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From the article : o3 (low) 75.7% 335K $20 o3 (high) 87.5% 57M $3.4K

jl6 2y ago
$3.4K is about what you might pay a magic circle lawyer for an opinion on a matter. Not saying o3 is an efficient use of resources, just saying that it’s not outlandish that a sufficiently good AI could be worth that kind of money.
- ant6n 2y ago
  What’s the liability insurance of the AI like
  - baq 2y ago
    Refer to IBM’s 1979 slide for details on that
- victorbjorklund 2y ago
  You pay that price to a law firm to get good service and to get a "guarantee" of correctness. You get neither from an LLM. Not saying it is not worth anything but you cant compare it to a top law firm.
  - nl 2y ago
    You absolutely do not get a "guarantee" of correctness (event with the airquotes) from any lawyer.
    - manquer 2y ago
      You can sue a lawyer giving certain kinds of bad advice and occasionally win . That is what the guarantee is about
      - bobxmax 2y ago
        You can probably sue Open AI for getting bad legal advice from ChatGPT too.
        throw-qqqqq 2y ago
        Sure, but can you also win the case ;)? On the bottom of ChatGPT.com I see a disclaimer: “ChatGPT can make mistakes. Check important info”. I don’t think you can succesfully sue with such caveat emptor.
mrandish 2y ago
When I saw these numbers back in the initial o3-ARC post, I immediately converted them into "$ per ARC-AGI-1 %" and concluded we may be at a point where each increased increment of 'real human-like novel reasoning' gets exponentially more compute costly. If Mike Knoop is correct, maybe R1 is pointing the way toward more efficient approaches. That would certainly be a good thing. This whole DeepSeek release and the reactions have shown by limiting the export to China of high-end GPUs, the US incentivized China to figure out how to make low-end GPUs work really well. The more subtle meta-lesson here is that the massive flood of investment capital being shoved toward leading edge AI companies has fostered a drag race mentality which prioritized winning top-line performance far above efficiency, costs, etc.

jl6 2y ago

$3.4K is about what you might pay a magic circle lawyer for an opinion on a matter. Not saying o3 is an efficient use of resources, just saying that it’s not outlandish that a sufficiently good AI could be worth that kind of money.

ant6n 2y ago
What’s the liability insurance of the AI like
- baq 2y ago
  Refer to IBM’s 1979 slide for details on that
victorbjorklund 2y ago
You pay that price to a law firm to get good service and to get a "guarantee" of correctness. You get neither from an LLM. Not saying it is not worth anything but you cant compare it to a top law firm.
- nl 2y ago
  You absolutely do not get a "guarantee" of correctness (event with the airquotes) from any lawyer.
  - manquer 2y ago
    You can sue a lawyer giving certain kinds of bad advice and occasionally win . That is what the guarantee is about
    - bobxmax 2y ago
      You can probably sue Open AI for getting bad legal advice from ChatGPT too.
      - throw-qqqqq 2y ago
        Sure, but can you also win the case ;)? On the bottom of ChatGPT.com I see a disclaimer: “ChatGPT can make mistakes. Check important info”. I don’t think you can succesfully sue with such caveat emptor.

mrandish 2y ago

When I saw these numbers back in the initial o3-ARC post, I immediately converted them into "$ per ARC-AGI-1 %" and concluded we may be at a point where each increased increment of 'real human-like novel reasoning' gets exponentially more compute costly. If Mike Knoop is correct, maybe R1 is pointing the way toward more efficient approaches. That would certainly be a good thing. This whole DeepSeek release and the reactions have shown by limiting the export to China of high-end GPUs, the US incentivized China to figure out how to make low-end GPUs work really well. The more subtle meta-lesson here is that the massive flood of investment capital being shoved toward leading edge AI companies has fostered a drag race mentality which prioritized winning top-line performance far above efficiency, costs, etc.