5 ms·

My personal hunch is that the “diffusion curve” for AI is slower than most people in this space think. Most businesspeople I talk to have only tried a basic Cop

by npilk 1mo ago

My personal hunch is that the “diffusion curve” for AI is slower than most people in this space think. Most businesspeople I talk to have only tried a basic Copilot chat and/or free ChatGPT. Many still haven’t used anything “AI” at all. As more use cases become practicable and cost-effective, more software will include AI, and more people will use AI with or without knowing. Also: note that the Wall Street analyst estimates Ed cites (and then declares impossible targets) are predictions by serious people with a lot of money at stake. Of course they could be wrong, but they’re not made up.

cogman10 1mo ago
> Also: note that the Wall Street analyst estimates Ed cites (and then declares impossible targets) are predictions by serious people with a lot of money at stake. I think Ed is wrong. But I have to push back. Wall street analysts are more likely to misrepresent precisely because they have money at stake. Much like ed has a pretty vested interest in saying the sky is falling (that's his brand at this point) the analysts have vested interests in saying everything is fine and keep investing. We can see similar behaviors with analysts like zero hedge, which every week write a new "the bubble is about to pop" article. A lot of this, IMO, is similar to a fact about the weather I'm probably misremember from stats. If you always predict "it will be sunny tomorrow" almost anywhere in the world you'll be right something like 80 to 90% of the time (citation needed). Analysts who always say "things are great and stocks will go up" will be right most of the time. The tricky thing has always been predicting when and if a pop will happen.
- npilk 1mo ago
  Yes, fair point, analysts tend not to be too critical. But they are still developing more detailed models to understand and project things like revenue and profit, so there's some rigor - at least more than company leadership just giving takes or stating platitudes.
deleted 1mo ago
[deleted]
ragall 1mo ago
Here's from a VC: https://x.com/bryce/status/2080766415716692385 https://x.com/bryce/status/2080766415716692385 "Have lost count of the number of CEOs I’ve talked to this week that are planning to be moved off OAI and Anthropic entirely by years end."
- npilk 1mo ago
  Interesting. I wonder what they will move to, and where it would be hosted.
  - ragall 1mo ago
    Self-managed open models hosted on GPU clouds.
    - npilk 1mo ago
      A lot of that demand could still flow through to AWS, Azure, etc. Maybe not at some startups, but I'd expect bigger companies would still use the hyperscalers even if they move off OAI/Anthropic. The real problem for the hyperscalers would be demand stalling out entirely (or maybe small local models getting good enough that people don't use the cloud).
      - ragall 1mo ago
        The infra expansion of the hyperscalers is predicated on, and financially justified by, the presumed future growth of OpenAI and Anthropic, and their projected revenue. If the latter is not going to happen, I expect OpenAI and Anthropic to go bankrupt, which will trigger a restructuring of the hyperscalers, which should flood the market with either training HW or training capacity at very low cost, and it's not necessarily the hyperscalers that will benefit the most: I believe some of the GPU clouds are using HW leased or bought form NVidia, and running inside second-tier datacenters, not the hyperscalers.

by npilk 1mo ago

cogman10 1mo ago

> Also: note that the Wall Street analyst estimates Ed cites (and then declares impossible targets) are predictions by serious people with a lot of money at stake. I think Ed is wrong. But I have to push back. Wall street analysts are more likely to misrepresent precisely because they have money at stake. Much like ed has a pretty vested interest in saying the sky is falling (that's his brand at this point) the analysts have vested interests in saying everything is fine and keep investing. We can see similar behaviors with analysts like zero hedge, which every week write a new "the bubble is about to pop" article. A lot of this, IMO, is similar to a fact about the weather I'm probably misremember from stats. If you always predict "it will be sunny tomorrow" almost anywhere in the world you'll be right something like 80 to 90% of the time (citation needed). Analysts who always say "things are great and stocks will go up" will be right most of the time. The tricky thing has always been predicting when and if a pop will happen.

npilk 1mo ago
Yes, fair point, analysts tend not to be too critical. But they are still developing more detailed models to understand and project things like revenue and profit, so there's some rigor - at least more than company leadership just giving takes or stating platitudes.

deleted 1mo ago

[deleted]

ragall 1mo ago

Here's from a VC: https://x.com/bryce/status/2080766415716692385 https://x.com/bryce/status/2080766415716692385 "Have lost count of the number of CEOs I’ve talked to this week that are planning to be moved off OAI and Anthropic entirely by years end."

npilk 1mo ago
Interesting. I wonder what they will move to, and where it would be hosted.
- ragall 1mo ago
  Self-managed open models hosted on GPU clouds.
  - npilk 1mo ago
    A lot of that demand could still flow through to AWS, Azure, etc. Maybe not at some startups, but I'd expect bigger companies would still use the hyperscalers even if they move off OAI/Anthropic. The real problem for the hyperscalers would be demand stalling out entirely (or maybe small local models getting good enough that people don't use the cloud).
    - ragall 1mo ago
      The infra expansion of the hyperscalers is predicated on, and financially justified by, the presumed future growth of OpenAI and Anthropic, and their projected revenue. If the latter is not going to happen, I expect OpenAI and Anthropic to go bankrupt, which will trigger a restructuring of the hyperscalers, which should flood the market with either training HW or training capacity at very low cost, and it's not necessarily the hyperscalers that will benefit the most: I believe some of the GPU clouds are using HW leased or bought form NVidia, and running inside second-tier datacenters, not the hyperscalers.