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LLM Inevitabilism

(tomrenner.com)
1612 points SwoopsFromAbove | 2 comments | | HN request time: 0s | 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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Msurrow ◴[] No.44568799[source]
> first signs of pulling back investments

I agree with you, but I’m curious; do you have link to one or two concrete examples of companies pulling back investments, or rolling back an AI push?

(Yes it’s just to fuel my confirmation bias, but it’s still feels nice:-) )

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1. 0xAFFFF ◴[] No.44569183[source]
Most prominent example was this one: https://www.reuters.com/technology/microsoft-pulls-back-more...
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2. durumu ◴[] No.44570468[source]
I think that's more reflective of the deteriorating relationship between OpenAI and Microsoft than an true lack of demand for datacenters. If a major model provider (OpenAI, Anthropic, Google, xAI) were to see a dip in available funding or stop focusing on training more powerful models, that would convince me we may be in a bubble about to pop, but there are no signs of that as far as I can see.