The field of generative AI is rapidly evolving, and one of its unintended consequences is the emergence of AI-generated content that can be difficult to distinguish from human-created content. This has significant implications for security, authenticity, and trust in digital artifacts. To address this challenge, researchers are exploring various techniques for watermarking AI-generated content, which involves embedding a unique identifier or signature into the content to verify its origin. One promising approach is the use of pseudorandom codes, which are designed to provide robustness against tampering or manipulation while maintaining detectability. Pseudorandom codes can be used to create watermarks that are resistant to attacks and can be verified publicly. The development of pseudorandom codes has the potential to make strong watermarks for generative AI more practical and deployable, especially in applications where security and authenticity are critical. However, further research is needed to determine the parameter ranges for which these schemes provide good security and to explore new approaches to building pseudorandom codes. Additionally, there is a need for publicly verifiable watermarks that can be used to authenticate digital artifacts beyond just AI-generated content.