But all work isn't done by LLMs at the moment and we can't be sure that it will be so the question is ridiculous.

Maybe one day it will be.. And then people can reevaluate their stance then. Until that time, it's entirely reasonable to hold the position that you just don't

This is especially true with how LLM generated code may affect licensing and other things. There's a lot of unknowns there and it's entirely reasonable to not want to risk your projects license over some contributions.

I use them all the time at work because, rightly or wrongly, my company has decided that's the direction they want to go.

For open source, I'm not going to make that choice for them. If they explicitly allow for LLM generated code, then I'll use it, but if not I'm not going to assume that the project maintainers are willing to deal with the potential issues it creates.

For my own open source projects, I'm not interested in using LLM generated code. I mostly work on open source projects that I enjoy or in a specific area that I want to learn more about. The fact that it's functional software is great, but is only one of many goals of the project. AI generated code runs counter to all the other goals I have.

I say let people hold this stance. We, agentic coders, can easily enough fork their project and add whatever the features or refinements we wanted, and use that fork for ourselves, but also make it available for others in case other people want to use it for the extra features and polish as well. With AI, it's very easy to form a good architectural understanding of a large code base and figure out how to modify it in a sane, solid way that matches the existing patterns. And it's also very easy to resolve conflicts when you rebase your changes on top of whatever is new from upstream. So, maintaining a fork is really not that serious of and endeavor anymore. I'm actually maintaining a fork of Zed with several additional features (Claude Code style skills and slash commands, as well as a global agents.md file, instead of the annoying rules library system, which I removed, as well as the ability to choose models for sub-agents instead of always inheriting the model from the parent thread; and yes, master branch Zed has subagents! and another tool, jjdag)

That seems like a win-win in a sense: let the agentic coders do their thing, and the artisanal coders do their thing, and we'll see who wins in the long run.

> just like almost all transportation is done today via cars instead of horses.

That sounds very Usanian. In the meantime transportation in around me is done on foot, bicycle, bus, tram, metro, train and cars. There are good use cases for each method including the car. If you really want to use an automotive analogy, then sure, LLMs can be like cars. I've seen cities made for cars instead of humans, and they are a horrible place to live.

Signed, a person who totally gets good results from coding with LLMs. Sometimes, maybe even often.

even if this was true or someday will be (big IF), is it worth looking for valid counter workflows? example: in many parts of the US and Canada the Mennonites are incredibly productive farmers and massive adopters of technology while also keeping very strict limits on where/how and when it is used. If we had the same motivations and discipline in software could we walk a line that both benefited from and controlled AI? I don't know the answer.

As someone who enjoys working with AI tools, I honestly think the best approach here might be bifurcation.

Start new projects using LLM tools, or maybe fork projects where that is acceptable. Don't force the volunteer maintainers of existing projects with existing workflows and cultures to review AI generated code. Create your own projects with workflows and cultures that are supportive of this, from the ground up.

I'm not suggesting this will come without downside, but it seems better to me than expecting maintainers to take on a new burden that they really didn't sign up for.

That would only be a world where the copyright and other IP uncertainties around the output (and training!) of LLMs were a solved and known question. So that's not the world we currently live in.