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Comment by halfadot | Hacker News Reader

> The article sort of goes sideways with this idea but pointing out that AI coding robs you a deep understanding of the code it produces is a valid and important criticism of AI coding.

No it isn't. There's literally nothing about the process that forces you to skip understanding. Any such skips are purely due to the lack of will on the developer's side. This lack of will to learn will not change the outcomes for you regardless of whether you're using an LLM. You can spend as much time as you want asking the LLM for in-depth explanations and examples to test your understanding.

So many of the criticisms of coding with LLMs I've seen really do sound like they're coming from people who already started with a pre-existing bias, fiddled with with for a short bit (or worse, never actually tried it at all) and assumed their limited experience is the be-all end-all of the subject. Either that, or they're typical skill issues.

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> There's literally nothing about the process that forces you to skip understanding.

There's nothing about C that "forces" people to write buffer overflows. But, when writing C, the path of least resistance is to produce memory-unsafe code. Your position reminds me of C advocates who say that "good developers possess the expertise and put in the effort to write safe code without safeguards," which is a bad argument because we know memory errors do show up in critical code regardless of what a hypothetical "good C dev" does.

If the path of least resistance for a given tool involve using that tool dangerously, then it's a dangerous tool. We say chefs should work with sharp knives, but with good knife technique (claw grip, for instance) safety is the path of least resistance. I have yet to hear of an LLM workflow where skimming the generated code is made harder than comprehensively auditing it, and I'm not sure that such a workflow would feel good or be productive.

Your point of view assumes the best of people, which is naive. It may not force you to skip understanding, however it makes it much easier to than ever before.

People tend to take the path of least resistance, maybe not everyone, maybe not right away, but if you create opportunities to write poor code then people will take them - more than ever it becomes important to have strong CI, review and testing practices.

Edit: okay, maybe I am feeling a little pessimistic this morning :)

People will complain about letting the LLM code because you won't understand every nuance. Then they will turn around and pip install a dependency without even glancing at the underlying code.

> No it isn't. There's literally nothing about the process that forces you to skip understanding. Any such skips are purely due to the lack of will on the developer's side

This is the whole point. The marginal dev will go to the path of least resistance, which is to skip the understanding and churn out a bunch of code. That is why it's a problem.

You are effectively saying "just be a good dev, there's literally nothing about AI which is stopping you from being a good dev" which is completely correct and also missing the point.

The marginal developer is not going to put in the effort to wield AI in a skillful way. They're going to slop their way through. It is a concern for widespread AI coding, even if it's not a concern for you or your skill peers in particular.

To add to the above - I see a parallel to the "if you are a good and diligent developer there is nothing to stop you from writing secure C code" argument. Which is to say - sure, if you also put in extra effort to avoid all the unsafe bits that lead to use-after-free or race conditions it's also possible to write perfect assembly, but in practice we have found that using memory safe languages leads to a huge reduction of safety bugs in production. I think we will find similarly that not using AI will lead to a huge reduction of bugs in production later on when we have enough data to compare to human-generated systems. If that's a pre-existing bias, then so be it.

> The marginal developer is not going to put in the effort to wield AI in a skillful way. They're going to slop their way through. It is a concern for widespread AI coding, even if it's not a concern for you or your skill peers in particular.

My mental model of it is that coding with LLMs amplified both what you know and what you don't.

When you know something, you can direct it productively much faster to a desirable outcome than you could on your own.

When you don't know something, the time you normally would have spent researching to build a sufficient understanding to start working on it can be replaced with evaluating the random stuff the LLM comes up with which oftentimes works but not in the way it ought to, though since you can get to some result quickly, the trade-off to do the research feels somehow less worth it.

Probably if you don't have any idea how to accomplish the task you need to cultivate the habit of still doing the research first. Wielding it skillfully is now the task of our industry, so we ought to be developing that skill and cultivating it in our team members.

I don't think that is a problem with AI, it is a problem with the idea that pure vibe-coding will replace knowledgeable engineers. While there is a loud contingent that hypes up this idea, it will not survive contact with reality.

Purely vibe-coded projects will soon break in unexplainable ways as they grow beyond trivial levels. Once that happens their devs will either need to adapt and learn coding for real or be PIP'd. I can't imagine any such devs lasting long in the current layoff-happy environment. So it seems like a self-correcting problem no?

(Maybe AGI, whatever that is, will change things, but I'm not holding my breath.)

The real problem we should be discussing is, how do we convince students and apprentices to abstain from AI until they learn the ropes for real.

> The real problem we should be discussing is, how do we convince students and apprentices to abstain from AI until they learn the ropes for real.

That's just it. You can only use AI usefully for coding* once you've spent years beating your head against code "the hard way". I'm not sure what that looks like for the next cohort, since they have AI on day 1.

* That is, assuming it's nontrivial.

> The real problem we should be discussing is, how do we convince students and apprentices to abstain from AI until they learn the ropes for real.

Learning the ropes looks different now. You used to learn by doing, now you need to learn by directing. In order to know how to direct well, you have to first be knowledgeable. So, if you're starting work in an unfamiliar technology, then a good starting point is read whatever O'Reilly book gives a good overview, so that you understand the landscape of what's possible with the tool and can spot when the LLM is doing (now) obvious bullshit.

You can't just Yolo it for shit you don't know and get good results, but if you build a foundation first through reading, you will do a lot better.

Totally agreed, learning the ropes is very different now, and a strong foundation is definitely needed. But I also think where that foundation lies has changed.

My current project is in a technical domain I had very little prior background in, but I've been getting actual, visible results since day one because of AI. The amazing thing is that for any task I give it, the AI provides me a very useful overview of the thing it produces, and I have conversations with it if I have further questions. So I'm building domain knowledge incrementally even as I'm making progress on the project!

But I also know that this is only possible because of the pre-existing foundation of my experience as a software engineer. This lets me understand the language the AI uses to explain things, and I can dive deeper if I have questions. It also lets me understand what the code is doing, which lets me catch subtle issues before they compound.

I suppose it's the same with reading books, but books being static tend to give a much broader overview upfront, whereas interacting with LLMs results in a much more focused learning path.

So a foundation is essential, but it can now be much more general -- such as generic coding ability -- but that only comes with extensive hands-on experience. There is at least one preliminary study showing that students who rely on AI do not develop the critical problem solving, coding and debugging skills necessary to be good programmers:

https://www.mdpi.com/2076-3417/14/10/4115

On vibe coding being self-correcting, I would point to the growing number of companies mandating usage of AI and the quote "the market can stay irrational longer than you can stay solvent". Companies routinely burn millions of dollars on irrational endeavours for years. AI has been promised as an insane productivity booster.

I wouldn't expect things to calm down for a while, even if real-life results are worse. You can make excuses for underperformance of these things for a very long time, especially if the CEO or other executives are invested.

> The real problem we should be discussing is, how do we convince students and apprentices to abstain from AI until they learn the ropes for real

I hate to say it but that's never going to happen :/

I'm a bit cynical at this point, but I'm starting to think these AI mandates are simply another aspect of the war of the capital class on the labor class, just like RTO. I don't think the execs truly believe that AI will replace their employees, but it sure is a useful negotiation lever. As in, not just an excuse to do layoffs but also a mechanism to pressure remaining employees: "Before you ask for perks or raises or promotions, why are you not doing more with less since you have AI? You know that soon we could replace you with AI for much cheaper?"

At the same time, I'll also admit that AI resistance is real; we see it in the comments here for various reasons -- job displacement fears, valid complaints about AI reliability, ethical opposition, etc. So there could be a valid need for strong incentives to adopt it.

Unfortunately, AI is also deceptively hard to use effectively (a common refrain of mine.) Ideally AI mandates would come with some structured training tailored for each role, but the fact that this is not happening makes me wonder about either the execs' competency or their motives.

Let me ask you this: do you understand the code your colleagues produce as well as the code you write yourself?

We most definitely should, especially so if you're working in a team or organization bigger than a handful of people. Because it's almost certain that you may need to change or interact with that code very soon in the lifetime of the project. When that happens you want to make sure the code aligns with your own mental model of how things work.

The industry has institutionalized this by making code reviews a very standard best practice. People think of code reviews mainly as a mechanism to reduce bugs, but turns out the biggest benefits (born out by studies) actually are better context-sharing amongst the team, mentoring junior engineers, and onboarding of new team-mates. It ensures that everyone has the same mental model of the system despite working on different parts of it (c.f. the story of the blind men and the elephant.) This results in better ownership and fewer defects per line of code.

Note, this also doesn't mean everybody reviews each and every PR. But any non-trivial PR should be reviewed by team-mates with appropriate context.

AI is not my coworker, with different tasks and responsibilities.

The comparison is oniy reasonable if most of your job is spent trying to understand their code, and make sure it did what you wanted. And with them standing next to you, ready to answer questons, explain anything I don't understand and pull in any external, relevant parts of the codebase.

Of course not that's a bit disingenuous. I would hope my colleagues write code that is comprehensible so it's maintainable. I think that if the code is so complex and inscrutable that only the author can understand it then it's not good code. AI doesn't create or solve this problem.

I do think when AI writes comprehensible code you can spend as much time as necessary asking questions to better understand it. You can ask about tradeoffs and alternatives without offending anybody and actually get to a better place in your own understanding than would be possible alone.

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