As artificial intelligence moves from pilot projects into production systems, the economics of paying for it change. A sponsored article from HPE, published by MIT Technology Review, argues that consumption-based pricing—buying AI one request at a time—offers flexibility but becomes difficult to budget for when usage is steady and business-critical. The piece cites Deloitte data showing worker access to AI rose 5% in 2025, and predicts the share of companies with at least 40% of AI projects in production will double within six months.
The article does not claim ownership is always cheaper. Instead, it describes a "crossover point" where sustained use makes dedicated capacity more economical than per-request pricing. That point depends on models, token balances, energy costs, and system design. A retrieval-heavy knowledge system, for example, may have a very different cost profile than a simple assistant.
Crucially, the author argues that owning infrastructure only creates value if the business can keep it productive. That requires an operating model for onboarding users, governing usage, and finding new workloads. The piece suggests leaders ask three questions: whether demand is steady and large enough, at what usage level ownership makes sense, and whether the organization can sustain adoption. Because this is a sponsored perspective from a hardware and services vendor, readers should treat it as an argument for capacity investment rather than an independent analysis.