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Beanie Babies, Tulips, NFTs, Web3 tokens were all obviously not going to change the world. The bubble was pure emotion and greed. All the cash inflows were spec

Beanie Babies, Tulips, NFTs, Web3 tokens were all obviously not going to change the world. The bubble was pure emotion and greed. All the cash inflows were speculation. Nvidia made 18 billion in profit last quarter, and expects to make 20 next quarter. That isn't speculation.

noirbot 2y ago
I mean, how much money did the NFT companies and Tulip vendors make? Nvidia isn't OpenAI. They're selling products that the bubble is built on, not the bubble itself. How much money is OpenAI or Anthropic making? Because that's what people are thinking is speculative value. My position has always been that Gemini/ChatGPT/Claude are all pretty great at a cost of Free, and grow increasingly questionable past that. My work is already limiting how many ChatGPT users we can afford with their price increases, and I'm pretty sure OpenAI is still not profitable. If ChatGPT is $50/month as a breakeven cost for them, how many people/companies will buy it then? Most jobs I've been at won't pay for JetBrains licenses that cost way less per head. I feel like the best comparison is something like Uber or AirBnB where it's easy to be excited about it when all the services are crazy discounted by free VC money, but when they have to start turning a profit, they're back to actually competing with other tools.
- gitfan86 2y ago
  The cost to run models of a specific quality goes down by about 90% a year. So the unprofitable $50/month cost becomes profitable in about 6 months. But the big deal isn't OAI being a profitable company. The big deal is that GPT6 will be 100x more useful in doing productive work. Tulips did not have cash flow like this. It was only people selling to speculators who hoped to sell again to another speculator.
  - noirbot 2y ago
    > The big deal is that GPT6 will be 100x more useful in doing productive work. Citation extremely needed. There's a lot of people and companies downstream of OpenAI speculating on that 100x that are gonna be in a lot of trouble if it's even just 10x, let along 5x. Again, not saying that none of this has any value, just that the value may well never live up to the cost. Uber's not a worthless company or service, but they're far from the values or profits they were pitching 10 years ago.
    - gitfan86 2y ago
      I would point you to tow pieces of data. 1. Drive a new Tesla with the latest Supervised FSD and measure how often you have to intervene to stop a crash. 2. Go back and look at your own expectations around AI two years ago. Did thing progress the way you expected or did they progress further?
      - oska 2y ago
        1. You are comparing 2 very different things. GPT6 is a generative LLM. Tesla's FSD is machine learning. 2. I have no expectations for 'AI' because the term is a nonsense label. I have followed and been excited by machine learning for a good number of years, and my expectations of progress were pretty much on par. The progress with LLMs has taken me a little by surprise, but I am also cognisant that their progress is being massively over-hyped presently, not least by ppl who call them 'AI' and then, even more foolishly, go on to talk about 'AGI' (a nonsense upon a nonsense).
        gitfan86 2y ago
        I agree that AGI is a meaningless term. I'm intentionally including FSD and LLMs under the same category of technologies that will have a huge impact. The point of this thread is that the demand for inference is going to skyrocket because AI is going to get a lot more useful.
        oska 2y ago
        Putting aside my (trenchant) philosophical issues with the term 'AI', I also don't think just pragmatically that it's a good categorical label. We both appear to agree that Machine Learning is a very powerful technology that will have huge impacts. Machine Learning requires (and will continue to require) a lot of compute and thus large costs but will also, almost certainly, produce great profits in some domains (FSD being one). It's a lot less clear to me that LLMs will 1) continue to require lots of compute beyond the short term (languages can get close to being 'solved') or 2) that LLMs will generate substantial profits because a) the model can escape capture from a monopoly player far more easily and b) while useful for translation, pulling summarised data from a corpus, recognition of voice commands, etc, none of these applications actually make for the kind of profound impacts that ML is capable of, because none of them transcend human ability like ML has the power to do.

noirbot 2y ago

I mean, how much money did the NFT companies and Tulip vendors make? Nvidia isn't OpenAI. They're selling products that the bubble is built on, not the bubble itself. How much money is OpenAI or Anthropic making? Because that's what people are thinking is speculative value. My position has always been that Gemini/ChatGPT/Claude are all pretty great at a cost of Free, and grow increasingly questionable past that. My work is already limiting how many ChatGPT users we can afford with their price increases, and I'm pretty sure OpenAI is still not profitable. If ChatGPT is $50/month as a breakeven cost for them, how many people/companies will buy it then? Most jobs I've been at won't pay for JetBrains licenses that cost way less per head. I feel like the best comparison is something like Uber or AirBnB where it's easy to be excited about it when all the services are crazy discounted by free VC money, but when they have to start turning a profit, they're back to actually competing with other tools.

gitfan86 2y ago
The cost to run models of a specific quality goes down by about 90% a year. So the unprofitable $50/month cost becomes profitable in about 6 months. But the big deal isn't OAI being a profitable company. The big deal is that GPT6 will be 100x more useful in doing productive work. Tulips did not have cash flow like this. It was only people selling to speculators who hoped to sell again to another speculator.
- noirbot 2y ago
  > The big deal is that GPT6 will be 100x more useful in doing productive work. Citation extremely needed. There's a lot of people and companies downstream of OpenAI speculating on that 100x that are gonna be in a lot of trouble if it's even just 10x, let along 5x. Again, not saying that none of this has any value, just that the value may well never live up to the cost. Uber's not a worthless company or service, but they're far from the values or profits they were pitching 10 years ago.
  - gitfan86 2y ago
    I would point you to tow pieces of data. 1. Drive a new Tesla with the latest Supervised FSD and measure how often you have to intervene to stop a crash. 2. Go back and look at your own expectations around AI two years ago. Did thing progress the way you expected or did they progress further?
    - oska 2y ago
      1. You are comparing 2 very different things. GPT6 is a generative LLM. Tesla's FSD is machine learning. 2. I have no expectations for 'AI' because the term is a nonsense label. I have followed and been excited by machine learning for a good number of years, and my expectations of progress were pretty much on par. The progress with LLMs has taken me a little by surprise, but I am also cognisant that their progress is being massively over-hyped presently, not least by ppl who call them 'AI' and then, even more foolishly, go on to talk about 'AGI' (a nonsense upon a nonsense).
      - gitfan86 2y ago
        I agree that AGI is a meaningless term. I'm intentionally including FSD and LLMs under the same category of technologies that will have a huge impact. The point of this thread is that the demand for inference is going to skyrocket because AI is going to get a lot more useful.
        oska 2y ago
        Putting aside my (trenchant) philosophical issues with the term 'AI', I also don't think just pragmatically that it's a good categorical label. We both appear to agree that Machine Learning is a very powerful technology that will have huge impacts. Machine Learning requires (and will continue to require) a lot of compute and thus large costs but will also, almost certainly, produce great profits in some domains (FSD being one). It's a lot less clear to me that LLMs will 1) continue to require lots of compute beyond the short term (languages can get close to being 'solved') or 2) that LLMs will generate substantial profits because a) the model can escape capture from a monopoly player far more easily and b) while useful for translation, pulling summarised data from a corpus, recognition of voice commands, etc, none of these applications actually make for the kind of profound impacts that ML is capable of, because none of them transcend human ability like ML has the power to do.