That might be a good thing after all, at least in a certain sense. Stack Overflow has been dying for the last ten years or so. In the first years there where a lot of good questions that were interesting to answer but that changed with popularity and it became an endless sea of low effort do my homework duplicates that were not interesting to answer and annoying to moderate. If this now gets handled by large language models, it could maybe become similar to the beginning again, only those questions that are not easily answerable by looking into the documentation or asking a chat bot will end up on Stack Overflow, it could be fun again to answer questions on Stack Overflow. On the other hand if nobody looks up things on Stack Overflow, it will be hard to sustain the business, maybe even when downscaled accordingly.

SO sold off their own data and made it insanely web-crawlable.

So makes sense that SO like users will use AI, not to mention they get the benefit of avoiding the neurotic moderator community at SO.

> I find this perspective so hard to relate to. LLMs have completely changed my workflow; the majority of my coding has been replaced by writing a detailed textual description of the change I want, letting an LLM make the change and add tests, then just reviewing the code it wrote and fixing anything stupid it did.

I use it in much the same way you describe, but I find that it doesn't save me that much time. It may save some brain processing power, but that is not something I typically need saving.

I extract more from LLM asking it to write code Infind tedious to write (unit tests, glue code for APIs, scaffolding for new modules, that sort of thing). Recently I started asking it to review the code I write and suggest improvements, try to spot bugs and so on (which I also find useful).

Reviewing the code it writes to fix the inevitable mistakes and making adjustments takes time too, and it will always be a required step due to the nature of LLMs.

Running tasks simultaneously don't help much unless you are giving it instructions that are too general that will take it a long time executiny - and the bottleneck will be your ability to review all the output anyway. I also gind that the broader is the scope of what I need it to do, the less precise it tends to be. I achieve most success by being more granular in what I ask of it.

My take is that while LLMs are useful, they are massively overhyped, and the productivity gains are largely overstated.

Of course, you can also "vibe code" (what an awful terminology) and not inspect the output. I find it unacceptable in professional settings, where you are expected to release code with some minimum quality.

If you ever end up working on large complicated code bases you'll likely have an easier time relating to the sentiment. LLMs are vastly better at small greenfield coding than for working on large projects. I think 100% of the people I've heard rave about AI coding are using them for small isolated projects. Among people who work on large projects sentiment seems to range from mildly useful to providing negative value.

> at least they are not as much a single point of failure

Yes, network effects and hyper scale produce perverse incentives. It sucks that Wikipedia can be gamed. Saying that, you'd need to be actively colluding with other contributors to maintain control.

Imagining that AI is somehow more neutral or resistant to influence is incredibly naive. Isn't it obvious that they can be "aligned" to favor the interests of whoever trains them?

Can I ask for some examples? I'm not this active on Wikipedia, so I'm curious where a narrative is being spread

Single point of failure?

Yeah u can download the entirety of Wikipedia if you want to. What's the single point of failure?