AI Has Replaced React Native Boilerplates
Remember when starting a new React Native project meant spending the first three days on things that had nothing to do with your actual app? You'd pick a boilerplate from GitHub, read through the README, realize it was two versions behind, spend an afternoon resolving dependency conflicts, then finally get to writing code. And if you didn't pick a boilerplate? You'd be manually setting up navigation, state management, linting, testing, folder structure, and authentication flows from scratch.
That era is ending.
AI code generation has reached the point where you can describe what you want and get working React Native code in seconds. Not pseudocode. Not a rough outline. Actual, production-quality components with proper styling and sensible structure.
The Boilerplate Problem
Boilerplates were always a compromise. They solved the "blank page" problem by giving you a starting point, but they came with baggage. Every boilerplate made assumptions about how you'd structure your app, which libraries you'd use, and what patterns you'd follow. If your needs diverged from those assumptions, you'd spend hours ripping out code you didn't want.
The popular React Native Boilerplate by TheCodingMachine tried to be light and simple. The ixartz starter went heavy with Expo, NativeWind, TypeScript, Jest, and Detox all preconfigured. Neither approach was wrong, but both forced you into decisions before you'd even figured out what you were building.
And then there was maintenance. Boilerplates go stale. React Native moves fast. A boilerplate that was cutting-edge in 2023 might be using deprecated patterns by 2025. You'd import someone else's technical debt from day one.
What Changed
AI code generation tools have matured to the point where they understand React Native's component model, Expo's conventions, and modern styling approaches like NativeWind. You can prompt for a three-screen onboarding flow with a login page, and get back something you could actually ship.
RapidNative calls this "prompt-based development." You describe a screen in plain English, and it generates working React Native and Expo code. Not just component stubs, but proper layouts with styling applied. The kind of thing that used to take a boilerplate plus two hours of customization.
Then there's the broader wave of AI-assisted coding. Cursor doesn't just autocomplete lines of code; it understands your whole project. Ask it to create a settings screen that matches your existing design patterns, and it will look at what you've already built and generate something consistent. That's not something a boilerplate could ever do.
Even traditional design tools are crossing over. Visual Copilot turns Figma designs into React code with a click. You design a screen, export it, and get components that look like your mockups. The translation step that used to take hours now takes seconds.
What Boilerplates Actually Solved
Let's be honest about what boilerplates were really doing. They solved three problems: initial scaffolding, configuration, and copy-paste UI code.
Initial scaffolding was about folder structure and project setup. Where do screens go? How do you organize components? What does the navigation tree look like? Boilerplates gave you an opinionated answer so you didn't have to think about it.
Configuration meant all the tooling: ESLint, Prettier, TypeScript, testing frameworks, pre-commit hooks. Getting all of those to play nicely together was tedious. Boilerplates did it once and shared the result.
Copy-paste UI code was the big one. Every app needs an auth flow. Every app needs a profile screen. Every app needs form inputs with validation. Boilerplates included these so you weren't writing them from scratch.
AI handles all three better than static templates ever could.
The New Workflow
When you start a new project today, you don't need to hunt for a boilerplate that matches your stack. You spin up a fresh Expo project, then tell your AI assistant what you need. "Create a tab navigator with Home, Search, and Profile tabs. Use NativeWind for styling." You get working code in seconds.
Need authentication? "Add a login screen with email and password fields, form validation, and a forgot password link." The AI generates it, and you tweak the details. You're not reading someone else's auth implementation and trying to understand their choices. You're guiding the generation of code that fits your project.
This isn't hypothetical. According to Builder.io's breakdown of the 2025 React and AI stack, the combination of AI code generation, component libraries, and modern frameworks has fundamentally changed how teams start projects. The scaffolding phase that used to take days now takes an afternoon.
What Still Matters
AI doesn't make everything easy. It's excellent at generating code that follows patterns, but it doesn't know which patterns are right for your specific situation. You still need to make architectural decisions. You still need to understand what the generated code is doing. You still need taste.
The difference is that AI lets you spend your time on the decisions that matter instead of the mechanical work of translating those decisions into code. You focus on "should this screen use a modal or navigate to a new page?" instead of "how do I wire up a stack navigator?"
Think of it like having a junior developer who's incredibly fast at typing but needs you to tell them what to build. The thinking is still on you. The execution got a lot cheaper.
Letting Go of the Old Way
Some developers resist this shift. They learned React Native the hard way, building everything by hand, and there's a certain pride in that. Typing out every line of code yourself feels more legitimate than generating it.
But that's ego, not efficiency. The goal isn't to write code. The goal is to ship products. If AI can generate the same component in thirty seconds that would take you thirty minutes, the only thing you're proving by doing it manually is that you have time to waste.
The best developers have always been lazy in the right ways. They automated repetitive tasks. They built abstractions. They used libraries instead of reimplementing everything. AI is just the next step in that progression.
Where This Goes
React Native is particularly well-positioned for AI-assisted development because of how it's structured. Components are self-contained. Patterns are consistent across projects. Tools like Expo enforce conventions that make AI output more predictable. According to Coaxsoft's analysis, this is why React Native is seeing such strong AI tooling development.
Boilerplates will still exist. Some teams have specific, complex needs that justify maintaining their own starters. But for most projects, the "blank page" problem is now solved with a prompt, not a git clone.
This is exactly the approach Nucleate takes. Instead of starting from a static boilerplate, you get a full Expo development environment with built-in AI that understands your codebase. Describe what you want, watch it appear, then iterate from there. The scaffolding phase that used to slow down every new project simply disappears.