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.