AI is an Amplifier. Are You Amplifying Engineering or Dysfunction?

Dave Farley recently shared results from a controlled study of 150 developers using AI tools. The data confirms a hard truth that the "vibe coding" crowd ignores: AI is a multiplier, not a magic wand.

The study revealed that while AI increases coding speed by 30-55%, it doesn't inherently fix code quality. In fact, the outcome depends entirely on who is holding the tool.

The "Slop" Myth vs. Economic Reality

We hear constantly that AI creates unmaintainable "slop." Farley’s data shows this is false—but with a catch.
For experienced developers, AI actually improved maintainability. Why? Because seasoned engineers don't let the LLM drive the architecture. They use AI to generate boring, idiomatic, and predictable code that fits into a rigorous design.

However, for those without strong engineering discipline, AI simply accelerates the creation of technical debt.

Automating the Dysfunction

This brings us to the core problem in the Enterprise.
If your organization currently lacks:

• A culture of TDD (Test-Driven Development),
• Modular architecture boundaries,
• Strict Code Review processes...

...then introducing GenAI won't make you "agile." It will just make you produce bad software 40% faster. You are automating your dysfunction.

The "Cognitive Debt" Trap

The biggest risk Farley highlights isn't the code itself—it's "Cognitive Debt."
When junior developers rely on AI to write logic they don't fully understand, they lose the ability to reason about the system when it inevitably breaks.

In my work modernizing legacy systems (Banking/Insurance), "fast" is often a trap. The dominant cost of software is maintenance (50-80% of TCO). Saving 2 hours on typing today is irrelevant if it costs 2 weeks of debugging next year.

The Strategy: Safe Modernization

To use AI safely in an enterprise environment, we must invert the hype:

1. Architecture First: Define the module boundaries and contracts before opening the chat window.
2. Constraint-Based Coding: Use AI to fill in the implementation details of a strictly defined interface, not to "invent" the solution.
3. Infrastructure as Code: If the AI suggests a console click, reject it. If it’s not in Terraform/OpenTofu, it doesn’t exist.

Conclusion

AI is a powerful amplifier.
Before you roll it out to your entire team, ask yourself: "Do we have an engineering culture worth amplifying?"

If the foundation is weak, speed is the last thing you need.