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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 da

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 2y 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 2y 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 2y 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 2y 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 2y ago
  A computer can never be held accountable. Therefore, a computer must never make a management decision.
nearbuy 2y 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 2y 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 2y 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 2y 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.

ryandrake 2y 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 2y 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 2y 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 2y 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 2y ago
A computer can never be held accountable. Therefore, a computer must never make a management decision.

nearbuy 2y 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 2y 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 2y 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 2y 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.