What kind of software are people building where AI can just one shot tickets? Opus 4.6 and GPT 5.4 regularly fail when dealing with complicated issues for me.
I dunno if Rust async or native platform API's which have existed for years count as new patterns, but if you throw even a small wrench in the works they really struggle. But that's expected really when you look at what the technology is - it's kind of insane we've even gotten to this point with what amounts to fancy autocomplete.
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.
> 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.
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)
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.
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.
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.
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
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.
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.
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
Of course not all tickets are complex. Last week I had to fix a ticket which was to display the update date on a blog post next to the publish date. Perfect use case for AI to one shot.
I'm using Opus on Claude Code and even on easy tasks, if you not review the changes properly, it creates tech debts. One of the most common issues is replicating the same logic with variables with different names (which makes grep harder to detect on future changes) in multiple places and lack of following project patterns. Even having a lot of .md files instructing to do the opposite.
I still didn't find a workflow without human interaction that can be that efficient and reliable.
I suppose at that point I’m wondering if it would have just been faster for… you, (I’m assuming) the developer to make that change and deploy it? Is the AI really faster on small changes like that, if you understand the platform/code/CI/CD enough???
Maybe for a non-dev it would be nice to submit a ticket and have it auto-fixed by an agent. But in the devs case, it feels like it would be faster to just do it manually.