Conclusion First: The Code Written by AI Has the Highest Value at the ‘Delivery Date’

When you have AI write code, the perceived development speed increases by 3 to 5 times. This is true. However, what happens when you have to touch that code 30 days later?

Code that is “written in a way that no one knows why or by whom” quietly resides in the production environment.

This is the “debt” of AI code. While human-written code also has its debts, the debt associated with AI code has a troublesome characteristic: even the author cannot explain the intent. This is because it was not written by a human.

In this article, we will convert this “invisible cost” into hourly rates from the perspective of small and medium-sized enterprises.

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What Is Happening: Voices from the Field on Hacker News

On Hacker News, developers who have deployed AI-generated code in production have been reporting the following:

• “AI-generated code passes initial tests but breaks within 30 days under edge cases.”
• “Bugs that were not detected during review surfaced in production, taking 2 to 3 times the original implementation time to fix.”
• “Because the intent of the code is unreadable, it ends up being a rewrite rather than a fix.”

The common thread is that initial costs decrease, but maintenance costs skyrocket.

This seems to be the case even for large corporations. Reports indicate that Apple is reassessing its internal AI-related budget. It is discussed in the context of the imbalance between short-term cost-cutting effects and the quality management and maintenance costs that arise later. If even tech giants are hitting this wall, small and medium-sized enterprises cannot afford to be complacent.

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Calculating Hourly Rates: Real Numbers for Small and Medium-Sized Enterprises

Let’s assume a local small and medium-sized enterprise and calculate specifically.

Assumptions

• Developer hourly wage: 3,500 yen (the market rate for local SMEs)
• Target: Functionality addition to an internal business system (medium scale)

Case A: Written by Humans

Case B: Written by AI

At first glance, Case B appears cheaper. The difference is 35,000 yen. However, there is a catch here.

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The Real Cost Is Not Just a ‘35,000 Yen Savings’

What is often overlooked in the above estimates is the difference in the quality of the maintenance process.

The 5 hours of maintenance in Case A is for “fixing code that I wrote myself.” Since the intent is understood, it can be fixed precisely.

The 25 hours of maintenance in Case B is for “decoding and fixing code written by someone else (AI).” Moreover, while AI’s code may look clean at first glance, there is no context for why that implementation was chosen. As a result, the “reading time” becomes enormous. When asking developers in the field, some even say, “It’s not fixing; it’s archaeology.”

Furthermore, the problem is that these 25 hours occur at unpredictable times. Small and medium-sized enterprises typically have one or two developers. If one of them suddenly spends 25 hours on this, other tasks will come to a halt.

If we hypothetically calculate this “cost of halting other tasks” at 1.5 times the hourly wage (including opportunity loss), we get:

25 hours × 3,500 yen × 1.5 = 131,250 yen

The initial savings of 42,000 yen (the difference between 40 hours and 12 hours) is completely wiped out.

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So, Should We Avoid Using AI Code? Not at All

At this point, I do not want to say, “AI is dangerous, so don’t use it.” Such a conclusion is meaningless.

The issue is the design of how to use it. To avoid losses from AI code, there are three rules that work effectively in the field for small and medium-sized enterprises.

Rule 1: Have AI Write ‘Disposable Code’

Bulk data transformations, CSV formatting, one-time scraping. AI excels at writing code that is “written, executed, and discarded”. Since maintenance does not occur, it does not become a debt.

In fact, a local manufacturing company processes monthly inventory data transformations using scripts written by AI. Each time, the code is discarded, and the required time is 15 minutes. Previously, outsourcing cost 30,000 yen per instance. This has reduced annual costs from 360,000 yen to nearly zero.

Rule 2: Operate Production Code as ‘AI Draft + Human Finishing’

Do not deploy AI-generated code directly into production. Use the AI output as a “draft” and have a human rewrite it with an understanding of the intent. This way, it can be treated as “code I wrote” during maintenance.

While this increases effort, it dramatically reduces maintenance costs after 30 days. In the above estimates, maintenance time can be compressed from 25 hours to about 8 hours.

Rule 3: Always Keep a Label Indicating ‘Written by AI’

This may seem trivial, but it is effective. Record in code comments or documentation that “this part was generated by AI, along with the date and the model used.” This is the greatest kindness to the person who will touch it 30 days later (often yourself). It serves as a warning: “Don’t trust this part too much.”

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What Apple’s Budget Cuts Indicate

Returning to the story of Apple reassessing its internal AI budget, this does not conclude that “AI cannot be used”; rather, it is a signal that large corporations are beginning to realize that ‘the costs of AI cannot be measured solely by initial investment.’

Large corporations have quality control teams, code review systems, and automated testing mechanisms. Even so, they are struggling with the costs that arise later.

Small and medium-sized enterprises do not have these. Therefore, it is essential to have an eye that judges from the outset with “maintenance costs included.”

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So, What Should We Do?

In summary, here’s what to keep in mind:

1. The ‘real cost’ of AI code is understood 30 days later. Do not judge solely based on initial savings.
2. Use AI to its fullest for disposable code. This is where cost-saving effects remain at 100%.
3. Treat production code as a ‘draft.’ The moment you deploy it as is, you are incurring debt for your future self 30 days later.
4. Estimate maintenance costs at ‘hourly wage × 1.5.’ Failing to include opportunity loss can lead to incorrect judgments.

The cost of writing code with AI is approaching nearly zero. However, when the cost of writing code decreases, the relative costs of ‘reading’ and ‘fixing’ increase.

Whether AI becomes a “weapon” or “debt” depends on whether you understand this structural change.

Small and medium-sized enterprises have few people. Therefore, how each individual uses their time is directly linked to management. If you find yourself repaying 30 minutes of AI-written code with 25 hours 30 days later—consider calculating whether you are unwittingly taking on such an “invisible loan.”