Demand-Side Subsynchronous Oscillations in Hyperscale AI Data Centers: Origins, Risks, Indirect Effects, Detection, and a Proposed Risk Assessment Framework

The rapid scaling of hyperscale AI data centers has introduced a novel class of electrical grid disturbance: demand-side subsynchronous oscillations (SSO). Unlike classical SSO — which originates from supply-side generator and transmission interactions — AI-induced SSO arises from the highly synchronised, periodic power cycling of large-scale distributed training workloads. As tens of thousands of GPU processors alternate between computation-intensive and communication-intensive phases in lockstep, aggregate facility power demand oscillates at subsynchronous frequencies. First-principles calculations project oscillation magnitudes of approximately 82% of rated capacity, translating to approximately 29 MW for a 50,000-GPU cluster and scaling to approximately 288 MW at planned 500,000-GPU-equivalent facilities — comparable to the 200 MW forcing that caused continent-wide grid oscillation in the documented 2019 Eastern Interconnection event. This review synthesises four primary technical sources: Tuckey (2026, Schneider Electric), Choukse et al. (2025, Microsoft/NVIDIA/OpenAI), Ko and Zhu (2025, UT Austin), and Li et al. (2024, University of Alberta). Beyond direct risks, the paper identifies five indirect effects of SSO as AI infrastructure scales: interconnection delays, PPA renegotiation exposure, stranded capacity, co-location conflicts, and grid planning feedback loops. Detection methodology is examined critically, including FFT vs. wavelet trade-offs and the 0.1 Hz resolution boundary of current 10-second window implementations. A sensitivity analysis demonstrates that partial synchronisation and software mitigation significantly reduce worst-case projections, while acknowledging that network jitter effects at extreme scale remain an open empirical question. This paper introduces the DS-SSORF (Demand-Side SSO Risk Assessment Framework) as a practical six-dimension operator screening tool — the first structured methodology for evaluating demand-side SSO exposure at AI data center facilities. An Open Data Standard proposal is presented as the recommended path from physics-based projection to empirically validated risk assessment. Conditions under which the risk framing is overestimated are explicitly identified in the spirit of scientific falsifiability.