AI did not kill software engineering. It moved the mistakes upstream.
AI did not kill software engineering. It moved the mistakes upstream.
If code is cheap now, why do so many teams still ship the wrong thing faster?
In a typical SMB, the pressure is reasonable: "We need a customer portal", "We need to automate ops", "We need an app by Q2". So the team picks a stack, hires a vendor, and starts building because progress is visible.
With AI, you get momentum immediately. It feels safer because the backlog burns down and demos look real.
But the expensive decisions were never in the code.
They were in the first week: what lives in Salesforce vs what becomes custom, whether you anchor on AWS or Microsoft, whether "we can switch later" is actually true once data models and workflows harden.
Then they show up at the end: reliability, observability, and incident response. Not because engineers forgot, but because those costs compound silently until the first outage hits customers, not QA.
I have seen teams save months of coding and still lose a year to rework and downtime.
The new bottleneck is judgment at the boundaries. The uncomfortable part is deciding who is accountable for those boundary calls before anyone writes a line.