i dont see anyone sane trusting ai to this degree any time soon, outside of web dev. the chances of this strategy failing are still well above acceptable margins for most software, and in safety critical instances it will be decades before standards allow for such adoption. anyway we are paying pennies on the dollar for compute at the moment - as soon as the gravy train stops rolling, all this intelligence will be out of access for most humans. unless some more efficient generalizable architecture is identified.

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heavyset_go 2 hours ago | parent | next [-]

> as soon as the gravy train stops rolling, all this intelligence will be out of access for most humans. unless some more efficient generalizable architecture is identified.

All Chinese labs have to do to tank the US economy is to release open-weight models that can run on relatively cheap hardware before AI companies see returns.

Maybe that's why AI companies are looking to IPO so soon, gotta cash out and leave retail investors and retirement funds holding the bag.

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PeterStuer 2 hours ago | parent | next [-]

They could still eliminate relatively cheap hardware.

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thin_carapace 2 hours ago | parent | prev [-]

i was under the impression that we were approaching performance bottlenecks both with consumer GPU architecture and with this application of transformer architecture. if my impression is incorrect, then i agree it is feasible for china to tank the US economy that way (unless something else does it first)

	▲	heavyset_go an hour ago \| parent [-]
		I think it just needs to be efficient or small enough for companies to deploy their own models on their hardware or cloud, for more inference providers to come out of the woodwork and compete on price, and/or for optimized models to run locally for users. Regarding the latter, smaller models are really good for what they are (free) now, they'll run on a laptop's iGPU with LPDDR5/DDR5, and NPUs are getting there. Even models that can fit in unified 64GB+ memory between CPU & iGPU aren't bad. Offloading to a real GPU is faster, but with the iGPU route you can buy cheaper SODIMM memory in larger quantities, still use it as unified memory, eventually use it with NPUs, all without using too much power or buying cards with expensive GDDR. Qwen-3.5 locally is "good enough" for more than I expected, if that trend continues, I can see small deployable models eventually being viable & worthy competition, or at least being good enough that companies can run their own instead of exfiltrating their trade secrets to the worst people on the planet in real-time.

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m00x 3 hours ago | parent | prev | next [-]

Several fintechs like Block and Stripe are boasting thousands of AI-generated PRs with little to no human reviews.

Of course it's in the areas where it doesn't matter as much, like experiments, internal tooling, etc, but the CTOs will get greedy.

	▲	slopinthebag 3 hours ago \| parent \| next [-]
		I don't think anybody is doubting its ability to generate thousands of PR's though. And yes, it's usually in the stuff that should have been automated already regardless of AI or not.
	▲	thin_carapace 2 hours ago \| parent \| prev [-]
		these companies contribute to swathes of the west's financial infrastructure, not quite safety critical but critical enough, insane to involve automation here to this degree

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slopinthebag 3 hours ago | parent | prev [-]

Even in webdev it rots your codebase unchecked. Although it's incredibly useful for generating UI components, which makes me a very happy webslopper indeed.

	▲	thin_carapace 2 hours ago \| parent [-]
		im grateful to have never bothered learning web dev properly, it was enlightening witnessing chat gpt transform my ten second ms paint job into a functional user interface