Basically all of my actual programming work has been done by LLMs since January. My team actually demoed a PoC last week to hook up Codex to our Slack channel to become our first level on-call, and in the case of a defect (e.g. a pagerduty alert, or a question that suggests something is broken), go debug, push a fix for review, and suggest any mitigations. Prior to that, I basically pushed for my team to do the same with copy/paste to a prompt so we could iterate on building its debugging skills.

People might still code by hand as a hobby, but I'd be surprised if nearly all professional coding isn't being done by LLMs within the next year or two. It's clear that doing it by hand would mostly be because you enjoy the process. I expect people that are more focused on the output will adopt LLMs for hobby work as well.

> It's clear that doing it by hand would mostly be because you enjoy the process.

This is gaslighting. We're only a few years into coding agents being a thing. Look at the history of human innovation and tell me that I'm unreasonable for suspecting that there is an iceberg worth of unmitigated externalities lurking beneath the surface that haven't yet been brought to light. In time they might. Like PFAS, ozone holes, global warming.

Ultimately you always have to trust people to be judicious, but that's why it doesn't make any changes itself. Only suggests mitigations (and my team knows what actions are safe, has context for recent changes, etc). It's not entirely a black box though. e.g. I've prompted it to collect and provide a concrete evidence chain (relevant commands+output, code paths) along with competing hypotheses as it works. Same as humans should be doing as they debug (e.g. don't just say "it's this"; paste your evidence as you go and be precise about what you know vs what you believe).

That's sounds like the perfect recipe for turning a small problem into a much larger one. 'on call' is where you want your quality people, not your silicon slop generator.

Sounds like a company on the verge of creating a mess that will require a rewrite in a year or so. Maybe an llm can do it.

I suspect this is more true than most people think. Today's bad code will be cleaned up by tomorrow's agents.

The other factor that gets glossed over is that llms create a financial incentive to create cleaner code, with tests, because the agent that you pay for will be more efficient when the code is easier to understand, and has clear patterns for extensibility. When I do code with llms, a big part of it is demonstration, i.e. pseudocoding a pattern/structure, asking the model if it understands, and then having it complete the pattern. I've had a lot of success with this approach.

> llms create a financial incentive to create cleaner code, with tests, because the agent that you pay for will be more efficient when the code is easier to understand, and has clear patterns for extensibility

Right, this is the kind of discussion we're having on my team: suddenly all of the already good engineering practices like good observability, clear tests with high coverage, clean design, etc. act as a massive force multiplier and are that much more important. They're also easier to do if you prioritize it. We should be seeing quality go up. It's trivial to explore the solution space with throwaway PoCs, collect real data to drive your design, do all of those "nice to have" cleanups, etc. The people who assume LLM = slop are participating in a bizarre form of cope. Garbage in, garbage out; quality in, quality out. Just accept that coding per se is not going to be a profession for long. Leverage new tools to learn more, do more, etc. This should be an exciting time for programmers.

> It's clear that doing it by hand would mostly be because you enjoy the process.

This will not happen until companies decide to care about quality again. They don't want employees spending time on anything "extra" unless it also makes them significantly more money.