[thin_carapace]

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.

[heavyset_go]

> 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.

[PeterStuer]

They could still eliminate relatively cheap hardware.

[thin_carapace]

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]

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.

[g947o]

I mean, they have been doing that for at least a year, and I haven't seen signs of US economy tanking?... You need to find some better arguments

[heavyset_go]

There aren't any released open-weight models that are "good enough" yet, but Qwen-3.5 is getting really damn close to the point where more than half of my LLM usage gets routed to it.

I suspect, but don't know, some fields of inquiry will be fruitful when it comes to "good enough" small models. Especially when it comes to constrained tasks like software development. Software development models don't have to generalize to anything a chatbot can be asked or tasked with, the space it's required to generalize on is pretty small compared to literally the whole world.

If I was a betting man, I'd put my money where my mouth is, but I'm not. I am betting with my time and focus that smaller local models are worth it, and will be worth it, though.

[slopinthebag]

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]

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

[m00x]

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]

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.

[sigseg1v]

Depends on your circle. On HN I would argue that there are still a fair number of people that would be surprised to see what heavy organizational usage of AI actually looks like. On a non programming online group, of which I am a member of several, people still think that AI agents are the same as they were in mid 2025 and they can't answer "how many R's are in the following word:". Same thing even when chatting with my business owner friends. The majority of the public has no clue of the scale of recent advancement.

[nerptastic]

Not arguing, but I just prompted Opus with a made up word and it responded with this:

“There are 4 Rs in the word “burberrorrly.” Here they are highlighted: burberrorrly (positions 3, 6, 7, 9)”

Obviously not a real word, but perhaps the fundamental concept remains

[thin_carapace]

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