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New models are constantly being trained, but they don't have to be constanatly trained. If OpenAI or Anthropic 'just' wanted to be a profitable business, they c

New models are constantly being trained, but they don't have to be constanatly trained. If OpenAI or Anthropic 'just' wanted to be a profitable business, they could ease up on that, but they're both racing to create a machine to can automate all or most human labour.

jlorentz1 1mo ago
If they stop racing, the wave of open models will pass them and their inference margins will drop to zero. A realistic model of their operating costs surely must include ongoing training.
- famouswaffles 1mo ago
  >If they stop racing, the wave of open models will pass them and their inference margins will drop to zero. 1. ChatGPT's ~billion weekly active users aren't going to give a shit about some open source model, and neither would most of Anthropic's Enterprise cutomers. 2. Open AI and Anthropic are in a race between themselves, not open source model trainers. There's a reason those models are consistently several months behind and often perform much worse than benchmarks indicate. In the first place, they're only as close as they currently are from the distillation attacks on Anthropic and OpenAI. If they slowed down, they would slow down too.
  - jlorentz1 1mo ago
    1. They absolutely would, eventually, if open models surpassed Ant & OAI's flagships. Keep in mind "surpassed" encompasses both output quality and cost-saving architecture innovations like DSA, which may not be possible to apply to old models (and may take advantage of new hardware!) 2. "They're only as close..." is not natural law. You really think open weights couldn't catch up to a fixed target if Beijing makes it a priority? And what happens to their valuations if they abandon the goal of building AGI? There is no strategic alternative to constant training for these companies, which is why they're, uh, constantly training.
    - famouswaffles 1mo ago
      1. Mainstream users don't care about benchmarks or whether some open weight model has technically surpassed GPT-X on a leaderboard. They care about whther GPT does what they want it to do. Capable Open source models already exist, and that hasn't caused ordinary chatGPT users to abandon chatGPT for them. Hell Anthropic exists, and that didn't cause that either. OpenAI still dwarfs Anthropic in the consumer space. Obviously, sufficiently large differences in capability can eventually matter like when Anthropic blew everyone away in coding at one point, but that's very different from saying OpenAI has to train a frontier model every few months or inference margins go to zero. 2. Nobody said anything about a fixed target. Not sure why you interpreted 'slow down' as 'freeze current models forever'. >And what happens to their valuations if they abandon the goal of building AGI? The capabilities these companies already have, combined with their growing userbases, revenue and distribution are plausibly enough to sustain trillion dollar businesses already. OpenAI is a company with a billion active users that has started running ads that reached ARR of $1 billion in the first 2 months and Anthropic is a company that hit $11B+ in revenue last quarter after a pretty massive jump.
      - jlorentz1 1mo ago
        Enterprises absolutely care about benchmarks (especially internal ones, but the headline benchmaxxed ones too), and the Ant coding thing is a great example. How long did that last again? A few months? Illustrates my point perfectly. Switching is easy. Why would a business have any loyalty to one text->text endpoint over another? The consumer market may be less responsive to quality, sure, but it is more responsive to cost which I mentioned. It's also just not as big. Well, I'm not really talking about freezing models forever either, I'm saying that nonstop training is a necessary part of their business. I don't think slowing down is untenable, I just think it's silly not to expect & account for ongoing training costs. That's all my original comment meant. I also don't understand why you think the open labs couldn't catch up to a given level of quality. If something's been done twice already, why can't a well funded team of experts somewhere else do it a third time? Sounds like wishful thinking.
        deleted 1mo ago
- JumpCrisscross 1mo ago
  > If they stop racing, the wave of open models will pass them How is this different from planes or cars? If Ford or Boeing zero line their R&D...well, we know what happens.
  - jlorentz1 1mo ago
    Two big differences are low switching costs and a higher rate of innovation.
    - JumpCrisscross 1mo ago
      > Two big differences are low switching costs and a higher rate of innovation The concept this entire thread seems to need is the difference between fixed and variable costs.
  - eeuej 1mo ago
    You’re the only person I’ve seen on here with strong foundations who should be allowed to talk about all things finance and valuation (besides me). lol
JumpCrisscross 1mo ago
> but they don't have to be constanatly trained This is an open question!

jlorentz1 1mo ago

If they stop racing, the wave of open models will pass them and their inference margins will drop to zero. A realistic model of their operating costs surely must include ongoing training.

famouswaffles 1mo ago
>If they stop racing, the wave of open models will pass them and their inference margins will drop to zero. 1. ChatGPT's ~billion weekly active users aren't going to give a shit about some open source model, and neither would most of Anthropic's Enterprise cutomers. 2. Open AI and Anthropic are in a race between themselves, not open source model trainers. There's a reason those models are consistently several months behind and often perform much worse than benchmarks indicate. In the first place, they're only as close as they currently are from the distillation attacks on Anthropic and OpenAI. If they slowed down, they would slow down too.
- jlorentz1 1mo ago
  1. They absolutely would, eventually, if open models surpassed Ant & OAI's flagships. Keep in mind "surpassed" encompasses both output quality and cost-saving architecture innovations like DSA, which may not be possible to apply to old models (and may take advantage of new hardware!) 2. "They're only as close..." is not natural law. You really think open weights couldn't catch up to a fixed target if Beijing makes it a priority? And what happens to their valuations if they abandon the goal of building AGI? There is no strategic alternative to constant training for these companies, which is why they're, uh, constantly training.
  - famouswaffles 1mo ago
    1. Mainstream users don't care about benchmarks or whether some open weight model has technically surpassed GPT-X on a leaderboard. They care about whther GPT does what they want it to do. Capable Open source models already exist, and that hasn't caused ordinary chatGPT users to abandon chatGPT for them. Hell Anthropic exists, and that didn't cause that either. OpenAI still dwarfs Anthropic in the consumer space. Obviously, sufficiently large differences in capability can eventually matter like when Anthropic blew everyone away in coding at one point, but that's very different from saying OpenAI has to train a frontier model every few months or inference margins go to zero. 2. Nobody said anything about a fixed target. Not sure why you interpreted 'slow down' as 'freeze current models forever'. >And what happens to their valuations if they abandon the goal of building AGI? The capabilities these companies already have, combined with their growing userbases, revenue and distribution are plausibly enough to sustain trillion dollar businesses already. OpenAI is a company with a billion active users that has started running ads that reached ARR of $1 billion in the first 2 months and Anthropic is a company that hit $11B+ in revenue last quarter after a pretty massive jump.
    - jlorentz1 1mo ago
      Enterprises absolutely care about benchmarks (especially internal ones, but the headline benchmaxxed ones too), and the Ant coding thing is a great example. How long did that last again? A few months? Illustrates my point perfectly. Switching is easy. Why would a business have any loyalty to one text->text endpoint over another? The consumer market may be less responsive to quality, sure, but it is more responsive to cost which I mentioned. It's also just not as big. Well, I'm not really talking about freezing models forever either, I'm saying that nonstop training is a necessary part of their business. I don't think slowing down is untenable, I just think it's silly not to expect & account for ongoing training costs. That's all my original comment meant. I also don't understand why you think the open labs couldn't catch up to a given level of quality. If something's been done twice already, why can't a well funded team of experts somewhere else do it a third time? Sounds like wishful thinking.
      - deleted 1mo ago
JumpCrisscross 1mo ago
> If they stop racing, the wave of open models will pass them How is this different from planes or cars? If Ford or Boeing zero line their R&D...well, we know what happens.
- jlorentz1 1mo ago
  Two big differences are low switching costs and a higher rate of innovation.
  - JumpCrisscross 1mo ago
    > Two big differences are low switching costs and a higher rate of innovation The concept this entire thread seems to need is the difference between fixed and variable costs.
- eeuej 1mo ago
  You’re the only person I’ve seen on here with strong foundations who should be allowed to talk about all things finance and valuation (besides me). lol

JumpCrisscross 1mo ago

> but they don't have to be constanatly trained This is an open question!