Show Notes: Jeremy Greenberg discusses the AI-powered audience simulator built by the Avenue Group. The tool allows users to provide a set of custom instructions for different audience segments, like research or interviews. It allows users to ask questions of qualitative and quantitative nature, and within minutes, results from simulated respondents are obtained. The tool mirrors the sentiment of collective segments and audiences, similar to chats or LLMs on a one-on-one basis. This tool is useful for collecting the opinions of celebrities, for example, Steve Jobs, highlighting the immense power of LLMs in capturing the distributions of the underlying population. Creating an Audience Jeremy discusses the process of updating the front end and the first section of the tool. He states the importance of setting this to create an audience, which is the global population interested in a specific topic, such as Americans drinking Coca Cola. This audience is then used to create sub-segments within the audience, each with its own criteria. For example, if the audience is comprised of decision-makers who decide on software for small businesses, they can segment them into different countries. The Creation of Segments The second section of the tool allows for the creation of segments. These segments can be categorized by industry, such as executives responsible for sourcing and procuring uniform rental services. For example, if the audience is comprised of executives in the food service industry, they can create a segment with one trait, such as "work in the food industry." The third section allows for the addition of more traits, such as "work in the food service industry," to further narrow down the audience. This allows for more targeted and targeted marketing efforts. An Example of Segmentation Jeremy uses the example of the janitorial services industry to identify the three segments. They create a review section that outlines the different traits and elements that comprise each segment, with a sample for each and a percentage base of the total. The group is asked questions about their current use of uniforms and key buying criteria. Jeremy recommends starting broad and going deeper with research, such as asking about the company, title, years in the industry, demographic information, and other relevant details. Open-ended questions can be added to gauge the industry's knowledge and understanding. For example, asking about the company's history and the number of vendors they work with could provide valuable insights. Quantitative questions can also be added to gauge the wallet fragmentation and the primary vendor's satisfaction level. For example, asking about the number of vendors they have for uniform rental services could provide insight into the distribution of the wallet. Additionally, asking about the top three criteria for selecting a vendor can help determine the industry's competitiveness. The Inspiration for Building the Tool The inspiration for building the tool came from research in academia. He cites a podcast called "Me, Myself, and AI" where they talked about research they’d done and hypothesis tested  on price sensitivity related to income and brand value, which demonstrated that AI can understand these factors. They also wanted to understand the distributions of different responses, mirroring the reality of the world. To achieve this, they worked with an advisor and member of a research team at the Wharton School. This allowed them to learn how to use the tool in more advanced and creative ways. The tool is currently being developed and is in the process of being bolted up with all its features and capabilities. Analyzing Responses from Segments Jeremy talks about the process of creating a tool for analyzing responses from different segments. He discusses the importance of creating a sequence of events within the tool, such as creating 60 different personas and interviewing each one individually. Th