Just want to clarify, this is not my Substack, I’m just sharing this because I found it insightful.
The author describes himself as a “fractional CTO”(no clue what that means, don’t ask me) and advisor. His clients asked him how they could leverage AI. He decided to experience it for himself. From the author(emphasis mine):
I forced myself to use Claude Code exclusively to build a product. Three months. Not a single line of code written by me. I wanted to experience what my clients were considering—100% AI adoption. I needed to know firsthand why that 95% failure rate exists.
I got the product launched. It worked. I was proud of what I’d created. Then came the moment that validated every concern in that MIT study: I needed to make a small change and realized I wasn’t confident I could do it. My own product, built under my direction, and I’d lost confidence in my ability to modify it.
Now when clients ask me about AI adoption, I can tell them exactly what 100% looks like: it looks like failure. Not immediate failure—that’s the trap. Initial metrics look great. You ship faster. You feel productive. Then three months later, you realize nobody actually understands what you’ve built.
Something any (real, trained, educated) developer who has even touched AI in their career could have told you. Without a 3 month study.
What’s funny is this guy has 25 years of experience as a software developer. But three months was all it took to make it worthless. He also said it was harder than if he’d just wrote the code himself. Claude would make a mistake, he would correct it. Claude would make the same mistake again, having learned nothing, and he’d fix it again. Constant firefighting, he called it.
As someone who has been shoved in the direction of using AI for coding by my superiors, that’s been my experience as well. It’s fine at cranking out stackoverflow-level code regurgitation and mostly connecting things in a sane way if the concept is simple enough. The real breakthrough would be if the corrections you make would persist longer than a turn or two. As soon as your “fix-it prompt” is out of the context window, you’re effectively back to square one. If you’re expecting it to “learn” you’re gonna have a bad time. If you’re not constantly double checking its output, you’re gonna have a bad time.
It’s still useful to have an actual “study” (I’d rather call it a POC) with hard data you can point to, rather than just “trust me bro”.
Untrained dev here, but the trend I’m seeing is spec-driven development where AI generates the specs with a human, then implements the specs. Humans can modify the specs, and AI can modify the implementation.
This approach seems like it can get us to 99%, maybe.