Once AI coding agents reach a high performance level, objective benchmarks become less important than a developer's subjective experience. Like a warrior choosing a sword, the best tool is often the one that has the right "feel," writes code in a preferred style, and integrates seamlessly into a human workflow.

Using AI to code doesn't mean sacrificing craftsmanship. It shifts the craftsman's role from writing every line to being a director with a strong vision. The key is measuring the AI's output against that vision and ensuring each piece fits the larger puzzle correctly, not just functionally.

The most significant productivity gains come from applying AI to every stage of development, including research, planning, product marketing, and status updates. Limiting AI to just code generation misses the larger opportunity to automate the entire engineering process.

With AI agents automating raw code generation, an engineer's role is evolving beyond pure implementation. To stay valuable, engineers must now cultivate a deep understanding of business context and product taste to know *what* to build and *why*, not just *how*.

Instead of asking an AI to directly build something, the more effective approach is to instruct it on *how* to solve the problem: gather references, identify best-in-class libraries, and create a framework before implementation. This means working one level of abstraction higher than the code itself.

While "vibe coding" tools are excellent for sparking interest and building initial prototypes, transitioning a project into a maintainable product requires learning the underlying code. AI code editors like Cursor act as the next step, helping users bridge the gap from prompt-based generation to hands-on software engineering.

To maximize an AI agent's effectiveness, establish foundational software engineering practices like typed languages, linters, and tests. These tools provide the necessary context and feedback loops for the AI to identify, understand, and correct its own mistakes, making it more resilient.

Instead of fighting for perfect code upfront, accept that AI assistants can generate verbose code. Build a dedicated "refactoring" phase into your process, using AI with specific rules to clean up and restructure the initial output. This allows you to actively manage technical debt created by AI-powered speed.

It's infeasible for humans to manually review thousands of lines of AI-generated code. The abstraction of review is moving up the stack. Instead of checking syntax, developers will validate high-level plans, two-sentence summaries, and behavioral outcomes in a testing environment.