From Demos to Durability

Why code quality must be the new benchmark for AI app building

Replit, Lovable, and dozens of other AI app builders are optimized to wow you with speed and amaze you with flashy functionality. They excel at building prototypes, demos, and internal tools.

The downside of optimizing for quick rewards is that you sacrifice code quality and depth. To move beyond prototypes and build software that is the foundation of lasting products and businesses, we must instead optimize for code quality and robust architecture.

An Example

Standard LLM AI-Generated Code

// app/(tabs)/hike/[id].jsx
<View style={{ backgroundColor: "#0B1217", flex: 1, padding: 16 }}>
  <Text style={{ color: "#E6EDF3", fontSize: 24, fontWeight: "700" }}>{title}</Text>
  <TouchableOpacity
    style={{ backgroundColor: "#3AA4FF", padding: 14, borderRadius: 12 }}
    onPress={() => {
      console.log("Hike started!");
      // any extra logic is jammed here inline
    }}
  >
    <Text style={{ color: "white" }}>Start</Text>
  </TouchableOpacity>
</View>

It compiles and runs, but this is the kind of thing you’d see from someone writing React Native for the first time:

• Colors, padding, fonts are all hardcoded inline. String literals everywhere. Nothing reusable.
• Layout, styling, and business logic mixed together in the same file, making it a mess to maintain.
• Hard to test the onPress functionality unless you lift it out.
• This wouldn’t pass any serious code review.

Production-Grade AI-Generated Code (from Woz)

// app-pages/hike/HikeDetailsStyles.ts
export interface HikeDetailsStyles {
  container: ViewStyle;
  title: TextStyle;
  primaryButtonStyles: ViewStyle;
}

export function useHikeDetailsStyles(): HikeDetailsStyles {
  const { colors, spacingPresets } = useStyleContext();

  const styles: HikeDetailsStyles = {
    container: { 
      backgroundColor: colors.bg.primary,
      flex: 1, 
      padding: spacingPresents.large
    },
    title: { 
      color: colors.fg.primary, 
    },
  };

  return styles;
};

// app-pages/hike/HikeDetailsFunctions.ts
export interface HikeDetailsFunctions {
  onStartPress: () => void;
}

export function useHikeDetailsFunctions(props: HikeDetailsProps): HikeDetailsFunctions {
 
  function onStartPress() {
    analytics.captureEvent("hike_started");
    // ...
  }

  return {
    onStartPress
  };
}

// app-pages/hike/HikeDetails.tsx
export interface HikeDetailsProps {
  title: string;
}

export function HikeDetails(props: HikeDetailsProps) {
  const { styles } = useHikeDetailsStyles();

  const { onStartPress } = useHikeDetailsFunctions(props);
  
  return (
    <View style={styles.container}>
      <Text style={styles.title}>{props.title}</Text>
        <CustomButton
          onPress={onStartPress}
          title={t("hike.startTracking")} 
          styles={styles.primaryButtonStyles}
        />
    </View>
  );
}

This version looks verbose at first glance, but it’s structured in the way real teams write software. The overall structure makes the codebase clean, easy to maintain, and scalable:

• Layout, styling, and business logic are in their own files, making the code easier to follow and maintain.
• Type safety everywhere makes changes or refactors more safe and predictable.
• Colors, spacing, and typography all come from shared presets instead of hardcoded inline values.
• Standardized reusable components for consistency throughout the app.
• Mature codebase details like i18n translations and analytics events for usage tracking.

The Hidden Work Behind Reliable Software

Today’s AI-generated apps cannot serve as the foundation of a real business.

Building the first version of an app is only 1% of the work. Lofty claims of “I just oneshotted this entire app with AI!!” miss the main point: the other 99% is the work of debugging, extending, scaling, and maintaining it for real users.

Yes, models will get better. They’ll write cleaner code, handle more edge cases, and even fix more of their own mistakes. But the fundamental issue doesn’t go away: ownership.

Businesses are owned by humans. Humans reap the rewards, and they also suffer the risks. At the end of the day, some human is signing their name next to the product. And if you’re the one signing, you or your team better understand the code your business depends upon.

Without that understanding, you are at the mercy of the AI that wrote it. If the AI can’t fix a mission-critical issue, and no human understands the system, your business is built on a foundation of sand. In short, you are f****d.

Imagine you’ve built a successful subscription app with AI. One day, a hidden bug charges customers multiple times for the same month. You don’t understand how the AI handles billing logic, so you can’t fix it quick enough. Within hours, furious customers are flooding social media, chargebacks are piling up, and cancellations are pouring in. The loss of revenue stings, but the real damage is the instant collapse of your customer’s trust.

What Is Good Code?

Good code isn’t sexy. 

• Good code is boring. 
• Good code explains itself.
• Good code is written once, read hundreds of times. 

It’s modular, predictable, and extensible. It’s code that another developer (or future you) can jump into six months later and instantly understand what is going on.

AI coding tools today do not produce code like this. They produce plastic software: shiny on the surface, cheap underneath, and guaranteed to break under pressure. There is no separation of concerns, no long-term thinking, no focus on readability or reusability. Code like this is incredibly hard to understand at any moment beyond the day it is written. Good for toy apps, prototypes, and impressing investors. Untenable in practice.

Why Did We End Up Here?

“Show me the incentive and I’ll show you the outcome” - Charlie Munger

So why does AI code look like this today? Why does it default to spaghetti instead of structure?

Because all the reward systems we’ve currently set up told it to do so.

• LLMs are trained to maximize immediate correctness, not think about long-term maintainability. If the code compiles and passes a test case, the model gets rewarded. Code quality is not currently an objective measure the model weighs upon.
• Short term success is easier to optimize. It is much simpler for a model to keep everything in one place (styles, logic, layout) than to separate concerns correctly. Keeping it all inline with each other makes it easier for the LLM to keep in memory… but impossible for a team to work with. They don't really understand the full mental model of the codebase, they only solve smaller, localized problems.
• The benchmarks that we reward model quality upon are shallow. The leaderboards care about solving LeetCode puzzles and small coding exercises, not architecting large pieces of high-quality software. They aren’t graded on how understandable it is to a human.

We’ve optimized for surface-level code, and that’s what we’ve received.

Redefining the Benchmark

Forget benchmarks like “can it solve LeetCode in X seconds” or “can the agent run autonomously for 200 minutes.” The real benchmark is simple: 

Can a human jump into the code six months later and still understand it? 

To enable LLM code generation to be more than a flashy gimmick and to serve high-risk, high-value use cases, this is where our focus must shift.

The Unavoidable Hard Work

AI will never care about code quality on its own. It has to be forced by the structure around it: architectural guidance, thorough reviews, and exhaustive testing. In practice, this is incredibly hard to do.

We need to treat AI as a junior developer. It’s talented and fast, but desperately in need of oversight and direction. Left alone, it will produce fragile, short-sighted code. Directed properly, it can help senior developers move significantly faster.

How does Woz direct it properly and produce this level of high-quality code? Well... that's a topic for a follow up post.

Woz’s Future of Software

Good code is boring code.

And boring code is the foundation of extraordinary software.

At Woz, we care more than anyone about code quality because we believe it’s the only way to make AI-augmented software development viable for serious products. Woz generated software feels like a senior development team spent months crafting it. It follows best practices, it’s clean, consistent, extensible, and always understandable by the humans who are ultimately responsible.

This is the benchmark that matters: not whether AI can hack together a demo, but whether humans and machines together can create code that lasts.

We’re obsessed with this standard, and we’re building a platform where thousands of entrepreneurs can build meaningful businesses with the help of structured AI. We invite every developer to see it for themselves: build your next mobile app on Woz, review the generated code, and tell us how we can make it even better.