AI is revolutionizing software development by accelerating code creation, yet it introduces a complex "AI Velocity Paradox" where the speed gains in coding do not necessarily translate into better outcomes downstream. Reports from Harness and DORA reveal that while AI tools enhance developers' productivity and speed up the deployment of code to production, they also lead to increased instability and software vulnerabilities due to insufficient feedback loops and quality assurance processes. The METR study highlights the shift in developers' roles from coding to tasks like specifying requirements, validating AI outputs, and integrating them into existing architectures, which can ultimately take longer and lead to quality concerns. The key to resolving these issues lies in mature DevOps practices, which emphasize robust feedback loops and high-quality internal platforms to manage the influx of AI-generated code effectively. The reports suggest that organizations with moderate automation in their continuous delivery processes achieve greater velocity gains, emphasizing the need for systemic health and investment in advanced DevOps to harness AI's potential fully.