What is an AI token, and why is it completely changing how we buy and use software? In this episode of the AI Explainer Series, Ben Kus, CTO of Box, breaks down the fundamental unit of artificial intelligence, the token, and explores how the shift from traditional "per-seat" pricing to consumption-based token models is reshaping the tech landscape. Ben dives into how AI agents consume tokens based on task complexity, the infrastructure and data center capacity required to power them, and how organizations like Box are introducing concepts like "AI Units" to map costs directly to the value of work completed. Plus, he shares critical strategies for managing your AI budget efficiently, including model selection, automatic model routing, and setting up administrative usage controls. 💬 Frequently Asked Questions (FAQs): Q: What exactly is an AI token? A: A token is the fundamental unit of data that an artificial intelligence system processes. It represents the basic unit of what the AI "thinks" about, encompassing both the inputs (prompts) and outputs (responses) of a model. Q: Why are software companies moving away from per-seat pricing? A: Traditional per-seat pricing doesn't account for the vast difference in workload between an AI agent that does a small task and one that performs highly complex, continuous operations. Token-based pricing ensures companies pay precisely for the actual computational work performed. Q: What is a Box AI Unit? A: A Box AI Unit is a standardized, small unit of work used to measure and charge for AI operations consistently across different underlying AI models and vendors. Q: How can companies prevent runaway AI token costs? A: Organizations should implement platforms with built-in administrative controls to monitor token usage by project or user. Additionally, employing strategies like automatic model routing and selecting the right model size for specific tasks can dramatically improve cost efficiency. Q: Does model selection affect token pricing? A: Yes. Different AI models from various vendors have different pricing structures and token costs. Matching the complexity of your task to the most cost-effective model size is key to running an efficient AI operation.

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