Source: The Pragmatic Engineer
Major tech companies are discovering that generative AI inference costs—particularly token consumption—are exceeding initial financial models, forcing real budget reallocations rather than theoretical cost-benefit discussions. The constraint is operational: which AI features companies can profitably ship now depends on unit economics rather than capability. Product roadmaps built on unlimited API access are colliding with cost reality. Engineering leaders face a choice between aggressive cost optimization, feature cuts, or accepting lower margins on AI-powered products. The economics favor closed-source models and in-house inference infrastructure.