Source: The Verge
Major tech companies are reporting that AI's capital intensity—the cost of training models and maintaining inference infrastructure—is eroding profit margins, contradicting the venture-backed scaling narrative. GPU scarcity, energy consumption, and compute costs are not declining as fast as Moore's Law suggested, forcing a collision between the hype cycle's assumption of exponential returns and actual unit economics. The shift from "how big can we build this" to "what's the unit economics at scale" has prompted investors to scrutinize ROI timelines and whether AI spending creates durable competitive advantages or simply triggers an industry-wide arms race with deteriorating margins.