I don't know a single white collar worker who isn't using AI for their job. Not like forced, but like "Oh damn, this bot thing can do a lot of tedious leg work for me".
To think that people won't pay $60-$80/mo to continue using it is wild to me. In a white collar environment it pays for itself in a few hours of use.
If you focus on how much value AI brings to people (mostly in time saved), the bubble hardly looks bubbly.
Did Uber die when a trip across town went from $3 to $13? No. It's giving more rides than ever, 10x more than when it was $3.
I'm sure there will be some losers, but unlike the dot-com boom, the entirety of the population already has all the tools needed to leverage AI.
But the question is how much are people willing to pay for AI.
I use AI every day at work, but I only pay $20. That's the most I will ever want to pay. And, so far, it gives me everything I need.
If OpenAI or Anthropic suddenly said "Sorry, the game is up. You'll have to pay $100/month now", I would 100% look into cheaper Chinese solutions.
I suspect the AI subscription (or API) economy is whale economy. You have a small, small percentage of users that are happy to pay whatever it takes - while the vast majority will either use the free tier, and then the next group will pay for the cheapest or next cheapest subscription.
EDIT: And I'll echo what another user wrote here. The VAST majority of office users around the world don't work for tech companies flush in cash. Even adding something like $20 - $100 subscriptions to every user is a serious enough financial obligation that it needs to go through budget planning / boards. Even more so for all the government workers around the world.
The video quotes sales of $2.5 trillion a year to payoff those investments. What workforce you divide it by is a bit up in the air, but let's use 500 millions, which is basically 100% of the workforce of US + Europe + some change. That gives you around $500 a month per employee (from hedge fund manager to flipping burgers at McDonald's).
And there will be competition. I see zero moat right now. The user specific part of the state of the model sits outside of the control of the model (it is basically my code base, or my prompts, all of which I can transfer to any competitor).
But AI use translates directly to time savings (if it works). You only need 1-2 hours of time savings per employee per week to break even on a $100/seat/month subscription.
anthropic and openai are a part of the ai economy, but not the only. the most valuable company in the world is nvidia, a massive player that benefits from the existance of the ow models.
what im trying to say is that we focus a bit too much on the labs, but the biggest part is in the infra that makes everything possible.
I spent $18k in credits last month, I have 3x 20x Anthropic accounts (for Fable to bypass limits) which runs me another $600ish p/m and then a chatgpt pro account another $200 p/m. The value I got out of it is easily 1-200x what I paid.
1 to 200 is a pretty big spread to between losing 18k dollars and making 3.6 milllion - do you have any actual numbers on the value produced from this 18k investment?
What are you using it for? I have to assume some kind of agentic workload. Also curious to know when (if ever) you would pivot to Chinese providers to save $$$
> Even adding something like $20 - $100 subscriptions to every user is a serious enough financial obligation that it needs to go through budget planning / boards
I doubt that. In my tiny town in India, office workers use the 1800 INR (20$) plans. 1800 INR/month is like... 4% of the salary these guys get. And since this is mostly MS-office and windows explorer and chrome based stuff in very small, non-tech companies, it pays for itself in literally a day. If they _could_ pay less they would. After all they pirate MS office. But the cheap chinese plans dont have any distribution, whereas OpenAI seems to have effectively marketed to them via IPL ads and such. No one there even knows about deepseek. It also doesn't help deepseek and such are focused on the coding market. No multimodal, office plugins, etc.
For personal stuff, people there use Meta AI - mostly because it is directly available in whatsapp and these days they are pushing it by adding a dedicated button for it right on the home page. Regardless, they still call it "chatgpt". OpenAI brand is unmatched.
Remember that 20$ is a subsidized rate openai is currently willing to provide. Once that goes down you think these guys would be willing to pay token based billing charges ?
It is not going to go away. Rather they have doubled down and opened 399 INR/mo (4$/mo) plans that as of late have GPT 5.6 Luna.
The reason this works is that you get lesser inference time compute used for queries on these cheaper plans which makes it sustainable and this is enough for the tasks these guys do.
And some local telcos are bundling this subscription as well, so most people just get it for "free". For example, I get Google AI Pro for free with my 350 INR/mo telco plan.
These companies have spent billions of investor dollars and they will need to recoup that cost soon. And then show year over year growth on top of that.
Unless they can massively scale down training and inference cost or implement AGI I don't know what their plan is. Just provide a subsidized plan for the next 10 or 20 years? Their costs are directly proportional to the amount of tokens the LLM produces. How is a monthly subscription plan supposed to account for such costs?
I remember the piracy era, and it used to be a joke whether anyone could actually stop it.
Eventually MS dealt with piracy by going after firms, and finally by offering something cheap enough.
Between the legal aspects, and the fact that its MS office, paying Rs 1800 a month is possible.
I’ve been trawling every source I can find to understand what the story on the ground is, and when it comes to productivity it’s a huge mixed bag. The variance in outcomes between independent coders, frontier labs, someone in SV and someone in India is mind-bending right now.
Most firms which talk about their AI plans are not seeing traction, and the AI projects are going to the same place that the ML projects used to go to die.
It’s at the individual level that I am seeing productivity gains, however that isn’t something firms are happy to hear right now.
Rofl, I already went for cheaper Chinese solutions. I can achieve more than I could when I was giving OpenAI 20 bucks a month, and for a fraction of the price.
I have no idea why people still use ChatGPT or Claude.
Most people don't understand how cheap models like DeepSeek and similar are. I have $5 in an account that I use for months. I can get very complex code for less than 50 cents.
Me too. Sinophobia may be the culprit, too many years watching fox or cnn is my guess?
I love chinese phones, cars, cities, people, and now AI models. There is a beautiful world out there to admire if people is willing to open their eyes.
You will spend thousands of dollars on hardware to run those at lower quality (quantized) than the benchmarks where they match March SOTA performance.
Source: I have the hardware to run those locally. I would never recommend it to anyone trying to save money. It’s so much cheaper to pay even Anthropic or OpenAI.
People may pay less than they are willing to pay as competition drives the price down. I paid $20/mo a while which I was happy with but I found I could get similar for free so pay $0 now.
The real question is not whether there will be a market for AI, it is obvious that there will be one (as it was obvious the Internet was a gigantic thing in the dotcom boom). It is rather whether the value will be in the models (and if so in which?) or the infrastructure or somewhere else.
Interesting thoughts from Dwarkesh Patel on the future of models [1]. In summary, no moat if a client can switch to a competitor from a dropdown when all models are similar-ish (so models are commodities), but to really compete with humans, AI needs to start "learning on the job", i.e. accumulating experience like an employee (as opposed to the training / inference approach where the model isn't learning after it is released). If your model does that, then you have vendor lock-in as you can't replace an experienced model with a competitor. Then the value is in the model.
As in the 90s, this could go in all sort of different directions, hard to make predictions. And remember the 90s: the value was in the browser (you had to purchase it, eg Netscape), then the browser came for free with the OS (no value in the browser), then the browser was the most important thing in the world as it was control over the default search engine, everyone thought portals was the most important thing to control, so ISPs had their own applications accessing the internet from their own portal, etc - none of those people were stupid.
I work for a large company and for any LLM I use professionally, whether programmatically or via a UI, I get to pick from a dropdown from Gemini, Claude, OpenAI and the open weight ones. We ain't locked in.
Yeah, and that is completely fine business-wise for all the businesses in that dropdown. Averaged over the whole TAM, they will all make a lot of money. The reason is that the total market becomes _bigger_ with every new model (upto a reasonable point of course, but for 5 models it is true), they don't end up competing for the same slices of the pie.
The core business for each of the 5 will not be companies like yours, but rather few hundred to few thousand companies each who exclusively use their model not for technical reasons, but because they were wined and dined or due to bundling with another service. The other hundreds of thousands of customers are all bonus. This is true for most categories of SaaS not just LLM inference.
If the vendor of a core piece of software that you are already locked into tells you they are raising prices and adding AI inference features, you are going to cancel whatever AI plan you have and use theirs. Or pay the amount anyways (which is even better business wise for them).
Eventually, semi-random differences that arise in their distribution may end up informing model capabilities as well, and _that_ is when true model-level differentiation may happen. But this is not necessary and if it comes true would just be a bonus.
The reason uber didn't loose that much business is because it replaced the established businesses and left most consumers with little alternative.
That's not the case with AI - unless the frontier labs achieve AGI or some sort of super intelligence that lets them create infinite economic value (at which point, any further discussion is pointless for obvious reasons), the average worker can do most of their tasks with 5% of the api costs using an open source model from China and achieve the same results.
> The reason uber didn't loose that much business is because it replaced the established businesses and left most consumers with little alternative.
The established businesses were awful. There was not a thriving and beloved taxi service in every city pre-Uber. Taxis were bad and very expensive.
Also did you forget that Lyft exists and normal taxis are still available? Taxi services had to improve their business when the ride sharing companies entered the market because they previously relied on their own grip on on-demand transportation to keep prices high and service poor.
Imagine suddenly a new taxi business was able to come into your city and provided taxi rides for 5 cents on the dollar. What would happen?
Edit: now add on top of that that even if uber tried to undercut them again by running losses as they did in the past, that new taxi business would not loose any money if the number of their rides went down because their operating costs simply scale with the number of rides - while uber was loosing money on every ride.
$80 per month in a 5,000 people Enterprise is 400k per month which translates to ~5m per year. That is easily in the 8-16% of their total IT budget. I don’t know you, but companies don’t like to increase recurring cost. Is AI good? Yes. Is it clear how it generates enough ROI to justify a material IT cost increase? No. This is the problem now, not how much white collar workers use AI, but what is the productivity narrative of it. Because AI is so generic, there is no clear specialized use case, like when Salesforce invented the CRM for example.
The thing that's a bit different about AI from a CRM is it translates pretty directly into time savings. You only need each employee to save 1-2 hours of work per month to break even with a $80/seat subscription.
It's true that it can easily save 1-2 hours per month, but it only seems worth it if those hours saved are spent doing something else productive/profitable.
It appears that a lot of the time saved is spent either 1) doing nothing instead, 2) waiting for the AI to finish, or 3) increasing the hours spent elsewhere down/up the chain for reviewing, fixing, auditing.
The obvious benefit of anything offering to save time is that now you have time for "higher order work", but often, we use the time saved to be lazy.
After all, that's the underlying reason we used the time-saving, corner-cutting tool in the first place.
That’s never really how efficiency gains tend to work except in places where entire industries ceased to exist (printers, weavers, clothes washers, etc).
That’s not the argument though. The argument is that conversion rates as per OpenAI are about 5% and to serve the other 95% you need to build significant infrastructure that costs hundreds of billions annually. So for every white collar worker that you know and uses it and pays for it there are 20 free loaders. And mind you, 5% conversion rate is extremely high already as a number per se. It won't magically reach 20% in a couple of years. Chances are it will fall particularly for the enterprise segment due to the advent of the Chinese models.
I agree that it's working good enough and that it's here to stay: mostly everybody around me uses AI too but...
> Did Uber die when a trip across town went from $3 to $13? No. It's giving more rides than ever, 10x more than when it was $3.
The competition from open-weights models is already putting pressure on how much this can be billed. That's today, with stuff like OpenAI announcing 80% cuts on terra and luna. Tomorrow, in addition to the pressure from open-weights models, there's going to be pressure from chinese hardware: Tencent, Alibaba, etc. are all already working on their own AI chips.
What good would, say, a SOTA open-weights model running on chinese chips (chinese chips located in China or elsewhere btw) do to, say, Meta or Oracle's insane AI investements?
And it doesn't even have to be chinese chips: what good AMD AI chips doing inference by having weights etched on silicon will do to OpenAI and Anthropic?
All the people, especially here on HN, who were explaining six months ago that OpenAI and Anthropic models where so good for coding that coding was solved once and for all can now run better open-weights models than the proprietary ones from six months ago. For a fraction of the price.
If people, instead of paying $20 per month pay $80 per month for their AI subscriptions, what makes you believe that money is going to go to one of these companies participating in this $2 trillion debt?
And really, in which new domain have we seen the first movers being able to keep a lifetime grip on the market without getting their arse handed to them by the competition? Things moves quickly today: I'm not sure OpenAI and Anthropic are the Ford equivalent for AI.
That AI is here to stay is a given. That people may be willing to pay more is likely (even though IMO they won't pay 4x more for something only marginally better). But that the current players are going to stay on top even though the competition closed the gaps to weeks?
How many are the white collar workers? 300 mln? even with $100 subscription each, the monthly revenue for your addressable market rises to $30 bln. If we increase the figure to half of humanity and they are 3 bn, you multiply it upto 10 and $300 bn monthly, $3.5 tn annually.
Now start cutting it down keeping in mind that not everyone is an office worker, does not need $100 plan, has access to cheaper models, could get ai through someone elses subscription or local models. I'm not sure how down it will get, but it can get down a lot and will have to be split among few players. At least for me it does not seem unrealistic that the revenue won't be enough for everyone.
After using it a bit I’m convinced it’s not, as a whole, a bubble.
Bubbles exist around it. Data center construction may end up overbuilding, much like the dark fiber thing during dot.com, since better chips and models will shrink power and space requirements over time.
But the core tech is the most powerful new thing I have seen since discovering the Internet, at least. Maybe more.
A bubble doesn’t mean a technology is useless. It just means it’s overvalued
In the last 30 years, we’ve had multiple bubbles in the tech industry. Every one of them was backed by real stuff that we still use today. They were still bubbles and a ton of people were hurt when they popped.
The railway bubble and the optical fiber bubble left the world with lots of railways and optical fibers. Once their original owners went bankrupt, the assets themselves were quite useful and changed the world.
The cryptocurrency bubble, however, didn't. Not really. At best you can use it to buy drugs. Some of the GPUs may have been repurposed for AI, but not enough to make a dent in AI, and the AI reesarchers who kicked off the AI bubble were having to buy their GPUs for high prices to compete with the miners, not low prices after they went bust, so we can't even say miners helped kick it off.
The creativity of the financial industry is quite insane. Combined with the scale-at-all-cost strategy in AI companies, this is a ticking time bomb, no doubt.
I think it is easy to forget that a revolutionary technology does not automatically make a viable business model. I think we are yet to see the real winners in this game.
Wall Street makes money from issuing debt, trading debt, and IPOs. They need to get the last two mega IPOs off before the curtain call on this era.
But they can only partially influence the market, they can’t control it. The question is if the market will let them get these IPOs off or if the jig is up.
Here’s an exercise for anyone curious: pull up the median stock in the S&P 500. Look at its P/E. Take 15 minutes and look through the company’s financials and figure out what you think about the quality of its earnings.
Then decide if that P/E is appropriate.
The bubble isn’t in just AI, the bubble is everywhere, and AI is its largest manifestation.
And capitalism also works with crashes taking unprofitable companies down. And frauds, eventually, gets companies valuation like Enron and FTX to zero. And people go to jail.
Now I'm not saying OpenAI or Anthropic are frauds. What I'm saying is that, eventually, things revert to what is just.
The late 90s SV tech-bros behind pets.com or webvan for example faked it for 18 months to 36 months or so. At some point when the expenses outnumber the revenues, capitalism does its thing.
It's a near guarantee that when things shall heat up and the stock market shall crash and people are going to say that "this time it's worse than 1929", we'll hear the brrrrrrrr sound of the money counterfeiters, very likely synchronizing their counterfeiting operations worldwide (US / EU at least) to print ever more money.
To think that people won't pay $60-$80/mo to continue using it is wild to me. In a white collar environment it pays for itself in a few hours of use.
If you focus on how much value AI brings to people (mostly in time saved), the bubble hardly looks bubbly.
Did Uber die when a trip across town went from $3 to $13? No. It's giving more rides than ever, 10x more than when it was $3.
I'm sure there will be some losers, but unlike the dot-com boom, the entirety of the population already has all the tools needed to leverage AI.
I use AI every day at work, but I only pay $20. That's the most I will ever want to pay. And, so far, it gives me everything I need.
If OpenAI or Anthropic suddenly said "Sorry, the game is up. You'll have to pay $100/month now", I would 100% look into cheaper Chinese solutions.
I suspect the AI subscription (or API) economy is whale economy. You have a small, small percentage of users that are happy to pay whatever it takes - while the vast majority will either use the free tier, and then the next group will pay for the cheapest or next cheapest subscription.
EDIT: And I'll echo what another user wrote here. The VAST majority of office users around the world don't work for tech companies flush in cash. Even adding something like $20 - $100 subscriptions to every user is a serious enough financial obligation that it needs to go through budget planning / boards. Even more so for all the government workers around the world.
And there will be competition. I see zero moat right now. The user specific part of the state of the model sits outside of the control of the model (it is basically my code base, or my prompts, all of which I can transfer to any competitor).
What's the capex expenditure of Magnificent 7 just this year? Close to $1tn?
what im trying to say is that we focus a bit too much on the labs, but the biggest part is in the infra that makes everything possible.
I doubt that. In my tiny town in India, office workers use the 1800 INR (20$) plans. 1800 INR/month is like... 4% of the salary these guys get. And since this is mostly MS-office and windows explorer and chrome based stuff in very small, non-tech companies, it pays for itself in literally a day. If they _could_ pay less they would. After all they pirate MS office. But the cheap chinese plans dont have any distribution, whereas OpenAI seems to have effectively marketed to them via IPL ads and such. No one there even knows about deepseek. It also doesn't help deepseek and such are focused on the coding market. No multimodal, office plugins, etc.
For personal stuff, people there use Meta AI - mostly because it is directly available in whatsapp and these days they are pushing it by adding a dedicated button for it right on the home page. Regardless, they still call it "chatgpt". OpenAI brand is unmatched.
The reason this works is that you get lesser inference time compute used for queries on these cheaper plans which makes it sustainable and this is enough for the tasks these guys do.
And some local telcos are bundling this subscription as well, so most people just get it for "free". For example, I get Google AI Pro for free with my 350 INR/mo telco plan.
Unless they can massively scale down training and inference cost or implement AGI I don't know what their plan is. Just provide a subsidized plan for the next 10 or 20 years? Their costs are directly proportional to the amount of tokens the LLM produces. How is a monthly subscription plan supposed to account for such costs?
Eventually MS dealt with piracy by going after firms, and finally by offering something cheap enough.
Between the legal aspects, and the fact that its MS office, paying Rs 1800 a month is possible.
I’ve been trawling every source I can find to understand what the story on the ground is, and when it comes to productivity it’s a huge mixed bag. The variance in outcomes between independent coders, frontier labs, someone in SV and someone in India is mind-bending right now.
Most firms which talk about their AI plans are not seeing traction, and the AI projects are going to the same place that the ML projects used to go to die.
It’s at the individual level that I am seeing productivity gains, however that isn’t something firms are happy to hear right now.
I have no idea why people still use ChatGPT or Claude.
I love chinese phones, cars, cities, people, and now AI models. There is a beautiful world out there to admire if people is willing to open their eyes.
https://news.ycombinator.com/item?id=49214008 https://news.ycombinator.com/item?id=49229621
The FAFO day of Anthropic and OpenAI arrived.
Source: I have the hardware to run those locally. I would never recommend it to anyone trying to save money. It’s so much cheaper to pay even Anthropic or OpenAI.
Interesting thoughts from Dwarkesh Patel on the future of models [1]. In summary, no moat if a client can switch to a competitor from a dropdown when all models are similar-ish (so models are commodities), but to really compete with humans, AI needs to start "learning on the job", i.e. accumulating experience like an employee (as opposed to the training / inference approach where the model isn't learning after it is released). If your model does that, then you have vendor lock-in as you can't replace an experienced model with a competitor. Then the value is in the model.
As in the 90s, this could go in all sort of different directions, hard to make predictions. And remember the 90s: the value was in the browser (you had to purchase it, eg Netscape), then the browser came for free with the OS (no value in the browser), then the browser was the most important thing in the world as it was control over the default search engine, everyone thought portals was the most important thing to control, so ISPs had their own applications accessing the internet from their own portal, etc - none of those people were stupid.
[1] https://www.youtube.com/watch?v=iewm45atodE
The core business for each of the 5 will not be companies like yours, but rather few hundred to few thousand companies each who exclusively use their model not for technical reasons, but because they were wined and dined or due to bundling with another service. The other hundreds of thousands of customers are all bonus. This is true for most categories of SaaS not just LLM inference.
If the vendor of a core piece of software that you are already locked into tells you they are raising prices and adding AI inference features, you are going to cancel whatever AI plan you have and use theirs. Or pay the amount anyways (which is even better business wise for them).
Eventually, semi-random differences that arise in their distribution may end up informing model capabilities as well, and _that_ is when true model-level differentiation may happen. But this is not necessary and if it comes true would just be a bonus.
That's not the case with AI - unless the frontier labs achieve AGI or some sort of super intelligence that lets them create infinite economic value (at which point, any further discussion is pointless for obvious reasons), the average worker can do most of their tasks with 5% of the api costs using an open source model from China and achieve the same results.
The established businesses were awful. There was not a thriving and beloved taxi service in every city pre-Uber. Taxis were bad and very expensive.
Also did you forget that Lyft exists and normal taxis are still available? Taxi services had to improve their business when the ride sharing companies entered the market because they previously relied on their own grip on on-demand transportation to keep prices high and service poor.
Edit: now add on top of that that even if uber tried to undercut them again by running losses as they did in the past, that new taxi business would not loose any money if the number of their rides went down because their operating costs simply scale with the number of rides - while uber was loosing money on every ride.
It appears that a lot of the time saved is spent either 1) doing nothing instead, 2) waiting for the AI to finish, or 3) increasing the hours spent elsewhere down/up the chain for reviewing, fixing, auditing.
The obvious benefit of anything offering to save time is that now you have time for "higher order work", but often, we use the time saved to be lazy.
After all, that's the underlying reason we used the time-saving, corner-cutting tool in the first place.
Huh? If that was not a joke, here is the history of CRM:
https://en.wikipedia.org/wiki/Customer_relationship_manageme...
Hmm. Today that'd be creepy.
> Did Uber die when a trip across town went from $3 to $13? No. It's giving more rides than ever, 10x more than when it was $3.
The competition from open-weights models is already putting pressure on how much this can be billed. That's today, with stuff like OpenAI announcing 80% cuts on terra and luna. Tomorrow, in addition to the pressure from open-weights models, there's going to be pressure from chinese hardware: Tencent, Alibaba, etc. are all already working on their own AI chips.
What good would, say, a SOTA open-weights model running on chinese chips (chinese chips located in China or elsewhere btw) do to, say, Meta or Oracle's insane AI investements?
And it doesn't even have to be chinese chips: what good AMD AI chips doing inference by having weights etched on silicon will do to OpenAI and Anthropic?
All the people, especially here on HN, who were explaining six months ago that OpenAI and Anthropic models where so good for coding that coding was solved once and for all can now run better open-weights models than the proprietary ones from six months ago. For a fraction of the price.
If people, instead of paying $20 per month pay $80 per month for their AI subscriptions, what makes you believe that money is going to go to one of these companies participating in this $2 trillion debt?
And really, in which new domain have we seen the first movers being able to keep a lifetime grip on the market without getting their arse handed to them by the competition? Things moves quickly today: I'm not sure OpenAI and Anthropic are the Ford equivalent for AI.
For all I know OpenAI is "La Mancelle":
https://fr.wikipedia.org/wiki/La_Mancelle
And Anthropic is Panhard.
That AI is here to stay is a given. That people may be willing to pay more is likely (even though IMO they won't pay 4x more for something only marginally better). But that the current players are going to stay on top even though the competition closed the gaps to weeks?
I don't buy it.
How many are the white collar workers? 300 mln? even with $100 subscription each, the monthly revenue for your addressable market rises to $30 bln. If we increase the figure to half of humanity and they are 3 bn, you multiply it upto 10 and $300 bn monthly, $3.5 tn annually.
Now start cutting it down keeping in mind that not everyone is an office worker, does not need $100 plan, has access to cheaper models, could get ai through someone elses subscription or local models. I'm not sure how down it will get, but it can get down a lot and will have to be split among few players. At least for me it does not seem unrealistic that the revenue won't be enough for everyone.
So it better be that api tokens are seriously overpriced. There is value at $20/month. I am not so sure if it’s $400/month or more.
Bubbles exist around it. Data center construction may end up overbuilding, much like the dark fiber thing during dot.com, since better chips and models will shrink power and space requirements over time.
But the core tech is the most powerful new thing I have seen since discovering the Internet, at least. Maybe more.
In the last 30 years, we’ve had multiple bubbles in the tech industry. Every one of them was backed by real stuff that we still use today. They were still bubbles and a ton of people were hurt when they popped.
The cryptocurrency bubble, however, didn't. Not really. At best you can use it to buy drugs. Some of the GPUs may have been repurposed for AI, but not enough to make a dent in AI, and the AI reesarchers who kicked off the AI bubble were having to buy their GPUs for high prices to compete with the miners, not low prices after they went bust, so we can't even say miners helped kick it off.
I think it is easy to forget that a revolutionary technology does not automatically make a viable business model. I think we are yet to see the real winners in this game.
As he explains here: https://youtu.be/NufJ7g63KSY?t=1233
But they can only partially influence the market, they can’t control it. The question is if the market will let them get these IPOs off or if the jig is up.
Here’s an exercise for anyone curious: pull up the median stock in the S&P 500. Look at its P/E. Take 15 minutes and look through the company’s financials and figure out what you think about the quality of its earnings.
Then decide if that P/E is appropriate.
The bubble isn’t in just AI, the bubble is everywhere, and AI is its largest manifestation.
Now I'm not saying OpenAI or Anthropic are frauds. What I'm saying is that, eventually, things revert to what is just.
The late 90s SV tech-bros behind pets.com or webvan for example faked it for 18 months to 36 months or so. At some point when the expenses outnumber the revenues, capitalism does its thing.
1. — "The Big Market Delusion: Valuation and Investment Implications" - https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3501688
The paper behind the "big market delusion" he discusses.
2. - "Do Stock Prices Fully Reflect Information in Accruals and Cash Flows About Future Earnings?" - https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2598
This is the "accrual anomaly" paper Boyle describes.
3. - "The ‘Incomplete Revelation Hypothesis’ and Financial Reporting" - https://publications.aaahq.org/accounting-horizons/article/1...
Paper explains why information can be public yet still not be fully incorporated into prices when extracting it takes effort.
4. - "Limited Arbitrage in Equity Markets" - https://www.hbs.edu/ris/Publication%20Files/Limited%20Arbitr...
Mitchell and Pulvino work on the "limits of arbitrage."
5. "How Pervasive Is Corporate Fraud?" - https://www.chicagobooth.edu/research/rustandy/social-impact...
The paper on about one-third of corporate fraud is detected
6. "There’s never been a better time to commit financial fraud" - https://www.economist.com/business/2026/07/29/theres-never-b...
7. "Firm Data on AI" - https://www.nber.org/system/files/working_papers/w34836/w348...
The Bank of England study on executive AI usage and productivity.
"Aswath Damodaran: Big Tech Has No Idea How AI Pays Off" - https://news.ycombinator.com/item?id=49229981