Senior engineers, whose identities are deeply tied to established workflows, are the most vocal critics of AI in coding. Unlike junior or non-engineers who readily adopt new methods, this group feels their extensive experience is being devalued by AI tools.

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?

High productivity isn't about using AI for everything. It's a disciplined workflow: breaking a task into sub-problems, using an LLM for high-leverage parts like scaffolding and tests, and reserving human focus for the core implementation. This avoids the sunk cost of forcing AI on unsuitable tasks.

Block's CTO observes a U-shaped curve in AI adoption among engineers. The most junior engineers embrace it naturally, like digital natives. The most senior engineers are also highly eager, as they recognize the potential to automate tedious tasks they've performed countless times, freeing them up for high-level architectural work.

AI agents function like junior engineers, capable of generating code that introduces bugs, security flaws, or maintenance debt. This increases the demand for senior engineers who can provide architectural oversight, review code, and prevent system degradation, making their expertise more critical than ever.

Contrary to the belief that AI levels the playing field, senior engineers extract more value from it. They leverage their experience to guide the AI, critically review its output as they would a junior hire's code, and correct its mistakes. This allows them to accelerate their workflow without blindly shipping low-quality code.

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.

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.

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.