"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
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.
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.
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.
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.
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.
The test is simple: have we seen great new products or improvements in the products we use over the past 12,24,36 months?
The only great new product I’ve used is my LLM of choice, and those labs seem to be hiring more humans than ever.
Maybe it’s true that Claude only just got good enough and that 12 months from now our day to day lives will be way better thanks to LLM-driven product improvements/breakthroughs.
My bet is that 12 months from now we will still have no great improvements and the claim will be “LLMs only got good enough in Feb 2027 so you can’t judge anything yet!”
AI only does what it is instructed to do. Without deep domain knowledge, results produced from simple prompts alone cannot be turned into production-ready products. In reality, creating detailed prompts, conducting continuous reviews, and providing iterative feedback after the prototype stage often takes even more time than building the prototype itself.
I'm about to throw away multiple months of LLM generated code for one of my side projects. I was really careful writing design specs and it wasn't even a new code base the LLM worked on, but still after several months of AI changes I feel my code degraded more and more into a subtle mess. Hard to explain, each individual change looked good and logical and on the surface the codebase looks fine, but looking at the whole picture everything is subtly wrong in multiple ways. The same goes for where I used AI for existing commercial code bases. I would love to have AI write production ready software for me, but it's just not there yet, there simply are things that good programmers and architects do that cannot be captured by the training loop of current generation LLMs.
I notice the same pattern when using LLMs to write longer text like reports or scientific papers, individually each section they write makes sense but overall the whole document feels off in a hard to describe way. I think it's where you can see the difference between human intelligence and whatever it is LLMs have, it's not the same thing. We are much slower and less able on the small scale but seems we can do some higher level reasoning that is still impossible for LLMs. That always becomes clear when you point an LLM at an obvious flaw it produced and it goes "You are absolutely right!" as if it's obvious in hindsight but when running multiple "Please look for issues" iterations it would never have spotted the issue by itself.
That said I think it will be absolutely fine writing a simple CRUD app for you e.g. using some popular JS framework, Tailwind for styling and a regular ORM, there's more than enough training data available for these things. But then again such software could be purchased before already e.g. as a SaaS template, I don't think LLMs are so revolutionary here, they just replace the template (but to be honest a good hand-written SaaS boilerplate is probably still better than a vibe coded one).
I like that one “ They understand what they're asking the model to produce. They review generated code with the same critical eye they'd apply to a junior engineer's pull request. They bring architectural thinking to the conversation, not just feature descriptions. They know when to push back on what the model suggests.” I use AI randomly recently and more and more see it as fantastic poc tool , also it’s really good at comparison existing tools and making some raw material for analysis
I think of it this way. If AI could build the product alone, then why would it ever be worth more than the tokens you spent on it? I think this would hold true 99% of the time. It's literally like asking someone "Can you pay me $1m for new gadget? I hired this other dude for $20p/h and he made it in a week". It simply doesn't add up.
Another take . If a (my) product is easy for AI to work with - I call it LLMable - that’s good metric or sign I move in the right direction . AI just guidelines me in that sense …
I wasn't strong-armed into shitting out products before LLMs, but sure, continue. Convince me this toil is a good thing.
edit: Bonus points if you do it without the word 'growth', my pockets/rental are, effectively, the same size. Can't have one without the other, I'm afraid.
Most of this piece reads like the Claude output I have to read after every prompt, cleaned up with an /elegant-writing skill or something. I'm so used to skimming text in this voice, it's hard to pay attention to this.
I honestly do not understand why people do this for writing that carries their signature, essentially. Do you want people to associate your name with skimmable fluff? We can talk to each other directly, please. The machine is helpful but it doesn't have to mediate every human interaction ever.
This begs the question of what the fundamentals really are. There are a bajillion bootcamps and learn-x-in-a-weekend books out there, most of which seem to focus on getting you familiar with basic syntax rather than teaching the how and why.
If memorising syntax and a standard library are no longer differentiators, then what exactly are you supposed to learn?
59 comments
[ 2.9 ms ] story [ 46.9 ms ] threadI 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.
- 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.
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.
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.
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.
The only great new product I’ve used is my LLM of choice, and those labs seem to be hiring more humans than ever.
Maybe it’s true that Claude only just got good enough and that 12 months from now our day to day lives will be way better thanks to LLM-driven product improvements/breakthroughs.
My bet is that 12 months from now we will still have no great improvements and the claim will be “LLMs only got good enough in Feb 2027 so you can’t judge anything yet!”
Yes: https://blog.google/security/chrome-stronger-with-every-upda...
This is becoming more and more true every day.
I notice the same pattern when using LLMs to write longer text like reports or scientific papers, individually each section they write makes sense but overall the whole document feels off in a hard to describe way. I think it's where you can see the difference between human intelligence and whatever it is LLMs have, it's not the same thing. We are much slower and less able on the small scale but seems we can do some higher level reasoning that is still impossible for LLMs. That always becomes clear when you point an LLM at an obvious flaw it produced and it goes "You are absolutely right!" as if it's obvious in hindsight but when running multiple "Please look for issues" iterations it would never have spotted the issue by itself.
That said I think it will be absolutely fine writing a simple CRUD app for you e.g. using some popular JS framework, Tailwind for styling and a regular ORM, there's more than enough training data available for these things. But then again such software could be purchased before already e.g. as a SaaS template, I don't think LLMs are so revolutionary here, they just replace the template (but to be honest a good hand-written SaaS boilerplate is probably still better than a vibe coded one).
AI doesn't write articles, that's still your job.
I have great results with AI assisted code bases and development
I am the harness
The 80/20 rule still stands.
edit: Bonus points if you do it without the word 'growth', my pockets/rental are, effectively, the same size. Can't have one without the other, I'm afraid.
I honestly do not understand why people do this for writing that carries their signature, essentially. Do you want people to associate your name with skimmable fluff? We can talk to each other directly, please. The machine is helpful but it doesn't have to mediate every human interaction ever.
If memorising syntax and a standard library are no longer differentiators, then what exactly are you supposed to learn?