Picture a startup with ten engineers hammering away on AI and one person lying awake over a $20,000 bill that hasn't arrived yet. That's the scenario we put to Jason Ward, who handles FinOps for AI at C.H. Robinson and recently joined the FinOps Foundation's AI working group. Jason's first answer is not glamorous: tag every AI resource so you know who owns it. The rest of the episode is what that makes possible. He walks through how C.H. Robinson runs AI across order entry, quoting, booking, and tracking, and why the team routes Anthropic models through Vertex AI to keep data locked down. Then come the metrics. Jason uses AI to dig through his own observability platform for signals he didn't know were there. One of them is how chatty a model is. That signal turned a prompt bloat alert into a bug in the code that kept retrying and burning tokens. Its opposite, context starvation, burns tokens too: a model with too little context keeps failing and trying again. We also cover why agentic and conversational workloads need separate baselines. Jason explains why cost per order is the easy win, and why most of the real work doesn't fit into neat discrete tasks. That's where his experimental cost per thought metric comes in, with reasoning ratio and cache hit rate alongside it. His advice is simple: your AI is the best tool you have for understanding your AI.

Two people, a wrong turn into a back alley, a community garden, and about thirty minutes of arguing about protocols on the streets of Tokyo. The guest is Angie Jones, VP of Developer Experience at the Agentic AI Foundation, fresh off launching AGNTCon + MCPCon in China before the Tokyo stop. She opens with what she learned there: a mobile-first, super-app world where the integration problem most of us obsess over barely exists, where every conversation about agents is really a conversation about the model, and where companies are now reaching for MCP and A2A precisely because they want to operate outside that ecosystem.