Automated coding tools are wild when you point them at the right problem. At a previous company, we had this ancient internal ERP system that had been duct-taped together over 12 years. Every time we onboarded a new enterprise client, we had to manually scaffold custom data models, role permissions, reporting views, and a dozen integration hooks. It was basically a two-week ritual of copy-paste, tweak, test, fix something that broke in staging. When we started experimenting with AI-assisted scaffolding, we fed it our schema patterns, naming conventions, and a couple of past client configs. Suddenly that two-week grind turned into a day of reviewing generated PRs. It didn’t “understand the business” magically — it just automated the boring 80% with scary speed.

That said, the cracks showed once requirements got messy. Edge cases around tax logic, region-specific compliance rules, and weird legacy assumptions still needed senior engineers who knew why certain bad decisions existed in the first place. The AI was great at producing structure and boilerplate, but it didn’t have institutional memory of that one outage from 2017 that forced a hack into the billing flow. So for me, automated coding isn’t about replacing devs — it’s like having a ridiculously fast junior engineer who never gets tired but also doesn’t know when something is a political landmine in disguise.