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Apple Will 'Watch Everything Burn' When the AI Bubble Bursts | Ed Zitron (www.macrumors.com)
submitted 1 month ago* by to c/apple@lemmy.zip
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(…) Apple Intelligence was both the worst and best thing to happen to Apple as far as the AI bubble goes.

It was a mass-radicalization of its users against AI – an attempt to cram a barely-functional series of add-ons that nobody asked for in the most-obtrusive way possible, with summaries that were almost immediately turned into memes and a new Siri that was, somehow, even worse than the old Siri.

And I think that told Apple to pump the brakes. It's barely spent anything on capex. It's barely done anything with AI. Despite headline after headline claiming it's "falling behind," nobody can really explain what it is it's falling behind on or why it matters. People hate Apple Intelligence, and I think Apple knows that, and so they're going to jingle the keys for the markets by putting "AI" on stuff without ever really putting their back into it.

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[–] 8 points 1 month ago* (2 children)

I think letting the others work out the kinks before making big investments makes sense. What is the benefit of being first vs. additional sales? Look at Microsoft with their OS LLM integration. They are backpeddling due to cost and user resistance. Their users can always use other general purpose LLMs.

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  • [–] 2 points 1 month ago (1 child)

    That would mean that they would be starting when other players are well established in the LLM space.

    I think they are waiting, or maybe even working on, the next paradigm in AI. There are a few new approaches, like JEPA, that are a lot less resource intensive, and which are way closer to actual intelligence.

    LLMs are at a very advanced state of diminishing returns, and are a bit of a dead end. They are not going to get much better. They need much higher resource investment for minimal gains, and are inherently bad in many use cases and scenarios.

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  • [–] 1 point 1 month ago*

    Thanks for the link. Latent embedding is nothing new, need to read up on it a bit to see how it works. True artificial intelligence needs some representation of ideas and concepts.. Language provides this and has plenty of training data, but is verbose and ambiguous. Using a compressed/latent representation totally makes sense, but I wonder how it's trained.

    I don't consider LLMs to be AI, but it's a step along the way. As you say though, it feels like a dead end.

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