"you haven’t done any of the hard thinking you would normally do in writing the code yourself"
It's true, I spend less time solving problems that arise naturally from the process of implementation. But implementation errors have a poor signal-to-noise ratio. For every error that exposes a real design problem there are 10 others that involve routine fixes: type errors, scope issues, import resolution, dependencies. There's a common argument I see being thrown around, which is "how will junior engineers develop into senior engineers if they don't get reps in with implementation?" But to me it feels like "programmer" and "architect" are becoming more and more orthogonal as the models improve in capability. If my goal is to be an architect, getting more reps in as an architect is in my best interest, and letting the agents do the dirty work frees up space for me to spend the mental effort where it counts. And anecdotally, the scope of projects I've started taking on has scaled roughly linearly relative to how much work I'm able to delegate, while the mental engagement has stayed constant.
| type errors, scope issues, import resolution, dependencies.
I write code myself and use the LLM to find mistakes then fix them manually. I recommend inverting the conventional wisdom on LLMs:
1. Don't use it to write code. It's a terrible
programmer. But it's an intelligent rubber duck
and a solid analysis tool.
2. Write the code yourself. It'll go faster than
figuring it out as you go along. It's just typing
if you have a plan.
3. Use an LLM to sanity check what you wrote.
Point out potential design issues or future problems
or breaks from convention. Decide based on your
experience how much you want to address these issues
before moving on.
Is this as fast as AI writing the code? Not at first but possibly over time. Maybe faster. Certainly better quality.
Plus you don't lose any of your skills in the process. You're still doing the gruntwork necessary to keep your skillset.
And the token cost is a small fraction of what it takes to get LLMs to write good code.
> Someone asks you to add a feature to an existing program
While I empathize with the tone, even before AI the creativity was largely at the feature definition step, not in the implementation.
Outside of the very few computer scientists working on novel algorithms, the vast majority of software development is a mapping problem between the feature request and the mundane technical details, something repeatedly (and correctly) mentioned here in the context of FAANG algorithm fixated interviews. This has now largely been automated by LLMs
What is left is just creativity part - defining the use cases and features to develop in the first place. But the corollary is that software engineers that start after the requirements have already been defined are obsolete, which is a sobering thought for any of us in that vocation.
Also typically new features resemble ones I implemented in the past and I won’t learn that much from implementing them. Often times I immediately know what to do and although I do enjoy typing I do prefer just having it done in way less time.
In the meantime I can concentrate on more interesting problems.
> Outside of the very few computer scientists working on novel algorithms,
It's a quite a bit broader than that: for instance most of science and engineering is heavily supported by simulations (very useful when the system you're considering doesn't have perfect spherical or cylindrical symmetry), and there is still tons of algorithm development going on. The world is vast, and thus so is the domain of programming.
And halfway through 2026, AI has become a very interesting and helpful partner in algo research too. If it does continue to pull away and zip off to ASI land, hopefully we can leverage the resulting magical technology and catch back up with it...
> It's a quite a bit broader than that: for instance most of science and engineering is heavily supported by simulations (very useful when the system you're considering doesn't have perfect spherical or cylindrical symmetry),
That isn't the vast majority of traditional software engineering work, and arguably is better called applied physics or applied science. Super interesting though - and definitely uses programming as a core skill/tool - but it leans heavily into traditional engineering and science.
This is not true. First of all, not all software is written in the context of a FAANG company with “feature requests”. Secondly, writing software is similar to the process of design, this comment reads like “the vast majority of handbag design is mapping problem between features and leather”, ignoring that both the design and implementation can be rewarding to work on. Eg. I’m working on a program for myself and the overall architecture of the program as well as some parts of its implementation are clever and compose well to make the codebase a joy to work in. I am not simply “mapping features to mundane technical details”. It is as much art as the skillfully hand-crafted handbag.
> I’m working on a program for myself and the overall architecture of the program as well as some parts of its implementation are clever and compose well to make the codebase a joy to work in. I am not simply “mapping features to mundane technical details”.
You said it: you are working on a "program for yourself". Hobbyist craft programming will always be here. The question is what kind of software engineering will be paid for, and a career can be built on.
I don't see much of a market for pure software engineers anymore. You also need to be a product manager, scientist, or have some other domain knowledge adjacent to software that relates to the real world.
I say this with empathy for those who just enjoyed the craft of designing and building software, and thought that alone would provide them a livelihood and career in perpetuity, but have found a big chunk of what they loved doing (and getting paid for) overtaken by AI coding agents.
I always think that stuff is funny because it clearly comes from a place of having only worked at faang-level-esque companies. I’ve only ever worked at messy mid-size software companies and have never once had a legit product manager guiding feature work, it’s generally been up to the developers to figure out what needs doing
One thing I just discovered is that I’m no longer interested in choosing a novel tech stack. What used to engage me now just feels shallow to me so I stick with what I already know.
Not sure if that’s fine though since I get to the point of a project being interesting a lot faster instead of wiring tools together.
I wonder if we're being overly selfish here and ignore the positive effects of democratizing programming and making software more accessible to less well funded causes and organizations. A good software engineer used to cost 100k/year, very few businesses could afford that.
I also disagree that AI results are lower quality. Codex Pro results I get are marvelous but they sometimes miss things that humans understand naturally - all of the edges - coherence of visuals, passage of time, etc.
One of the things that's neat about the software field in this day and age is that you can see when someone started out 20+ years ago, when things were pretty easy (relatively) and the boom was still on the horizon.
It's pretty common that someone who was born around the right time to the right people in the right location to become a millionaire by writing some php may do something else, but still consider themselves an engineer.
Reading posts on HN, I notice that even across different programming domains, surprisingly similar problems emerge. Seeing this, I can't help but think that programming might ultimately be a matter of organizational theory. Conway's Law speaks to this as well. Code structure and approach shift significantly depending on how an organization is structured. From that perspective, it makes sense why Gen AI coding yields such starkly divided opinions.
Zooming out, the debate largely boils down to the downsides of severed junior pipelines and lost tacit knowledge on one hand, versus the upside of being able to build everything you envisioned on the other.
Honestly, I suspect that programming in the future will become something like the art of bonsai, a form of sculptural cultivation. Just as you dig furrows for water to flow through, and once the water emerges it follows those furrows, Gen AI will pour out an immense volume of code, and we will shape that code into form. It is like when you build with IoC based frameworks. If you do not adopt hexagonal architecture, the code inevitably converges toward the patterns the community recommends anyway. Ultimately, it is less about individual code fragments and more about hoping those fragments do not go wrong, contained by guardrails that ensure they do not exceed certain thresholds. If that is the case, programmers might end up focusing on questions like, "How much loss is acceptable?"
Many programmers say, "You need to know the entire codebase." But as someone who has delivered over 30 projects with codebases of 60,000 lines or more, I know that once my own code alone surpasses 40,000 lines, it becomes nearly impossible to hold it all in memory. So I document it in broad categories. And the next day, I have to retrace where I left off and refresh my memory, which takes a long time. This is one area where Gen AI genuinely excels.
Personally, what I have noticed when looking at other people's code is that those who work at a low level often struggle with high level integration, and vice versa. When cognitive resources become concentrated, specialization deepens in one area while others are neglected. And how one manages those cognitive resources ultimately diverges depending on the domain and the specific problem at hand. Just as the mindset required for architectural design differs from the mindset required for diving deep into implementation.
My workflow, broadly speaking, has not changed. My main job has always been to grasp a legacy codebase, open up the black box, connect debug logs one by one, find safe footholds, and add features. The only difference now is that the black box called "legacy codebase" has simply become "Gen AI code." It is not like I could ever claim my own code was bug free to begin with. Ultimately, what mattered was which bugs to contain and how far to wrap them. Open source programmers, however, are different. Their code blocks serve as their cognitive solutions and their reputation, so their approach differs from delivery oriented code. That is because code quality itself contributes to their prestige. For me, it is the brands and the number of companies I have delivered to that build my reputation. That distinction seems to divide programmers at a fundamental level.
In that sense, I think future programming work will split into roughly three categories. The first is the exceptionally talented group that creates the datasets Gen AI will consume. In other words, the renowned open source groups we already admire. In AI labeling terms, they will be the ones producing the gold standard answer sheets. I believe they will survive and remain admired, just as they are now, though their numbers will shrink even further.
The second group will be those who make Gen AI code flow through industries and organizations. When Gen AI pours out code like water, they will be the ones managing the channels to prevent it from flooding. Most people will migrate into this role.
The third group is QA, and I think they will survive. They will execute Gen AI code and relay to the channel managers when the watercourses are going wrong.
In truth, things are already moving in this direction. It is like how no one thinks about how OOP inheritance maps to vtables anymore. People just treat objects as abstract concepts and move on.
Personally, I think the overall flow in Gen AI coding will shift toward implementing system state in the form of state transition diagrams, a return to the primitive form of Parnas's theory of information hiding. Parnas's theory states that "modules should hide design decisions that are likely to change." Humans interpreted this as something like private variables in OOP, but then leaked internal implementations to the outside anyway. When business policies end up scattered across controllers and services, information hiding has effectively failed. The decisions most likely to change are not hidden in one place. They are dispersed throughout the system. But in the Gen AI era, ironically, information hiding might actually become easier to achieve. You just define the state transition from "payment pending" to "payment completed," and that is it. Ultimately, we might end up circling back to earlier approaches, do you not think?
Anyway, I am not asking for someone else's answer. I need to find my own way. Because my way and someone else's way are different, just as my programming and someone else's programming are different.
Programming is ultimately the work of constructing a mental model, and that mental model is inherently personal.
For me the biggest issue with AI coding model is not that it writes code, or how it writes code, or whether it will replace me or not. I mean, these are real problems, but just not the ones that makes the difference to me daily. Instead what sets my feeling about the AI is simple: the experience of working with AI models, because it's the worse experience of my life so far.
Every single LLM will make up stuff, go down rabbit holes I didn't even ask it to visit and infuriate me all day long by doing ALMOST what I asked it to do, but just not quite. Take "yes" to one question as an approval to some other question. Come up with a plan but one you review the plan and accept it it hits some minor issue and then throws away the plan and do whatever it wants. Or when I reject some line of code or don't approve a command it wants to run 9/10 times it just tries to re-add or re-run it as if my rejection was meaningless. And no amount of rules and markdown files ever seems to change its behaviour for long, if at all.
It's like working with a sociopath who just doesn't remember anything that happened 5 minutes ago.
It's the classic case of "my computer doesn't do what I want, only what I tell it".
And all of that would be fine if it didn't pretend to be a human, if all the UX didn't create the illusion that you interact with some intelligent being, because as long as I remember that this is a cli tool it's all good, I manage my expectations, but the experience sooner or later makes you annoyed and frustrated - and if this was a person they would either stop or you wouldn't never work with them again.
it just a higher level of abstraction but not high enough that it replaces developers, I don't think the LLMs are even capable of reaching that level. Saying the creative process is gone is laughable. A lot of people make these claims and only have an internal tool that no one uses to show for it.
I built a "simple" storage application recently, uploading large files from a phone is not trivial, you can't just command Claude to do it without giving it good context.
I’ve been successfully shipping software since 1998. I just got the first payment on a greenish-field software product I’m doing on the side solo (that I couldn’t have done solo in 2019 without the tools). This is my workflow:
1. Client asks you to add a feature(s)
2. Spend two weeks unpaid walking the client through scoping down to the most minimal viable set of features that tests the business hypothesis and roadmap
2. I wrote up a reasonably exhaustive bullet list and sent it to a CGPT to write a draft formal definition of features describing what it should (and should not) do, how users can access it and what the suite of tests that we will need through TDD
3. Chat for 30 minutes with CGPT researching which data structures, algorithms, code libraries and external services might serve best to implement this feature.
4. Generate mockups and data schema alongside CGPT, to build the new feature, the tests to make sure it works as expected, and the documentation telling users how to use it and telling other engineers what they’ll need to know to maintain and debug it.
5. Generate minimum code to test the full data workflow.
6. Send repo and or working application binary to Claude and Gemini to critique
7. Adjust as needed. Deploy for client review and acceptance in sandbox. Promote to production
8. GOTO3 and loop
I can do in a week what would have taken a month a decade ago
Whenever I let these tools look at existing repos they are too influenced by what's already there.
I could even say "feel free to completely refactor or rewrite anything" and they'll still just do small performative changes.
I've now changed my workflow to only using AI for prototyping and rewriting by hand once I can see something is viable. Takes longer but the results are always much better.
For websites this work. But now you are a core developer of the GTA6 engine. Your task is to create a fast efficiënt occlusion mapper. One that works from all angles. Has cavities and rt support. Good luck doing this with Claude. It's not gonna achieve this. Because only a handful of games achieved this partly. Maybe next gen AI can do it. But for complex tasks LLMs are still not good enough.
Current AI cannot be comparable with "machinery and assembly lines".
Current AI companies stole much data from Internet without permissions, and used the knowledge of many people directly shamelessly.
It cannot resolve many practical problems well, even in programming area.
Do you want the software-assistant to do the important or enjoyable things instead of you? Do you want your assistant in real life to eat the delicious food instead of you?
How do you want to define "clearly positive impact on the economy"? NVDA and a bunch of other stocks are way up. If you got in early, you're sitting pretty. I'd call that positive for those lucky people.
It's true, I spend less time solving problems that arise naturally from the process of implementation. But implementation errors have a poor signal-to-noise ratio. For every error that exposes a real design problem there are 10 others that involve routine fixes: type errors, scope issues, import resolution, dependencies. There's a common argument I see being thrown around, which is "how will junior engineers develop into senior engineers if they don't get reps in with implementation?" But to me it feels like "programmer" and "architect" are becoming more and more orthogonal as the models improve in capability. If my goal is to be an architect, getting more reps in as an architect is in my best interest, and letting the agents do the dirty work frees up space for me to spend the mental effort where it counts. And anecdotally, the scope of projects I've started taking on has scaled roughly linearly relative to how much work I'm able to delegate, while the mental engagement has stayed constant.
I write code myself and use the LLM to find mistakes then fix them manually. I recommend inverting the conventional wisdom on LLMs:
Is this as fast as AI writing the code? Not at first but possibly over time. Maybe faster. Certainly better quality.Plus you don't lose any of your skills in the process. You're still doing the gruntwork necessary to keep your skillset.
And the token cost is a small fraction of what it takes to get LLMs to write good code.
While I empathize with the tone, even before AI the creativity was largely at the feature definition step, not in the implementation.
Outside of the very few computer scientists working on novel algorithms, the vast majority of software development is a mapping problem between the feature request and the mundane technical details, something repeatedly (and correctly) mentioned here in the context of FAANG algorithm fixated interviews. This has now largely been automated by LLMs
What is left is just creativity part - defining the use cases and features to develop in the first place. But the corollary is that software engineers that start after the requirements have already been defined are obsolete, which is a sobering thought for any of us in that vocation.
In the meantime I can concentrate on more interesting problems.
It's a quite a bit broader than that: for instance most of science and engineering is heavily supported by simulations (very useful when the system you're considering doesn't have perfect spherical or cylindrical symmetry), and there is still tons of algorithm development going on. The world is vast, and thus so is the domain of programming.
And halfway through 2026, AI has become a very interesting and helpful partner in algo research too. If it does continue to pull away and zip off to ASI land, hopefully we can leverage the resulting magical technology and catch back up with it...
That isn't the vast majority of traditional software engineering work, and arguably is better called applied physics or applied science. Super interesting though - and definitely uses programming as a core skill/tool - but it leans heavily into traditional engineering and science.
Seems to me you can get feature requests from user feedback.
I also expect someone will figure out a way to have AI "use" your software and suggest features
You said it: you are working on a "program for yourself". Hobbyist craft programming will always be here. The question is what kind of software engineering will be paid for, and a career can be built on.
I don't see much of a market for pure software engineers anymore. You also need to be a product manager, scientist, or have some other domain knowledge adjacent to software that relates to the real world.
I say this with empathy for those who just enjoyed the craft of designing and building software, and thought that alone would provide them a livelihood and career in perpetuity, but have found a big chunk of what they loved doing (and getting paid for) overtaken by AI coding agents.
Not sure if that’s fine though since I get to the point of a project being interesting a lot faster instead of wiring tools together.
I also disagree that AI results are lower quality. Codex Pro results I get are marvelous but they sometimes miss things that humans understand naturally - all of the edges - coherence of visuals, passage of time, etc.
It's pretty common that someone who was born around the right time to the right people in the right location to become a millionaire by writing some php may do something else, but still consider themselves an engineer.
Zooming out, the debate largely boils down to the downsides of severed junior pipelines and lost tacit knowledge on one hand, versus the upside of being able to build everything you envisioned on the other.
Honestly, I suspect that programming in the future will become something like the art of bonsai, a form of sculptural cultivation. Just as you dig furrows for water to flow through, and once the water emerges it follows those furrows, Gen AI will pour out an immense volume of code, and we will shape that code into form. It is like when you build with IoC based frameworks. If you do not adopt hexagonal architecture, the code inevitably converges toward the patterns the community recommends anyway. Ultimately, it is less about individual code fragments and more about hoping those fragments do not go wrong, contained by guardrails that ensure they do not exceed certain thresholds. If that is the case, programmers might end up focusing on questions like, "How much loss is acceptable?"
Many programmers say, "You need to know the entire codebase." But as someone who has delivered over 30 projects with codebases of 60,000 lines or more, I know that once my own code alone surpasses 40,000 lines, it becomes nearly impossible to hold it all in memory. So I document it in broad categories. And the next day, I have to retrace where I left off and refresh my memory, which takes a long time. This is one area where Gen AI genuinely excels.
Personally, what I have noticed when looking at other people's code is that those who work at a low level often struggle with high level integration, and vice versa. When cognitive resources become concentrated, specialization deepens in one area while others are neglected. And how one manages those cognitive resources ultimately diverges depending on the domain and the specific problem at hand. Just as the mindset required for architectural design differs from the mindset required for diving deep into implementation.
My workflow, broadly speaking, has not changed. My main job has always been to grasp a legacy codebase, open up the black box, connect debug logs one by one, find safe footholds, and add features. The only difference now is that the black box called "legacy codebase" has simply become "Gen AI code." It is not like I could ever claim my own code was bug free to begin with. Ultimately, what mattered was which bugs to contain and how far to wrap them. Open source programmers, however, are different. Their code blocks serve as their cognitive solutions and their reputation, so their approach differs from delivery oriented code. That is because code quality itself contributes to their prestige. For me, it is the brands and the number of companies I have delivered to that build my reputation. That distinction seems to divide programmers at a fundamental level.
In that sense, I think future programming work will split into roughly three categories. The first is the exceptionally talented group that creates the datasets Gen AI will consume. In other words, the renowned open source groups we already admire. In AI labeling terms, they will be the ones producing the gold standard answer sheets. I believe they will survive and remain admired, just as they are now, though their numbers will shrink even further.
The second group will be those who make Gen AI code flow through industries and organizations. When Gen AI pours out code like water, they will be the ones managing the channels to prevent it from flooding. Most people will migrate into this role.
The third group is QA, and I think they will survive. They will execute Gen AI code and relay to the channel managers when the watercourses are going wrong.
In truth, things are already moving in this direction. It is like how no one thinks about how OOP inheritance maps to vtables anymore. People just treat objects as abstract concepts and move on.
Personally, I think the overall flow in Gen AI coding will shift toward implementing system state in the form of state transition diagrams, a return to the primitive form of Parnas's theory of information hiding. Parnas's theory states that "modules should hide design decisions that are likely to change." Humans interpreted this as something like private variables in OOP, but then leaked internal implementations to the outside anyway. When business policies end up scattered across controllers and services, information hiding has effectively failed. The decisions most likely to change are not hidden in one place. They are dispersed throughout the system. But in the Gen AI era, ironically, information hiding might actually become easier to achieve. You just define the state transition from "payment pending" to "payment completed," and that is it. Ultimately, we might end up circling back to earlier approaches, do you not think?
Anyway, I am not asking for someone else's answer. I need to find my own way. Because my way and someone else's way are different, just as my programming and someone else's programming are different.
Programming is ultimately the work of constructing a mental model, and that mental model is inherently personal.
I just hope I can survive into the next era.
Every single LLM will make up stuff, go down rabbit holes I didn't even ask it to visit and infuriate me all day long by doing ALMOST what I asked it to do, but just not quite. Take "yes" to one question as an approval to some other question. Come up with a plan but one you review the plan and accept it it hits some minor issue and then throws away the plan and do whatever it wants. Or when I reject some line of code or don't approve a command it wants to run 9/10 times it just tries to re-add or re-run it as if my rejection was meaningless. And no amount of rules and markdown files ever seems to change its behaviour for long, if at all. It's like working with a sociopath who just doesn't remember anything that happened 5 minutes ago. It's the classic case of "my computer doesn't do what I want, only what I tell it".
And all of that would be fine if it didn't pretend to be a human, if all the UX didn't create the illusion that you interact with some intelligent being, because as long as I remember that this is a cli tool it's all good, I manage my expectations, but the experience sooner or later makes you annoyed and frustrated - and if this was a person they would either stop or you wouldn't never work with them again.
I built a "simple" storage application recently, uploading large files from a phone is not trivial, you can't just command Claude to do it without giving it good context.
1. Client asks you to add a feature(s)
2. Spend two weeks unpaid walking the client through scoping down to the most minimal viable set of features that tests the business hypothesis and roadmap
2. I wrote up a reasonably exhaustive bullet list and sent it to a CGPT to write a draft formal definition of features describing what it should (and should not) do, how users can access it and what the suite of tests that we will need through TDD
3. Chat for 30 minutes with CGPT researching which data structures, algorithms, code libraries and external services might serve best to implement this feature.
4. Generate mockups and data schema alongside CGPT, to build the new feature, the tests to make sure it works as expected, and the documentation telling users how to use it and telling other engineers what they’ll need to know to maintain and debug it.
5. Generate minimum code to test the full data workflow.
6. Send repo and or working application binary to Claude and Gemini to critique
7. Adjust as needed. Deploy for client review and acceptance in sandbox. Promote to production
8. GOTO3 and loop
I can do in a week what would have taken a month a decade ago
Whenever I let these tools look at existing repos they are too influenced by what's already there.
I could even say "feel free to completely refactor or rewrite anything" and they'll still just do small performative changes.
I've now changed my workflow to only using AI for prototyping and rewriting by hand once I can see something is viable. Takes longer but the results are always much better.
I am developing my own app 2+ years manually, and not use "AI" at all.
Using AI usually can cause many problems, which the author pointed at.
Programming should be enjoyable, not an annoyed or unhappy thing.
You choosing to do something in one specific manner is more the exception that proves the rule then any rebuttal.
Current AI companies stole much data from Internet without permissions, and used the knowledge of many people directly shamelessly.
It cannot resolve many practical problems well, even in programming area.
Do you want the software-assistant to do the important or enjoyable things instead of you? Do you want your assistant in real life to eat the delicious food instead of you?
“Age of AI” but most people actively dislike it and hasn’t had a clearly positive impact on the economy after ~4 years.
How does this mean this isn't an age of AI? Because you're The Representative of Programmers?
> so many not-real-programmers
Oh sorry I missed this part. It seems that you really are!
https://news.ycombinator.com/newsguidelines.html