Software development has a ‘996’ problem

The article draws a critical parallel between the grueling ‘996’ work culture (9 a.m. to 9 p.m., 6 days a week) and the emerging trend of using AI to generate massive volumes of code. Author Matt Asay argues that just as human burnout rarely leads to innovation, using AI to brute-force code generation results in bloated, derivative, and unmanageable software.

The High Cost of Code Churn

Evidence from GitClear and GitHub suggests that while AI helps developers code significantly faster, it correlates with a spike in ‘code churn’—lines of code that are modified or deleted within two weeks. The data shows an increase in copy-pasted code and a decrease in refactoring. This creates a trap where the constraint on innovation is viewed as the number of characters typed rather than clarity of thought, leading to codebases that are harder to secure and maintain.

Code is a Liability, Not an Asset

A central argument is that software development is a decision-making process, not a typing contest. Every line of code shipped represents a liability that requires debugging and maintenance. The article emphasizes that:

• Senior engineering is defined by knowing what code not to write.
• AI-generated bloat creates a larger surface area for complexity and technical debt.
• Innovation requires ‘slack’ time for deep thinking, which is lost if developers are constantly acting as janitors for AI-generated output.

Human-Centric AI Strategy

To avoid building a ‘996 culture on silicon,’ the author suggests using AI to handle drudgery (boilerplate, unit tests) specifically to buy back time for high-value human tasks:

• Framing the problem: Determining if a feature is actually necessary for the customer.
• Ruthless editing: Celebrating ‘negative code’ commits that delete complexity rather than adding to it.
• Owning the blast radius: Ensuring engineers maintain enough system understanding to debug outages without relying on the AI that wrote the code.

Mentoring question

Are you using the efficiency gained from AI tools to simply ship more features faster, or are you investing that saved time into refining problem definitions and reducing the overall complexity of your codebase?

Source: https://www.infoworld.com/article/4094801/software-development-has-a-996-problem.html