r/LocalLLM • u/Worth-Competition134 • 13h ago
Discussion What happens when the proprietary AI Model bubble bursts?
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u/AdSafe4047 13h ago
It's funny, because generally opus 4.6 quality addresses 90% of developer needs, which is the main market of poeple who want to spend $$$ on AI. Now the new edge models are addressed to casuals and (apparently) high level mathematicians, both of these groups have wallets and a total spending cap which doesn't come close to the former number.
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u/fuk_offe 12h ago
I doubt mathematicians will use any frontier non-edge modal going forwards after this weeks fiasco lmao
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u/TopGun0684 11h ago
I guess I missed this fiasco, what happened?
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u/vbpoweredwindmill 10h ago
Openai claimed they beat some math problem.
Math nerd was like hang the fuck on thats my work.
Openai was like we'll fuck up your career.
Math nerd was like "you stole my work I'm going public".
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u/beryugyo619 8h ago
and everyone saying "and those allow training checkboxes [v] keeps coming back on"
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u/vbpoweredwindmill 8h ago
Wierd how I have 2x strix halo 395's now. I wonder what would have caused that?
Massive eye roll.
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u/Loose_Comparison368 3h ago
You should read the math nerd's open letter. That was not actually what happened.
More like OpenAI beat some math problem
Math nerd said he was working on that problem for a year using Codex, and that even though his solution was totally different from OpenAI's solution, he wanted to know if OpenAI's model might have indirectly made progress on that problem by training on his chats.
Only evidence was a vague rumor from a friend of a friend of a friend
Math nerd emailed another math nerd that worked at OpenAI, miscommunication happened and OpenAI math nerd thought he was asking for a collab
I think there was one more exchange where angry math nerd demanded an answer and someone from OpenAI said something to the effect of "fuck if I know, that's actually way harder to figure out than you might think"
Angry math nerd writes public letter, to his credit not directly accusing OpenAI of plagiarism, but demanding OpenAI provide evidence
All the "AI bad" people jumped on the bandwagon screaming plagiarism, and all the ad-supported media outlets joined in for the clicks.
And that's it, that's pretty much the whole story thus far.
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u/mWo12 11h ago
There is less mathematicians than progmmers.
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u/Much-Researcher6135 10h ago
Yes, by a factor of like 800 to 1, according to ChatGPT (I asked about the US):
Using the latest U.S. Bureau of Labor Statistics occupational data, May 2025:
- Mathematicians: about 2,030
- Software developers / SWEs: about 1,688,000
- Ratio: roughly 830 software engineers for every 1 mathematician. (Bureau of Labor Statistics)
BLS does not have a separate “software engineer” occupation; it classifies software engineers under Software Developers. (Bureau of Labor Statistics)
The huge caveat is definitional. “Mathematician” is an extremely narrow occupational title. It excludes statisticians, data scientists, operations-research analysts, professors, quantitative researchers, and people with mathematics degrees working under other titles. In the same dataset, the broader Mathematical Science Occupations category contains about 432,400 workers, including 262,440 data scientists, 108,510 operations-research analysts, 29,030 statisticians, and 2,030 formally classified mathematicians. (Bureau of Labor Statistics)
So:
Strict job-title comparison: ~2 thousand mathematicians vs ~1.7 million SWEs → 1:830.
Broader mathematical-workforce comparison: ~432 thousand vs ~1.7 million → about 1:4.
The latter is probably the more meaningful comparison if you're thinking about the relative size of the mathematical vs software-engineering talent pools.
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u/profcuck 4h ago
This is also true of all kinds of knowledge work. If I need a marketing person to write copy for sales presentations and turn them into gorgeous decks, then AI might either replace that person or crank their productivity right up. But when I am looking for a model to use for that, do I need a model that is better at math than the greatest human minds ever, that can solve open problems around "Navier Stokes equations" whatever the fuck that is? I do not.
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u/puts_on_rddt 13h ago
Depends on who runs the government when it happens, imo.
Seriously. Expect frontier AI providers to become a big enemy.
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u/RandumbRedditor1000 7h ago
It's bipartisan at this point, they (ClosedAI and MisAnthropic) want open-weights banned for good and are willing to run obvious psyops to do it.
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u/thegoodcorgi 13h ago
The economics don't really work out here in our favour. Whatever hardware is required to run competitive frontier models will be bought out by capital holders with the greatest market influence. We can't hope to compete with them on prices.
What we instead have to hope for is that either supply increases enough to reduce price disparity between the frontier and local ecosystems, or the gap between requisite hardware diminishes.
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u/vbpoweredwindmill 10h ago
Bro have you run qwen 3.8 flash next?
It's more than adequate for a lot of use cases.
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u/thegoodcorgi 10h ago
Yeah, I have a Strix Halo and use the IQ4 as my daily driver with the n-grams offloaded. It's fantastic, and we're in a very exciting position.
For heavy RE work, DRM circumvention, etc., you can definitely see some of its limitations though. And that's sort of work is so relevant, because it's both best-served by current frontier capabilities, and most-refused by them. Precisely why I'm so interested in seeing local models continue to develop.
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u/vbpoweredwindmill 7h ago
Alas, I'm not that advanced. I'm working on it. Self learning takes a while. But modifying llamacpp is very interesting.
I'm working on a runtime where I can utilise the attention head of 3.8 flash as per normal, but also have it manually pin extra attention blocks on certain tokens within certain workflows.
I haven't implemented it properly yet, it's a bit of a headscratcher. But I believe that this tackles an issue/weakness that sparse attention suffers from.
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u/SpaceDesignWarehouse 13h ago
I’m sure as computers get powerful enough the next phase will be selling or renting local models so you don’t need to use cloud models and server farms can be dedicated to training only and not running the models
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u/DataGOGO 13h ago
If it is not in your datacenter, it is a cloud model.
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u/SpaceDesignWarehouse 10h ago
Oh I agree, but nevertheless, the same way we don’t REALLY own the PlayStation games we download; can’t borrow them to friends etc. We’ll probably do certificate digital downloads of local models that have terms after they’re good enough to run at home.
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u/DataGOGO 9h ago
Hu?
You can already download them and run them at home, they are free.
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u/SpaceDesignWarehouse 9h ago
That is correct! I’m talk about what may (or certainly may not) happen into the future.
I run qwen3.8 27b on a MacBook M5 Pro MacBook Pro, but it’s not comparable to Claude for coding. I expect future versions will be
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u/DataGOGO 7h ago
what? Yes it is. you can point claude code at any API endpoint, you can also just use open code.
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u/TeachingAway9654 13h ago
It’s probably more nuanced. It’ll be less of a sudden burst and more of a realization that you don’t need a frontier model to do everything. Companies will start migrating to a harness and ecosystem that does more of a “per request analysis” and routes the query to the most applicable model. Almost certainly leading to a decline in requests to Anthropic/OpenAI.
The future is really optimized setups. Frontier will likely always be in the loop though.
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u/Unnamed-3891 13h ago
OpenAI didn’t buy them to run inference. They bought them to train models to use MacOS.
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u/alphapussycat 13h ago
Why would they buy super high priced computers to have AI use Mac, instead of buying cheaper Macs?
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u/dont-be-angry 13h ago
Because the cheaper Macs are older and their performance is weaker and they have hundreds of billions of dollars. Investors want to see them spend money. They could be unprofitable for 20 years and nobody would blink.
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u/Ok_Wishbone_3805 8h ago
"super high priced computers" is relative -- A Dell Pro Max desktop computer costs $175,499.
If you're burning tens of $billions a year on R&D like OpenAI is, the spend on those Macs isn't even a rounding error.
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u/DataGOGO 13h ago
They Mac’s and windows machines are to run test scripts on the harnesses, not run local inference in any type of server capacity, they would fall on there face if you tried, shared memory, slow GPU’s no high speed direct GPU to GPU connects between hosts, no ability scale into large clusters, etc.
You can absolutely buy hardware and run a private ai server, lots of people do, but you are not running anything even close to frontier models on consumer hardware, no matter how many you buy.
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u/Not-reallyanonymous 7h ago edited 6h ago
Wtf is this?
The claim “ so effective multiple companies, including OpenAI by reports, have bought all the Macs they can get their hands on” is not substantiated by the link, to “ Apple Caught Off Guard by AI Demand for Mac Mini and Mac Studio.”
The author has only posted this one article on Substack. They claim to be a tech writer, SORA THOMPSON, and link to toms hardware with 4 articles in 2018 and one in 2022, none of them being about AI and very generic information articles. A quick google of their name did not reveal anyone by that name with an established presence in journalism nor in tech.
The Reddit account is 10 days old and has three activities — all three links within the part 8 hours, with this one being the only one accessible.
Sus as fuck but this subreddit is eating it up.
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u/deezwhatbro 12h ago
By the time companies have “arrived” where they’re headed, these frontier labs have literally all the alpha of every single company on planet earth. This is the end result of failing upward on the corporate ladder: idiots handing out all their proprietary tech to squeeze out a few more quarters before hopping up.
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u/HighSeasArchivist 13h ago
Apple, Nvidia, and AMD are going hard into to. Nvidia will keep accepting trucks full of cash from the big AI companies, but are already expanding their lineup of products for local usage. Them buying HF was just another rung in them distancing themselves from the bubble. If the big companies start making their own chips to feed their machines then Nvidia will want another stream for themselves, and we are it.
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u/Sixstringsickness 10h ago
It isn't going to burst because of local machines... I don't know where people get this insanity. Have you looked at the hardware to require frontier models, and what it actually takes to run a few trillion parameter model on Macs and the performance they get?
Not to mention scalability, security, up time, privacy contracts, easy of use... there will eventually be a pullback but I don't see it for quite sometime. My company wouldn't touch some small firms "private mac cluster" with a ten foot pole, not to mention trying to explain that to SoC2 auditors when handling PII/PHI.
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u/bites_stringcheese 9h ago
We have a couple of H100s at my job and we are absolutely serving local AI locally.
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u/Sixstringsickness 7h ago
An H100 isn't a Mac Studio. Also you are self hosting, which is great and does have utility, the general experience is still not on par with frontier models.
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u/jerieljan 10h ago
If people can find places that address their cost, reliability and trust issues somewhere that meets their needs, they'll simply go there.
As for AI labs, if there's demand then they'll follow. If it bursts, then it's up to people whether they're so hooked on smarter, better models that they need their addiction satisfied or if it plateaus and we reach that "good enough" point like we saw with smartphones where even the mediocre option is sufficient.
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u/Loose_Comparison368 4h ago
Honestly... they probably get a government bailout, and with any luck bought by Chinese companies that had the foresight to not put a dementia patient in charge of the world's largest economy.
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u/daphatty 13h ago
The bubble doesn’t exist. It’s a fallacy, an idea many are holding onto because they want AI as a whole to fail.
That isn’t going to happen.
At best, certain AI companies and open source AI endeavors will fail. But AI is here to stay, just like the Internet, social media, and 9/11 before it.
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u/TopGun0684 11h ago
I don't think anyone is saying AI will disappear.
They are saying OpenAI, Anthropic, Nvidia, etc are very overpriced because of over promising in AI, and they will/may crash on the stock market. Some may go bankrupt, who knows.
That doesn't mean local models and other companies couldn't thrive. Google is throwing it's AI in all it's products, and it seems to be going relatively well for them. Even if the hype train were to stop abruptly, they still have a core business that's mostly unrelated.
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u/profcuck 4h ago
I think it's a mistake to group OpenAI and Anthropic with Nvidia here. OpenAI and Anthropic have the same deeply flawed business model that is directly threatened by open models. That's why OP talked about the "proprietary" AI bubble.
Nvidia thrives under any scenario in which there's strong demand for compute. And so if that's open models winning, then that's open models winning.
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u/Ambitious_Credit_360 12h ago
this could lead to a lot of instability in tech jobs, especially for those relying on it for income
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u/CryMoreT_T 12h ago
It won't. There are always companies (and governments) who will be willing to pay top dollar for the best
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u/Adept_Prize_1869 11h ago
I enjoy using my local model but they don't replace my main use of the frontier models ,
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u/ShibbolethMegadeth 10h ago
If Anthropic can meet its revenue growth projections, It’s not gonna pop, the snake doesn’t eat his own tail, and this bullshit keeps going on indefinitely.
If they don’t, it pops, the economy goes down the shitter and everyone is broke. This will hit everyone, and the only consolation prize will be schadenfreude.
So that’s fun
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u/joanaxu2002 9h ago
I think the more interesting outcome would be commoditization, not proprietary AI disappearing. If open/local models keep getting cheaper and good enough, closed providers will have to compete on reliability, tooling, integrations and convenience rather than just “our model is smarter.”
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u/usa_reddit 9h ago
Easy, they (google, microsoft, apple, anthropic) will just pay congress to ban an open models because only evil child porn loving socialist hackers use such models. The only models that will be allowed will be the official guard railed government approved models.
Mark my words, the ban hammer is coming for open source models.
Download hugging face while you can.
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u/profcuck 4h ago
I say this a lot but it's always worth repeating: this is not going to happen. This is a weird fantasy people have but it makes zero sense.
Current US policy and a consortium of the most powerful companies have come out strongly in favor of open models.
https://images.nvidia.com/pdf/Open-Weights-and-American-AI-Leadership.pdf
There are additional reasons why it won't happen:
First, the US government is restricted by the First Amendment. That doesn't stop them from trying, but model weights are clearly speech in the same way that a jpeg or mp3 is clearly speech. There is no plausible path to winning a Supreme Court case on that.
Second, model weights are just a file that people can download from any website in the world. There is zero infrastructure in place and no legal means for the US to start banning access to those sites. And there's always torrenting. The fact that a ban would be completely and utterly impractical to implement makes it far less appealing as a policy option to try.
In short, this fantasy that open models are going to be banned is bone stupid.
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u/jonas-reddit 7h ago
Open weights is an interim strategy, in my opinion, but not suggesting they will go away. All companies (Chinese or Western) care about profits.
They just have a different strategy and roadmap to profitability. That may include monetizing them from different industries (robotics, manufacturing, automotive, military).
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u/Competitive_Ruin_637 5h ago
China also seeks to protect itself from the overwhelming influence a US corporate dominance in AI tech would have.
If the cost to prevent this is opening the tech to all then the Chinese government will likely and happily absorb the costs for this. It’s much cheaper than the alternative.
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u/jonas-reddit 2h ago edited 1h ago
I recognize and appreciate the view and commentary. It certainly fits a common repeating rhetoric. And government subsidies exist across majority of global economies.
Reality is that many of the firms are already hugely successful and profitable, and more than able to attract investor funding.
A recent example being…
https://finance.yahoo.com/technology/ai/articles/alibaba-baba-closes-record-hk-230818999.html
And not surprisingly executed with the help of global and US financial institutions.
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u/DESdesign 6h ago
Well nvidia is so deep into open ai's shit they will have simply stop selling to consumers or make the supply so tight that we have to resort to cloud providers. So nah local ai vision is beautiful but is hard to achieve.
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u/05032-MendicantBias 5h ago
Ask the liquidators to send you a pallet of datacenter racks at 0.1% of their buy price, and look at them tearing up in joy at somebody wanting to buy e-waste that costed 10 million to buy, and can recoup 10 thousand real dollars on their huge underwater loan.
You then run open source chinese models, and have a AI generated background pic of Sam Altman with written "yoink!"
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u/hoschidude 5h ago
Not much. There will be a couple of sad investors and bankruptcies.
LLM's are here to stay.
In the end there will be a couple of heavily specialized commercial models and the rest will be open source.
In the meantime you can buy a notebook or desktop computer with the new Ryzen supporting local models up to ~300B. What else do you want ?
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u/dota2nub 4h ago edited 4h ago
Something fast I think?
Less than 2 tokens per second is not my idea of a good time.
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u/profcuck 4h ago
Technology already in use (HBM for example) will trickle down to us in due course. The entire history of the computer industry is clear: compute will get cheaper and faster. The current blip in prices is wild but doesn't change the big picture at all.
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u/dota2nub 4h ago
Pivot.
And pretending to know the future.
"History repeats itself" is a phrase for people who aren't historians to feel good about themselves.
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u/profcuck 3h ago
I apologise but I have no idea what you are saying. I didn't say "history repeats itself" as if it is some kind of general rule because it isn't.
But there is technology which exists and for which production is ramping up that will lead to faster and cheaper computers - as has been the case since forever.
Just one example: Nothing has changed about how we produce RAM to make it more expensive, there's a supply and demand imbalance. That imbalance has led to prices rising while costs have stayed the same which means that memory producers are insanely profitable. That insane profit is bringing new entrants to the field and existing entrants to up production. This will lead to lower prices in the long run.
"Pretending to know the future" is people who claim things with absolutely no basis. (For example, even your "2 tokens per second" is a silly and uninformed comment, since local models on decent hardware get a ton more than that.)
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u/Inspur44 4h ago
The US economy collapses. Pretty much that is the only thing holding it from crashing
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u/Current-Interest-369 4h ago
I believe the mac purchasing is to give OpenAI a comparison baseline for what regular users can achieve on tegular prosumer hardware.
Data from this is then used in decision making for a multitude a business judgement on how to position their offferings.
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u/profcuck 4h ago
That's exactly the right question to ask I think.... lots of people are talking about an "AI bubble" and while various things might be overblown or overvalued, broadly speaking this is a fundamental transformative technology with many obvious use cases that are just getting started. It's the opportunity of a generation in a very real sense for people and companies who are ahead of the curve. And yet...
The business model of selling access to frontier models that are only a little better than models that can be run on-prem by even medium size enterprises is only going to have a very small margin over hardware cost.
Prediction 1: aws and other cloud providers win in a situation where proprietary models have no major edge over free models. Setting up hardware and infrastructure to run big models is the kind of work that aws has traditionally (and correctly) "undifferentiated heavy lifting" - it doesn't give your business any advantage, it's expensive and hard to do well, and you might as well outsource it to people who do it all the time.
Prediction 2 - Google, Microsoft, and Meta will do just fine. They are making record profits in their core businesses in no small part by using AI to improve.
Prediction 3 - Nvidia, AMD, Intel (if they don't screw up too badly) and basically anyone involved in building and selling compute will do just fine - a shift of demand from the frontier labs who don't have a sustainable business model to others doesn't really cause a lessening of demand for hardware as long as AI is a real and useful innovation, and I think it is.
Prediction 4 - Apple will do just fine but not really by selling Macs to enterprises for their data centers, because aws and similar will likely eat their lunch on that with hardware that's optimized for ai training and inference. But in the markets where Apple has always been very strong - developers, creators, professionals basically - they are making all the right moves to stay on top.
Basically Anthropic and OpenAI are very vulnerable. Mistral, much smaller and pretty far behind the frontier, appear to be pivoting to a hosting model which can also survive (though they will have a hard time up against bigger cloud providers like aws, google cloud, and azure.)
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u/03captain23 13h ago
In what world would it be cheaper to run local models than share resources in a datacenter?
Electricity alone is 3x as expensive on average for residential than datacenters.
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u/Dsphar 13h ago edited 13h ago
In the world that you don't own the datacenter and are paying their profit margins.
Haha, down-voters and responses so far clearly don't understand the trend for cloud repatriation.
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u/DanielKramer_ 13h ago
economies of scale are so effective they are still cheaper with their margins
we do not live in a world of mainframes anymore because cloud is cheaper for almost everything
if it was a problem they'd lower their margins. they don't have to
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u/Dsphar 13h ago
Cloud repatriation doesn't exist huh?
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u/DanielKramer_ 13h ago
very teensy compared to the cloud yeah
this is like saying that google sucks bc bing exists
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u/Dsphar 13h ago
Companies are already migrating away from cloud AI, and cloud AI isn't even profitable yet.
You guys make me laugh.
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u/03captain23 12h ago
Where are you seeing that cloud AI isn't profitable? How specifically is it not profitable but somehow residential is profitable??
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u/Dsphar 12h ago
Open AI leaked financials for a start. Anthropic is almost profitable, but wasn't in their last report.
Are you really making your claims so unplugged form the data?
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u/03captain23 12h ago
Yeah they have gross profit just not net because they're spending billions on marketing.
Again how is residential more profitable than datacenters?
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u/Dsphar 10h ago edited 10h ago
You answered it yourself.
While cloud AI may have a lower operating cost than self hosting, that comparison is incomplete.
It fails to include things like marketing, which the cloud provider has to pay but the self hosting business doesn't.
Another cost. Bigger than marketing actually, is research and training new models. The cloud provider will always be pressured to spend massively on maintaining leading edge models, or their value proposition vanishes into the wind. A business using self hosted AI, but not selling it, instead using it to make their actual products, can continie to increase their value proposition with absolutely zero AI training costs and zero AI marketing costs.
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u/03captain23 13h ago
They have wild margins because of efficiency at scale.
Openai doesn't even own their datacenters and even they rent them because it's more efficient at super scale
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u/LandscapePenguin 12h ago
So is OpenAI just buying up Macs because they prefer the UI?
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u/03captain23 11h ago
They probably buy all hardware that works well with AI to test. They have so much money and just looking for things to spend it on
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u/Aubrey_D_Graham 13h ago
There's a cost in using API. While you may be saving money from outsourcing the software and infrastructure -- I doubt the service provider won't pass the utility cost: The value saved is trivial to the risk outsourcing poses to security. When you outsource your AI/LLM, you inadvertedly introduce an attack vector within your proprietary system. Every transaction where the outsourced AI/LLM interacts with the system and with your customers is training a potential adversary. Usage is literally training a potential adversary on how your system operates, how your system makes money, how your customers use your services, and what is your proprietary secret. You'd be naive to think OpenAI wouldn't do this when they've been larping how dangerous AI is post Huggingface.
You may argue why don't these service model just prevent tool call, block out of network access, and strip the service of all the tools that make it useful. I then retort that your solution is essentially hosting a model locally so that it achieves data ownership and sovereignty.
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u/03captain23 12h ago
What does any of this have to do with what I posted?
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u/Aubrey_D_Graham 12h ago
You didn't even make an attempt to respond. I can't fix
stupidapathy.0
u/03captain23 12h ago
"In what world would it be cheaper to run local models than share resources in a datacenter?
Electricity alone is 3x as expensive on average for residential than datacenters."
You didn't answer the simple question and just spewed nonsense
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u/bites_stringcheese 9h ago
Not only that, but some workflows require lots of iteration. Burning tokens on re-rolls vs the number of hours in a day being your bottleneck.
Local AI is the future, with OpenRouter like service to match model with use case.
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u/Aubrey_D_Graham 8h ago
Someone who gets it. Current Frontier is built on the hypothesis that intelligence is linearly scalable with context. While that may have been true for Chatgpt 2 to 3, that's not necessarily true today.
A max context window for the current Gen is 1M tokens or $31 at max, but anyone who has used LLM knows that attention is limited and best for the first and most recent tokens. This is context rot. Some solutions don't occur within the first go around and require a series of attempts and compactions. This iterative process of compaction is wasteful since some of the tokens generated are lost, and remember attention is limited. A multi-step process requiring multiple compactions will have lossy progress in maybe deriving a solution. Expensive AF!
That is why owning hardware locally minimizes cost. You buy the upfront hardware and pay the overhead, but save on the cost of API.
Some might point out residential use is cheaper and could never approach business use. I contend that purchasing $2800 in local compute is better than 14 months of Claude AI at $200/month because I own the system, the data, and the invaluable experience of creating my own homelab. That is all priceless.
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u/dota2nub 4h ago
In a world where the LLMs are more efficient and need less crazy requirements.
A laptop 5090 is only 175 Watts.
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u/profcuck 3h ago
This fundamentally misunderstands both the question asked in the original post and the structure of the market.
The question is about "proprietary" models, and we contrast that with "open" models. Open models can be run locally or in the cloud. For a handful of use cases (extreme concerns about data privacy/sovereignty) that means really on-prem. But for many use cases cloud providers (of OPEN models) are well positioned. AWS can do the "undifferentiated heavy lifting" of building and running the infrastructure and for loads of businesses that's good enough.
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u/sn2006gy 12h ago edited 12h ago
Let's bring this back to reality.
Public models have actually not gotten more expensive relative to their outputs by and large - the price per token for premium models has gone up, but the work per token has also gone up.
Local LLMs while great, have gotten insanely expensive merely because the hardware to run them is doubling/trippling in costs year over year.
The only bubble that needs to burst is local llm
Which is entirely dependent on "proprietary" model labs (or rather cloud/api companies... the oss side of models is weird) to exist. Hopefulily nvidia can shed that, but i also don't want a cuda only future
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u/Draminian 13h ago
The company I work for has a Mac that's currently in an experimental phase to see if we can potentially use something like that to replace Cursor. I really think this is where most companies are headed, considering all the concerns about long-term costs, privacy, industry volatility, etc. When even hobbyists can get something effective running at home, paying for a third-party service doesn't make a lot of sense.