Yes — AI writes most of the code shipped by indie developers in 2026. Here is the honest version of what works and what does not.
undefined
Architectural decisions for a new system (it needs your judgement about constraints AI cannot see). Deep navigation of large unfamiliar codebases without help. Fixing the underlying problem rather than the symptom. Debugging concurrency, race conditions and subtle distributed-system issues. Following non-obvious conventions in your specific codebase. The gap shrinks quarterly; specifying clearly closes most of it.
Use the AI as a strong pair, not as a contractor. Review every change. Run tests. Push back when the AI suggests something that looks fine but is wrong in your context. The 10x productivity claim is real for many tasks; it becomes a 0.1x liability for the tasks where you trust the AI without reviewing.
Cursor: best in-editor experience for most developers. Claude Code: best for terminal-native and multi-file refactors. Copilot: best for tight, inline completion. Devin: best for autonomous long-running tasks (at $500/mo). Heaven Code Studio: best for Flutter and for non-coders building apps from prompts. There is no single best — your stack and platform pick the tool.
Flutter-native code generation with live preview, multi-file orchestration, deterministic Dart-to-spec fallback, and persistent project memory across sessions.
For non-coders: the easiest path to a working app on the App Store. For coders: a fast Flutter pair that knows your project.
Sovereign — your code does not flow through a third-party LLM provider in the hot path. Free forever.
For well-defined tasks and reviewed changes, yes — many production systems contain large amounts of AI-written code. For unreviewed changes in critical paths, no. The bar is the same as for any contractor: review the work before deploying.
Will change what programming means. Routine implementation work is increasingly AI-handled. Architectural judgement, taste, system design, and the parts of programming that require deep context understanding are still human work. The job is shifting toward "architect + reviewer + AI orchestrator"; the demand for these skills is rising, not falling.
Yes — given a clear bug description, stack trace or failing test, AI is often faster than a human at locating and fixing. Where it struggles: bugs that require understanding why the system was designed this way, or that come from race conditions and emergent distributed behaviour.
Use AI as a first pass; review with a security mindset. AI sometimes generates code with subtle vulnerabilities (SQL injection, auth bypass, secret leaks) that look fine on read-through. Use static analysis, run security tests, and treat AI-written security code as you would a junior developer's — useful, but verify.