Now, I’m no MBA, but that seems like a bad business plan…
no wonder why OpenAI is losing alot of money.
What is the actual “cost” after they buy the hardware, is that $1000 really pure power usage cost?
The problem is that the hardware has a 5 or 6 year depreciation schedule on paper, but NVIDIA keeps saying that their next generation chip will be twice as good as their last chip so there is a FOMO schedule of like every two years.
that’s the $84,000 question. They’re filling datacenters with the fastest possible equipment and need it to be 10x faster, That hardware is dinosaur fodder a year after they install it.
I’m curious as well. My knowledge is probably quite outdated, but from what I understood the training part is what’s expensive and then querying the model is pretty cheap. Is it still true (or was it ever) that the generated answers on search engines are cheaper to generate than the actual search results?
It is sorta. Training is orders of magnitudes more intensive than inference, but we infer billions of times within a model generation.
I find that hard to believe, I recently had to uninstall co-pilot after it weaseled its way into my search bar. Its not an exageration to say that my PC literally ran cyberpunk 2077 with pathtracting better than it ran the fucking windows search bar with co-pilot.
The author is right and wrong. Its subsidised but not by anthropic. The power users who use their plans to the limit are subsidised by the rest of the users. Im an AI hater but I do think anthropic will be profitable next year. Their revenue growth is insane and looks to just be getting started. Claude code took enterprise by storm and now cowork is out.
Good thing I don’t personally pay them anything
Oh, you are going to pay. The bubble is going to fuck us all quite thoroughly.
Honestly Google is likely to beat openAI and Anthropic as things are.
OpenAI and Anthropic have to buy/rent their hardware from Nvidia, while Google is making their own TPU hardware. Google’s hardware costs on AI is way lower, every dollar they spend on it goes a lot farther.
And unlike the other two, they’re already a profitable company. They’re making record profits right now. They don’t have a desperate need to figure out how to make back billions on their AI models, they can just keep offering Gemini at a comparatively cheap price and wait for anthropic and open AI to bankrupt themselves.
Plus they have a hook with the common folk, the phone steers you toward Gemini (Android phones, obviously, and Apple currently partners with Google for Gemini for iPhone…).
For Claude and OpenAI, you have to explicitly want to go out of your way to use them, or use them indirectly through another service that has a hook.
Claude seems to have some software developers explicitly preferring them, though a alot of the corporate money is on Microsoft and Microsoft leveraged Visual Studio and Github to become the business-friendly frontend, and sure, you can use Anthropic models too… Though Microsoft ultimately has control of what is reasonably available and how much each one costs. Anthropic has a shot but I could see Microsoft pivot to really mess with Anthropic. The one gap in Microsoft strategy is the “native AI” workflow where Claude Code has won hearts and minds, but it uses massively more tokens for frankly marginal or sometimes negative value compared to a more curated use in-editor.
OpenAI I see as the most exposed. Lot’s of data showing they are suffering from people being over the fad of going out of their way to use ChatGPT, especially since their phones have started embracing ‘default’ Chatbot. Software developers that are inclined to use LLM are also inclined to be pretty dismissive of anything other than either Anthropic or open weight models, depending on their inclination. Also Altman seemed the most agressive in committing to spending money they didn’t have, though all of them exhibit this to some extent.
I predict Microsoft ultimately pivots to in-house models and convinces the businesses to go that way. Apple may continue with Gemini or roll their own eventually. Anthropic currently has the stronger position between OpenAI and them, but I think you are right that both have risk of just being left behind.
I really really really don’t want evil corporation Google to dominate even more.
I prefer plailny greedy corporations over evil ones
They’re all evil, so we just have to exploit the ones that offer us some value. If Google is cheaper, and has the ability to damage the others, then Google it is.
Google is shaping up to fare better than the others, but I dont think that means success. They, too, are spending more than its making, just at a less drunken rate than some competitors.
OpenAI and Anthropic aren’t less evil than Google.
They aren’t great, though I do think Google is worse. And far too powerful
Google is only worse by virtue of their reach. OpenAI and Anthropic don’t have the reach yet, but they absolutely will get there given the chance.
Before Google had the reach it has now, it was widely regarded as a comparitive ‘good guy’ and people believed in the “don’t be evil”. Lo and behold once they got going, “don’t be evil” went away.
It’s gonna come crashing down pretty soon. It’s gonna hurt all of us. It won’t hurt the people responsible nearly enough.
pretty soon
people have been saying that for some time though
The thing is this really depends on the speed of some financial events, not some technical failing.
Notably, if OpenAI has to cancel any of their commitments to buy hardware because they find they have neither the money nor can secure even more debt to cover, that event would potentially cause the bubble to pop, even for hypothetical companies that may have been more responsible and might have a viable business approach. Those commitments are coming up, and a lot of analysis struggles to see how they will fund those commitments.
The thing with this bubble is that the investors don’t get the nuance and will flee at signs of trouble in any of OpenAI, Anthropic, or a handful of others, and Altman’s leadership has made trouble at OpenAI very likely, but the investors don’t believe it and won’t believe it’s unique to OpenAI, even if it would be.
What people? All the credible people I read say that things fall apart Q2/Q3 2027 as debt and profit obligations are due.
The only thing that changed is now there is an energy crisis coming, so it’s possible that might force the bubble to pop sooner if all the systemic risk aligns.
The bubble will pop, I think a lot of people are just baffled by how big it’s getting.
reminder than during 2019 there were streaming services popping left and right, all showing tremendous growth because they started from zero, and articles were about how bad Netflix was doing due to having practically no growth compared with the competition (they already had a massive subscriber base). Twist? Netflix was the only streaming service that was actually making a profit, the rest were a massive loss but big growth.
Needless to say most of those streaming services died; who remembers DC streaming service, or Yahoo’s? While Netflix is basically as stong as ever, despite the prevalent enshitification happening through the whole industry.
Point of the story? shareholders don’t care about stable profitable business, only cancerous growth. AI is like that, zero profits, ton of cost, but as long as they show growth the shareholders are happy, regardless of how cooked the books are.
2019 Yahoo
My immediate thought, there is no way Yahoo! Screen survived into 2019.
I looked it up and Yahoo! Screen (which featured Community season 6) was shutdown in January 2016. But Yahoo! View launched in late 2016 (as a Hulu-like replacement), and that did shutter in mid 2019.
So Yahoo! was already dead, but it also died for real in 2019.
Imagine having a streaming service so bad it fails twice
Isn’t that kind of Yahoo!'s business model?
Actually, when Yahoo was the search giant, before Google went mainstream, they were pretty damn good at what they did.
With how shit Google is these days, I kinda wonder if Yahoo could dust out their search engine from two decades back and it would just be… better.
Netflix was also late to streaming because their mail service subscriptions were THE major player
Late to streaming? Netflix was the first big time streaming service that I ever heard of. The main reason their streaming service was able to take off like it did is that nobody else of significance thought that streaming was worth pursuing. What other companies were offering streaming services at anything approaching scale before Netflix?
YouTube and Hulu were basically all starting about the same time. But RealPlayer was the first big one.
Netflix just had the layout that everyone uses now. The Cable networks had streaming services, just not on demand. YouTube and Hulu also pioneered the on demand layout. YouTube focused on personal experiences so maybe that’s why you’re forgetting them
YouTube started in 2005, but was not really a “streaming service”, it hosted random internet posted videos. The concept of engaging with the big content rights holders wasn’t remotely in sight back then.
Hulu came out a year after Netflix started streaming, by about a year. Hulu was inspired by Netflix’s move to have actual traditional media content as a streaming service instead of ad-hoc video uploads like youtube.
RealPlayer offered technology for websites to provide videos, they themselves I don’t recall being a streaming platform in and of itself.
Whatever one may say about Netflix, they were right there in the beginning with streaming traditional, professional media content. Yes, video playback over the internet wasn’t new, but that’s a technical detail that enables, but is not the core of the “streaming service” business model.
late to streaming, but practically the first subscription based system to watch movies/tv online.
First years of Netflix were the best, the product began degrading quite early on. but that was mostly companies realizing that instead of licensing their content on Netflix, they can make their own platforms.
I think people forget that there is also the problem of being “too early” where people or the technology isn’t ready yet. Netflix timed their entry perfectly.
There are so many defunct websites or businesses that no one has ever heard of that were precursors to modern day services we view as conveniences.
it’s not about being the first, just the first one when the technology/cost are just right.
who remembers DC streaming service, or Yahoo’s?
Quibi will always have a place in my heart. Or, at least, my golden arm
Of course it is, it’s essentially a scam. They just need enough humans to keep investing until they check out and run with a bailout.
thats why they are peddling it to governments for “surveillance AI”
Funny thing is, the US government doesn’t even have nearly enough money to bail all these mfa out. So we are heading into uncharted territory here
Of course they don’t, that’s why they’re building bunkers. Thinking it’ll slow us down, as we’ll open their bunkers like cans of tuna. A bunker only works for so long, then the survivors start hunting for them like delicious shipwrecks.
they are going to argentina. apparently NZ has blocked thiels compound.
I don’t think the bunkers are to avoid bad financial decisions, more so to stave off something like rogue ASI or a biosphere collapse which in any circumstance won’t work in the long-run.
Yeah, but it’s not like they would be smart enough to know that
And that’s why they’re trying underhanded tactics to inflate earnings and IPO directly into the index funds, so every American’s 401K will legally have to rebalance and invest in them. They’re racing to fleece retirement funds before the bubble bursts.
Not financial advice, of course :p but people should really consider getting their stuff out and into self-directed funds or whatever it is US people do to not depend on auto-allocated funds.
Money printer go brrrrrrr
I don’t get why companies get to legally bailout like this. Why do people have to suffer for their bullshit? Enslave the CEOs if you have to make things right, leave the people out of it.
That’s simple, because the people making laws and overseeing the adherence to those laws are great buddies with those same CEOs.
So, corruption.
Though i do agree with you, there is no such thing as too big to fail. Government shouldn’t have any handouts to corporations.
These levels of corruption are frustrating; money shouldn’t decide the law.
No handouts to corporations, indeed. Make them pay.
Many applications are suboptimal to say the least but what’s been done with alpha fold and recently in mathematics is very far from a scam. Not to bring up what’s also been accomplished in cyber security. These models are proving open problems that have been around for decades and finding serious vulnerabilities. The issue is consistency and efficiency. Of course the other issue in making them stronger is continual learning and long horizon planning. I think too much investment came in too quickly and what is provided to the masses currently isn’t consistent or efficient enough. That said as a math and comp sci grad and someone who works in the field it’s been absolutely mind blowing to watch what’s already been done. In 2010 the concept of an artificial mind solving something like the Erdős unit distance conjecture would have been seen as pure sci-fi, maybe something we would achieve closer to 2100 than 2026.
For reference, it took Uber about 17 years to become profitable and Spotify 18. They were hemorrhaging cash for over a decade and a half before finally hitting their stride. As for the current AI development it’s honestly from 2017 when the white paper on transformers came out where shit started getting serious, so it’s been about 9 years since investors were serious. Before that point it was all passion projects, absolute moon shots as they call them.
Both Uber and Spotify (and AWS too) had economics of scale going for them - the more users they have, the more the infrastructure could be leveraged. This does NOT work for LLMs. More users means using more compute, more advanced tasks (like coding) uses exponential amounts of compute. A single user running a complex task can make 8 Blackwell GPUs run full tilt, and you don’t even have any guarantee that the output will be useable.
There are a few narrow areas where LLMs might be successful, like scanning for security vulnerabilities or searching large amounts of documents. The massive amount of money invested will never be recouped with these usage scenarios.
I don’t think anyone is assuming it will stay at its current efficiency and there will be zero improvements. A lot of the everyday AI use cases will likely be pushed to someone’s personal device aka your phone. In the same way a lot of Uber and Spotify is handled by your personal device today. What we’ve seen for years now is the development of these gargantuan models that are then condensed down into much smaller models with 90%+ of the same effectiveness. Simultaneously we will see and are seeing devices sold with better NPU’s for edge compute for AI the same we’ve seen the push for more edge compute to manage other services such as Uber and Spotify.
Across this thread and others there’s like this implicit assumption AI will never progress beyond where it is right now in spite of the evidence of its almost exponential growth. It’s really interesting.
Although, most people aren’t talking about Alphafold when they’re talking about AI. They’re usually specifically referring to the generative transformer models that are currently all the rage.
I doubt anyone would care too much about a linear regression model, or multi-layer peceptron , for example.
I’m pretty sure a lot of them don’t know the difference or understand how mind breaking it is that some of these achievements are happening. Of course alpha fold is old news and the solutions to the Erods problems is something that should be raising eyebrows. These models are fundamentally just math and a model that’s better than humans at math can theoretically design a stronger model than us.
solving something like the Erdős unit distance conjecture
Tell me you listen to media news cycle without understanding what that actually mean without telling me that.
That’s not exactly what happened, isn’t it.
Not to bring up what’s also been accomplished in cyber security
Multiple new vectors of attacks, automation of attack pipelines…
Got anything besides my quotes to give your argument credibility?
How much do they spend when I pay nothing?
I think they might’ve broken the laws of math there, as they’re certainly still spending a non-zero amount.
ax+c
It just means they lose more money per paying user, I guess.
they are spending infinite money for every $1 i pay them
spending money that nvidia gave them via chips
I’m quite happy to use their compute power for frivolous bullshit if it hastens their enshittification and demise.
“Hey Claude, can you begin work on an e-commerce site written in visual basic?”
*Two microseconds later… *
“Your free usage limit has been reached”
“Ok Claude see you tomorrow, maybe we’ll think about a rewrite in Turbo Pascal”
We need this in Esperanto and the lost languages of Lutruwita.
“I need a triple A cooking game with blackjack and hookers, all written in SQL.”
“But that’s a database langu-”
“Did I stutter?”
In fact, forget the SQL!
So, in UQL?
Agreed, but hey no need to pile the the hate on Pascal, modern ones like FPC/Lazarus are pretty cool actually :)
TURBO PASCAL FOR LYFE
You don’t need to shout!
Also, camelCase:
TurboPascalForLyfe
That’s not good business
so these crazy prices i hear about being implemented (like at github) should actually be at least 10x higher?
10x higher to break even :)
To break even on operating expenses, not even counting debt payments, depreciated capital value, or future recapitalization costs.
*Operating expenses before nvidia raises their prices so they can somehow make the line go up enough to justify it’s massive evaluation.
Definition of a Bubble. These AI huckster keep stringing investors on though. Sadly, I think these public IPOs coming up for Space X, OpenAI, and Anthropic will fall short of expectation and trigger the bubble popping.
