The hosts look at utility functions as the mathematical basis for making AI systems. They use the example of a travel agent that doesn’t get tired and can be increased indefinitely to meet increasing customer demand. They also discuss the difference between this structured, economic-based approach with the problems of using large language models for multi-step tasks.
This episode is part 2 of our series about building smarter AI agents from the fundamentals. Listen to Part 1 about mechanism design HERE.
AI is at a fascinating inflection point. Companies are discovering that implementing generative AI isn't the simple lift they imagined, and the initial hype is giving way to deeper, more thoughtful questions. This moment of recalibration provides the perfect backdrop for our exploration of utility functions as the mathematical backbone for truly effective agentic AI systems.
What if we could build an AI travel agent that never tires, scales infinitely, and truly understands your personal preferences? Not just in a conversational way, but in a mathematically precise manner that optimizes for exactly what matters to you? That's the promise of utility functions – a concept borrowed from economic theory that allows us to quantify how much satisfaction or “utility” someone derives from different options or combinations of goods.
In this episode, we break down why large language models (LLMs) fall short when tasked with complex optimization problems. They are good at sounding like people and predicting text, but they weren’t made to find the best solution to complex problems with real-world limits. We explain how utility functions are a clear, reliable way to model individual preferences across factors like luxury, speed, and budget when planning travel.
From indifference curves to marginal rates of substitution, we explore the economic principles that allow us to create personalized optimization systems. We discuss how these functions can account for substitutes (like choosing between similar airlines), complements (like needing both outbound and return flights), and diminishing returns (where each additional dollar spent on luxury yields less benefit than the previous one). These concepts create the foundation for a system that can truly understand what matters to you when planning travel.
This episode is your gateway to understanding how classical economic theory provides the framework for next-generation AI agents that can reliably act on your behalf in the real world.
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