What High-Throughput Engineers do Differently and Why AI Widens the Gap

This post identifies and elaborates on six fundamental engineering practices that high-throughput engineers leverage, which are significantly amplified by AI: 1. Small, single-responsibility PRs: AI makes managing many small PRs more feasible and reliable. 2. Spec-driven development: AI agents use detailed specs (work items, acceptance criteria) as context for execution, echoing graph engineering principles. 3. Fast, trustworthy dev loop: High test coverage and fast builds provide the spec for agents and enable autonomous testing/fixing. 4. Bounded parallelism: Engineers limit active human contexts to 2-4 high-cognition tasks, offloading routine work to agents. 5. Warm contexts: Providing options for cheap and fast context switching (multiple git worktrees, remote dev environments) is crucial as agents generate more work. 6. Fast review: Reciprocal reviewer networks and AI-assisted review (where compliant) become critical bottlenecks as code generation speeds up. Leaders should invest in review capacity, fast feedback loops, structured work, parallel environment options, and sustainability.