I work on and maintain a 900k+ LoC codebase with mixed languages, runtimes, deployment models, cloud and airgapped ops and it's 99% written by AI. The difference I see the most between our approach and others is that we see the end-user product as an output of our context engineering internal machine, we focus on building that machine and we measure the output like madmen across all and any qualitative software metrics and signals (traces,collectors,otel,snapshots,profilers,custom debuggers,videos and more). For any change we do, less than 5% of wall time is an AI doing reasoning or coding, 95% is running the verification deterministically. Of that 5%, about 90% is spent on non-feature aligned code.

Velocity and quality is through the roof, "10x" would be a regression at this point, the bottleneck is only figuring out what is relevant/good to deliver, the implementation is the output of the machine.