OP here, some anticipations: I modeled the impact of AI-assisted development on the SDLC as a non-linear dynamical system.
By calibrating an ODE (Validation Capacity) against 1.6 million file-touch events from 27 repositories, the model reveals a saddle-node bifurcation. Simply put: AI increases generation volume, but if QA interception isn't scaled proportionally, the queue saturates with rework, causing net delivery to collapse mathematically.
The paper includes the formal derivation, the empirical validation (including an operational regime classifier based on file closure rates), and the full Python replication suite.
I'd appreciate any mathematical or architectural critique on the queueing model and the filter chain formalization.
antmenn•38m ago
By calibrating an ODE (Validation Capacity) against 1.6 million file-touch events from 27 repositories, the model reveals a saddle-node bifurcation. Simply put: AI increases generation volume, but if QA interception isn't scaled proportionally, the queue saturates with rework, causing net delivery to collapse mathematically.
The paper includes the formal derivation, the empirical validation (including an operational regime classifier based on file closure rates), and the full Python replication suite.
I'd appreciate any mathematical or architectural critique on the queueing model and the filter chain formalization.