> If you can go from producing 200 lines of code a day to 2,000 lines of code a day, what else breaks? The entire software development lifecycle was, it turns out, designed around the idea that it takes a day to produce a few hundred lines of code. And now it doesn’t.
It is so embarrassing that LOC is being used as a metric for engineering output.
LOC is useful here not because it's a metric for output but because it's a metric for _understandability_. Reviewing 200 lines is a very different workload than reviewing 2000.
That's assuming the 200 lines are logical and consistent. Many of my most frustrating LLM bugs are caused by things that look right and are even supported by lengthy comments explaining their (incorrect) reasoning.
Vibe Coding (and LLMs) did not create undisciplined engineering organizations or engineers. They exposed and accelerated them.
Plenty of engineers have loose (or no!) standards and practices over how they write coee. Similarly, plenty of engineering teams have weak and loose standards over how code gets pushed to production. This concept isn't new, it's just a lot easier for individuals and teams who have never really adhered to any sort of standards in their SDLC to produce a lot more code and flesh out ideas.
Bad engineers continue being bad, good engineers continue being good.
I personally don’t know any colleagues who were good engineers just because they wrote code faster. The best engineers I know were ones who drew on experience and careful consideration and shared critical insights with their team that steered the direction of the system positively.
> Claude, engineer a system for me, but do it good. Thanks!
People in the future are going to wonder what the hell we were thinking, when 30 years down the line everything is a hot mess of billions of lines of code generated by LLMs that no human has read almost any of it and is no longer possible for anyone to maintain neither with nor without LLMs. And the LLM generated garbage will have drowned out all of the good quality code that ever existed and no one will be able to find even human generated code anymore on the internet.
Makes me want to just give up programming forever and never use a computer again.
By then, the fix will be easy. Fire up the latest LLM, point it at your codebase and tell it "rewrite this from scratch. do it well. fix the architecture mistakes"
We can do this today too (but definitely hopefully future LLMs make better architectural decisions). With Claude, I've been working on an application for the last 2 months. I didn't have a great vision of what I wanted when I started but I didn't want that to slow me down. The architecture is terrible - Claude separated some functionality into different classes but did a bad job at it and created a big ball of mud. Now that I finally have my vision locked down and implemented (albeit poorly), it'd be a great time to throw it away and start over. It'd be interesting to see the result and see how long it takes.
Like with a lot of things in this space, it depends where you invest your effort. If you care about quality design and good code, you can definitely get there - but that doesn't happen by default.
With the right investment, we could certainly have tooling that creates and maintains very good designs out of the box. My bet is that we'll continue chasing quick and hacky code, mostly because that's the majority of the code that it was trained on, and because the majority of people seem to be interested in a quick result vs a long-term maintainable one.
If you like sci-fi takes on software systems, check out Vernor Vinge "A Fire upon the deep" and sequels. I recall ship systems software is something like all the code humanity has ever written, plus centuries of LLM churn. One of the protagonists is a space faring software developer particularly good with legacy code.
We are used to thinking about software like in the article, a program that runs deterministically in an OS. Where we are headed might be more like where the LLM or AI system is the OS, and accomplishes things we want through a combination of pre-written legacy software, and perhaps able to accomplish new things on the fly.
By then AI would be good enough to clean them all up....like I dont get these dooming scenarios they always assume that we are going to be stuck with LLMs and there wont be anything new coming.
There is nothing in the post to support the statement. An interesting personal confession, but it does not establish that vibe coding and agentic engineering are converging as a general phenomenon.
As a piece of meat, I look forward to charge rates of $10,000 an hour, to fix code out the vibe code generation.
> People in the future are going to wonder what the hell we were thinking, when 30 years down the line everything is a hot mess of billions of lines of code generated by LLMs that no human has read
--
It's just as likely that people will be surprised that we used to have billions of lines of human generated code, that no LLM ever approved.
This is wishful thinking. The force of the market is "number go up". Quality increasingly has less and less of a role in the equation. You will eat your slop, and you will like it. It will be the only choice you have.
But the quality of code was already very bad due to market forces. Most code at large companies is notoriously poor despite the talent density, because the incentives are not there to tackle tech debt or improve code quality.
With such a low baseline, there is an optimistic perspective that LLMs could improve the situation. LLMs can produce excellent code when prompted or reviewed well. Unlike human employees, the model does not worry about getting a 'partially meets expectations' rating or avoid the drudgery of cleaning up other people's code.
Have you noticed that the coding agents get really close to the solution on the first one shot and then require tons of work to get that last 10% or 5%?
If we shift the paradigm of how we approach a coding problem, the coding agents can close that gap. Ten years ago every 10 or 15 minutes I would stop coding and start refactoring, testing, and analyzing making sure everything is perfect before proceeding because a bug will corrupt any downstream code. The coding agents don't and can't do this. They keep that bug or malformed architecture as they continue.
The instinct is to get the coding agents to stop at these points. However, that is impossible for several reasons. Instead, because it is very cheap, we should find the first place the agent made a mistake and update the prompt. Instead of fixing it, delete all the code (because it is very cheap), and run from the top. Continue this iteration process until the prompt yields the perfect code.
Ah, but you say, that is a lot of work done by a human! That is the whole point. The humans are still needed. The process using the tool like this yields 10x speed at writing code.
This was often true when writing code manually to be fair.
You could get to "something that works" rather fast but it took a long time to 1) evaluate other options (maybe before, maybe after), 2) refine it, 3) test it and build confidence around it.
I think your point stands but no one really knows where. The next year or so is going to be everyone trying to figure that out (this is also why we hear a lot of "we need to reinvent github")
The problem of life in general is the last 5-10% is always the hardest. And it makes no economic sense in many cases to invest in trying to make that last part mechanised.
I believe the llm providers went with the wrong approach from the off - the focus should’ve been on complementing labour not displacement. And I believe they have learned an expensive lesson along the way.
Perhaps I've missed a few weeks worth of progress, but I don't think that AIs have become more trustworthy, the errors are just more subtle.
If the code doesn't compile, that's easy to spot. If the code compiles but doesn't work, that's still somewhat easy to spot.
If the code compiles and works, but it does the wrong thing in some edge case, or has a security vulnerability, or introduces tech debt or dubious architectural decisions, that's harder to spot but doesn't reduce the review burden whatsoever.
If anything, "truthy" code is more mentally taxing to review than just obviously bad code.
There are techniques for improving our confidence in our software: unit testing, integration testing, fuzz testing, property-based testing, static analysis, model checking, theorem proving, formal methods, etc. The LLM is not only a tool for generating lines of code. It can also generate lines of testing. The goal is that the tests are easier to audit by the humans than the code.
Correct me if I’m wrong Simon, but weren’t you highly optimistic about llm’s and agentic-use of them?
I believe this is a common fault of not being able to zoom out and look at what trade offs are being made. There’s always trade-offs, the question is whether you can define them and then do the analysis to determine whether the result leaves you in a net benefit state.
The problem with vibe coding closer is that the agentic makes a very plasticy samey feel unless you work with something that makes it unique or can pass a template through it.
Claude often does things in more detail, and even better, than I would, in the first pass. But I don't understand how anybody stands comments generated by an LLM?
It's seriously the thing that worries (and bothers) me the most. I almost never let unedited LLM comments pass. At a minimum.
Most of the time, I use my own vibe-coded tool to run multiple GitHub-PR-review-style reviews, and send them off to the agent to make the code look and work fine.
It also struggles with doing things the idiomatic way for huge codebases, or sometimes it's just plain wrong about why something works, even if it gets it right.
And I say this despite the fact that I don't really write much code by hand anymore, only the important ones (if even!) or the interesting ones.
Also, don't even get me started on AI-generated READMEs... I use Claude to refine my Markdown or automatically handle dark/light-mode, but I try to write everything myself, because I can't stand what it generates.
I find that the best thing about generating documentation with LLM's is that it gets me angry enough to rewrite it correctly.
"Ugh, no! Why would you say it like that? That's not even how it works! Now, I need to write a full paragraph instead of a short snippet to make sure that no future agents get confused in the same way."
It is pure arrogance to expect that machines will never be able to code as good as a skilled human.
And AI generated code should be different than human code. AI has infinite memory for details. AI doesn’t need organizational patterns like classes. Potentially AI can write code that is more performant than any human.
Will it look like garbage? Sure. Will the code be more suited to the task? Yes.
> The thing that really helps me is thinking back to when I’ve worked at larger organizations where I’ve been an engineering manager. Other teams are building software that my team depends on.
> If another team hands over something and says, “hey, this is the image resize service, here’s how to use it to resize your images”... I’m not going to go and read every line of code that they wrote.
The distance of accountability of the output from its producer is an important metric. Who will be held accountable for which output: that's important to maintain and not feel the "guilt".
So, organizations would need to focus on better and more granular building incentives and punishment mechanisms for large-scale software projects.
Vibe coding is just coding now. Writing assembly used to be a thing too until higher and higher languages were created. LLM is like that except it compiles English to code. This scares lot of professionals understandably.
This is a timely observation and feels right to me. I needed to get a relatively simple batch download -> transform -> api endpoint stood up. I wrote a fairly detailed prompt but left a lot of implementation details out, including data sources.
Opus 4.7 built it about 90% the same way I would, but had way more convenience methods and step-validations included.
It's great, and really frees me
up to think about harder problems.
I think I'm just too opinionated to go there. If I see something that works fine, but isn't the way I'd do it, it doesn't matter if a human or an LLM wrote it I'm still in there making it match my vision.
So you are going to waste everyone's time getting another developer to write code the way you want? This resonates with me because at my company I get this all the time. At that point, you might as well close my PR and do it yourself, whatever way you want. I really like the advice from the book 0 2 1, to assign different areas of responsibility to people, so that there is no conflict.
I concur, and I think that is one of the most difficult aspects of reviewing another's code. It's difficult for me to sometimes differentiate between what is acceptable vs. what I would have done. I have to be very conscious to not impose my ideals.
I am experimenting with writing en entire TypeScript compiler[1] with AI assistant. I've spent 4 months on it already. It might not be successful at the end of the day but my thinking is that if LLMs are going to write a lot of the code I better learn how this can and can not work. I've learned a lot from this project already. I think we're still in charge of design and big ideas even if all of the code is written by AI
I'm also experimenting with it more and more. Now I'm trying to create a 2D side-scrolling shooter with it, running in the browser. When it was relatively small, it did a good job. As the codebase and docs/ files that I'm using get larger it starts hallucinating, especially when the context gets at about 50% usage (Codex w/ gpt5.5). As in, it'll literally forget to update parts of the code.
e.g, I change velocity of player to '200' and of bullets to '300', and it only updated the bullet velocity. Then told me the player was already 'at the correct value' even though it was set to 150. Things like that.. :)
> I know full well that if you ask Claude Code to build a JSON API endpoint that runs a SQL query and outputs the results as JSON, it’s just going to do it right. It’s not going to mess that up. You have it add automated tests, you have it add documentation, you know it’s going to be good.
> But I’m not reviewing that code. And now I’ve got that feeling of guilt: if I haven’t reviewed the code, is it really responsible for me to use this in production?
Answer: it wholly depends upon what management has dictated be the goal for GenAI use at the time.
There seems to be a trend of people outside of engineering organizations thinking that the "iron triangle" of software (and really, all) engineering no longer holds. Fast, cheap, good: now we can pick all three, and there's no limit to the first one in particular. They don't see why you can't crank out 10x productivity. They've been financially incentivized to think that way, and really, they can't lose if they look at it from an "engineer headcount" standpoint. The outcomes are:
1) The GenAI-augmented engineer cranks out 10x productivity without any quality consequences down the line, and keeps them from having to pay other people
or
2) The GenAI-augmented engineer cranks out 10x productivity with quality consequences down the line, at which point the engineer has given another exhibit in the case as to why they should no longer be employed at that organization. Let the lawyers and market inertia deal with the big issues that exist beyond the 90-day fiscal reporting period.
Either way, they have a route to the destination of not paying engineers, and that's the end goal.
If you don't like that way of running a software engineering organization, well, you're not alone, but if nothing else, you could use GenAI to make working for yourself less risky.
> And that feels about right to me. I can plumb my house if I watch enough YouTube videos on plumbing. I would rather hire a plumber.
I don't buy this argument at all. I think if we could pay $20/month to a service that would send over a junior plumber/carpenter/electrician with an encyclopedic knowledge of the craft, did the right thing the majority of the time, and we could observe and direct them, we'd all sign up for that in a heartbeat. Worst case, you have to hire an experienced, expensive person to fix the mess. Yes, I can hear everyone now, "worst case is they burn your house down." Sure, but as we're reminded _constantly_ when we read stories about AI agent catastrophes -- a human could wipe your prod database too. wHy ArE yOu HoLdInG iT tO a DiFfErEnT sTaNdArD???
The business side of the house is getting to live that scenario out right now as far as software goes. Sure you've got years of expertise that an LLM doesn't have _yet_. What makes you think it can't replace that part of your job as well?
You're comparing paying $20 for an AI plumber to paying hundreds/thousands for a traditional plumber.
But that's not what the author is talking about in that passage you quoted. What he's saying is that, if you can pay $20 for an AI plumber, then it stands to reason that eventually you will be able to pay $30 to a company that manages AI plumbers for you, so that you don't even have to go to the trouble of supervising the plumber. Most people will choose the $30.
> It used to be if you found a GitHub repository with a hundred commits and a good readme and automated tests and stuff, you could be pretty sure that the person writing that had put a lot of care and attention into that project.
I think this highlights a problem that has always existed under the surface, but it's being brought into the light by proliferation of vibeslop and openclaw and their ilk. Even in the beforetimes you could craft a 100.0% pure, correct looking github repo that had never stood the test of production. Even if you had a test suite that covers every branch and every instruction, without putting the code in production you aren't going to uncover all the things your test suite didn't--performance issues, security issues, unexpected user behavior, etc.
As an observer looking at this repo, I have no way to tell. It's got hundreds of tests, hundreds of commits, dozens of stars... how am I to know nobody has ever actually used it for anything?
I don't know how to solve this problem, but it seems like there's a pretty obvious tooling gap here. A very similar problem is something like "contributor reputation", i.e. the plague of drive-by AI generated PRs from people (or openclaws) you've never seen before. Stars and number of commits aren't good enough, we need more.
I'd be lying if I said I was not worried about the future. I am not necessarily worried in the sense that there will be some grave, impeding doom that awaits the future of humanity.
Rather, I just feel like I have to constantly remind myself of the impermanence of all things. Like snow, from water come to water gone.
Perhaps I put too much of my identity in being a programmer. Sure, LLMs cannot replace most us in their current state, but what about 5 years, 10 years, ..., 50 years from now? I just cannot help be feel a sense of nihilism and existential dread.
Some might argue that we will always be needed, but I am not certain I want to be needed in such a way. Of course, no one is taking hand-coding away from me. I can hand-code all I want on my own time, but occupationally that may be difficult in the future. I have rambled enough, but all and all, I do not think I want to participate in this society anymore, but I do not know how to escape it either.
> If you can go from producing 200 lines of code a day to 2,000 lines of code a day, what else breaks? The entire software development lifecycle was, it turns out, designed around the idea that it takes a day to produce a few hundred lines of code. And now it doesn’t.
It is so embarrassing that LOC is being used as a metric for engineering output.
LOC is useful here not because it's a metric for output but because it's a metric for _understandability_. Reviewing 200 lines is a very different workload than reviewing 2000.
That's assuming the 200 lines are logical and consistent. Many of my most frustrating LLM bugs are caused by things that look right and are even supported by lengthy comments explaining their (incorrect) reasoning.
[delayed]
LOC is the worst metric for engineering output, except for all the others - Churchill
Agreed. And, LOC has historically been one of the things we've collectively fought against management for how to evalute a "productive" developer!
At least "mentions of LOC" is now a great metric for "how clueless is this person"
Vibe Coding (and LLMs) did not create undisciplined engineering organizations or engineers. They exposed and accelerated them.
Plenty of engineers have loose (or no!) standards and practices over how they write coee. Similarly, plenty of engineering teams have weak and loose standards over how code gets pushed to production. This concept isn't new, it's just a lot easier for individuals and teams who have never really adhered to any sort of standards in their SDLC to produce a lot more code and flesh out ideas.
Bad engineers continue being bad, good engineers continue being good.
I personally don’t know any colleagues who were good engineers just because they wrote code faster. The best engineers I know were ones who drew on experience and careful consideration and shared critical insights with their team that steered the direction of the system positively.
> Claude, engineer a system for me, but do it good. Thanks!
People in the future are going to wonder what the hell we were thinking, when 30 years down the line everything is a hot mess of billions of lines of code generated by LLMs that no human has read almost any of it and is no longer possible for anyone to maintain neither with nor without LLMs. And the LLM generated garbage will have drowned out all of the good quality code that ever existed and no one will be able to find even human generated code anymore on the internet.
Makes me want to just give up programming forever and never use a computer again.
By then, the fix will be easy. Fire up the latest LLM, point it at your codebase and tell it "rewrite this from scratch. do it well. fix the architecture mistakes"
We can do this today too (but definitely hopefully future LLMs make better architectural decisions). With Claude, I've been working on an application for the last 2 months. I didn't have a great vision of what I wanted when I started but I didn't want that to slow me down. The architecture is terrible - Claude separated some functionality into different classes but did a bad job at it and created a big ball of mud. Now that I finally have my vision locked down and implemented (albeit poorly), it'd be a great time to throw it away and start over. It'd be interesting to see the result and see how long it takes.
Will work just as good as today or 20 years ago.
Are you suggesting AI coding was as good 20 years ago as it is today?
I think they're being sarcastic, saying that rewrites from scratch have rarely worked well (whether done by AI or humans).
"Make sure to double check everything, and MAKE NO MISTAKES!!!"
"YOU'RE A SENIOR SOFTWARE ENGINEER!!!"
"Write me a really cool game, that will make me lots of money, fast!"
Make me a 1hr episode of my favorite book. Make it as lore accurate as possible. Plot out the script for the next 100 episodes.
I see your point, however: EA sports has been doing this for literally the entire lifetime of gaming as an industry
First, most software is already a hot mess.
Second, LLM code can be less of a hot mess than human written code if you put in the time to train/prompt/verify/review.
Generating perfect well patterned SOLID and unit tested code with no warnings or anti-patterns has never been easier.
Like with a lot of things in this space, it depends where you invest your effort. If you care about quality design and good code, you can definitely get there - but that doesn't happen by default.
With the right investment, we could certainly have tooling that creates and maintains very good designs out of the box. My bet is that we'll continue chasing quick and hacky code, mostly because that's the majority of the code that it was trained on, and because the majority of people seem to be interested in a quick result vs a long-term maintainable one.
Right, but it takes one to know one. Many don’t have the ability to decipher what’s good stable output or not
I'm generally pro "llm assisted coding" or whatever you want to call it. But I do somethings think about the Butlerian Jihad from Dune.
https://en.wikipedia.org/wiki/Dune:_The_Butlerian_Jihad
If you like sci-fi takes on software systems, check out Vernor Vinge "A Fire upon the deep" and sequels. I recall ship systems software is something like all the code humanity has ever written, plus centuries of LLM churn. One of the protagonists is a space faring software developer particularly good with legacy code.
We are used to thinking about software like in the article, a program that runs deterministically in an OS. Where we are headed might be more like where the LLM or AI system is the OS, and accomplishes things we want through a combination of pre-written legacy software, and perhaps able to accomplish new things on the fly.
By then AI would be good enough to clean them all up....like I dont get these dooming scenarios they always assume that we are going to be stuck with LLMs and there wont be anything new coming.
> Makes me want to just give up programming forever and never use a computer again.
LLMs aren’t the first thing to come along and change how people develop applications.
You had the rise of frameworks like Django, Rails, etc. Also the rise of SPAs. And also the rise of JS as a frontend+backend language.
In a 3-5 yeats we’ll have adapted to the new norm like we have in the past
There is nothing in the post to support the statement. An interesting personal confession, but it does not establish that vibe coding and agentic engineering are converging as a general phenomenon.
As a piece of meat, I look forward to charge rates of $10,000 an hour, to fix code out the vibe code generation.
Why does it matter, as long as it accomplishes the task?
> People in the future are going to wonder what the hell we were thinking, when 30 years down the line everything is a hot mess of billions of lines of code generated by LLMs that no human has read
--
It's just as likely that people will be surprised that we used to have billions of lines of human generated code, that no LLM ever approved.
If that is the case market forces would likely favor hand written code and all the slop will be forgotten (unless the slop works fine and is stable).
The market is hardly as rational as people would like to hope it is, though it does at least have its own twisted sort of internal consistency.
This is wishful thinking. The force of the market is "number go up". Quality increasingly has less and less of a role in the equation. You will eat your slop, and you will like it. It will be the only choice you have.
But the quality of code was already very bad due to market forces. Most code at large companies is notoriously poor despite the talent density, because the incentives are not there to tackle tech debt or improve code quality.
With such a low baseline, there is an optimistic perspective that LLMs could improve the situation. LLMs can produce excellent code when prompted or reviewed well. Unlike human employees, the model does not worry about getting a 'partially meets expectations' rating or avoid the drudgery of cleaning up other people's code.
Have you noticed that the coding agents get really close to the solution on the first one shot and then require tons of work to get that last 10% or 5%?
If we shift the paradigm of how we approach a coding problem, the coding agents can close that gap. Ten years ago every 10 or 15 minutes I would stop coding and start refactoring, testing, and analyzing making sure everything is perfect before proceeding because a bug will corrupt any downstream code. The coding agents don't and can't do this. They keep that bug or malformed architecture as they continue.
The instinct is to get the coding agents to stop at these points. However, that is impossible for several reasons. Instead, because it is very cheap, we should find the first place the agent made a mistake and update the prompt. Instead of fixing it, delete all the code (because it is very cheap), and run from the top. Continue this iteration process until the prompt yields the perfect code.
Ah, but you say, that is a lot of work done by a human! That is the whole point. The humans are still needed. The process using the tool like this yields 10x speed at writing code.
This was often true when writing code manually to be fair.
You could get to "something that works" rather fast but it took a long time to 1) evaluate other options (maybe before, maybe after), 2) refine it, 3) test it and build confidence around it.
I think your point stands but no one really knows where. The next year or so is going to be everyone trying to figure that out (this is also why we hear a lot of "we need to reinvent github")
The problem of life in general is the last 5-10% is always the hardest. And it makes no economic sense in many cases to invest in trying to make that last part mechanised.
I believe the llm providers went with the wrong approach from the off - the focus should’ve been on complementing labour not displacement. And I believe they have learned an expensive lesson along the way.
Perhaps I've missed a few weeks worth of progress, but I don't think that AIs have become more trustworthy, the errors are just more subtle.
If the code doesn't compile, that's easy to spot. If the code compiles but doesn't work, that's still somewhat easy to spot.
If the code compiles and works, but it does the wrong thing in some edge case, or has a security vulnerability, or introduces tech debt or dubious architectural decisions, that's harder to spot but doesn't reduce the review burden whatsoever.
If anything, "truthy" code is more mentally taxing to review than just obviously bad code.
There are techniques for improving our confidence in our software: unit testing, integration testing, fuzz testing, property-based testing, static analysis, model checking, theorem proving, formal methods, etc. The LLM is not only a tool for generating lines of code. It can also generate lines of testing. The goal is that the tests are easier to audit by the humans than the code.
Correct me if I’m wrong Simon, but weren’t you highly optimistic about llm’s and agentic-use of them?
I believe this is a common fault of not being able to zoom out and look at what trade offs are being made. There’s always trade-offs, the question is whether you can define them and then do the analysis to determine whether the result leaves you in a net benefit state.
The problem with vibe coding closer is that the agentic makes a very plasticy samey feel unless you work with something that makes it unique or can pass a template through it.
Claude often does things in more detail, and even better, than I would, in the first pass. But I don't understand how anybody stands comments generated by an LLM?
It's seriously the thing that worries (and bothers) me the most. I almost never let unedited LLM comments pass. At a minimum.
Most of the time, I use my own vibe-coded tool to run multiple GitHub-PR-review-style reviews, and send them off to the agent to make the code look and work fine.
It also struggles with doing things the idiomatic way for huge codebases, or sometimes it's just plain wrong about why something works, even if it gets it right.
And I say this despite the fact that I don't really write much code by hand anymore, only the important ones (if even!) or the interesting ones.
Also, don't even get me started on AI-generated READMEs... I use Claude to refine my Markdown or automatically handle dark/light-mode, but I try to write everything myself, because I can't stand what it generates.
I find that the best thing about generating documentation with LLM's is that it gets me angry enough to rewrite it correctly.
"Ugh, no! Why would you say it like that? That's not even how it works! Now, I need to write a full paragraph instead of a short snippet to make sure that no future agents get confused in the same way."
It is pure arrogance to expect that machines will never be able to code as good as a skilled human.
And AI generated code should be different than human code. AI has infinite memory for details. AI doesn’t need organizational patterns like classes. Potentially AI can write code that is more performant than any human.
Will it look like garbage? Sure. Will the code be more suited to the task? Yes.
I would only add one caveat to this:
Code that is organized well and operates coherently in the first place, by an LLM or not, will be easier to iterate on, by an LLM or not.
Agentic engineering? That reads to me a little like amateur oncologist. How are you defining engineering?
Can agentic engineers adhere to a similar code of ethics that a professional engineer is sworn to uphold?
https://www.nspe.org/career-growth/nspe-code-ethics-engineer...
The problem of calling what most of us do "engineering" predates LLMs by a good 15-20 years.
> The thing that really helps me is thinking back to when I’ve worked at larger organizations where I’ve been an engineering manager. Other teams are building software that my team depends on.
> If another team hands over something and says, “hey, this is the image resize service, here’s how to use it to resize your images”... I’m not going to go and read every line of code that they wrote.
The distance of accountability of the output from its producer is an important metric. Who will be held accountable for which output: that's important to maintain and not feel the "guilt".
So, organizations would need to focus on better and more granular building incentives and punishment mechanisms for large-scale software projects.
As agents get better at code we trust them to produce more of it. There are still bugs to find, but the haystack gets bigger.
So the number of bugs to find remains constant but the amount of code to review scales with the capability of the agent.
Vibe coding is just coding now. Writing assembly used to be a thing too until higher and higher languages were created. LLM is like that except it compiles English to code. This scares lot of professionals understandably.
This is a timely observation and feels right to me. I needed to get a relatively simple batch download -> transform -> api endpoint stood up. I wrote a fairly detailed prompt but left a lot of implementation details out, including data sources.
Opus 4.7 built it about 90% the same way I would, but had way more convenience methods and step-validations included.
It's great, and really frees me up to think about harder problems.
I think I'm just too opinionated to go there. If I see something that works fine, but isn't the way I'd do it, it doesn't matter if a human or an LLM wrote it I'm still in there making it match my vision.
That's not how most organizations work, AI or not.
What do you mean?
100%. I don't think any senior programmer ever looks at another developer's code and says, "Oh yeah, that's just the way I'd do it."
But I assume you don't go and change all your co-workers code just because they didn't do it how you would have done it?
Even the most toxic places I've worked that kind of behavior would totally get you canned.
So you are going to waste everyone's time getting another developer to write code the way you want? This resonates with me because at my company I get this all the time. At that point, you might as well close my PR and do it yourself, whatever way you want. I really like the advice from the book 0 2 1, to assign different areas of responsibility to people, so that there is no conflict.
I concur, and I think that is one of the most difficult aspects of reviewing another's code. It's difficult for me to sometimes differentiate between what is acceptable vs. what I would have done. I have to be very conscious to not impose my ideals.
I am experimenting with writing en entire TypeScript compiler[1] with AI assistant. I've spent 4 months on it already. It might not be successful at the end of the day but my thinking is that if LLMs are going to write a lot of the code I better learn how this can and can not work. I've learned a lot from this project already. I think we're still in charge of design and big ideas even if all of the code is written by AI
[1] https://github.com/mohsen1/tsz
I'm also experimenting with it more and more. Now I'm trying to create a 2D side-scrolling shooter with it, running in the browser. When it was relatively small, it did a good job. As the codebase and docs/ files that I'm using get larger it starts hallucinating, especially when the context gets at about 50% usage (Codex w/ gpt5.5). As in, it'll literally forget to update parts of the code.
e.g, I change velocity of player to '200' and of bullets to '300', and it only updated the bullet velocity. Then told me the player was already 'at the correct value' even though it was set to 150. Things like that.. :)
> I know full well that if you ask Claude Code to build a JSON API endpoint that runs a SQL query and outputs the results as JSON, it’s just going to do it right. It’s not going to mess that up. You have it add automated tests, you have it add documentation, you know it’s going to be good.
> But I’m not reviewing that code. And now I’ve got that feeling of guilt: if I haven’t reviewed the code, is it really responsible for me to use this in production?
Answer: it wholly depends upon what management has dictated be the goal for GenAI use at the time.
There seems to be a trend of people outside of engineering organizations thinking that the "iron triangle" of software (and really, all) engineering no longer holds. Fast, cheap, good: now we can pick all three, and there's no limit to the first one in particular. They don't see why you can't crank out 10x productivity. They've been financially incentivized to think that way, and really, they can't lose if they look at it from an "engineer headcount" standpoint. The outcomes are:
1) The GenAI-augmented engineer cranks out 10x productivity without any quality consequences down the line, and keeps them from having to pay other people
or
2) The GenAI-augmented engineer cranks out 10x productivity with quality consequences down the line, at which point the engineer has given another exhibit in the case as to why they should no longer be employed at that organization. Let the lawyers and market inertia deal with the big issues that exist beyond the 90-day fiscal reporting period.
Either way, they have a route to the destination of not paying engineers, and that's the end goal.
If you don't like that way of running a software engineering organization, well, you're not alone, but if nothing else, you could use GenAI to make working for yourself less risky.
Simon,
Just piggy backing on this post since I'm early:
Would love to see your take on how the AI and Django worlds will collide.
> And that feels about right to me. I can plumb my house if I watch enough YouTube videos on plumbing. I would rather hire a plumber.
I don't buy this argument at all. I think if we could pay $20/month to a service that would send over a junior plumber/carpenter/electrician with an encyclopedic knowledge of the craft, did the right thing the majority of the time, and we could observe and direct them, we'd all sign up for that in a heartbeat. Worst case, you have to hire an experienced, expensive person to fix the mess. Yes, I can hear everyone now, "worst case is they burn your house down." Sure, but as we're reminded _constantly_ when we read stories about AI agent catastrophes -- a human could wipe your prod database too. wHy ArE yOu HoLdInG iT tO a DiFfErEnT sTaNdArD???
The business side of the house is getting to live that scenario out right now as far as software goes. Sure you've got years of expertise that an LLM doesn't have _yet_. What makes you think it can't replace that part of your job as well?
You're comparing paying $20 for an AI plumber to paying hundreds/thousands for a traditional plumber.
But that's not what the author is talking about in that passage you quoted. What he's saying is that, if you can pay $20 for an AI plumber, then it stands to reason that eventually you will be able to pay $30 to a company that manages AI plumbers for you, so that you don't even have to go to the trouble of supervising the plumber. Most people will choose the $30.
I literally do pay $20 a month to have a plumber service on call.
And that includes materials, labor, and will be there the instant you need them?
> It used to be if you found a GitHub repository with a hundred commits and a good readme and automated tests and stuff, you could be pretty sure that the person writing that had put a lot of care and attention into that project.
I think this highlights a problem that has always existed under the surface, but it's being brought into the light by proliferation of vibeslop and openclaw and their ilk. Even in the beforetimes you could craft a 100.0% pure, correct looking github repo that had never stood the test of production. Even if you had a test suite that covers every branch and every instruction, without putting the code in production you aren't going to uncover all the things your test suite didn't--performance issues, security issues, unexpected user behavior, etc.
As an observer looking at this repo, I have no way to tell. It's got hundreds of tests, hundreds of commits, dozens of stars... how am I to know nobody has ever actually used it for anything?
I don't know how to solve this problem, but it seems like there's a pretty obvious tooling gap here. A very similar problem is something like "contributor reputation", i.e. the plague of drive-by AI generated PRs from people (or openclaws) you've never seen before. Stars and number of commits aren't good enough, we need more.
I'd be lying if I said I was not worried about the future. I am not necessarily worried in the sense that there will be some grave, impeding doom that awaits the future of humanity.
Rather, I just feel like I have to constantly remind myself of the impermanence of all things. Like snow, from water come to water gone.
Perhaps I put too much of my identity in being a programmer. Sure, LLMs cannot replace most us in their current state, but what about 5 years, 10 years, ..., 50 years from now? I just cannot help be feel a sense of nihilism and existential dread.
Some might argue that we will always be needed, but I am not certain I want to be needed in such a way. Of course, no one is taking hand-coding away from me. I can hand-code all I want on my own time, but occupationally that may be difficult in the future. I have rambled enough, but all and all, I do not think I want to participate in this society anymore, but I do not know how to escape it either.