For coding, "it seems to work for me" is good enough. For software engineering, it's not.
For coding, you can use AI to write your code. For software engineering, you can't.You can 100% use AI for software engineering. Just not by itself, you need to currently be quite engaged in the process to check it and redirect it.
But AI lowers the barrier to writing code and thus it brings people will less rigour to the field and they can do a lot of damage. But it isn't significantly different than programming languages made coding more accessible than assembly language - and I am sure that this also allowed more people to cause damage.
You can use any tools you want, but you have to be rigorous about it no matter the tool.
This is a pretty common sentiment. I think it equates using AI with vibe-coding, having AI write code without human review. I'd suggest amending your rule to this:
> For coding, you can use AI. For software engineering, you can't.
You can use AI in a process compatible with software engineering. Prompt it carefully to generate a draft, then have a human review and rework it as needed before committing. If the AI-written code is poorly architected or redundant, the human can use the same AI to refactor and shape it.
Now, you can say this negates the productivity gains. It will necessarily negate some. My point is that the result is comparable to human-written software (such as it is).
Just don't expect to get decent code often if you mostly rely on something like cursor's default model.
You literally get what you pay for.
That's my policy in each of my clients and it works fine, if AI makes something simpler/faster, good for the author, but there's 0, none, excuses for pushing slop or code you haven't reviewed and tested yourself thoroughly.
If somebody thinks they can offset not just authoring or editing code, but also taking the responsibility for it and the impact it has on the whole codebase and the underlying business problem they should be jobless ASAP as they are de facto delegating the entirety of their job to a machine, they are not only providing 0 value, but negative value in fact.
Even in less desperate teams, as productivity grows with AI (mine does, even if I don't author code with it it's tremendous help in just navigating repos and connecting the dots, it saves me so much time...) the reviewing pressure increases too, and with that fatigue.
It is not a worthwhile use of my time to similarly "coach" LLM slop.
The classic challenge with junior engineers is that helping them ship something is often more work than just doing it yourself. I'm willing to do that extra work for a human.
These discussions are always about tactics and never operations.
Obviously there’s nuance (I’ll take slop food for starving people over a healthy meal for a limited few if we’re forced to choose), but the perverse incentives in society start to take over if we allow ourselves to be ok with slop. Continuously chasing the bottom of the barrel makes it impossible for high quality to exist for anyone except the rich.
Put another way: if we as a society said “it is illegal to make slop food”, both the poor and the rich would have easy access to healthy food. The cost here would be born by the rich, as they profit off food production and thus would profit less to keep quality high.
If it's all crap anyway, why pay more?
Automobiles are large, expensive purchases with a relatively small set of options though... For most purchases, it's impossible to determine quality ahead of time.
Our current parasitic elites are just enriching themselves. Arguing about economic systems is pointless if the ruling class hates the common people.
No, not if I have to maintain it.
Code is liability. LLM written PRs often bring net negative value: they make the whole system larger, more brittle, and less integrated. They come at the cost of end user quality and maintainer velocity.
I get the “AI sucks” argument from a programmers point of view. It’s weird looking and doesn’t care about “code smells” or about rearranging the code base’s deck chairs just the way you like. From an owner’s or client’s perspective, human programmers suck. You want big standard CRUD app? Like a baby’s first Django app? That’s going to take at least 6 months for some reason. They don’t understand your problem domain and don’t care enough to learn it. They work 15 minutes on the hour, spend 45 on social media or games, and bill you $200/hr. They “pair program” for “quality” to double their billed rate for the same product. They bill you for interns learning how to do their job on your dime. On top of that there is still a very good chance the whole project will just be a failure. Or I can pay Anthropic $20/month and text an AI requirements on my phone when I’ve got 5 minutes of down time. If it doesn’t work I just make a new one and try again. Even if progress on AI stopped today, the world is now so much better for consumers of programs. Maybe not for developers unless you’re writing the AI and getting paid in the millions. Good for them. I’m glad to see the $200/hr Stack Overflow copy and pasters go do something else.
The difference is that a human can learn and grow.
From your examples, it sounds like we're talking about completely different applications of code. I'm a software engineer who is responding to the original topic of reviewing PRs full of LLM slop. It sounds like you are a hobbyist who uses LLMs to vibe code personal apps. Your use case is, frankly, exactly what LLMs should be used for. It's analogous to how using a consumer grade 3d printer to make toys for your kids is fine, but nobody would want to be on the hook for maintaining full scale structural systems that were printed the same way.
Vibing and good enough is a terrible combination, as unknown elements of the system grow at a faster rate than ever.
Using LLMs while understanding every change and retaining a mental model of the system is fine.
Granted, I see vibe+ignorance way too often as it is the short-term path of least resistance in the current climate of RTO and bums in seats and grind and ever more features.
LLMs can make mistakes. Humans can't.
I think think the underlying problem people have is they don't trust themselves to review code written by others as much as they trust themselves to implement the code from scratch. Realistically, a very small subset of developers do actual "engineering" to the level of NASA / aerospace. Most of us just have inflated egos.
I see no problem modelling the problem, defining the components, interfaces, APIs, data structures, algorithms and letting the LLM fill in the implementation and the testing. Well designed interfaces are easy to test anyway and you can tell at a glance if it covered the important cases. It can make mistakes, but so would I. I may overlook something when reviewing, but the same thing often happens when people work together. Personally I'd rather do architecture and review at a significantly improved speed than gloat I handcrafted each loop and branch as if that somehow makes the result safer or faster (exceptions apply, ymmv).
LLMs can make mistakes. Humans can't."
In the rare case when there is a human that is consistently persistently confidently wrong like AI, a project can identify that person and easily stop wasting their time working with that person. With masses of people being told by the vocal AI shills how amazing AI is, projects can easily be flooded with confidently wrong aaI generated PRs.
in my experience these tests don't test anything useful
you may you have 100% test coverage, but it's almost entirely useless but not testing the actual desired behaviour of the system
rather just the exact implementation
Brittle meaningless tests tend to lock bad decisions in, and prevent meaningful refactoring.
Bad tests simply are code debt, and dramatically increase the future cost of rework and adaptation.
I set boundaries during design where I choose responsibilities, interfaces and names. Red Green Refactor is very useful for beginners who would otherwise define boundaries that are difficult to test and maintain.
I design components that are small and focused so their APIs are simple and unit tests are incredibly easy to define and implement, usually parametrized. Unit tests don't keep me "sane", they keep me sleeping well at night because designing doesn't drive me mad. They don't define how the "program" is supposed to work, they define how the unit is supposed to work. The smaller the unit the simpler the test. I hope you agree: simple is better than complex. And no, I don't subscribe to "you only need integration tests".
Otherwise, nice battery of ad hominems you managed to slip in: my understanding of quality software is lacking, my problem is my approach to engineering and I'm an immature developer. All that from "LLMs can automate most of the boring stuff, including unit tests with 100% coverage." because you can't fathom how someone can design quality software without TDD, and you can't steelman my argument (even though it's recommended in the guidelines [1]). I do review and correct the LLM output. I almost always ask it for specific test cases to be implemented. I also enjoy seeing most basic test cases and most edge cases covered. And no, I don't particularly enjoy writing factories, setups, and asserts. I'm pretty happy to review them.
[1] https://news.ycombinator.com/newsguidelines.html Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize. Assume good faith.