AI tools are commoditizing the act of writing code (software development). The durable skill and key differentiator is now software engineering: architecting systems, creating great user experiences, and applying taste. Building something people want to use is the new challenge.

Product leaders must personally engage with AI development. Direct experience reveals unique, non-human failure modes. Unlike a human developer who learns from mistakes, an AI can cheerfully and repeatedly make the same error—a critical insight for managing AI projects and team workflow.

Working with generative AI is not a seamless experience; it's often frustrating. Instead of seeing this as a failure of the tool, reframe it as a sign that you're pushing boundaries and learning. The pain of debugging loops or getting the right output is an indicator that you are actively moving out of your comfort zone.

AI coding tools can rapidly build the first 70% of an application, but the final 30%—the complex, unique features that define your vision—will consume the vast majority of your development time. This is a critical reality check for anyone starting with these tools.

The process of struggling with and solving hard problems is what builds engineering skill. Constantly available AI assistants act like a "slot machine for answers," removing this productive struggle. This encourages "vibe coding" and may prevent engineers from developing deep problem-solving expertise.

AI coding assistants won't make fundamental skills obsolete. Instead, they act as a force multiplier that separates engineers. Great engineers use AI to become exceptional by augmenting their deep understanding, while mediocre engineers who rely on it blindly will fall further behind.

The traditional definition of a developer, centered on mastering programming languages, is becoming obsolete. As AI agents handle code generation, the most valuable skills are now clarity of thought, understanding user needs, and designing robust systems, opening the field to new personas.

AI coding tools disproportionately amplify the productivity of senior, sophisticated engineers who can effectively guide them and validate their output. For junior developers, these tools can be a liability, producing code they don't understand, which can introduce security bugs or fail code reviews. Success requires experience.

Despite creating code that could replace junior and senior developers, the author argues AI is a tool for enhancement. The key skills for future developers are not just coding, but the ability to effectively direct AI through prompting and validate its output via debugging. This mirrors how computers augmented, rather than eliminated, mathematicians.