The rise of AI coding tools has revolutionized software development, but also introduced new legal and ethical challenges. Large Language Models (LLMs), trained on vast codebases, can generate code remarkably similar to existing open-source projects, raising concerns about intellectual property rights and licensing. This post delves into the complexities of LLM license compliance, exploring the fine line between leveraging open-source code and unintentional copyright infringement. A groundbreaking study introduces LiCoEval, a benchmark designed to assess LLMs' ability to adhere to licensing requirements. By analyzing 14 popular LLMs, the research reveals a surprising gap between AI's code generation prowess and its understanding of licensing implications. The study uses a novel metric, LICO (License Compliance), to evaluate how well LLMs provide accurate licensing information for generated code, especially for copyleft licenses. This reveals a widespread issue of inaccurate license attribution, even among top-performing models. One interesting finding is the variation in compliance between general and code-specific LLMs, as well as between open and closed-source models, hinting at the influence of training data and model transparency on licensing outcomes. This research has important implications for the future of AI-assisted software development, highlighting the need for improved training processes and tools that promote ethical and legal code generation practices. The findings urge both LLM creators and users to address the legal and ethical challenges of integrating AI-generated code into software projects. As we move forward, fostering collaboration between the AI and open-source communities will be crucial to ensure responsible innovation and protect the rights of open-source developers.