Join host Will Jarvis as he delivers a wide-ranging conference presentation covering the macro case for property taxes, the mechanics of AI and large language models, and what these technologies mean for assessment offices. Will draws on his background as a mass appraisal researcher and CEO of ValueBase to explain why property taxes are the most efficient form of taxation for economic growth, how the historical Georgist movement shaped American assessment practices, and why AI tools—from machine learning valuation models to LLMs—are poised to transform day-to-day workflows for assessors while requiring careful validation and human oversight.

• 00:00 - Introduction to the presentation and Will Jarvis's background in mass appraisal research and founding ValueBase
• 03:24 - The big picture: total factor productivity, slowing economic growth since the 1970s, and why it matters
• 06:35 - Why property taxes are more efficient than income and sales taxes for driving economic growth
• 10:09 - Henry George and the historical roots of American property tax policy
• 12:33 - Tax revolts, vertical and horizontal equity challenges, and the capacity crisis in assessment offices
• 15:25 - Three revolutions in valuation: from the Sommer System ledger method to CAMA to the AI era
• 18:00 - Why AI is happening now: cheaper compute, the Attention Is All You Need breakthrough, and how LLMs actually work
• 24:24 - Sam Altman's Moore's Law for Everything thesis and its connection to property tax
• 27:00 - The emerging core skill: managing AI models with real-world context and validating outputs
• 30:14 - What LLMs are and aren't: not databases, not search engines, and not magic
• 33:09 - Machine learning valuation models: gradient boosted trees, overfitting risks, and the big five property characteristics
• 37:22 - Practical LLM use cases for assessors: drafting correspondence, summarizing, research, and brainstorming
• 39:38 - Key pitfalls: hallucinations, math errors, out-of-date knowledge, and data privacy considerations