5 ms·

> each model is roughly 2x profitable on its own, but each next model costs 10x the last. The whole thing only works if scaling keeps delivering. This is a dec

> each model is roughly 2x profitable on its own, but each next model costs 10x the last. The whole thing only works if scaling keeps delivering. This is a decent argument, but it's not the death knell you think. Models are getting 99% more efficient every 3 years - to get the same amount of output, combined with hardware and (mostly) software upgrades - you can use 99% less power. The number of applications where AI is already "good enough" keeps growing every day. If the cost goes down 99% every three years, it doesn't take long until you can make a ton of money on those applications. If AI stopped progressing today, it would take probably a decade or longer for us to take full advantage of it. So there is tons of forward looking revenue that isn't counted yet. For the foreseeable future, there are MANY MANY uses of models where a company would not want to host its own models and would be GLAD to pay an 4-5x cost for someone else to host the model and hardware for them. I'm as bullish on OpenAI being "worth" $730B as I was on Snap being worth what it IPO'd for - which it's still down about 80% (AFTER inflation, or about ~95% adjusting for gold inflation). But guess what - these are MINIMUM valuations based on 50-80% margins - i.e. they're really getting about ~$30B - the rest is market value of hardware and hosting. OpenAI could be worth 80% less, and they could still make a metric fuck-ton of money selling at IPO with a $1T+ market cap to speculative morons easily... Realistically, very rich people with high risk tolerance are saying that they think OpenAI has a MINIMUM value of ~$100B. That seems very reasonable given the risk tolerance and wealth.

kortilla 8mo ago
> Models are getting 99% more efficient every 3 years The LLM industry has only be around for like 4 years. Extrapolating trends from that is pretty naive.
dfp33 8mo ago
"If AI stopped progressing today, it would take probably a decade or longer for us to take full advantage of it." AI stopped progressing, or LLMs? I really dislike people throwing the term AI around.
- grosswait 8mo ago
  For the purposes of their argument, I don’t think the distinction matters.
blmarket 8mo ago
> 99% more efficient every 3 years It's 2x efficiency. Then I'd take 50% less power instead of ridiculous 99% less power.
- sigmoid10 8mo ago
  GPT-4 came out 3 years ago and you can run comparable models for 1% of the cost nowadays. That is not 2x efficiency. That's two orders of magnitude in end-to-end compute efficiency.
  - swingboy 8mo ago
    How do we know how much it costs? Or is this just based off the token pricing?
    - Xunjin 8mo ago
      That's the bingo of the question... The entire argument is token pricing, which can be subsidized.
  - danielparsons 8mo ago
    you're looking at nearly the entire curve of the tech's development. that's like saying lightbulbs became 99% more energy efficient and therefore will become another 99% more energy efficient. but most techs follow an S curve.
    - MengerSponge 8mo ago
      But S curves are boring and dont moon
    - robotresearcher 8mo ago
      > most techs follow an S curve. All techs, eventually.
    - sigmoid10 8mo ago
      >you're looking at nearly the entire curve of the tech's development That's a pretty strong statement that would need some data or at least a mathematical argument to back it up. Otherwise it's like saying in the 1980s that PCs with 640kB RAM have reached their pinnacle in terms of what users can expect in real life benefits and there's no reason to keep pushing the tech.
moron4hire 8mo ago
We said all the same shit about VR, dude. Even had a global pandemic show up to boost everyone's interest in the key market of telepresence. Turns out the merry go round can stop abruptly.
- solumunus 8mo ago
  Did we?! You and Mark Zuckerberg maybe.
  - mixdup 8mo ago
    "Am I nothing to you?" --Tim Cook
- christophilus 8mo ago
  No. Like many of us, I never saw much value in VR. LLMs have undeniable value that is general and broad. Now, does that mean OpenAI has a moat? No, it does not.
  - moron4hire 8mo ago
    We also said that about VR.
  - player1234 8mo ago
    [dead]
christoff12 8mo ago
> Models are getting 99% more efficient every 3 years - to get the same amount of output, combined with hardware and (mostly) software upgrades - you can use 99% less power. Even if true, this still doesn't bend the curve when paying for the next model. > If AI stopped progressing today, it would take probably a decade or longer for us to take full advantage of it. So there is tons of forward looking revenue that isn't counted yet. If this is true, it's true for the technology overall, and not necessarily OpenAI since inference would get commoditized quickly at that point. OpenAI could continue to have a capital advantage as a public stock, but I don't think it would if the music stopped.
- XenophileJKO 8mo ago
  I would actually like to see the real math currently. The market adoption has increased a lot. The cost to serve has come down a lot per token. Model sizes have not increased exponentially recently (The high point being the aborted GPT-4.5), most refinement recently seems to be extending training on relatively smaller models. When you take this into account together, the relative training to inference income/cost ratio likely has actually changed dramatically.
robotpepi 8mo ago
ok, but everything depends on your numbers being correct. 99% improved efficiency seems kind of a way too optimistic prediction.
janalsncm 8mo ago
When models get cheaper to run for OpenAI, they also get cheaper for everyone else. It gets commoditized. AI might be able to do more, but most people aren’t going to pay for a thing they could get for free. See the many models on Huggingface as examples of that. And as the number of things AI is “good enough” at increases, the list of things on the frontier that people will want to pay OpenAI for shrinks. Even if OpenAI can consistently churn out PhD level math, most companies don’t care about that. So a necessary (but not sufficient) condition for the math to work out is that frontier tasks still exist and are profitable. This is why CEOs keep hyping up AGI. But what they really want is for developers to keep paying to get AI to center a div.
- intrasight 8mo ago
  > get cheaper to run Irrelevant. The model is the moat > most companies don’t care about that. Wrong. They will use the model that gives them an edge. If they are using a PhD but their competitors are using Einstein, they will lose. > center a div For sure a common use case, but is bot what the CEO is concerned about with AI.
  - vidarh 8mo ago
    > Wrong. They will use the model that gives them an edge. If they are using a PhD but their competitors are using Einstein, they will lose. For some tasks that matters. But for a lot of tasks, "good enough but cheaper" will win out. I'm sure there will be a market for whichever company has the best model, but just like most companies don't hire many PhD's, most companies won't feel a need for the highest end models either, above a certain level. E.g. with the release of Sonnet 4.6, I switched a lot of my processes from Opus to Sonnet, because Sonnet 4.6 is good enough, and it means I can do more for less. But I'm also experimenting with Kimi, Qwen, Deepseek, and others for a number of tasks, including fine-grained switching and interleaving. E.g. have a cheap but dumb model filter data or take over when a sub-task is simple enough, in order to have the smart model do less, for example.
    - intrasight 8mo ago
7thpower 8mo ago
I love that you are already confident fitting a curve. I want some of that swagger in my life.
- NickGarfinkel 8mo ago
  I was thinking the same thing.
paulcole 8mo ago
> Models are getting 99% more efficient every 3 years How many years total are you basing this on?
- GolfPopper 8mo ago
  Obligatory XKCD: https://xkcd.com/605/ https://xkcd.com/605/
lelanthran 8mo ago
> Models are getting 99% more efficient every 3 years Ugh. Someone has to do this: https://xkcd.com/605/ https://xkcd.com/605/
- agentica_ai 8mo ago
  [dead]
shruubi 8mo ago
> Models are getting 99% more efficient every 3 years - to get the same amount of output, combined with hardware and (mostly) software upgrades - you can use 99% less power. This is such a poor argument for a number of reasons. 1. Three years ago is basically when the "AI race" really kicked off amongst the frontier companies. You're effectively comparing a car from the 1920/30's to a modern car. 2. Past performance is not an indicator of future performance. You can't just say that LLM's will grow and improve at a fixed rate for all time, that isn't how they or anything else works in the real world. 3. Since it's an open secret that companies like Anthropic and OpenAI are running their models at a loss, a static 99% cheaper every three years arc still puts these companies at a net negative position unless compute, energy and water all somehow start getting 99% cheaper every three years.

kortilla 8mo ago

> Models are getting 99% more efficient every 3 years The LLM industry has only be around for like 4 years. Extrapolating trends from that is pretty naive.

dfp33 8mo ago

"If AI stopped progressing today, it would take probably a decade or longer for us to take full advantage of it." AI stopped progressing, or LLMs? I really dislike people throwing the term AI around.

grosswait 8mo ago
For the purposes of their argument, I don’t think the distinction matters.

blmarket 8mo ago

> 99% more efficient every 3 years It's 2x efficiency. Then I'd take 50% less power instead of ridiculous 99% less power.

sigmoid10 8mo ago
GPT-4 came out 3 years ago and you can run comparable models for 1% of the cost nowadays. That is not 2x efficiency. That's two orders of magnitude in end-to-end compute efficiency.
- swingboy 8mo ago
  How do we know how much it costs? Or is this just based off the token pricing?
  - Xunjin 8mo ago
    That's the bingo of the question... The entire argument is token pricing, which can be subsidized.
- danielparsons 8mo ago
  you're looking at nearly the entire curve of the tech's development. that's like saying lightbulbs became 99% more energy efficient and therefore will become another 99% more energy efficient. but most techs follow an S curve.
  - MengerSponge 8mo ago
    But S curves are boring and dont moon
  - robotresearcher 8mo ago
    > most techs follow an S curve. All techs, eventually.
  - sigmoid10 8mo ago
    >you're looking at nearly the entire curve of the tech's development That's a pretty strong statement that would need some data or at least a mathematical argument to back it up. Otherwise it's like saying in the 1980s that PCs with 640kB RAM have reached their pinnacle in terms of what users can expect in real life benefits and there's no reason to keep pushing the tech.

moron4hire 8mo ago

We said all the same shit about VR, dude. Even had a global pandemic show up to boost everyone's interest in the key market of telepresence. Turns out the merry go round can stop abruptly.

solumunus 8mo ago
Did we?! You and Mark Zuckerberg maybe.
- mixdup 8mo ago
  "Am I nothing to you?" --Tim Cook
christophilus 8mo ago
No. Like many of us, I never saw much value in VR. LLMs have undeniable value that is general and broad. Now, does that mean OpenAI has a moat? No, it does not.
- moron4hire 8mo ago
  We also said that about VR.
- player1234 8mo ago
  [dead]

christoff12 8mo ago

> Models are getting 99% more efficient every 3 years - to get the same amount of output, combined with hardware and (mostly) software upgrades - you can use 99% less power. Even if true, this still doesn't bend the curve when paying for the next model. > If AI stopped progressing today, it would take probably a decade or longer for us to take full advantage of it. So there is tons of forward looking revenue that isn't counted yet. If this is true, it's true for the technology overall, and not necessarily OpenAI since inference would get commoditized quickly at that point. OpenAI could continue to have a capital advantage as a public stock, but I don't think it would if the music stopped.

XenophileJKO 8mo ago
I would actually like to see the real math currently. The market adoption has increased a lot. The cost to serve has come down a lot per token. Model sizes have not increased exponentially recently (The high point being the aborted GPT-4.5), most refinement recently seems to be extending training on relatively smaller models. When you take this into account together, the relative training to inference income/cost ratio likely has actually changed dramatically.

robotpepi 8mo ago

ok, but everything depends on your numbers being correct. 99% improved efficiency seems kind of a way too optimistic prediction.

janalsncm 8mo ago

When models get cheaper to run for OpenAI, they also get cheaper for everyone else. It gets commoditized. AI might be able to do more, but most people aren’t going to pay for a thing they could get for free. See the many models on Huggingface as examples of that. And as the number of things AI is “good enough” at increases, the list of things on the frontier that people will want to pay OpenAI for shrinks. Even if OpenAI can consistently churn out PhD level math, most companies don’t care about that. So a necessary (but not sufficient) condition for the math to work out is that frontier tasks still exist and are profitable. This is why CEOs keep hyping up AGI. But what they really want is for developers to keep paying to get AI to center a div.

intrasight 8mo ago
> get cheaper to run Irrelevant. The model is the moat > most companies don’t care about that. Wrong. They will use the model that gives them an edge. If they are using a PhD but their competitors are using Einstein, they will lose. > center a div For sure a common use case, but is bot what the CEO is concerned about with AI.
- vidarh 8mo ago
  > Wrong. They will use the model that gives them an edge. If they are using a PhD but their competitors are using Einstein, they will lose. For some tasks that matters. But for a lot of tasks, "good enough but cheaper" will win out. I'm sure there will be a market for whichever company has the best model, but just like most companies don't hire many PhD's, most companies won't feel a need for the highest end models either, above a certain level. E.g. with the release of Sonnet 4.6, I switched a lot of my processes from Opus to Sonnet, because Sonnet 4.6 is good enough, and it means I can do more for less. But I'm also experimenting with Kimi, Qwen, Deepseek, and others for a number of tasks, including fine-grained switching and interleaving. E.g. have a cheap but dumb model filter data or take over when a sub-task is simple enough, in order to have the smart model do less, for example.
  - intrasight 8mo ago

7thpower 8mo ago

I love that you are already confident fitting a curve. I want some of that swagger in my life.

NickGarfinkel 8mo ago
I was thinking the same thing.

paulcole 8mo ago

> Models are getting 99% more efficient every 3 years How many years total are you basing this on?

GolfPopper 8mo ago
Obligatory XKCD: https://xkcd.com/605/ https://xkcd.com/605/

lelanthran 8mo ago

> Models are getting 99% more efficient every 3 years Ugh. Someone has to do this: https://xkcd.com/605/ https://xkcd.com/605/

agentica_ai 8mo ago
[dead]

shruubi 8mo ago

> Models are getting 99% more efficient every 3 years - to get the same amount of output, combined with hardware and (mostly) software upgrades - you can use 99% less power. This is such a poor argument for a number of reasons. 1. Three years ago is basically when the "AI race" really kicked off amongst the frontier companies. You're effectively comparing a car from the 1920/30's to a modern car. 2. Past performance is not an indicator of future performance. You can't just say that LLM's will grow and improve at a fixed rate for all time, that isn't how they or anything else works in the real world. 3. Since it's an open secret that companies like Anthropic and OpenAI are running their models at a loss, a static 99% cheaper every three years arc still puts these companies at a net negative position unless compute, energy and water all somehow start getting 99% cheaper every three years.