AI datacenters hit diseconomies of scale as inference shifts demand smaller
abhiadesai · x · 2026-07-22
The post argues that datacenters face diseconomies of scale outside training.
As workloads shift toward inference, the author says the winning direction is smaller and faster infrastructure.
It quotes a detailed example about Oracle’s Wisconsin datacenter: the project has become a case study in how credit risk is priced into AI buildouts. Because We Energies’ tariff requires collateral from developers rated below A-, Oracle’s BBB- rating triggers a $7B letter of credit plus $100M+ per year in costs on a $15B, 1GW site tied to a $300B OpenAI contract. Wisconsin regulators declined to revisit the rule, and Oracle is now challenging it in county court.
The broader point is that many states are designing large-load tariffs to prevent ratepayers from absorbing stranded-asset risk from hyperscaler capex binges.
Related event: AI Competition Shifts to Infrastructure and Power Constraints(4 posts)→
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