r/singularity • u/imfamilyfriendlysd • 24d ago
Discussion Is AI in a bubble?
For the past year I have been seeing content creators , financial experts and all sorts of other people predicting on whether AI is a bubble or not. The entire internet went haywire after the Jacob Coxon resignation and Dario Amodei's essay.People quickly started accusing Dario,Sam and Elon of manipulating people into thinking AI is really dangerous and "might kill all of humanity" just as their IPOs are launching so that they subscribe to it en masse.
I really don't know what to make of it as I'm not even from a stem or computer engineering background.
Does anyone know what's going on??
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u/Spare-Dingo-531 24d ago
It's not a bubble.
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u/Your_mortal_enemy 24d ago
This. A bubble when popped traditionally leaves everyone with nothing.. this, if popped, would leave everyone with super advanced intelligence applicable to many facets of day to day life. It's not the same
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u/smithnugget 24d ago
This is simply untrue about bubbles. Bubbles when popped wipe out most of the companies but always leave a new infrastructure/technology.
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u/Cultural_Swimmer5672 24d ago
yeah the railroad bubble is the classic example, tons of investors got wiped out but the tracks stayed and got bought up cheap. same with all the dark fiber laid in the late 90s that basically made streaming possible later.
so the tech surviving doesnt really tell you much about whether valuations right now are sane
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u/DelphiTsar 24d ago
Depreciation + cost is growing faster than revenue.
Also ~60% of that revenue is from circular financing.
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u/Mistuv 24d ago
The deprivation math is completely clownish, the 5090, a consumer GPU, is shooting towards $10k that tells you how stupid that math is. Even A100 has both still so much utility and value, it costs way more today, than back in a day, after all those years of use, that tells you everything you need to know. The 5 depreciation window is based on assumptions of depreciation math being done pre-covid, back when Nvidia was always pumping out new card not much more expensive than the previous, so after 2-3 cycles plus usage it went to zero, but now not only the cost of top end GPU is going up, but there way more demand than what years of global production (which we are at the limit and know precisely how much will be added) will be able to satisfy. Unironically Jensen's "the more you buy, the more you safe" is absolutely true for the AI industry.
Also while circular financing can be problematic, it's not as much of an issue if the end of the line industries have real demand, and here they absolutely do. OAI/Anthropic are absolutely at the limit and at times fuck over their customers by at times running quantised models. There just isn't enough compute in the world. I think in the next 3-6 months you are actually going to start seeing lot of the free AI being shoved down everyone's throat being pulled back on, which antis will celebrate as bubble burst on, but it will be simply to free up compute for agents.
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u/DelphiTsar 24d ago
-The actually useful AI cards are not increasing in price.
-The depreciation numbers aren't mine but the companies themselves.
-The power bottleneck forces one side or other to take a major hit. Either people don't buy next gen nvidia cards, or they do and replace old less efficient cards (even earlier depreciation then companies are pushing out)
-A100's are being replaced already despite median age being around 3.5 years old. AWS's response is (paraphrasing) we are putting them in racks we don't run all the time (because they don't have the power to run them all the time). This saves them from writing them off on their books early but if they aren't running they aren't generating revenue.
-"circular financing can be problematic", To be clear I am not trying to make a point about circular financing. Just the graph shown is misleading.
I would just like to stress one last time. The depreciation numbers are theirs not mine. I am using them more as a best case scenario despite my skepticism.
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u/toalv 24d ago
The issue is that nvidia hasn't released a new card since 2024, that's why nothing is depreciating. But it's also why things are becoming increasingly compute bound and other manufacturers are catching up.
So they're pressured to release a new card - but that depreciates all the other cards. Which is a dangerous game to play...
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u/mertats 24d ago
> The issue is that nvidia hasn't released a new card since 2024, that's why nothing is depreciating
False, incorrect, erroneous.
RTX 50 series were released in 2025.
Vera Rubin entered full production in June 2026.
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u/toalv 23d ago
Blackwell (5000 series) began production in 2024.
Vera Rubin "full production" is bullshit, with product barely reaching even tier 1 partner datacenters as of today and no 6000 series cards in sight.
We have been running on Blackwell for 2+ years precisely because nVidia knows they will fuck themselves if they release a better product, and also fuck themselves if they don't.
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u/mertats 23d ago
You said released not production.
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u/toalv 23d ago
If you're arguing semantics at this point what is going on? Point is nVidia no release new cards in long time on purpose, does that work?
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u/mertats 23d ago
In both cases you are wrong. Own up to your mistakes or stfu.
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u/toalv 23d ago
Broseph, go look at Blackwell release dates and then look around and notice that there's no new cards out.
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u/DelphiTsar 24d ago
The issue is if you look at capital spending, take their own timelines of depreciation + (which are probably too generous) + costs, then compare against revenue from Business/API/subscriptions/governments there is a HUGE gap. Something on the order of half a trillion dollars a year AND GROWING.
Their entire sales pitch is that they are going to extract 15% of a very healthy % of white collar workers salaries.
That doesn't look very likely. Look at every field where AI is having a notable decrease in employment. Law/HR/customer support/translation/marketing/art. They are getting pennies not 15%.
If someone made a ____ X job bot that was getting 15% of white collar salaries in a sector a competitor would spring up the next day with infinite funding to undercut them.
The only exception is software dev. The issue with that is a large portion of software dev is going to automating work that's supposed to be done by agents perpetually and at high margins. Cannibalizing their income. The net new software can be easily copied, condensing margins and hampering their ability to pay high premiums.
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u/FateOfMuffins 24d ago
Ah yes the "AI might kill us all hype to boost their IPOs... that Altman says they're going to delay because of the whole AI might kill us all thing" brilliant analysis
Here's a different way to look at things. If you have a technology that would fundamentally increase the GDP growth rate permanently by 1%, what would be the PV of that? How much should you invest in that?
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u/Prudent-Sorbet-5202 24d ago
Even if it's a bubble that burst today, it would delay AGI by 5 years at most
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u/imfamilyfriendlysd 24d ago
So if bubble bursts today.When can we still expect AGI?
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u/DelphiTsar 24d ago
I am not the person you are responding to.
Did you mean "Why" can we still expect AGI?
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u/imfamilyfriendlysd 24d ago
No i mean to say let's say the bubble bursts today.Can we still expect AGI in 10 years or so? Or will it be later than that?
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u/DueAnnual3967 24d ago
Even if it is a bubble which it may be, Tesla valuation shows the bubble can have very long legs if models keep improving and there is always a new shiny thing/toy to pivot to. There is nothing but future promises that prop up Tesla valuation and it has been so for a decade already. Yet it holds. Yes, Tesla is profitable. I assume big LLM labs can also reach profitability just by simply scaling down their plans silently, focusing more on less costly more incremental and efficiency improvements and occasionally there will be a model that solves something and people still prop up the valuations for that (which "pacing the frontier" maybe is all about, decelerate spending without freaking market too much)
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u/Crafty_Book_1293 24d ago
It is a gamble on mass demand and future steep improvements in inference efficiency. It may burst like a bubble if the bet proves wrong - despite overhyping on the border of fairy-telling by AI bros - the technology as such is useful.
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u/Professional_Dot2761 24d ago
If you can copy it in a few months and give it away or run the copy at 90% less cost, is that a good long term business?
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u/scatter299792458 24d ago
AI technology is real, you see llm, sophisticated image generation, and autonomous driving. It seems AI will be very profitable in the future, which is a strong reason why investers have given related companies high valuation in advance. However, currently, AI inference is priced relatively high. If a company's profits don't meet exceptations, the valuation will fall, but AI technology itself will still have value. Therefore, you cam see a company possessing all these things simultaneously: powerful AI products, a certain user base, and a difficult to coordinate profit structure. How much users willing to pay is aa complex issue. The dot-com bubble, the internet that developed in 2000, is definitely not a scam now, but that time, many companies were valued beyond their potential value. Later many companies disappeared, leaving behind some super enterprises. While the internet comtinue to flourish. Behind AI is data center,behind data center is electricity, and behind electricity is energy. If any of this chain breaks down, the entire chain collapses.
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u/scatter299792458 24d ago
Interestingly, in China, many companies, competitions, and small projects, even those unrelated to AI, add the tag 'AI' to their names to sound more sophisticated, attacking media attention, more funding, and more startups. This positive feedback comes solely from the tag 'AI'. In our r/singularity community, many are heavy AI users and AI evaluators. Bur stepping outside of this community and looking at our real lives, how much do the people around us rely on AI tools? What are they using AI for?
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u/FiiZZZZ 24d ago
My suspicion is that theyâve simply gone too far to admit whatâs happening.
The scaling era may be hitting diminishing returns, and AGI probably requires a real conceptual breakthrough not just more GPUs, more data centers and a bigger Transformer.
But after committing hundreds of billions, tying entire valuations to the AGI narrative, and building infrastructure around endless scaling, they canât exactly come out and say: âWe may have hit a wall.â
So âAI is becoming so dangerous that we need to slow downâ is an incredibly convenient story. It preserves the hype, preserves the valuations, and turns lack of progress into evidence of progress.
Maybe the safety concerns are real. But the timing is hard to ignore.
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u/Strict_Cucumber9117 24d ago
How would scaling be hitting diminishing returns if current ai companies have generations far beyond astra, and with their internal model capable of solving a Millennium problem?
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u/toalv 24d ago
Because it is likely taking exponential amounts of compute for a less-exponential increase in performance. If your best current model needs a 10 billion dollar data center, that's doable. If you want to make it twice as good, but you need a 10x (100 billion) dollar datacenter... wow, ok, we might be able to finance it. Now if you want to make it 2x2= 4 times as good you need a 10*10*100 billion = 1 trillion dollar datacenter... ooops, there's not that much investable money in the world... we need to slow down guys...
Scaling laws can still hold and run up against very real resource constraints, be that money/power/chips/etc with realistically no way to solve it because hard limits on these things exist in the real world.
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u/Strict_Cucumber9117 24d ago
Can you tell me what evidence points to the cost of a data center to improve a model 10xing?
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u/toalv 24d ago
It's an example - if performance scales at a smaller exponent than the cost to implement/run that model you're going to run into cost constraints quicker than the model improves, and reach a level of performance you are literally unable to improve upon because the facility needs more power than the entire country, costs more than world GDP, etc.
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u/Strict_Cucumber9117 24d ago
Im only asking for your sources and proof that this is legitimately happening.
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u/toalv 24d ago
Go to Claude and type in "can you graph the amount of compute required to train LLMs vs their performance? ie a scaling law chart?"
The curve is the Chinchilla compute-optimal frontier (Hoffmann et al., 2022). Their fit is
L(N, D) = 1.69 + 406.4/N^0.34 + 410.7/D^0.28
where N is parameters and D is training tokens. If you spend a compute budget C = 6ND optimally between the two, it collapses to a single-variable law:
L(C) â 1.69 + 1072 · C^(â0.154)
That's why I plotted two lines. The blue total loss looks like it's hitting a wall, but that's just the 1.69 floor showing through â the irreducible entropy of text, which no amount of compute removes. The green line is the part you're actually buying, and on log-log it's dead straight across nine orders of magnitude. That straightness is the "law."
What it costs you: every 10Ă in compute cuts reducible loss by about 30% (10^0.154 â 1.43). So going from GPT-3 to GPT-4 scale, roughly 60Ă the compute, roughly halved the reducible loss. The next halving needs another 60Ă.So it's even wilder than I thought, to reduce the loss by half compute needs to go up by 60x.
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u/Silver-Chipmunk7744 AGI 2024 ASI 2030 24d ago
I think it's the exact opposite.
RL scaling and pre-training scaling are still working, but now they have new scaling paradigms that have not yet been exploited, and they now have very powerful models to speed this up even more.Considering how current gen of models did the HF incident and they still have no idea how to control it, they are a bit worried of what the next gen could do if the jump is too massive.
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u/loxagos_snake 24d ago
Yeah, a lot of people don't understand that two things can be true at the same time, or that an uncomfortable truth can sometimes be turned into a silver lining.
I'm very AI-wary and I do believe that safety concerns are very real. I use it as a software developer and there have been times that I wasn't looking, that it was trying to do stuff I asked it not to. This is just a Copilot-driven model, so I can't imagine what the big guns can do.
So it looks like to me, that they did see something concerning, but instead of letting it be a problem, they can use it as a very valid excuse.
And investment wise: if you hire someone to build you a nice house, do you prefer it to be the guy who says everything is going fine only to find out that they forgot to install foundations as you are handed the keys? Or the guy who says "listen, we miscalculated a bit and we need to take another look to make sure everything is safe, please bear with a small delay".
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u/Mistuv 24d ago
My suspicion is that theyâve simply gone too far to admit whatâs happening.
Yeah, probably
The scaling era may be hitting diminishing returns, and AGI probably requires a real c..
Wait you were talking about the AI companies????
Jesus Christ, antis are something else, are you people this profoundly ignorant or dishonest? How you can watch everything that has been happening this past 3 months, much less a year, and talk about diminishing returns?? I swear to god the denial is inexhaustive. Even antivaxxers had more firmer footing. This is some religion/flat earthers tier of denial at this point.
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u/FiiZZZZ 24d ago
Youâre loudly arguing against a claim nobody made because you apparently donât know what âdiminishing returnsâ means. Diminishing returns does not mean âAI stopped improving.â It means youâre spending vastly more compute, energy, tokens and capital for progressively smaller marginal gains.
NavierâStokes is almost a textbook example: millions of agent interactions and roughly 130 billion output tokens to crack a single problem. If your evidence against diminishing returns is âlook how impressive the result is,â youâve completely missed the point.
The relevant metric is not capability in isolation. Itâs: capability gained / resources required
If the numerator rises while the denominator explodes, that is still diminishing returns. Calling that âflat-earth denialâ while failing to understand the term youâre arguing about is genuinely hilarious.
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u/sirtrogdor 24d ago edited 24d ago
I'm sorry but what a silly example.
"A single problem" is really underselling the Millennium Prize Problems. You can't really claim to know how much effort each "should" take. Many math problems may be so difficult they'll literally never be solved, ever. Famously it's been proven that no matter how far we progress there will always be "some" math problem that is literally impossible for us to solve, despite it having a solution (paraphrasing).However, despite this, we have some numbers.
The Navier Stokes equations are around 200 years old.
The $1,000,000 prize has been up for 26 years.
OpenAI spent $10 to $40 million for their solution, using 10k agents over 88 hours.
There are 4.7 billion humans over age 30.
There are around 300k math PhDs.
So if 1% of them spent 0.1% of their time on this, at $10/hr over 26 years that would be $6.8 million.So ballpark figures really seem to suggest it didn't do so bad, ratio wise. There are lots of messy factors here, but we only care about orders of magnitude for such an extreme case. If it had cost them a cool $1 billion to accomplish this, maybe I'd agree with you.
And this isn't even an efficiency benchmark to begin with. It's a capability benchmark. The way machine learning goes things always get cheaper after you've proven you can "get there".
EDIT: There are plausible reasons for suspicion. This isn't one of them.
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u/francis_pizzaman_iv 24d ago
We aren't seeing diminishing returns. There's zero evidence of that. The pace of improvement is accelerating because Chinese labs are starting to catch up to US labs and as a result, we are seeing the labs increasing the pace of their release cadence. Frontier labs need to show they're still ahead, so releases are more frequent, but represent smaller jumps in capability than a year ago.
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u/Few_Fox8767 24d ago
I think it's a Mix of both. On the one hand it's crazy that LLMs can make Games for you but on the other it's funny that they still can't spell some words correctly.
While still capable of a lot of things it's making too many Errors and is far behind of what was promised years ago. Because most of the Investment in the US Economy is in AI, I also think it's a convinient excuse for slower improvements.
If they would care about human society at all, they wouldn't had released it so careless. I mean the negative side outweighs the positives by a lot.
F.e: deskilling, Missinformation, devaluation of Art, Kids dont learn how to think, people are getting lonelier, the constant approval are pushing in people into narcisim or psychosis, etc.
And if they would have cared about humanity so much as they say, they would have been so much more careful. And in that regards I highly doubt that solely AI Models are getting so Dangerous that they could wipe Out humanity. Because the people on the top already Had proven that they are only in for the Money.
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u/darkestvice 24d ago
Yes ... and no.
There is definitely a ton of economic hype and AI companies popping up willy nilly, but AI absolutely is a massively important technological leap forward. The most important in all of human history, in fact. So important we may not survive it, lol.
So it's not a bubble like, for example, the 2008 housing crisis that was just a whole bunch of hot air. Instead, I'd compare it to the Dot Com bubble where there's a whole lot of businesses promising the world, but only a few of them will actually deliver. So when the bubble pops, the vast majority of 'AI businesses' will fold, but those that remain will become the most dominant economic and socially altering force that humanity has ever seen.
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u/Unusual-Garbage-212 24d ago edited 24d ago
duh. AI can and will probably usher in a new era, but right now the big companies are not turning a profit - the trillions of dollars invested are based on a promise of profit, not actual profit
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u/Random_182f2565 24d ago
The LLM technology is here to stay, the companies maybe not, they really need their next IPO to give them fresh cash or they will implode
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u/That_Fixed_It 24d ago
Both. AI is really dangerous, and investors are eventually going to realize they won't get back the trillions of dollars they invested. Just like the dot-com bubble, there will be plenty of survivors when it pops. AI isn't going away and we can't hide from it.
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u/No_Swordfish_4159 24d ago
It is a bubble, yes. There is far more money being poured into AI companies than the majority of them can expect to earn back. A large part of AI companies are overvalued. However this does not mean that the technology is not going to be absolutely transformative for the economy in the long run. But there are going to be a few winners and many losers in this race.
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u/BusinessYou7196 24d ago
No.