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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 th

by jdw64 2mo ago

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.

movedx01 2mo ago
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.
- watwut 2mo ago
  > even though more code is generated than ever, we are not observing an equivalent rise in software quality or usefulness More code produced by a company that has history of low quality ... means more low quality. A company that is unable to ship useful features will produced useless features whether they generate code or not.
- jdw64 2mo ago
  I don't think so. GPT CODEX and Claude Code are already saying they use AI in their own codebases, and they have huge numbers of active users. Can that really be called a failure? In my view, it's just that the existing infrastructure layer is so thick that it's not immediately visible—but AI adoption is already quite widespread across many companies. People say program quality has declined, but I don't think so. The average quality of programs has improved significantly. You can see this by looking at open-source architecture books from 10 to 15 years ago. That's why I think we need to first define what we actually mean by 'code quality' before discussing this issue. Realistically, this discussion could easily drift into a debate about code quality. But if you look at older books on open-source architecture, there were many issues—runtime null references, confusing callback references, diamond dependencies, and so on. These days, many of those problems are caught by linters and other tools. At the micro level, code quality has definitely risen compared to the past. The real problem is that programs are getting larger. The minimum requirements for a program to be viable have gone up, while the available workforce hasn't kept pace. But in terms of quality, I think we can confidently say that overall code quality has improved compared to the past. The issue is the gap between micro-level code hygiene and macro-level semantic coherence. And the key question is how we can maintain that macro-level semantic coherence while using AI-generated code. I think these are fundamentally different problems.

by jdw64 2mo ago

movedx01 2mo ago

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.

watwut 2mo ago
> even though more code is generated than ever, we are not observing an equivalent rise in software quality or usefulness More code produced by a company that has history of low quality ... means more low quality. A company that is unable to ship useful features will produced useless features whether they generate code or not.
jdw64 2mo ago
I don't think so. GPT CODEX and Claude Code are already saying they use AI in their own codebases, and they have huge numbers of active users. Can that really be called a failure? In my view, it's just that the existing infrastructure layer is so thick that it's not immediately visible—but AI adoption is already quite widespread across many companies. People say program quality has declined, but I don't think so. The average quality of programs has improved significantly. You can see this by looking at open-source architecture books from 10 to 15 years ago. That's why I think we need to first define what we actually mean by 'code quality' before discussing this issue. Realistically, this discussion could easily drift into a debate about code quality. But if you look at older books on open-source architecture, there were many issues—runtime null references, confusing callback references, diamond dependencies, and so on. These days, many of those problems are caught by linters and other tools. At the micro level, code quality has definitely risen compared to the past. The real problem is that programs are getting larger. The minimum requirements for a program to be viable have gone up, while the available workforce hasn't kept pace. But in terms of quality, I think we can confidently say that overall code quality has improved compared to the past. The issue is the gap between micro-level code hygiene and macro-level semantic coherence. And the key question is how we can maintain that macro-level semantic coherence while using AI-generated code. I think these are fundamentally different problems.