I’m guessing the lost funds are bleeding out to energy concerns. Correct? Asides from brief construction contracts for data centers, I’m unsure how one makes such large sums of money disappear.
There is a lot to unpack here, finance is interesting.
Investors spend money to build companies on the expectation that those companies will be worth more money than they spent in the future.
One way they spend that money is on the acquisition of bonds, which are firm guarantees against future debts. Bonds are often part of other financial structures (you’ll see them a lot in a hedge, which is a way to limit liability on an investment in exchange for some upside).
If I’m reading the article correctly, those bonds had a condition where they could be converted to shares at a certain price, so the investment and the hedge were part of the same instrument.
It’s not accurate to say that the money could have been deployed otherwise, one way to think about this is that large banks have very large, often self-issued, credit limits. Companies also have self-issued credit limits, related to the banks.
In this case specifically, some amount of this money is going to come out of the IPO, the 80 billion doesn’t exist in any other way.
tl;dr The 80 billion that ‘vanished’ here wasn’t actually spent on anything, it’s valuation against those bonds. It’s also an exciting headline.
Anyways, I hope this was helpful, these companies are definitely getting a lot of investment (running up their credit cards) in the hopes of a big payout, but this doesn’t have anything to do with private equities current running speculation on data center construction or power consumption, or any of those big problems.
From what I understood it’s not really money “lost”, it’s that the company was valued a certain amount and, with a bit of that hype dying down, it’s now valued 80 Billion less.
I’m not an expert on… pretty much any argument in this thread though, so if someone more knowledgeable can chime in I’d appreciate too.
See here’s the thing: it isn’t really their money, so they don’t give a shit. If it was, things would be… ✨different✨
Anthropic has committed to spending at least $518 billion over a decade on cloud services, computing and infrastructure.
They’re betting everything on AGI. I don’t think anyone there at any point has even hoped that the tiny initial revenue streams would cover the absolutely gigantic upfront costs of setting everything up.
Now, whether they’ll actually reach AGI before this thing implodes is anyone’s guess. The people who claim that they absolutely will are full of shit just like the ones claiming they absolutely will not. Nobody knows and only time will tell.
Plus I mean what good can an AGI do for me? I’m still not interested in the slightest in paying someone to atrophy my brain.
AGI “use” has ethical issues of its own in its own right. But if we dismiss those (we shouldn’t) they will be extremely useful.
Imagine a personal assistant that will organize your life and manage all the trivial but tedious tasks in your life. There is a reason why crazy rich people all hire personal assistants.
It’s because they’re lazy, stupid, and have more money than they know what to do with. I don’t have any of those luxuries.
Not going to argue with you here. But your name is quite ironic. Lol.
I have no time or respect for the owner class, sorry if it came across as aggressive towards you, lol.
AGI isn’t for us, it’s to replace us. AGI is the pipedream they have been selling of actually reducing your workforce by offloading it all onto AGI agents. They either don’t reach AGI before their debt obligations come due in 2027 causing the bubble to burst and causing the next great recession or the active AGI before that happens and everyone slowly loses their jobs causing the next great recession.
Lucky for us we can provide superior results for 1/1000th the cost lol.
Yeah, I think they forgot about competition or assumed they wouldn’t have it by the time it matters.
It only works if they stop training new models and can actually follow their 6 year depreciation schedule on their GPUs. But that would make Jensen Huang cry, and they do have competition right now. You can’t put a bunch of effort into creating one AGI model because then a few months later OpenAI or Anthropic releases a slightly better model and all your contracts go away next year.
Oh, AGI isn’t for you. Its for the AI cultists to worship once they summon it. The AI cultists think they can control thier god, even thought it will be integrated into everything if it is truly super intelligent. Then they will realize that they didn’t learn from all of the dystopia Sci fi warning them about summoning their god.
Either that, or its all marketing bullshit.
Superfly 😎
What good could hundred thousand of the world’s best scientists do for me if they had decades to study every question I asked and could answer them immediately?
Nothing I guess if you absolutely want to figure it all out by yourself.
Good thing they’re working on it regardless of if I pay a subscription to a technofeudalist, lol
Now, whether they’ll actually reach AGI before this thing implodes is anyone’s guess.
I’m with you but in a professional environment, there is simply no way around AI anymore. It’s too good to not use unfortunately.
I am in software and I don’t think it’s that clear cut tbh. There’s definitely a right and wrong way to use AI. I think a lot of companies are moving fast and breaking things right now, but a lot of them are going to find out that the thing they broke is the product and they won’t be able to slop their way out of a problem they slopped their way into.
At a micro scale, AI is pretty good at writing workable code quickly. This obviously boosts productivity at the individual level, especially in the short term. At a macro scale, good maintainable software requires higher order thinking that transformer based models simply cannot do. So a lot of the stochastic micro decisions an AI makes lead to technical debt that is not trivial to remove, and can’t really be resolved by the AI itself, at least not in a way that won’t contribute to the problem.
These issues can be curbed by the programer at the point of origin, before it gets out of hand, but obviously that requires more judicious use which cuts into the productivity gains. Because writing code has never been the hard part of software.
TL;DR: fully embracing the slop machine will eventually poison your product in a way that is not really easily recoverable. It is useful, but not “too good to not use.”
TL;DR: fully embracing the slop machine will eventually poison your product in a way that is not really easily recoverable. It is useful, but not “too good to not use.”
We are encouraged to fully embrace the slop machine for production code where I work. No token limit currently. We’ll see how this will play out in a few years lmao.
I’m glad I’ve never had to work in an office much, I suppose.
So I’m a carpenter. AI has zero relevance to my job.
Brain atrophy is only a risk if you don’t reallocate the time saved by applying AGI to an issue you would otherwise have to figure out yourself.
Use it on problems you can’t bear to manage and not on something for which you have a passion.
It could be incredible if society doesn’t collapse.
The problems I can’t bear to manage are not manageable by a chat bot, lol.
Plus with the incoming climate apocalypse, I’d rather be more self reliant and not less.
I’m just going to throw in my 2 cents on the AGI issue since I have a better understanding than average. I’m currently a computational neuroscience PhD student. I don’t work in theoretical neuroscience currently but that’s my hope after I finish my doctorate.
I don’t believe current transformer architectures are capable of AGI and all big AI companies just seem to be adding more parameters to established transformer architectures.
Tap for spoiler
From what I understand transformer architectures act similarly to primary sensory cortices, they model complex probability densities. These companies appear to be banking on the idea that adding more training data, having smaller networks working together with different expertise, and having the train of thought (phonological loop) modeled as part of the PDF is enough to reach AGI. I disagree.
While all of those things are interesting solutions to parts of the problem I don’t think they address some of the bigger issues.
- in biological intelligence memory recall is built into the same system as information processing. Associations between things are learned by simultaneously recalling and processing
- decision making and higher order processing do not fit into a transformer architecture
- temporal encodings are only made, in the transformer system, via the attention mechanism which is much less efficient than a more biological mechanism.
This is really interesting. Do you know of anywhere that I could read more about comparing neuronal function to transformer architecture?
Good question! Unfortunately, I haven’t found a good source myself. I’ve been trying to piece it together based on my theoretical/mathematical neuroscience background and self-study about transformer architectures. The problem is the point neurons in transformers don’t map to neurons in cortex. You have to instead look at the bigger functions that are being computed by each system.
The best single source to learn more about theoretical neuroscience is Neuronal Dynamics: From Single Neurons to Networks and Models of cognition. But its super math heavy. If you want to easier to digest videos I recommend Artem Kirsanov on youtube. For transformer architecture 3 Blue 1 Brown has a great series of videos about it on YouTube as well.
I’m not afraid of a little math so I’ll give the book a try, thanks for the recommendation!
Just remembered another great book kind of bridging both topics that’s a great introduction to the field. Its called Models of the Mind by Grace Lindsay. It follows the history of computational/theoretical neuroscience and how we got to our modern understanding. Its written for a more general audience than Neuronal Dynamics and gives a great overview of the field.
I kinda feel like actual AI will not be made by humans, but by a model that is able to reprogram and upgrade itself until it has some sort of agency. That said, my experience in the field is from the 90s so I really know fuck all that’s relevant today.
Generally the benchmark for AGI has been “human level intelligence” meaning that from there on AI is also the best AI researcher and human input is no longer needed in developing it. That’s where the concept of singularity or intelligence explosion stems from.
The narrative here tends to be that it goes from AGI to ASI (Artificial Super Intelligence) very rapidly but in my view there’s not a significant difference between the two. AI that is as intelligent as a human but not more would still process information about a million times faster so effectively it would still be superintelligent.
That makes sense. That said, I’m not sure superintelligence is just a matter of processing speed. It’s an interesting question - give me a complex problem and say a thousand years to work on it. I kinda feel there’s some problems I’m just not equipped to solve, no matter how much processing time I have. But of course, that’s impossible to test. And in computer terms, you could copy a whole team of me to work together. Hmm.
Yeah, I concur that the difference between AGI and ASI is likely moot. If an intelligent machine is able to upgrade itself to be more intelligent - well, I think we’d all do that if we could! I’m not sure that more intelligence and faster processing are the same thing, but I’m also not sure that we could distinguish between the two in practical terms.
That’s perfectly reasonable. I mostly take issue with the people claiming absolute certainty either way.
Haven’t there been developments in biological computers recently? Wouldn’t that be the key to AGI? I am a total idiot so I know zero about what you are doing or talking about. It just makes more sense for a computer to think like us if it has the same hardware as us is how I see it.
There are positives and negatives to using cells for computing. There are very complex systems that are responsible for making sure cells grow in the correct architecture and are supported property via blood vessels and glial cells. There is also definitely something to be said about glial cells themselves having a role in computation (whole other topic I won’t get into) not just neurons.
If were just looking at the input output relationship with neurons we can model it pretty well using neuromorphic hardware which is designed to respond like a neuron. There are still gaps but and there are scientists who believe it isn’t close enough.
Our current hardware can model neurons closely as well but its less efficient at it. The current AI models use point neurons which takes a lot less computing power but I don’t think they’re the way to AGI. Personally I’m a fan of the adaptive exponential leaky integrate and fire model or the spike response model
i wish them ruin, you made my day
Removed by mod
Yup, because they’re still losing hundreds of dollars for every dollar spent. Their economics still rely on a delusion that the hardware outlay is a fixed cost that can be recovered after model training by selling cheaper compute. Instead, models continue to be released not only to match competition, but because models still do not do a fraction of what they are promised and being sold to do. So capital costs are not fixed.
So AI hyperscalers have a lose/lose/lose/lose choice of either growing at a rate that requires unsustainable subsidies, stopping growth and admitting models can’t do what they and a thousand other startups have been selling them for, charging actual costs, or any combination of the three.
If people were charged anything near the actual costs, much less with a profit margin, demand would implode fast enough to create a sonic boom.
Add to this that incremental improvement comes at exponentially more and more cost. So they spend ungodly amounts more in hopes that the next incremental improvement will push them over the magic line beyond where the are.
Some impressive feats but the issues holding it back from the ‘replace the whole economy’ sort of ambition they imagine are very stubborn. Notably it still confidently declares incorrect information (a significant volume of software developement works around by rigging auto-retries until it stumbles into ‘correctness’, and of course the people pumping out slop art/books aren’t bound by ‘correctness’) and they still have no clear path to autonomous control of humanoid robotics (as crazy advanced as the online GenAI experience, they are still struggling to demonstrate even the most basic manual labor tasks).
“we lose money on every sale but we make it up in volume!”
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.
My monthly salary as a software developer is 80 USD
There’s a reason so many AI projects have turned out to be Actually Indians.
🐤
I hope it pops
I just recently installed Strata which runs on qwen 3.8 flash next. I’m getting over 50 toks. Now a deepseek v4 version is out along with glm 5.3 flash. It’s slower but useable and very capable. All this on a 5080 16GB and 128GB ddr5.
The future for me is going to be open weights and on your own hardware.
How do you run qwen 3.8 flash next on 16gb vram, if you don’t mind me asking ?
At 3 tokens a second and calling it useble I would guess?
I see people claiming that shit all the time and it’s baffling.
They said 50- i looked up strata, and it uses smaller quantizations/optimizations of that model depending on your system.
I have 48g vram and 192g system ram and get ~15t/s on qwen 3.8 flash next at Q4. Off the top of my head. I think Q6 is 12.
Nice setup. I’m running Qwen3.8 27B at 17GB with a 32k context window on a 3090. It does most of what I need for everyday use and its all local, private, no internet connection needed.
The gap between running an LLM locally and cloud API is closing fast.
That’s how it needs to be, and how it should be. Otherwise, we are all permanently screwed by big tech, subscribed and licensed to even own a computer by 2035; MicroSlop says open-source AI is good somehow, but we will see through their actions if they lock down their new line of NVIDIA Spark laptops to an approved number of models. I heard rumours that Deekseek might be banned in America; I can’t see how that would work though.
Deepseek is open weights. You can just download and use it locally. I would never run deepseek using their servers. Or z.ai servers. That I agree with China is still pretty bad.
Running on your hardware is always going to be less efficient than running in a data center. You can run small models on green data centers - best of both worlds (unless you need privacy then run local).
Ever since I switched to Cortecs I’m spending like 20 cents / month for my LLM usage cuz I mostly use small models and try using them responsibly.
Yeah but getting your own hardware is getting expensive too.
Only because there is artificial scarcity in play
There’s plenty of actual scarcity to go around too
I mean that sounds great and I’m happy for you, but you’re working with hardware that costs more than most people on here’s cars when it’s available at all. How viable would your workflow be on an 8gb 1080ti with 32gb of ddr4?
I mean, it’s not really a toy. It’s a business expense. I use it everyday to make money.
Running locally makes little sense anyway because computing multiple replies at once costs the same as computing one. A home setup isn’t going to get utilization anywhere close to optimal.
Is there a way to access this without having to pay for/have an account?
Anthropic logged net loss of over $80 billion in first half of 2026, WSJ reports October 20, 2026 3:41 PM EDT Updated 51 mins ago
Oct 20 (Reuters) - Anthropic reported a net loss of more than $80 billion in the first half of the year and signaled it could spend as much as $750 billion in the coming years to secure more computing capacity, the Wall Street Journal reported on Tuesday.
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, the report said, citing a draft prospectus for its IPO.
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The figures show the hefty costs of the AI race even as investors look harder at whether the money companies are pouring into chips and data centers will pay off.
Anthropic’s revenue jumped to $16.3 billion in the six months ended June 30 from $1.1 billion a year ago, while AI infrastructure spending nearly quadrupled to almost $9 billion, the Journal said.
Anthropic did not respond to a request for comment and Reuters could not immediately verify the report.
ANTHROPIC’S IPO VALUATION UNDER SCRUTINY
Reuters reported first in September that Anthropic posted a net loss of $42 billion in 2025, as part of a series of exclusives from its IPO prospectus. The company has benefited from the popularity of Claude Code, its coding assistant that developers use to write software and build apps.
But some current investors and prospective investors who attended Anthropic’s investor day last week are weighing a lower valuation for the AI company after its latest financial disclosures, four people familiar with the matter had told Reuters.
The investors who attended the event in San Francisco discussed a valuation of around $1.5 trillion, three of the people had said, a significant step down from the roughly $2 trillion valuation previously expected for the November share sale.
Anthropic told investors at the meeting that its third-quarter revenue jumped 45% to $16.8 billion from the previous quarter, and that it was profitable on an adjusted basis during the quarter.
A cooler reception for Anthropic’s IPO could weigh on the wider AI market, whose near-term prospects investors see as hinging largely on the share sale’s success, ahead of rival OpenAI’s potential listing next year.
Reporting by Aditya Soni and Jaspreet Singh in Bengaluru; Editing by Sahal Muhammed
Yes, look up Ed ZItron on Youtube. He’ll be talking about it by Friday at the latest if I know him.
The full article isn’t very long like a handful of paragraphs. TL;DR they lost $72 billion in Q3 despite revenue going up a lot. Same year same quarter was just $13 billion in net loss
they lost $72 billion in Q3 despite revenue going up a lot.
Despite? More like because. If compute is subsidized, every extra compute adds to the loss.
72 billion was a paper loss. 8 billion loss is still a lot though!
Can you elaborate on that a bit more, I figured thats what this was trying to say
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, the report said, citing a draft prospectus for its IPO.
But I didn’t really understand it?
Pre-IPO investors fund the company but it isnt like they give all the money upfront. They do it in pieces and can increase or decrease as targets are reached, failed, or pivoted. This keeps the investors less likely to lose money. As debts and losses are shown the value of the company dropped and how much money is on tap for the future decreases. In this case 72B of funding was “lost”. This is paper money because they only had it on paper they didnt have the gold in hand.
Essentially their potential value to their current investors dropped 72B.
The reported net loss is real as they used more money than they made.
Neither factor is going to bankrupt or close the company considering they still have a bunch on hand and their investors still seem to think theres a possibility of profit (just less than before).
That’s my non-expert understanding anyway.
The paper loss is still critically important though.
“Oh don’t worry about that 64 billion. That’s just something or other the accountants dreamed up. You know what those buzz-harshing assholes are like.”
It’s not as important as you might think. Counter intuitively it’s tied to success as a risky start up. Or arguably they should have got better terms during one of their raises - but hindsight and all that.
For the record I wouldn’t by them for 1.5 trillion dollars.
Its important to the people deciding whether to give them more money.
As a selling point that they can deliver incredible ROI?
This IPO is going to be one of the biggest rug pulls in history.
So far
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 does Amazon every year, no?
No… The worst year Amazon ever had was a net loss of $1.4 Billion back in 2000. Most years in the early days they were at a loss of a few dozen million. At the time, they drew a lot of criticism for running red way longer. If Amazon ran at their worst year for 50 years, they still would have lost less money than Anthropic did.
They haven’t operated at a loss in over a decade.
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.
bUt It CoUlD cUrE cAnCeR
Not with an LLM you won’t.
You act as though those billions of dollars spent towards other things (like cancer research) would somehow be better.
…yes?
?
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.
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?
My experience too. The source never backs up what the AI claims. It always just makes shit up.
That’s a lot of words just to say you’re an idiot that would rather offload important critical thinking skills to a piece of software that a rational person wouldn’t trust to provide a cookie recipe.
Better to be an idiot, than to be an jackass. I wouldn’t trust you to be a good person.
Also, the cornbread recipe the AI gave me an hour ago is pretty damn good. Perfectly adjusted to my ingredient list, tools, and pan size, and specifics like whether I want the food to be moist or dry.
Rather be a jackass with a brain than a drooling moron that can’t figure out a cornbread recipe without consulting fancy autocorrect.
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. 👍
alphafold actually COULD have and still got dissolved https://www.aimodeling.com/en/news/slug/deepmind-dissolves-alphafold-team-anthropic-pivot
glorified autocorrect and plagiarism/nonatribition.
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.
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.
yep. roided up eliza’s
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
I mean, local models with RAG exist.
You are describing local models and they’re getting pretty damn good, especially at coding.
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.
I dunno, isn’t this the same scheme that the ride share companies and food delivery services?
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?
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
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.
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.
The Italian government has just passed an emergency decree that authorizes AI-powered face recognition in public spaces without a judge’s authorization.
Ai is a panopticon
This is my take as well.
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.
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.
Modern companies are only profitable in late stage capitalism if they fall into one of they two categories: scams or gambling.
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…
Building on other people’s work to provide examples for something is actually one of the things an LLM can be good for.
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
See the problem is that people expect AGI to learn on its own. LLMs sorta do with big enough context and writing memories, but… They also don’t. They don’t get better at reasoning or anything, they only get better at solving the tasks they’ve written notes on. Which are themselves often pretty bad notes.
They do learn they use search tools and create long term memories. Mechanically that is just tool use it isn’t magic. Do you need these things as emergent properties for them to be real? That would be cool but I don’t think it takes away what is possible now.
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.
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.
As a training data it’s possible less then useful, unsupervised data like that is actively harmful. As a central repository of everyone’s private conversations it can be in a way useful, but I can’t understand how anyone could be OK with a private company reselling your most private data.
Good.
No wonder they’re panicking so hard over open weight Chinese models
I firmly believe OpenAI, Anthropic, etc., will all eventually collapse under their own weight, meanwhile open weight distillations of their models will become the de facto standard for AI usage. There’s just no way they can ever make up all the money they’ve burned in R&D, especially when distillations are right behind them.
they have to pay off those datacenters somehow, the only one whos partially laughing to the bank is jensen.
What happens to Nvidia when the AI bubble pops though? They will lose so much revenue. Now they should be perfectly sustainable still given that they can sell GPUs to the same customers they used to. But will shareholders like that?
90% of Anthropic money is enterprise API use which is far less subsidised than their subscriptions being 10% of their income. OpenAI on the otherhand relies on the subs.
There is still going to be a big bubble bursting. It’ll be interesting to see how each company handles it.
Check out what portion of Anthropic revenues turned out to be processing distillation for others, namely Chinese market players.
A huge portion of their revenue basically came from their competitors using their horsepower against them to build the next, better mouse trap
Fortunately they keep pushing ahead so those competitors will need to keep paying them to distill.
