So these companies will be profitable if training stops? Is that even a real possibility?

Any given company could stop training tomorrow, and, as some others have said here, they'd be generating quite a bit of profit until their models visibly fell behind, however long that ended up taking, at which point they'd probably just fall over completely.

Over the whole industry? No; they can never, ever stop training, or they'll cease to be useful at all very soon.

Training is what keeps the models up-to-date on current events, which includes new programming languages, frameworks, and techniques. It's already been observed that using LLM assistance on some types of programming is much more effective than others, based on how well-represented they are in the training data: if everyone stopped training tomorrow, and next month a new programming language came out, none of them would ever be able to help you program in that new language.

This can be extended to other aspects of programming, too. If training stopped, coding assistants would gradually start giving you wrong answers on how to implement code for APIs, frameworks, and languages that continued to evolve, as they will always do, in much subtler (and likely harder-to-debug) ways than how they'd deal with a new language whose existence they don't even know about.

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naravara a day ago | parent | prev [-]

The impetus to continue training at the pace they are is driven by the competition. So if the money starts drying up, then they’ll naturally slow down because they’ll have to figure out how to do more with less.

I suspect that once the models hit a point of “good enough” for certain use cases companies will start putting R&D focus in other areas that may be less expensive. Like figuring out how to run more efficiently, UI/UX conventions that help users get what they’re trying to accomplish in fewer steps, various kinds of caching of requests, etc. So the cost to serve tokens over time should only come down, and will probably start coming down more rapidly as the returns to model training slow down.

That’ll probably be a while though, because each successive model tends to be a lot better than the last.

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WarmWash a day ago | parent | next [-]

What's interesting to note is that the "intelligence" labs can squeeze out of an H100, an almost 4 year old GPU, is dramatically higher than what they got out of it in 2022.

It hints that once these labs get a good enough "everyday model", they can work on efficiency so they can serve these models on old hardware. Which is almost certainly already happening.

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pier25 a day ago | parent | prev | next [-]

> So if the money starts drying up, then they’ll naturally slow down because they’ll have to figure out how to do more with less.

Meanwhile companies like Google will keep investing on training...

Anthropic's CEO has suggested all AI companies should slow down training but obviously this is only beneficial for companies that can't afford to keep training.

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hbn a day ago | parent | prev [-]

> UI/UX conventions that help users get what they’re trying to accomplish in fewer steps

If we can expect the past 15 years of software UI/UX history to continue, it's more likely they'll spend the money on making the UI/UX more confusing, removing features, and making basic tasks take more steps than they do today.

	▲	naravara 6 hours ago \| parent [-]
		That’s because the past 15 years were dictated by Web 2.0 companies that make their money off keeping you glued to the screen. A AI assistant would work more like Planet Fitness where the goal is to figure out how to convince you to keep paying them while using the facilities as little as possible. A big part of that might just be steering you towards repos of existing solutions to the problem you’re trying to solve rather than helping you vibe code a solution yourself. Over time they’ll be able to accrue a whole pile of canned functions that’s all automatically documented and audited and it’ll be able to plug and play those rather than having to rewrite. The security implications of this give me a headache to contemplate to be honest.