The AI Economic Paradox: Why Cheaper Inference Is Making AI More Expensive - Edgee Blog

The paradox of AI economics lies in the fact that while AI inference costs are decreasing rapidly, enterprise AI budgets are escalating even faster due to the unforecastable and volatile nature of these expenses. This phenomenon mirrors the trajectory of cloud computing but at an accelerated pace, where low unit costs encourage expansive adoption, leading to unpredictable and hard-to-manage expenses that are not directly visible in invoices. The key challenges include the lack of centralized visibility in AI adoption, the multiplicative nature of agentic workflows, and the difficulties in attributing costs accurately. The solution involves embracing disciplines such as routing, compression, real-time constraints, and cost explainability to manage AI expenses effectively and ensure that costs remain bounded and forecastable, rather than relying on stable unit prices. Ultimately, the focus should be on engineering the predictability of AI spend, rather than predicting token prices, to navigate the complexities of AI as an infrastructure.