Porting iOS to Android Takes Days Now. Why Use Cross-Platform?

Cross-platform frameworks exist because building native apps twice is expensive. React Native, Flutter, web wrappers. They all solve the same problem: you don't have time to maintain two codebases.

But what if the second codebase only took a week?

AI can now translate your iOS implementation to Android (or vice versa) with surprising fidelity. Not generate from scratch, but translate, preserving your architectural decisions, your edge case handling, your hard-won fixes.

I experienced this building Kibbler. The iOS app took about 6 weeks to build, iterating through 183 commits. The Android version took 52 commits over 9 days.

Some context: I'm not a mobile developer. My background is backend and infrastructure. I'd never written Kotlin before, never used Jetpack Compose, and my last Android experience was a toy app in 2015. I also wasn't working on this full-time. Those 9 days were scattered across other work.

AI could look at my iOS implementation and produce the equivalent in Kotlin with Jetpack Compose. I didn't need to learn Android idioms from scratch or spend weeks ramping up. The iOS app was the specification.

The Platform Quirks Are Still Real

Now, I'm not going to pretend AI made Android development effortless. The commit history tells a different story, one of genuine platform struggles that no amount of abstraction can hide:

Android has opinions. When your phone locks, Android's Doze mode aggressively kills network connections. My SSE streams would just... die. The fix wasn't trivial: detect stream interruption, defer recovery polling until the user actually unlocks (polling in Doze mode wastes battery and fails anyway), then gracefully resume. None of this exists on iOS.

The keyboard is chaos. Getting edge-to-edge layout working with the soft keyboard required a manual 22% height correction specifically for Gboard. Why 22%? I still don't know. But it works.

Security APIs have edge cases. EncryptedSharedPreferences can corrupt itself after OS updates, throwing AEADBadTagException on launch. This is a known Android issue with no fix except catching it and resetting the keystore. Fun.

Haptic feedback varies wildly. iOS has a clean UIFeedbackGenerator API with consistent behavior. Android has HapticFeedbackConstants where CONFIRM and REJECT only work on Android 11+, so you fall back to CONTEXT_CLICK for broader device support.

But the Platform Gifts Are Real Too

For all the quirks, native development hands you capabilities that web apps simply can't match.

Voice comes free. Both iOS and Android ship with speech recognition APIs and audio processing you can tap into. iOS has SFSpeechRecognizer with on-device mode. Android has SpeechRecognizer (though it often uses cloud by default). Both platforms give you access to echo cancellation so the mic doesn't pick up TTS output. You still need to handle voice activity detection yourself, but the building blocks are there.

What's the web equivalent? The Web Speech API exists, but it typically requires network round-trips for decent accuracy, with no true on-device processing. You don't get easy access to OS-level echo cancellation. And on iOS, WebViews have more restricted API access than Safari itself.

This pattern repeats everywhere. Haptics, secure storage, background processing, push notifications, biometric auth. Native platforms hand you polished, optimized implementations. Web apps get polyfills and "best effort."

Why This Argues For Native

Because I had the iOS implementation as a reference, AI could help me find the right Android-native solution. Not a lowest-common-denominator workaround, but the actual correct way to handle it on Android.

When AI found that Doze mode was killing streams, it didn't suggest some hacky keep-alive. It implemented proper lifecycle-aware recovery that respects Android's battery optimization. When keyboard handling was broken, it used Android's WindowInsets API correctly, not some framework shim.

The New Equation

The old trade-off was:

• Web/Cross-platform: 1x development time, 0.7x quality on each platform
• Native: 2x development time, 1.0x quality on each platform

The new equation with AI-assisted porting:

• Native: 1.2x development time, 1.0x quality on each platform

Build your first platform thoroughly. Let AI translate to the second platform. Fix the platform-specific issues as they come up, because they will. The result is two genuinely native apps that feel right to users on each platform.

The Experience Matters

Native apps can take full advantage of what each platform offers. The animations, the gestures, the system integrations, the little details that make an app feel like it belongs. Cross-platform frameworks have gotten better at this, but there's still a gap when you want to push the boundaries.

And now, with AI making the porting process faster, the cost of going native is lower than it used to be.

Cross-platform frameworks existed to solve a resource problem: you couldn't afford to build twice. AI shrinks that initial cost, but it doesn't eliminate the ongoing one. Two codebases means two sets of bugs, two dependency trees, two places to implement every future feature. That maintenance burden is real, and for some teams it's the deciding factor.

But if you care about the details that make an app feel right on each platform, native is more accessible than it used to be. For me, that trade-off is worth it.