The Zerohedge analysis frames AI economics through electricity demands and data center realities, yet primary energy data reveal deeper constraints. U.S. Energy Information Administration reports document data center electricity consumption rising from 70 TWh in 2022 toward projected 150-200 TWh by 2030 under current buildout rates, independent of model training intensity. European Network of Transmission System Operators for Electricity grid assessments similarly highlight transmission bottlenecks that predate recent AI investments, with interconnection queues exceeding five years in multiple regions. These records contrast with secondary market projections emphasizing exponential capability growth without equivalent infrastructure timelines. One perspective centers on capital allocation patterns observed in prior cycles, where hyperscaler capex announcements outpaced actual utilization rates documented in quarterly filings. A second view examines policy instruments such as the U.S. Inflation Reduction Act tax credits for clean generation, which prioritize deployment schedules measured in years rather than quarters. A third notes enterprise productivity metrics from OECD digital economy papers showing incremental gains in specific sectors without broad displacement effects. The original coverage correctly identifies user pricing disconnects but understates regulatory permitting durations for new generation capacity, which average 4-7 years per Federal Energy Regulatory Commission records. Patterns across these documents indicate that valuation adjustments will align more closely with measured build rates than with capability announcements alone.