alt Hacker NewsThe 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.
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> 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.
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.