Software engineering is evolving from line-by-line coding to managing fleets of AI agents. This new paradigm resembles a sorcerer casting spells, demanding skills in high-level direction, prompt engineering, and oversight, rather than manual implementation.
AI tools disproportionately amplify the productivity of top performers, making them exceptional. A manager's highest leverage activity is to focus the majority of their time on unblocking and supporting these individuals to maximize the team's overall output.
An effective engineering manager acts like the support team in an operating room. Their primary role is to empower their top engineers (the "surgeons") by looking around corners, anticipating organizational hurdles, and having solutions ready before they are even asked.
An internal OpenAI team maintains a codebase written entirely by AI. By removing the "escape hatch" of manual coding, they are forced to solve fundamental problems in providing better context and documentation to the AI, thus uncovering best practices for agent interaction.
Silicon Valley is biased towards open-ended knowledge work like software engineering. However, a larger, often ignored opportunity for AI lies in automating the repeatable, deterministic business processes that power most of the non-tech economy, from customer support to operations.
Data from OpenAI reveals a massive and growing productivity gap. Engineers who actively use the AI coding assistant Codex are opening 70% more pull requests than their peers, indicating a significant boost in efficiency and a widening skill divide.
In the rapidly advancing field of AI, building products around current model limitations is a losing strategy. The most successful AI startups anticipate the trajectory of model improvements, creating experiences that seem 80% complete today but become magical once future models unlock their full potential.
AI tools are most readily adopted for tedious tasks engineers dislike, such as performing code reviews, fixing lint errors, and managing CI processes. This automation makes the core job of an engineer more focused on creative, high-impact work, thereby increasing job satisfaction.
The true second-order effect of AI isn't just a single massive solo company. It's a "golden age" of B2B SaaS, where a one-person unicorn will rely on hundreds of other small, hyper-specialized software startups to handle its various functions.
Companies fail with AI when executives force it on employees without fostering grassroots adoption. Success requires creating an internal "tiger team" of excited employees who discover practical workflows, build best practices, and evangelize the technology from the bottom up.
The "bitter lesson" of AI applies to product development: complex scaffolding built around model limitations (like early vector stores or agent frameworks) will inevitably become obsolete as the models themselves get smarter and absorb those functions. Don't over-engineer solutions that a future model will solve natively.