AI is restructuring engineering teams. A future model involves a small group of senior engineers defining processes and reviewing code, while AI and junior engineers handle production. This raises a critical question: how will junior engineers develop into senior architects in this new paradigm?

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.

In 10 years, AI will generate vast amounts of high-quality code, similar to the leap in image generation. The developer's role will shift from writing code to curation and design, exercising intent and critical judgment to select the best output from a sea of AI-generated options.

The traditional, decades-long path to becoming a senior engineer is no longer practical. Aspiring engineers should instead focus on mastering AI coding assistants. You can be highly effective by learning how to prompt, guide, and debug AI-generated code, bypassing the need for deep foundational knowledge.

Top-performing engineering teams are evolving from hands-on coding to a managerial role. Their primary job is to define tasks, kick off multiple AI agents in parallel, review plans, and approve the final output, rather than implementing the details themselves.

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.

Experience alone no longer determines engineering productivity. An engineer's value is now a function of their experience plus their fluency with AI tools. Experienced coders who haven't adapted are now less valuable than AI-native recent graduates, who are in high demand.

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.