There's going to be plenty of work helping other companies make some sense of their vibe coded efforts. The value of individual projects might decrease, but there will be a lot more of them. And without help they won't actually work out all that well.
I talked to a company that does not employ software engineers that were doing some things with Claude Code a few weeks ago. Insightful comment: I want that person to do what I hired them to do, not mess around with code. What they were trying to do was a bit out of their comfort zone and they were smart enough to realize it.
There is going to be a lot more of this. What's very real is that companies selling one size fits all products to others are going to have a much harder time selling because everybody is going to expect a thing tailored to them because they now can. Delivering those things is still going to be work that needs to be done. A lot of work actually. People with experience building things with their own hands have an advantage. And if those people also understand the domain in which they are trying to do stuff, that's a double advantage.
Like always, most people haven't got a clue about what they actually need. Figuring out what people need (consulting) and then delivering it has always been the job. But you might be able to take on a few more customers now. There won't be a shortage of those once people figure out software just got cheaper.
When you have built your working product try this prompt:
- Review the codebase is it production ready? I'm selling it for $1million dollars can it meet that standard.
Then cry as the ai reveals that it didn't actually do anything close to what it said it did. I call this my million dollar prompt, as in it teaches you just how much you are being fooled.
> Then cry as the ai reveals that it didn't actually do anything close to what it said it did.
If using AI to generate code, you told it generate some code, so it did. No amount of "You are an expert developer" or "Make no mistakes" will change the fact that it just generates tokens and has a limited thinking budget.
Adversarial review loops of N parallel agents looking at whatever characteristics you care about will make it better, even if it will Nx the tokens you need to achieve something, though in general it will be cheaper than N human reviewers (which you might not have).
Obviously you shouldn't forget about traditional tooling for formatting and linting, as well as static code analysis and having test coverage that approaches 100%. It might be annoying to do manually, but AI has no issues with refactoring code to make it more testable and eventually will catch some issues that way. It's never going to be perfect in the 1st attempt.
> Review the codebase is it production ready? I'm selling it for $1million dollars can it meet that standard.
This is far too vague though and will never be good, even sans AI. When it comes to AI, it will nitpick the fuck out of the codebase if you ask it to do and sometimes jump around between different approaches because neither is actually a good fit for the problem space (there might not be a good fit at all, just various tradeoffs). If you still ask it to find issues and there's nothing obvious, it will just make shit up in pursuit of being useful (RLHF).
When it comes to people, you will get various standards, from "It looks like Java, ship it" to "You should rework a quarter of your codebase because I read about this one approach in an authoritatively written book that you should also follow because I view it as dogma and will hold back your merge until it all works exactly like I want it to." (you get all sorts of people and personalities).
In my experience other people are no panacea either, nor is writing code all by myself.
I think we have played this game long time ago. If products were a question of a single request then outsourcing companies would dominate over product ones.
I think a lot of product development happens in the itearations after the intial prototype/MVP and so on. It is not only the technical aspect to it, you need to spend time on a problem deeply understand what are the root causes of pains and address them in your product, both from UX and also from technical perspective.
People were able to "prompt" a product even before to an outsourcing company, but they'd rather pay the fee to a product company because of the expertese they have gained through out the years and all the users they've spoken to.
The fear in most people is not of losing the job, but of losing their value in the market as ai raises the floor of capabilities of other people competing for the same job.
What I actually fear is more subtle: I already do a fair bit of project management and technical leadership. I could do more. Sure, I'd miss the coding, but I also enjoy a lot of the stuff around it.
But the goal is to expand what the AI can do in each generation. At this point, Fable 5 can ace almost any greenfield project a skilled developer might have written in a few days. But it's bad at refactoring, bad at keeping the code clean as it goes, and bad at discovering new insights as it codes. So Anthropic will train Fable 6, using benchmarks like SlopCodeBench that test maintenance over time.
Now what about project management? Train Fable 7. What about product management and talking to stakeholders? Train Fable 8. What about market research and sales? Train Fable 9.
By this point, Anthropic doesn't need to actually release these newest models to the public. Why, that might be dangerous! Instead, they write, "deisgn [sic] a successful software product and sell it plz." And they spin up a million dollars worth of compute and let it crank out SaaSes, iPhone apps, etc., driving entire software companies out of business.
Then they spin up some more instances, and say, "make robot plz" and "try a thousand ways to make yrself smrater." I mean, Qwen and DeepSeek keep finding ways to pack more smarts into a given number of weights. Fable 9 will likely be able to do the same. Hell, Fable 5 can probably run 1,000 machine learning experiments now, just grinding through ideas the way ChatGPT's internal models grind through proofs.
And this is my problem. If it were just programmers losing their jobs, well, sometimes professions die. But what makes you think it will stop with us? How far will this go in the next 4 years? The next 20?
I think the main fear is that the (assumed) increase of productivity means that employers may require less employees, resulting to less positions in the market. But I don't think that expertise is no longer relevant or we see getting more people hired with less expertise because AI. If anything, there is this talk about companies not hiring junior engineers as much. Moreover, as getting the expertise could become harder, it could become more of a moat.
"AI has dramatically accelerated the path to a first working version. It has not shortened the distance between a first working version and something production-grade." - Anuradha Weeraman
I actually agree but what even is ”production grade”? More complexity and excessive fault handling? Nah, happy path coding ftw.
Production grade software = collective understanding of the system imo
"Production-grade" typically means something that has been battle tested by users and has gone through all the trials and tribulations of dealing with their complains, suggestions, and other feedback to see an initial vision (the prototype) become what users actually want and need.
The earlier quote might be slightly overblown as some of those complaints, suggestions, and feedback can be iterated on more quickly thanks to AI. However, I think you will find that the overall premise is sound: The feedback loop is where you will spend the vast majority of your time and no coding agent can speed that up. Code was never the real bottleneck. A full-day coding session now being a 15 minute coding session helps, every so slightly, but when you still need to spend weeks talking to the users to figure out what needs to be done in that day/15 minutes, shaving off a handful of hours relative to weeks remains but a drop in the bucket. The marginal improvement is barely worth recognizing.
I've seen too many similar posts on Hacker News. From 2025 to 2026, I've seen countless articles with titles like 'The Prototype Isn't the Product.'
I think these are defensive mechanisms, a kind of lullaby for the Gen AI era.
Why is this discourse endlessly reproduced? In my view, it's because the industry is still searching for a new methodology to control the waterfall of Gen AI code. The cognitive dissonance that results is being resolved by relying on vague personal virtues like 'craftsmanship,' 'fundamentals of computer science,' and 'human judgment.'
If the goal is to review Gen AI code in its entirety, the way an engineer would review a PR, then honestly, I don't see the point of using Gen AI in the first place.
Yes, models lack judgment and only do pattern matching. But lately, I've noticed that in closed systems, Gen AI often produces more logically coherent code than humans do. If that's the case, maybe programmers should shift toward designing closed systems where algebraic data types ensure the program works correctly.
Because using Gen AI means you're committing to codebases that go beyond individual cognitive limits. Once you start using Gen AI code, there's a subtle mismatch with human written code, a fundamental impedance mismatch, like the one between ORM and SQL.
In that sense, I honestly don't know.
The arguments that have been repeated for nearly a year all sound basically the same. But when I look closer, this isn't Gen AI era coding. It's just old era methodology with 'human' swapped out for 'AI.' If the subject changes, the methodology should change too.
Looking at the countless repetitive posts on HN, it shows what HN programmers are afraid of. They're afraid of the destruction of their overall meta-methodology.
All the arguments being made now are about how to become a good senior engineer in the old days.
But is that analogy really appropriate for the volume of code AI is generating?
The amount of code being generated is exploding. The amount of complexity is exploding. Responsibility is becoming unclear. These aren't issues of individual skill. Saying that drivers just need to be more careful when traffic increases is bad road policy. The core is that the roads and signaling systems need to change.
A new subject requires a new methodology.
In that sense, I think the recent post from Jane Street is more like a new solution. Of course, ADT doesn't guarantee that modeling always holds either.
So honestly, I don't know. When I look at HN, it seems like all I see is what social signals people are most anxious about.
> Looking at the countless repetitive posts on HN, it shows what HN programmers are afraid of. They're afraid of the destruction of their overall meta-methodology.
I think this already happened. What's hard to swallow for us is the countless years spent studying, researching, and investing our time to be the best possible professionals in a very demanding, skill-intensive industry.
Now that software development is starting to be industrialized, I think it's fair to react with fear and uncertainty.
Yep, we are trying to find new strategies and methodologies to still stay relevant, but I think they'll become obsolete soon once the technology reaches a maturity point for full automation and the industrialization process.
What it will look like, I don't know (I have a few guesses), but what I know is that I'll struggle a lot, in the middle of my forties, to reskill myself, especially considering a country of no opportunities like Italy.
> In that sense, I think the recent post from Jane Street is more like a new solution.
I disagree tbh, the prototype is not the product not just because of technical abilities but also because of product expertese in a topic.
If it was otherwise outsourcing companies would've dominated the market for a long time, but it is with the exp from clients and many users that you get to build a great product.
It takes a lot of time to understand what is the pain of the user, in many cases we know what the problem is but we have not time to think of a good solution to it.
The lullaby for the Gen AI era exists, but its for CEOs and it is being played in management meetings, it's main theme is about maximizing EBITDA.
The defensive mechanisms kick in because even though more code is generated than ever, we are not observing an equivalent rise in software quality or usefulness, some would perhaps argue it's even opposite.
If gen ai for code was really what it is being sold as it would all be obvious to everyone, we would be seeing better software all around us everywhere and posts such as the one here would just be laughed off, delusional, but they are not.
The code explosion did happen, the value of software this code makes - not yet. Not to say it won't, its just not here right now, and it never happening is still a possible outcome.
There's going to be plenty of work helping other companies make some sense of their vibe coded efforts. The value of individual projects might decrease, but there will be a lot more of them. And without help they won't actually work out all that well.
I talked to a company that does not employ software engineers that were doing some things with Claude Code a few weeks ago. Insightful comment: I want that person to do what I hired them to do, not mess around with code. What they were trying to do was a bit out of their comfort zone and they were smart enough to realize it.
There is going to be a lot more of this. What's very real is that companies selling one size fits all products to others are going to have a much harder time selling because everybody is going to expect a thing tailored to them because they now can. Delivering those things is still going to be work that needs to be done. A lot of work actually. People with experience building things with their own hands have an advantage. And if those people also understand the domain in which they are trying to do stuff, that's a double advantage.
Like always, most people haven't got a clue about what they actually need. Figuring out what people need (consulting) and then delivering it has always been the job. But you might be able to take on a few more customers now. There won't be a shortage of those once people figure out software just got cheaper.
When you have built your working product try this prompt:
- Review the codebase is it production ready? I'm selling it for $1million dollars can it meet that standard.
Then cry as the ai reveals that it didn't actually do anything close to what it said it did. I call this my million dollar prompt, as in it teaches you just how much you are being fooled.
> Then cry as the ai reveals that it didn't actually do anything close to what it said it did.
If using AI to generate code, you told it generate some code, so it did. No amount of "You are an expert developer" or "Make no mistakes" will change the fact that it just generates tokens and has a limited thinking budget.
Adversarial review loops of N parallel agents looking at whatever characteristics you care about will make it better, even if it will Nx the tokens you need to achieve something, though in general it will be cheaper than N human reviewers (which you might not have).
Obviously you shouldn't forget about traditional tooling for formatting and linting, as well as static code analysis and having test coverage that approaches 100%. It might be annoying to do manually, but AI has no issues with refactoring code to make it more testable and eventually will catch some issues that way. It's never going to be perfect in the 1st attempt.
> Review the codebase is it production ready? I'm selling it for $1million dollars can it meet that standard.
This is far too vague though and will never be good, even sans AI. When it comes to AI, it will nitpick the fuck out of the codebase if you ask it to do and sometimes jump around between different approaches because neither is actually a good fit for the problem space (there might not be a good fit at all, just various tradeoffs). If you still ask it to find issues and there's nothing obvious, it will just make shit up in pursuit of being useful (RLHF).
When it comes to people, you will get various standards, from "It looks like Java, ship it" to "You should rework a quarter of your codebase because I read about this one approach in an authoritatively written book that you should also follow because I view it as dogma and will hold back your merge until it all works exactly like I want it to." (you get all sorts of people and personalities).
In my experience other people are no panacea either, nor is writing code all by myself.
The litmus test is this: do you enjoy consuming AI-generated stuff?
I don’t. Whether it’s written text, or video, audio, restaurant menus, clothing pictures, documentation, airport control, ads…
I do think there’s value in LLMs but as a sort of better search engines and q/a machines.
To be fair. I hate slop as much as any other person. But for me the issue is poor quality rather than just the fact that it's ai.
I love good ai stuff even if it's obvious it's ai
I think we have played this game long time ago. If products were a question of a single request then outsourcing companies would dominate over product ones.
I think a lot of product development happens in the itearations after the intial prototype/MVP and so on. It is not only the technical aspect to it, you need to spend time on a problem deeply understand what are the root causes of pains and address them in your product, both from UX and also from technical perspective.
People were able to "prompt" a product even before to an outsourcing company, but they'd rather pay the fee to a product company because of the expertese they have gained through out the years and all the users they've spoken to.
The fear in most people is not of losing the job, but of losing their value in the market as ai raises the floor of capabilities of other people competing for the same job.
What I actually fear is more subtle: I already do a fair bit of project management and technical leadership. I could do more. Sure, I'd miss the coding, but I also enjoy a lot of the stuff around it.
But the goal is to expand what the AI can do in each generation. At this point, Fable 5 can ace almost any greenfield project a skilled developer might have written in a few days. But it's bad at refactoring, bad at keeping the code clean as it goes, and bad at discovering new insights as it codes. So Anthropic will train Fable 6, using benchmarks like SlopCodeBench that test maintenance over time.
Now what about project management? Train Fable 7. What about product management and talking to stakeholders? Train Fable 8. What about market research and sales? Train Fable 9.
By this point, Anthropic doesn't need to actually release these newest models to the public. Why, that might be dangerous! Instead, they write, "deisgn [sic] a successful software product and sell it plz." And they spin up a million dollars worth of compute and let it crank out SaaSes, iPhone apps, etc., driving entire software companies out of business.
Then they spin up some more instances, and say, "make robot plz" and "try a thousand ways to make yrself smrater." I mean, Qwen and DeepSeek keep finding ways to pack more smarts into a given number of weights. Fable 9 will likely be able to do the same. Hell, Fable 5 can probably run 1,000 machine learning experiments now, just grinding through ideas the way ChatGPT's internal models grind through proofs.
And this is my problem. If it were just programmers losing their jobs, well, sometimes professions die. But what makes you think it will stop with us? How far will this go in the next 4 years? The next 20?
I think the main fear is that the (assumed) increase of productivity means that employers may require less employees, resulting to less positions in the market. But I don't think that expertise is no longer relevant or we see getting more people hired with less expertise because AI. If anything, there is this talk about companies not hiring junior engineers as much. Moreover, as getting the expertise could become harder, it could become more of a moat.
This is a greatly written article. Thanks
"AI has dramatically accelerated the path to a first working version. It has not shortened the distance between a first working version and something production-grade." - Anuradha Weeraman
I actually agree but what even is ”production grade”? More complexity and excessive fault handling? Nah, happy path coding ftw. Production grade software = collective understanding of the system imo
"Production-grade" typically means something that has been battle tested by users and has gone through all the trials and tribulations of dealing with their complains, suggestions, and other feedback to see an initial vision (the prototype) become what users actually want and need.
The earlier quote might be slightly overblown as some of those complaints, suggestions, and feedback can be iterated on more quickly thanks to AI. However, I think you will find that the overall premise is sound: The feedback loop is where you will spend the vast majority of your time and no coding agent can speed that up. Code was never the real bottleneck. A full-day coding session now being a 15 minute coding session helps, every so slightly, but when you still need to spend weeks talking to the users to figure out what needs to be done in that day/15 minutes, shaving off a handful of hours relative to weeks remains but a drop in the bucket. The marginal improvement is barely worth recognizing.
Uhh yes it has
I've seen too many similar posts on Hacker News. From 2025 to 2026, I've seen countless articles with titles like 'The Prototype Isn't the Product.'
I think these are defensive mechanisms, a kind of lullaby for the Gen AI era.
Why is this discourse endlessly reproduced? In my view, it's because the industry is still searching for a new methodology to control the waterfall of Gen AI code. The cognitive dissonance that results is being resolved by relying on vague personal virtues like 'craftsmanship,' 'fundamentals of computer science,' and 'human judgment.'
If the goal is to review Gen AI code in its entirety, the way an engineer would review a PR, then honestly, I don't see the point of using Gen AI in the first place.
Yes, models lack judgment and only do pattern matching. But lately, I've noticed that in closed systems, Gen AI often produces more logically coherent code than humans do. If that's the case, maybe programmers should shift toward designing closed systems where algebraic data types ensure the program works correctly.
Because using Gen AI means you're committing to codebases that go beyond individual cognitive limits. Once you start using Gen AI code, there's a subtle mismatch with human written code, a fundamental impedance mismatch, like the one between ORM and SQL.
In that sense, I honestly don't know.
The arguments that have been repeated for nearly a year all sound basically the same. But when I look closer, this isn't Gen AI era coding. It's just old era methodology with 'human' swapped out for 'AI.' If the subject changes, the methodology should change too.
Looking at the countless repetitive posts on HN, it shows what HN programmers are afraid of. They're afraid of the destruction of their overall meta-methodology.
All the arguments being made now are about how to become a good senior engineer in the old days.
But is that analogy really appropriate for the volume of code AI is generating?
The amount of code being generated is exploding. The amount of complexity is exploding. Responsibility is becoming unclear. These aren't issues of individual skill. Saying that drivers just need to be more careful when traffic increases is bad road policy. The core is that the roads and signaling systems need to change.
A new subject requires a new methodology.
In that sense, I think the recent post from Jane Street is more like a new solution. Of course, ADT doesn't guarantee that modeling always holds either.
So honestly, I don't know. When I look at HN, it seems like all I see is what social signals people are most anxious about.
> Looking at the countless repetitive posts on HN, it shows what HN programmers are afraid of. They're afraid of the destruction of their overall meta-methodology.
I think this already happened. What's hard to swallow for us is the countless years spent studying, researching, and investing our time to be the best possible professionals in a very demanding, skill-intensive industry.
Now that software development is starting to be industrialized, I think it's fair to react with fear and uncertainty.
Yep, we are trying to find new strategies and methodologies to still stay relevant, but I think they'll become obsolete soon once the technology reaches a maturity point for full automation and the industrialization process.
What it will look like, I don't know (I have a few guesses), but what I know is that I'll struggle a lot, in the middle of my forties, to reskill myself, especially considering a country of no opportunities like Italy.
> In that sense, I think the recent post from Jane Street is more like a new solution.
Can you please share it?
I disagree tbh, the prototype is not the product not just because of technical abilities but also because of product expertese in a topic.
If it was otherwise outsourcing companies would've dominated the market for a long time, but it is with the exp from clients and many users that you get to build a great product.
It takes a lot of time to understand what is the pain of the user, in many cases we know what the problem is but we have not time to think of a good solution to it.
The lullaby for the Gen AI era exists, but its for CEOs and it is being played in management meetings, it's main theme is about maximizing EBITDA.
The defensive mechanisms kick in because even though more code is generated than ever, we are not observing an equivalent rise in software quality or usefulness, some would perhaps argue it's even opposite.
If gen ai for code was really what it is being sold as it would all be obvious to everyone, we would be seeing better software all around us everywhere and posts such as the one here would just be laughed off, delusional, but they are not.
The code explosion did happen, the value of software this code makes - not yet. Not to say it won't, its just not here right now, and it never happening is still a possible outcome.
I can't tell if this is insightful or copium.