Generative Artificial Intelligence (GenAI) is an emerging technology that creates relevant text, images and other content from prompts. Large Language models (LLMs) are the most widely used of these GenAI forms. This technology already has applications in business and education. This paper tests GenAI's ability to apply physics to global problems and arrive at viable solutions. When an idea is created by a human, it is merely a culmination of that person's experiences and prior knowledge, ordered into a new concept. This research proposes that it should be possible to replicate the process by a machine learning algorithm and, due to its vast database, a far more informed and coherent idea should be the result. This research tested how well AI could tackle some global challenges and compared the results to how well these same challenges could be addressed by physicists. The data collection process was to have a dynamic conversation with each of the participants and work with them to create a number of ideas and solutions that apply physics to a selection of global issues. This process was repeated with both Bing AI and ChatGPT-4, where they were prompted to return ideas to the same issues. Each of the ideas were then coded to a marking scheme adapted from the OECD DAC criteria for development evaluation. While Bing AI did not prove itself to be capable of unique idea creation, ChatGPT-4 returned valuable data. ChatGPT-4 excelled at providing efficient, coherent and sustainable results whilst it performed significantly worse than humans in versatility and profitability. The findings show that at the present time, AI cannot work as an idea generation tool on its own due to lacking in accuracy and versatility. It is best applied in tandem with humans where it can be used to generate a series of ideas to a problem which physicists refine the results. [Note: The volume number (19) and publication year (2024) shown on the PDF are incorrect. The correct volume number is 20 and the correct publication year is 2025.]