As AI writes more code, language simplicity becomes more critical, not less. Humans must be able to verify the AI's output, and languages with complex, hidden mechanisms make this harder, as an AI might use an obscure feature without considering human readability.

Human developers may prefer longer files, but AI coding assistants process code in smaller chunks. App developer Terry Lynn intentionally keeps his files small (under 400 lines) to reduce the AI's context window usage, prevent it from getting lost, and improve the speed and accuracy of its code generation.

The cost of generating code with AI is trivial, shifting the primary expense to its maintenance, validation, and deployment. This inverts the traditional software engineering model where human code production was the main bottleneck, making code's complexity a liability.

Unlike normal technical debt, 'agentic technical debt' compounds rapidly. Without persistent, written architectural constraints, AI coding tools re-derive foundational decisions in each session, causing the codebase to drift incoherently. The solution is to document architectural principles before building to give the AI context and prevent entropy.

Many software development conventions, like 'clean code' rules, are unproven beliefs, not empirical facts. AI interacts with code differently, so engineers must have the humility to question these foundational principles, as what's 'good code' for an LLM may differ from what's good for a human.

The 'Don't Repeat Yourself' (DRY) principle primarily helps humans manage complexity. Since an AI can easily identify and refactor all instances of duplicated code on demand, the need for perfect, upfront abstraction diminishes. Developers can commit 'minor heresies' and clean them up later.

Avoid building one AI agent to do everything. Instead, create a hierarchy with a 'manager' agent that delegates tasks to specialized sub-agents (e.g., for coding, research). This prevents context overload and performance degradation, mirroring an effective human team structure for scalable automation.

In the AI era, technical debt isn't 'ugly' code but anything that slows an AI agent's feedback loop. This includes slow tests, messy system boundaries that the AI will replicate, and poor runtime performance. The focus shifts from human readability to machine-drivable efficiency and safety.

While developers leverage multiple AI agents to achieve massive productivity gains, this velocity can create incomprehensible and tightly coupled software architectures. The antidote is not less AI but more human-led structure, including modularity, rapid feedback loops, and clear specifications.