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LLM Inevitabilism

(tomrenner.com)
1613 points SwoopsFromAbove | 2 comments | | HN request time: 0.503s | source
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lsy ◴[] No.44568114[source]
I think two things can be true simultaneously:

1. LLMs are a new technology and it's hard to put the genie back in the bottle with that. It's difficult to imagine a future where they don't continue to exist in some form, with all the timesaving benefits and social issues that come with them.

2. Almost three years in, companies investing in LLMs have not yet discovered a business model that justifies the massive expenditure of training and hosting them, the majority of consumer usage is at the free tier, the industry is seeing the first signs of pulling back investments, and model capabilities are plateauing at a level where most people agree that the output is trite and unpleasant to consume.

There are many technologies that have seemed inevitable and seen retreats under the lack of commensurate business return (the supersonic jetliner), and several that seemed poised to displace both old tech and labor but have settled into specific use cases (the microwave oven). Given the lack of a sufficiently profitable business model, it feels as likely as not that LLMs settle somewhere a little less remarkable, and hopefully less annoying, than today's almost universally disliked attempts to cram it everywhere.

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alonsonic ◴[] No.44570711[source]
I'm confused with your second point. LLM companies are not making any money from current models? Openai generates 10b USD ARR and has 100M MAUs. Yes they are running at a loss right now but that's because they are racing to improve models. If they stopped today to focus on optimization of their current models to minimize operating cost and monetizing their massive user base you think they don't have a successful business model? People use this tools daily, this is inevitable.
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dbalatero ◴[] No.44570964[source]
They might generate 10b ARR, but they lose a lot more than that. Their paid users are a fraction of the free riders.

https://www.wheresyoured.at/openai-is-a-systemic-risk-to-the...

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Centigonal ◴[] No.44572286[source]
This echoes a lot of the rhetoric around "but how will facebook/twitter/etc make money?" back in the mid 2000s. LLMs might shake out differently from the social web, but I don't think that speculating about the flexibility of demand curves is a particularly useful exercise in an industry where the marginal cost of inference capacity is measured in microcents per token. Plus, the question at hand is "will LLMs be relevant?" and not "will LLMs be massively profitable to model providers?"
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amrocha ◴[] No.44572513[source]
The point is that if they’re not profitable they won’t be relevant since they’re so expensive to run.

And there was never any question as to how social media would make money, everyone knew it would be ads. LLMs can’t do ads without compromising the product.

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1. Centigonal ◴[] No.44572620[source]
I can run an LLM on my RTX3090 that is at least as useful to me in my daily life as an AAA game that would otherwise justify the cost of the hardware. This is today, which I suspect is in the upper part of the Kuznets curve for AI inference tech. I don't see a future where LLMs are too expensive to run (at least for some subset of valuable use cases) as likely.
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2. TeMPOraL ◴[] No.44573316[source]
I don't even get where this argument comes from. Pretraining is expensive, yes, but both LoRAs in diffusion models and finetunes of transformers show us that this is not the be-all, end-all; there's plenty of work being done on extensively tuning base models for cheap.

But inference? Inference is dirt cheap and keeps getting cheaper. You can run models lagging 6-12 years on consumer hardware, and by this I don't mean absolutely top-shelf specs, but more of "oh cool, turns out the {upper-range gaming GPU/Apple Silicon machine} I bought a year ago is actually great at running local {image generation/LLM inference}!" level. This is not to say you'll be able to run o3 or Opus 4 on a laptop next year - larger and more powerful models obviously require more hardware resources. But this should anchor expectations a bit.

We're measuring inference costs in multiples of gaming GPUs, so it's not an impending ecological disaster as some would like the world to believe - especially after accounting for data centers being significantly more efficient at this, with specialized hardware, near-100% utilization, countless of optimization hacks (including some underhanded ones).