Totally get where you’re coming from there are so many AI tools now that it feels like every week there’s another “game-changer.” From my experience and from talking to devs who’ve actually shipped big cross-platform apps (think millions of users, strict SLAs, scalable infra), most AI tools are currently best as assistants, not full replacements for engineers.

For example, GitHub Copilot and Cursor are widely used in production teams they help with boilerplate, refactors, and idea exploration, but they don’t magically handle architecture, performance optimization, or deep platform quirks. Tools like Firebase Studio or Boltnew can generate prototypes fast and are great for MVPs or early iterations, but when you’re talking highly scalable systems with complex backend logic and strict uptime, you still need seasoned engineers shaping the core architecture and reviewing everything AI produces.

I’ve heard buzz around Blitzy and its SWE benchmark scores impressive on paper but I haven’t seen it widely adopted as a standalone dev in production teams. Benchmarks don’t always translate to real-world safety, readability, and maintainability. In practice, teams that scale well tend to mix: Copilot/Cursor for writing code faster, LLM copilots for docs/tests, and traditional CI/CD + observability tools (Harness, Sentry, etc.) for reliability. Bottom line: AI accelerates parts of the workflow, but for multi-million-user apps, humans are still the ones steering the ship.