The article presents a theoretical and mathematical framework for AI-driven Schumpeterian growth, arguing that AI is a "meta-factor" reshaping innovation and adoption dynamics. It states that once the recursive term G(x,m) surpasses a critical threshold, the system transitions from linear growth to nonlinear acceleration, resulting in a higher steady-state growth rate, lower effective diffusion lag, and increased sensitivity to feedback perturbations. Historically, AI marks a departure from prior industrial revolutions by augmenting the process of augmentation itself, aligning with Schumpeter's "creative destruction" but with endogenous recursion. The policy consequence is that traditional industrial policies separating innovation and adoption are inadequate; instead, policies must synchronize innovation and adoption to sustain a stable recursive loop. This involves encouraging open innovation ecosystems, building absorptive capacity, and monitoring systemic thresholds. The article concludes that AI acts as both the spark and fuel of modern innovation, challenging linear assumptions in traditional growth models and calling for new frameworks like the recursive innovation–adoption model to capture the AI-driven economy's emergent structure.