Outside a relatively small world of circular investment and FOMO feeding FOMO the general consensus seems to be “let it burn.”
It appears very unlikely we will ever see an IPO of OpenAI. Anthropic appears less doomed, but still iffy at best. Tons of other large, but little discussed, AI startups are just dead-companies-walking at this point.
The likes of AWS are showing good headline numbers but are taking out massive debt to build infrastructure that looks increasingly unneeded. Those with capacity are looking to offload it, quickly. Yes AWS has “committed contracts” for this capacity but if those commitments are with shaky AI startups then it’s mostly just fluff PR and these hyperscalers will get left holding the bag on all this debt.
> Are you saying people haven’t been throwing unneeded GPU capacity on the market? That is happening.
That, by itself, doesn't have to mean anything.
AWS is far more compute capacity than Amazon needs, but that's not because Amazon misjudged how much capacity they need. They built it out to sell it to others, leveraging know-how and economies of scale to do so very profitably.
SpaceX has put a ton of excess capacity on the market.
Meta tanked chip stocks by saying it was considering the same.
Worries about compute overcapacity, via folks saying they want to offload capacity, is literally the thing that nuked the “situational awareness” fund last week.
It’s long term demand that matters, not just “price.” High prices without true demand is the literal definition of a bubble.
SpaceX is selling compute to a willing buyer. You can't point to one side of the trade as evidence for under/over capacity. The way we usually judge such things is the price. If the price for compute is going up, which it is, then that's the thing to look at.
It's holding up the entire US stock market and therefore the entire US economy and the dollar value. You probably don't want this particular Atlas to shrug.
The “Let it burn” attitude doesn’t mean people think they are immune.
Yes the overall market will take a hit but, like a forest fire we need a healthy burn to just wipe out the weaker players so the older more mature trees can get on with it. Yes the big trees will get burned a bit but they’ll be fine in the long run.
We need a good brush fire to just wipe out all the iffy startups and investors that over-indexed here. Thats what people want with “let it burn.”
I do. The faster it happens, the faster a recovery begins and we can devote resources back to doing more practical things. Get it over with as soon as possible, rip the bandaid off, insert your idiom of choice.
Anthropic is almost purely a model company. They own close to no data centers.
If models are becoming commodities, and the main bottleneck is actually serving them at scale, Anthropic does not appear particularly well positioned.
If AWS had a ~60-80% margin for decades, I see no reason why inference can't have a ~60-80% margin for quite some time.
The problem is, if costs continue to drop ~90% for the same level of quality every 18 months, demand is unlikely to grow 10x to keep the revenue stable.
Who knows. Jevon's paradox. But the cost/quality is dropping too fast that it's hard for me to imagine demand keeps up long term to keep revenues (and profits) GROWING.
More and more companies are signing up with OpenAI and Anthropic at near exponential growth to automate everyday tasks. If anything, these two should manage to IPO just fine. The rest of the downstream startups probably won't make it.
Of course we know it. It’s been obvious since at least 2023. Everyone in AI oversells, except a few companies that built
There is no AGI coming anytime soon no matter how much hype is being thrown around. We are not in the singularity. However, peak bullshit is NEAR.
The trick the frontier labs have done is define AGI as "better than humans at the vast majority of valuable knowledge work" which is definitely not what most people think it means.
Even if we agree with this take (and I do think it's a likely take that you're right on the long term), it doesn't change that it seems likely we're in a bubble, and it probably will pop.
We see a similar paradigm with lots of revolutionary technology. The initial promise is high, people get very excited, lots of money pours in, and.... 15-30 years go by before we start seeing real impact across the economy at large.
It's just a real slog to actually implement and roll out new tech.
So take robots: I can promise you that you won't see robotics in every household in the next decade (especially so if we exclude the current market of robot vacuums). Even if a company makes an incredibly capable robot "today" (and to be clear - they are not) it won't have time to scale out production, reduce costs, generate a used market that's accessible to less wealthy consumers, deal with regulatory hurdles and quality problems that only pop up in real-world usage, etc...
It's just slower than you're implying.
The change very well will happen (I'm inclined to agree that things are going to shift). That doesn't mean that the current investment is sane and will pay off.
So many historical examples of this, just two here real quick:
- Ford built his first automobile in 1896, founded a company in 1901, went out of business, got sued by ALAM, didn't build more than 10k Model T's until 1910, then only finally hit real scale (of low hundred of thousands of units) in 1913: More than a decade to "basic scale". Household ownership didn't hit 60% until 1929... 30+ years later.
- The initial web enthusiasm, followed by the dot-com crash in early 2000s...
Robotics in the household has been the realm of science fiction for decades and it still hasn't happened. We get dedicated, compact machines like dishwashers and washing machine/dryers, that's it. Most recent innovation has been the automatic vacuum.
If you want someone else to do household chores, hire someone. You can pay someone to do your house chores for years for the cost that these things will have initially.
>Most recent innovation has been the automatic vacuum
Which are pretty useless for a lot of home layouts and degree of putting cords etc. away. I took a look a few years back and got a stick vac instead. (And have a monthly housekeeper who does a lot more than a robo-vac would.)
> Robotics in the household has been the realm of science fiction for decades and it still hasn't happened.
Wasnt AI science fiction (research) for decades but just took couple of years after Chatgpt to become mainstream. Why do you that wont happen to robotics?
We don't have sci-fi AI, and in any case "this thing is now possible, therefore [much more difficult thing] will be possible soon" is not a rational thought.
Current frontier models are absolutely sci-fi AI by any measure that existed up until the late 2010s. If you showed a current frontier agentic AI, with bidirectional speech, tool use etc. to a person from 2010, they'd say it can't be real, there must be a person inside that mechanical turk.
The cost of a timeshare robot will come down first, so you'll be hiring a robot before you own one. But that might not take so long to occur as you imagine.
All robotic home appliances will feature cameras with which people will be watching you and microphones with which people will be listening to you, all the time, forever.
> I would argue we still have not even really gotten started.
I agree, but that doesn't mean there isn't a bubble.
It is possible we'll have all those changes and they generate a lot of revenue for very few players but, still, a lot of the remaining players fail.
In particular, I worry about robotics. It is clearly becoming a China-only game. The west just doesn't have the industrial manufacturing critical mass to play it.
Yep, that is what the bet is. The build out will continue, even if it looks different.
The Internet was a 'bubble' at one point, and after it crashed in 2000, it didn't go away, it continued to build out. We're still using the Internet after the Internet bubble popped.
On a $100B AI datacenter, some 60-70% of the center needs to be replaced every 2-6 years. These companies claim the hardware lasts 6 years while simultaneously claiming they are relying on new hardware to lower inference costs (implying much more frequent updates).
Over 40 years, that's 7-20 hardware changes, that turns into between $420B and $1.4T of ongoing investment (not accounting for inflation). The $30B that is called "infrastructure" only accounts for 2-7% of the overall bill.
This is NOTHING like fiber buildouts because the fiber lasts the whole 40 years with ZERO replacements and very close to 100% of the cost is infrastructure rather than a tiny percentage.
I've been through several network upgrades. You are saying the fiber in the ground lasts a long time, but all the equipment on either end gets replaced every ~5 years. I agree this build out seems pretty crazy, but people were saying the same thing with the internet. Arguing if the equipment has a replacement rate of 2-6 years or 30-40 years, I think isn't the part that makes this a bubble.
For fiber, the main costs were not the fiber itself, but acquiring the rights to lay the fiber and the labor to do so. The equipment at either end has a lifespan of somewhat less that 50 years, but that's a minor cost.
Conversely, for the data centers we're talking about, the cost of the things that need replacing every five years is one of the primary costs.
"The line separating investment and speculation, which is never bright and clear, becomes blurred still further when most market participants have recently enjoyed triumphs. Nothing sedates rationality like large doses of effortless money. After a heady experience of that kind, normally sensible people drift into behavior akin to that of Cinderella at the ball. They know that overstaying the festivities ¾ that is, continuing to speculate in companies that have gigantic valuations relative to the cash they are likely to generate in the future ¾ will eventually bring on pumpkins and mice. But they nevertheless hate to miss a single minute of what is one helluva party. Therefore, the giddy participants all plan to leave just seconds before midnight. There’s a problem, though: They are dancing in a room in which the clocks have no hands."
Warren Buffett 2000
https://www.berkshirehathaway.com/2000ar/2000letter.html
There is something I have been pondering recently. If we compare the cost of AI subscriptions (let's say Claude's 100/month) to a median developer salary (let's say 100k/year to 200k/year), the difference is orders of magnitude. This fills like a gap that needs to close. I suspect llms are too cheap right now but will raise their prices to a point where only big companies will be able to afford subscriptions to use them. I think soon we will see models that are only sold at very high prices.
This is something I have been pondering recently. If I compare the cost of a plunger ($23.99 on Amazon) to a median plumber salary ($62,970 per year per BLS), the difference is orders of magnitude. This feels like a gap that needs to close. I suspect plungers are too cheap right now but will raise their prices to a point where only big companies will be able to afford subscriptions to use them. I think soon we will seen plungers that are only sold at very high prices.
Open weight models tell a different story. Inference is not that much more expensive than what a 100$ plan would allow and will only get cheaper (for current capability models of course, frontier not so much).
Also, something else to add. At first I thought no one wants to build data centers in hotter areas in the middle of deserts (many places in the American continent). So nobody would spend money building a data center in the Chihuahuan Desert for instance. However, a game of latencies will either require cover llm access from these areas, or make people move closer to the other data centers. In the former, llm prices will go up; in the latter, there will be a migration towards data centers that increase the price of the areas around.
Is this range just Silicon Valley or what is this? Even including just Europe, you're looking at a lower bracket of 10k. If you expand to the rest of the world... Or do you think rich cities in the USA, where developers make 100k+ per year, can alone sustain this industry?
salary cost to the company is a lot higher than gross salary (which itself is a lot higher than net salary). you can guestimate total salary cost to be about 1.5x gross salary, so for the lower bound: 100k / 1.5 = $66,666.67 = €57,813.34.
€57-114k p.a. is well within the order of magnitude of yearly gross developer salaries in Western Europe (e.g. Germany).
I mean we are due our 6-8 year financial crash that we won't learn from. Once again it'll be coming from the USA's feral financial investments all to be bailed out by the tax player whilst the rest of the world picks up the pieces. Maybe its time we moved away from the petro-dollar if the USA can't be trusted to keep its finances in order.
There's a bubble around data centers, mainly. That's fueled by projected demand of AI and assumptions companies make about how the pie for that revenue is going to be divided up.
What's very real is the rapidly growing amount of revenue for both OpenAI and Anthropic. That's already tens of billions per year and growing quite rapidly. Investments against that kind of revenue aren't completely horrible. To a point. But at the multi trillion dollar valuation level, of course there are going to be issues with living up to those expectations.
In my view some of the base assumptions are looking not so solid currently. It's not a given that OpenAI and Anthropic will end up with most of the revenue. The Chinese trust Silicon Valley just about as much as vice versa. Which is why they are doing their own models, chips, and data centers. This is driving a rapid commoditization for things like frontier models, open model weights, and chips. This in turn gives countries outside the US a lot of options to stay independent. Which burst the bubble that all that global revenue was going to flow towards Silicon Valley. Some of that still might. But that will have to happen based on cost and merit.
There are also geopolitical circumstances that cause most data center plans to be bottle necked on permitting, chip shortages, grid connectivity, availability of gas turbines, gas, solar panels, inverters, batteries, water, and other resources. As it turns out, you can't just willy nilly plan for hundreds of GW of data centers and expect those to pop into existence overnight along with all the needed infrastructure. Most of the announced/planned capacity for this will likely not be realized. Certainly not this decade. 5-10% by 2035 would be a lot given all the constraints and scarcity. No amount of reality distortion can change the physical constraints on this topic.
The good news is that most of the money needed for this hasn't been spent yet. And what has been spent won't be going to waste. Up and running data centers are a hot commodity right now. They won't be running idle if a bubble bursts. But probably investors dreaming of multi trillion dollar IPOs might be a bit more cautious now that SpaceX stock is trading well below its IPO value.
Note that the “AI Bubble” term used here is only defined in the context of market speculation. If you are not an investor of AI companies then there is nothing to worry about for you. If you are an investor, then you should know that people can’t really predict when bubbles burst. Every prediction in the markets is a speculation and some investors can simply bets against the popular expectations to make good money.
Movements of AI stocks shouldn’t be confused with “AI as a technology” and “AI as a business”. Market valuation is a different game.
> The same thing happened with their massive investment in Anthropic. They accounted for $53.4 billion due to deals with Anthropic last quarter. He said if you follow one Anthropic dollar through the earnings release, it's counted in AI business revenue, chips business, and AWS segment revenue.
That is insane if that is true, is that even legal?
It is. However remember that when the bubble pops it all works in the reverse direction too. Suddenly you have to mark down investment losses, missed revenue, and written off commitments.
Companies can go from looking really good to a complete financial mess almost overnight when all that leverage and self-reinforcing stuff unwinds. See last weeks headlines for one such scenario.
The title's clickbait (from The Register, of all people - colour me shocked!).
There's nothing really groundbreaking at all in there, just "chips are expensive, and open weights models hosted locally in enterprise could displace Claude/GPT"
Nvidia already seems to be pivoting to "Local inferencing" with stuff like RTX Spark laptops.
The problem is that local inference machines won't allow anywhere close to their current margins or gross sales figures. At the same time, the huge overbuild of GPUs is going to crash server sales and prices for around a half-decade as companies try to avoid hardware upgrades or buy cheap, used equipment to save costs.
I think Nvidia will survive, but they'll be back to something like a 1T (or lower) valuation.
Nvidia has hordes of raucous gamers on the prowl waiting to tear their chips out of their hands the moment hyperscaler demand falters; plus, China has made sure there will always be plenty of open models to run for hobbyists and neoclouds, even if training compute were to drop off a cliff. They'll be just fine.
I want to see the number of gamers that tear the chips from the dead hands of hyperscalers to make up for the implosion of that market.
If I had to guess, half of humanity does not have interest in dedicated gaming chips.
Memory, that's another story.
The gpus will be too expensive and a lot of them are not even close to useable for gaming. Any consumer gpus maybe but I suspect that nvidia will be able to pivot more quickly producing consumer gpus again.
The market for video game hardware is absolutely puny in comparison to the datacenter GPU boom.
I saw it put quite well in a comment on reddit:
> In 2020, the gaming segment was 47% of revenue at around 8 billion. Today it's doubled to 16 billion, or 7% of revenue. That's right, data center went from 6 billion 2020 to around 198 billion today.
Even if gaming revenue doubles again when the AI bubble pops, their total revenue will still drop by something like 80%. I'm neither smart nor dumb enough to be confident about whether that's something Nvidia can survive.
I think Nvidia surviving is pretty certain. They are not particularly leveraged to my understanding and their commitments are for their own products. Debt is at 12,34 billion so not that much even at their gaming revenue comparison.
Now their market cap is most likely destroyed for forever... Still I little doubts about them continuing to exist.
Totally agree. For a lot of tradefolks maps seem to be the actually more important location. Still Google obviously but it's less slopified in comparison to search. For now at least.
well there are just 2 sources for website profits:
1. money from advertisements (from views, visits): you need to be on google search results at the bear minimum.
2. money from non-advertisements, mostly-offline (eg. you sell preminum stuff/service): you CAN hand out pamphlets to your service website IF your ROI per visit is so good to be true...
but even in the 2nd case, it really helps to be on the google's search result
It's entirely possible China has a little bit of a bubble too when it comes to development investment vs demand. But obviously the bubble in the US is basically a whale compared to whatever small fish China is.
> I caution anyone looking at API prices: they dropped the price, but is it actually less expensive?
> Yeah, they dropped the price, but count the number of tokens you're tossing into it and see if it's actually cheaper. It's good for marketing, but the rub is how much you're actually using.
The level of discourse is so horrible now, I don't have words. Are these the ones making predictions on AI bubble?
This is poorly stated in the podcast, but the underlying point is correct: while cost-per-token is going down, overall token use is way up. This is causing the cost of using LLMs in corporations to skyrocket.
Of course, the price of tokens going down makes it cheaper. You can now afford to throw millions of tokens at solving a problem that would have been impossible for AI to solve at all a year ago for any price.
People completely lack imagination about this stuff. The main problem right now with AI isn't even AI successfully producing code at a reasonable cost, it's human coordination and review that is the bottleneck.
I think it's both. It's easy to run up a massive bill with AI without much to show for it, which is why token-maxxing has now been replaced by cost-awareness and AI budgeting such as Uber's "max 10% of salary per employee".
Cost isn't just token price though - it's (number-of-tokens-used x price-per-token), and there are large differences in token efficiency between different models and harnesses. Increasingly we're seeing benchmark sites focusing on "cost per completed task" as a cost metric, and it's not always the cheapest tokens that win.
I agree that ultimately AI/coding cost is just part of the picture - at the end of the day it's about software development cost, which for time being involves humans.
Outside a relatively small world of circular investment and FOMO feeding FOMO the general consensus seems to be “let it burn.”
It appears very unlikely we will ever see an IPO of OpenAI. Anthropic appears less doomed, but still iffy at best. Tons of other large, but little discussed, AI startups are just dead-companies-walking at this point.
The likes of AWS are showing good headline numbers but are taking out massive debt to build infrastructure that looks increasingly unneeded. Those with capacity are looking to offload it, quickly. Yes AWS has “committed contracts” for this capacity but if those commitments are with shaky AI startups then it’s mostly just fluff PR and these hyperscalers will get left holding the bag on all this debt.
> infrastructure that looks increasingly unneeded
This is the crux that needs to be substantiated. Without substantiation none of your other arguments hold up.
Are you saying people haven’t been throwing unneeded GPU capacity on the market? That is happening.
Frankly it’s the opposite scenario (that’s there’s all this demand) which is struggling for any hard evidence.
> Are you saying people haven’t been throwing unneeded GPU capacity on the market? That is happening.
That, by itself, doesn't have to mean anything.
AWS is far more compute capacity than Amazon needs, but that's not because Amazon misjudged how much capacity they need. They built it out to sell it to others, leveraging know-how and economies of scale to do so very profitably.
They only need companies to suddenly need their GPU compute if those committed contracts don't make it.
How hard can that be?
Which players, specifically, are you talking about? Everything you said is opinion passed off as fact.
SpaceX has put a ton of excess capacity on the market.
Meta tanked chip stocks by saying it was considering the same.
Worries about compute overcapacity, via folks saying they want to offload capacity, is literally the thing that nuked the “situational awareness” fund last week.
It’s long term demand that matters, not just “price.” High prices without true demand is the literal definition of a bubble.
SpaceX has put a ton of excess capacity on the market that was subsequently absorbed by Anthropic. There's a reason why Grok is no longer free.
Meta said it was considering doing the same because they saw how valuable excess capacity was.
You're misinterpreting what actually happened.
SpaceX is selling compute to a willing buyer. You can't point to one side of the trade as evidence for under/over capacity. The way we usually judge such things is the price. If the price for compute is going up, which it is, then that's the thing to look at.
It's holding up the entire US stock market and therefore the entire US economy and the dollar value. You probably don't want this particular Atlas to shrug.
The “Let it burn” attitude doesn’t mean people think they are immune.
Yes the overall market will take a hit but, like a forest fire we need a healthy burn to just wipe out the weaker players so the older more mature trees can get on with it. Yes the big trees will get burned a bit but they’ll be fine in the long run.
We need a good brush fire to just wipe out all the iffy startups and investors that over-indexed here. Thats what people want with “let it burn.”
You can’t threaten me with two good times. Long term that would help correct the overemphasis on the U.S. economy and AI.
I do. The faster it happens, the faster a recovery begins and we can devote resources back to doing more practical things. Get it over with as soon as possible, rip the bandaid off, insert your idiom of choice.
It's inevitable though. The funny-money has outpaced actual money by lightyears at this point.
On the plus side, our interest rates aren't 0% right now, so there's some room there.
On the down side, our national debt generation now exceeds 125% of GDP and bond rates are shooting up because nobody wants to buy our debt.
Anthropic is almost purely a model company. They own close to no data centers.
If models are becoming commodities, and the main bottleneck is actually serving them at scale, Anthropic does not appear particularly well positioned.
If AWS had a ~60-80% margin for decades, I see no reason why inference can't have a ~60-80% margin for quite some time.
The problem is, if costs continue to drop ~90% for the same level of quality every 18 months, demand is unlikely to grow 10x to keep the revenue stable.
Who knows. Jevon's paradox. But the cost/quality is dropping too fast that it's hard for me to imagine demand keeps up long term to keep revenues (and profits) GROWING.
More and more companies are signing up with OpenAI and Anthropic at near exponential growth to automate everyday tasks. If anything, these two should manage to IPO just fine. The rest of the downstream startups probably won't make it.
Of course we know it. It’s been obvious since at least 2023. Everyone in AI oversells, except a few companies that built There is no AGI coming anytime soon no matter how much hype is being thrown around. We are not in the singularity. However, peak bullshit is NEAR.
The trick the frontier labs have done is define AGI as "better than humans at the vast majority of valuable knowledge work" which is definitely not what most people think it means.
I would argue we still have not even really gotten started.
What do we have in the decade ahead? Robotics in every household, models 10x+ faster and more intelligent than today.
Really no significant impact in life sciences, R&D, and 'offline' world / robotics today as of yet, which is where most of the value will live.
Even if we agree with this take (and I do think it's a likely take that you're right on the long term), it doesn't change that it seems likely we're in a bubble, and it probably will pop.
We see a similar paradigm with lots of revolutionary technology. The initial promise is high, people get very excited, lots of money pours in, and.... 15-30 years go by before we start seeing real impact across the economy at large.
It's just a real slog to actually implement and roll out new tech.
So take robots: I can promise you that you won't see robotics in every household in the next decade (especially so if we exclude the current market of robot vacuums). Even if a company makes an incredibly capable robot "today" (and to be clear - they are not) it won't have time to scale out production, reduce costs, generate a used market that's accessible to less wealthy consumers, deal with regulatory hurdles and quality problems that only pop up in real-world usage, etc...
It's just slower than you're implying.
The change very well will happen (I'm inclined to agree that things are going to shift). That doesn't mean that the current investment is sane and will pay off.
So many historical examples of this, just two here real quick:
- Ford built his first automobile in 1896, founded a company in 1901, went out of business, got sued by ALAM, didn't build more than 10k Model T's until 1910, then only finally hit real scale (of low hundred of thousands of units) in 1913: More than a decade to "basic scale". Household ownership didn't hit 60% until 1929... 30+ years later.
- The initial web enthusiasm, followed by the dot-com crash in early 2000s...
Robotics in the household has been the realm of science fiction for decades and it still hasn't happened. We get dedicated, compact machines like dishwashers and washing machine/dryers, that's it. Most recent innovation has been the automatic vacuum.
If you want someone else to do household chores, hire someone. You can pay someone to do your house chores for years for the cost that these things will have initially.
>Most recent innovation has been the automatic vacuum
Which are pretty useless for a lot of home layouts and degree of putting cords etc. away. I took a look a few years back and got a stick vac instead. (And have a monthly housekeeper who does a lot more than a robo-vac would.)
> Robotics in the household has been the realm of science fiction for decades and it still hasn't happened.
Wasnt AI science fiction (research) for decades but just took couple of years after Chatgpt to become mainstream. Why do you that wont happen to robotics?
We don't have sci-fi AI, and in any case "this thing is now possible, therefore [much more difficult thing] will be possible soon" is not a rational thought.
> We don't have sci-fi AI
Current frontier models are absolutely sci-fi AI by any measure that existed up until the late 2010s. If you showed a current frontier agentic AI, with bidirectional speech, tool use etc. to a person from 2010, they'd say it can't be real, there must be a person inside that mechanical turk.
The cost of a timeshare robot will come down first, so you'll be hiring a robot before you own one. But that might not take so long to occur as you imagine.
People have been talking about this for literally a hundred years at this point. It's not a thing and isn't gonna be one.
What it we would prefer that there were not other people in our homes for interpersonal reasons?
All robotic home appliances will feature cameras with which people will be watching you and microphones with which people will be listening to you, all the time, forever.
The absolutely massive scale and influence of the Web happened after the dot com burst.
> I would argue we still have not even really gotten started.
I agree, but that doesn't mean there isn't a bubble.
It is possible we'll have all those changes and they generate a lot of revenue for very few players but, still, a lot of the remaining players fail.
In particular, I worry about robotics. It is clearly becoming a China-only game. The west just doesn't have the industrial manufacturing critical mass to play it.
Yep, that is what the bet is. The build out will continue, even if it looks different.
The Internet was a 'bubble' at one point, and after it crashed in 2000, it didn't go away, it continued to build out. We're still using the Internet after the Internet bubble popped.
On a $100B AI datacenter, some 60-70% of the center needs to be replaced every 2-6 years. These companies claim the hardware lasts 6 years while simultaneously claiming they are relying on new hardware to lower inference costs (implying much more frequent updates).
Over 40 years, that's 7-20 hardware changes, that turns into between $420B and $1.4T of ongoing investment (not accounting for inflation). The $30B that is called "infrastructure" only accounts for 2-7% of the overall bill.
This is NOTHING like fiber buildouts because the fiber lasts the whole 40 years with ZERO replacements and very close to 100% of the cost is infrastructure rather than a tiny percentage.
I've been through several network upgrades. You are saying the fiber in the ground lasts a long time, but all the equipment on either end gets replaced every ~5 years. I agree this build out seems pretty crazy, but people were saying the same thing with the internet. Arguing if the equipment has a replacement rate of 2-6 years or 30-40 years, I think isn't the part that makes this a bubble.
For fiber, the main costs were not the fiber itself, but acquiring the rights to lay the fiber and the labor to do so. The equipment at either end has a lifespan of somewhat less that 50 years, but that's a minor cost.
Conversely, for the data centers we're talking about, the cost of the things that need replacing every five years is one of the primary costs.
It's obvious. Most people were expecting this.
"The line separating investment and speculation, which is never bright and clear, becomes blurred still further when most market participants have recently enjoyed triumphs. Nothing sedates rationality like large doses of effortless money. After a heady experience of that kind, normally sensible people drift into behavior akin to that of Cinderella at the ball. They know that overstaying the festivities ¾ that is, continuing to speculate in companies that have gigantic valuations relative to the cash they are likely to generate in the future ¾ will eventually bring on pumpkins and mice. But they nevertheless hate to miss a single minute of what is one helluva party. Therefore, the giddy participants all plan to leave just seconds before midnight. There’s a problem, though: They are dancing in a room in which the clocks have no hands." Warren Buffett 2000 https://www.berkshirehathaway.com/2000ar/2000letter.html
There is something I have been pondering recently. If we compare the cost of AI subscriptions (let's say Claude's 100/month) to a median developer salary (let's say 100k/year to 200k/year), the difference is orders of magnitude. This fills like a gap that needs to close. I suspect llms are too cheap right now but will raise their prices to a point where only big companies will be able to afford subscriptions to use them. I think soon we will see models that are only sold at very high prices.
This is something I have been pondering recently. If I compare the cost of a plunger ($23.99 on Amazon) to a median plumber salary ($62,970 per year per BLS), the difference is orders of magnitude. This feels like a gap that needs to close. I suspect plungers are too cheap right now but will raise their prices to a point where only big companies will be able to afford subscriptions to use them. I think soon we will seen plungers that are only sold at very high prices.
Open weight models tell a different story. Inference is not that much more expensive than what a 100$ plan would allow and will only get cheaper (for current capability models of course, frontier not so much).
Also, something else to add. At first I thought no one wants to build data centers in hotter areas in the middle of deserts (many places in the American continent). So nobody would spend money building a data center in the Chihuahuan Desert for instance. However, a game of latencies will either require cover llm access from these areas, or make people move closer to the other data centers. In the former, llm prices will go up; in the latter, there will be a migration towards data centers that increase the price of the areas around.
Who cares about a few ms more latency on an LLM API? Maybe for voice, but most other use-cases are quite latency insensitive.
Yeah, the customer support use case is already grinding me even worse than offshoring to India.
They are building data centers in the Chihuahuan Desert right now. Meta has a huge one planned right outside El Paso.
LLMs are not as cheap on enterprise plans.
I don't understand this argument.
Go to OpenRouter and look at all of the unsubsidized providers.
> 100k/year to 200k/year
Is this range just Silicon Valley or what is this? Even including just Europe, you're looking at a lower bracket of 10k. If you expand to the rest of the world... Or do you think rich cities in the USA, where developers make 100k+ per year, can alone sustain this industry?
salary cost to the company is a lot higher than gross salary (which itself is a lot higher than net salary). you can guestimate total salary cost to be about 1.5x gross salary, so for the lower bound: 100k / 1.5 = $66,666.67 = €57,813.34.
€57-114k p.a. is well within the order of magnitude of yearly gross developer salaries in Western Europe (e.g. Germany).
I mean we are due our 6-8 year financial crash that we won't learn from. Once again it'll be coming from the USA's feral financial investments all to be bailed out by the tax player whilst the rest of the world picks up the pieces. Maybe its time we moved away from the petro-dollar if the USA can't be trusted to keep its finances in order.
There's a bubble around data centers, mainly. That's fueled by projected demand of AI and assumptions companies make about how the pie for that revenue is going to be divided up.
What's very real is the rapidly growing amount of revenue for both OpenAI and Anthropic. That's already tens of billions per year and growing quite rapidly. Investments against that kind of revenue aren't completely horrible. To a point. But at the multi trillion dollar valuation level, of course there are going to be issues with living up to those expectations.
In my view some of the base assumptions are looking not so solid currently. It's not a given that OpenAI and Anthropic will end up with most of the revenue. The Chinese trust Silicon Valley just about as much as vice versa. Which is why they are doing their own models, chips, and data centers. This is driving a rapid commoditization for things like frontier models, open model weights, and chips. This in turn gives countries outside the US a lot of options to stay independent. Which burst the bubble that all that global revenue was going to flow towards Silicon Valley. Some of that still might. But that will have to happen based on cost and merit.
There are also geopolitical circumstances that cause most data center plans to be bottle necked on permitting, chip shortages, grid connectivity, availability of gas turbines, gas, solar panels, inverters, batteries, water, and other resources. As it turns out, you can't just willy nilly plan for hundreds of GW of data centers and expect those to pop into existence overnight along with all the needed infrastructure. Most of the announced/planned capacity for this will likely not be realized. Certainly not this decade. 5-10% by 2035 would be a lot given all the constraints and scarcity. No amount of reality distortion can change the physical constraints on this topic.
The good news is that most of the money needed for this hasn't been spent yet. And what has been spent won't be going to waste. Up and running data centers are a hot commodity right now. They won't be running idle if a bubble bursts. But probably investors dreaming of multi trillion dollar IPOs might be a bit more cautious now that SpaceX stock is trading well below its IPO value.
Note that the “AI Bubble” term used here is only defined in the context of market speculation. If you are not an investor of AI companies then there is nothing to worry about for you. If you are an investor, then you should know that people can’t really predict when bubbles burst. Every prediction in the markets is a speculation and some investors can simply bets against the popular expectations to make good money.
Movements of AI stocks shouldn’t be confused with “AI as a technology” and “AI as a business”. Market valuation is a different game.
I'm not an intentional investor in AI companies. I have index funds, which still expose me to the risk of AI froth.
Although yes, AI as a technology is still in its early stages, and I believe it's a sound technology for us to work with.
I also think we're solving the wrong problem (removing office work) versus solving problems in the sciences, like running and monitoring biotech labs.
> The same thing happened with their massive investment in Anthropic. They accounted for $53.4 billion due to deals with Anthropic last quarter. He said if you follow one Anthropic dollar through the earnings release, it's counted in AI business revenue, chips business, and AWS segment revenue.
That is insane if that is true, is that even legal?
It is. However remember that when the bubble pops it all works in the reverse direction too. Suddenly you have to mark down investment losses, missed revenue, and written off commitments.
Companies can go from looking really good to a complete financial mess almost overnight when all that leverage and self-reinforcing stuff unwinds. See last weeks headlines for one such scenario.
The title's clickbait (from The Register, of all people - colour me shocked!).
There's nothing really groundbreaking at all in there, just "chips are expensive, and open weights models hosted locally in enterprise could displace Claude/GPT"
well if bubble pops, everyone will die EXCEPT google
UNLESS openAI/etc actually succeeds in making AGI that never hallucinates and goes over the current LLM limitations
as for google... well they own the web
(+google has plenty of other revenue sources, so it can just pay out its AI survival)
if you're a website owner, would you welcome chatgpt/etc's data-collection bots?
but... as for google's bots... you need your website to be on the Google search results...
I expect Nvidia to survive the crash on its own, and everyone else except Softbank to be bailed out with US taxpayer money.
Nvidia already seems to be pivoting to "Local inferencing" with stuff like RTX Spark laptops.
The problem is that local inference machines won't allow anywhere close to their current margins or gross sales figures. At the same time, the huge overbuild of GPUs is going to crash server sales and prices for around a half-decade as companies try to avoid hardware upgrades or buy cheap, used equipment to save costs.
I think Nvidia will survive, but they'll be back to something like a 1T (or lower) valuation.
Nvidia has hordes of raucous gamers on the prowl waiting to tear their chips out of their hands the moment hyperscaler demand falters; plus, China has made sure there will always be plenty of open models to run for hobbyists and neoclouds, even if training compute were to drop off a cliff. They'll be just fine.
True, but Nvidia has 10x'd their market cap since the AI boom. They'll be fine, but their current financials can't be sustained by gamers alone.
I want to see the number of gamers that tear the chips from the dead hands of hyperscalers to make up for the implosion of that market. If I had to guess, half of humanity does not have interest in dedicated gaming chips. Memory, that's another story.
The gpus will be too expensive and a lot of them are not even close to useable for gaming. Any consumer gpus maybe but I suspect that nvidia will be able to pivot more quickly producing consumer gpus again.
The market for video game hardware is absolutely puny in comparison to the datacenter GPU boom.
I saw it put quite well in a comment on reddit:
> In 2020, the gaming segment was 47% of revenue at around 8 billion. Today it's doubled to 16 billion, or 7% of revenue. That's right, data center went from 6 billion 2020 to around 198 billion today.
Even if gaming revenue doubles again when the AI bubble pops, their total revenue will still drop by something like 80%. I'm neither smart nor dumb enough to be confident about whether that's something Nvidia can survive.
I think Nvidia surviving is pretty certain. They are not particularly leveraged to my understanding and their commitments are for their own products. Debt is at 12,34 billion so not that much even at their gaming revenue comparison.
Now their market cap is most likely destroyed for forever... Still I little doubts about them continuing to exist.
yeah you can't expect gamers to pay enormous "investment bank money" just to play games...
plus, even old gpus run games fine
> but... as for google's bots... you need your website to be on the Google search results...
Why? Depends totally on what kind of website you are running.
Totally agree. For a lot of tradefolks maps seem to be the actually more important location. Still Google obviously but it's less slopified in comparison to search. For now at least.
well there are just 2 sources for website profits:
1. money from advertisements (from views, visits): you need to be on google search results at the bear minimum.
2. money from non-advertisements, mostly-offline (eg. you sell preminum stuff/service): you CAN hand out pamphlets to your service website IF your ROI per visit is so good to be true...
but even in the 2nd case, it really helps to be on the google's search result
> well if bubble pops, everyone will die EXCEPT google
Also, possible Apple since they haven't gone into deep debt to finance 'AI buildout'.
They’re also primed to capture the on-device AI market in 3-5 years time when a “good enough” model can run locally.
Apple is also just partnering with Google.
https://blog.google/company-news/inside-google/company-annou...
Do they even have any direct exposure to the whole AI bubble?
If not, I'd say they don't even count in the "everyone"-
Their exposure could be net positive. If other companies fail they will still be around to pick up their customers.
The bubble in the US pops maybe. China is only limited by chips, not costs.
It's entirely possible China has a little bit of a bubble too when it comes to development investment vs demand. But obviously the bubble in the US is basically a whale compared to whatever small fish China is.
[dead]
> I caution anyone looking at API prices: they dropped the price, but is it actually less expensive?
> Yeah, they dropped the price, but count the number of tokens you're tossing into it and see if it's actually cheaper. It's good for marketing, but the rub is how much you're actually using.
The level of discourse is so horrible now, I don't have words. Are these the ones making predictions on AI bubble?
This is poorly stated in the podcast, but the underlying point is correct: while cost-per-token is going down, overall token use is way up. This is causing the cost of using LLMs in corporations to skyrocket.
Do you first agree that not only is the cost-per-token going down, cost per task is going down which obviously encourages people to use it more?
If a car company releases a new car with higher mileage, would you suggest that cars are getting costlier?
Of course, the price of tokens going down makes it cheaper. You can now afford to throw millions of tokens at solving a problem that would have been impossible for AI to solve at all a year ago for any price.
People completely lack imagination about this stuff. The main problem right now with AI isn't even AI successfully producing code at a reasonable cost, it's human coordination and review that is the bottleneck.
I think it's both. It's easy to run up a massive bill with AI without much to show for it, which is why token-maxxing has now been replaced by cost-awareness and AI budgeting such as Uber's "max 10% of salary per employee".
Cost isn't just token price though - it's (number-of-tokens-used x price-per-token), and there are large differences in token efficiency between different models and harnesses. Increasingly we're seeing benchmark sites focusing on "cost per completed task" as a cost metric, and it's not always the cheapest tokens that win.
I agree that ultimately AI/coding cost is just part of the picture - at the end of the day it's about software development cost, which for time being involves humans.
[flagged]