Speaking during an interview on CNBC's "Squawk on the Street" segment earlier this week, CEO of cybersecurity giant Palo Alto Networks Nikesh Arora implored the tech industry to lower the cost of AI.

During the segment, the chief executive argued that the cost to use large language models (LLMs) has to drop by 20 percent by 2027 — and 90 percent by 2028 — for the tech to be useful to enterprises.

"We need to see the pricing for AI come down," Arora said.

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[–] 32 points 2 months ago (7 children)

Prices for services like this do not ever decrease. By 2028, costs will likely be double what they are now.

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  • [–] 18 points 2 months ago (1 child)

    More than that. At the current discount prices, LLM providers bleed money like mad. They will run out of cash soon if they don't raise the prices to profitable levels. Just double won't cover it.

    Which will lead to most customers dropping the LLM services stat. Which in turn will force the LLM providers to up their prices even more...

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  • [–] 13 points 2 months ago

    When my last employer opened up a publicly available chatbot they asked us not to test it because of how much it costs. It was a sales bot, we had recently had the company decimated by redundancies.

    A common act of protest was to abuse the public chatbot from a non work device.

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  • [–] 10 points 2 months ago

    The last 50 years has shown that the new model is to get a sector of the economy or consumers dependent on a service then once the competition has faded away, jack up the price. Competition either never shows up or collaborates on pricing.

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  • [–] 4 points 2 months ago (2 children)

    Depends entirely on whether or not Moore's law has hit a physical limit yet.

    If we're still doubling computational every X years for the same cost, we'd be able to see same models cheaper. If we're however finally hitting physical limits, then yeah. It won't be cheaper and we can't just smash more context in it like there's no tomorrow.

    Then the most likely breakthrough would have to be DNA storage (tri point storage, rather than bi point storage) and then we'd need fast read/write to DNA storage. As that'd theoretically allow for more context for the LLMs, context is the biggest bottleneck

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