AI server power demand to surpass conventional systems by 2027

The shift toward AI-optimized hardware is fundamentally altering the data center cost structure, as infrastructure and cooling needs now scale at nearly the same rate as compute. For the streaming ecosystem, this indicates that the long-term cost of AI-driven personalization and encoding will be dictated by power security rather than silicon availability. Supply constraints in primary hubs may force a migration of non-latency-sensitive workloads to secondary markets with better grid access. Watch the adoption rate of liquid cooling and onsite power generation, as Gartner suggests these will become essential to protecting margins by 2027.

The projection of 565TWh for 2026 aligns with broader findings that power availability is the primary bottleneck for AI scaling. In May 2026, Goldman Sachs Research reported that U.S. data center demand alone is forecast to double to 66GW by 2027, up from 31GW in 2025. This surge is creating acute regional pressure; the PJM Interconnection, which serves 65 million people in the U.S., saw capacity market prices spike tenfold for the 2026/2027 period, largely attributed to data center load growth. Per Sightline Climate in April 2026, between 30% and 50% of large-scale data centers scheduled for 2026 opening face delays or cancellations due to these grid constraints and long lead times for electrical equipment like transformers.

Institutional analysts are also highlighting the infrastructure investment required to meet this demand. The International Energy Agency (IEA) projected in June 2026 that global data center energy use could reach 945TWh by 2030, with AI tasks being up to 1,000 times more energy-intensive than traditional web queries. To address this, high-density facilities are shifting toward "onsite power" strategies; a June 2026 Bloom Energy report noted that one-third of data centers are expected to use 100% onsite power by 2030 to bypass utility interconnection delays. Simultaneously, tech giants including Amazon and Google increased their combined planned capital expenditure for 2026 by an estimated 75% to secure the energy-ready sites necessary for next-generation AI training and inference.