Designing computer chips is incredibly complex and time-consuming. But what if AI could automate the process? New research explores how large language models (LLMs), the technology behind ChatGPT and Bard, can revolutionize chip design. Imagine telling an AI your desired chip specifications and having it generate the code, debug errors, and even create test simulations, all automatically. This vision is closer than you think. Researchers are investigating how LLMs can understand hardware description languages (HDLs) like Verilog, essentially turning human-readable design specs into the complex code required for manufacturing. One exciting development is the use of LLMs to automatically repair C/C++ code for High-Level Synthesis (HLS), a process that converts high-level programming languages into hardware-specific code. This significantly speeds up the design process and reduces the need for manual intervention. Another breakthrough involves automating testbench generation. Testbenches are crucial for verifying that a chip design functions correctly. Traditionally, creating these test environments is manual and laborious. However, new research demonstrates how LLMs can automatically generate these tests, saving designers significant time and effort. While promising, several challenges remain. LLMs sometimes produce incorrect or nonsensical outputs ('hallucinations') and can struggle with the nuanced complexity of chip design. Ensuring data privacy and seamlessly integrating LLM-driven tools into existing design workflows are also crucial. Despite these hurdles, the potential for LLM-aided chip design is enormous. As researchers address these challenges, we may soon see a future where AI plays a central role in creating the next generation of faster, more efficient, and more powerful computer chips.