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760 points MindBreaker2605 | 2 comments | | HN request time: 0.48s | source
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sebmellen ◴[] No.45897467[source]
Making LeCun report to Wang was the most boneheaded move imaginable. But… I suppose Zuckerberg knows what he wants, which is AI slopware and not truly groundbreaking foundation models.
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ACCount37 ◴[] No.45897970[source]
That was obviously him getting sidelined. And it's easy to see why.

LLMs get results. None of the Yann LeCun's pet projects do. He had ample time to prove that his approach is promising, and he didn't.

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camillomiller ◴[] No.45898122[source]
LLMs get results is quite the bold statement. If they get results, they should be getting adopted, and they should be making money. This is all built on hazy promises. If you had marketable results, you wouldn't have to hide 20+ billion dollars of debt financing into an obscure SPV. LLMs are the most baffling piece of tech. They are incredible, and yet marred by their non-deterministic hallucinatory nature, and bound to fail in adoption unless you convince everyone that they don't need precision and accuracy, but they can do their business at 75% quality, just with less human overhead. It's quite the thing to convince people of, and that's why it needs the spend it's needing. A lot of we-need-to-stay-in-the-loop CEOs and bigwigs got infatuated with the idea, and most probably they just had their companies get addicted to the tech equivalent of crack cocaine. A reckoning is coming.
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ACCount37 ◴[] No.45898220[source]
LLMs get results, yes. They are getting adopted, and they are making money.

Frontier models are all profitable. Inference is sold with a damn good margin, and the amounts of inference AI companies sell keeps rising. This necessitates putting more and more money into infrastructure. AI R&D is extremely expensive too, and this necessitates even more spending.

A mistake I see people make over and over again is keeping track of the spending but overlooking the revenue altogether. Which sure is weird: you don't get from $0B in revenue to $12B in revenue in a few years by not having a product anyone wants to buy.

And I find all the talk of "non-deterministic hallucinatory nature" to be overrated. Because humans suffer from all of that too, just less severely. On top of a number of other issues current AIs don't suffer from.

Nonetheless, we use human labor for things. All AI has to do is provide a "good enough" alternative, and it often does.

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ripe ◴[] No.45899125[source]
> Frontier models are all profitable.

This is an extraordinary claim and needs extraordinary proof.

LLMs are raising lots of investor money, but that's a completely different thing from being profitable.

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1. jonas21 ◴[] No.45902241[source]
Dario Amodei from Anthropic has made the claim that if you looked at each model as a separate business, it would be profitable [1], i.e. each model brings in more revenue over its lifetime than the total of training + inference costs. It's only because you're simultaneously training the next generation of models, which are larger and more expensive to train, but aren't generating revenue yet, that the company as a whole loses money in a given year.

Now, it's not like he opened up Anthropic's books for an audit, so you don't necessarily have to trust him. But you do need to believe that either (a) what he is saying is roughly true or (b) he is making the sort of fraudulent statements that could get you sent to prison.

[1] https://www.youtube.com/watch?v=GcqQ1ebBqkc&t=1014s

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2. f33d5173 ◴[] No.45902337[source]
He's speaking in a purely hypothetical sense. The title of the video even makes sure to note "in this example". If it turned this wasn't true of anthropic, it certainly wouldn't be fraud.