AI has changed the speed of building
Scaffolding, interface code, tests, documentation, refactoring and straightforward integrations can move dramatically faster with capable AI tools. That changes the economics of an MVP and lets smaller teams explore more ideas.
Production is a systems problem
A real SaaS product is not one code-generation task. Authentication, tenant isolation, payments, permissions, data migrations, integrations, background jobs, observability, deployment and customer support all interact. The engineering value is in making those parts behave as one reliable system.
The model cannot own the consequences
AI can suggest an architecture, but someone still has to decide which data may be exposed, how billing state is trusted, what happens when a webhook fails, how permissions are enforced and how the product recovers when an upstream service is unavailable.
The right question is leverage
We do not see AI as an alternative to engineering. We use it as leverage inside engineering. The strongest result is faster implementation combined with experienced review of the decisions that become expensive later.