Why AI Model Collapse Is Bullish for Real Developers
A Quick Recap: Model Collapse and Why It Matters
Last month, I wrote about model collapse — the compounding degradation that happens when AI systems start training on their own outputs — in the context of software development. It’s not just a theoretical concern for AI researchers anymore. It’s already affecting how software gets written. As LLM-generated content floods the internet, the risk isn’t just bad answers — it’s a feedback loop of diminishing quality.
I argued then that human expertise isn’t obsolete — it’s more valuable than ever. And this week’s headlines only reinforce that point.
Two Headlines, One Direction
This week, two stories stood out:
AI-generated content is polluting the internet. Forums, blogs, docs, tutorials—more and more of it is AI-generated sludge: content that looks correct but lacks insight, context, or any real rigor. The article I linked doesn’t explicitly mention this, but GitHub and Stack Overflow are just as vulnerable as Reddit. That means the foundational resources for learning, troubleshooting and (of course) future AI training are becoming less reliable by the day.
Amazon’s CEO says AI will drive layoffs. Andy Jassy told Amazon employees to prepare for cuts (for what probably feels like the thousandth time in the last two years) as generative AI “improves productivity.” This feels less like a genuine transformation and more like the usual justification for shrinking headcount under the banner of innovation. Or maybe just shortsighted — history teaches us that productivity gains usually unlock new opportunities, not fewer people.
These stories aren’t isolated. They point in the same direction: a tech ecosystem increasingly dominated by superficial LLM output and short-term thinking.
The Sludge Layer is Getting Thicker
We’re already seeing it in software development. AI-generated code is everywhere — on GitHub, in internal wikis, in Slack threads. But a lot of it is mediocre at best, subtly dangerous at worst.
Not necessarily obviously broken, but flawed in ways only experienced engineers will catch:
Inefficient where performance matters.
Insecure in edge cases.
Misusing concurrency primitives.
Composing the wrong abstractions.
This isn’t just “low quality.” It’s convincing garbage of the kind that passes a typical code review but quietly corrodes your system over time.
The Internet Is Becoming Hostile to Beginners
As the signal-to-noise ratio drops, newcomers are the first to suffer. Search results are polluted. Documentation is autogenerated and half-broken (but looks more complete and sounds more authoritative than ever). Stack Overflow is filled with LLM regurgitations that are confidently wrong.
The barrier to entry hasn’t gotten lower. It’s just gotten weirder. Code literacy isn’t enough anymore. You need code fluency. And fluency isn’t something you prompt a chatbot for. It’s something you build through real experience, real projects, and by learning alongside people who’ve been there before.
I’ve found it helpful to reconnect with this dynamic myself by occasionally teaching intro-level programming to adult students. It’s a powerful reminder of how fragile early intuition can be — and how much harder it will be when the foundational resources are polluted with plausible-sounding nonsense.
Ironically, many of the most enthusiastic promoters of AI coding assistants are very experienced engineers — either early adopters or building these tools themselves. But they may have forgotten what it’s like to start from zero. The tooling looks empowering from the top of the mountain, but from the bottom, it can often be disorienting and opaque.
We must take care to avoid building a developer ecosystem where the bottom rungs of the ladder start to rot.
Real Developers Don’t Fear or Follow AI—They Wield It
For experienced engineers, this won’t feel like a disruption — it’ll feel like leverage. Not necessarily right now, but soon, as more teams realize just how noisy and unreliable the information space has become.
Veteran software developers don’t need help with what to type. They need leverage on what to build. And that’s where AI can shine if you know what you’re doing.
Great software developers:
Use models to scaffold and stub, not to architect.
Inspect and test output instead of trusting it blindly.
Debug not just the bug, but the flawed reasoning behind it.
Know when to throw it all out and write it by hand.
This is a moment of divergence. The engineers who understand systems, tradeoffs, and long-term complexity aren’t going to be replaced. They’re becoming the ones everyone else will increasingly depend on.
Expertise Is a Moat
This isn’t about resisting the future. It’s about understanding where the value actually lies. Sure, anyone can generate code now. But writing and deploying the right code? Knowing when to apply a design pattern and when to walk away from it? That’s the moat.
In a world where the average code quality is trending down, real engineers stand out more than ever. The people who know software — who’ve watched systems live, evolve, and sometimes die — are the ones who will lead, architect, and mentor through the chaos.
Let’s Build Smarter, Not Just Faster
I work with teams that want to build software that lasts. Not AI-churned scaffolding, but real infrastructure: Resilient, performant, and maintainable. If your team is navigating this moment and wants to stay sharp (or just ship fewer AI-induced regressions), let’s talk. Reach out. I’d love to help you build smarter.
