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I think the author might need a reality check. In almost every company the usage of AI is becoming more and more valuable. Even if progress in models freezes an

I think the author might need a reality check. In almost every company the usage of AI is becoming more and more valuable. Even if progress in models freezes and halts, the current state is too valuable. I have talked to multiple people in multiple companies and LLMs are becoming indispensable at so many things. Yes chatgpt might or might not conquer the world but the enterprise usage I don't see decreasing (and I am not talking about customer facing LLM usage which might be very stupid).

janalsncm 1y ago
There is a difference between valuable and profitable. I think anyone who wants to say there isn’t a bubble needs to solve two problems: 1) Inference is too damn expensive. 2) The models/products aren’t reliable enough. I also personally think talking to industry folks isn’t a silver bullet. No one knows how to solve #2. No one. We can improve by either waiting for better chips or using bigger models, which has diminishing returns and makes #1 worse. Maybe OpenAI’s next product should be selling dollars for 99 cents. They just need a billion dollars of SoftBank money, and they can do 100 billion in sales before they need to reraise. And if SoftBank agrees to buy at $1.01 the business can keep going even longer!
- ryandrake 1y ago
  I think AI will be useful to industries/companies where #2 is unimportant: Where the quantity of product is far more important than its quality. Disturbingly, this describes the market for a lot of industries/companies.
- hu3 1y ago
  > The models/products aren’t reliable enough. People aren't reliable enough. Nature isn't reliable enough. For most uses, all that is needed is a system to handle cases where it is not reliable enough. Then suddenly it becomes reliable enough.
  - th0ma5 1y ago
    There are different kinds of reliable. There is the reliability of certain failure for instance. These things are unreliable in a way that the other things that you listed are not.
  - Juliate 1y ago
    Nature is reliable in that we have rules (physics) and practices (engineering) to base our process upon. People aren't reliable, for a specific value of reliable. We expect of technology(machines, software, AI, whatever) to be: deterministically reliable (knowing their failure modes), and significantly more reliable than humans at what they do (because that's what we rely upon, why we use them to replace humans at what they, in ways humans can't: harder, faster, stronger).
  - void-pointer 1y ago
    A computer can never be held accountable. Therefore, a computer must never make a management decision.
- nearbuy 1y ago
  It seems like every 6 months they come out with a new model that's as good as the previous generation but with a fraction of the inference cost. Inference cost for a given level of quality has been dropping fast. E.g. GPT-4.1 nano outperforms the old GPT-4 model but costs 300x less. Right now, the API cost for asking a single question costs about a fiftieth of a cent on their cheap 4.1-nano model, up to about 2 cents on their o3 model. This is pretty affordable. On the other end of the spectrum, if you're maxing out the context window on o3 or 4.1, it'll cost you around $0.50 to $2. Pricy, but on 4.1, this is like inputting several novels worth of text (maybe around 2000 pages).
  - janalsncm 1y ago
    That would be great news for OpenAI if they could somehow prevent people from buying their own computers. Because as inference costs come down for OpenAI, their customers also get access to better and better commodity hardware (and also better models to run on it). And commodity models become more and more capable all the time, even if they’re not the best.
    - nearbuy 1y ago
      It counters the claim that "inference is too damn expensive". You can argue that cheap inference is actually bad for OpenAI because it makes it easier to run models on commodity hardware, but then you're arguing against your point that inference was too expensive for OpenAI to be sustainable. Which is it? Is inference too expensive or too cheap? For now, OpenAI's models are closed source, so if you find their models offer the best value for your use case, you don't have the option of running it on your own hardware. If a competitor releases better products for cheaper, OpenAI will fail, just like any other company would.
      - janalsncm 1y ago
        It’s both. For an increasing number of tasks, it’s too cheap. People will be able to do simple things on their own hardware. Things OpenAI would love to charge high margins on. There’s probably an 80/20 rule on the horizon. And for others, it’s too expensive. The frontier is constantly being pushed, so they can’t stop improving or they will fall behind. Google at least makes their own chips so they can control their costs somewhat.
arm32 1y ago
That's besides the point, though. How are they gonna meet the shareholder's expectations of future revenue? When AI becomes equally expensive as a human, what happens then?
- n_ary 1y ago
  When AI becomes equally expensive as a human, layoff humans to continue finance AI because, it is easy to fire and rehire but expensive(more precisely acknowledging failure and bad decision making to hurt leadership ego) to remove and become independent from complex integrations. Edit: I am just sharing how our CTO responds to the massive push of AI into everything, because integration of a non deterministic system has massive cost and eventually once the thing is made deterministic, the additional steps add expenses which finally makes the entire solution too expensive compared to the benefit. This is not my opinion, just sharing how typical leaderships hope to tackle the expense issue.
skybrian 1y ago
It seems like the question is whether you believe in cost-based or value-based pricing. The cost of AI for the same amount of power is going down a lot year-to-year. [1] If market prices go down with costs, then we see something like solar power where it’s everywhere but suppliers don’t make money, not even in China. Or maybe customers spend a lot more on more-expensive models? Hard to say. [1] https://simonwillison.net/2025/Feb/9/sam-altman/ https://simonwillison.net/2025/Feb/9/sam-altman/
cowanon2222 1y ago
The author's tone is over the top, but I think this quote is true: > Large Language Models and their associated businesses are a $50 billion industry masquerading as a trillion-dollar panacea for a tech industry that’s lost the plot. There are very real use cases with high value, but it's not an economy-defining technology, and the total value is going to fall far short of projections. On bottom lines, the overall productivity gain from AI for most companies is almost a rounding error compared to other factors.
- HeatrayEnjoyer 1y ago
  Hopefully you are right, but I fear this is a very naive and premature judgement. At one point the internet was a $50B industry insisting it would be a $1T industry. It even had a bubble, bursting and burying entire companies. Yet, $1T was nevertheless a profound underestimation.
  - wrs 1y ago
    Is it? There's certainly $1T of other businesses built with the internet, but the internet business, itself, was rapidly commoditized. The valuable things were the applications built on it, not the network. The argument here is that nobody's found the $1T applications built on AI foundation models yet, but OpenAI is valued as if they have, because their demo chatbot took off out of peoples' curiosity and people are extrapolating that accident exponentially into the future.
  - janalsncm 1y ago
    It’s not enough to be right eventually. Being too early is as good as wrong.
  - cowanon2222 1y ago
    The internet bubble is probably a good analogy. It took almost 20 years and several rounds of failed businesses for the internet to have the impact that was originally promised. The big internet companies of the 90s are not where the money was ultimately made. Similarly, the current LLM vendors and cloud providers are likely not where the money will ultimately be made. Some startup 10-15 years from now will likely stack a cheaply hosted or distributed LLM with several other technologies, and create a whole new category of use cases we haven't even thought of yet, and that will actually create the new value. It's basically the Gartner hype cycle in action.
Gerardo1 1y ago
> In almost every company the usage of AI is becoming more and more valuable. It's certainly becoming more common, and there are lots of people who want it to be valuable, and indeed believe it's valuable. Personally, I find it about as valuable as a really, really good intellisense. Which is certainly valuable, but I feel like that's way off from the type/quality/quantity of value you're suggesting.
- n_ary 1y ago
  I also find the intellisense aspect of it good though the price is still too high when my local IDE can do 1/10th of that for a long time. Additionally, LLMs are sort of using old day google mastery to find the right result quickly to save a huge waste of wading through junk and SEO spam, which translates to productivity but then we are again balanced out because this gained productivity was lost once SEO spam took off a decade back. I am indifferent about this gain again, as anything with mass adaptation tends to devolve in garbage behavior eventually, slowly the gains will again be eaten up.
- kristjank 1y ago
  This is the comment I wanted to write before scrolling down. Information retrieval in general is really improving computer-related tasks, but I think present or visible is a much better term to describe it than valuable.
croes 1y ago
Where is the more of value coming from if everyone uses the same tool to do the same things? Your emails, presentations etc. will all look the same and what’s worse so will the emails, presentations etc. of scammers and phishers.
archagon 1y ago
Why is there a ChatGPT button on my gaming mouse? https://www.yankodesign.com/2025/04/27/razers-first-vertical-gaming-mouse-also-comes-with-a-dedicated-chatgpt-button/ https://www.yankodesign.com/2025/04/27/razers-first-vertical... How is this not indicative of a massive bubble?
kunley 1y ago
At these companies it becomes more and more forced, but not more and more valuable. Please make a distinction between what people say and what can be measured
jimbokun 1y ago
I noticed there is not a single number in your comment.

janalsncm 1y ago

There is a difference between valuable and profitable. I think anyone who wants to say there isn’t a bubble needs to solve two problems: 1) Inference is too damn expensive. 2) The models/products aren’t reliable enough. I also personally think talking to industry folks isn’t a silver bullet. No one knows how to solve #2. No one. We can improve by either waiting for better chips or using bigger models, which has diminishing returns and makes #1 worse. Maybe OpenAI’s next product should be selling dollars for 99 cents. They just need a billion dollars of SoftBank money, and they can do 100 billion in sales before they need to reraise. And if SoftBank agrees to buy at $1.01 the business can keep going even longer!

ryandrake 1y ago
I think AI will be useful to industries/companies where #2 is unimportant: Where the quantity of product is far more important than its quality. Disturbingly, this describes the market for a lot of industries/companies.
hu3 1y ago
> The models/products aren’t reliable enough. People aren't reliable enough. Nature isn't reliable enough. For most uses, all that is needed is a system to handle cases where it is not reliable enough. Then suddenly it becomes reliable enough.
- th0ma5 1y ago
  There are different kinds of reliable. There is the reliability of certain failure for instance. These things are unreliable in a way that the other things that you listed are not.
- Juliate 1y ago
  Nature is reliable in that we have rules (physics) and practices (engineering) to base our process upon. People aren't reliable, for a specific value of reliable. We expect of technology(machines, software, AI, whatever) to be: deterministically reliable (knowing their failure modes), and significantly more reliable than humans at what they do (because that's what we rely upon, why we use them to replace humans at what they, in ways humans can't: harder, faster, stronger).
- void-pointer 1y ago
  A computer can never be held accountable. Therefore, a computer must never make a management decision.
nearbuy 1y ago
It seems like every 6 months they come out with a new model that's as good as the previous generation but with a fraction of the inference cost. Inference cost for a given level of quality has been dropping fast. E.g. GPT-4.1 nano outperforms the old GPT-4 model but costs 300x less. Right now, the API cost for asking a single question costs about a fiftieth of a cent on their cheap 4.1-nano model, up to about 2 cents on their o3 model. This is pretty affordable. On the other end of the spectrum, if you're maxing out the context window on o3 or 4.1, it'll cost you around $0.50 to $2. Pricy, but on 4.1, this is like inputting several novels worth of text (maybe around 2000 pages).
- janalsncm 1y ago
  That would be great news for OpenAI if they could somehow prevent people from buying their own computers. Because as inference costs come down for OpenAI, their customers also get access to better and better commodity hardware (and also better models to run on it). And commodity models become more and more capable all the time, even if they’re not the best.
  - nearbuy 1y ago
    It counters the claim that "inference is too damn expensive". You can argue that cheap inference is actually bad for OpenAI because it makes it easier to run models on commodity hardware, but then you're arguing against your point that inference was too expensive for OpenAI to be sustainable. Which is it? Is inference too expensive or too cheap? For now, OpenAI's models are closed source, so if you find their models offer the best value for your use case, you don't have the option of running it on your own hardware. If a competitor releases better products for cheaper, OpenAI will fail, just like any other company would.
    - janalsncm 1y ago
      It’s both. For an increasing number of tasks, it’s too cheap. People will be able to do simple things on their own hardware. Things OpenAI would love to charge high margins on. There’s probably an 80/20 rule on the horizon. And for others, it’s too expensive. The frontier is constantly being pushed, so they can’t stop improving or they will fall behind. Google at least makes their own chips so they can control their costs somewhat.

arm32 1y ago

That's besides the point, though. How are they gonna meet the shareholder's expectations of future revenue? When AI becomes equally expensive as a human, what happens then?

n_ary 1y ago
When AI becomes equally expensive as a human, layoff humans to continue finance AI because, it is easy to fire and rehire but expensive(more precisely acknowledging failure and bad decision making to hurt leadership ego) to remove and become independent from complex integrations. Edit: I am just sharing how our CTO responds to the massive push of AI into everything, because integration of a non deterministic system has massive cost and eventually once the thing is made deterministic, the additional steps add expenses which finally makes the entire solution too expensive compared to the benefit. This is not my opinion, just sharing how typical leaderships hope to tackle the expense issue.

skybrian 1y ago

It seems like the question is whether you believe in cost-based or value-based pricing. The cost of AI for the same amount of power is going down a lot year-to-year. [1] If market prices go down with costs, then we see something like solar power where it’s everywhere but suppliers don’t make money, not even in China. Or maybe customers spend a lot more on more-expensive models? Hard to say. [1] https://simonwillison.net/2025/Feb/9/sam-altman/ https://simonwillison.net/2025/Feb/9/sam-altman/

cowanon2222 1y ago

The author's tone is over the top, but I think this quote is true: > Large Language Models and their associated businesses are a $50 billion industry masquerading as a trillion-dollar panacea for a tech industry that’s lost the plot. There are very real use cases with high value, but it's not an economy-defining technology, and the total value is going to fall far short of projections. On bottom lines, the overall productivity gain from AI for most companies is almost a rounding error compared to other factors.

HeatrayEnjoyer 1y ago
Hopefully you are right, but I fear this is a very naive and premature judgement. At one point the internet was a $50B industry insisting it would be a $1T industry. It even had a bubble, bursting and burying entire companies. Yet, $1T was nevertheless a profound underestimation.
- wrs 1y ago
  Is it? There's certainly $1T of other businesses built with the internet, but the internet business, itself, was rapidly commoditized. The valuable things were the applications built on it, not the network. The argument here is that nobody's found the $1T applications built on AI foundation models yet, but OpenAI is valued as if they have, because their demo chatbot took off out of peoples' curiosity and people are extrapolating that accident exponentially into the future.
- janalsncm 1y ago
  It’s not enough to be right eventually. Being too early is as good as wrong.
- cowanon2222 1y ago
  The internet bubble is probably a good analogy. It took almost 20 years and several rounds of failed businesses for the internet to have the impact that was originally promised. The big internet companies of the 90s are not where the money was ultimately made. Similarly, the current LLM vendors and cloud providers are likely not where the money will ultimately be made. Some startup 10-15 years from now will likely stack a cheaply hosted or distributed LLM with several other technologies, and create a whole new category of use cases we haven't even thought of yet, and that will actually create the new value. It's basically the Gartner hype cycle in action.

Gerardo1 1y ago

> In almost every company the usage of AI is becoming more and more valuable. It's certainly becoming more common, and there are lots of people who want it to be valuable, and indeed believe it's valuable. Personally, I find it about as valuable as a really, really good intellisense. Which is certainly valuable, but I feel like that's way off from the type/quality/quantity of value you're suggesting.

n_ary 1y ago
I also find the intellisense aspect of it good though the price is still too high when my local IDE can do 1/10th of that for a long time. Additionally, LLMs are sort of using old day google mastery to find the right result quickly to save a huge waste of wading through junk and SEO spam, which translates to productivity but then we are again balanced out because this gained productivity was lost once SEO spam took off a decade back. I am indifferent about this gain again, as anything with mass adaptation tends to devolve in garbage behavior eventually, slowly the gains will again be eaten up.
kristjank 1y ago
This is the comment I wanted to write before scrolling down. Information retrieval in general is really improving computer-related tasks, but I think present or visible is a much better term to describe it than valuable.

croes 1y ago

Where is the more of value coming from if everyone uses the same tool to do the same things? Your emails, presentations etc. will all look the same and what’s worse so will the emails, presentations etc. of scammers and phishers.

archagon 1y ago

Why is there a ChatGPT button on my gaming mouse? https://www.yankodesign.com/2025/04/27/razers-first-vertical-gaming-mouse-also-comes-with-a-dedicated-chatgpt-button/ https://www.yankodesign.com/2025/04/27/razers-first-vertical... How is this not indicative of a massive bubble?

kunley 1y ago

At these companies it becomes more and more forced, but not more and more valuable. Please make a distinction between what people say and what can be measured

jimbokun 1y ago

I noticed there is not a single number in your comment.