AI Coding Cost Proposal Focuses on Merged Pull Requests as HPE Reports Savings From Model Routing

An MIT Technology Review article recommends measuring AI coding costs per merged pull request rather than relying on token counts or usage caps, and reports that HPE has reduced token costs by 30 to 50% through a hybrid model platform. The proposed measure divides total AI-assisted development costs by the number of changes that pass review and are merged, with rework and defects tracked alongside it. The article describes routing routine coding tasks to open-weight models and reserving frontier models for harder work. At HPE, the platform routes workloads from more than 150 internal applications to on-premises open-weight models; the reported savings apply to token costs.

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There is not enough continuous observation data to plot a trend yet.