This thesis gives insight into how architects and other designers can make use of generative AI tools for generating novel conceptual designs to assist in the creative process. To do this, I examine the potential uses of generative AI platforms such as Midjourney, DALL-E 2, and Stable Diffusion in architecture and design. I study the use of these generative AI platforms in producing complex designs that can be compared to those generated by existing architecture generative tools. The method used for demonstrating the capabilities of the mentioned AI platforms is to use the same prompts for each platform and run multiple tests to make a more accurate comparison of results. A number of tests are conducted, ranging from the design of buildings and architectural spaces by including factors such as traditional architectural styles, complex forms from nature, and the combination of famous architects' styles. Therefore, It helps to test how well AI can handle complex ideas that are difficult for humans to envision and difficult to implement using algorithmic tools such as Grasshopper. As part of the thesis, I survey machine learning architectures used in image-based generative AI and provide comprehensive examples of how the most popular AI tools (Midjourney, DALL-E 2, and Stable Diffusion) translate speculative concepts into novel images.