The Hater's Guide to the AI Bubble(wheresyoured.at)
wheresyoured.at
The Hater's Guide to the AI Bubble
https://www.wheresyoured.at/the-haters-gui/
167 comments
>can't conceive of a use of this which is beneficial beyond a marginal improvement in content consumption
AlphaFold is having a big influence in medical research. There's more to AI than chatbots.
It's quite interesting the work they are doing there now - article on it https://www.labiotech.eu/in-depth/alpha-fold-3-drug-discover... I've got a personal interest because my sister has ALS and I think an in silico breakthrough is the only thing that could fix that before her dying.
AlphaFold is having a big influence in medical research. There's more to AI than chatbots.
It's quite interesting the work they are doing there now - article on it https://www.labiotech.eu/in-depth/alpha-fold-3-drug-discover... I've got a personal interest because my sister has ALS and I think an in silico breakthrough is the only thing that could fix that before her dying.
I don't think that anyone is dismissing machine learning itself.
This bubble is more about generative AI, and especially language models.
Machine learning has been there for quite a while and is a useful tool. But it's only a tool among many other ones, like programming languages and libraries. It's not a product. At most it can be the engine of a specific feature.
Machine learning has been there for quite a while and is a useful tool. But it's only a tool among many other ones, like programming languages and libraries. It's not a product. At most it can be the engine of a specific feature.
> but everything it produces needs careful checking for errors, mistakes and 'hallucinations' by someone with some level of expertise in a subject
Nah you just post it, if people point out the mistakes the comment is treated as a positive engagement by the algorithm anyway, unfortunately for anyone that cares.
Nah you just post it, if people point out the mistakes the comment is treated as a positive engagement by the algorithm anyway, unfortunately for anyone that cares.
I too am deeply skeptical of the current economic allocation, but it’s typical of frontier expansions in general.
Somehow, in AI, people lost sight of the fact that transformer architecture AI is a fundamentally extractive process for identifying and mining the semantic relationships in large data sets.
Because human cultural data contains a huge amount of inferred information not overtly apparent in the data set, many smart people confused the results with a generative rather than an extractive mechanism.
….To such a point that the entire field is known as “generative” AI, when fundamentally it is not in any way generative. It merely extracts often unseen or uncharacterized semantics, and uses them to extrapolate from a seed.
There are, however, many uses for such a mechanism. There are many, many examples of labor where there is no need to generate any new meaning or “story”.
All of this labor can be automated through the application of existing semantic patterns to the data being presented, and to do so we suddenly do not need to fully characterize or elaborate the required algorithm to achieve that goal.
We have a universal algorithm, a sonic screwdriver if you will, with which we can solve any fully solved problem set by merely presenting the problems and enough known solutions so that the hidden algorithms can be teased out into the model parameters.
But it only works on the class of fully solved problems. Insofar as unsolved problems can be characterized as a solved system of generating and testing hypothesis to solve the unsolved, we may potentially also assail unsolved problems with this tool.
Somehow, in AI, people lost sight of the fact that transformer architecture AI is a fundamentally extractive process for identifying and mining the semantic relationships in large data sets.
Because human cultural data contains a huge amount of inferred information not overtly apparent in the data set, many smart people confused the results with a generative rather than an extractive mechanism.
….To such a point that the entire field is known as “generative” AI, when fundamentally it is not in any way generative. It merely extracts often unseen or uncharacterized semantics, and uses them to extrapolate from a seed.
There are, however, many uses for such a mechanism. There are many, many examples of labor where there is no need to generate any new meaning or “story”.
All of this labor can be automated through the application of existing semantic patterns to the data being presented, and to do so we suddenly do not need to fully characterize or elaborate the required algorithm to achieve that goal.
We have a universal algorithm, a sonic screwdriver if you will, with which we can solve any fully solved problem set by merely presenting the problems and enough known solutions so that the hidden algorithms can be teased out into the model parameters.
But it only works on the class of fully solved problems. Insofar as unsolved problems can be characterized as a solved system of generating and testing hypothesis to solve the unsolved, we may potentially also assail unsolved problems with this tool.
Different algorithms do different things but “generative” AI can certainly come up with new stories and images and with different algorithms AI can work with not fully solved problems like protein folding.
Coming up with new arrangements of bits is not a particularly hard problem on its own, but the current crop of ai is certainly able to do that in the extractive process, in fact randomness is a key part of training and inference. But making new things from old parts does not constitute innovation, insofar as the arrangements follow known paths.
That doesn’t make it non useful. It just makes it non innovative.
Trial and error within a defined problem space is an area where automation can definitely be useful. Once again though, the result is not innovation but rather automation of labor.
There is a -lot- of labor requiring mind numbing repetition or iteration. The vast majority of labor falls into this category, and exists in fundamentally solved problem spaces, but still is complex enough that the algorithms involved are opaque. This is where the current type of AI can work miracles when trained with enough oblique data.
That doesn’t make it non useful. It just makes it non innovative.
Trial and error within a defined problem space is an area where automation can definitely be useful. Once again though, the result is not innovation but rather automation of labor.
There is a -lot- of labor requiring mind numbing repetition or iteration. The vast majority of labor falls into this category, and exists in fundamentally solved problem spaces, but still is complex enough that the algorithms involved are opaque. This is where the current type of AI can work miracles when trained with enough oblique data.
I believe this is a "good" bubble in the sense that the 19th century railroad bubble and original dot com bubble both ended up invested in infrastructure that created immense value.
That said, all of these LLMs are interchangeable, there are no moats, and the profit will almost entirely be in the "last mile," in local subject matter experts applying this technology to their bespoke business processes.
That said, all of these LLMs are interchangeable, there are no moats, and the profit will almost entirely be in the "last mile," in local subject matter experts applying this technology to their bespoke business processes.
America's railroad boom isn't a great example, it got us the worst rail infrastructure in the world, built by private monopolies solely for maximum short-term profit, i.e. moving freight and not passengers, and now American industry is largely gone and we're stuck with rail infrastructure that is useless to almost everyone and it costs far more to maintain it than it's even worth.
America's internet infrastructure, like the railroads, was also left in the hands of private monopolies and it is also a piece of shit compared to other countries. It's slow and everyone pays far too much for it and many are still excluded from it because it's not profitable enough to run fiber to their area.
The AI bubble won't leave behind any new infrastructure when it bursts. Just millions of burned out GPUs that get sent to an e-waste processing plant where they are ground up into sand, trillions of dollars wasted, many terawatt hours of energy wasted, many billions of liters of freshwater wasted, and the internet being buried under an avalanche of pseudorandomly-generated garbage.
America's internet infrastructure, like the railroads, was also left in the hands of private monopolies and it is also a piece of shit compared to other countries. It's slow and everyone pays far too much for it and many are still excluded from it because it's not profitable enough to run fiber to their area.
The AI bubble won't leave behind any new infrastructure when it bursts. Just millions of burned out GPUs that get sent to an e-waste processing plant where they are ground up into sand, trillions of dollars wasted, many terawatt hours of energy wasted, many billions of liters of freshwater wasted, and the internet being buried under an avalanche of pseudorandomly-generated garbage.
> "good" bubble in the sense
how can massively buying hardware that will have to be thrown away in a few years be a "good" bubble in the sense of being a lasting infrastructure investment?
how can massively buying hardware that will have to be thrown away in a few years be a "good" bubble in the sense of being a lasting infrastructure investment?
Why would that hardware "have to be thrown away"? I've seen quite old GPUs still in use; given the current demand, I expect the vast majority of hardware used in these data centers to see a lot more extended use than most other types of electronics around the world (e.g. phones).
GPUs in data centers have short lifespans.
https://www.tomshardware.com/pc-components/gpus/datacenter-g...
https://www.tomshardware.com/pc-components/gpus/datacenter-g...
Oh, interesting - it's about failure rate following degradation after prolonged use. I didn't think of that - but my take is that if companies like Google are actually using those components until they 100% exhaust what the components are capable of, then we can argue about whether this use of resources is better than an alternative use of them, but it's by definition not a waste of resources.
I am pretty optimistic that as long as hardware capacity exists, people will find ways of using it. Whether it will be profitable or not is another story of course.
Rivers overflowing with legacy hardware and villages incinerating boards for their metals, and the caustic effects on people & their environment that causes, are already happening. The hardware capacity exists only as long as it is operational and within a few generations. Perhaps we should be careful before building Manhattan-sized data centers.
Up to a point it is better than having additional compute sitting idle at the edge, economies of scale and all that, but after some point it becomes excess and wasteful, even if people figure out ways to entertain themselves with it.
And if people don't want to pay what it costs to improve and maintain these city-sized electronic brains? Then it all becomes waste, or the majority transformed into office or warehouse space or something else.
Proceeding with combined 1% (US GDP)-sized budgets despite this risk being an elephant in the room is what makes it a bubble.
Up to a point it is better than having additional compute sitting idle at the edge, economies of scale and all that, but after some point it becomes excess and wasteful, even if people figure out ways to entertain themselves with it.
And if people don't want to pay what it costs to improve and maintain these city-sized electronic brains? Then it all becomes waste, or the majority transformed into office or warehouse space or something else.
Proceeding with combined 1% (US GDP)-sized budgets despite this risk being an elephant in the room is what makes it a bubble.
Aren't AI GPUs a drop in the bucket compared to consumer electronics?
Nvidia sold ~3M blackwells in 2025: https://wccftech.com/nvidia-has-sold-over-three-million-blac...
Compare that to laptops which sell in tens of millions per manufacturer: https://en.wikipedia.org/wiki/List_of_laptop_brands_and_manu...
Plus, it's way easier to collect boards for recycling from a centralized data center.
Nvidia sold ~3M blackwells in 2025: https://wccftech.com/nvidia-has-sold-over-three-million-blac...
Compare that to laptops which sell in tens of millions per manufacturer: https://en.wikipedia.org/wiki/List_of_laptop_brands_and_manu...
Plus, it's way easier to collect boards for recycling from a centralized data center.
Nvidia had $35.6 billion in data center revenue vs $2.5 billion "Gaming and AI PC" revenue in the 4th quarter of 2024 so data center stuff stuff is like 93% of it
I completely agree with a lot of your points; the whole thing is quite stupid. My only objection is that the infrastructure will probably not go unused. And if we are lucky, those uses will be better than helping teenagers cheat themselves out of a good education.
The prices are falling down. I do a lot of Machine Learning and sometimes work with large datasets. The ability to (1) put all data in VRAM and (2) have the results in hours/days instead of weeks is amazing - and in the past it wouldn't be easy for a normal researcher like me. Now I can have access to these beefy machines, do my research and publish the results without taking a loan from my bank.
One large but forgotten effect of the dotcom bubble was an excess fiber capacity, that allowed smooth growth of internet in the following 25 years---average internet speed in the US is 200 Mbps, and a significant number of households is on a gigabit uplink. I take your point that GPU hardware amortizes away faster than fiber, but that's true of all computing hardware: the average lifecycle of a server is around five years.
The models themselves, and methods and knowledge used to build and use them, are part of the "infrastructure" being built.
You're redefining infrastructure. A supply and demand model is not infrastructure. A Taylor expansion method is not infrastructure.
that's the point - learning how to train the models and run them at scale is the "infrastructure" funded by the bubble that will be useful after it pops.
Completely agree. I would ask also what “infrastructure” the dotcom bubble created?
Data centers and fiber optic connections across the world.
They’re referring to this: https://en.wikipedia.org/wiki/Dark_fibre
I wouldn't call that wasted yet. It's just latent. Someone has to invent the startup that uses dark fibre maps to figure out exactly how far away one can build a house away from civilization and still have a 1000 MB/s connection.
But the large majority of this was created after the bubble bursted, no?
All over rural New England you'll find abandoned rail lines. Many of these were used for passenger service between walkable towns. Now, Boston area commuter rail sucks big time. Towns now have commercial stips "served" by cars-only "stroads."
How well used do you think those AI data centers are going to be?
How well used do you think those AI data centers are going to be?
Ok, but what's the infrastructure that will remain after the AI bubble that can be retooled like railroads or dot com?
Maybe we'll all be able to afford secondhand H100s and finally get to see raytraced cyberpunk at 4k120
I keep saying to people - "if you have a good idea that can make use of large amounts of really really cheap GPUs to do something genuinely useful - get ready for a massive glut of spare capacity". I still haven't thought of anything, unfortunately...
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These are sort of compute-focused GPUs, right? I bet a lot of university labs would like them.
I wonder if ubiquitous, user-friendly finite elements analysis tools could become a boon for 3D printers.
I wonder if ubiquitous, user-friendly finite elements analysis tools could become a boon for 3D printers.
Hopefully they can be repurposed for something like cheap drug discovery rather than shitcoin mining.
If that ends up being the case then we could all genuinely agree that good has eventually emerged from the compute/inference infrastructure that LLMs paid for. I hope that comes to pass.
Yeah but we all know it's definitely gonna be shitcoin mining
I would love to build a render farm out of cheap decommissioned GPUs...
I hope it will bring us reliably good memory bandwidth in consumer devices, an area where many hardware vendors are skimping.
Lots of in-depth analysis, but I think the author is very clearly emotionally invested to the point that they are only drawing conclusions that justify and support their emotions. I agree that we’re in a bubble in the sense that a lot of these companies will go bankrupt, but it won’t be Google or Anthropic (unless Google makes a model that’s an order of magnitude better or order of magnitude cheaper with capability parity). Claude is simply too good at coding in well-represented languages like Python and Typescript to not pay hundreds of dollars a month for (if not thousands, subsidized by employers). These companies are racing to have the most effective agents and models right now. Once the bottleneck is clearly humans’ ability the specify the requirements and context, reducing the cost of the models will be the main competitive edge, and we’re not there yet (although even now the better you are at providing requirements and context, the more effective you are with the models). I think that once cost reduction is the target, Google will win because they have the hardware capabilities to do so.
> Claude is simply too good at coding in well-represented languages like Python and Typescript to not pay hundreds of dollars a month for (if not thousands, subsidized by employers).
I think the cost is more in thousands to cover inference. And, no, I don’t think it’s been proven out that an engineer is so much more productive to justify thousands of dollars a month cost. The models are great for greenfield projects. But a lot of engineering is iterating and maintaining an existing code base——a code base that the engineer is fluent in. So the time savings is writing code specific enough to implement a new feature vs writing a prompt specific enough that the AI can write code specific enough to implement a new feature. The difference between those two tasks is the time savings.
Say that difference is like 10%. You save 10% of your time by using AI, meaning you have 4 more hours a week than you did before. Are you going to spend 4 more hours writing code? No. Some will be spent in meetings. Some will be spent reading Hacker News. Maybe you’ll get two hours a week of additional coding time. So you’re really only increasing your output by 5%.
The so the employer gets 5% more from you if you have AI. If your salary is 10k per month, they wouldn’t pay more than $500. Per month. And you’re probably costing Anthropic >$10k in inference costs per _week_. The economics just don’t make sense.
You can sub out the numbers here and play around with the scenario. I think the cost of inference needs to drastically fall. And I don’t think that happens soon. What might happen 10 years from now is developers are given a laptop with a built-in GPU for AI inference that does much better code auto-complete using AI. That’s something an employer can pay 3k-5k for _once_ as a hardware investment. But the future of AI coding won’t be agents. It won’t be prompt-engineering. The models aren’t going to get much better. It will be simple and standard and useful but unimpressive. It’s going to feel boring. It’s going to feel boring. When it’s working, when it’s mature, when it becomes economical, it always feels boring. And that’s a good thing.
I think the cost is more in thousands to cover inference. And, no, I don’t think it’s been proven out that an engineer is so much more productive to justify thousands of dollars a month cost. The models are great for greenfield projects. But a lot of engineering is iterating and maintaining an existing code base——a code base that the engineer is fluent in. So the time savings is writing code specific enough to implement a new feature vs writing a prompt specific enough that the AI can write code specific enough to implement a new feature. The difference between those two tasks is the time savings.
Say that difference is like 10%. You save 10% of your time by using AI, meaning you have 4 more hours a week than you did before. Are you going to spend 4 more hours writing code? No. Some will be spent in meetings. Some will be spent reading Hacker News. Maybe you’ll get two hours a week of additional coding time. So you’re really only increasing your output by 5%.
The so the employer gets 5% more from you if you have AI. If your salary is 10k per month, they wouldn’t pay more than $500. Per month. And you’re probably costing Anthropic >$10k in inference costs per _week_. The economics just don’t make sense.
You can sub out the numbers here and play around with the scenario. I think the cost of inference needs to drastically fall. And I don’t think that happens soon. What might happen 10 years from now is developers are given a laptop with a built-in GPU for AI inference that does much better code auto-complete using AI. That’s something an employer can pay 3k-5k for _once_ as a hardware investment. But the future of AI coding won’t be agents. It won’t be prompt-engineering. The models aren’t going to get much better. It will be simple and standard and useful but unimpressive. It’s going to feel boring. It’s going to feel boring. When it’s working, when it’s mature, when it becomes economical, it always feels boring. And that’s a good thing.
* And you’re probably costing Anthropic >$1k in inference costs per _week_. Not >$10k. Typo.
Hard to justify the valuations of these companies based on the potential market for what is essentially an IDE plugin.
OpenAI was arguably an oom ahead at one point, and competitors caught up in about a year. So I’m not sure even an advantage like that is insurmountable. Like we saw with Anthropic, you just need a group of key researchers to leave the incumbent and start their own thing—they’ll then have a pretty good shot at catching up.
It is not clear at all if Claude will actually be profitable, are there enough people who will actually pay the subsidized costs especially if they end up being a significant fraction of an additional dev's salary.
I think there's enough people willing to pay Claude's token rates today, either via subscription or via proxy through e.g. Cursor, that they can effectively turn the "new model R&D cost" knob to whatever their financials can support, and they'll survive for as long as their models can remain competitive.
The challenge all these frontier labs have is: Their existing models can have high token profitability, but they have to invest everything they've got (and everything they're given) into new model R&D, because if they don't xAI will beat them, or Anthropic will beat them, or Google will. That's the nature of frontier spaces like this.
But the flipside is, model capability will plateau, they probably are already, and as that happens it becomes safer to aim for profitability. And I have zero doubt that OpenAI and Anthropic can find profitability. xAI, Perplexity, Mistral, and the other labs, I'm less sure about.
The challenge all these frontier labs have is: Their existing models can have high token profitability, but they have to invest everything they've got (and everything they're given) into new model R&D, because if they don't xAI will beat them, or Anthropic will beat them, or Google will. That's the nature of frontier spaces like this.
But the flipside is, model capability will plateau, they probably are already, and as that happens it becomes safer to aim for profitability. And I have zero doubt that OpenAI and Anthropic can find profitability. xAI, Perplexity, Mistral, and the other labs, I'm less sure about.
I think the author's take is overly bleak. Yes, he supports his claim that AI businesses are currently money pits and unsustainable. But I don't think it's reasonable to claim that AI can't be profitable. This whole thing is moving so extremely fast. Models are getting better by the month. Cost is rapidly coming down. We broadly speaking still don't know how to apply AI.
I think it's hubris to claim that, in the wake of this whole bubble noone will figure out how to use AI to provide value and noone will be profitable.
"Cost is rapidly coming down" but capital expenditures are still high. They'll have to charge for this eventually, no?
Not necessarily. The ppl and firms making the capital expenditures can go bankrupt for instance. The world will carry on without them, while the infrastructure they built with those expenditures continues to provide value, just to someone else, and now at a dramatically lower capital cost.
We could compare it to the railroad boom, and the telecom boom - in both cases vast sums capital expenditures were made, and reasonable people might have concluded that eventually these expenses would have to be reimbursed through higher prices. However, in both cases, many firms simply went bankrupt and all that excess infrastructure went time to serve humanity for decades at lower cost.
We could compare it to the railroad boom, and the telecom boom - in both cases vast sums capital expenditures were made, and reasonable people might have concluded that eventually these expenses would have to be reimbursed through higher prices. However, in both cases, many firms simply went bankrupt and all that excess infrastructure went time to serve humanity for decades at lower cost.
I am so, so glad you brought up what should be the obvious conclusion here. "B-but they spent all that money, how do they get it back!?" "That's the fun part, they don't."
Creative destruction is a woefully underappreciated force in capitalism. Shareholders can lose everything. Debt can be restructured or sold for pennies on the dollar. Debt can go unsold and unpaid, and the creditors can lose everything.
I think here it has to be mentioned that bankruptcy in the United States actually works very differently to bankruptcy in the European Union, where creditors have a lot more legal means at their disposal to haunt you if you try risky plays like taking on more debt to moonshot your way out of your current debt. In a funny way, a country's bankruptcy laws are their most important ones when it comes to wealth transfer.
Creative destruction is a woefully underappreciated force in capitalism. Shareholders can lose everything. Debt can be restructured or sold for pennies on the dollar. Debt can go unsold and unpaid, and the creditors can lose everything.
I think here it has to be mentioned that bankruptcy in the United States actually works very differently to bankruptcy in the European Union, where creditors have a lot more legal means at their disposal to haunt you if you try risky plays like taking on more debt to moonshot your way out of your current debt. In a funny way, a country's bankruptcy laws are their most important ones when it comes to wealth transfer.
It's also a common misconception in housing debates!
"B-but the developer always has to make the money back so rents & prices can never go down!" "That's the fun part, they don't!"
The builder/buyer/lender/landlord/etc can go bankrupt but as long as the building actually got built, it will carry on and benefit the rest of us, regardless of what happened to the ppl who paid for it to be built.
Also fun when landlords claim to be "housing providers" "No actually, the housing will still be there, even if you sell, even if you lose your shirt and get foreclosed on"
"B-but the developer always has to make the money back so rents & prices can never go down!" "That's the fun part, they don't!"
The builder/buyer/lender/landlord/etc can go bankrupt but as long as the building actually got built, it will carry on and benefit the rest of us, regardless of what happened to the ppl who paid for it to be built.
Also fun when landlords claim to be "housing providers" "No actually, the housing will still be there, even if you sell, even if you lose your shirt and get foreclosed on"
IDK, there are whole developments in FL that are just going to be bulldozed. Same thing happened in TX after the S&L crash 35 years ago.
I’m not familiar with either, but feel free to share a link.
If the buildings, I assume single family houses, were built in places that few people want to live, or places people are simply forced to live because they can’t live where they want to live, then it wouldn’t shock me if someone chose the bulldozer as the best option. I’d argue that the root cause is bad land use law that doesn’t allow construction in the places people really want to live, but that’s a whole other topic of course :)
My argument, both for AI, railroads, telecom and construction, is that if you built something tangible and useful, it can outlive your financial arrangements, and go on to serve humanity, even if you go belly up.
But for residential construction, location is everything, you could build a theoretically useful building, but in a location that few people want to voluntarily live in, far from jobs services, shops, friends, and family, etc. Ofc, you could build a railroad between two unpopular destinations, or run a fibre optic line between two places with little demand, and those would probably not outlive your finances and might be torn up or abandoned too.
If the buildings, I assume single family houses, were built in places that few people want to live, or places people are simply forced to live because they can’t live where they want to live, then it wouldn’t shock me if someone chose the bulldozer as the best option. I’d argue that the root cause is bad land use law that doesn’t allow construction in the places people really want to live, but that’s a whole other topic of course :)
My argument, both for AI, railroads, telecom and construction, is that if you built something tangible and useful, it can outlive your financial arrangements, and go on to serve humanity, even if you go belly up.
But for residential construction, location is everything, you could build a theoretically useful building, but in a location that few people want to voluntarily live in, far from jobs services, shops, friends, and family, etc. Ofc, you could build a railroad between two unpopular destinations, or run a fibre optic line between two places with little demand, and those would probably not outlive your finances and might be torn up or abandoned too.
I looked it up since I was also curious. There’s this story making the rounds right now in FL: https://youtu.be/ia1qP1DmJdg
It seems as if these are developments which didn’t get completed.
It seems as if these are developments which didn’t get completed.
“The world will carry on without them”. Sure but at the end of the day it’s not because companies can go bankrupt that debts etc magically disappear. It still impact other companies.
Not directly, no.
The impact of firms and people going bankrupt that other people making investment and lending decisions will see risk more clearly and may (for a time) be less greedy and stupid when they make capital allocation decisions.
Debts can & do magically disappear. To be clear someone paid for the lost money, but at that stage it's far too late for them to be able to do anything about it, let alone raise prices.
Here's an example: Founder A founds a startup with equity funding from B & C. Later they take loans from D & E. They spend all the money but never become profitable. None of the original investors or lenders is interested in pumping in good money after bad. They voluntarily declare bankruptcy or they default on a loan and D or E forces them into bankruptcy. Either way, whatever is left of the company's assets are sold to reimburse, in part, the loan D & E made. A, B & C got nothing.
A, B, C, D, and E, all lost real money.
But by the time this loss is crystalized, there is no way any of them can go back in time to raise prices to pay for it. It's gone and so is the company. The only thing they can do is act differently in the future.
The impact of firms and people going bankrupt that other people making investment and lending decisions will see risk more clearly and may (for a time) be less greedy and stupid when they make capital allocation decisions.
Debts can & do magically disappear. To be clear someone paid for the lost money, but at that stage it's far too late for them to be able to do anything about it, let alone raise prices.
Here's an example: Founder A founds a startup with equity funding from B & C. Later they take loans from D & E. They spend all the money but never become profitable. None of the original investors or lenders is interested in pumping in good money after bad. They voluntarily declare bankruptcy or they default on a loan and D or E forces them into bankruptcy. Either way, whatever is left of the company's assets are sold to reimburse, in part, the loan D & E made. A, B & C got nothing.
A, B, C, D, and E, all lost real money.
But by the time this loss is crystalized, there is no way any of them can go back in time to raise prices to pay for it. It's gone and so is the company. The only thing they can do is act differently in the future.
“Debts can & do magically disappear. To be clear someone paid for the lost money,” you contradict yourself.
So no it doesn’t magically disappear. A bankruptcy somewhere is a loss for others somewhere else. Even cutting dept to pennies on the dollar means lenders are losing money. Bankruptcy is not a magic trick…
So no it doesn’t magically disappear. A bankruptcy somewhere is a loss for others somewhere else. Even cutting dept to pennies on the dollar means lenders are losing money. Bankruptcy is not a magic trick…
It is not about profitability alone, but whether benefits are net positive for society over the long term.
Profitability is easy with current standards. Get the users. Make them dependent. Increase the price. Make AI mandatory. List goes on.
> Profitability is easy with current standards. Get the users. Make them dependent. Increase the price. Make AI mandatory. List goes on.
"Easy". "Just" get more users and "just" increase prices to somehow cover hundreds of billions of invested dollars and hundreds of millions of running costs.
It's that easy. I'm surprised none of the companies mentioned in the article thought of that.
"Easy". "Just" get more users and "just" increase prices to somehow cover hundreds of billions of invested dollars and hundreds of millions of running costs.
It's that easy. I'm surprised none of the companies mentioned in the article thought of that.
> I'm surprised none of the companies mentioned in the article thought of that.
I was just stating the obvious. That is what they are doing.
I was just stating the obvious. That is what they are doing.
Currently they are throwing money onto the fire. Almost all of them are not just unprofitable. They are abysmally unprofitable.
The only reason they are raising prices is to try and recoup some of the ongoing operational costs. In the end it will only be Google left standing because Google has unlimited money, and they can price dump indefinitely long, and offer most things for free.
The only reason they are raising prices is to try and recoup some of the ongoing operational costs. In the end it will only be Google left standing because Google has unlimited money, and they can price dump indefinitely long, and offer most things for free.
From a quick skim, at least 90% of the article is about profitability. The remaining 10% is mostly bragging.
We are pretty much plateauing in base model performance since gpt4. It's mostly tooling and integration now. The target is also AGI so no matter your product you will get measured on your progress towards it. With new "sota" models popping up left and right you also have no good way of user retention because the user is mostly interested in the models performance not the funny meme generator you added. looking at you openai...
"They called me bubble boy..." - some dude at Deutsche.
"They called me bubble boy..." - some dude at Deutsche.
So, how do you feel about the recent IMO stuff? Don't they cause a consistency problem for your view that we've plateaued-- to me at least, I felt we were something like two years away from this kind of thing.
Probably very expensive to run of course, probably ridiculously so, but they were able to solve really difficult maths problems.
Probably very expensive to run of course, probably ridiculously so, but they were able to solve really difficult maths problems.
> So, how do you feel about the recent IMO stuff?
It's not real, they are cheating on benchmarks. (Just like the previous many times this was announced.)
It's not real, they are cheating on benchmarks. (Just like the previous many times this was announced.)
The biological brain of the top human IMO guy runs on 20 watts. I wonder how much electricity Google used to match that performance.
>We are pretty much plateauing in base model performance since gpt4.
Reasoning models didn't even exist at the time, LLMs were struggling a lot with math at the time, now it's completely different with SOTA models, there have been massive improvements since gpt4.
Reasoning models didn't even exist at the time, LLMs were struggling a lot with math at the time, now it's completely different with SOTA models, there have been massive improvements since gpt4.
The transformer paper was published in 2017, and within 8 years (less so, if i'm being honest), we have bots that passed the Turing test. To people with shorter term memories, passing the turing test was a big deal.
My point is that even if things are pleatuing, a lot of these advancements are done in step change fashion. All it takes is one or two good insights to make massive leaps, and just because things are plateauing now, it's a bad predictor for how things will be in the future.
My point is that even if things are pleatuing, a lot of these advancements are done in step change fashion. All it takes is one or two good insights to make massive leaps, and just because things are plateauing now, it's a bad predictor for how things will be in the future.
What is noone? You sure put a lot of confidence in it
Are we in a bubble that's going to pop and take a large part of the economy with it? Almost certainly. Does it mean that the AI is a scam? Not really. After all, the Internet did not disappear after the dotcom burst, and, actually, almost everything we were promised by the dotcoms became reality at some point.
"doing everything on the internet" definitely worked out, but I don't see why that implies "GPU accelerated LLMs will replace large swathes of human labor" will also be true
I disagree on the “doing everything on the internet”. Social network is something we definitely “do” on the internet nowadays but I wouldn’t say that this “worked out”, and we’re only now starting to grasp how badly it’s working.
But agreed on the overall meaning of the comment, LLMs promises are still exaggerated.
But agreed on the overall meaning of the comment, LLMs promises are still exaggerated.
That's not what I'm saying. What dotcoms prove is that some technology can be a bubble and a real technological revolution at the same time, there is no contradiction here. "AI is a bubble and I probably shouldn't invest all my savings in NVDA" is a valid point, "AI is a bubble and therefore stupid and will never work" is not
If there's anything that can be reliably predicated to be true over multiple decades it's that capitalism will continually seek to reduce labour costs and automate everything.
You can bet that even if the specific forms attempted in this interval don't take hold, they will eventually.
You and I are too expensive, and have had too much power.
You can bet that even if the specific forms attempted in this interval don't take hold, they will eventually.
You and I are too expensive, and have had too much power.
> If there's anything that can be reliably predicated to be true over multiple decades it's that capitalism will continually seek to reduce labour costs and automate everything.
what about improved life quality? what about an explosion of types of jobs?
> You and I are too expensive, and have had too much power.
do you think the average citizen (or the collective) have MORE power or LESS power than 100 years ago, than 200 years ago?
what about improved life quality? what about an explosion of types of jobs?
> You and I are too expensive, and have had too much power.
do you think the average citizen (or the collective) have MORE power or LESS power than 100 years ago, than 200 years ago?
Than 100 years ago? Very much might be less, honestly, given modern police, media, and surveillance power and the decimation of labor unions since then.
What about productivity gains going to the top 1%? What about income inequality? Capitalism is not labor friendly in the slightest.
I never said anything about life quality, only that automation is an inevitability.
What I said vs what you imagined I said are two different things.
What I said vs what you imagined I said are two different things.
Fair enough. Maybe I read too much between the lines. Apologies.
Worth noting that the essay acknowledges that there are ways that people use this stuff and actually like it. Saying it's a scam is about those uses being orders of magnitude less valuable than the companies involved (and credulous media) claim, and even orders of magnitude less than the amount of money that they are actively investing in this stuff. Saying it's a bubble is not a claim that it will go away entirely and never be seen again, it's a claim that reality will eventually manifest and result in massive upheaval as companies go bankrupt, valuations plummet, and associated downstream effects.
See, the problem when making predictions is that the timeframe is effectively the prediction. I don't know what will happen. When I saw GPT-3 I thought it was hot garbage and never took it seriously. As a result I now have large error bars about what the future holds.
What we got from the Internet was some version of the original promises, on a significantly longer timescale, mostly enabled by technology that didn't exist at the time those promises were made. "Directionally correct" is a euphemism for "wrong".
What we got from the Internet was some version of the original promises, on a significantly longer timescale, mostly enabled by technology that didn't exist at the time those promises were made. "Directionally correct" is a euphemism for "wrong".
> almost everything we were promised by the dotcoms became reality at some point.
remember the blockchain bubble? used much blockchain lately? are blockchains changing anything?
I'm not sure most people took the blockchain hype seriously, and the basic number goes up feature of bitcoin continues.
> Outside of OpenAI, Anthropic and Anysphere (which makes AI coding app Cursor), there are no Large Language Model companies — either building models or services on top of others' models — that make more than $500 million in annualized revenue (meaning month x 12), and outside of Midjourney ($200m ARR) and Ironclad ($150m ARR), according to The Information's Generative AI database, and Perplexity (which just announced it’s at $150m ARR), there are only twelve generative AI-powered companies making $100 million annualized (or $8.3 million a month) in revenue. Though the database doesn't have Replit (which recently announced it hit $100 million in annualized revenue), I've included it in my calculations for the sake of fairness.
I think this is among the most unhinged paragraphs I've ever read in my entire life. It deeply, metaphysically, struggles to frame what its presenting in a bad light, but the data is so overwhelmingly positive that it just can't do it.
"Ugh, there's only twelve companies basically none of which existed two years ago making over a hundred million dollars in revenue. What a failure of an industry. And only three of them are making a half a billion? What utter failures. See, no one is using any of this stuff!!"
I think this is among the most unhinged paragraphs I've ever read in my entire life. It deeply, metaphysically, struggles to frame what its presenting in a bad light, but the data is so overwhelmingly positive that it just can't do it.
"Ugh, there's only twelve companies basically none of which existed two years ago making over a hundred million dollars in revenue. What a failure of an industry. And only three of them are making a half a billion? What utter failures. See, no one is using any of this stuff!!"
Keep up the good work, but this could be said with more strength and in far fewer words by removing the indulgent rambling.
The bubble will pop, just like the web bubble popped; and that’s going to suck. AI technologies will remain and be genuinely transformative, just like the web remained and was transformative (for good and ill).
It's a source of constant amusement to me that "arguments" used for AI are indistinguishable from "arguments" used for crypto.
(With a caveat that LLMs actually do have their uses)
(With a caveat that LLMs actually do have their uses)
I think they are somewhat distinguishable. Going by the HN consensus most people thought cryptocurrency was not much use beyond crime and gambling. On the other hand I think most people see that if AI achieves human level thinking it'll be able to do human like jobs which would be a big deal economically.
That's a huge if that you can only accept if you buy into "AGI has been achieved internally" marketing.
I'm thinking more that AGI will happen within a few years. It's one of the reasons for the present financial weirdness. Throwing so much money into current AI would make no sense if it stays as it is. It only makes sense if you think the tech will improve.
The nasdaq composite p/e at its peak during the dotcom bubble breached 200.
Today we're at 40, and Nvidia alone is at 49.
As much as everyone wants this to be a bubble: it isn't. ChatGPT was the fastest "thing" in history to reach 100M MAUs, and is believed to be a top 5 most visited website today, across the entire internet. Cursor was the fastest company in human history to reach $500M in revenue. Midjourney, the company no one talks about anymore, is profitable and makes over $200M in revenue.
Being brutal here: HackerNews is in the bubble. Yeah, there's some froth, there's some overvaluation, some of these companies will die. But I seriously do not understand how people can see these real, hard statistics, not fake money like VC dollars or the price of bitcoin but fucking deep and real shit and still say "nah its like crypto all over again".
48% of survey respondents to a recent survey said they've used ChatGPT for therapy [1]. FORTY EIGHT PERCENT. There is no technology humanity has ever invented that has seen genpop uptake this quickly, and its not dropping. This is not "oh, well, the internet will be popular soon, throw money at it people will eventually come". This is: "we physically cannot buy enough GPUs to satisfy demand, our services keep going down every week because so many people want to pay for this".
[1] https://sentio.org/ai-blog/ai-survey
Today we're at 40, and Nvidia alone is at 49.
As much as everyone wants this to be a bubble: it isn't. ChatGPT was the fastest "thing" in history to reach 100M MAUs, and is believed to be a top 5 most visited website today, across the entire internet. Cursor was the fastest company in human history to reach $500M in revenue. Midjourney, the company no one talks about anymore, is profitable and makes over $200M in revenue.
Being brutal here: HackerNews is in the bubble. Yeah, there's some froth, there's some overvaluation, some of these companies will die. But I seriously do not understand how people can see these real, hard statistics, not fake money like VC dollars or the price of bitcoin but fucking deep and real shit and still say "nah its like crypto all over again".
48% of survey respondents to a recent survey said they've used ChatGPT for therapy [1]. FORTY EIGHT PERCENT. There is no technology humanity has ever invented that has seen genpop uptake this quickly, and its not dropping. This is not "oh, well, the internet will be popular soon, throw money at it people will eventually come". This is: "we physically cannot buy enough GPUs to satisfy demand, our services keep going down every week because so many people want to pay for this".
[1] https://sentio.org/ai-blog/ai-survey
S&P 500 P/E is 30 and historically used to be much lower. Some of it can be surely attributed to increasing inequality and increased wealth of the hyperwealthy who have no other option than to store their money in stocks absent a hypergrowth market, even at lower expected profit. But "growth" can only so long serve as an argument to justify a 40-50 P/E vs. a 25-30 as in other parts of the stock market. If that growth stalls, there is a lot of room to fall, even if the companies will still be profitable and won't go under. Ed at no point claims that Nvidia, Microsoft or Google would cease to exist as companies, as they can of course be very profitable in smaller markets than those that are currently being priced in and without AI contributing to revenue. But companies that purely rely on AI for revenue will have a hard time to ever turn a profit and there is not a single example to proof otherwise. And if that happens, you might also see -20%, -30% or more on some of these larger players that will survive.
>we physically cannot buy enough GPUs to satisfy demand
is caused by mispricing - VC money is used to pay for the GPUs but the product is mostly given away for free. If companies just charged the public what the service cost to provide usage would go down dramatically.
is caused by mispricing - VC money is used to pay for the GPUs but the product is mostly given away for free. If companies just charged the public what the service cost to provide usage would go down dramatically.
If they had to actually fund R&D and footprint expansion with customer money: Absolutely yes. But, that's exactly what VC money is for. If VC money can cover expansion while customer money can cover per-token incremental costs, eventually they won't need to expand quite as much, and then profitability should catch up.
In other words: How literally every tech business that has ever worked has worked. This isn't news. This isn't novel. This is just how it works.
If you want me to feel fright that the world is coming to an end, wake me up when Google (one of the world's largest frontier AI labs by any measure) isn't posting $35B in profit on a 39% gross margin every quarter, or when Meta (who is reported to be paying AI researchers nine figure comp packages) isn't making $16B on 40%. The amount of money these companies make is disgusting, its so disgusting that they can blow a hundred billion on GPUs and key people, they could write it all to zero two years later, and its all a teeny tiny little blip on their graphs, it becomes the third line-item in their quarterly board meetings, behind far more important stuff. The reason why some of you get so freaked out is because you literally cannot comprehend the scale these companies operate at, and how financialized their operations are.
In other words: How literally every tech business that has ever worked has worked. This isn't news. This isn't novel. This is just how it works.
If you want me to feel fright that the world is coming to an end, wake me up when Google (one of the world's largest frontier AI labs by any measure) isn't posting $35B in profit on a 39% gross margin every quarter, or when Meta (who is reported to be paying AI researchers nine figure comp packages) isn't making $16B on 40%. The amount of money these companies make is disgusting, its so disgusting that they can blow a hundred billion on GPUs and key people, they could write it all to zero two years later, and its all a teeny tiny little blip on their graphs, it becomes the third line-item in their quarterly board meetings, behind far more important stuff. The reason why some of you get so freaked out is because you literally cannot comprehend the scale these companies operate at, and how financialized their operations are.
> But I seriously do not understand how people can see these real, hard statistics, not fake money like VC dollars
You're commenting this under an article showing how deeply unprofitable most of "AI" companies are. Revenue isn't profit. Midjourney is probably the only company that is profitable.
> 48% of survey respondents to a recent survey said they've used ChatGPT for therapy [1]. FORTY EIGHT PERCENT.
No idea why you provide this frankly scary statistic. It's not proof that this isn't a bubble.
> This is: "we physically cannot buy enough GPUs to satisfy demand, our services keep going down every week because so many people want to pay for this".
It's called a mania. Manias always end.
You're commenting this under an article showing how deeply unprofitable most of "AI" companies are. Revenue isn't profit. Midjourney is probably the only company that is profitable.
> 48% of survey respondents to a recent survey said they've used ChatGPT for therapy [1]. FORTY EIGHT PERCENT.
No idea why you provide this frankly scary statistic. It's not proof that this isn't a bubble.
> This is: "we physically cannot buy enough GPUs to satisfy demand, our services keep going down every week because so many people want to pay for this".
It's called a mania. Manias always end.
The irony is that I asked ChatGPT to make a summary in french. However, i'm tired of the AI bubble and seeing half of my twitter feed filled w AI announcements and threads
Reddit and LinkedIn especially has become a cesspool of generated content, thankfully its pretty easy to spot and block
It’s not bad for summarizing or translating.
I like categorize AI outputs by prompt + context input information size vs output information size.
Summaries: output < input. It’s pretty good at this for most low-to-medium stakes tasks.
Translate: output ≈ input but in different format/language. It’s decent at this, but requires more checking.
Generative expansion: output > input. This is where the danger is. Like asking for a cheeseburger and it infers a sesame seed bun because that matches its model of a cheeseburger. Generally that’s fine. Unless you’re deathly allergic to sesame seeds. Then it’s a big problem. So you have to be careful in these cases. And, at best, the anything inferred beyond the input is average by definition. Hence AI slop.
I like categorize AI outputs by prompt + context input information size vs output information size.
Summaries: output < input. It’s pretty good at this for most low-to-medium stakes tasks.
Translate: output ≈ input but in different format/language. It’s decent at this, but requires more checking.
Generative expansion: output > input. This is where the danger is. Like asking for a cheeseburger and it infers a sesame seed bun because that matches its model of a cheeseburger. Generally that’s fine. Unless you’re deathly allergic to sesame seeds. Then it’s a big problem. So you have to be careful in these cases. And, at best, the anything inferred beyond the input is average by definition. Hence AI slop.
Hey, it's the guy who has been predicting the imminent collapse of AI for three years now! As I understand, he's a former video game journalist and being anti-AI is now his full-time thing. Saying it's all useless, fake, evil, etc.
A poor man's Gary Marcus, basically.
A poor man's Gary Marcus, basically.
So being a former video game journalist, makes all his arguments void.
Thank you for your imput!
Thank you for your imput!
Pretty much. What else would you expect from a non-technical activist who's railing against science/tech? What you see is what you get.
I hope Ed Zitron will keep saying that the biggest AI companies in the world are not making any profit for as long as the biggest AI companies in the world are not making any profit.
I find that fact alone a bit alarming.
You don't need to be a technical expert to understand that it's worrying how the entire media industry is pushing for everyone, everywhere, all of the time, to lean on a tech where the biggest providers are not profitable.
You also don't need to be a technical expert to see how much of a failure it is for the entire media industry to interview Sam Altman, let him spew out utter gibberish, and not even question him on it.
I find that fact alone a bit alarming.
You don't need to be a technical expert to understand that it's worrying how the entire media industry is pushing for everyone, everywhere, all of the time, to lean on a tech where the biggest providers are not profitable.
You also don't need to be a technical expert to see how much of a failure it is for the entire media industry to interview Sam Altman, let him spew out utter gibberish, and not even question him on it.
Not just Altman, but also Amodei, Pichai and Nadella. These guys are desperate now.
>biggest AI companies in the world are not making any profit
Companies that are exploding in popularity and expanding as fast as possible are not expected to make a profit. This is not unusual in the slightest.
>the entire media industry is pushing for everyone, everywhere, all of the time
No, people use AI because they want to use AI. New users arrive on their own. If you take a closer look at what the legacy media is actually saying, they tend to have a negative slant against AI. Yet people still show up. And will continue to show up.
>Sam Altman, let him spew out utter gibberish, and not even question him on it
If Altman is pissing you and Ed off, he's doing at least something right. That said, I follow AI news every single day and I barely even glance at what Altman is saying. Here lies one of the biggest follies of the anti-AI crowd. Zitron et al. think that they can make AI go away by canceling Altman.
Companies that are exploding in popularity and expanding as fast as possible are not expected to make a profit. This is not unusual in the slightest.
>the entire media industry is pushing for everyone, everywhere, all of the time
No, people use AI because they want to use AI. New users arrive on their own. If you take a closer look at what the legacy media is actually saying, they tend to have a negative slant against AI. Yet people still show up. And will continue to show up.
>Sam Altman, let him spew out utter gibberish, and not even question him on it
If Altman is pissing you and Ed off, he's doing at least something right. That said, I follow AI news every single day and I barely even glance at what Altman is saying. Here lies one of the biggest follies of the anti-AI crowd. Zitron et al. think that they can make AI go away by canceling Altman.
Dunno if I need to comment on the first point since "will AI be able to Uber itself" is talked to death by zitron, and if we're disagreeing on their ability to turn a profit later on, then talking it out isn't gonna change much.
>>the entire media industry is pushing for everyone, everywhere, all of the time
>No
Yes it is. You can't just "no" this. People aren't just "happily using AI and getting bothered by the EVIL AI haters!!!". The push for AI is literally made of threats "You WILL get left behind", "people WILL replace you". Even if it is undeniably disruptive, you can't not call that pressure on a very large scale.
Just because I ask copilot once every few days to write me a piece of boilerplate doesn't invalidate the fact that my workplace has been made to believe prompting is an "essential skill" that must be enforced via mandatory e-learnings.
>think that they can make AI go away by canceling Altman.
I didn't say that and I don't think that. That last paragraph's basically bait.
>>the entire media industry is pushing for everyone, everywhere, all of the time
>No
Yes it is. You can't just "no" this. People aren't just "happily using AI and getting bothered by the EVIL AI haters!!!". The push for AI is literally made of threats "You WILL get left behind", "people WILL replace you". Even if it is undeniably disruptive, you can't not call that pressure on a very large scale.
Just because I ask copilot once every few days to write me a piece of boilerplate doesn't invalidate the fact that my workplace has been made to believe prompting is an "essential skill" that must be enforced via mandatory e-learnings.
>think that they can make AI go away by canceling Altman.
I didn't say that and I don't think that. That last paragraph's basically bait.
>is talked to death by zitron
Yes, Zitron talks all the time. Loudly and insultingly. Here's a suggestion: he should put his money where his mouth is. If he honestly thinks the market for AI is actually 100x smaller than reported and ready to collapse any moment now, he could easily bet against the market. He could earn millions and look like a genius. Make it all public, let his fans join too. You could join. Why not? He supposedly knows the hard truths, he brought the receipts, etc.
But he won't. That's too real. Endlessly prophesying the end of AI is more lucrative.
>You can't just "no" this.
Fundamentally my argument is that the media has little say in this. Take all the new releases this week. Another ~200 million new users will shuffle their way in. Not through fear or fervor, just basic curiosity and a need for getting things done. Models will continue to get better and this trend will continue. It's just how it is.
>I didn't say that and I don't think that. That last paragraph's basically bait.
In any case, I'll leave the Sam Altman discussion to you and Ed.
Yes, Zitron talks all the time. Loudly and insultingly. Here's a suggestion: he should put his money where his mouth is. If he honestly thinks the market for AI is actually 100x smaller than reported and ready to collapse any moment now, he could easily bet against the market. He could earn millions and look like a genius. Make it all public, let his fans join too. You could join. Why not? He supposedly knows the hard truths, he brought the receipts, etc.
But he won't. That's too real. Endlessly prophesying the end of AI is more lucrative.
>You can't just "no" this.
Fundamentally my argument is that the media has little say in this. Take all the new releases this week. Another ~200 million new users will shuffle their way in. Not through fear or fervor, just basic curiosity and a need for getting things done. Models will continue to get better and this trend will continue. It's just how it is.
>I didn't say that and I don't think that. That last paragraph's basically bait.
In any case, I'll leave the Sam Altman discussion to you and Ed.
That's a nice suggestion but also an overdone one. I've seen this "epic own" as much as you've probably seen anti-ai "epic owns" (I need to spend less time looking at AI discourse). It's yet another taunt but I'll bite anyways, shorting has no lower bound, it's risky as hell. I don't wanna be a trader.
Back to the media thing. I still don't fully agree that media coverage has absolutely no influence on the success of a technology or that the best solution always wins (which, correct me if I'm wrong, is the argument you're making). If my fundamental argument is "it has an impact" and yours is "it doesn't" then we've reached a stalemate and can drop that topic too.
Have a good day, it hasn't been nice talking with you, but it's helped me realize that I should care less when I have no stake in it.
Back to the media thing. I still don't fully agree that media coverage has absolutely no influence on the success of a technology or that the best solution always wins (which, correct me if I'm wrong, is the argument you're making). If my fundamental argument is "it has an impact" and yours is "it doesn't" then we've reached a stalemate and can drop that topic too.
Have a good day, it hasn't been nice talking with you, but it's helped me realize that I should care less when I have no stake in it.
Yes but the main thrust of his criticism is the fragility of the whole 'AI' stack, where every part of the stack is unprofitable, unstable and inter-reliant - if any one of those parts goes tits up - everybody else goes with them.
Also, a lot of AI 'users' arrive, search for an actual use case, find none, and then move on.
Also, a lot of AI 'users' arrive, search for an actual use case, find none, and then move on.
What's clear is that the hype has reach such a critical mass that people are comfortable enough to publicly and shamelessly extrapolate extraordinary claims based purely on gut feeling. Both here on HN and by complete laymen elsewhere.
AI-optimist or not, that's just shocking to me.
AI-optimist or not, that's just shocking to me.
A while back I saw a comment here that was basically like, we don't need to worry about copyrights because they won't matter once LLMs are able to create an OS from scratch for the cost of tokens. Which is just a batshit insane not thought through stance to have.
But then I think about the real actual planning decisions that were made based on the claims about driving cars and Hyperloop being available "soon" that made people materially worse off due to differed or canceled public transportation infrastructure.
But then I think about the real actual planning decisions that were made based on the claims about driving cars and Hyperloop being available "soon" that made people materially worse off due to differed or canceled public transportation infrastructure.
> people are comfortable enough to publicly and shamelessly extrapolate extraordinary claims based purely on gut feeling
What's the problem with that? Why shouldn't people feel comfortable sharing their vision of the future, even if it's just a "gut feeling" vision? We're not going to run out of ink.
What's the problem with that? Why shouldn't people feel comfortable sharing their vision of the future, even if it's just a "gut feeling" vision? We're not going to run out of ink.
I guess I expect higher standards than the kind of confident extrapolation you find in pseudo-science. And "vision of the future" is your euphemistic rewrite.
If that's clearly stated I obviously have no problem with people's fanciful speculation. But these are claims in the format: "X will be replaced in a couple of years, how should we adapt as a society?" etc etc.
Although if there is some small possibility that X will be replaced in a couple of years shouldn't people be able to consider it?
Small possibility some natural disaster ends all life on earth in two years. Large possibility it will within decades
But hey more fun to pretend the chatbot will turn into Terminator
But hey more fun to pretend the chatbot will turn into Terminator
Well... whether the machines take over or just give us a common enemy, at this stage, the rise of Skynet seems to me like the highest likelihood path for global coordination.
Well, since it's hard to prove a negative, that then applies to basically everything. Look, people can "consider" whatever they want to. But I'll certainly complain about the normalization of making sloppy extraordinary claims on gut feeling. It's like some /r/wallstreetbets user trying to predict the future by the shapes & patterns of stock graphs. It's intellectually lazy and suggests lacking critical thinking.
I don’t doubt this but it might help to include some examples if you have any close at hand.
From my perspective it's basically in every other comments section of AI related articles. Here's a particularly spicy one from today: https://news.ycombinator.com/item?id=44646797
This is quite refreshing to read, while I would classify myself more in the group of “optimists”, I do believe there is a severe lack of skepticism, and those that share negative or more conservative views are indeed held to different standard to those who paint themselves as "optimists". Unlike other trends before, the wave of grifters in the AI space is atounding, anything can be “AI-powered” as long as its a wrapper/ chatbot
The analysis is just bogus. He is basically comparing two years of inflated AI capex estimates to a low-ball estimate of one year of trailing revenue.
Let's unpack that a bit.
Capex is spending on capital goods, with the spending being depreciated over the expected lifetime of the good. You can't compare a year of capex to a year of revenue: a truck doesn't need to pay for itself in year 1, it needs to pay for itself over 10 or 20 years. The projected lifetime of datacenter hardware bought today is probably something like 5-7 years (changes to the depreciation schedule are often flagged in earnings releases, so that's a good source for hard data). The projected lifetime of a new datacenter building is substantially longer than that.
Somehow Zitron manages to not make a comparison that's even more invalid than comparing one year of Capex to one year of revenue: he basically ends up comparing a year of revenue to two years of Capex. So now the truck needs to pay for itself in six months.
They way you'd need to think about this is to for example consider what the return on the capital goods bought in 2024 was in 2025. But that's not what's happening here. Instead the article is basically expecting a GPU that's to be paid for and installed in late 2025 to produce revenue in early 2025. That's not going to happen. In a stable state, this would not matter so much. But this is not a stable state. Both capex and revenue are growing rapidly, and revenue will lag behind.
What about the capex being inflated and the revenue being low-balled?
None of us really know for sure how much of the capex spending is on things one might call AI. But the pre-AI capex baseline of these companies was tens of billions each. Probably some non-AI projects no longer happen so that the companies can plow more money into AI capex, but it absolutely won't be all of it like the article assumes. As another example, why in the world is Tesla being included in the capex numbers? It's just blatant and desperate padding of the numbers.
As for the revenue, this is mostly analyst estimates rather than hard data (with the exception of Microsoft, though Zitron is misrepresenting the meaning of run rate). Given what he has to say about analysts elsewhere, seems odd to trust them here. But more importantly, they are analyst estimates of a subset of the revenue that GPUs/TPUs would produce. What happens when Amazon buys a GPU? Some of those GPUs will be used internally. Some of them will be used to provide genai API services. Some might be used to provide end-user AI proucts. And some of them will be rented out as GPUs. Only the two middle ones would be considered AI revenue.
I don't know what the fair and comparable numbers would be, am not aware of a trustworthy public source, and won't even try to guess at them. But when we don't know what the real numbers are, the one thing we should not do is use obviously invalid ones and present them as facts.
> I am only writing with this aggressive tone because, for the best part of two years,
Zitron's entire griftluencer schtick has always been writing aggressive and often obscenity-laden diatribes. Anyway, please don't forget to subscribe for just $7/month, and remember that he just loves to write and has no motive for clickbait or stirring up some outrage.
Let's unpack that a bit.
Capex is spending on capital goods, with the spending being depreciated over the expected lifetime of the good. You can't compare a year of capex to a year of revenue: a truck doesn't need to pay for itself in year 1, it needs to pay for itself over 10 or 20 years. The projected lifetime of datacenter hardware bought today is probably something like 5-7 years (changes to the depreciation schedule are often flagged in earnings releases, so that's a good source for hard data). The projected lifetime of a new datacenter building is substantially longer than that.
Somehow Zitron manages to not make a comparison that's even more invalid than comparing one year of Capex to one year of revenue: he basically ends up comparing a year of revenue to two years of Capex. So now the truck needs to pay for itself in six months.
They way you'd need to think about this is to for example consider what the return on the capital goods bought in 2024 was in 2025. But that's not what's happening here. Instead the article is basically expecting a GPU that's to be paid for and installed in late 2025 to produce revenue in early 2025. That's not going to happen. In a stable state, this would not matter so much. But this is not a stable state. Both capex and revenue are growing rapidly, and revenue will lag behind.
What about the capex being inflated and the revenue being low-balled?
None of us really know for sure how much of the capex spending is on things one might call AI. But the pre-AI capex baseline of these companies was tens of billions each. Probably some non-AI projects no longer happen so that the companies can plow more money into AI capex, but it absolutely won't be all of it like the article assumes. As another example, why in the world is Tesla being included in the capex numbers? It's just blatant and desperate padding of the numbers.
As for the revenue, this is mostly analyst estimates rather than hard data (with the exception of Microsoft, though Zitron is misrepresenting the meaning of run rate). Given what he has to say about analysts elsewhere, seems odd to trust them here. But more importantly, they are analyst estimates of a subset of the revenue that GPUs/TPUs would produce. What happens when Amazon buys a GPU? Some of those GPUs will be used internally. Some of them will be used to provide genai API services. Some might be used to provide end-user AI proucts. And some of them will be rented out as GPUs. Only the two middle ones would be considered AI revenue.
I don't know what the fair and comparable numbers would be, am not aware of a trustworthy public source, and won't even try to guess at them. But when we don't know what the real numbers are, the one thing we should not do is use obviously invalid ones and present them as facts.
> I am only writing with this aggressive tone because, for the best part of two years,
Zitron's entire griftluencer schtick has always been writing aggressive and often obscenity-laden diatribes. Anyway, please don't forget to subscribe for just $7/month, and remember that he just loves to write and has no motive for clickbait or stirring up some outrage.
> he basically ends up comparing a year of revenue to two years of Capex
He appears to only be doing that for the seven companies cumulatively, and in each company's case is only comparing year with year.
> Both capex and revenue are growing rapidly, and revenue will lag behind.
Even if his capex estimates are inflated, unless they're off by magntitudes, isn't the ratio between the two figures still alarming? What was, say, Amazon's initial capex for AWS compared to the revenue? Or in any other cases where long-term investment bore fruit?
> What happens when Amazon buys a GPU?
What else are they using GPUs for? Luna cloud gaming? Crypto mining?
He appears to only be doing that for the seven companies cumulatively, and in each company's case is only comparing year with year.
> Both capex and revenue are growing rapidly, and revenue will lag behind.
Even if his capex estimates are inflated, unless they're off by magntitudes, isn't the ratio between the two figures still alarming? What was, say, Amazon's initial capex for AWS compared to the revenue? Or in any other cases where long-term investment bore fruit?
> What happens when Amazon buys a GPU?
What else are they using GPUs for? Luna cloud gaming? Crypto mining?
Jeff wants 8K RTX on all his screens
> Let's unpack that a bit.
I’m not accusing you of anything, just giving the feedback that this line makes your post sound like it is AI slop. This is an extremely typical phrase when you prompt any current AI with some variation of “explain this post”. Honestly, the verbosity of the rest of your post also reinforces this signal. The typo here also indicates cutting and pasting things together “Given what he has to say about . But more importantly,”
If it is not AI slop, then hopefully you can use this feedback for future writing.
I’m not accusing you of anything, just giving the feedback that this line makes your post sound like it is AI slop. This is an extremely typical phrase when you prompt any current AI with some variation of “explain this post”. Honestly, the verbosity of the rest of your post also reinforces this signal. The typo here also indicates cutting and pasting things together “Given what he has to say about . But more importantly,”
If it is not AI slop, then hopefully you can use this feedback for future writing.
All written by hand, and you can check the style against thousands of posts I've made on HN over almost two decades.
(I explained the issue briefly in the first paragraph, but the article is 15k words. Hard to convincingly rebut that without the details.)
But thanks for the superficial style feedback and ignoring the substance.
(I explained the issue briefly in the first paragraph, but the article is 15k words. Hard to convincingly rebut that without the details.)
But thanks for the superficial style feedback and ignoring the substance.
Please don't do this here, it's easy enough for you to see their comment history. Believe it or not, people still care about writing comments on HN in an informative and engaging manner.
Thanks, Ed Zitron.
This article is to me like a glass of ice water to somebody in hell.
SoftBank is also more cautious and the "$500 billion" Stargate project that was hyped in the White House will just build a single data center by the end of 2025:
https://www.wsj.com/tech/ai/softbank-openai-a3dc57b4
https://www.wsj.com/tech/ai/softbank-openai-a3dc57b4
Best rant I have read in such a long time. Subscribed despite the fact that I am all-in on AI for coding (plus much more) and disagree completely with the author's point of view.
With current LLMs, my productivity is increased by at least 50%. This will only get better over time as efficiency is gained and hardware gets cheaper.
How are you measuring your productivity? There are studies [0][1] that indicate it's common for people to self-assess that their productivity using LLMs increased by 20-40%, when in fact it decreased based on objective, controlled measures.
[0] https://storage.googleapis.com/gweb-research2023-media/pubto...
[1] https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
[0] https://storage.googleapis.com/gweb-research2023-media/pubto...
[1] https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
who you going to believe? random studies or your own lying eyes?
You say “random studies” like you’re trying to discredit them in some way. But they’re not “random”, they’re controlled, documented, objective and published for you to go and read the results.
damn he doesn't say when the shorts should start
Shorting markets is kind of a specialised skill. Judging from the dot com crash you want to wait till there's a substantial drop and people start talking about a crash, then it'll bounce back about half the way and that's when.
Markets can stay irrational longer than you can stay solvent
make a technology very affordable, get people hooked. Then when LLM have basically destroyed the open web, charge more for accessing and searching that wealth of human created knowledge. Profit $$$
Ethical approach? hell no. What do you expect from an unregulated capitalistic system.
Ethical approach? hell no. What do you expect from an unregulated capitalistic system.
> What do you expect from an unregulated capitalistic system.
Competition, fortunately
Competition, fortunately
> Competition, fortunately
so there's no competition when there are no rules and regulations... ? interesting.
all those sports without rules or regulations, like american football where anything goes.
so there's no competition when there are no rules and regulations... ? interesting.
all those sports without rules or regulations, like american football where anything goes.
huh? I'm saying adding rules and regulations reduces competition yes, by definition it adds barriers to entry. We can argue how high those barriers ought to be.
Highly regulated industries: healthcare, banking, aviation
Less regulated industries: web software, e-commerce, entertainment
It is easier for startups to get started in the latter, harder in the former.
Highly regulated industries: healthcare, banking, aviation
Less regulated industries: web software, e-commerce, entertainment
It is easier for startups to get started in the latter, harder in the former.
These sound very much in tone like the criticisms of Web 1.0
AI/LLMs are an infant technology, it’s at the beginning.
It took many many years until people figured out how to use the internet for more than just copying corporate brochures into HTML.
I put it to you that the truly valuable applications of AI/LLMs are yet to be invented and will be truly surprising when they come (which they must of course otherwise we’d invent them now).
Amdahl says we tend to overestimate the value of a new technology in the short term and underestimate it in the long term. We’re in the overestimate phase right now.
So I’d say ignore the noise about AI/LLMs now - the deep innovations are coming.
AI/LLMs are an infant technology, it’s at the beginning.
It took many many years until people figured out how to use the internet for more than just copying corporate brochures into HTML.
I put it to you that the truly valuable applications of AI/LLMs are yet to be invented and will be truly surprising when they come (which they must of course otherwise we’d invent them now).
Amdahl says we tend to overestimate the value of a new technology in the short term and underestimate it in the long term. We’re in the overestimate phase right now.
So I’d say ignore the noise about AI/LLMs now - the deep innovations are coming.
> It took many many years until people figured out how to use the internet for more than just copying corporate brochures into HTML.
It was immediately clear for many people how it could be used to express themselves. It took a lot of years to figure out how to kill most of those parts and turn the remainder into a corporate hellscape thats barely more than corporate brochures.
It was immediately clear for many people how it could be used to express themselves. It took a lot of years to figure out how to kill most of those parts and turn the remainder into a corporate hellscape thats barely more than corporate brochures.
AI is not infant. LLMs? yes. But not AI as a whole. Conflating the two is part of the problem when deciding what is useful and profitable.
AI is to LLM
what
The Internet is to the World Wide Web
what
The Internet is to the World Wide Web
Actually Amara, not Amdahl I think.
His comments about Apple ring true to my ears. Apple is definitely lagging behind in the "AI" world, but that is really what they tend to do. They aren't the first company but they are usually the best. Historically, they wait until everyone else makes the mistakes and then introduce something better. I guess they felt like they couldn't wait for the "AI" trend to blow over; probably because Siri is just not very good.
I think that Apple will hold on to their "AI" stuff for a while longer and wait until it really dies down. Then they will introduce a much better Siri and get rid of the "summarize your email" and "re-write this sentence" bullshit.
I think that Apple will hold on to their "AI" stuff for a while longer and wait until it really dies down. Then they will introduce a much better Siri and get rid of the "summarize your email" and "re-write this sentence" bullshit.
>I have written hundreds of thousands of words with hundreds of citations, and still, to this day, there are people who claim I am somehow flawed in my analysis...
Says the PR guy who discovered AI a couple of years ago and now knows it all and that all the AI experts are wrong.
I mean it's a good rant but I don't think he gets the bigger picture.
Says the PR guy who discovered AI a couple of years ago and now knows it all and that all the AI experts are wrong.
I mean it's a good rant but I don't think he gets the bigger picture.
What a nasty dismissal, "he's not a tech guy anyways, he could never understand anything surrounding AI".
Quoting the end of the article ad verbatim:
> And remember that you, as a regular person, can understand all of this. These people want you to believe this is black magic, that you are wrong to worry about the billions wasted or question the usefulness of these tools.
You are currently being "these people".
You don't need a huge technical baggage to understand that OpenAI still operates at a loss, and that there are at the very least some risks to consider before trying to rebuild all of society on it.
I've seen many people on HN (or maybe it was also you the other times) give this same reply again and again, "what do you know? You've not made your research, and if you made research, you don't have reliable sources, and if you have reliable sources, you're not seeing the bigger picture, and if you are seeing the bigger picture, you're not a tech guy, so what do you know?"
This essentially comes back to what the article also says, you are somehow held to crazy fucking standards if you ever say anything remotely critical, and then people will come up in HN threads and say "the human brain is basically also autocomplete, so genAI will be as good as the human brain soon™" (hey, according to your reply, shouldn't people be experts in the human brain to be able to post stuff like this?)
Quoting the end of the article ad verbatim:
> And remember that you, as a regular person, can understand all of this. These people want you to believe this is black magic, that you are wrong to worry about the billions wasted or question the usefulness of these tools.
You are currently being "these people".
You don't need a huge technical baggage to understand that OpenAI still operates at a loss, and that there are at the very least some risks to consider before trying to rebuild all of society on it.
I've seen many people on HN (or maybe it was also you the other times) give this same reply again and again, "what do you know? You've not made your research, and if you made research, you don't have reliable sources, and if you have reliable sources, you're not seeing the bigger picture, and if you are seeing the bigger picture, you're not a tech guy, so what do you know?"
This essentially comes back to what the article also says, you are somehow held to crazy fucking standards if you ever say anything remotely critical, and then people will come up in HN threads and say "the human brain is basically also autocomplete, so genAI will be as good as the human brain soon™" (hey, according to your reply, shouldn't people be experts in the human brain to be able to post stuff like this?)
AI is a temporary buoy for FAANG and the tech industry to keep the financial markets happy while they switch to their next source of growth:
Military contracts.
I hope people understand the irony, but to spell it out: they need to live on government money to sustain growth.
Corporate welfare while 60% of the USA population doesn't have the money to cover a 1000$ emergency.
Military contracts.
I hope people understand the irony, but to spell it out: they need to live on government money to sustain growth.
Corporate welfare while 60% of the USA population doesn't have the money to cover a 1000$ emergency.
> Military contracts. They need to live on government money to sustain growth.
Meta makes 99% of its revenue from advertising (according to the article). Google, similarly, makes most of its money from advertising.
Tesla makes money by selling cars (there's no indication the government is going to transform their fleets to Tesla vehicles; in fact, they're openly hostile to EVs).
Apply needs to rely on US government military contracts for continued growth? What?
Amazon, the company that sells toothpaste and cloud services needs to rely on US government military contracts?
Consider me not convinced by the story you tell.
Meta makes 99% of its revenue from advertising (according to the article). Google, similarly, makes most of its money from advertising.
Tesla makes money by selling cars (there's no indication the government is going to transform their fleets to Tesla vehicles; in fact, they're openly hostile to EVs).
Apply needs to rely on US government military contracts for continued growth? What?
Amazon, the company that sells toothpaste and cloud services needs to rely on US government military contracts?
Consider me not convinced by the story you tell.
Microsoft's $22 billion (wasted) IVAS was a fiasco, now they are doubling down:
https://breakingdefense.com/2025/01/army-kickstarts-possible...
Of course it won't work. These tech companies have no clue about the real world and humans.
https://breakingdefense.com/2025/01/army-kickstarts-possible...
Of course it won't work. These tech companies have no clue about the real world and humans.
OP is speaking about the next source of growth, not existing revenue streams.
> next source of growth
How large is the US military contract market for the kinds of products and services these companies produce?
For reference, their combined 2024 revenue was around $2 Trillion.
How large is the US military contract market for the kinds of products and services these companies produce?
For reference, their combined 2024 revenue was around $2 Trillion.
Surveillance? They have an exceptionally valuable product if they package it right.
> Surveillance? They have an exceptionally valuable product if they package it right.
So valuable that it will be next main source of growth (what was claimed) for Amazon, Apple, Alphabet, Microsoft, Nvidia, Meta, and Tesla?
The US military budget is less than $1 trillion per annum. these companies had a combined revenue of $2 trillion. For military contracts to be THE new source of growth, the military budget would have to be how much larger?
So valuable that it will be next main source of growth (what was claimed) for Amazon, Apple, Alphabet, Microsoft, Nvidia, Meta, and Tesla?
The US military budget is less than $1 trillion per annum. these companies had a combined revenue of $2 trillion. For military contracts to be THE new source of growth, the military budget would have to be how much larger?
> For military contracts to be THE new source of growth, the military budget would have to be how much larger?
To be fair, it wasn't suggested that the growth would be equivalent to or surpassing of past growth, just growth of some kind. The budget doesn't necessarily have to become any larger, they just need a piece of the pie.
To be fair, it wasn't suggested that the growth would be equivalent to or surpassing of past growth, just growth of some kind. The budget doesn't necessarily have to become any larger, they just need a piece of the pie.
"...being entirely blunt, I am an AI skeptic. I think AI and LLM are somewhat interesting but a bit like self-driving cars 5 years ago - at the peak of a VC-driven hype cycle and heading for a spectacular deflation.
My main interest in technology is making innovation useful to people and as it stands I just can't conceive of a use of this which is beneficial beyond a marginal improvement in content consumption. What it does best is produce plausible content, but everything it produces needs careful checking for errors, mistakes and 'hallucinations' by someone with some level of expertise in a subject. If a factory produced widgets with the same defect rate as ChatGPT has when producing content, it would be closed down tomorrow. We already have a problem with large volumes of bad (and deceptive!) content on the internet, and something that automatically produces more of it sounds like a waking nightmare.
Add to that the (presumed, but reasonably certain) fact that common training datasets being used contain vast quantities of content lifted from original authors without permission, and we have systems producing well-crafted lies derived from the sweat of countless creators without recompense or attribution. Yuck!"
I'll be interested to see how long it takes for this "spectacular deflation" to come to pass, but having lived through 3 or so major technology bubbles in my working life, my antennae tell me that it's not far off now...