The Hidden Cost of AI-Assisted Coding No One is Talking About

As the adoption of AI coding assistants grows, teams are utilizing them to produce code at an unprecedented pace. This capability allows engineering teams to ship more features faster than ever before. However, the downstream effect is that we are also generating more code, requiring more reviews, and inevitably introducing more bugs into our systems.

While we have learned to successfully guide AI to produce solutions that are functionally correct, resilient, and robust in production, there is one critical aspect of software engineering that is frequently overlooked in this new paradigm: maintainable, readable, and extendable code.

The Code Review Bottleneck

With the sheer volume of code being produced, there is tremendous pressure on developers to review pull requests rapidly. Even with the advent of AI-assisted code review tools, we are not at a stage where we can blindly trust what an LLM outputs. AI reviewers might catch syntax issues or basic security flaws, but they lack the deep architectural context required to evaluate the long-term viability of a design.

Human engineers still need to carefully read and understand the generated code. When code lacks clarity, the review process slows down significantly or, worse, reviewers mentally fatigue and approve substandard structural code just to unblock the pipeline.

Functional, But Structurally Flawed

When our codebases grow at such an accelerated rate, we often end up with features that work perfectly but lack modularity and readability. AI tends to favor immediate solutions over principled software design. It frequently generates code that does not adhere to established best practices, such as SOLID principles or functional programming paradigms.

You might find sprawling files with multiple responsibilities, heavily coupled components, and duplicated logic scattered across the application. The code "functions", but it becomes a fragile monolith that is incredibly difficult to untangle later.

The Barrier for the Team

This lack of structural integrity makes it extremely challenging for anyone—especially new team members—to onboard effectively. When the code is not self-documenting and logically organized, developers struggle to understand the core flow, debug issues, or confidently add new, high-quality code.

The ultimate result is an environment where the complexity continuously piles up. If left unchecked, the project reaches a point of no return: a state where developers must spend the majority of their time fighting technical debt rather than building new value.