Picture this scenario: a freshly migrated app is in production, users are happy, and then the crash reports start rolling in. Not sporadically. Consistently. Every user who tries to view their profile is welcomed with a crash.
The frustrating part? The code compiled without warnings. The test suite passed. The converted Swift code looked clean and modern. The AI agent did what seemed like a flawless job converting 50,000 lines of Objective-C to Swift in just a few days.
This is the nightmare scenario that keeps CTOs up at night when they're considering AI-assisted migrations. And it's not a hypothetical scenario.
Here's what you need to know: AI tools are brilliant at converting syntax, but they fundamentally misunderstand how memory management works differently between Objective-C and Swift. Those misunderstandings create a specific category of bugs that are invisible until they hit production.
Let me walk you through the five memory management mistakes that you need to watch out for. These aren't edge cases. They're systematic patterns that emerge from how AI tools approach code conversion.
Understanding these will help you avoid shipping bugs to production, whether you're doing the migration yourself or evaluating someone else's work.
When "Non-Null" Assumptions Crash Your App
Consider a common Objective-C pattern:
- (NSString *)getUserName {
if (self.user) {
return self.user.name;
}
return nil;
}
An AI tool will typically convert this to:
func getUserName() -> String {
if let user {
return user.name
}
return "" // AI "fixes" the nil return
}
If you're lucking, your AI agent will write more idiomatic Swift and use a computed property.
var username: String {
if let user {
return user.name
}
return "" // AI "fixes" the nil return
}
This looks reasonable at first glance. The AI even handled the optional unwrapping properly. But there's a fundamental misunderstanding here: the AI has changed the method's contract without understanding why.
In the original Objective-C code, returning nil was a signal "no user is logged in," "data isn't available yet," or "the operation failed." Other parts of the codebase might be checking for nil to make decisions. When the AI converts this to return an empty string instead, it breaks that implicit contract. Suddenly, code that was checking "is there a user?" gets an empty string and proceeds as if there is one, leading to crashes downstream when it tries to access data that doesn't exist.
The real issue runs deeper than this one example. AI migration tools operate on a flawed assumption: if an Objective-C method lacks explicit nullability annotations, it should return a non-optional type in Swift. This makes sense from a syntax perspective: cleaner code, fewer optionals to unwrap. But it ignores decades of Objective-C convention where returning nil was standard practice.
Legacy Objective-C codebases rarely have comprehensive nullability annotations. They were written before _Nullable and _Nonnull existed, or by teams that never adopted the annotations because they were optional and the code worked fine without them. The AI doesn't know which methods might return nil in edge cases (network failures, cache misses, race conditions). It just sees an unannotated return type and makes its best guess.
What makes this particularly insidious is that these bugs don't show up in basic testing. Your happy-path tests pass because normal conditions return valid values. The crashes only appear when edge cases hit (slow network, background data refresh, user logging out while a request completes). Suddenly, your app is crashing in production with force-unwrap failures, and you're hunting through thousands of lines of converted code trying to figure out which of the AI's assumptions was wrong.
The fix requires actually understanding the business logic. You need to trace through the original Objective-C code and ask: Did this method ever intentionally return nil? Was that nil being used as a signal? Should this be an optional type, or should we handle the nil case differently now that we're in Swift?
It's the kind of semantic analysis that AI tools simply can't do. They see syntax, not intention.
The Observer Pattern Time Bomb
Here's a pattern that appears in almost every legacy iOS codebase: Key-Value Observing. KVO is one of those Objective-C features that works perfectly fine until you forget about it, and then it destroys you. The problem is that observing relationships creates implicit dependencies between objects, and if you don't clean them up properly, you get crashes when those objects are deallocated.
Objective-C developers learned to be meticulous about removing observers in dealloc. It was drilled in through painful debugging sessions. But when AI tools convert KVO code to Swift, something interesting happens: they convert the syntax perfectly while completely missing the lifecycle implications.
Take this common pattern:
- (void)viewDidLoad {
[super viewDidLoad];
[self.dataManager addObserver:self
forKeyPath:@"status"
options:NSKeyValueObservingOptionNew
context:NULL];
}
- (void)dealloc {
[self.dataManager removeObserver:self forKeyPath:@"status"];
}
Your AI agent will convert this to:
override func viewDidLoad() {
super.viewDidLoad()
dataManager.addObserver(
self,
forKeyPath: "status",
options: .new,
context: nil
)
}
deinit {
dataManager.removeObserver(self, forKeyPath: "status")
}
Syntactically correct. But here's what the AI agent doesn't understand: Swift has better ways to do this. The converted code still uses stringly-typed key paths and requires manual cleanup in deinit. It's Objective-C thinking translated to Swift syntax, not idiomatic Swift. Modern Swift provides safer alternatives such as property observers (willSet and didSet), Combine publishers, and, since iOS 11, the KeyValueObservation API that automatically handles removal when the token is deallocated. In even newer Swift versions, the @Observable macro makes this pattern obsolete. AI tools don't apply these modern patterns. They preserve the old ones because one-to-one conversion is the safe choice.
The real danger comes when the converted code interacts with Swift's object lifecycle. Objective-C's and Swift's ARC systems are both deterministic, but Swift introduces different ownership conventions, especially around optional references and closure captures. These differences can change deinitialization order and reference lifetime expectations. The result can be crashes when observers weren't removed due to timing changes, or weak references becoming nil earlier than expected.
What makes this particularly tricky is that these bugs are intermittent. They depend on deallocation timing, which varies based on system memory pressure and the specific order of operations. Your tests might pass a hundred times and fail on the hundred-and-first because the timing was slightly different.
The solution isn't converting KVO syntax. It's deciding whether to keep KVO at all, and if so, ensuring the lifecycle management is bulletproof in Swift's memory model.
When Notification Center Becomes a Memory Leak Factory
Notification Center is everywhere in older iOS apps. It's a convenient way to decouple components, and Apple even encourages it in their documentation. But it's also a notorious source of memory leaks when not handled carefully.
The problem with AI-converted notification code isn't that it doesn't work. It's that it works just well enough to hide the leak until your app has been running for a while. Imagine an app where memory usage climbs steadily over hours of use, eventually getting killed by the system. The culprit? Dozens of view controllers are still registered as notification observers long after they should have been deallocated.
When AI tools convert the notification registration code, they handle the syntax perfectly:
NotificationCenter.default.addObserver(
self,
selector: #selector(handleDataUpdate(_:)),
name: NSNotification.Name("DataUpdated"),
object: nil
)
They even convert the selector syntax correctly, adding the @objc attribute to the handler method. Everything compiles. Everything runs. But here's what they miss: the observer needs to be removed, and in Swift, that's often forgotten because the patterns have changed.
In Objective-C, you always remove observers in dealloc. It was mandatory, beaten into developers by crashes. But Swift developers often use the block-based observation API that returns a token, which you store and which automatically removes the observer when deallocated. Or they use Combine publishers, which handle cleanup automatically when the subscription is released. The old selector-based API is legacy thinking.
What compounds the problem is that modern iOS versions have made notification observer removal more forgiving. You often don't get immediate crashes if you forget to remove an observer anymore. You just get memory leaks. The leaked view controller sticks around, its observer still registered, consuming memory. Do this enough times and your app balloons.
I've debugged apps where the memory profiler showed dozens of instances of the same view controller. The user had navigated to that screen many times, and every single instance was still alive because each was registered as a notification observer. The AI migration had perfectly converted the registration code but hadn't added the cleanup in deinit, and the original Objective-C dealloc method's cleanup got lost in translation.
The fix isn't complicated. Add proper cleanup, or better yet, migrate to modern Swift patterns. If you must use selector-based observers, still remove them in deinit. But if you switch to the block-based API introduced in iOS 9, observer tokens automatically unregister when deallocated. Combine’s NotificationCenter.Publisher offers the same safety. AI tools don't make those architectural decisions. They convert what's there and move on, leaving you with code that appears fine but quietly leaks memory.
The Retain Cycle Maze
Swift's capture semantics for closures are an elegant feature of the language ... until you realize that AI tools fundamentally misunderstand them. The problem stems from how differently Objective-C blocks and Swift closures handle memory management, and how AI tools try to map between them.
In Objective-C, you use __weak and __strong modifiers to control how blocks capture variables. It's explicit and verbose, but once you learn the pattern, it's straightforward. Swift uses capture lists at the beginning of the closure: [weak self] or [unowned self]. Different syntax, similar concept ... except AI tools struggle to determine when these captures are actually necessary.
I see two failure modes repeatedly in AI-converted code. First, AI tools will convert every block with a weak self to a closure with [weak self], even when it's not needed. This creates unnecessary optional unwrapping throughout your code. You end up with chains of guard let self else { return } in closures that would never create a retain cycle anyway. It's defensive programming taken to an extreme, making the code harder to read and reason about.
The second failure mode is worse: missing the [weak self] when it actually matters. This happens when the AI doesn't understand the object relationship graph. A closure that's stored as a property, passed to a long-lived completion handler, or used in a notification callback needs weak capture. But if the AI doesn't recognize that the closure will outlive the current scope, it creates a retain cycle that keeps objects alive indefinitely.
Here's a pattern you might encounter. The original Objective-C code:
__weak typeof(self) weakSelf = self;
self.locationManager.updateBlock = ^(CLLocation *location) {
__strong typeof(weakSelf) strongSelf = weakSelf;
[strongSelf processLocation:location];
};
Your AI agent might convert this Objective-C snippet to:
locationManager.updateBlock = { location in
self.processLocation(location)
}
Clean, readable Swift. But it's wrong. The closure is stored in a property of an object that self owns, creating a retain cycle. The view controller (or whatever object owns locationManager) never deallocates. The location manager keeps running. The device's battery drains. Users complain.
The correct Swift version is:
locationManager.updateBlock = { [weak self] location in
self?.processLocation(location)
}
But determining when to use weak vs strong vs unowned requires understanding the ownership graph and object lifetimes. AI tools don't have that understanding. AI agents pattern-match on syntax, not semantics.
What makes this particularly frustrating is that these bugs don't crash immediately. They just leak memory slowly. Users don't report "crash on launch." They report "app gets slow after using it for a while" or "battery drains faster than it should." These are subtle, insidious bugs that take days to track down, and they're scattered throughout AI-converted codebases.
The @objc Annotation Explosion
The last memory management mistake is more subtle, but it has real performance implications: AI tools love sprinkling @objc annotations everywhere. They do this because it's the safe choice. If something might need Objective-C exposure for KVO, selectors, or dynamic dispatch, adding @objc ensures it compiles and works. But this safety comes at a cost.
Every @objc annotation increases your binary size. It prevents Swift compiler optimizations. It exposes internal implementation details to the Objective-C runtime. And it can cause namespace collisions when you have methods with the same name but different types (which Swift allows, but Objective-C doesn't).
When reviewing AI-driven migrations, you might see entire classes marked with @objc, or every method in a class marked individually. The AI agent sees that one method needs to be called via a selector for a notification handler, so it marks everything just to be safe. Or it sees KVO usage and assumes all properties need @objc exposure.
The insidious part is that this works perfectly fine. Your app runs, features function, no crashes. But your binary is larger than it needs to be, your code is slower than it should be, and you're preventing the Swift compiler from doing its job. Over time, as you add more features, the accumulated overhead becomes noticeable.
If you profile your app after removing unnecessary @objc annotations, you may notice measurable reductions in binary size and improved performance. The exact impact varies by project size and compiler settings, but the effect is real. Every unneeded @objc annotation disables certain compiler optimizations and increases runtime exposure, which accumulates as your codebase grows.
The challenge is that determining which @objc annotations are necessary requires understanding the entire codebase's architecture. You need to trace call sites, check for selector usage, and verify KVO dependencies. It's tedious work, but it's also exactly the kind of semantic analysis that AI agents can't do reliably.
What This Means for Your Migration
If you're reading this and thinking, "This sounds like a nightmare," I have good news and bad news.
The bad news is that these five memory management issues appear in virtually every AI-assisted migration. They're not edge cases or rare bugs. They are systematic limitations of how AI tools understand code conversion. You will likely encounter them if you use AI tools for migration.
The good news is that they're all preventable with proper code review by someone who understands both Objective-C and Swift memory models. The AI does the heavy lifting of converting 70% of your codebase quickly and accurately. But that remaining 30%, the semantic understanding, the architectural decisions, the memory management correctness, requires human expertise.
This is why I advocate for a hybrid approach:
Let AI tools handle the mechanical conversion work. They're fast, cheap, and excellent at syntax translation. But pair that with an experienced code review that looks explicitly for these memory management patterns. Someone who's debugged retain cycles knows what to look for. Someone who's hunted memory leaks through Instruments knows where AI tools make assumptions.
The result is a migration that's 40-60% cheaper than pure manual work, completed in 6-8 weeks instead of 6-12 months, and, critically, without the production crashes, memory leaks, and performance issues that plague unsupervised AI migrations.
Because nobody wants to wake up to crash reports flooding in from production.
Next Steps
If you're considering migrating your Objective-C codebase, let's start with a conversation. I offer a free 30-minute consultation to discuss your project, timeline, and whether migration makes sense right now.
For projects that move forward, I provide a comprehensive paid assessment that gives you a detailed roadmap, accurate timeline, and fixed-price quote. Whether you proceed with me or not, you'll have a valuable document for decision-making.
With 15 years of Apple development experience and as the founder of Cocoacasts, I've analyzed dozens of small and large codebases, and know exactly what to look for.
In the next post, we'll explore the runtime behavior and type system issues that cause AI-migrated code to compile successfully but break in production. Subscribe to get it in your inbox when it's published.