By Jeffrey A. Newman Esq.
Engineers and planners are hard at work refashioning our national electrical grid in order to meet the increasing demand for electricity. U.S. data centers used about 183 TWh in 2024, roughly 4% of national electricity use, with projections that demand will more than double to around 426 TWh by 2030, largely due to AI workloads. Forecasts see U.S. data center power demand reaching 100+ GW by the mid‑2030s, potentially 10–15% of total U.S. demand when you include telecom and semiconductor loads.
Transmission and distribution are presently the binding constraints because most U.S. high‑voltage lines are decades old, and 70% of transmission is at or near end of life, which limits how much new data‑center load can be served. AI programs are helping to design and optimize grid operations (forecasting demand, redispatching generation, and reducing losses), with some use cases showing 10–60% reductions in energy consumption for specific industrial or building loads. AI‑enabled “smart transformers” and advanced control systems can cut distribution losses by around 15% and improve voltage profiles, effectively freeing up capacity without building new lines.
Data center operators are using AI for workload scheduling, dynamic cooling control, and demand response agreements, which can shave peaks and allow partial operation in constrained regions while waiting for full grid upgrades. Policymakers and utilities are starting to treat data centers as structural load drivers, revising long‑term forecasts and planning dedicated “electric highways” and regional upgrades, but timing and permitting remain central bottlenecks.
AI techniques can reduce data center energy use mainly by optimizing cooling, workload placement, and hardware power management, often cutting total facility energy 10–30% in well‑tuned deployments. These techniques reduce waste but do not eliminate the need for efficient hardware and good physical design. Machine learning models analyze temperature, airflow, and equipment data to spot hotspots and inefficiencies, then adjust chillers, pumps, and fans in real time to maintain just‑enough cooling. Supervised and reinforcement learning schedulers place workloads on servers and racks to balance utilization and thermal load, minimizing the number of active servers and cooling demand.
AI models flag failing or inefficient components in HVAC, power distribution, and servers (e.g., stuck valves, degraded fans, failing PSUs), allowing targeted maintenance before they waste significant energy.
Anomaly detection on telemetry streams catches configuration drift and mis‑tuned systems (like persistently overcooled zones), helping operators maintain energy‑optimal settings over time.
Jeffrey Newman is a whistleblower lawyer whose firm represents whistleblowers revealing violations of export controls, tariff evasions, money laundering, healthcare fraud and other kinds of WB cases. The firm represents individuals both in the United States and other countries. Mr. Newman and his firm staff also represent many physicians across the country who become whistleblowers in healthcare fraud cases. Whistleblower laws in the U.S. allow individuals anywhere with information about export control violations or tariff fraud to reveal the information under The False Claims act or through the Securities and Exchange Commission’s Whistleblower Program. The Firm’s website is at www.JeffNewmanLaw.com and attorney Newman can be reached at Jeff@Jeffnewmanlaw.com or at 978-880-4758
