Tokens are the atomic units of data that large language and multimodal models read, write, and reason over to generate outputs. Token economics studies how AI consumption is metered, attributed, and connected back to business outcomes. Token consumption at large enterprises is climbing far faster than per-token prices are falling, and AI spend is increasingly becoming a CEO-level concern rather than an engineering line item. This talk examines token economics of custom-built agents, and the structural reasons behind their high costs. It surveys the emerging technologies and research to enhance token economics. Attendees will leave with a practical framework for locating and reducing their own token costs.
Yiwen Zhang is a Principal Applied GenAI Architect at AWS, where she designs and deploys generative AI applications and enterprise integration strategies for strategic customers. She holds a PhD in computational statistics and has spent a decade building and deploying AI/ML and data pipelines. She focuses on addressing challenging deployment questions such as ROI, AI agent performance tuning, cost management, and resilience. Her goal is to help enterprises build generative AI agents into “outcome as a service” solutions.
