> 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.
> 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.
That is real bad advice
The rules and guidelines for good code have not changed. Don't change that for the sake of today's Ai and limitations