zlacker

>>tablet+(OP)
I’d love to see the authors of effusive praise of generative AI like this provide the proof of the unlimited powers of their tools in code. If GAI (or agents, or whatever comes next …) is so effective it should be quite simple to prove that by creating an AI only company and in short order producing huge amounts of serviceable code to do useful things. So far I’ve seen no sign of this, and the best use case seems to be generating text or artwork which fools humans into thinking it has coherent meaning as our minds love to fill gaps and spot patterns even where there are none. It’s also pretty good at reproducing things it has seen with variations - that can be useful.

So far in my experience watching small to medium sized companies try to use it for real work, it has been occasionally useful for exploring apis, odd bits of knowledge etc, but overall wasted more time than it has saved. I see very few signs of progress.

The time has come for llm users to put up or shut up - if it’s so great, stop telling us and show and use the code it generated on its own.

>>grey-a+ba
I think we're talking past each other. There's always been a threshold: above it, code changes are worth the effort; below it, they sit in backlog purgatory. AI tools so far seem to lower implementation costs, moving the threshold down so more backlog items become viable. The "5x productivity" crowd is excited about this expanded scope, while skeptics correctly note the highest value work hasn't fundamentally changed.

I think what's happening is two groups using "productivity" to mean completely different things: "I can implement 5x more code changes" vs "I generate 5x more business value." Both experiences are real, but they're not the same thing.

https://peoplesgrocers.com/en/writing/ai-productivity-parado...

>>marxis+ne
My friends at companies where AI tools are either mandated or heavily encouraged report that they're seeing a significant rise in low-quality PRs that need to be carefully read and rejected.

A big part of my skepticism is this offloading of responsibility: you can use an AI tool to write large quantities of shitty code and make yourself look superficially productive at the cost of the reviewer. I don't want to review 13 PRs, all of which are secretly AI but pretend to be junior dev output, none of which solve any of the most pressing business problems because they're just pointless noise from the bowels of our backlog, and have that be my day's work.

Such gatekeeping is a distraction from my actual job, which is to turn vague problem descriptions into an actionable spec by wrangling with the business and doing research, and then fix them. The wrangling sees a 0% boost from AI, the research is only sped up slightly, and yeah, maybe the "fixing problems" part of the job will be faster! That's only a fraction of the average day for me, though. If an LLM makes the code I need to review worse, or if it makes people spend time on the kind of busywork that ended up 500 items down in our backlog instead of looking for more impactful tasks, then it's a net negative.

I think what you're missing is the risk, real or imagined, of AI generating 5x more code changes that have overall negative business value. Code's a liability. Changes to it are a risk.