Watch · narrated walkthroughs
When one engineer holds the full context, plan, and intent for a feature, driving it end-to-end with an AI coding assistant can beat splitting the work across a team. This series builds the argument from evidence: why shared development pays a coordination tax that grows with team size, why context and cognitive load — not typing speed — are the real bottleneck, what AI assistants actually change by compressing the design-implement-test-refactor loop into a single continuous flow, when the single-conductor model delivers faster and more consistent results, and where genuine collaboration still wins. Grounded in Brooks' communication-overhead law, Conway's Law, cognitive-load and flow research, the GitHub Copilot productivity randomized trial, and the DORA delivery-performance findings.