Can AI tools build backends, not just frontends?

A common misconception is that AI tools only generate pretty interfaces. The reality is more capable:

What full-stack AI tools generate:

Database schemas: Tables, relationships, indexes based on your requirements.

API endpoints: Server-side code that handles data operations, business logic, and communication with external services.

Authentication: User signup, login, session management, password reset flows.

Business logic: Rules about who can do what, calculations, validations.

Server functions: Background jobs, scheduled tasks, webhook handlers.

How it typically works:

With Lovable/Bolt: You describe what you want ('users should be able to create teams and invite members'). The tool generates both the UI and the backend logic to support it.

With Supabase: Database operations, real-time subscriptions, and edge functions provide backend capabilities. AI tools integrate these seamlessly.

With Cursor: You work with backend code directly, getting AI assistance for API routes, database queries, and server logic.

Limitations:

Complex business logic: Multi-step processes with many edge cases may need refinement.

Performance optimisation: AI generates working code but not necessarily optimised code.

Integrations: Unusual third-party services may require more manual work.

The 'frontend only' perception is outdated. Modern AI tools build real, deployable applications with working backends.