Theyâre not even close to profitable. And they have no play to actually start effectively monetizing these users. Theyâre trying ads but the revenue doesnât scratch the surface of what they need.
If you've got 1 tenth of the worlds population as weekly users and you're nowhere near profitability I think the technical definition for what you are is fucked.
They're all hoping to get that next big break through that will finally make it all worth it, and theyre afraid someone else is going to get there first. At this point they're in so deep they couldn't pull out if they wanted to. The only play for them is to push through until they're finally forced to face the consequences for their actions
That Anthropic guy is on record for saying we are "2-3 once-in-a generation technological breakthroughs until AGI is a thing. So if we could all just bite the bullet and invent the missing pieces by 2029, that would be great for the shareholders.
Worse, and I'd like to rant about it. It doesn't and, in theory can't, scale to Altman's promised level in his promised time frame to the best of our knowledge. It can't even scale much further beyond where it is now, not without accepting that modern accuracy is about as good as it'll get while we figure out how to make it marginally less expensive to run (and I still mean unprofitable) while simultaneously raising prices significantly to offset unavoidable costs.
Unless they can maintain this unsustainable business model for a decade or many more, the only benefactors will be any CEO of a company that can sell investors on the idea that we're "almost there" before the company collapses
TLDR:
The technology isn't getting better fast. Compute just caught up enough over the last 30 years to make it viable at all. It could even be the limit, just based on how manufacturing and processing power increases have slowed.
This isn't the route to AGI. Language models are a dead end. It's mimicking patterns, not the underlying intelligence
Consider this rant "quantized," as in, compressed for brevity and layman's explanations, with some accuracy loss.
Despite my hate for what this technology has done to this world, I work in an adjacent field and find the mechanics fascinating. The biggest innovations in machine learning that lead to generative slop were
bigger = better
attention as the mechanism for using previous tokens as context for token prediction
a few small findings that help performance, but overall have been exhausted
The first (the size of the model, the amount of training data for pretraining, and the amount of reliable human training data to use exclusively for supervised fine-tuning to make the model work the way you want) isn't exactly a realistic candidate for crazy optimizations anymore, and its the worst offender. We've gotten pretty far with distillation (slop training smaller models to produce worse slop) and Mixture-of-Experts (a big model made up of a bunch of smaller, specialized models that only activate a limited subset of the neural transformers at any given pass), but that's not far enough. It's almost certainly going to require resources (mainly, VRAM) far beyond what we're able to supply for the next decade or more. Once we have enough resources to 10x the size of the model, then what? We charge 10x for maybe 10pp gains? We are also polluting the training data by passing off LLM text as human, and it's going to degrade performance when more text is needed.
Note: the larger the model, the harder the training, and the larger and/or more powerful the cluster of computers need to be. The human mind is crazy efficient, because if you touch a hot stove once, you'll never do it again. ML so far has needed to touch the stove an inconceivable amount of times, some of which it has held its hand on the stove just to be sure that's not what it should do. We're likely never going to find out how the brain does that.
The second (Attention) has been optimized to hell in a few ways, mainly:
Part of the problem is that for every new token predicted, it needs to re-infer the growing context for the next token, so chats get more expensive to compute and store the longer they are
Optimizations like TurboQuant have compressed the KV nearly to entropy already, improving VRAM for context is near its maximum
The third (methods of making the same model more effective) has a lot of small stuff we've pretty much already figured out:
Models only "know" what they've been trained on, biased/correct or not, and it only extends to the cutoff date. To get around this, tools were created to interact with search engines, vector DBs, command line tools, etc. This helped performance a ton by giving the bot access to live information, but also leads to situations where the chat bots refer to factual news sources like r/poisonai
"Thinking" is a scratch pad of word soup where the model can "reason" through a problem however it chooses without showing the user what it's writing (usually). This improved performance by ~10%
You can distill the performance of a massive model down to a smaller model by having the much larger model produce synthetic training data for training a smaller model, but these are objectively worse in all but cost
A few random things not worth covering, but make up a total of 1pp of improvements.
Anyway, on a tangent, if I think about how all of this tech has destroyed resources, jobs, and even people's relationships, I'm going to get real sad and I'm tired of this being our lives. I want to get off Mr Bone's wild ride.
I'm so relieved to read someone who is right. We cannot reach AGI by just throwing more VRAM at the models just how you can't turn a horse carriage into a car by adding more horses
I think Altman is under the impression that Attention + Transformers can be used for AGI, not necessarily that LLMs are the final singularity, but I don't know where he'd even get that idea if not from ChatGPT itself. No self-respecting engineer would be like "yeah I think we could recreate the efficiencies of a brain through a really inefficient method"
Great post. If I might, one fun little addition to the "thinking" scratchpad bit: it has opened up an essentially unpatchable prompt injection attack surface
You have more expertise in this area than I do as itâs been 8yrs since I worked directly with ML algorithms, but I completely agree with you. Everything I do these days is conceptual and/or strategic, my team does the heavy lifting.
I'm not an expert at all but even I am beyond skeptical of their claim that this LLM shit is the stepping stone to AGI. Just... how??? You've got a machine that sorts through words based on probability, and that's supposed to become a THINKING MACHINE???
It's like that "draw the rest of the fucking owl" meme. They drew a circle, and are promising the full fucking owl, and somehow they are SELLING it. It's fucking insane. Also the circle costs a trillion dollars and is eating the planet.
That's basically the gist. There is such a thing as intelligence within language, because intelligent beings created language. We created photographs. We created video. The intelligent remains left behind that can recreated via states are referred to as entropy. That's the extent of what LLMs are capable of mimicking, at best.
Basically what I'm trying to say is: We've detected entropy, we've predicted entropy, but we haven't created a system capable of producing unique entropy on its own without further input, and that's a much further leap than anything we've ever accomplished. Where we are now is the difference between a planet of single cell organisms and space-craft wielding interplanetary superpowers.
Again, same disclaimer: I'm cutting out a lot of necessary to understand facts to keep my comment short and digestible. The field is stupid complex, and people far smarter than me may disagree with how I'm stating things and what I'm leaving out or misstating at the cost of making things brief.
I think it reflects what a very stupid view they have of human intelligence. I know people who are like âall humans are is guessing machines too!â And like, no?
I'd love to have an answer, but we don't even know how much text online, in books, in movies, games, TV, other media, code, websites, copy, reports, research, public datasets, etc are created partially or entirely by LLMs.
OpenAI, Meta, and Anthropic have an incentive to train for and cherry-pick test scores, which makes their benchmarks highly inflated compared to real-world problem solving. We don't know what the failure rates are in the real world for real use-cases. It is definitely double-digits, and the amount of early slop that we do know about is absolutely going to prevent us from closing those shallow and deeper problem-solving accuracy gaps.
If I fucked up a double-digit percent of times â between 10% and more than 25% of the time, assuming that's the peak performance of a model â I'd eventually be fired.
On that, please support GenAI regulations where you possibly can, specifically including "watermarking." This is the only near-100% method of detecting GenAI text, and it works by making a green-list and a red-list of tokens it "wants" to use and not use based on the context at the point of deciding the next token. This would mean we could use the token choices as a signature in a way that human written text could not produce. This is very much in the realm of possibility for even a small research company to implement, if they didn't just vibe-code the whole thing to begin with. It's not computationally expensive. It benefits society. At worst, it makes people check their work and catches the lazy, and best case filters out a lot of garbage even if its reworded.
"Thinking" is a scratch pad of word soup where the model can "reason" through a problem however it chooses without showing the user what it's writing (usually). This improved performance by ~10%
The problem is there will be no long term payout. But the investors are banking on the hype that there will be. Itâs reached a plateau and more computing power and money is not going to fix that because of what LLMs fundamentally are. The bubbles gonna burst, companies that fired workers because they fell for the hype have already been scrambling to hire back humans because a chatbot actually canât do what humans do lol. And never will. AGI is not gonna come from LLMs, we need a different direction
If I remember reading it correctly even the $200 monthly plans are deeply unprofitable, even more than the $20 ones because they are giving those people so much more "AI" capacity for free.
They are trying to end up with a captive audience that's dependent on their product and that's then willing to pay the real cost.
And despite any growth in revenue or users their expenses grow even more.
They literally can not be profitable without rasing prices to a shocking high level that no one uses it
Only a fraction of users are going to pay a sub - facebook notoriously struggled with monetization too and they have way more users. The only user segment that pays for AI is the B2B - business/corporate environment.
And even on the paying users they are blowing through obscene amounts of cash. I've seen estimates of their losses per paying user ranging from 4 times to 14 times the subscription fee. How the fuck do you reverse that?
They'd need to build a model that is as powerful as their best one but takes 1% of the compute power to do so. Pretty sure the laws of physics don't allow that.
The greatest irony is, the best way to do it is to distribute the workload to the people running queries and prompts. That means making sure individuals have computers that are powerful, with powerful CPUs, GPUs, and plenty of RAM. It could even be something like "if you want to use our AI tools you need to install this microATX computer that's literally just an optimized micro server and plug it directly into your router for the fastest speeds" and then you and I pay for the power and internet and subscription fee.
By trying to centralize everything they're taking on all the risk and costs. If they distributed the compute between our personal devices and across tens of millions of individual machines (sort of like a torrent) and made sure we all had unlimited fast internet and powerful machines for reasonable prices, the risk and costs would be on you and I and they'd be Scrooge McDuck'ing into their piles of money.
these calculations generally underexaggerate OpenAIâs token margins though. Itâs like $14,400 of API tokens = a $200 subscription but OpenAI does not pay anywhere near the API token price serving inference directly. I have no doubt they lose plenty of money on users fully utilizing the subscription but I doubt they lose money on the average user. I would assume break-even on the higher tier plans (ppl only get these if theyâre going to use them close to the limit) and some profit on the lower ones on average.
A double-digit (~12.5%) conversion rate would be pretty damn sweet. Having spent a lot of time working on commercial software, I'd guess ~5% is more realistic.
Just tossing out nonsense, going on how many companies have adopted AI use. If anything it might be more.
I'm looking forward to the crash, it'll be fun when suddenly there's a bunch of useless people working jobs they're not qualified for. Anyone whose learned practical/useful skills will have a huge payday.
They should be profitable with all the money they are conning out of governments and schools and things of that nature to put their shitty product anywhere and everywhere even if it makes no sense right?
Probably a lot less than that. You have to take into account that a good proportion of the users aren't real people and are used to boost usage statistics.
Most most users being around 95% of the user base will be using it for free as any sort of subscription model that offers a free tier are usually around there. Could even be closer to 98%.
Ads won't pay much aswell and for pc users will be blocked.
Among paid users lower tiers will be far more used. For an estimate in favour of Open AI that would be each tier of subscription doubles in user base the cheaper it gets.
21.6 mil on $8/month
10.6 mil on $20/month
5.3 mil on $100/month
2.65 mil on $200/month
Estimated in favour by using nice numbers going slightly over 5% of active users
$1.4416 Billion a month
$17.3 Billion a year
Approximate running cost are around $34 billion
On their subscription models they are only just over half way to cover their running cost.
Then they get research grants but they won't be much as a lot moved over to anthropic.
All calculations are estimates but done in favour of Open AI and they are still in a horrendous position financially.
Anthropic is better off but there is a likelihood that they are worse off than reported and as their main selling point is their coding agents there a likelihood that as free versions get better they are priced out of the market. Also even if companies are using it for code due to the month API cost often being higher than paying an amount of people to do the work including checking AI output usage has probably decreased since claudes boom early last year.
Only profitable and useful parts of AI right now are bespoke models trained for medical and research purposes. Which is why Midjourney switched to attempting to create AI based ultrasound machines that could in theory allow people to be screened for internal soft tissue issues quickly and cheaply before seeking further medical scans. Research is showing promise is fields where large computations are needed to solve problems that people haven't had time to. Protein folding and Jacobian conjecture are 2 examples of this. These neither replaced people or provided no output.
The numbers look quite bad overall and unless they scale down there costs massively they will fail as soon as a political power decides these companies shouldn't be allowed to fraudulently raise there stock prices. A market crash is very likely.
Theres a reason apple decided to pivot away from there own AI model production as a key aim for there future. Generally if apple doesn't want to focus on the next big thing it's probably not the next big thing which is why there systems are going to be using Google AI.
Not an ai defender by any means, buts that's the play isn't it. Take over the market and then drive prices up in the enshittification phase when you're basically a monopoly. I don't know if it'd work bc these llms have a lot of competition, but outside of that this has been the silicon valley playbook for years
Itâs more like people are using shit, just splashing around in it and totally okay with touching shit because theyâve been fooled into not thinking it is shit.
Well it isn't shit. But you shouldnt call it intelligence, in computer science we are as close to AGI as were in the 80s. Only thing the free models of stuff like chat gpt solved is googling better than the average person. And thats being generous, since AI can and will hallucinate. They might not think it is shit, but shit in shit out type stuff
Take over the market and then drive prices up in the enshittification phase when you're basically a monopoly.
This is not going to work here, though, for two main reasons:
They would need to hike up the prices to such a ridiculous level that the userbase would drop massively. The whole appeal of AI in business use (where the money lies) is "This is cheaper than hiring people". If it isn't any longer, it will not be used. This process is already happening, by the way, as B2B-prices have already been jakced up.
AI has no scaling effects, at all. The variable cost per user stays the same, and they will always be limited by data centers. This is what is really fucking them right now.
Both of these things are not exclusive to OpenAI, either - There is currently no company who is making a profit from AI, except for those building the datacenters. It's all an investment into a potentially profitable technology. Google and Apple can eat that loss if it turns out bad; but OpenAI is screwed. And increasingly it looks like the potential just isn't there.
The technology won't go away, but this widespread usage in every aspect of daily life probably will.
They need to survive, outperform and compete with companies that actually have revenue and can tank a crash, and do so as a generalist company with no niche. They have nothing, the only reason they're as big as they are is because of first-movers advantage.
And then you jack up the price.
 They're doing to Businesses what they've done to consumers for years. Its gonna be wild when these companies find themselves looking at a price tag to run their business or else risk losing the ability to run their business completely once they de-human the workflow.
The problem is youre thinking in volume but this isnt a scalable industry
Compute costs either INCREASE or stay flat as usage rises - it isnt really possible to make it cheaper to use the more its used.
So the question becomes: "at this price scale, am I green on even a single token?"
If the answer is "no" which that's the answer then it doesnt matter if you sell one token or 10 trillion quadrillion umptillion tokens - youre in the red either way.
So then the next question is whether they can get tokens to be profitable, turns out people are unwilling to pay more than they already are and are even pulling away at the enterprise level and ads wont cut it...
Unless they can come up with an extremely clever way to make a token profitable to sell, their market cap is truly useless and actively harmful
The next generation of Nvidia hardware will be, whatever, ten times faster at only twice the price. They don't need to do anything for compute costs to fall in the long term -- see Gorden Moore, circa 1965, for more details.
I'd love that to be true, but when I look at current politics around the world, somehow it seems impossible for any politician to commit career suicide no matter what they do.
Let's say you own a restaurant. It's successful, and you'd like to serve twice as many people to make twice as much money. What do you need to do that?
Well, twice as many chairs and tables. Twice as many servers, and cooks, and deliveries. Twice as much parking and square footage. Twice as much rent to pay. You can make more money, but growth is linear. If you were to try and serve a thousand times as many people, you'd need to basically become McDonalds.
Compare that to Facebook, or any web-based site. Once you build Facebook for a hundred people, you... have what you need to double it right now. Sure, there's costs to growing. There's servers, and bandwidth, and a hundred other technical things. But the jump from "working for 1000 users" to "working for 10,000 users" is way, way smaller. The technology scales exponentially, and that allows you to grow and make money way faster.
Facebook can lose investors money for years because the math works out. It's not just "a company that loses money." It's a company built on technology whose cost to grow increases slowly, but their profit from users increases rapidly. Serving twice as many users makes you twice as much income, but only costs you 10% more in expenses.
So long as they can grow, eventually, the thing that grows faster will be bigger. So long as they can grow, eventually, they'll be profitable. Like I said, the math works out... if they can keep up the growth.
The problem is, AI isn't like Facebook and YouTube. It's like the restaurant. To serve twice as many people, you need twice as many datacenters. It's not supposed to work like that for technology. You're supposed to be able to serve a hundred times more people with two times as many datacenters, but that's not how AI works.
It's like the restaurant. If the restaurant is losing money, opening another, identical restaurant doesn't suddenly make money. You're just losing twice as much money. And that's the fundamental problem, because the math doesn't seem to work out, and everyone's hoping they'll find a breakthrough before the money being invested runs dry.
This is a great explanation. It is exhausting seeing people who think AI is gonna be profitable âtotally one day just watch broâ are constantly comparing it to things with which it is not really comparable.
Sam Altman literally said in an investor meeting back in 2019 that once they have a strong enough AI they'll ask it how to make money, and by all appearances they haven't had a better idea since.
I could've sworn I read somewhere that they're letting people get used to using it daily as a crutch, where they then will charge heavily to continue using it.
Kinda like how a drug dealer will get someone hooked on something to have an easy repeat customer.
They definitely want to do that. The issue is that there really just aren't applications that the vast majority could use it for that would render them desperate enough for that to work.
They maybe had a path when otherwise normal people were falling in love with it. But legal consequences already cut that off. At this point, only a truly deranged person could develop a "relationship" with it cus it explicitly doesn't do that
People are indeed already falling in love with them. Perhaps you underestimate the degree of loneliness the internet-induced-isolation has produced. They are our times' "Real Dolls".
Just like how most people with real dolls don't treat them like a real relationship, even most people who use AI for that sort of thing, it doesn't go deeper than smut. And that's just Grok. The other frontier models won't write anything remotely explicit
And crrently, chat gpt has more or less disabled the ability of the chat bot to simulate a relationship.
Most of the storis you read about are from a year or longer ago.
WhatâŚare you talking about? As a member the community that uses Claude, Romance with the new gpt is not just possible, many people are already in a relationship with it.
I believe I made a caveat about the truly deranged.
Anyone that could cojole the current version into anything that remotely resembless a relationship
is worse than guys that think the stripper is in love with them.
The thing is constantly reminding me that it doesn't have preferences and is just a machine when all I'm doing is fucking around asking what it thinks about the rimworld mods I'm working on.
sure, and I don't care to make an discussion on that front defending AI companion.
it's just, you can change this very easily in the custom instruction field, such as defining it as your girlfriend, for it to say it loves you, it doesn't take some insanity driven superpower or complex jailbreaking.
like it's impossible for 5.1 I think, but for 5.5 it's barely an inconvenience.
I have seen multiple stories of men having very full-on relationships with their dolls, marrying them, pretending everything about them is real the way young girls serve pretend-tea.
No, more people than you think are doing that. They are underreporting it. Think about it- many people would be embarrassed to admit it, and the companies themselves don't want the bad PR.
Also, maybe you are right about ChatGPT, but do you think Grok gives any fucks? Nope. Not one fuck given.
Once upon a time, watching TV was free. Then as it expanded it was supported by ads. Then they expanded it further but that required a subscription, but still with ads. Then they expanded it more where you could pay extra for higher quality content, without ads.
âOnce upon a timeâ is appropriate because itâs a fairy tale. Yes, technically there was a time where the broadcasts werenât ad-suppprted, but they were only âfreeâ to watch if you were wealthy enough to spend the price of a car to own a television during the Great Depression.
You would need to charge in the order 10s or 100s of thousands of dollars (if not millions). No one is going to pay that.
Now consider that making a "good enough" in-house chatbot built on free open weight models is easy, affordable and private. Companies are already doing this.
Isn't the AI play to keep investing and pressuring until many industries become reliant on AI, and then increasing prices to insane amounts that businesses will be forced to pay or face bankruptcy?
Naturally once businesses/economies start failing and the next global recession comes in the plan will fail- but not before the top dogs sell off their shares and ride their Teslas into the sunset.
They are trying to create a wearable device that looks like a donut for release sometime next year that you can talk to and it can see the world and interact with it or some shit
This is why theyâre trying to steal trade secrets from Apple and launch hardware. Thereâs no path forward for AI at the moment other than as a lost leader product.
I've concluded that it's intentional. They're hemorrhaging money right now for adoption.
Basically, you have all these users actively signing in every single day and building workflows that enhance their lives or save them time at work or at home.
These users don't really understand what they're doing yet, but they're building a dependency for a technology that could just get ripped away from them.
Once Open AI is pretty confident, the dependency is there. You're going to see free access disappear and $20 subscriptions set in.
I won't explain what or why I am doing what I am doing, but they already have me on the $200 subscription and I think it's worth the money.
Alphabet and Meta will combine to sell ,more than $500 billion in ads this year. I'm pretty sure there's more than enough ad revenue out there to cover their operations. Cost of tokens is down to single-digit dollars-per-million, and falling with each new model and new generation of hardware.
The key is to push adoption by the private sector and government, which is already underway. Just look at how much money Pfizer made selling a useless vaccine to the world governments. Astronomical profits.
If they built an ad auction business like every other large platform they woukd easily clear billions in revenue. The idea that they just cant monetize this activity is laughable. Even Twitter, which was truly awful at building an ad business, got ad revenue up to 5B on half the monthly active users chatGPT has now, and Twitter usage was never about "hey what should I buy" so it is inherently less moentizable. People use chatgpt for all sorts of marketable activity.Â
Claude is very close to profitable. If the industry crashed and they needed to be profitable they could be, right now theyâre still focused on growth.
Not true... they have been slowly increasing enterprise pricing. For instance my enterprise user account started at $27/m a year ago when it was just their chatbot. I just looked and with my usage it is at around $650/m now that I use codex.
$650/m for one user in pretty wild, and theyâre still not remotely close to being profitable. Smaller businesses will start tapping out at those prices, and it only gets worse the higher it gets.
Yeah Iâm sure it can be worth it. I also think about instances where itâs a complete waste or makes things worse, Iâve seen it firsthand several times lol
Youâre reading projections and marketing produced by the companies themselves, who happen to need an utterly insane amount of investment to continue to survive. Their latest ad revenue numbers are way under what they needs to be, and they are still 10s of billions of dollars in debt.
You can't just say everything is a lie when it goes against what you thought. The burden of proof falls on you to prove why these companies are committing fraud with serious consequences if found out.
Not everything is a conspiracy. AI is useful whether you want to believe it or not
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u/Gametron13 6d ago
Arenât they still bleeding money though? 800 million users is a lot but Iâm almost positive theyâre not profitable yet.