Why Verbose Languages Like Java Are Better for Reviewing AI-Generated Code

Mar 28, 2026

I spent 30 minutes reviewing an AI-generated Python function last week. The code was 12 lines long. It compiled. Tests passed. But I couldn’t shake the feeling that something was off.

What type was order["total"]? A float? An int? A Decimal? What happens if the key doesn’t exist? What values can order["status"] have?

I ended up tracing through three other files to understand the data shape. That’s when I realized: verbosity isn’t bloat anymore — it’s verification.

The Problem Nobody Talks About

Teams adopting AI coding assistants hit a new bottleneck fast: code review speed.

The AI writes code in seconds. But reviewers? They’re stuck holding context in their heads, trying to verify code that looks plausible but lacks the structural cues they need to trust it.

Atlassian’s ICSME’25 study surveyed 118 practitioners and found something telling: 81% of developers say readability is still crucial even with LLMs in the loop. The top motivation? Reducing long-term maintenance costs.

But here’s the kicker: AI-generated code in TypeScript and Python tended to be longer and less maintainable than human-written code. Meanwhile, Java, Kotlin, Go, and Scala showed negligible differences.

Why? Because verbose languages encode intent in their structure.

What “Boilerplate” Actually Does

I used to complain about Java’s verbosity. Who wants to write public class OrderService when Python lets you just write the function?

Turns out, that “boilerplate” is now signal.

record Order(OrderStatus status, BigDecimal total) {}
enum OrderStatus { COMPLETED, PENDING, CANCELLED }

public class OrderService {
    public BigDecimal totalCompletedOrders(List&lt;Order&gt; orders) {
        return orders.stream()
                .filter(order -> order.status() == OrderStatus.COMPLETED)
                .map(Order::total)
                .reduce(BigDecimal.ZERO, BigDecimal::add);
    }
}

Before I read a single implementation line, I know:

Input: List<Order> — a list of Order records
Output: BigDecimal — precise decimal arithmetic, no floating point surprises
Status: closed enum — only three possible values
Nullability: explicitly handled (records require values)

Now compare this:

def total_completed_orders(orders):
    return sum(order["total"] for order in orders if order["status"] == "COMPLETED")

Concise? Yes. But I have to hold all this in my head:

What’s in orders? Dictionary? Object? Something else?
What type is total? Could be float, int, string, Decimal…
What if "status" key is missing?
What if "COMPLETED" is typo’d as "completed"?
What other status values exist?

The Python version requires me to read surrounding code to verify correctness. The Java version verifies itself from the signature alone.

Why This Matters for AI Code

When an AI generates code, it doesn’t have the full mental model of your codebase. It makes assumptions. Sometimes those assumptions are wrong.

In verbose languages, wrong assumptions surface immediately:

// AI tries this:
public int totalCompletedOrders(List&lt;Order&gt; orders) {
    return orders.stream()
            .filter(order -> order.status() == OrderStatus.COMPLETED)
            .map(Order::total)
            .reduce(0, Integer::sum);  // Type error: BigDecimal can't sum to int
}

The compiler catches the type mismatch. The reviewer sees it instantly.

In Python:

# AI tries this:
def total_completed_orders(orders):
    return sum(order["total"] for order in orders if order["status"] == "completed")

This runs. Tests might even pass (if the test data uses lowercase strings). But the bug — inconsistent casing for status — slips through. The reviewer must remember to check casing conventions.

The Readability-Efficiency Tradeoff Shifts

For years, we optimized for writing speed. Fewer keystrokes, more concise syntax, more “expressive” code.

But with AI assistants, writing speed isn’t the bottleneck anymore. Verification speed is.

Consider the comparison:

Aspect	Concise Languages	Verbose Languages
Write speed	Fast	Slower
AI generation	Natural	More tokens
Review speed	Slow (context needed)	Fast (signature tells all)
Error visibility	Hidden	Explicit
Maintenance	Higher cognitive load	Lower cognitive load

The Atlassian study found AI-generated Python tends to “drift” — it doesn’t look like human-written Python. But AI-generated Java? It looks like human-written Java because the structure forces consistency.

Common Mistakes When Choosing Languages for AI Projects

Mistake 1: Dismissing verbose languages as “enterprise bloat”

I’ve seen teams choose Python for AI projects because “we can iterate faster.” Six months in, they’re drowning in implicit bugs — wrong types, missing keys, inconsistent enums. Review takes longer than writing.

Mistake 2: Prioritizing generation speed over verification speed

AI generates Python faster than Java. But if reviewing Python takes 3x longer, you’ve lost ground.

Mistake 3: Assuming tests catch everything

Tests verify behavior, not intent. A function that returns a float when you need a Decimal passes tests until precision errors compound six months later.

When Verbosity Wins

Use verbose languages when:

Code review is a bottleneck — If your team spends more time reviewing than writing, verbosity pays off
Multiple reviewers — Explicit contracts mean less tribal knowledge
Long-lived codebases — Maintenance costs dwarf initial development
Safety-critical domains — Financial, medical, anything where precision matters

Stick with concise languages when:

Prototyping — Throwaway code that won’t be reviewed deeply
One-person projects — You hold all context anyway
Scripting — Short-lived automation where maintenance isn’t a concern

The New Mental Model

Old mental model: Verbosity = inefficiency

New mental model: Verbosity = verification cues encoded in syntax

When an AI writes:

public OrderResult processOrder(OrderRequest request, @NonNull UserContext user) {
    // 50 lines of logic
}

I see a contract: OrderRequest goes in, OrderResult comes out, user can’t be null. I can verify the contract before reading the implementation.

When an AI writes:

def process_order(request, user):
    # 50 lines of logic

I see… parameters with no type, no nullability, no return type. I must read all 50 lines to understand what the function expects and returns.

What I Changed

After that 30-minute Python review, I proposed an experiment to my team:

For new services that AI will help write, we use Java or Kotlin. For scripts and prototypes, Python is fine.

Three weeks in:

Average review time dropped from 45 minutes to 15 minutes
AI-generated code bugs caught at compile time: 12
Bugs that slipped to runtime: 1 (down from ~5 per week)

Summary

In this post, I explained why verbose languages like Java are better for reviewing AI-generated code. The key point is that verbosity isn’t bloat anymore — it’s verification cues encoded in syntax.

In the AI coding era, the language that wins isn’t the one that’s fastest to write. It’s the one that’s fastest to verify.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Atlassian ICSME'25 Study
👨‍💻 Reddit Discussion: Verbose Languages and AI

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!