Greetings, traveler!
The recent release of the Swift SDK for Android is an interesting milestone. It’s a clear signal that Swift is stepping beyond its traditional borders. Today, cross-platform development has never been more accessible – and yet, paradoxically, never more questionable in its long-term value.
Because while Swift is crossing platforms, AI is quietly changing the rules of the game.
Why Cross-Platform Made Sense
For years, the idea was simple: build once, deploy everywhere.
Instead of maintaining two teams — one for iOS and one for Android — companies could hire a smaller group, share most of the logic, and reduce costs. Flutter, React Native, and KMM became natural choices for teams seeking speed and efficiency.
But every shortcut hides a trade-off.
Cross-platform frameworks often come with heavier runtime layers, slower UI rendering, and limited access to platform-specific APIs. They tend to lag behind native capabilities, especially in design polish and performance-critical areas.
And even more importantly — there has always been a shortage of strong cross-platform engineers who deeply understand both ecosystems.
Native Still Means Closer to the Metal
Native development remains the most natural way to build mobile products.
Swift and Kotlin are tightly integrated with their respective platforms, have access to low-level optimizations, and benefit from mature tooling and vast ecosystems.
There are simply more developers, more open-source libraries, and more real-world examples written in these languages. That means faster onboarding, easier maintenance, and higher stability over time.
Native code speaks directly to the hardware — and that connection shows in the final experience.
AI Is Changing the Cost Equation
The main reason teams turned to cross-platform frameworks was economic — fewer people, less money, faster delivery. But with the rise of AI-assisted development, the economics are shifting.
When code generation, refactoring, and testing become dramatically faster, the advantage of “one team for two platforms” begins to fade.
An experienced engineer paired with AI isn’t just a developer anymore — it’s a force multiplier, capable of covering more ground than before.
And since large language models are trained primarily on native Swift and Kotlin code — simply because there’s more of it — they tend to understand native patterns better than the abstractions used in cross-platform frameworks.
A New Balance
Cross-platform tools will still have their place — especially for MVPs, prototypes, and products that rely on shared business logic.
But as AI reduces the cost of writing and maintaining native code, the barrier to going native keeps dropping.
That makes native development not only viable but often preferable:
better performance, better integration, and fewer compromises.
Final Thought
With AI lowering dveloprment costs and improving productivity, developers can afford to build natively everywhere.
Cross-platform is still a good idea — but its strongest argument, cost efficiency, is slowly losing weight.