Designing computer chips is incredibly complex. Imagine trying to predict a circuit's performance just from its code – like guessing how fast a car will go based on its blueprint. Traditional methods struggle with this, especially with today’s massive chip designs. However, a new technique called VeriDistill is changing the game. It leverages the power of large language models (LLMs), the same technology behind AI chatbots, to “read” Verilog (a hardware description language) and estimate key performance metrics like area and delay. VeriDistill doesn't just process code; it learns from the underlying circuit structure through a clever technique called knowledge distillation. Think of it as an experienced engineer mentoring an AI apprentice. This approach lets VeriDistill outperform existing methods, providing faster and more accurate feedback to chip designers. This breakthrough could revolutionize chip development, speeding up design cycles and ultimately leading to more powerful and efficient electronics. While promising, the research also reveals the limitations of LLMs in fully grasping the complexities of circuit design, suggesting that further advancements are needed to unlock their full potential. This also points to the fascinating possibility that LLMs might be developing their own internal representations of logic synthesis, suggesting that these AI models are learning more than we initially intended.