Enterprise AI Economics: Effective Cost per Useful Output
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On October 20, 2026, MIT Technology Review published the article "The New Economics of Enterprise AI," noting that enterprise AI is shifting from a phase of scattered experiments to a foundational production layer, and that the standard for measuring AI costs should move from per-token or per-GPU-hour billing to "the actual cost per effective business outcome." The article analyzes three models for enterprise AI deployment: self-built, on-demand consumption, and integrated private AI platforms, and points out that owning GPUs only improves economics when demand is sufficient and operational discipline is in place—for example, routing tasks to the lowest-cost models and applying optimizations such as KV-cache reuse. This is currently a single report with no follow-up coverage and no contradictions with other reports.
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- MIT Technology Review · AIThe new economics of enterprise AI
Enterprise AI is moving from scattered experiments to a foundational production layer, and the yardstick should shift from token or GPU-hour costs to the actual cost per effective business outcome. The piece analyzes three models — self-build, on-demand consumption, and integrated private AI platforms — and notes that owning GPUs only improves the economics with sufficient demand and operational discipline, such as routing tasks to the lowest-cost models and reusing KV-cache.
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