Why AI Turned Enterprise Software from a Fixed Cost into a Variable One

For decades, enterprise software was treated as a fixed capital expense — a license paid once and depreciated over time. AI has disrupted that model by introducing usage-based pricing, where large language models charge per token processed, meaning costs scale directly with user activity. Unlike traditional SaaS seats, two users performing the same task can generate vastly different costs depending on prompt length and complexity. Running AI hardware on-premises does not eliminate the variable cost problem, as GPU clusters often sit 70–80% idle and models become outdated within months, creating a continuous refresh burden. Analysts argue that AI inference should instead be treated as a cost of goods sold — a per-transaction input expense requiring gross-margin discipline rather than capital depreciation logic.