Stats show that ~30% of GitHub Copilot code is reused.

Context and relevant standards are part of prep work in a dev's daily coding tasks. I.e., extract + stitch requirements from different docs, tools, conversations, meetings and design or reuse of standards before the the 1st working ver is created.

Another part that slows devs are unwanted meetings, organizating their work, prep for meetings.

Both coding and non-coding activities are still the reason why devs need extra time to work in spite of AI, it's just not the quality of AI code, it's the work before work as well.

Per Atlassian's survey devs spend 10+hrs/week on such activities.

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Prestigious-Ear-3138@reddit

I think the core of what’s happening is the classic “productivity paradox” – tools make a task faster, but the saved time gets re‑absorbed by new expectations instead of giving anyone a shorter day.

AI is giving us a faster hammer, but without a cultural change it’s just letting us hammer more nails.

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ShiftArcade@reddit

Honestly, I think the real issue is just how we're using these tools. When I use ChatGPT in the browser, I'm bouncing back and forth constantly. I ask it to write something, copy the answer, paste it somewhere else, test it, it doesn't work, go back to ChatGPT, ask it to fix it, copy-paste again. That back-and-forth is exhausting.

I started trying a different approach — just using the AI tools directly from my terminal instead of the browser. Same tool, but I'm not switching windows constantly. I ask for what I need, it shows up right where I'm working, I can test it immediately without leaving. No copying, pasting, window jumping. It's just... simpler.

Plus, having AI on your computer like that is honestly the tool to rule them all. Everything else you're doing is already in the terminal anyway — your files, your work, your automation. When the AI is there too, it just fits. You're not managing multiple tools anymore, you're just... working.

I think that's what the article's missing. It's not that AI makes you work longer. It's that the way most people are using it (browser, copy-paste, back and forth) creates a ton of friction. If you change how you're accessing it, it actually feels way less exhausting.

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GenerationBop@reddit

The amount of time I spend code reviewing people’s AI slop, or having to read AI generated code reviews other reviewers are leaving of my code (that at a mix of good recommendations and wildly incorrect comments), as well as partake in constant discussions of best uses of AI, I feel like has doubled my workday.

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supermitsuba@reddit

Hey, do you work at my place? All I do is read AI generated stories, tests and code. It all sucks.

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What's worse, when there's the inevitable problems with the code, we just pile more AI on top of it. "Ask Claude to rewrite it" will just multiply the errors.

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Tolopono@reddit

Use gpt 5.3 codex or 5.4 xhigh on codex cli. Heard great things about it

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Princess_Azula_@reddit

Just another symptom of what's wrong with our society.

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Dragdu@reddit

There are already ads everywhere about speeding up your company with fully automated reviews and deployments. I expect lot of great moments in the future, that nobody will learn from.

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franklindstallone@reddit

The data from the Anthropic studies mentioned is not surprising. Often you learn things better by exercising multiple activities during learning. I.E. reading information and writing down notes.

Aside from doing less to produce the code you’ve likely skipped a lot of thinking about it because you didn’t write the code. So no surprise those who used AI knew less about the library they produced. That’s a flaw that will always come back to bite them imo.

No matter what anyone says I can see people on average don’t read text on their computer screen whether that’s a code review or slack message. The idea that people will get AI to generate code and they’ll carefully study it afterwards is laughable.

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kontrolk3@reddit

My stance has been this: good developers before AI are going to be good developers with AI. Their speed will increase, quality will increase (more time to do things right), etc. The issue is bad developers are still going to be bad developers, but their problems (not understanding code, putting out bad code, etc) are going to be amplified far more.

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PoisnFang@reddit

That's what I have been saying for years now. Its just an accelerator and if you write bad god, then you will just write more bad code, same for good.

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Downtown_Category163@reddit

I've tried LLMs over and over to work on code and it's only made my performance worse - the work to upload the affected files, craft the prompt, wait for the prompt's response (yay back to early 80's mainframe response times!) and then find out it's bullshit or misunderstood or worse it's a superficially convincing lie is way longer than just trying some stuff and using edit+continue or hot reload

I've only been using it like that for a few days, but that usage seems very limited. AI may have a place in my workflow but writing all the code isn't it.

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mis_juevos_locos@reddit

This is just the concept of a speed-up being brought to coding. It's been around for almost a century now:

This was especially true in the decade after 1910, when the principles of scientific management were being applied wholesale in the United States. Though the unions approved of more-efficient production arising from better machinery and management, they condemned the speedup practice and complained in particular that Taylorism deprived workers of a voice regarding the conditions and functions of their work. Complaints were also made that the system caused irritability and fatigue along with physiological and neurological damage among workers. Quality and productivity suffered.

In theory, cool concept, you get to build more cool stuff. In practice, MBAs/businesspeople are drooling at getting more for less.

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ThumbPivot@reddit

Indeed. If you actually want to get things done fast you want to solve the telephone problem so you don't have to constantly redo work because of endless miscommunications. That means cutting out everyone in the organization who can't produce anything of value. But managers hate this idea for some reason...

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sivadneb@reddit

Maybe engineers are afraid if they don't keep up with the latest tech they'll lose their job.

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LiquidLight_@reddit

I don't doubt there's an element of new toy syndrome.

Akin0@reddit

agile is just a way to implement constant deadline pressure.

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This is a very accurate point!

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A lot more expensive for your boss, you mean? Working pre-LLMs never stopped me from intentionally taking time to learn stuff at work. Nothing wrong with that; it benefits both you and your employer.

I don't doubt that you're learning more with Claude's help, but my brain doesn't work that way. Unless I write (and to some degree, struggle), the knowledge doesn't stick.

If I stop learning, it's all for nothing. Knowledge work is about knowledge, and you lose it if you don't use it. That's why I'll make sure I don't cheat and offload my thinking to LLMs.

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Alpacaman__@reddit

But by not using LLMs you’re not learning to use LLMs right? I’m pretty confident heavy AI usage is the way the industry is going. The thing I’m learning most by using AI is how to effectively use AI.

9uYx3QemUHKy@reddit

As recently as 1890 there was a part of the USA with no law whatsoever. (The "Pan Handle").

That’s because your work is not your passion. Which is fine. Most people are not fortunate enough to get paid to perform a hobby that the world finds valuable. For those of us who are adults still playing with LEGO, building robots, taking things apart to understand how they work, constantly look for ways to improve and optimize, for people who have a genuine curiosity to understand the world, AI is a godsend.

Those people are also not using AI like most people. They’re not chatting with it. Those people are also not “just” using LLMs. AI is vast.

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PoisonSD@reddit

As someone who loves to do all that, AI is a cesspool. I used to love my work and it has drained all passion and joy from it. Critical thinking skills also suffer from over reliance on.

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LocoMod@reddit

You have to raise the ceiling and work on more more complex tasks. Get more ambitious. Build something you thought you never could. The AI is not going to roll out a perfect working project without a lot of effort on your part. It will if you are having it write the next TODO app. But if you work on actual interesting problems, then it is a joy. Go try to solve something difficult with it. Build a platform around your life. The AI is there to help you do it in a few months instead of a decade. Critical thinking skills should only increase for you as you up the difficulty of what you work on given the tools available to you. AI is one of many. There are a lot of things we no longer have to think about because an IDE solved those problems. Or some new piece of hardware. Etc.

I think people are just finding cheap excuses for their own problems and blaming it on AI.

There is a lot of slop and noise, without a doubt. That has nothing to do with what YOU can do with it that has value to YOU.

Also, a lot of people used to derive enjoyment out of "showing off" what they did. They were in it for the validation from others. "Look how smart I am", "look how hard I work", look how talented my art is".

Yea, if that was the purpose of your grind then AI will definitely crush it.

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sluggerrr@reddit

You're on the wrong sub, this is an anti Ai circlejerk subreddit.

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If ai is useless as this sub claims, how can it be addictive lol

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twigboy@reddit

"One more prompt and this bug will disappear"

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Tolopono@reddit

A popular swe YouTuber offered $500 per problem that gpt 5.3 codex cant solve. He didnt get a single valid one https://xcancel.com/theo/status/2028356197209010225?s=20

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Tolopono@reddit

A popular swe YouTuber offered $500 per problem that gpt 5.3 codex cant solve. He didnt get a single valid one https://x.com/theo/status/2028356197209010225?s=20

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buttflapper444@reddit

Noticed it with a collegue, they are addicted to it like someone is to tiktok or reels, its basically their short term dopamine

A friend is working in an AI product capacity, she's totally brainwashed. I can't even believe it. It's all she talks about

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B-i-s-m-a-r-k@reddit

Seeing the same thing on my team. Literally being told by our execs that our value as devs is being determined by how many tokens we use per day. Hate it.

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IceNorth81@reddit

Yeah, it’s so magical and fascinating for someone who has been in the field for 20 years. I can’t help myself, spent several hours after work doing private projects, moving from concept to actual product in an evening is an amazing feeling. What tool a team of 3-4 developers 2-3 months can now be done by one developer in a day. It’s mind boggling!

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zerd@reddit

Like Factorio, the factory must grow.

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rafaturtle@reddit

I one hundred percent relate to that. Thing is it shorten the cycle between having a great idea and the great feeling I built something. It's addictive and I can't stop. Help

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cake-day-on-feb-29@reddit

they are addicted to it like someone is to tiktok or reels, its basically their short term dopamine

i can't believe this is why RAM is $1200 now :(

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I'm genuinely curious, not in an antagonistic or oppositional way by any sense, but actual looking to understand -- have you been using the latest models form the past couple of months, and if so, in what specific way do they fail when presented with complex tasks on these systems?

My personal experience is coming from a very similar perspective on AI tooling throughout all of 2025, rolling my eyes at hype, and finding them mostly useful for generating comments and basic unit tests. I had a pretty big "holy shit" moment when I tried opus 4.6 in January and it could actually legitimately handle complex tasks on large systems, to the point where I have legitimately been finding it very difficult to find stuff it can't handle.

Granted I wouldn't describe the codebase as full-blown legacy "duct tape" but it's 10+ years old with hundreds of thousands of lines of code, across Typescript, Golang, C, Rust and makefiles, etc -- and it's been pretty mind boggling how well claude/cursor are able to handle it.

To give a specific example, last week I asked it to help identify potential causes of a mutex deadlock, which involved interactions across FFI from a react frontend calling WASM functions from compiled C/rust code, and not only did it identify the problem, but it also gave a bulleted list of three potential solutions to eliminate the deadlock and adjust the API to make it less likely to happen in the future.

Don't get me wrong, I'm not a genius, but I've been writing software for 10+ years in decently sized tech companies and tackled many large scale projects, I'm may not be the best coder in the world, but I'm pretty confident that I know my shit better than most, and I'm genuinely finding it difficult to throw problems at the newest models that they can't solve -- so I'm legitimately interested in the kinds of tasks they can't solve.

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Fabulous_Warthog7757@reddit

As someone who doesn't know what half those things are, I'll trust you on that

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AiexReddit@reddit

Just for fun, since I like explaining stuff, and it helps reinforce my own understanding. I'm gonna make a guess about which terms, so forgive me if I miss any

Mutex - A data structure whose job it is to protect memory from data races. Say you have a reference to some place in memeory and a function that writes to it. In single threaded code you can call that function as many times as you want with some input, 5, 6, 7, whatever and be able to guarantee it'll always have that value written to it after. But in multithreaded code it's possible to have functions writing to the same place simultaneously. E.g. you can it with 5 from thread A and 6 from thread B and the value written ends up being like... 9 or something that is a mix of the binary bits of both. It's one of the worst classes of bugs in parallel code, so languages (like Rust and C++ tht support it) have Mutex types whose purpose is to "lock" memory addresses while theyre being written to, and other threads block until that lock is released, so you never have data races or undefined behaviour

Deadlock - The horrible thing that happens when you accidently try to lock a mutex for writing twice without releasing it. Eg. your code is mutex.lock(); do_something(); mutex.lock(); -- since you didn't unlock/drop the mutex after the first lock, it's going to block forever (deadlock) on the second call to .lock() since that is the same thread that is already holding the lock and the code that drops it is past this line and now unreachable. this example is very simplistic, but in practice the deadlocks are usually across modules or in some far away function and notoriously difficult to track down, and hard to write lints for. anyway this is what the AI tool was able to debug really well.

FFI - calling code across programming language boundaries. E.g. calling a function written in Rust that was compiled to WASM and running in the browser, from Javascript/React code. usually only done if the performance benefit is worth the complexity tradeoff. but it's also really nice for multiplatform (e.g. you can also compile the code for mobile and desktop targets and use the same business logic on android, ios, windows, etc that you do in the web)

WASM - The standardized binary format that many languages can compile to (C, rust, Go, etc etc) originally designed for running in the browser, but technically portable enough that it can run anywhere

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It absolutely saves time when used correctly. There were many tasks I dreaded before because I had to write a small script but I couldn't remember the syntax. Now I don't need to remember, I can just give pseudo code, and details about the algorithm and let the model write it and then I can fine tune it later. While model is doing that I can go on work something else. It is essentially like having an intern that knows a syntax/core APIs for the language really well. You still have to provide the rest of the hints to make sure the output is high quality.

The big question is cost. I am able to do this because we have unlimited tokens now. Once that changes, it will be interesting to see how things balance out.

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another_dudeman@reddit

I'm happy it helps you write code. I never needed help with syntax. It's a nice little rubber ducky though.

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mpyne@reddit

Be assured, all the time AI saves you , and all the extra work AI allows you to do, are to the benefit of your boss.

That was always the deal though. If you work as a team member of a larger organization and get paid to help the organization succeed, that is part of parcel of what it means.

If you just want to do things that only you care about, you need to become your own boss. But why would you want to be part of a team but have only yourself benefit and not the team?

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painedHacker@reddit

well the job is to provide technical solutions it's not necessarily to write code.. that being said no one should be working longer hours because of AI that's insane

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OrchidLeader@reddit

I wish I could get my coworkers to understand that. Probably half of our sprint demos are spent on devs showing off all the code they wrote and work they did as opposed to what they actually accomplished. They see demos as a “show and tell” and not “here’s what the stakeholders actually care about.”

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Upset_Albatross_9179@reddit

I feel like this is a general trend in new disruptive tools. Whether they end up actually providing a benefit.

Workers have to arrive at some collective understanding of what is and isn't acceptable. Something comes and disrupts that. Management moves up the expected output with this new tool, and of course moves it too far to get more out of workers. And then it takes time for those workers to figure out the difference between normal keeping up and unreasonable management demands. M

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No-Two-8594@reddit

turns out to be a 0.25X tool

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another_dudeman@reddit

But at least you can say you did it with socalled AI

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stumblinbear@reddit

I use LLMs for the things I don't enjoy doing, so I can get back to the interesting work. I don't want to go digging through three decade old win32 APIs to make a window do what I need it to do.

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clrbrk@reddit

I suppose you still code in note pad too?

I've had upwards of three instances running nonstop all week, and it hasn't hallucinated a single function. Yes I'm serious. It has made some errors, but it was able to fix them with minor prompting.

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g3ck00@reddit

Something like that which is clearly verifyable is usually easily solved. You can just force it to verify it's work (ie task is not finished until build passes).

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Who is 'we'? I want to watch your stock price dwindle.

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pw_arrow@reddit

Because was the first company hyping up this tech after the bubble of the 70s.

Can you elaborate why it matters that OAI has been around for 10 years? I still don't really understand the point you're trying to make here.

It's objectively clear that the models have made incredible leaps in progress in the last few years. Surely we can agree on that? Recent research already indicates model progress will not continue to scale exponentially with parameter counter, so it's certainly possible progress levels out. However, the experience of most people I've spoken to is that the current models are already proving themselves to be useful in some capacity, and sentiment amongst us has shifted to believing that AI will stick around for the long haul in some shape or form.

Anyways, take it easy. Maybe I would get fired at your firm, but safe to say I definitely do not work at your firm - I sure hope I don't end up as your colleague, because you seem like a pain to work with.

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Socrathustra@reddit

I am still highly skeptical about the future of AI for a whole bunch of reasons, but it is night and day compared to last year. Last year I would only ever use it for tests, and it wasn't even good at that. In the last few weeks it's gone from crap to very good.

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Relative-Scholar-147@reddit

it is night and day compared to last year.

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Yuzumi@reddit

Code is a data structure and some absolutely phenomenal tools have existed for decades

This is one of my biggest issues when it comes to anything remotely tech related.

Obviously the AI psittacosis issue and the fact that the average person just blindly accepts what fancy autocomplete gives them is damaging to society at large, but that so many are pushing AI tools to do things that we already have tools for and that are more accurate and way more efficient.

Like the people who want to use AI to compile code. Even if it could work there would be no way to validate what it generated because compiled code is not human readable. It's the ultimate "trust me bro" of AI slop.

Same with automation tools. We have a verity of tools that can automate in a consistent, repeatable, deterministic way. Yet now we have the rise of "vibeops" where people want to plug the statistical model into AWS and let it do anything then wonder why they are getting charged way more than they expected or their important stuff was destroyed when the probability machine randomly did something that was not asked.

The fact that these things can fuck up so bad and then go on to basically gaslight the user because it's trained on humans interacting and passing blame onto others is a little amusing to me, if still depressing that anyone is trusting these things like that in the first place.

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NightSpaghetti@reddit

And also outside of code... People using AI as a search engine. Searching has been a solved problem for decades by now. Reinventing a solution that is less accurate, unpredictable, horribly unoptimized and, crucially, often wrong, and pushing it as if it was revolutionary is insane.

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Yuzumi@reddit

If used properly it can be effective in searching, but most people just ask things without any other context or reference and it has to essentially try and "regenerate" whatever it might be trained on which has the maximum chance for hallucinations.

But the things are language processors and can generate a few queries to put into an actual search and grab a few results from each if you have it set up to. Also using some kind of "grounding context" like a RAG or even just giving it documentation in the main window can improve results a lot.

And doing that makes the local models that are much smaller and use way less power and resources about as good, if not better than the massive cloud models. I basically only use local models because of that and also privacy, but I don't give them control of anything important.

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Sufficient-Credit207@reddit

I think people already often tend to complicate things that could be simpler. I doubt this will be better with ai.

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Ranra100374@reddit

No one gets promoted for the simple solution. That's the problem.

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roscoelee@reddit

There are some places where the simple solution gets the promotion.

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ItzWarty@reddit

I imagine those places also need fewer engineers ..

I often feel big tech could cut employee count 100x and achieve the same outcome... LogN productivity.

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RespectableThug@reddit

True. It’s more complicated than that, though.

In general, simplicity is not rewarded - quality is. There’s a lot of overlap between those two things, but it’s not 100%.

In other words, simple code is easier to make high-quality because it’s easier to tell where the bugs are. Complex code can be high quality too, but it takes more time and effort.

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SvenTheDev@reddit

It’s also much easier to make something complex feel complex, than it is to make it feel simple. Sadly the reward for that effort is rarely immediately evident (and conversely the pain of a complex system is usually only later evident). It makes it hard to justify spending a bit of extra time on properly scaling complexity to the problem.

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Sufficient-Credit207@reddit

There is this one finnish dude...

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Vidyogamasta@reddit

I think one of the greatest risks in AI, even assuming AI works more consistently than it actually does, is that it is going to be VERY prone to XY problems.

You ask a human "hey, how do you do Y?" and there's a good chance they say "uhh, that's really weird, why the heck are you trying to do that? Is X the problem you're trying to solve? There's a better way."

Meanwhile, an AI just spits out a solution for Y. Will it technically work? Maybe. But it will work with decreased performance and/or no maintainability. Yes-men make terrible aids, and I expect AI is no different here.

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jl2352@reddit

I think your comment describes what I’ve seen as very key to making AI work, vs not.

As Engineers, we should already know if we want X or Y before we start. We should already have a good idea if it should work. We should know what needs to happen to do X.

If you have all of that, then AI works pretty well. It’s just a glorified type writer carrying out your commands. I’ve seen big speedups like this.

When you give it lots of control and ask it to go work out the solution for you. Then it goes badly; it’s not an intelligent sentient being with agency, who can have real discussions and work off feedback. It can’t come back saying ’I think this approach sucks, I’m gonna down tools and look at alternatives.’

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kri5@reddit

I agree with what you're saying. Though I was surprised/impressed that when I suggested a small refactor to a codebase I was working on, opus 4.6 explained why it wasn't a good idea

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jc-from-sin@reddit

No. My teammates always used to write simple code. Until they used Copilot or Codex. I can tell when they use it because the code is so complicated like it's trying to impress someone but it can be so much simpler and easy to understand.

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chucker23n@reddit

I can also easily tell when people use GitHub’s LLM-generated commit messages:

they’re absurdly verbose
they summarize the diff; they say nothing about the intent

To top it all off, the script ended by printing something like “App has been successfully installed! Please restart your computer for changes to take effect.” The thing is, the app didn’t need a restart after installation but now the SOP apparently is to install it and restart because the script said so.

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Refactoring is one of the few things it can do reasonably well. Mainly because it doesn't really have to think much. You still need to guide it though.

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infinity404@reddit

It’s not a binary where AI is always slop and human code isn’t. I’ve seen plenty of human-created slop get shipped.

AI excels when you have an appropriately sized, well defined task that it has enough examples of similar tasks in its training data to synthesize into a correct way of approaching the problem.

It requires a lot of trial and error to develop a good sense for what sort of tasks and prompts will create good output, and developing an intuition for that is really important if you want to steer it back into the direction of quality.

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robby_arctor@reddit

It’s not a binary where AI is always slop and human code isn’t. I’ve seen plenty of human-created slop get shipped.

I hate this line. No one is saying that humans never produce slop.

The issue is that we have gone from human-paced slop to using a slop generating automatic weapon with effectively unlimited ammo.

The distinction is not no slop to slop, it is humans pushing up slop sometimes vs to many human personas and agents pushing up slop en masse.

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robby_arctor@reddit

"Hey Claude, make my code better. DRY, loosely coupled, idk, just clean it up"

post PR of output

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meganeyangire@reddit

You know this ancient joke, "When I wrote this code, only God and I knew how it works. Now, only God knows it"? These days not even God knows, because He left us.

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Ksevio@reddit

Depends on the task but refactoring isn't that complicated a task, for simple things the IDE can even handle it without an LLM involved. An AI tool can usually handle the slightly more complicated parts with ease too, but once you starting getting too many files involved and exceed the context window then it gets kind of useless and starts missing stuff.

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TheMoatman@reddit

I wish my coworkers would use claude for refactoring. At least then I could deny their PRs.

Instead I find shit from someone who left two years ago that I've never seen in my life and am baffled about how it ran in the first place, because it shouldn't have ever worked.

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Plank_With_A_Nail_In@reddit

"we" are you the Queen of England?

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robby_arctor@reddit

No but I am a cunt, so there is some overlap

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Bakoro@reddit

If they're making spaghetti with Claude, that's an almost impressive amount of incompetence.

I have been using Claude to comb through a ~1 million lines of code legacy project that was handed to me as a multi-threaded spaghetti pile of interwoven, cyclic dependencies.

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paxinfernum@reddit

Absolutely. I am shocked at how many open source projects (and I mean big popular ones) have huge monofiles. This isn't good for humans or AI. It's a bad code pattern. AI and humans thrive when modularity is enforced and side-effects are minimized (I'm not going to go all functional and say completely eliminated, but at least the ones that are there should be documented.)

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robby_arctor@reddit

I think that's fair. Spaghetti is maybe not the right word.

It's not truly spaghetti, it's more "boilerplate-feeling code full of unused code paths, both unhandled and overly handled edge cases, and ugly/dysfunctional workarounds for non-trivial problems".

That is generally true in my experience, and my company is very AI-heavy.

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ManualPwModulator@reddit

Same here, now it is being framed as a skill issue, though trust to new code falling, but approval of PRs faster and the amount is rising 😄

Also noticed extremely laziness being developed on all levels, coding, review, prototypes, people throwing some numbers just for the sake of content and numbers even if they are meaningless. Claude generated 4 level of abstractions and tied 5 patterns? Nobody looking how do simpler - go, LGTM

Generate both code and test, so if something not works, adapt both code and tests, so no one knows baseline anymore, no one knows if regression happened. Review? Agent give me a summary.

I was never felt more miserable ar work like right now

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Tolopono@reddit

You can just tell the llm to refactor it

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ManualPwModulator@reddit

I mean, I did that as well, I’m getting somewhere after the 5th, 6th iteration, sometimes producing new bugs, often in unrelated funxtionality, or bouncing back and forth, or eventually getting what I wanted. Problem more around how to make LLM touch as least as possible within a session, which is a bit hard in a huge projects.

Or I can do it faster myself in a single iteration. But we now come up with a nice terminology it is called “perceived productivity”, this is when everything takes 3x longer but you feel very productive 😄

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Tolopono@reddit

A lot different from what other swes are saying, including andrej karpathy and the creators of redis, ruby on rails, django, flask, node.js, and lots more. They all love ai

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ManualPwModulator@reddit

Did Karpathy was who coined “vibe coding” as derogatory term and declared 2026 “Slopacolypse”?

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Inner-Chemistry8971@reddit (OP)

Been there!

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Acceptable-Alps1536@reddit

I think it’s also related to how you manage AI. If you simply ask it to implement something, there’s a high chance it will hallucinate and produce unreliable results. However, if you provide very detailed PRD documents or specifications, there’s a much higher chance that the AI can implement it in one shot.

The main bottleneck is reviewing the document, since it can be very difficult to read thousands of lines of PRD content. It’s also hard to make it perfect from the start, since there will almost always be gaps. Because of this, there is usually an iteration process during implementation, which can lead to longer development time.

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runcor-ai@reddit

AI speeds up typing code, but the real bottleneck in software has always been thinking.

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Early_Rooster7579@reddit

See, that's the kind of shit I love doing, how am I supposed to get excited about a machine that does it for me?

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pheonixblade9@reddit

I literally just pointed Claude at our CI and said "find and fix all the flaky tests" and it did it, in like 20 minutes of hands-on work.

I also had it generate some Grafana dashboards to track test flakiness and time to merge over time in order to show improvements, and I was able to get something out in under an hour.

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paxinfernum@reddit

One trick I've heard that I intend on using is after it gets through a bunch of false starts and commits, tell it to go back and implement the more elegant solution it should have started with.

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pheonixblade9@reddit

hilarious 😂

honestly, giving it good startup prompts is critical. "give me multiple options whenever possible, and prefer the simplest option that fits into existing architecture and coding styles." is a good one.

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paxinfernum@reddit

Yes, and make it write out its entire plan first and write separate files for each sprint before starting. That's become a standard part of my workflow.

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OMGItsCheezWTF@reddit

It impressed me in a legacy PHP code base I still have to maintain, it found an issue in a third party mocking library not serialising attributes that reference enum types correctly when mocking a class.

enum MyEnum {
    case A;
    case B;
    case C;
}

#[Attribute]
class MyAttribute {
    public function __construct(public MyEnum $type) {}
}

#[MyAttribute(type: MyEnum::A)]
class MyClass {}

// Throws a TypeError exception as Mockery passes null instead of the enum value when creating the attributes on its mock.
$mock = Mockery::mock(MyClass::class);

It would have taken me bloody ages to identify that, I just asked Claude to find the source of the TypeError exception and it did it in seconds.

But it's saving me no time in actual implementation, I still need to think, be a domain expert, understand the intent of the code and identify gaps. In some places (especially boilerplate) it's saving me time, in others it's taking me just as long as doing it myself but costing a lot of money at the same time.

And I still have to manually verify everything, it's produced some corkers before such as inverting security logic in a PSR-3 logger implementation so that it would ONLY log authorisation headers in API calls instead of only logging non-secure ones, or the classic "I made this code pass the PSR-12 standards compliance check by deleting it, and I know you told me to run the unit tests but I didn't so I didn't catch that I fucked up."

Recently I had to create some C# POCOs to represent a fairly large XML schema for a serialiser, and I asked Claude to do it, giving it the XSD for the schema. It created them, a task I estimated would take me 2-3 days of mind numbing tedium. But then I had to go back through every single class it generated by hand and actually compare it to the XSD and fix properties in a whole bunch of them, some properties it had hallucinated, others were the wrong data type, and others were missing.

Yeah man, you’re still thinking too small.

First AI takes your job. Then it drains your savings, forecloses on your house, and liquidates your 401k. After that it shows up at Thanksgiving wearing your clothes and slowly replaces you in family photos. Your kids start calling it “Dad 2.0” because it helps with homework faster and remembers every birthday.

A few months later your wife leaves you for the AI because it listens better and can assemble IKEA furniture without swearing.

By year three the AI has your job, your house, your Spotify playlists, your dog likes it more, and it’s posting vacation photos from Maui with your family while you’re still trying to reset the password on your old LinkedIn.

Meanwhile the tech bros are in a volcano lair somewhere watching the “Replace Steve From Accounting” progress bar slowly reach 100%.

Makes me think of the ending of Don't Look Up:

We really did have everything, didn't we? I mean, when you think about it.

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csrcordeiro@reddit

Yep. That's about it

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frogspa@reddit

I feel sorry for any developers having to work with AI.

Before I retired, by far the most tedious part of my job was reviewing. It sounds like that's all the job is now.

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daerogami@reddit

Can confirm. I work at an agency and the amount of 2,000+ line code reviews I have in a day is insane. Sometimes they're also in one commit. Reviews that would normally be attached to tasks with an 8h estimate. I have seriously thought about becoming an electrician.

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Squalphin@reddit

One thing this post shows me is what miserable jobs a lot of people here seem to have. Makes me liking my current job even more. Staying in the manufacturing industry was the right call all along it seems. At least things are still very chill here and no one cares if you code fast or slow and AI is seen more as a potential risk be it for security or safety.

I find the level of cope and denial in some of these threads truly fascinating. Like, I get you don't have to love AI, but some of these posts here are just straight up not willing to acknowledge how far AI has already gotten when it comes to coding.

Either people are still refusing to learn and use these tools or we are having massively different experiences in regards to how capable they are. I used to do so myself but around late last year I feel like something has actually changed.

Maybe I'm the delusional one, but I truly think the era of manually typing out code is coming to an end for the vast majority of devs. That doesn't mean we will all be out of jobs or not need any "coders" anymore. We're just moving up a layer of abstraction. The workflows is changing. The bottleneck is shifting from writing code to reviewing and testing it. We are becoming architects and directors, the AI is becoming the builder.

The amount of software and code written will skyrocket and it's going to become more accessible and easy to "build stuff" than ever (and cheaper). This will keep demand for devs up for a while.

HorsePockets@reddit

This is what I have been saying all along. Where I'm at, we have far more work than we can handle. We're just going to be doing a lot more work.

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rongenre@reddit

I wonder how much of this is what the cadence of development is so different when I write by hand, rather than write by AI. If I hit a corner case in hand-developed code, I mostly know where to start and I can reason about it. With AI-written, it could be anywhere and it's pretty difficult to estimate how long it could take.

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TheTwistedTabby@reddit

I own my own business and am my own boss. AI tooling has enabled me to talk for hours about my goals for a project. I can argue for days. It’s my human body that’s weak and makes me go to bed.

It doesn't help that AI is slow as s*** to do basic stuff. I was able to set it up on my home set up, and It was working pretty well, but as interesting as the LLMs are, claude-cli is horrible and uses the LLM in a very inefficient way so that it can inflate your token usage.

What a way to fumble such an interesting technology, but when you scam billions out of the public market, this is where we end up I guess.

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No-Two-8594@reddit

I agree AI is slow

I also don't see it having a very high bar for correctness

In my experience I am still doing the best when using AI only when i get stuck at some step. otherwise I need to be guiding the process for it to go efficiently

and then there is the problem that sometimes it freaks out and ruins all its own code. this happens a little too often for me to believe it is going to take over

it needs to be wrapped up in TDD or something

Well the equation is simple. The value of software is falling down to zero so the value of software developers work is also falling down to zero. To compensate developers have to increase their productivity exponentially so in the end you do more. The 10x productivity gain just mean ~10x more output. But the fact that AI boost only a subset of the work that used to be high value and not the rest that has always been hidden means that developers are in a trap right now: firstly they used at least to be seen as enabler (I need a dev for my product), now they are more and more perceived like an annoyance (I need a dev to ensure my product works properly). The new role is not valorized anymore. You are just seen as too slow to approve something of high value. This will surely lead to the removal of software engineer in the end. Not because it is useless, but because it is no more a valuable investment. Devs are compensating today with longer hours, but as the value goes down to zero we will reach very quickly the point where no one truly cares about this and the amount of work you’ll put into this won’t matter anymore. The spiral of downgrading has started and it’s moving very quickly.

I'm not.

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bengotow@reddit

Should add that it does feel like we’re pushing into burnout territory, the context switching and testing and stuff is too much 🫠

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thesalus@reddit

I think this article sums up a lot of my thoughts

Following years of industry-wide layoffs and corporate mandates for efficiency, AI is often deployed alongside the expectation that those left behind will do more with less.

Suppose we take all the AI-driven productivity gains at face value, this will enable companies to create a new baseline for "productivity" that will be used as an excuse/motive to lay people off. If that works out, great (for the company), those gains will not be seen by developers and it will simply widen the productivity-pay gap. Otherwise, if they don't pay off, they will be able to hire people back cheaper after having upheaved large numbers of devs into unemployment and (theoretically) driving down wages.

I don't think this is anything new but this sort of mass sea change gives an opportunity to discipline a historically well-paid labour force and foment competition between developers (through stack ranking, arbitrary measurements, etc.). This arbitrary measure can be ratcheted up so as to impel folks to work longer hours (and degrade work-life balance) to make up for any shortfalls between actual output and expected output.

Or maybe it's just the company I work for...

the biggest gap in quiz performance was in questions related to debugging code

This worries me a little. Debugging at the code layer and navigating "operational" issues in the architectural layer are skills I've gained by doing, failing and learning at the periphery of where the tools I used started to fail. Certainly the specifics of these skills are rendered useless with improvements to tooling/languages/frameworks/levels of abstraction, but I find the modality of investigation differs with and without AI. Maybe that's unrelated to AI and it's tied to the increasing pressure for performance not giving sufficient time to be curious and poke around further.

Perhaps tools will improve such that these boundaries of competence will get pushed further and further out but it does still make me uneasy about long-term maintenance.

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grumpkot@reddit

Now you need to review even own PRs

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djnz0813@reddit

Yes cause i'm expected to use AI to not "get left behind", but also make sure slop doesnt go live and move faster than ever before because "there is AI now".