1 link tagged with all of: productivity-paradox + technical-debt
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AI has made producing code, tests, policies, and organizational structures nearly free, but maintaining and understanding them hasn't gotten cheaper. This creates a trap where systems accumulate layers of infrastructure faster than they provide value, turning the factory into its own largest customer.
- Steve Yegge's AI agent system (Wheelhouse) grew to 600,000 lines of supporting code—nearly matching the 1.2M lines of the actual product itself—complete with constitutional governance, legal rulings, and a "Head of Wheelhouse Law" role, all created in under ten weeks.
- AI removes the friction that historically forced teams to justify new policies, tests, and documentation. What's cheap to generate becomes expensive to maintain: every new rule creates potential contradictions, every test needs monitoring, every document is a possible source of confusion.
- The real failure mode isn't obvious incompetence—each individual addition looks sensible. The problem is capacity-seeking-utilization: once you have agents producing work constantly, the system needs more coordination infrastructure to manage that work, which creates more things to maintain.
- Engineering telemetry shows the gap between activity and value: teams with high AI adoption completed 21% more tasks and merged 98% more PRs, but review time jumped 91%, PR size ballooned 154%, and bugs per developer rose 9%.