# How A Google Engineer Uses Claude Code At His Startup - Max Ghenis Founder of Policy Engine Source: https://www.youtube.com/watch?v=4apWdJR9-iY Recap page: https://rapidrecap.app/video/4apWdJR9-iY Generated: 2025-09-17T22:03:18.427+00:00 --- ## Quick Overview Max Ghenis, founder of Policy Engine, explains how AI tools like Claude can be used to analyze policy impacts and streamline research by automating tasks such as writing code and summarizing complex data, ultimately enhancing the efficiency and depth of policy analysis. **Key Points:** - Policy Engine uses AI to analyze the impact of economic policies across all 50 states and the UK, simulating outcomes for different policy proposals. - The company leverages AI tools like Claude to generate code for data analysis, overcoming limitations in human capacity for complex calculations. - AI models are used to analyze economic policies, allowing for the simulation of impacts on different income groups and policy recommendations. - The process involves using AI to analyze existing policy documents, extract relevant data, and then build models to predict future impacts. - Policy Engine's approach allows for faster, more accurate, and more comprehensive policy analysis than traditional methods. - The company aims to democratize policy analysis by making complex data accessible and understandable to a broader audience. ![Screenshot at 00:00: Max Ghenis, founder of Policy Engine, is interviewed about how AI is used to analyze public policy impacts, demonstrating the company's website with policy analysis charts.](https://ss.rapidrecap.app/screens/4apWdJR9-iY/00-00-00.png) **Context:** This video features an interview with Max Ghenis, founder of Policy Engine, a company that uses AI and large language models to analyze the economic impacts of public policy. Ghenis discusses how his company leverages technology to simulate the effects of various policies on different populations, providing valuable insights for policymakers and the public. ## Detailed Analysis Max Ghenis, founder of Policy Engine, explains how his company utilizes AI, specifically models like Claude, to analyze the economic impact of public policies. He highlights how these AI tools automate complex tasks like coding and data simulation, which would otherwise be time-consuming and resource-intensive for humans. Policy Engine focuses on providing detailed analyses of policies such as tax bills and benefit programs, allowing users to see how these policies affect different income groups and households across various states. Ghenis emphasizes that AI enables them to create more comprehensive and accessible policy analysis, making it easier for policymakers and the public to understand the potential consequences of legislation. He also notes the importance of open-source models and the ability to test and iterate on policy proposals rapidly using AI. The company's goal is to democratize policy analysis, making it more understandable and actionable for a wider audience, ultimately leading to better-informed policy decisions. ### AI in Policy Analysis - AI tools like Claude automate code generation and data simulation for economic policy analysis, enabling more comprehensive and accessible insights. ### Policy Engine's Approach - The company uses AI to analyze existing policies, simulate impacts on different income groups, and provide data-driven recommendations to policymakers. ### Benefits of AI in Policy - AI speeds up analysis, allows for more detailed simulations, and makes complex economic data more understandable for a broader audience. ### Open-Source Models - Policy Engine utilizes open-source AI models, promoting collaboration and transparency in policy analysis. ### Future of Policy Analysis - AI integration in policy analysis promises more efficient, accurate, and accessible decision-making for governments and the public. ![Screenshot at 00:00: Max Ghenis, founder of Policy Engine, is interviewed about using AI for policy analysis, with the Policy Engine website displayed.](https://ss.rapidrecap.app/screens/4apWdJR9-iY/00-00-00.png) ![Screenshot at 01:15: Max Ghenis discusses his background and how he uses AI tools for policy analysis, referencing a LinkedIn post.](https://ss.rapidrecap.app/screens/4apWdJR9-iY/00-01-15.png) ![Screenshot at 02:30: Ghenis explains the process of using AI to analyze policy impacts, detailing how models are built and utilized.](https://ss.rapidrecap.app/screens/4apWdJR9-iY/00-02-30.png) ![Screenshot at 04:00: Ghenis shares his personal journey and insights gained from working with AI in policy analysis.](https://ss.rapidrecap.app/screens/4apWdJR9-iY/00-04-00.png) ![Screenshot at 05:30: The discussion shifts to the effectiveness and limitations of AI in policy analysis, highlighting the need for human oversight.](https://ss.rapidrecap.app/screens/4apWdJR9-iY/00-05-30.png) ![Screenshot at 08:15: Ghenis explains how AI is used to generate specific policy analysis models and simulations.](https://ss.rapidrecap.app/screens/4apWdJR9-iY/00-08-15.png) ![Screenshot at 10:00: The importance of test-driven development in AI-driven policy analysis is discussed.](https://ss.rapidrecap.app/screens/4apWdJR9-iY/00-10-00.png) ![Screenshot at 11:55: A user asks about the requirements for using AI in policy analysis, prompting a discussion on data and model inputs.](https://ss.rapidrecap.app/screens/4apWdJR9-iY/00-11-55.png) ![Screenshot at 15:00: Ghenis discusses the challenges and successes of applying AI to policy analysis, mentioning specific examples.](https://ss.rapidrecap.app/screens/4apWdJR9-iY/00-15-00.png) ![Screenshot at 18:00: The conversation moves to the future of AI in policy and its potential impact on governance and decision-making.](https://ss.rapidrecap.app/screens/4apWdJR9-iY/00-18-00.png)