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Research & sources.

Clarke's positioning responds to a clearly documented gap: AI infrastructure is scaling at unprecedented speed, but inconsistent capacity models, speculative interconnection requests, and the absence of standardized definitions are creating billion-dollar planning uncertainties across the industry.

AI infrastructure growth acceleration

International Energy Agency (2025)
Energy and AI
Global data center electricity demand is projected to exceed 1,700 TWh by 2035 in the Lift-Off Case — reaching 4.4% of global electricity demand.
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RAND Corporation (2025)
AI's Power Requirements Under Exponential Growth
Global AI data center power demand could reach 68 GW by 2027 and 327 GW by 2030, compared with total global data center capacity of just 88 GW in 2022.
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Goldman Sachs (February 2025)
AI to Drive 165% Increase in Data Center Power Demand by 2030
Global power demand from data centers will increase 50% by 2027 and by as much as 165% by the end of the decade.
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Deloitte (2024)
The AI Era: Meeting Power Demands for Data Centers
The AI-driven data center expansion represents the largest compute infrastructure buildout in history.
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Inconsistent capacity modeling & metrics

IEA 4E EDNA (2025)
Data Centre Energy Use: Critical Review of Models and Results
Projections for 2030 range from just over 200 TWh to nearly 8,000 TWh — a factor of almost 40.
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Fragmented demand signals & planning uncertainty

World Resources Institute (September 2025)
Powering the US Data Center Boom: Why Forecasting Can Be So Tricky
Utilities are being flooded with "speculative" interconnection requests — duplicate filings and "phantom" load that will never be built.
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CNBC (October 2025)
Utilities Grapple with Multibillion Question: How Much AI Data Center Power Demand Is Real?
Tech companies are shopping the same big projects around to multiple utilities as they look for quickest access to power.
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Utility Dive (November 2025)
A Fraction of Proposed Data Centers Will Get Built. Utilities Are Wising Up.
Five to ten times more interconnection requests exist than data centers actually being built.
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The need for standardization & transparency

GridLab (March 2025)
Practical Guidance and Considerations for Large Load Interconnections
Introducing a standardized and transparent load-side interconnection process is essential to control speculation.
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Brookings Institution (2024)
The Future of Data Centers
Rapid expansion of AI infrastructure requires new frameworks for coordination built on shared definitions and transparent methodologies.
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These sources establish Clarke's market context: unprecedented AI infrastructure scaling, capacity models diverging by factors of 40×, speculative demand distorting utility planning, and explicit calls for standardized definitions and transparent methodologies — the foundational challenges Clarke addresses.