As always with Zitron, grab a beverage before settling in.

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[–] 29 points 1 year ago* (last edited 1 year ago) (13 children)

Like Zitron says in the article, we’re 3 years into the AI era and there is not a single actually profitable company. For comparison, the dot-com bubble was About 5-6 years from start to bust. It’s all smoke and mirrors and sketchy accounting.

Even if/when the AI hype settles and perhaps the tech finds its true (profitable) calling, the tech itself is still insanely expensive to run and train. It’s going to boil down to Microsoft and/or X owning nuclear power plants, and everyone else renting usage from them.

People are making money in AI, but like always, it’s the founders and C-suite, while the staff are kicked to the curb. It’s all a shell game and everyone that has integrated AI into their lives and company workflows, is gonna get the rug pulled out from under them.

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  • [–] 8 points 1 year ago (4 children)

    and there is not a single actually profitable company

    This is a little misleading, because obviously FAANG (and others) are all building AI systems, and are all profitable. There are also tons of companies applying machine learning to various areas that are doing well from a profitability standpoint (mostly B2B SaaS that are enhancing extant tools). This statement is really only true for the glut of "AI companies" that do nothing but produce LLMs to plug into stuff.

    My personal take is that this is just revealing how disconnected from the tech industry VCs are, who are the ones buying into this hype and burning billions of dollars on (as you said) smoke and mirrors companies like Anthropic and OpenAI.

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  • [–] 9 points 1 year ago*

    The thing is, companies like Google, Facebook, Amazon and Microsoft are already profitable, so it could lose them huge amounts of money, with no real meaningful benefit to user retention or B2B sales, but the companies as a whole would still be profitable. It could be a huge money black hole, but they continue to chase it out of unjustified FOMO and in an attempt to keep share prices high through misplaced investor confidence.

    Apple’s share price has taken a pretty big hit from the perception that they’re “falling behind” on AI, even if they’ve mostly just backed away from it because users didn’t like it when it was shoved in their face. Other companies are probably looking at that and saying “hey, we’d rather keep the stock market happy and our share prices high rather than stop wasting money on this”.

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  • [–] [S] 6 points 1 year ago

    This is an interesting take in that only doing one thing but doing it well has been, historically, how businesses thrived. This vertical integration thing and startups looking to be bought out instead of trying to make it on their own (obviously, VCs play a role in this) has led to jacks of all trades.

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  • [–] [S] 2 points 1 year ago (7 children)

    I don't think it's going to come down to these absurd datacentres. We're only a few years off from platform-agnostic local inference at mass-market prices. Could I get a 5090? Yes. Legally? No.

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  • [–] 5 points 1 year ago (1 child)

    I have to think that most people won't want to do local training.

    It's like Gentoo Linux. Yeah, you can compile everything with the exact optimal set of options for your kit, but at huge inefficiency when most use cases might be mostly served by two or three pre built options.

    If you're just running pre-made models, plenty of them will run on a 6900XT or whatever.

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  • [–] [S] 3 points 1 year ago

    I don't expect anyone other than ... I don't even know what the current term is ... geeks? batshit billionaires? to be doing training.

    I'm very much of the belief that our next big leap in LLMs is local processing. Once my interactions stay on my device, I'll jump in.

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  • [+] 3 points 1 year ago (4 children)
  • [–] [S] 3 points 1 year ago (3 children)

    There are already large local models. It's a question of having the hardware, which has historically gotten more powerful with each generation. I don't think it's going to be phones for quite some time, but on desktop, absolutely.

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  • [+] 2 points 1 year ago (2 children)
  • [–] [S] 3 points 1 year ago

    For the security tradeoff of sensitive data not heading to the cloud for processing? Not all businesses, but many would definitely see value in it. We're also discussing this as though the options are binary ... models could also be hosted on company servers that employees VPN into.

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  • [–] 1 point 1 year ago*

    Keywords: NPU, unified RAM

    Apple is doing it, AMD is doing it, phones are doing it.

    GPUs with dedicated VRAM are an inefficient way of doing inference. They've been great for research purposes, into what type of NPU may be the best one, but that's been answered already for LLMs. Current step is, achieving mass production.

    5 years sounds realistic, unless WW3.

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