Cost versus value: Managing agentic AI system performance

Per-token pricing has stopped being a useful measure for what enterprises actually pay for gen AI. A conversation with David Tepper, CEO of Pay-i, delves into the new economics of measurement.

For the first two years of gen AI adoption, most enterprises focused on access, experimentation, and deployment. As agents move into production, a different set of questions is emerging: How should leaders think about the economics of systems whose costs scale with usage rather than users? What happens when information, software, and even coding itself become increasingly generated on demand? The decision to scale an agent is increasingly becoming a complex and fast-changing economics decision, not a technical one.

To explore these ideas in greater depth, McKinsey Senior Partner Lari Hämäläinen spoke with David Tepper, CEO and cofounder of Pay-i, who has spent the past three years building measurement and economics infrastructure for enterprise agentic systems (see sidebar “About Pay-i”). Tepper spent 19 years at Microsoft, where he held leadership roles related to Azure’s internal gen AI consumption strategy, and holds an early gen AI patent dating back to 2011.

‍