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.
Billion is officially the new million.
And that’s without paying royalties to content creators as they should be required to do
also before court cases for defamation. Two cases in Germany hold Google executives responsible for false claims (by the Google AI) of sexual crime claims against an innocent man.
Good. If you want to make AI a form of communication then that’s the voice of your company. You’re endorsing whatever it says.
yeah. It should be the same with Facebook. Here in Australia, it seems like nothing is against the community standards anymore (not spam, porn or even violent threats)
Trash app, it’s why everyone should just stop using it. I ceased doing so, 6 years ago and it’s been bliss without it.
or counting the $1.5 trillion in debt the big AI players are hiding in subsidiaries
yeah the circle-jerk nvidia has created with coreweave and so many others just to wreck the gpu and memory markets, all so these hideously expensive AI racks can SIT IN THEIR FUCKING PACKAGING ON A LOADING DOCK because they don’t have power or cooling built is mindboggling
ITS THE FUTURE YOU JUST DON’T UNDERSTAND!!!
the future of aging like fine piss lol
“The court finds Anthropic guilty and orders them to relinquish the whole of their company to the Group Representing All Artists”.
Artists : “Wait -”.
Dario Amodei : “Fuck yes! Good luck paying back those 80 billions, losers!”
Why haven’t they just asked Claude how to make money with their business model? /s
They have.
-
Wait for excited CEOs to fire all their staff.
-
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.
-
$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.
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
An entire industry built around burning cash, water, and electricity to generate questionable answers based on glorified autocorrect. It’s astounding.
Throw another few trillion and a couple dozen rain forests at it. Completely rational behavior.
glorified autocorrect and plagiarism/nonatribition.
yep. roided up eliza’s
Have you actually used it recently? It’s become incredibly good. Calling it a glorified autocorrect is like calling ASML’s lithography machines a glorified polaroid camera.
Now now this is the frediverse. Your only allowed to ignore facts and hate on ai around here.
There’s plenty of good reasons to hate AI but in case you missed the notice on the door your not allowed to have nuance. It’s all or nothing.
lol… it’s hardly limited to the fediverse bud, people are learning why the economy is fucked: AI
why can’t they buy a console? AI.
OR A GPU.
OR AN SSD.
OR RAM.
OR A FUCKING SPINNING DISK HDD.
why does the food in the adverts look like nightmare fuel? AI.
why does the president think he’s popular? AI.
Tim Sweeney castigates Kenny - why? AI.
Musk can’t synthesize a personality so he snorts ketamine and AI.
where’s the upside, ASSHOLES? Your garbage is fucking up the entire planet, great fuckin work FANBOYS, shove that up your ass for NUANCE
In 20 years - if there’s still a civilization - they’re all gonna look back and wonder how so many were so fuckin dumb.
In 20 years we ALWAYS look back and wonder how so many were so fucking dumb. Thats literally history. Theres never a point where there isn’t some major fucking bunch of absolute idiots trying to ruin everything.
Ai is just this decades attempt at ruining everything.
ok, so it’s like asbestos and nuclear waste runoff, that’s not enough reason to hate AI for you?
instead of stopping the stupidity you’re like: find the nuance.
sexhaver87@sh.itjust.works - lol doubt sheetzoos@lemmy.world -
listen kids, if you’re not brave enough to let chatgpt give you a riposte don’t fucking participate ya little shits
That’s entirely enough reason to hate it.
But it’s also objectively fucking stupid to act as if this is some new historic event in human history and not just yet another dumb fuck move in a long line of dumb fuck moves.
Plenty of good reasons to be pissed off. You don’t have to lie and bullshit to make up more is what I’m getting at.
bUt It CoUlD cUrE cAnCeR
alphafold actually COULD have and still got dissolved https://www.aimodeling.com/en/news/slug/deepmind-dissolves-alphafold-team-anthropic-pivot
Not with an LLM you won’t.
obligatory…
You act as though those billions of dollars spent towards other things (like cancer research) would somehow be better.
?
Fucking yes it would be better to put hundreds of billions of dollars every year into curing cancer/HIV/ Type 1 diabetes/alzheimers, literally any disease. Curing a major illness like those would actively help hundreds of thousands or millions of people around the world every year.
What does AI help? My fucking manager in sending cryptic emails that raise more questions than answers?
Dump a few hundred billion into disease research, or fusion power, or just into housing for all of us.
AI helped me with my voting ballot. I asked Kagi’s AI to research the ambiguous candidates and propositions. I had it put them into a political alignment chart - left/centrist/rightwing. Addendums for candidates included things like whether they had criminal convictions or corruption, stance on vaccinations, how they feel about “tough on crime”, the homeless, ect.
For propositions, the political alignments had a fourth entry added, where the yes/no included the wealth level of backers for a position.
This is very useful, completing in a hour what would have taken a week of research. Very useful for digging into the mechanical details of a proposition, like whether it prevented the replacement or supplementing of a measure.
How do you know that anything it displayed was accurate for those candidates? Every single time I’ve asked it to include source links, those source links don’t actually support the AI’s statement.
My experience too. The source never backs up what the AI claims. It always just makes shit up.
One of the candidates was appointed to their office by a Republican governor, and elevated more recently by a Democratic one. The AI referred to official government records.
Well, that’s one candidate. And a reference to an official gov doc doesn’t meant the info was extracted or summarized accurately.
How about the others?
…yes?
Remember to never miss the /s
Marking your sarcasm is the coward’s way out
With a .ml account you need to. I’ve heard way more absurd statements said with a straight face from .ml users
It’s cowardly to be understood?
Okay, boss. 👍
But have you considered that it’s like REALLY good at programming? /s
What’s stupid is, you could even just keep just the programming part, and it would become much more efficient and not ramble on with explanations etc.
If an LLM was something you could train on your GitHub repo or an internal knowledge base, and ask it natural language questions about that, AND it didn’t need an 800 acre datacenter to do it, I wouldn’t have so many hang-ups about it. Or maybe even train a model in a programming language and have a model that’s tailor made for writing C, or LISP, or Brainfuck or something.
But that’s not what they are. They only function if you hoover up everything the light touches and feed it to the machine, to the point where rare, irreplaceable literature is being shredded apart for training data. And god help you if you try to take your ball and go play somewhere else as the AI boosters profess you must do, they will follow you there and scrape you anyway
You are describing local models and they’re getting pretty damn good, especially at coding.
I mean, local models with RAG exist.
Stop_trying_to_make_AI_happen.jpg
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
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.
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.
The loss, up from $13 billion a year earlier, was driven by $72 billion in accounting charges tied to changes in the value of financial instruments Anthropic issued to raise money
This right here is how all of this will come crashing down. Anthropic and OpenAI, along with several associated companies, are using accounting tricks to hide their debts and obligations. These two are trying to maintain hype for investors so they can cash in on an IPO with total disregard for long-term fiscal sustainability. At some point the bill will come due, and you can bet the C-Suite will have already made out like bandits.
Don’t forget, though, that there’s another cashflow that they’re banking on: total surveillance.
AI can’t do a lot of things, but the thing it’s most suited for is creating a society run like a prison to try and keep the discontented masses in check.
Not enough people realize this. They think the component shortage is a side effect, but it’s to push you towards cloud services on underpowered devices that rely on it.
Imagine the censorship when every service is monitored and controlled by them. Imagine the market capture when they can see everyone’s financials and trade secrets. When they can cut off journalists and activists with one click.
Haven’t heard anything new lately, but there is a push to eliminate physical currency for, what I presume, is a similar reason -> make everything trackable.
People have been trying to eliminate physical currency for decades in not longer. That shit predates computers.
This is quite obvious, it hasn’t happened at a global scale yet because people have to agree on which currency it would be
I think this will backfire too. I think they way underestimate the amount of people already using “underpowered devices” that will just continue to do so without the new cloud services shit.
Basically everyone I know us using a 5-10+ year old laptop, for example. And they just use it fine, sure it’s pokey, and it’s not everyone, but it’s a lot.
The Italian government has just passed an emergency decree that authorizes AI-powered face recognition in public spaces without a judge’s authorization.
I’m not even sure it’s great at that. Is this not a function of any industry struggling to find a usecase eventually ending up with the largest US employer and cash cow: the DoD?
LLMs (‘AI’), not so great, but it can get (probabilistic, but fascists don’t care) descriptions of video feeds into easily searched text. Gobs of GPUs in datacenters paired with surveillance devices all over the place however presents a greater threat, where there’s an (evil) will, a way will be forthcoming. Nice of Ukraine to point out how fragile they are. There’s a deathstar analogy there somewhere.
Yeah, the type of people pushing this don’t even care about the inaccuracy either. If it “catches a black man” with facial recognition, even if it’s the wrong person it won’t matter “they are all criminal thugs anyway.” See also the ICE bull shit and basically deporting anyone even remotely Mexican looking, citizen or not.
Ai is a panopticon
This is my take as well.
Modern companies are only profitable in late stage capitalism if they fall into one of they two categories: scams or gambling.
This right here is how all of this will come crashing down
No, this is pretty normal, and more importantly, doesn’t actually cost the company cash. They gave lenders bonds that can be converted to stock, so this loss on their books is pure paper loss, the same way wealth in owning stock in this money losing company is pure paper wealth.
These numbers are just illustration for simplicity, but imagine a company borrowing $20 billion and telling the lender that you’ll either pay back the $20 billion plus interest, or they can opt to convert it into stock worth 10% of the company. When the company is worth $100 billion, the bondholders won’t convert to stock. But if the value of the company shoots up on paper to where that same stock would be worth $100 billion, the lenders would rather take the stock than the cash repayment. And because it’s worth $80 billion more, the company will have to record that on the books as replacing $20 billion in bonds for $100 billion in stock, and an $80 billion loss compared to their last statement.
But look at the underlying financial cash flow. When that happens, the company actually takes $20 billion in debt off their books. They don’t have to repay that loan anymore. So it improves their cash flow position at exactly the same time that they’re booking a loss.
The whole thing will come crashing down for totally different reasons, relating to operational cash flow, not the actual accounting shenanigans where one kind of paper number gets converted to another kind of paper number.
While you explained this correctly (as far as I understand it), there is part of this everyone keeps making an assumption on the outcome, the “or they can opt to convert it into stock” part. I’m really curious what the interest rate they gave the early investors to entice them when the company was much further from a sure thing. If the interest rate was decent, that final number is going to start becoming awfully enticing to just collect on instead of getting stock that might collapse.
That’s the tradeoff, of course. If a company liquidates, it pays its debts first, then its preferred stockholders, then its common stockholders. But each step down the line gives a greater upside in exchange for less certainty in getting paid at all. Bondholders get paid at higher priority, but have the exact amount they get paid defined by a specific mathematical formula. So they won’t get any additional benefit from the company becoming super valuable. Or they can get in the back of the line to see what unlimited upside they have from the company’s value going up a lot.
Also, Anthropic’s bonds aren’t exactly low interest. They’re right there at the edge between junk bond and investment grade.
It’s not entirely transparent though. It’s a paper loss in terms of cash but they did have to give the stock. At their crazy valuations, even 1% is a big deal.
I dunno, isn’t this the same scheme that the ride share companies and food delivery services?
With the difference that all those services mostly outsource their asset costs to their drivers or have very little investment there. Unlike anthropic who have billions in costs related to all the compute they need to service their customers (obligations to other companies, datacenters and, not to forget, ever rising interest on all their debt).
Definitely, the cost of doing business for these AI companies is orders of magnitude higher than Uber or Lyft
You mean the ones who had to massively pivot their entire business model, because they were relying on self-driving cars being practical in 2019?
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.
Commodities, a famously low-margin game. I wonder what the new companies that pick over the corpses of Anthropic and OpenAI will be like!
And if it does, it won’t ever cost as much as they need it to, to justify their valuations
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.
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.