Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools

How do the productivity effects of AI evolve across successive generations of tools, and to what extent do task-level gains ultimately translate into final output? We study these questions in the context of software development, using data on more than 500,000 GitHub developers combined with their AI usage telemetry. In a matched event study design, we find that autocomplete, interactive coding agents, and autonomous coding agents each significantly increase coding activity (“commits”), with respective cumulative effects of 30%, 180%, and 240%. These gains, however, attenuate sharply across the production hierarchy: the 240% cumulative effect falls to 80% for the number of projects, and to 30% for actual releases. This pattern is consistent with the weak-link hypothesis: the strong productivity gains from AI are attenuated by human bottlenecks in the production chain, with an estimated elasticity of substitution of 0.23 between AI and human effort, which indicates strong complementarities. We further confirm these results across four major software marketplaces, finding a sharp increase in the number of new apps but no increase in total usage. Large task-level AI productivity gains have therefore translated only partially into shipped and used software thus far.