[razoorka]

The gap between "AI is a 90% solution" and "100% required for production" is enormous. In my bubble, AI-generated code is maybe 70% useful, often less. The remaining 30% isn't minor polish—it's:

Understanding system architecture constraints Handling edge cases AI doesn't know exist Debugging when AI-generated code breaks in production Knowing when AI's "solution" creates more problems than it solves

That last 30% is what separates engineers from prompt writers. And it's not getting smaller—if anything, it's growing as systems get more complex.

[NuclearPM]

Why are systems getting more complex?

[razoorka]

Because growth always adds entropy.

Every new product, integration, or business line introduces new edge cases, dependencies, and coordination paths. What starts as a clean architecture turns into a network of overlapping constraints - legacy data formats, different latency expectations, regulatory quirks, “temporary” patches that become permanent.

You can manage complexity for a while, but you can’t eliminate it. Every layer that simplifies work for one team usually adds hidden coupling for another. Over time, the system stops being a single design and becomes an ecosystem of compromises.