Why haven’t they just asked Claude how to make money with their business model? /s
They have.
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Wait for excited CEOs to fire all their staff.
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Crank up the prices.
2 only works if regulatory capture in place to prevent cheaper competition from open models.
That’s why they keep trying to play the “AI is so dangerous” narrative and why you keep hearing about “rogue AI agents” which in reality someone had to instruct to break the law, and they probably used some of their no-guardrails internal-use models (so Claude Mythos rather than Fable).
Claude Mythos and Fable are the exact same model, the branding only reflects which one has more restrictive classifiers in front of it during deployment. But the weights are identical.
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Goddamm… This could easily have housed every man, woman, and child in this country. But no, no free housing while we squander wealth with our stupid ideas.
All those loans are getting hot. They’re starting to toss them around and trying to hide them.
I have strong doubts they can ever make this particular approach to AI work at all, let alone in time to cover their bets.
This path looks like a dead end. But, I don’t know jack about shit.
The issue is they are too expensive, it’s not really a limitation of the technology, well it might be, but that’s not the issue here. They are paying Nvidia Billions for their entire 2026/27 GPU runs and they can’t get the customers to cover that. It’s like $200 pm for their max plan. It’s almost cheeper to hire a human in India.
There’s a reason so many AI projects have turned out to be Actually Indians.
My monthly salary as a software developer is 80 USD
🐤
Inference is cheap and getting cheaper. Not sure about training.
I hope it pops
This IPO is going to be one of the biggest rug pulls in history.
Yes. If you follow the money, investors in OpenAI are the usual suspects, a Kushner in there (admittedly the “best” kushner) and the early investors were guaranteed returns of up to 18% (bank CD currently around 5.3%) which stemmed from a $500B injection from the US government. Not only will it be a rug pull, but a government built one. Truly appaling, we let the fox in the henhouse.
Cant have Healthcare for our people, but can pump and dump the world economy.
So far
$8B real loss on $16B revenue. ($72B is a bondholder gain (accounting loss) from conversion). $9B in training costs not counted as loss, even though it is worthless whenever next model version is released.
$561B in forced spending on compute over next 10 years, with $750B total compute costs lined up. Structural losses are baked in for a few years.
But, when the word guessing program gains consciousness, it will be capable of doing the job of every worker in the world, and eager to do those jobs, and anybody who owns Anthropic shares will be a trillionaire in an economy that totally works and is healthy. So, better invest now!
Musk giving 1 share (about $160) to all US children with a Trump account, will make the next generation cheer for skynet genocide and oppression. “For a while, it added 10-20% to my net worth.”
even though it is worthless whenever next model version is released.
For most version releases I actually doubt that, I’m pretty sure they just keep training the model and release checkpoints every few months. Architectural changes, however, will indeed invalidate the previously trained models, and occasionally they have to do that to keep up with the competition.
But if you do architectural changes even just once a year, that means you’d have to depreciate those training costs over just that one year to be truthful. Meaning they might as well be losses rather than depreciating capital investments.
There is a genuine “training corpus” that is an accumulated asset and grows and feeds new models. The vast majority of training cost, that should be expensed, is the training time compute. Neither of these are expensed. There was about 13 major US model releases in September. At least 7 of these replaced a 1 month old previous model family. Open AI gpt sol went from 5.6 to 6 to 6.1 in about 2 months.
Fast model releases not only lock in structural losses, they juice revenue growth illusions. Model use rate generally drops by 66% 4 weeks after release, because of competition, and a surge of use on release for evaluation.
New models are trained on older models, so even if the older model is not longer at par, without it the new model couldn’t exists
I’m totally spamming this across the thread with copy/vaste.
Stop_trying_to_make_AI_happen.jpg
The ai crash of 2026 can’t come soon and fast enough
The RAM cartel is figuring out a new excuse for a price hike before allowing it.
Billion is officially the new million.
Can’t wait to end up holding the bag for this because they’re “too big to fail” again.
So much of their revenue is government contracts. Especially with the Pentagon.
Really can’t be overstated how they’ve wormed themselves into the Military Industrial Complex.
A lot of their revenue is also investment from hardware and software vendors that are using the revenue from the AI companies for those investments.
It’s like the Enron scheme, but instead of one company inventing fake companies to move money around to increase revenue and drive up prices to hide the fact they’re hemorrhaging money while the C-suite tries to divest their own money from the company, it’s multiple companies working together to move money around to increase revenue and drive up prices to hide the fact they’re hemorrhaging money while the major investors try to find a way to cash out.
Anthropic is anxious to do their IPO because they know their business model is fucked, and their shareholders want out.
they’re hemorrhaging money
Are they? This is productive capital being rented out at a profit to the end users. And the end users are, as often as not, big public institutions with near bottomless accounts.
I might argue they are rapidly exhausting scare natural resources. And you’re welcome to do the material analysis on whether dumping 50 gal of water and 100 Mbtus of energy through a digital engine to make a “Farting Peach Says It’s Voting For The Green Party in 2026!” viral Facebook ad is benefiting humanity.
But merely observing that money is changing hands between big economic players doesn’t let us know if something is profitable. You’re describing the basic functions of a financial economy.
Anthropic is anxious to do their IPO because they know their business model is fucked
That’s the popular line. People said the same thing about Tesla when it went public.
It’s always us normies that end up footing the bill and feeling the pain (sometimes literally to the point of dying).
Every fucking time.
It is obvious to me that AI computing is a COMMODITY, in that it will inevitably be something that can not be effectively gate-kept.
These two major US companies (OpenAI and Anthropic) are playing the game of “locking in” a monopoly in a product category that has yet to mature. This is a pretty old capitalist game, of course… Uber, Microsoft, facebook,and many others have played this game in the past.
But, unlike OSes or word processing file compatibility I don’t see a “network effect” in that an open source/weight model can/will do 99% of what a proprietary model can, will do it “transparently,” will eventually become so optimized that it can run locally, etc. etc.
I mean: maybe its name recondition that is worth “trillions” but a race to be “Xerox” or “Kleenex” or even “PC” (as in IBM PC) did not really work out well for these companies.
I think that even the established players (MS, Google, etc.) are biding their time, making sure that they don’t get locked out while non-proprietary solutions become feasible.
I means I know that much of the issues have to do with the limited availability of hardware, but locking down supply is not sustainable for the “big 2” and sooner or later more adequate memory and GPU supply will enter the market…
It will never reach the scale of use they claim unless it becomes a commodity.
And if it does, it won’t ever cost as much as they need it to, to justify their valuations
Commodities, a famously low-margin game. I wonder what the new companies that pick over the corpses of Anthropic and OpenAI will be like!
I dont think a local model will do 99% of what the big ones can, and even if it could, it would require an enormous amount of memory and cpu to do it.
People pay for a good user interface and convenience, that’s well known. Netflix and Spotify are built on that idea and people starting using them even over free pirated stuff :)
Qwen3.8-flash-next broke the vram requirement with its new architecture (which is a preview of their 4.0 architecture). You can run a 177b parameter model on 12gb VRAM, 64gb ram and 80gb SSD at 30tok/s and the speed scales almost linearly with increases to vram.
Incidentally, this was proven at almost the exact time all the calls for “regulating local models because they’ll spread into the toasters” started.
You absolutely can have local models that run on consumer grade hardware, and the proprietary companies know it and are scared to death.
Interesting.
on 12gb VRAM, 64gb ram and
What an excellent time to have 8gb VRAM and 32gb RAM with the current pricing -.-
Are these really able to be as good as chat gpt? Because every time I’ve tried a model like that, it has not been able to be nearly as good. I have 48 gb memory on my machine so I can’t run this particular one though.
I would buy more ram and a whole new computer if the market wasn’t insane.
It is good enough to be useful for programming. It can do clearly defined tasks like find the root cause of a bug. Using it for writing code is frustrating because it is slow and produces lots of tokens. Meanwhile Opus 5.5 is good enough that you can pretty much rely on it doing exactly what you say (and not what you meant), faster, more context efficient and you get a ton of usage with the cheapest subscription.
I can see this changing, though. If the open-weight models reach the level of Claude 5.5 and Anthropic stops subsidizing they might be on pretty equal footing. I don’t see how further improving the model would help, except maybe if somebody invents a different way to manage context that remembers all the important details over a long time so it actually “learns”.
i would not recommend using the SSD to run local LLMS,its slow
best: VRAM only
good: VRAM + RAM
bad: SwappingI don’t think they’re insinuating running inferences from the SSD. But you need somewhere to store the weights for the GPU to load.
i am trying to say: you need the SSD to store it but not swap to it due to its slowness
I won’t pretend to be an expert but my understanding is that the architecture loads experts from the SSD on demand with the RAM serving as a hot cache and the VRAM running the active experts.
As I said above I was able to run 70tok/s on my 4090w64GB ram and a samsung pcie4.0 ssd.
I haven’t run the benchmarks myself, but I can say the quality of response I was getting seemed on par with the frontier models I use for work (Claude opus/fable) and the published benchmarks for the model compete with those same models.
It is a q3 quantization so there is some small loss of accuracy, but from what I’ve understood those are modest losses in the worst case.
There are models that don’t need to consume the entire worlds data infinitively. We’ve had then before llms, things like the pi ai camera can do ai processing on image with something like 8mb of on board storage.
The problem is anthropic, open ai, who ever are trying to do the stupidist approach and assume they can brute force money and power at the problem and don’t understand that they need exponentially infinite data in a way that is unobtainable.
In an ideal world there would be a million little small local models that so specific tasks and can be ran on consumer hardware.
Maybe not “today” but today is still early days.
I’ve been consuming lots of “build your own local AI coding agent” kind of content lately. No doubt this is the same as the guys that are welding together their own versions of Shelby Cobras and stuff… but in a few years you’ll be able to buy a “server” with enough GPU and ram to host strong open models and serve them to your little office.
I work in a small company, and if AI plays out the way Anthropic sees it’d their AI bill will be $100k every year (fire 2-3 people, pay for AI so that the remaining people do more). At that level of expenditure, why wouldn’t my poss pay as much as a copy machine $25k, lets say, go give “local AI” a chance?
The stock valuations that most tech companies carry (and certainly the 2-3 trillion that is being talked about for Anthropic) speak to potential future revenue.
To me, I see computing and actual “model” advances meeting the need within a few years… if not by entrenched companies like Nvidia, then by insurgent chipmakers or new (Chinese or otherwise) chipmakers.
In 10 years you’ll have “opus” is not better running in your phone and probably something more powerful in a PC.
There is a lot of this going on in your assumptions
A local LLM is more convenient than cloud. But it is way less efficient. The cloud LLM processes multiple people’s tasks at once because generating one token is mostly loading things into GPU memory, the compute required for inference is not that big. When doing multiple at once you can load one stage onto the GPU and then do that stage for a number of tokens for essentially the same cost.
maybe they can do like 60% of the big ones?
I dont know, maybe, but I think either way, local models will be a fringe interest for technical people, and not something that will affect the income for these big tech Ai corps in any significant way.
Because you simply can’t run a model with all the worlds knowledge on your home computer.
Someone said that maybe there will be small specialized models that only know about a certain thing, and sure, that’s possible.
You also can’t run a model with all the worlds knowledge on a supercomputer. That simply doesn’t exist. To think that even the largest frontier models can do that is a mistake.
The real question is: will the median user be able to run basically everything they need locally, and how much compute would they need to do that? These companies are betting the farm that it will never happen, and that they’ll be able to charge whatever they want in rent to a captive audience.
But I think these tech CEOs and ML researchers talking about “AI killing us all” is related via the aphorism: It’s easier to imagine the end of the world than the end of capitalism. They can see the writing on the wall, but can’t imagine a future for themselves.
“A MINOR SETBACK ON THE PATH TO THE SINGULARITY!” -
I’m doing my part by constantly asking chatgpt questions without ever logging in or paying them money. Eventually they’ll all go belly up and maybe we can ditch this shit.
80 billion could have bought a whole lot of Chinese EV’s to resale and they could have made a profit.
i wish them ruin, you made my day
Idk man, to me, it feels like we are further from AGI than fusion reactors actually being hooked up to the grid, considering LLMs are still just glorified autocorrects, needing a ton of prompt injections from their providers. And yet, the AI companies make it seem like it’s right around the corner (obviously, because $$$), whereas to me it feels like we are still at least 50 years away. Maybe AGI will be the next fusion generator chase.
Ironically there was notable news in the fusion reactor space this week as Gemini 4, building on the work of Matt Landreman and GPT-6 Astra a little while ago, expanded counterexamples to Grad’s conjecture which had claimed exact solutions for controlling a key aspect of fusion were impossible.
So fusion may actually be more around the corner thanks to the advances of capabilities of AI which helped prove that the fusion problems aren’t as hard as they were thought to be for decades.
And if you actually believe that, I have an amazing business proposition to you. So, there is a bridge in NY, and I can sell it to you for very little money…
They did manage to do something different, though. I thought at one point that proof assistants should offer to automatically upload your proofs to a central repository so everyone can benefit from them. But such a system would require many to opt in for it to be useful.
What AI does best is giving you existing solutions. It even adapts them if they don’t quite fit. Because many people now discuss their ideas with AI, ideas may spread the same way as well when the conversations are used as training data.
So no, definitely not AGI but valuable. Though as typical of explosively growing startups the value does not come from the technology but from activity bordering on criminal that lawmakers can’t keep up with.
AGI has been achieved more or less because it just means self sufficient at completing tasks. An LLM is just a component these days because they are Vision-Audio-Language-Models with multi frequency ‘thought’ loops. The next letter is A for action where they just have high frequency outputs for direct computer and robot use.
Just because it is bad AGI doesn’t mean it is not AGI right
