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  • Oct 22, 2026, 10:57 AM

    @lune @jcoglan

    When people say 'this is possible only with LLM-generated programming' what they often actually means is 'this is possible only with massively subsidised automation'. An LLM is probably not the most efficient way of getting to the end point, but when the compute for the LLM is subsidised by a factor of 10-100x, if the other technique requires 20% as much compute it's still vastly more expensive if you can't burn someone else's money on it.

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  • Oct 22, 2026, 8:43 PM

    @radex

    With respect, this entire LLM phenomenon is a Gold Rush.

    Yes, you're right, under one definition of cost. Yet marginal serving cost (GPUs, power, datacenter rent per token served). Here price is probably well above cost.

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  • Oct 22, 2026, 7:55 PM

    @david_chisnall @lune @jcoglan this is the part that's been boggling my mind! Every time when an impressive "see! LLM can do *this* now!" happens - there's inevitably prior art. Which seems consistent with how LLMs work in general

    It's just traditionally all these problems have been deemed too expensive by those in charge of money

    But somehow these corporations decided to burn orders of magnitude *more* of money and make it everyone else's problem AND they keep getting rewarded for it!? WTF!?

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  • Oct 22, 2026, 8:57 PM

    @david_chisnall @lune @jcoglan Isn't it amazing what can be achieved when you task what is a supercomputer at a problem? Subsidised monkeys and typewriters on a scale not seen before. Or fling enough shit against a wall and eventually a visually appealing pattern will appear.

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  • Oct 23, 2026, 2:24 AM

    @david_chisnall @lune @jcoglan

    Do you have sources for the claimed subsidy size?

    From what I can tell, API payment is profitable to the large providers, Enterprise seats are profitable or neutral, and paid consumer subscription plans are at most 2x subsidized.

    Source: I ran models similar to this for a smaller company for a couple of years.

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