Topics & Intelligence

Explore by Category

Navigate the full spectrum of GPU datacenter finance and AI infrastructure topics.

19 Topics Covered

Market Intelligence Architecture

Navigating AI Datacenter Economics by Domain

The AI datacenter finance landscape spans multiple interconnected domains—from GPU hardware economics and lease structures to grid interconnection queues and regional incentive programs. Our category taxonomy organizes this complexity into navigable market segments, each with dedicated data feeds, analytical tools, and strategic frameworks.

Every category connects to our proprietary indices: the GPU Lease Rate Index (GLRI) tracks pricing across 20+ providers, the Time-to-Power Score (TTPS) quantifies interconnection risk, the Curtailment Stress Score (CSS) monitors grid reliability, and the Project Attractiveness Yield (PAY) synthesizes site-level investment metrics.

Whether you're deploying capital into AI infrastructure, operating compute at scale, or advising on datacenter transactions, understanding the interplay between GPU economics, power availability, and regulatory environments is essential.

Hub

GPU Lease Rates & Costs

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Residual Values & Depreciation

Explore detailed guides and market data

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Hub

Colocation & Infrastructure

Explore all colocation & infrastructure resources and data

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Liquid Cooling & Thermal

Explore detailed guides and market data

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Pro

Regional Incentives & Programs

Premium analytics tool on our platform

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Financing & Economics

Explore detailed guides and market data

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ERCOT Curtailment & Grid

Explore detailed guides and market data

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Hub

Site Readiness & Power/Fiber

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Hub

GPU Availability & Supply

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Pro

Zoning & Permitting Risks

Premium analytics tool on our platform

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Pro

Water Risk & Cooling

Premium analytics tool on our platform

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Pro

Fiber Latency & Network

Premium analytics tool on our platform

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Live Data

GPU Lease Rate Index

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Live Data

Power-Adjusted Yield (PAY)

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Pro

AI-Ready Sites

Premium analytics tool on our platform

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Live Data

Curtailment Risk & Grid Stress

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Live Data

Interconnection Queue Insight

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Hub

Index Trackers & Hubs

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Live Data

Methodology & Glossary

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Understanding Categories

Category FAQ

What's the difference between Hub and Live Data categories?

Hub categories aggregate multiple content types—guides, market data, and tools—around a broad topic like GPU leasing or colocation. Live Data categories link directly to our platform tools with real-time feeds, such as the GLRI index or curtailment trackers. Hubs are ideal for learning; Live Data is for active monitoring and decision-making.

Why are some categories marked as PRO?

PRO categories provide access to premium platform features—advanced calculators, proprietary datasets, or real-time alerting. These tools require significant infrastructure to maintain and represent our deepest market intelligence. Free categories demonstrate our methodology; PRO tools unlock the full analytical capability for institutional users.

How do categories connect to your indices?

Each category maps to one or more proprietary indices. GPU-related categories connect to GLRI (pricing) and residual value curves. Power and grid categories connect to TTPS (interconnection timing) and CSS (curtailment risk). Site and regional categories integrate PAY scores that synthesize multiple indices into investment-grade metrics.

Can I use category data for investment analysis?

Absolutely. Our categories are structured to support institutional due diligence. Hub categories provide educational context and methodology. Live Data tools offer the quantitative inputs for financial models—lease rate benchmarks, queue timing estimates, risk scores, and site comparisons. Many users start in Hubs, then graduate to PRO tools as their analysis deepens.

How do you maintain data accuracy across categories?

We aggregate data from primary sources: ISO/RTO filings for power markets, SEC disclosures for public operators, equipment vendor APIs for hardware pricing, and proprietary surveys from our operator network. Each data stream has defined refresh cadences—daily for pricing feeds, weekly for queue data, quarterly for residual value curves.

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