AI Code Becomes ‘Debt’ in 30 Days—Writing Costs 1/10, Fixing Costs 10 Times. The Hidden Landmines Small Businesses Must Avoid

GitHub Copilot allows you to write code that used to take an hour in just five minutes. This is a fact. But have you ever considered what the code generated in those five minutes might trigger 30 days later?

To get straight to the point, the cost of writing with AI has decreased to 1/10, but the cost of fixing has ballooned to 10 times. This is the reality of “AI code debt” that is currently unfolding in development environments around the world. This issue is particularly fatal for small and medium-sized enterprises (SMEs) compared to large corporations.

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Debt Accumulation in 30 Days—What’s Happening?

According to an analysis released by GitClear in 2024, since the advent of AI-assisted code generation, the proportion of “moved code” and “copied and pasted code” has surged. In other words, AI is often not designing new logic but rather cutting and pasting existing patterns.

On the surface, it works. It passes tests. However, about 30 days after generation, problems begin to surface. Why 30 days? Because that’s when the code is deployed in a production environment and exposed to actual user traffic and edge cases.

What specifically happens?

• Dependency Inconsistencies: The training data referenced by the AI is outdated, calling deprecated APIs.
• Security Holes: Input validation is weak, and SQL injection countermeasures are missing.
• Performance Degradation: It appears to work, but as data volume increases, the N+1 problem explodes.

To fix these issues, humans must first decipher the intent behind the code written by AI. If it’s code you wrote yourself, you understand the context. However, decoding hundreds of lines of AI-generated code to understand “why it was written this way” can take longer than rewriting it from scratch.

In one overseas startup, it was reported that code for a microservice generated by AI caused a memory leak 30 days after going live, requiring about eight times the original development cost for recovery and fixes. “Code that cost 50,000 yen to write ended up costing 400,000 yen to fix,” they said.

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Security Bugs Are Discovered Faster Than They Are Fixed

A research team at Stanford University published a paper in 2023 revealing that developers using AI code assistance tools were more likely to write security-vulnerable code compared to those who did not use such tools. Moreover, troublingly, developers using AI were more confident that their code was secure.

Therein lies a structural problem.

Thanks to advancements in AI, the speed of discovering vulnerabilities has dramatically increased. Tools like Snyk, SonarQube, and GitHub Advanced Security can detect security bugs the moment code is pushed. The time taken for discovery ranges from a few seconds to a few minutes.

On the other hand, fixing the discovered bugs remains a human task. It requires understanding context, investigating the scope of impact, rewriting tests, and deploying fixes. This can take hours to days.

In other words, the structure is as follows:

• Bug Generation Speed: Accelerated by AI (as code volume increases, so do bugs)
• Bug Discovery Speed: Accelerated by AI (due to faster scanning tools)
• Bug Fixing Speed: Remains human (unchanged)

While generation and discovery have become ten times faster with AI, fixing remains at human speed. As a result, unfixed security bugs accumulate. This is serious for SMEs. Large corporations can have dedicated security teams to handle this. However, for SMEs with only two or three engineers, continuously responding to security alerts that arise daily is not realistic.

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‘Silent Failures’—Working but Wrong

Another issue often overlooked in the field is the “silent failures” of code corrected by AI.

A study reported by Microsoft’s research team in 2024 found that about 41% of code automatically corrected by AI coding agents was not actually corrected correctly. It passes tests. No errors are thrown. However, the returned values are subtly incorrect.

For example, in a case where the field name of a response changed due to an API specification update, the AI only rewrote the field name. However, it failed to address the change in data type from string to integer. As a result, numbers were displayed as strings on the frontend, leading to incorrect sorting. It took two weeks for this to be discovered, only after receiving complaints from users.

Such “silent failures” are discovered later than traditional bugs and are difficult to identify in terms of impact. This is because nothing shows up in the error logs. It’s working but wrong. This is the scariest part.

Consider this in the context of an SME. What if the amount calculation in an order management system was “quietly” incorrect? What if the inventory count was slightly off? By the time you realize it, months of data could be contaminated. The cost of fixing it is not the only issue; it becomes a matter of trust.

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So, Should SMEs Avoid AI Code Generation?

No. They should use it. However, they must not misuse it.

There are three key points.

1. Define the Scope for AI to “Write”

AI excels at generating routine processes. CRUD API endpoints, validation logic, and templates for test code. For these “pattern-defined” elements, let AI handle them to reduce costs to 1/10.

Conversely, the core parts of business logic—such as pricing calculations, access control, and payment processing—should be written by humans. If you leave this to AI, you will face the brunt of debt 30 days later.

2. Estimate “Review Costs” from the Start

Even if the labor cost for writing code with AI is 50,000 yen, if review and validation cost 150,000 yen, then the total cost is 200,000 yen. Accidents happen because this is estimated as “development can be done for 50,000 yen.”

SMEs should incorporate from the outset that “review labor will take 2 to 3 times the generation labor” when introducing AI code generation. Even so, it will still be cheaper than traditional handwritten development. However, deploying without any review is a suicidal act.

3. Institutionalize the “30-Day Check”

For code generated by AI, record the generation date as metadata. Then, automatically trigger an alert 30 days later to recheck its behavior in the production environment. This alone will significantly increase the early detection rate of “silent failures.”

No elaborate system is required. Create a spreadsheet listing “AI Generated Code,” recording the generation date, target files, and the 30-day check date. Even a company with only two engineers can manage this.

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The True Cost Lies in the “Invisible Areas”

The biggest trap of AI code generation is that the dramatic decrease in visible costs makes it hard to notice the inflation of invisible costs.

The cost of writing has decreased to 1/10. That’s great. However, consider the costs of review, security responses, fixing issues after 30 days, and the cost of trust erosion due to silent failures. When these are summed up, has the total cost really decreased?

The answer is, “It will decrease if used correctly; it will explode if used carelessly.”

For SMEs, AI code generation can undoubtedly be a weapon. It has the potential to achieve the same development speed as large corporations with only 1/10 of the workforce. However, if not handled correctly, a weapon can hurt you.

When you receive a proposal saying, “We can make it cheaply with AI,” ask back:

“So, what will the cost be 30 days later?”

Whether they can answer that question will reveal whether that development partner is trustworthy.