Superhuman adopted AI coding tools using a three-quarter plan: 1) Unrestricted experimentation with centralized budget approval. 2) Analysis and measurement using self-reported PR labels. 3) Observing a sustained increase in engineering throughput from 4 to 6 PRs per engineer per week.
Related Insights
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.
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.
Instead of codebases becoming harder to manage over time, use an AI agent to create a "compounding engineering" system. Codify learnings from each feature build—successful plans, bug fixes, tests—back into the agent's prompts and tools, making future development faster and easier.
Coastline Academy frames AI's value around productivity gains, not just expense reduction. Their small engineering team increased output by 80% in one year without new hires by using AI as an augmentation tool. This approach focuses on scaling capabilities rather than simply shrinking teams.
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.
