6 ms·

> Everyone is in too deep to now admit that there’s a problem I'm not sure how to square this with the dramatic improvement in LLM capabilities in the last 8-9

> Everyone is in too deep to now admit that there’s a problem I'm not sure how to square this with the dramatic improvement in LLM capabilities in the last 8-9 months. If anything, it makes the earlier investments look prescient?

ForHackernews 2mo ago
No, because the LLMs will keep getting more efficient and capable. Distillation and quantization will mean firms spending trillions on giant data centres are left holding the bag. I suspect Apple ends up laughing all the way to the bank. https://github.com/microsoft/BitNet https://github.com/microsoft/BitNet
- InsideOutSanta 2mo ago
  Everyone who initially failed at this stumbled backward into victory.
- skybrian 2mo ago
  I suppose there is some limit, but it’s a bit hard to believe that Google won’t find a good use for more data centers.
InsideOutSanta 2mo ago
The problem is that the dramatic improvement in capabilities is not translating to a dramatic increase in revenue.
- DiscourseFan 2mo ago
  The technology is too hard to capitalize on. It’s far more democratic than, say, an iPhone, or a search engine. Anyone can download a model to their computer and start toying with it, how do you profit off of that? Even if everyone was constantly tokenmaxxing (which we cannot, since the process gets fucked up if you let it run entirely on its own), it probably still wouldn’t be marginally profitable.
- erwald 2mo ago
  "Anthropic and OpenAI generate a lot of revenue with relatively few employees – an estimated $9M and $5.5M in revenue per employee (RPE), respectively. If either company were to go public, it would have a higher RPE than any public tech company on Forbes’ Global 2000 list." https://epoch.ai/data-insights/revenue-per-employee-ai-companies https://epoch.ai/data-insights/revenue-per-employee-ai-compa...
  - toomuchtodo 2mo ago
    This assumes they do not have to increase prices to be profitable, and that they will continue to have customers when customers can switch to open models at similar performance. As an analogy, Uber could crank up rates after the VC growth play was over to stoke revenue and profits because they have a duopoly with Lyft. LLM consumers can switch to Kimi models fairly trivially today, and whatever the frontier open model landscape looks like later. Model training and development is expensive, self hosted inference on open models not so much. https://www.wheresyoured.at/the-openai-bubble/ https://www.wheresyoured.at/the-openai-bubble/ has the math. (a component of my work is currently building scaffolding so our organization can swap out commercial inference providers for on prem inference infra to derisk against the eventual rug pull when the math gets icky for LLM providers, while consuming as much subsidized tokens as we can until then, when it makes sense to use tokens for work)
    - lenerdenator 2mo ago
      The question will be whether customers can switch. Can you install a near-SOTA model on a cluster in a data center? Of course. Compliance and operations are the sticking points. I work in healthcare IT, and it's amazing how tight the data compliance requirements are. I can't have someone in Canada look at prod data. If we told hospitals that we were handing off PHI/PII to Chinese models, they'd end our relationship due to the long history China has of hacking Western networks and computers. They don't care how open and cheap things are. Then, you have to keep up-to-date on the latest technology and right-size things in a very fluid market. If you sign a contract for hosting the model on a data center that's running what the SOTA is now in hardware, and someone comes through with a data center hardware or software product that makes that data center contract a disadvantage (maybe it's too expensive and the other party won't budge on the price), you might have to factor that into your offering's price, and that could put you at a disadvantage in your marketplace. Google, MS, etc. all want to leverage the cloud model to make this be less of an issue for you, for a price. They have the ability to update you with the SOTA stuff in the data centers, because they're the ones driving that SOTA. They can say they host in the US and develop most of their stuff in the US. Will that be enough of a moat? Probably not for the levels of spending that are happening now, but over the long term, probably.
- budsniffer952 2mo ago
  >not translating to a dramatic increase in revenue. Completely false. AI and AI related revenues are growing exponentially.
  - dgellow 2mo ago
    Not for the companies using the LLMs…
    - budsniffer952 2mo ago
      Are you denying that AI revenues are growing? Or are you just adding nonsense about "yeah but yeah but no value"?
      - dgellow 2mo ago
        I’m saying there is no proof that companies _paying for AI_ are seeing a positive effect to their ROI. If you have such a proof, please share, that would be a massive news
  - TheOtherHobbes 2mo ago
    Expenditure on compute is growing even more exponentially.
  - jerf 2mo ago
    I know it's easy to forget, and InsideOutSanta kind of anchored the conversation on "revenue", but profit is ultimately what matters. Back when Silicon Valley was merely insane rather than bat-guano crazy insane, it was commonly observed that it's not hard to build a business around selling a dollar for 95 cents. The point being that it doesn't necessarily mean much when you have a business doing that, because of course the demand will be insane. It doesn't mean you have a viable business. You don't know you have a viable business until you transition to selling a dollar for $1.03. Many a VC-funded business that looked successful, even wildly successful, has run aground on that transition, or at least, suddenly stopped looking so wildly successful. If AI-related expenses are also growing exponentially, and they are growing exponentially faster, it doesn't matter that revenue is growing exponentially. The AI funding has also now absolutely baked in exponential growth of expenses, because that's how debt works. A slow exponential, hopefully, but an exponential none-the-less. Something Hacker News needs to be periodically reminded of is that we are the field getting the most out of AI, and it's not even close. That's great for us. But the stocks aren't priced for "a pretty nice coding tool". They're priced for every field in the world getting even more value out of this than our field is getting now. That is, frankly, not happening anywhere near fast enough for the spending and stock valuations. When you don't have all the engineering guardrails that are present in software engineering [1], suddenly the AI is, ahem, exponentially less useful. As I say in that post, watch your AI actually doing something, even the frontier models. Watch the thinking traces. Watch how many times they bang into a guardrail of some sort; a failing test, a failing compile, a linter failure, a bash script that doesn't work, all those things. How much value would you get out of an AI coding assistant if the first time it banged into a guard rail it was done and you had to stop using it for that task? How much value would you get out of an AI coding assistant if instead it silently failed and just proceeded forward with errors that you lack the infrastructure to easily detect? In the first case, it would be fairly modest, almost certainly not worth the money, and in the second, it would be worth paying to not use. Even in our field, while the rate of code output has increased substantially, the rate of value generation increase has been quite a bit more modest. I have observed, and heard from a number of other places, that while my own output has increased somewhat we still generally can't plan on being able to work with other teams at much faster a rate than we used to. There's a viable business here but I can't see how all these companies expect to be returning all this revenue in any financially sensible period of time. They're all spending like if only they spend enough they can own about %900 of the market in three years. They can't all do that, even accounting for "AI makes the market bigger". And they're wildly vulnerable to some new solution coming out that obsoletes all this spending, like an ASIC that starts running a popular model directly (especially if model capabilities plateau), meaning that all this nVidia GPU spending is so much dead silicon. Or someone comes out with a much more efficient way to train models. There has to be some insight we're missing; humans do not learn what they do by having the entire contents of the Internet poured through their head hundreds of times over. We are far more efficient with our training data. What if someone works out a solution to that and we don't need to spend billions on GPUs but only millions? The whole spending proposition could collapse overnight and the companies that suddenly have three orders of magnitude too much hardware and the debt to match would be up a creek without a paddle. [1]: https://jerf.org/iri/post/2026/programming_is_engineering/ https://jerf.org/iri/post/2026/programming_is_engineering/
- ac29 2mo ago
  The article notes Google Cloud revenue grew 82% YoY
  - paxys 2mo ago
    How much did Google spend to get that increase?
  - inigyou 2mo ago
    Why do people choose the cloud with a history of randomly deleting billion-dollar accounts?
    - manarth 2mo ago
      UniSuper? (The claim felt so wild I wanted to check, and indeed, the private Google Cloud for the $125bn Australian pension fund was accidentally deleted by a provisioning misconfiguration. Any others?)
      - zdragnar 2mo ago
        IIRC, the files for Toy Story 2 were accidentally deleted during production, and the film was only saved because someone on maternity leave had a backup at home. Turns out you can fuck up self hosting too.
      - inigyou 2mo ago
        Yes, Google randomly deleted UniSuper for basically the same reason they randomly ban individual customers: they don't care. Relying on them for anything is a huge mistake.
- wongarsu 2mo ago
  Source? Has Anthropic's annualized revenue not quadrupled in the last 7 months? And OpenAI's annualized revenue quadrupled since January 2025? Which is only unimpressive by comparison to Anthropic's meteoric revenue growth I'd be with you if you claimed that the revenue hasn't translated into substantial profits. Being able to spend a lot of money to get less money back is not that impressive. But revenue by itself is on a dramatic rise as capabilities improve
  - InsideOutSanta 2mo ago
    > Anthropic's annualized revenue That's not a meaningful number, and even if it were, quadrupled isn't nearly enough.
- paxys 2mo ago
  Moreover there’s no guarantee that eventual AI profits (if any) will go to the companies investing all this cash. If the worst case scenario of Chinese labs building and serving frontier-level models on 2nd tier nvidia hardware comes to be then what will be left of all the “hyperscalers”?
- raincole 2mo ago
  Except it did get translated to a dramatic increase in revenue. "Dramatic increase" is a ridiculous understatement here, by the way.
- thewebguyd 2mo ago
  Ans so far, the dramatic improvements have come with an increase in API costs. Even if, hypothetically, Fable or a Fable-class model could seriously replace some headcount, it'll only gain further traction of it's actually cheaper than hiring humans. $50/MTok is expensive. Wouldn't be unreasonable to expect somewhere between ~$3k-$5k/month/developer in spend. Cheaper than a Junior in the HCoL areas (in the US), but not much cheaper in lower-to-average COL areas. Most acceleration will come from having the headcount + giving said headcount $3k-$5k/month in token budget, so now it just becomes a very expensive dev tool rather than a headcount replacement tool. The idea that a $30k/year API bill will replace 2 $100k developers falls part outside of SFC/NYC. No CFO of a mid-market company in a LCOL area is signing off on $3k/month/dev API bills. They'll just hire juniors and cap their spend at $200/month.
grey-area 2mo ago
For certain values of ‘dramatic improvement’. Is lots more important work being done with LLMs? Not much sign of it yet, they’ve been helpful for experts at times (e.g. vuln research or maths research) but that hardly justifies the vast sums for Google investors.
throwaway27448 2mo ago
Presumably at some point you need a measurable productivity return yea? Maybe organizations are not built around skill and aptitude so much as liability, which LLMs cannot provide barring (very welcome and also very unlikely) legislation in the US.
- budsniffer952 2mo ago
  >Presumably at some point you need a measurable productivity return yea? At what point? This technology is brand new. Did you think we were going to double productivity in 3 years? Capacity is being built. It's hard to build data centres, there are no chips, there is no memory, it's hard to get talent, we don't have the energy to power the facilities. No one knows where this is going. We are scratching the surface. There is an absolute boom happening, and yet every day I have log onto Hacker News and read this nonsense about everything falling apart. Are we living in the same universe??? So-called "technologists" saying, "meh, it's not that cool". Okay. Guess what? You're not Michael Burry. Nobody cares or will care that you "called it". Look around this place: you aren't even slightly contrarian.
  - TheOtherHobbes 2mo ago
    "We would be profitable if we had the resources but we don't," isn't the smackdown argument you seem to believe it is. There used to be a thing where successful tech companies were profitable right out of the gate, and very successful companies doubled those profits for years, and companies who bought and used the tech could point to clear, actioned, benefits and cost savings. Now it's all "This will be really, really profitable one day, probably, if the omens align and we can deal with all of the problems."
- WarmWash 2mo ago
  The infamous 2025 MIT study that found almost all AI pilots in companies were failing, also found that virtually every worker was using AI many times a week if not daily. Turns out people just use their personal AI accounts rather than company ones. Which would make sense if you want to claim the work the AI does as your own.
vrganj 2mo ago
I'm not sure I've seen what I would call dramatic improvement since maybe GPT4? Sure, things got better. But I'd call it iterative more than revolutionary. I still wouldn't trust any of the models to do anything meaningful unattended. They all still do dumb shit all the time. Plus, even if they were genuinely dramatically better, the businesses sure as hell aren't. They're burning money left and right, they have no moat, Chinese open models are basically equivalent these days. What's the path to profitability, or hell, break-even? How do you envision this being anything but a giant money pit?
- budsniffer952 2mo ago
  [flagged]
  - dgellow 2mo ago
    It doesn’t matter… are those companies using AI getting a positive ROI? So far there is no signs it is the case, unless you’re yourself selling AI stuff
    - budsniffer952 2mo ago
      >are those companies using AI getting a positive ROI? Yes. >So far there is no signs it is the case How could you possibly know this?
      - weakfish 2mo ago
        How could you? Can _someone_ in this thread _please_ provide a source?
      - TheOtherHobbes 2mo ago
        Because there are almost no "We used AI to save money, improve our services, and gain more customers" success stories. There's a lot of "We fired a lot of people because we're sheep and now we're having to hire some of them back" stories. And a lot of "A few engineers are doing a lot more, but we're not quite sure how to turn that into actual money" stories. And even more "We told everyone to tokenmaxx, and they did, and then we realised it was costing too much, so we stopped," stories. But there really hasn't been a deluge of "AI has cut costs and increased profits while also improving quality" stories. There has been a small outbreak of vibe-startups offering fairly generic services - mostly marketing and adjacent - who are doing okay, possibly. But established tech? Doubt.
finnthehuman 2mo ago
> not sure how to square this with the dramatic improvement in LLM capabilities A good tech demo doesn’t matter to the business if the products don’t become profitable at the scale the investment chased.
epolanski 2mo ago
It's an internet/railroad issue again. Tech is real, impact is gigantic, long term winners hard to predict, capex spending hard to recoup soon, if ever. And differently than internet or rails, you don't build once and maintain later, but enter a loop of ever increased spending to keep on top of the arms race and ever exploding usage.

ForHackernews 2mo ago

No, because the LLMs will keep getting more efficient and capable. Distillation and quantization will mean firms spending trillions on giant data centres are left holding the bag. I suspect Apple ends up laughing all the way to the bank. https://github.com/microsoft/BitNet https://github.com/microsoft/BitNet

InsideOutSanta 2mo ago
Everyone who initially failed at this stumbled backward into victory.
skybrian 2mo ago
I suppose there is some limit, but it’s a bit hard to believe that Google won’t find a good use for more data centers.

InsideOutSanta 2mo ago

The problem is that the dramatic improvement in capabilities is not translating to a dramatic increase in revenue.

DiscourseFan 2mo ago
The technology is too hard to capitalize on. It’s far more democratic than, say, an iPhone, or a search engine. Anyone can download a model to their computer and start toying with it, how do you profit off of that? Even if everyone was constantly tokenmaxxing (which we cannot, since the process gets fucked up if you let it run entirely on its own), it probably still wouldn’t be marginally profitable.
erwald 2mo ago
"Anthropic and OpenAI generate a lot of revenue with relatively few employees – an estimated $9M and $5.5M in revenue per employee (RPE), respectively. If either company were to go public, it would have a higher RPE than any public tech company on Forbes’ Global 2000 list." https://epoch.ai/data-insights/revenue-per-employee-ai-companies https://epoch.ai/data-insights/revenue-per-employee-ai-compa...
- toomuchtodo 2mo ago
  This assumes they do not have to increase prices to be profitable, and that they will continue to have customers when customers can switch to open models at similar performance. As an analogy, Uber could crank up rates after the VC growth play was over to stoke revenue and profits because they have a duopoly with Lyft. LLM consumers can switch to Kimi models fairly trivially today, and whatever the frontier open model landscape looks like later. Model training and development is expensive, self hosted inference on open models not so much. https://www.wheresyoured.at/the-openai-bubble/ https://www.wheresyoured.at/the-openai-bubble/ has the math. (a component of my work is currently building scaffolding so our organization can swap out commercial inference providers for on prem inference infra to derisk against the eventual rug pull when the math gets icky for LLM providers, while consuming as much subsidized tokens as we can until then, when it makes sense to use tokens for work)
  - lenerdenator 2mo ago
    The question will be whether customers can switch. Can you install a near-SOTA model on a cluster in a data center? Of course. Compliance and operations are the sticking points. I work in healthcare IT, and it's amazing how tight the data compliance requirements are. I can't have someone in Canada look at prod data. If we told hospitals that we were handing off PHI/PII to Chinese models, they'd end our relationship due to the long history China has of hacking Western networks and computers. They don't care how open and cheap things are. Then, you have to keep up-to-date on the latest technology and right-size things in a very fluid market. If you sign a contract for hosting the model on a data center that's running what the SOTA is now in hardware, and someone comes through with a data center hardware or software product that makes that data center contract a disadvantage (maybe it's too expensive and the other party won't budge on the price), you might have to factor that into your offering's price, and that could put you at a disadvantage in your marketplace. Google, MS, etc. all want to leverage the cloud model to make this be less of an issue for you, for a price. They have the ability to update you with the SOTA stuff in the data centers, because they're the ones driving that SOTA. They can say they host in the US and develop most of their stuff in the US. Will that be enough of a moat? Probably not for the levels of spending that are happening now, but over the long term, probably.
budsniffer952 2mo ago
>not translating to a dramatic increase in revenue. Completely false. AI and AI related revenues are growing exponentially.
- dgellow 2mo ago
  Not for the companies using the LLMs…
  - budsniffer952 2mo ago
    Are you denying that AI revenues are growing? Or are you just adding nonsense about "yeah but yeah but no value"?
    - dgellow 2mo ago
      I’m saying there is no proof that companies _paying for AI_ are seeing a positive effect to their ROI. If you have such a proof, please share, that would be a massive news
- TheOtherHobbes 2mo ago
  Expenditure on compute is growing even more exponentially.
- jerf 2mo ago
  I know it's easy to forget, and InsideOutSanta kind of anchored the conversation on "revenue", but profit is ultimately what matters. Back when Silicon Valley was merely insane rather than bat-guano crazy insane, it was commonly observed that it's not hard to build a business around selling a dollar for 95 cents. The point being that it doesn't necessarily mean much when you have a business doing that, because of course the demand will be insane. It doesn't mean you have a viable business. You don't know you have a viable business until you transition to selling a dollar for $1.03. Many a VC-funded business that looked successful, even wildly successful, has run aground on that transition, or at least, suddenly stopped looking so wildly successful. If AI-related expenses are also growing exponentially, and they are growing exponentially faster, it doesn't matter that revenue is growing exponentially. The AI funding has also now absolutely baked in exponential growth of expenses, because that's how debt works. A slow exponential, hopefully, but an exponential none-the-less. Something Hacker News needs to be periodically reminded of is that we are the field getting the most out of AI, and it's not even close. That's great for us. But the stocks aren't priced for "a pretty nice coding tool". They're priced for every field in the world getting even more value out of this than our field is getting now. That is, frankly, not happening anywhere near fast enough for the spending and stock valuations. When you don't have all the engineering guardrails that are present in software engineering [1], suddenly the AI is, ahem, exponentially less useful. As I say in that post, watch your AI actually doing something, even the frontier models. Watch the thinking traces. Watch how many times they bang into a guardrail of some sort; a failing test, a failing compile, a linter failure, a bash script that doesn't work, all those things. How much value would you get out of an AI coding assistant if the first time it banged into a guard rail it was done and you had to stop using it for that task? How much value would you get out of an AI coding assistant if instead it silently failed and just proceeded forward with errors that you lack the infrastructure to easily detect? In the first case, it would be fairly modest, almost certainly not worth the money, and in the second, it would be worth paying to not use. Even in our field, while the rate of code output has increased substantially, the rate of value generation increase has been quite a bit more modest. I have observed, and heard from a number of other places, that while my own output has increased somewhat we still generally can't plan on being able to work with other teams at much faster a rate than we used to. There's a viable business here but I can't see how all these companies expect to be returning all this revenue in any financially sensible period of time. They're all spending like if only they spend enough they can own about %900 of the market in three years. They can't all do that, even accounting for "AI makes the market bigger". And they're wildly vulnerable to some new solution coming out that obsoletes all this spending, like an ASIC that starts running a popular model directly (especially if model capabilities plateau), meaning that all this nVidia GPU spending is so much dead silicon. Or someone comes out with a much more efficient way to train models. There has to be some insight we're missing; humans do not learn what they do by having the entire contents of the Internet poured through their head hundreds of times over. We are far more efficient with our training data. What if someone works out a solution to that and we don't need to spend billions on GPUs but only millions? The whole spending proposition could collapse overnight and the companies that suddenly have three orders of magnitude too much hardware and the debt to match would be up a creek without a paddle. [1]: https://jerf.org/iri/post/2026/programming_is_engineering/ https://jerf.org/iri/post/2026/programming_is_engineering/
ac29 2mo ago
The article notes Google Cloud revenue grew 82% YoY
- paxys 2mo ago
  How much did Google spend to get that increase?
- inigyou 2mo ago
  Why do people choose the cloud with a history of randomly deleting billion-dollar accounts?
  - manarth 2mo ago
    UniSuper? (The claim felt so wild I wanted to check, and indeed, the private Google Cloud for the $125bn Australian pension fund was accidentally deleted by a provisioning misconfiguration. Any others?)
    - zdragnar 2mo ago
      IIRC, the files for Toy Story 2 were accidentally deleted during production, and the film was only saved because someone on maternity leave had a backup at home. Turns out you can fuck up self hosting too.
    - inigyou 2mo ago
      Yes, Google randomly deleted UniSuper for basically the same reason they randomly ban individual customers: they don't care. Relying on them for anything is a huge mistake.
wongarsu 2mo ago
Source? Has Anthropic's annualized revenue not quadrupled in the last 7 months? And OpenAI's annualized revenue quadrupled since January 2025? Which is only unimpressive by comparison to Anthropic's meteoric revenue growth I'd be with you if you claimed that the revenue hasn't translated into substantial profits. Being able to spend a lot of money to get less money back is not that impressive. But revenue by itself is on a dramatic rise as capabilities improve
- InsideOutSanta 2mo ago
  > Anthropic's annualized revenue That's not a meaningful number, and even if it were, quadrupled isn't nearly enough.
paxys 2mo ago
Moreover there’s no guarantee that eventual AI profits (if any) will go to the companies investing all this cash. If the worst case scenario of Chinese labs building and serving frontier-level models on 2nd tier nvidia hardware comes to be then what will be left of all the “hyperscalers”?
raincole 2mo ago
Except it did get translated to a dramatic increase in revenue. "Dramatic increase" is a ridiculous understatement here, by the way.
thewebguyd 2mo ago
Ans so far, the dramatic improvements have come with an increase in API costs. Even if, hypothetically, Fable or a Fable-class model could seriously replace some headcount, it'll only gain further traction of it's actually cheaper than hiring humans. $50/MTok is expensive. Wouldn't be unreasonable to expect somewhere between ~$3k-$5k/month/developer in spend. Cheaper than a Junior in the HCoL areas (in the US), but not much cheaper in lower-to-average COL areas. Most acceleration will come from having the headcount + giving said headcount $3k-$5k/month in token budget, so now it just becomes a very expensive dev tool rather than a headcount replacement tool. The idea that a $30k/year API bill will replace 2 $100k developers falls part outside of SFC/NYC. No CFO of a mid-market company in a LCOL area is signing off on $3k/month/dev API bills. They'll just hire juniors and cap their spend at $200/month.

grey-area 2mo ago

For certain values of ‘dramatic improvement’. Is lots more important work being done with LLMs? Not much sign of it yet, they’ve been helpful for experts at times (e.g. vuln research or maths research) but that hardly justifies the vast sums for Google investors.

throwaway27448 2mo ago

Presumably at some point you need a measurable productivity return yea? Maybe organizations are not built around skill and aptitude so much as liability, which LLMs cannot provide barring (very welcome and also very unlikely) legislation in the US.

budsniffer952 2mo ago
>Presumably at some point you need a measurable productivity return yea? At what point? This technology is brand new. Did you think we were going to double productivity in 3 years? Capacity is being built. It's hard to build data centres, there are no chips, there is no memory, it's hard to get talent, we don't have the energy to power the facilities. No one knows where this is going. We are scratching the surface. There is an absolute boom happening, and yet every day I have log onto Hacker News and read this nonsense about everything falling apart. Are we living in the same universe??? So-called "technologists" saying, "meh, it's not that cool". Okay. Guess what? You're not Michael Burry. Nobody cares or will care that you "called it". Look around this place: you aren't even slightly contrarian.
- TheOtherHobbes 2mo ago
  "We would be profitable if we had the resources but we don't," isn't the smackdown argument you seem to believe it is. There used to be a thing where successful tech companies were profitable right out of the gate, and very successful companies doubled those profits for years, and companies who bought and used the tech could point to clear, actioned, benefits and cost savings. Now it's all "This will be really, really profitable one day, probably, if the omens align and we can deal with all of the problems."
WarmWash 2mo ago
The infamous 2025 MIT study that found almost all AI pilots in companies were failing, also found that virtually every worker was using AI many times a week if not daily. Turns out people just use their personal AI accounts rather than company ones. Which would make sense if you want to claim the work the AI does as your own.

vrganj 2mo ago

I'm not sure I've seen what I would call dramatic improvement since maybe GPT4? Sure, things got better. But I'd call it iterative more than revolutionary. I still wouldn't trust any of the models to do anything meaningful unattended. They all still do dumb shit all the time. Plus, even if they were genuinely dramatically better, the businesses sure as hell aren't. They're burning money left and right, they have no moat, Chinese open models are basically equivalent these days. What's the path to profitability, or hell, break-even? How do you envision this being anything but a giant money pit?

budsniffer952 2mo ago
[flagged]
- dgellow 2mo ago
  It doesn’t matter… are those companies using AI getting a positive ROI? So far there is no signs it is the case, unless you’re yourself selling AI stuff
  - budsniffer952 2mo ago
    >are those companies using AI getting a positive ROI? Yes. >So far there is no signs it is the case How could you possibly know this?
    - weakfish 2mo ago
      How could you? Can _someone_ in this thread _please_ provide a source?
    - TheOtherHobbes 2mo ago
      Because there are almost no "We used AI to save money, improve our services, and gain more customers" success stories. There's a lot of "We fired a lot of people because we're sheep and now we're having to hire some of them back" stories. And a lot of "A few engineers are doing a lot more, but we're not quite sure how to turn that into actual money" stories. And even more "We told everyone to tokenmaxx, and they did, and then we realised it was costing too much, so we stopped," stories. But there really hasn't been a deluge of "AI has cut costs and increased profits while also improving quality" stories. There has been a small outbreak of vibe-startups offering fairly generic services - mostly marketing and adjacent - who are doing okay, possibly. But established tech? Doubt.

finnthehuman 2mo ago

> not sure how to square this with the dramatic improvement in LLM capabilities A good tech demo doesn’t matter to the business if the products don’t become profitable at the scale the investment chased.

epolanski 2mo ago

It's an internet/railroad issue again. Tech is real, impact is gigantic, long term winners hard to predict, capex spending hard to recoup soon, if ever. And differently than internet or rails, you don't build once and maintain later, but enter a loop of ever increased spending to keep on top of the arms race and ever exploding usage.