On July 8, 2026, Capria Ventures hosted an AMA session titled “Inference Economics: The Hidden Math Behind AI Success” featuring Philip Kiely, AI researcher, author of Inference Engineering, and engineer at BaseTen.
The session explored one of the most important—and often overlooked—aspects of building AI products: the economics of inference. Drawing on his experience working with hundreds of AI companies, Philip shared practical frameworks for designing cost-efficient AI systems, selecting the right models for different tasks, and preparing infrastructure to support rapid growth. The discussion covered topics including unit economics, total cost of ownership, open-source models, dedicated inference, caching strategies, routing, and the future of edge AI.
Key takeaways included the importance of measuring AI costs using business outcomes rather than token usage, optimizing models only after achieving product-market fit, continuously evaluating open-source alternatives, building strong evaluation frameworks, and treating inference economics as a core product discipline. The session reinforced that long-term AI success is not determined by using the most powerful model, but by building AI systems that deliver sustainable business value through the right balance of cost, performance, and reliability.
