AI Is Making Developers Dumb

LLMs don't create an abstraction. They generate code. If you are thinking about LLMs as a layer of abstraction, you are going to have all kinds of problems.

Leaky abstractions is a really appropriate term for LLM-assisted coding.

The original "law of leaky abstractions" talked about how the challenge with abstractions is that when they break you now have to develop a mental model of what they were hiding from you in order to fix the problem.

(Absolutely classic Joel Spolsky essay from 22 years ago which still feels relevant today: https://www.joelonsoftware.com/2002/11/11/the-law-of-leaky-a... )

Having LLMs write code for you has a similar effect: the moment you run into problems, you're going to have to make sure you deeply understand exactly what they have done.

Not all of those abstractions are equally leaky though. Automatic memory management for example is leaky only for a very narrow set of problems, in many situations the abstraction works extremely well. It remains to be seen whether AI can be made to leak so rarely (which does not meant that it's not useful even in its current leaky state).

If we just talk in analogies: a cup is also leaky because fluid is escaping via vapours. It's not the same as a cup with a hole in it.

Llms currently have tiny holes and we don't know if we can fix them. Established abstractions are more like cups that may leak but only in certain conditions (when it's hot)

> (My favorite use of coding LLMs is to ask them to help me understand code I don't yet understand. Even when it gets the answer wrong, it's often right enough to give me the hints I need to figure it out myself.)

Agree, specially useful when you join a new company and you have to navegate a large codebase (or bad-maintained codebase, which is even worse by several orders of magnitude). I had no luck asking LLM to fix this or that, but it did mostly OK when I asked how it works and what the code is trying to code (it includes mistakes but that's fine, I can see them, which is different if it was just code that I copy and paste).

This "it's the same as the past changes" analogy is lazy - everywhere it's reached for, not just AI. It's basically just "something something luddites".

Criticisms of each change are not somehow invalid just because the change is inevitable, like all the changes before it.

When a higher level of abstraction allows programmers to focus on the detail relevant to them they stop needing to know the low level stuff. Some programmers tend not to be a fan of these kinds of changes as we well know.

But do LLMs provide a higher level of abstraction? Is this really one of those transition points in computing history?

If they do, it's a different kind to compilers, third-party APIs or any other form of higher level abstraction we've seen so far. It allows programmers to focus on a different level of detail to some extent but they still need to be able to assemble the "right enough" pieces into a meaningful whole.

Personally, I don't see this as a higher level of abstraction. I can't offload the cognitive load of understanding, just the work of constructing and typing out the solution. I can't fully trust the output and I can't really assemble the input without some knowledge of what I'm putting together.

LLMs might speed up development and lower the bar for developing complex applications but I don't think they raise the problem-solving task to one focused solely on the problem domain. That would be the point where you no longer need to know about the lower layers.

Last year I learned a new language and framework for the first time in a while. Until I became used to the new way of thinking, the discomfort I felt at each hurdle was both mental and physical! I imagine this is what many senior engineers feel when they first begin using an AI programming assistant, or an even more hands-off AI tool.

Oddly enough, using an AI assistant, despite it guessing incorrectly as often as it did, helped me learn and write code faster!

This is something that I struggle with for AI programming. I actually like writing the code myself. Like how someone might enjoy knitting or model building or painting or some other "tedious" activity. Using AI to generate my code just takes all the fun out of it for me.

> I like to build things, the faster the better.

what's the largest (traffic, revenue) product you've built? quantity >>>> quality of code is a great trade-off for hacking things together but doesn't lend itself to maintainable systems, in my experience.

Have you seen it work to the long term?

> I've tolerated writing my own code for decades.

The only reason I got suckd into this field was because I enjoyed writing code. What I "tolerated" (professionally) was having to work on other people's code. And LLM code is other people's code.

I've accepted this way of working too. There is some code that I enjoy writing. But what I've found is that I actually enjoy just seeing the thing in my head actually work in the real world. For me, the fun part was finding the right abstractions and putting all these building blocks together.

My general way of working now is, I'll write some of the code in the style I like. I won't trust an LLM to come up with the right design, so I still trust my knowledge and experience to come up with a design which is maintainable and scaleable. But I might just stub out the detail. I'm focusing mostly on the higher level stuff.

Once I've designed the software at a high level, I can point the LLM at this using specific files as context. Maybe some of them have the data structures describing the business logic and a few stubbed out implementations. Then Claude usually does an excellent job at just filling in the blanks.

I've still got to sanity check it. And I still find it doing things which looks like it came right from a junior developer. But I can suggest a better way and it usually gets it right the second or third time. I find it a really productive way of programming.

I don't want to be writing datalayer of my application. It's not fun for me. LLMs handle that for me and lets me focus on what makes my job interesting.

The other thing I've kinda accepted is to just use it or get left behind. You WILL get people who use this and become really productive. It's a tool which enables you to do more. So at some point you've got to suck it up. I just see it as a really impressive code generation tool. It won't replace me, but not using it might.

I don't think the author is saying it's a dichotomy. Like, you're either a disciple of doing things "ye olde way" or allowing the LLM to do it for you.

I find his point to be that there is still a lot of value in understanding what is actually going on.

Our business is one of details and I don't think you can code strictly having an LLM doing everything. It does weird and wrong stuff sometimes. It's still necessary to understand the code.

I like coding on private projects at home; that is fun and creative. The coding I get to do at work inbetween waiting for CI, scouring logs, monitoring APM dashboards and reviewing PRs, in a style and abstraction level I find inappropriate is not interesting at all. A type of change that might take 10 minutes at home might take 2 days at work.

I resonate so strongly with this. I’ve been a professional software engineer for almost twenty years now. I’ve worked on everything from my own solo indie hacker startups to now getting paid a half million per year to sling code for a tech company worth tens of billions. I enjoy writing code sometimes, but mostly I just want to build things. I’m having great fun using all these AI tools to build things faster than ever. They’re not perfect, and if you consider yourself to be a software engineer first, then I can understand how they’d be frustrating.

But I’m not a software engineer first, I’m a builder first. For me, using these tools to build things is much better than not using them, and that’s enough.

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You’re exactly right on the rage part, and that’s not something I’ve seen discussed enough.

Maybe it’s the fact that you know you could do it better in less time that drives the frustration. For a junior dev, perhaps that frustration is worth it because there’s a perception that the AI is still more likely to be saving them time?

I’m only tolerating this because of the potential for long term improvement. If it just stayed like it is now, I wouldn’t touch it again. Or I’d find something else to do with my time, because it turns an enjoyable profession into a stressful agonizing experience.

Is it just me or has this been a year or two off for at least a year or two now?

> As a friend commented, "these language models should never have been made available to the general public", only to researchers.

That feels to me like a dystopian timeline that we've only very narrowly avoided.

It wouldn't just have been researchers: it would have been researchers and the wealthy.

I'm so relieved that most human beings with access to an internet-connected device have the ability to try this stuff and work to understand what it can and cannot do themselves.

I'm giving a programming class and students uses LLMs all the time. I see it as a big problem because:

- it puts focus on syntax instead of the big picture. Instead of finding articles or posts on Stack explaining things beyond how to write them. AI give them the "how" so they don't think of the "why"

- students almost don't ask questions anymore. Why would they when an AI give them code?

- AI output contains notions, syntax and API not seen in class, adding to the confusion

Even the best students have a difficult time answering basic questions about what have been seen on the last (3 hours) class.

What do you think is the big difference between these tools and calculators?

I wish I had an LLM as a student because I couldn’t afford a tutor and googling for information was tedious.

It’s the college’s responsbility now to teach students how to harness the power of LLMs effectively. They can’t keep their heads in the sand forever.

it's particularly bad for students who should be trying to learn.

at the same time in my own life, there are tasks that I don't want to do, and certainly don't want to learn anything about, yet have to do.

For example, figuring out a weird edge case combination of flags for a badly designed LaTeX library that I will only ever have to use once. I could try to read the documentation and understand it, but this would take a long time. And, even if it would take no time at all, I literally would prefer not to have this knowledge wasting neurons in my brain.

I wonder if it would work to introduce a company policy that says you should never commit consensus aren't able to explain how it works?

I've been using that as my own personal policy for AI-assisted code and I am finding it works well for me, but would it work as a company company policy thing?

One of the advantages of working with people who are not native english-speakers is that, if their english suddenly becomes perfect and they can write concise technical explanations in tasks, you know it's some LLM.

Then if you ask for some detail on a call, it's all uhm, ehm, ehhh, "I will send example later".

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And our memory has declined. He was right

Good job he wrote that down.

It's really interesting how minor changes in your workflow can completely wreck productivity. When I'm at work I spend at least 90% of my time in emacs, but there are some programs I'm forced to use that are only available via Win32 GUI apps, or cursed webapps. Being forced to abandon my keybinds and move the mouse around hunting for buttons to click and then moving my hand from the mouse to the keyboard then back to the mouse really fucks me up. My coworkers all use MSVC and they don't seem to mind it all because they're used to moving the mouse around all the time; conversely a few of them actually seem to hate command-driven programs the same way I hate GUI-driven programs.

As I get older, it feels like every time I have to use a GUI I get stuck in a sort of daze because my mind has become optimized for the specific work I usually do at the expense of the work I usually don't do. I feel like I'm smarter and faster than I've ever been at any prior point in my life, but only for a limited class of work and anything outside of that turns me into a senile old man. This often manifests in me getting distracted by youtube, windows solitaire, etc because it's almost painful to try to remember how to move the mouse around though all these stupid menus with a million poorly-documented buttons that all have misleading labels.

This is the reason I don’t use auto completing IDEs. Pretty much vanilla emacs. I do often use syntax highlighting for the language, but that’s the limit of the crutches I want to use.

I think the issue is that a lot of orgs are systematically using the tool poorly.

I’m responsible for a couple legacy projects with medium sized codebases, and my experience with any kind of maintenance activities has been terrible. New code is great, but asking for fixes, refactoring, or understanding the code base has had an essentially 2% success rate for me.

Then you have to wonder, how the hell orgs expect to maintain/scale and more code from fewer devs, who don’t even understand how the original code worked?

LLMs are just a tool but overreliance on them is just as much of a code smell as - say - deciding your entire backend is going to be in Matlab; or all your variables are going to be global variables - you can do it, but I guarantee that it’s going to cause issues 2-3 years down the line.