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.

$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.

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.

You absolutely do not get a "guarantee" of correctness (event with the airquotes) from any lawyer.

You can sue a lawyer giving certain kinds of bad advice and occasionally win . That is what the guarantee is about

You can probably sue Open AI for getting bad legal advice from ChatGPT too.

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.

What’s the liability insurance of the AI like

Refer to IBM’s 1979 slide for details on that