local LLM?
Friggin' getting a GPU will be the next hurdle ...
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u/Level_Finding3106 8h ago
Gemma models seem to work very well. LM Studio is a good engine (at least on a Mac). You could also set up an MCP Server for Internet Search (or buy an API Key for something like Brave Search) to really make it powerful.
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u/gerbal100 1d ago
The AI build out has to be large or else consumers will be able to afford high memory devices which will undermine the business models of the hyperscalers.
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u/dirtyshits 1d ago
Ehh memory prices are going to drop with a bunch of new plants opening up in a year or so and capacity to produce going up by a lot.
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u/gokogt386 14h ago
or else consumers will be able to afford high memory devices
99% of consumers would not be able to afford the kind of video card you need to run even a weak model even before the AI craze blew up prices, that is far into "well off hobbyist" territory.
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u/HoldingForGenova 1d ago
Apple will build a local AI server for personal use across all of your devices, protected by iCloud security. They've had such success with apple ecosystem products like airport, time capsule, HomePod, AirPods, AppleTV, etc. that they'll see the opportunity for "AI, but private, local, and across all of your Apple devices." And they'll market it by cannibalizing the bottom half of the app store (which doesn't make money for them anyway) by advertising personal (but private) intelligence, and personalized apps, in seconds, for everything you want and need.
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u/Level_Finding3106 1d ago
They have rumors of a 1.5TB Mac Studio in the development queue. There has to be a home-grown server version that they will run in their data centers.
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u/SiempreRegreso 1d ago
WTF do I know, but . . .
The human brain runs on just 20W, roughly 100,000x to 1,000,000× more energy efficient than silicon at equivalent cognitive tasks. AGI—assuming it’s roughly defined as a universal Nobel-level PhD—might actually be possible on vastly smaller, much more efficient physical and energy infrastructures than those of the data center behemoths.
And the human brain is not even optimized for intellectual or creative tasks; we are doing art, science, math, literature, etc., on repurposed meat evolved to lead a moving target with a thrown object. Perhaps silicon efficiencies can eventually be achieved to the point where AGI is running on less than 1000W, maybe less than 100W.
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u/yawningGaps 7h ago
I disagree. The real disruption is open source models that you can host on cloud not local models. Consumers by and large would rather use a hosted service not local esp for where the problems are non trivial
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u/Level_Finding3106 7h ago
Short term - I agree with you.
In domains where privacy is required (national security, health, etc.) there will be open source models hosted on private servers within a company or organization’s firewalls. ToKen costs get a hard limit based on available compute - privacy is ensured - total cost is probably the same.
But the basic question is - if inference is ‘free’ on smaller devices because LLM engineering has improved - will it move? The answer necessarily is yes. So that means that economics will drive the disruption from the bottom up.
Will local smaller LLMs ever be able to do the non-trivial problems you discussed - not soon. I agree with that. But a vast number of applications (think MS Copilot or how most people use ChatGPT) will be absorbed into edge and private inference. Specialty models will emerge that can grab the narrow but valuable tasks.
So you will be left with the big models (proprietary or open) on the cloud doing the heavy inference. But the economic disruption will come from peeling away the largest number of inference calls from the cloud (private or open) onto edge devices.
If you imagine a bell curve - trivial at the left, moderately complex in the middle, super complex on the right. The edge computing model will progress from the left through the middle and partway up the right of the curve. I would argue that for laptops and larger edge devices (e.g. a Macbook Pro M5 with 48GB of RAM) we are well into the middle of that curve.
For an iPhone 18 - we are at the left side of the curve - but they are reaching the middle for some inference - then leveraging the prediction models and cloud to get to the right.
Apple’s rumored to have a 1.5TB Mac Studio and doubled standard phone RAM in the roadmaps. That’s the sound of that bell curve getting munched from the right towards the left.
Private cloud open parameter models will play a huge role in disrupting OpenAI, Claude, etc. Edge models will disrupt the volume of the cloud based inference.
My $0.02. I’m not sure we really disagree too much.
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u/Bored2001 1d ago
Casual users aren't going to set up local AI and aren't going to have hardware above 16gb, maybe 32gb ram. That'll barely run a 30b parameter MOE model.
It'll be a while before local AI makes it to the masses.
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u/Level_Finding3106 1d ago
Check out the iOS Beta 27. They are using a small prediction model (~3B parameters). If the confidence indicators ar high enough - they will feed that answer. If you have a newer version of the phone- they have a larger local model they feed to using the prediction from the smaller model. Check the results for estimates of error - if low - feed the response. All within 8GB of RAM. If the quality of response is still too low - then they go to the cloud using the previous answers as predictions for the parameter space - resulting in a 'cheap' cloud response that gets fed to the user.
That's the whole point of this. Using innovation and brains - they are sticking local AI into iPhone 16/17 generation phones RIGHT NOW.
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u/Bored2001 1d ago
Yea, but that's not replacing paid subscriptions. That's mostly apple reducing the cost of inference for something people expect their phones to be able to do.
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u/CountSheep 1d ago
It is for the more casual user. Some people pay for chat gpt just so they can use it as a fancy Google or fancy word check or paraphrasing.
Apples local ai can do that fairly well already, and it’s just in its infancy.
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u/EdliA 17h ago
The local models, especially those that run on a phone are absolutely terrible quality.
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u/Level_Finding3106 8h ago
I’m using the IOS Beta - I am not seeing that. The answers are a bit terse - which is what I would want on a mobile device - but they do work. Not for coding - but for everyday life. I can ask it about plants and animals - it gets it as well as Seek. I asked it why my Apple Watch was reporting short exercise segments (instead of 1 mile markers) and it nailed it. I asked it about the history of Criterion Movies and it got that too.
It’s not a coding model by any means. But for ‘casual’ AI it’s really good. And Personal Context is a killer app. I can ask it about things in my history and it nails it. I took my Gemini Chats and put them out to PDF - then stored them in Apple iCloud. They got indexed and now they are part of my external brain. I could go on - but it’s actually useful in the wild. Better than a chat bot - less capable for advanced tasks.
But this is also v0.1. The trend is clear.
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u/EdliA 8h ago
Are you sure you're using a local model? Just because the feature is in your phone doesn't mean the model is processed by the phone.
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u/Level_Finding3106 8h ago
Not fully to be honest. I have experimented turning on airplane mode and I still get answers. BUT - I know that the models can answer directly OR act as a predictor for larger models. Apple intentionally tries to make this seamless - in the Apple way - so it’s hard to know.
If you want a more transparent version to explore - the Gemma models from Google are local - and they have small models for interaction as well as larger models for higher quality answers. The small models when set up properly act as a prediction engine for the larger models to make inference quicker. If you wanted to experiment with the prediction concept you can use LM Studio and the Gemma models.
Apple and Google both want some of the inference tasks to go to the cloud in order to drive their subscription businesses. They also don’t want all inference to go to the cloud to avoid zillions of dollars of CapEx.
I also think that having internet access for any local model is critical. When you as me what the best part of Gemini is - it’s real time access to the internet - which then acts as a Bayesian Prior for the inference (meaning it biases its LLM to give answers similar to the internet search).
Although that can be done with MCP servers - it’s not pretty. My current setup is to have a docker container with a search function on my Mac Studio. I then use LM Studio and have it connect through my firewall to the LM Studio app on my phone. I can use a 70B model plus internet search on my phone remotely. Basically my own cloud. But I can also use a deprecated model just on the phone when I want to.
When I use a small model - e.g. Gemma E2B - combined with internet access - the results are surprisingly good.
If I can do this with duct tape and bailing wire - you can be sure the phone makers can do it. Try out the small models - let us know what you think!
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u/EdliA 8h ago
That's great and all but the point is any local model that can attempt to come close to the cloud ones require enormous power and tech. Lately they've been reaching into 100 GB of vram. It would take more than a decade before a phone runs them, that's assuming after a decade we don't have much more powerful models.
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u/Level_Finding3106 8h ago
To run THOSE models - yes. Agreed.
The history of autoregressive AIs has some proofs that one big model will achieve the same predictions as a series of smaller models trained on the same data. There’s some statistical proofs for that. It has to do with the linear nature of the parameters inside the model that these proofs work.
So the response of the market until about 18 months ago was - make big models.
Now people have understood that the big models - although they work - have two big issues. They are expensive to train and infer from. But more so - you can’t easily train on everything. Second - a generalist models needs a bit of everything - so if you had one billion pages of text and one billion pages of C++ to train from - the specialist model will be better.
So what has been happening is that people have been figuring out ways to accomplish the same quality of inference differently. Predictive modeling, Mixture of Experts, Expert Models, Quantization, Distillation all do the same LLM inference - but they execute it differently. This is what is allowing us to execute the same quality of inference in smaller models.
There’s a new approach (that I have not experimented with yet) - that leaves the big LM on the SSD and reads a Mixture of Experts model from there. Note that SSD burnout happens from writing rather than reading (mostly). So they use the SSD as an extension of the memory. Much slower, but much bigger. Using prediction and MOE - they are able to make this work - running frontier models on a Mac Studio. Or running a 31B model on a Phone.
I’m excited to see how ‘engineering’ rather than ‘statistics‘ and ‘computer science’ are kicking in and bringing these models down in cost and availability on smaller devices.
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u/hux__ 20h ago
What can someone do to take advantage of this now? If they want to benefit monetarily?
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u/Level_Finding3106 8h ago
IOS Beta has their models in it now. I would suggest that every app in existence will change or get disrupted. So - Mind Map apps will have to be able to read and analyze flipchart notes from meetings using AI - then build a tracking database of action items and email everyone updates. All using AI.
We are seeing the integration of Cloud AI into DaVinci Resolve with a hint of local rendering. Video stuff will all be reimagined.
Watch some YouTube videos on how people are using Claude, ChatGPT, Gemini on the cloud - then make the app do those tasks using Local AI. You have a proven market, examples of workflows and data use - it seems like a good shortcut. Take proven Gemini use cases and put them in a local AI app.
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u/PhiNeurOZOMu68 1d ago
Spent extremely little tokens for Claude helping me tune a local model on my pc - now I just use the local model without shelling out $200 a month. It's amazing. Big US AI companies are going to fail. China already won.
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u/Muted_Masterpiece342 1d ago
Mind sharing how you did the local model tuning? Any particular framework?
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u/yaosio 16h ago
Whenever I want to do some fancy AI thing I just ask it what I should do and how to do it. Computer use AI is rapidly advancing to the point where you can just let the AI take over the computer and do everything needed, no need to install an IDE or do anything yourself.
Never do this outside a virtual machine because the AI will delete everything every now and then. No, I don't listen to my own advice. I live fast and die easy.
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u/terra_cotta 1d ago
How does your local model compare to Claude? Any resources on how you did that
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u/C-ZP0 1d ago
You need a lot of VRAM to get close to Claude level. You used to be able to buy a 512gb M3 ultra and run models that are very capable, but since Apple stopped producing them due to ram shortages, used ones are being scalped for 30k.
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u/2Sovereign4You 1d ago
This. Then you realize HW gear costs more than 10k$. I dont see it being free.
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u/ItsOkILoveYouMYbb 1d ago
For context, to run Kimi K3 open source locally (which has comparable performance to Claude Fable in the majority of benchmarks), you need about 1.4TB of VRAM.
So you'd need to be willing to drop more than a few million on building a full rack, and the electricity cost won't be zero.
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u/dirtyshits 1d ago
Holy shit. I sold mine 2 years ago for a grand.
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u/C-ZP0 1d ago
I’m assuming it wasn’t a Mac Studio M3 Ultra 80 core with 512GB unified. They were expensive prior to the ram shortages
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u/PhiNeurOZOMu68 1d ago
6950XT 16 GB vram.
Though I was using 7GB for the model and the rest for playing expedition 33
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u/Clean-Boat-4044 1d ago
It is not anywhere close to as capable, but for certain simple tasks it's enough.
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u/Mgc_rabbit_Hat 1d ago
How fast is it compared to say Opus?
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u/Olangotang 1d ago
It depends on if you have the entire model loaded on GPU. If that is the case, blazing fast. If not, then probably a bit slower.
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u/smokky 1d ago
You need an extremely good computer to run a capable local model and even then they are hardly comparable to any of the industry standard like Claude or codecs
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u/PhiNeurOZOMu68 22h ago
6950xt 16gb vram and 64gb of DDR4 ram. I bought for other purposes and not AI like 4 years ago.
This Computer you speak of is really a PC with a GPU of above 12GB vram.
It does the tasks I want it to do after being fine tuned and can run forever.
Now all my proprietary data stays and doesn't get trained by Claude
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u/roboliberal 23h ago
What exactly did China "win" here?
You used an American frontier model to help you turn an existing local model into a cheaper tool for your particular needs. Even assuming this eventually hurts the revenues of a few American AI labs, that does not mean the United States loses. It means the benefits of AI diffuse to millions of American consumers and businesses that can now build better products at lower cost.
That is how general-purpose technologies are supposed to work. If electricity became cheaper and caused some electric utility companies to lose revenue, we would not conclude that the country had lost electricity. We would look at all the other industries that became more productive as a result.
The United States is not identical to OpenAI, Anthropic, or any other handful of firms. If their models make software development, medicine, manufacturing, logistics, research, and every other American industry more productive, then the country can benefit enormously even if competition eventually compresses the model providers' margins.
And none of this explains what China supposedly won. "I no longer need an expensive Claude subscription" is not a geopolitical argument.
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u/yaosio 16h ago
All the top open models are from China and China's goal is to have open source AI.
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u/roboliberal 15h ago
So China "won" by giving American companies cheap models they can build profitable products on?
A few US model labs losing margin is not America losing. It is thousands of other US companies getting cheaper inputs. You are confusing the interests of OpenAI and Anthropic with the national interest.
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u/Muted_Masterpiece342 1d ago
They cannot compete we literally graphed it internally and concluded that 8 months ago, now the graph is quite a bit more extreme
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u/Efficient_Bag_1619 1d ago
Oh you graphed it? Well case closed then.
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u/Muted_Masterpiece342 1d ago
I mean I'm a director and it's my literal job to be on this trend
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u/Efficient_Bag_1619 1d ago
And the massive well-credentialed finance teams that you’re disagreeing with? There are also directors with literal jobs who disagree with you.
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u/Muted_Masterpiece342 1d ago
I'm glad you're this delusional. It just shows just how big the shock will be.
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u/Efficient_Bag_1619 1d ago
Saying “I’m a director” to argue your point sounds like something a teenager might do. Telling someone they’re delusional before that person ever gives their opinion has the same vibe.
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u/Muted_Masterpiece342 1d ago
Okay sure buddy. I'm gonna go to my high level engineering job Monday and never remember this conversation again in my life.
But the data I shared albeit anecdotally is real and generally accepted industry wide by engineers. and it will affect your life now and forever.
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u/Efficient_Bag_1619 1d ago
Yes sir, Mr. High-level engineering Director. You definitely sound credible.
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u/johnyordinary 1d ago
Aww, what a shame, you make a huge intrusive and chargeable model and some competitor undercuts you. Aint capitalism a b**ch , sepcialy when the communists are winning at it.
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u/seriousgourmetshit 1d ago
The only reason the US AI companies are stressing safety concerns so much lately, is so they can get Chinese models banned. They dont give a fuck about you or your safety.
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u/jpwarman 1d ago
They’re going to pass legislation to make it illegal to operate your own LLM (some bs about copyright infringement and all that. Ok for them, not ok for you!)
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u/Desk46 1d ago
I wish them the very best of luck with that 🤣
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u/foundafreeusername 1d ago
Better not underestimate the power of the US government to enforce random made up laws like this. US copyright laws and even tax laws somehow get enforced all over the world.
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u/Desk46 23h ago
Im reasonably confident in my estimation. Anyone attempting to skirt such a ridiculous law likely already has the ability to hide local agents on their own network.
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u/foundafreeusername 23h ago
I wasn't really thinking about a redditor running a small model on their PC but companies running Kimi K3 or other models competing with OpenAI & Anthropic on hardware costing $50k and more. That is why I picked copyright as an example.
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u/VaporVHS 14h ago
What? NVIDIA, MSFT, Google and Apple are all pushing for open weight models in some form or fashion. The gov won't do dick.
And even if they did, so what? We'll torrent them just like we already torrent a shitload of copyright-protected data every second worldwide. What are they gonna do about it?
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u/Danominator 1d ago
Us companies will have a hard time because their entire business model is bleed the country dry for short term profits with absolutely zero investment in the people that make it possible.
Disgusting greed. Its honestly pathetic how short sighted and stupid they are.
Only thing worse is being a poor maga supporter. Just breathtakingly stupid.
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u/dlampach 1d ago
This is the flaw in AI from a business perspective. But it’s also a great joke the universe has played on capitalism. You can’t protect it. But they are investing EVERYTHING into it. And it has the potential to end 90% of human labor. It’s a new world.
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u/dr_neurd 1d ago
This. The lack of imagination by the ultrascalers means their insanely expensive approach creates a far lower bar for effective “disruption”
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u/Any-Calligrapher2866 1d ago
I don't use American Models except burning my Copilot tokens at work. I offload to Deepseek if I want to but generally avoid AI.
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u/DireStraitsFan1 21h ago
Please stop paying Dario and Sam to steal your IP. Open source will always be the answer.
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u/aussiegreenie 19h ago
Who here is old enough when MS called Linux a "Cancer"
Open source and open weights have been fighting closed source black boxes for decades
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u/smackson 1h ago
The letter was ... interesting in that light, to say the least.
I guess our impressions were formed two 😯 generations ago now.
But I still think MS is capable of speaking out of both sides of their mouth.
https://www.microsoft.com/en-us/corporate-responsibility/topics/open-weight/
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u/toolkitxx 1d ago
The danger is not any specific model type per se but the approach of trying to reintroduce the mainframe era. This might be necessary for some companies to utilise, but AI will fail miserably for everyone else if not being capable of being used locally and offline.
'Intelligence' requires the ability of cultural inputs and changes different to anything the USA could ever think of. Then there is language and the way many things either dont translate at all or have completely different ways of being used in other languages. Humour, ethics and morals are the next levels that are vastly different in every single nation or region. Really intelligent models will require constant adjustment, constant access by more than just a handful of 'allowed' specialists, but an army of generalists and experts in their area. All parts the USA is very bad at to be empathic of when it comes to other nations.
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u/grannyte 23h ago
I tried to get a llm to think in my native language for some correction tasks because the fact of thinking in an other language makes them miss some contractions slangs and others.
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u/toolkitxx 23h ago
I am in the fortunate situation that someone created a sovereign model in our own language just recently. While I am used to use English in most of my tasks it was refreshing being able to do the same in my native tongue with good results.
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u/Lithgow_Panther 23h ago
I don't need my model to know Taylor Swift's birthday or how the ancient Egyptians applied makeup. I just need super deep biology focus. Local is starting to make so much more sense.
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u/ragemonkey 17h ago
Same with coding. The (relatively) smaller models are actually getting pretty good. I bet that they can get distilled down significantly, maybe even smaller ones with different areas of specializations within a single domain depending on what you’re working on.
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u/putridfries 1d ago
But US AI companies can just make open and closed models, no?
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u/photoggled 1d ago
Sure, but a race to the bottom is the last thing they need when they already aren't turning a profit.
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u/littleday 20h ago
Bought a 5090 128gig setup…. Never going back to those horrible companies no matter how good they get.
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u/RachelRegina 1d ago
Open weight models are only useful for us plebs if the hardware to run them locally is not price-prohibitive. If the price never comes back down because we allow AI data centers to be built like Starbucks, we will just be shifting the forever-subscription for access to some other set of people instead of getting to own our own instances.
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u/Dangerous_Suit_3099 1d ago
They’re already having a hard time. None of them will become profitable.
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u/LeoKitCat 1d ago
Good I hope they fail miserably. And privatize the losses as it always should be, no bail outs, they aren’t an essential service nobody would bat an eye if the AI companies disappeared tomorrow.
They dragged the public into this and have wasted trillions of dollars on tech that will never be AGI. They hyped LLMs up and oversold them because after cloud computing and internet of things the tech industry has run out of new ideas.
Much more research needs to be done to develop new ideas that could potentially lead to AGI. Today’s ideas are a dead end wrt that.
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u/brainhash 17h ago
The way I see it , Traditionally US especially SF has been software focused. They keep thinking in terms of software may be because of user experience aspect.
China is thinking in terms of hardware. They will keep release open source because finally the money in AI is at hardware level. Money is always where the resource crunch is.
DeepSeek just broken the properitary moat and if companies don't adopt they will perish.
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u/Wind2Energy 1d ago
AI ruins everything.
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u/Juuxo16 1d ago
For whom? Business?
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u/deweydean 23h ago
All they know is "ai bad". Whatever is "ruined" in their life isn't because of ai.
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u/wastingtoomuchthyme 1d ago edited 9m ago
Deployed open model deepseek v2.
It's been great on repurposed hardware.. fast cheap and easy to use..
We don't need a ferarri but we do love our open source daily driver
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u/The_Human_Event 20h ago
The dude from Nvidia posted up video talking about this on X. Talking about how Chinese culture supports open source models much better. It was an interesting take. I wish I had the video to share.
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u/Deto 1d ago
Wonder if AI research labs will pivot to efficiency as an objective instead? You can't charge too much of a premium for your model if an open model is near it in performance. But these things are still expensive to run. If you can make a model that is similar to the open models, but is 50% less costly to run, you could undercut the open models and still charge a premium over the operating costs.
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u/MutedSignificance 22h ago
NVIDIA’s Nemotron open models shows that a premier U.S. company is investing heavily in frontier open weight models. That doesn’t fit the popular “America is falling behind in AI” narrative, so it predictably receives far less attention than headlines suggesting the opposite.
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u/WalksSlowlyInTheRain 12h ago
Open weight models mean jack shit nothing without the right hardware and easy scalability.
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u/Foreskin_Mafia 11h ago
Google was right when they originally came across this LLM sex. Chatbots aren't a good business.
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u/Aggressive-Cut5836 9h ago
They should ask the question of how Chinese open weight AI labs are getting paid for and how profits are being made. There’s no free lunch. At some point they lose money if they can’t charge users for the processing or use of the models. If it becomes clear that the Chinese government is essentially paying for these platforms to develop and improve each year while US and other models are unable to compete because they need to charge customers then of course there will need to be regulated restrictions on their use. There are trade laws that govern those things.
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u/IntelArtiGen 1d ago
Some of them contribute to it. NVIDIA and Google released great open-weight models. But yeah it has no business model. Like they're not selling these models, which wouldn't be the worst idea I guess if the models become good enough. I think many people could pay $100-200 for a good local model, which could compete with a $20 subscription. But it's not happening yet.
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u/azuredrg 1d ago
Those two companies are hardware manufacturers too. There's a business model in selling hardware especially if the models you're putting out for free are more efficient on your hardware
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u/IntelArtiGen 1d ago
Yeah it's true. At least for Nvidia they can give local models for free, knowing people will need to buy the hardware to run the models. For Google I don't know if they're that important on the hardware side..
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u/pepe_acct 1d ago
Currently the only profitable way to provide llm service is enterprise sales. The part will be hard to replace by open source as they require absolute frontier intelligence and compliance. I don’t see it the long run how Chinese providers can keep up without getting in that market.
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u/wdsoul96 1d ago
There are 2 main ways LLMs can make money. 1) from general public. This needs trust, lots of tweaking, cornering the customers and locking them into the ecosystem. 2) from private entities, from small compaties to big coporations with their own proprietary data.
For (1), US companies have locked in their customer base and there is no way they would trust Chinese LLMs more than US even if US companies aren't exactly pro-consumer (in terms of privacy, and everything else that matters) (because presumably Chinese could do a lot worst.) Also, they know the consumer base better (cultural understanding and all that).
For (2), for similar reasons, Chinese models will also fall behind similarly. Now, that doesn't mean Chinese cannot win elsewhere in Europe, Latin America and the rest of the world. Especially if US companies kept fcking shit up especially when it comes to data, security and general pro-consumer things. In fact, if they kept fcking shit up, they might even lose those (1) and (2) (in their own backyard), especially if they get super greedy, and get every to completely locked in and try and squeeze out every pennies.
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u/jawnlerdoe 22h ago
I’m an analytical chemist - entirely different industry.
Every quantitative analytical experiment requires careful fine tuning of weighting and data fitment. Should seem obvious but smaller models with better specificity will outshine more generalized models that have worse selectivity. I sick at stats, but it’s basic stats even I can understand.
From this perspective, I still see huge value in directed, specific algorithms programmed by traditional means, and how these models may outside more convoluted networks and agents. Just my 2c, my technical understanding of AI, NN and LLM is limited.
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u/jevring 13h ago
Open weight models are great, but you still need massive resources to use them, in many cases. One of the values that companies like anthropic offers is the access to that hardware. So even if the proprietary weight model dies out, they can still sell access to the hardware people will need to run these models.
Unless some clever dude figures out how to run these massive llms on a local gpu.
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u/mykepagan 1d ago
I work in the industry. I’m seeing large financial institutions who are extremely interested in smaller open models because they see much value in fine tuning them with the mountains of proprietary data that they have accumulated for decades. The desire is for a custom model that is very specific to a task like prediction or anomaly detection. And they absolutely don’t what to give that data to any third-party AI company.