In 2025, AI has become integral to code generation, with companies like Microsoft and Google reporting that AI generates 30% of their code, indicating the evolution of AI coding tools into a core component of engineering workflows. However, the adoption of these tools has brought challenges, as studies reveal they can introduce up to 41% more bugs, necessitating a full-stack approach to development tools, including AI-powered code review, QA testing, refactoring, and documentation tools. Companies are increasingly opting for diverse AI dev tool stacks to enhance productivity and address the limitations of AI coding tools, with strategies ranging from comprehensive stacks encompassing the entire development lifecycle to partial or essential stacks focusing on key areas like coding and code review. This shift towards an AI dev tool tech stack aims to streamline the software development process and maximize the productivity gains promised by AI, while also reducing the bottlenecks and manual workload introduced by increased code generation.