Building an Elite AI Engineering Culture in 2026

> The data on AI's differential impact is now overwhelming. Faros AI's Productivity Paradox Report (10,000+ developers, 1,255 teams) found that high-AI-adoption teams completed 21% more tasks and merged 98% more pull requests — but PR review time increased 91%, creating a critical bottleneck at human approval. At the organizational level, any correlation between AI adoption and performance metrics evaporated. This is Amdahl's Law applied to software: a system moves only as fast as its slowest link.

We've definitely faced the review bottleneck on the open-source project that I maintain. ( github.com/robotmcp/ros-mcp-server )

> Vertical slice architecture — organizing code by feature with each slice self-contained — is emerging as the AI-friendly pattern because it maximizes context isolation.

> Three architectural principles are gaining consensus: "token efficiency" as a design constraint (structuring code to minimize the context an AI model needs for any given task), explicit over implicit everywhere (explicit types, explicit error handling, explicit interfaces), and co-location of related code. These principles aren't new, but AI has given them renewed urgency.

I found the architecture section of his article (which is pretty far down) to be the most interesting.