When AI Writes Faster Than You Can Understand

AI coding agents have revolutionized software development, enabling tasks that once took weeks to be completed in hours. These agents can explore code repositories, comprehend existing patterns, create code, run tests, and even initiate pull requests—all while a human struggles to grasp the initial files. This shift introduces a new challenge: the bottleneck is no longer code writing, but understanding what is being built.

Many engineering teams embrace AI coding tools to expedite development. However, a concerning consequence emerges: the volume of code engineers are expected to review can surge faster than their comprehension capabilities. This becomes particularly challenging during migrations or when integrating unfamiliar frameworks. Reviewing AI-generated code alongside other modifications while still needing to deliver promptly can be overwhelming. Simply reading more code doesn't scale to address this issue. A new approach is required.

Traditional workflows, such as Ticket → Developer understands problem → Developer designs solution → Developer writes code → Review, are becoming less effective with AI agents. The improved workflow should follow this pattern: Ticket → AI investigation → Architecture proposal → Human understanding → AI implementation → Human review.

Before delegating code writing to an AI agent, request it to explain the functionality's current location, the existing system's operation, involved components, the required changes, minimal reasonable modifications, applicable patterns, potential breakages, and testing methods.

The objective isn't to slow down AI; the aim is to ensure engineers fully comprehend the changes before the code multiplies. Engineers don't need to grasp every line of AI-generated code. Instead, they must grasp the system well enough to answer five key questions: Where does this feature reside? What is the surrounding architecture?

What is changing? Why does this implementation make sense? What could go wrong? By answering these questions, engineers can review extensive changes without manually reconstructing every line of code. Their value lies in understanding the problem, making sound technical decisions, and validating AI-produced results.

AI coding agents serve as valuable learning tools. When encountering unfamiliar code, don't ask merely, "What does this code do?" Instead, ask, "Teach me this implementation. Assume I understand the old architecture, but I'm new to this framework. Map the concepts to things I already know." You can further ask, "Why was this approach chosen?"

"What are the alternatives?" "What assumptions does this code make?" and "What are three ways this implementation could be wrong?" This transforms the AI from a mere code generator into a senior engineer working alongside you. If the AI solely writes your code, your reliance on it grows. However, if AI aids in understanding the system, your engineering skills improve alongside the AI.

While learning everything about a new framework may seem tempting, it's rarely practical in a fast-paced project. Instead, focus on grasping a small set of concepts that explain most of the encountered code. For a new UI framework, concentrate first on components, state properties, events, rendering, lifecycle, composition, data flow, and testing.

Learn how your organization's architecture utilizes these concepts. You don't need to become an expert in the framework before contributing; merely achieving a sufficient understanding to build a correct mental model is sufficient.

Maintain a simple system map of your application when everything changes simultaneously. This map should include an application overview, platform components, UI elements, state management, routing, backend APIs, data services, and testing frameworks. Leverage AI to maintain this documentation as the system evolves. Over time, this system map becomes your mental model of the application and facilitates comprehension of subsequent AI-generated changes.

The goal isn't to understand every line but to achieve predictive understanding, where you can look at a ticket and know roughly where it belongs, examine the proposed implementation and understand why the AI agent chose that approach, and finally review the pull request and identify potential issues. While you may not comprehend every line, understanding enough to predict the system's behavior is a far more scalable skill in an AI-assisted engineering environment.

AI makes engineering judgment more crucial than ever. Responding to faster AI development by merely working faster yourself is a mistake. You can't compete with an AI agent at code generation. Instead, move yourself further upstream: understand the problem, shape the architecture, let AI implement, validate the result, and learn from the implementation.

Engineers who excel in this environment won't be those who write the most code but those who can understand systems, make sound decisions, and maintain human control while AI operates at machine speed.