The paper explores the application of large language models (LLMs) in design and manufacturing, highlighting their potential to streamline and enhance various stages of the workflow. The authors evaluate LLMs' capabilities in generating designs from natural language, creating design spaces, preparing designs for manufacturing, evaluating design performance, and searching for optimal designs. They use GPT-4 as a case study, demonstrating its effectiveness in tasks such as designing a cabinet, a quadcopter, and a wooden box. The paper also discusses the ethical implications and potential risks associated with the use of LLMs, including job displacement, intellectual property issues, and dual-use concerns. To address these challenges, the authors propose strategies for mitigating risks and improving the integration of LLMs into design and manufacturing workflows. The study concludes that while LLMs show promise, significant work is needed to fully realize their potential and address existing limitations.The paper explores the application of large language models (LLMs) in design and manufacturing, highlighting their potential to streamline and enhance various stages of the workflow. The authors evaluate LLMs' capabilities in generating designs from natural language, creating design spaces, preparing designs for manufacturing, evaluating design performance, and searching for optimal designs. They use GPT-4 as a case study, demonstrating its effectiveness in tasks such as designing a cabinet, a quadcopter, and a wooden box. The paper also discusses the ethical implications and potential risks associated with the use of LLMs, including job displacement, intellectual property issues, and dual-use concerns. To address these challenges, the authors propose strategies for mitigating risks and improving the integration of LLMs into design and manufacturing workflows. The study concludes that while LLMs show promise, significant work is needed to fully realize their potential and address existing limitations.