You’ve probably spent the last year watching demos of AI generating entire apps in seconds. Chat interfaces spitting out React components, Python scripts, even full-stack projects. It feels like magic. It feels like the future. But here’s the thing nobody wants to tell you: most of that code is dead on arrival, and the real problem isn’t that it exists—it’s that we’ve made it so easy to create that we’ve forgotten how to care.
I’m not talking about the polished, VC-backed products. I’m talking about the slopcode. The thousands of tiny projects, side hustles, and weekend experiments that get pushed to GitHub, announced on Hacker News, and then quietly deleted within months. The data doesn’t lie: a recent analysis found that the majority of AI-generated code projects are abandoned and removed from public repositories within a year. The buzzword is “frictionless creation.” The reality is frictionless disposal.
Let me give you a real example. A friend of mine—let’s call him Dave—spent six hours last Saturday using an AI coding agent to build a tool that converts meeting notes into action items. He posted it on Product Hunt, got 47 upvotes, and two days later he forgot it existed. When I asked him why he didn’t maintain it, he shrugged: “It was just a test. I already moved on to the next thing.” That’s the silent crisis of the AI era: the psychological sunk cost of code has evaporated.
Before AI, writing even a simple app took days. You had to think about architecture, debugging, edge cases. That effort created a bond. You felt ownership. You wanted to see it through. But when you can generate the same thing in minutes, the bond is gone. The project becomes a disposable coffee cup—used once, then thrown away. And the landfill? That’s the internet. A growing pile of half-finished, unmaintained, and eventually broken software.
Here’s the twist that flips the narrative: the bottleneck in software has never been creation—it’s maintenance and attention. We have infinite capacity to generate code, but finite capacity to care for it. Every new AI-generated project doesn’t add value; it adds noise. It competes for the same limited pool of user attention, developer time, and server resources. The result isn’t a golden age of innovation—it’s a digital wasteland.
This isn’t a Luddite rant. I use AI tools every day. But I’m terrified of what happens when the cost of creation drops to zero. History shows that when something becomes too easy to produce, we produce too much of it—and value collapses. The ease of creation is the engine of oblivion.
What can you do? Start by asking yourself: is this project something I would still build if I had to write every line by hand? If the answer is no, maybe you shouldn’t build it at all. Because the real cost isn’t the compute—it’s the attention you’re stealing from the world. And the world is already too full of noise.
So next time you see a demo of AI generating an entire app in 30 seconds, remember: the hard part isn’t making the app. It’s making it matter. We don’t have a code shortage. We have a meaning shortage. And no amount of generation can fix that.
FAQ
Q: Is this just a problem with hobby projects, not professional software?
A: No. The same dynamics apply to corporate environments. Teams are using AI to generate more features faster, but maintenance debt grows exponentially. Abandoned internal tools and microservices are becoming a major cost center.
Q: What’s the practical implication for someone building with AI today?
A: Treat every AI-generated codebase as a liability. Before you deploy, plan for maintenance. If you can't commit to at least a year of support, don't release it publicly. The internet doesn't need more orphans.
Q: Isn’t this just a natural evolution—some projects fail, others succeed?
A: The failure rate is normal, but the scale is not. When creation becomes frictionless, the volume of failures skyrockets, creating signal pollution. It becomes harder for genuinely useful projects to get noticed. The abundance of garbage makes the diamonds invisible.