The code review process is a crucial aspect of professional software development, yet it's often neglected in university CS programs. Code reviews serve multiple purposes, including quality control, skill growth, and providing feedback to coworkers. There are three approaches to code reviews: rule-based, AI-powered, and human-led. Rule-based tools offer thoroughness and speed but can produce false positives and lack conversational capabilities. AI-powered reviewers provide insights into the code's structure and potential issues but may not always understand the context or be deterministic. Human code reviewers bring valuable expertise and foresight to the review process, but their reviews can be inconsistent and prone to interpersonal conflicts. The ideal approach is often a combination of all three, with rule-based tools catching negatives, AI reviewers fixing higher-order errors, and human reviewers providing clairvoyance and contextual understanding. However, in practice, this comprehensive approach may not be feasible, and teams must find an 80/20 solution that balances the strengths and weaknesses of each approach. Ultimately, leveraging both AI and human capabilities will be crucial for effective software development teams.