Source: SiliconANGLE
The AI industry is establishing measurement standards—tokens, model calls, API usage—that benefit incumbents like OpenAI and Anthropic while obscuring the true economics of AI deployment for buyers. Enterprises optimizing for these metrics become dependent on specific vendors' pricing structures and architectural choices rather than optimizing for business outcomes like accuracy, latency, or total cost of ownership. This mirrors cloud providers' use of egress fees and proprietary services to create lock-in. The difference: AI metrics are being positioned as industry standards before alternative measurement frameworks solidify, giving early leaders outsized control over how enterprises evaluate and budget AI.