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The AI Infrastructure Thesis

The Thesis in 60 Seconds

AI infrastructure requires power. Power takes years to secure. Five companies positioned early and now hold over $75 billion in contracted infrastructure revenue from hyperscalers. Two adjacent players selling GPU compute add another $57 billion. The market values all seven at approximately $132 billion combined, roughly parity with the contracted base, while speculative AI plays with zero revenue trade at similar or higher multiples. Either contracted infrastructure with creditworthy hyperscaler counterparties is worth its nominal contract value and nothing more, or the market is still catching up to the expansion capacity, renewal optionality, and execution proof that long-duration infrastructure typically commands above nominal. This thesis argues the latter, distinguishes infrastructure from compute, acknowledges what could prove it wrong, and provides the milestones to track.

01

Everyone's watching the AI race through the wrong lens.

The focus is on models, chips, talent. The actual constraint is power. Grid capacity took decades to build and will take decades to expand. The companies that secured allocations early own something that can't be replicated on relevant timelines.

02

The obligations have already cascaded.

Enterprises are embedding AI into production systems. Hyperscalers have sold capacity forward to those enterprises. Data center operators control the power those hyperscalers need. Each layer locked in before the next. The flywheel is already running.

03

Markets are mispricing the result.

Speculation with no revenue trades at premiums. Infrastructure generating real cash gets discounted for "execution risk." The asymmetry is structural. It's also temporary.

Read the Thesis
Contents 5 Parts · 16 Sections · ~75 min read

About This Thesis

What this is

An investment thesis arguing that five specific companies are undervalued based on their contracted revenue and power positions: APLD, GLXY, CIFR, WULF, and RIOT. IREN and NBIS are included as adjacent positions with different risk profiles (GPU cloud rather than infrastructure).

What this isn't

Financial advice. I'm not a financial advisor. I don't know your situation, risk tolerance, or investment goals. The thesis could be wrong. I could be missing something important. Execution risk is real. Counterparty risk is real. I've tried to present the bear cases honestly, but I'm still the one writing them.

How to use it

As one input among many. Test the logic against your own understanding. Verify the sources. Every figure cites SEC filings, earnings transcripts, or official press releases. Read the bear cases as if you're trying to talk yourself out of the trade. Consider what I might be wrong about. Form your own view. Size positions for the possibility that I'm completely wrong.

Updates

Material changes to contracts, execution milestones, or thesis-relevant developments will be reflected in updates. The current version reflects information available through May 2026.

All figures sourced to SEC filings, earnings transcripts, and official releases.

The constraint is physical. The mispricing is temporary. The thesis is testable.

Begin with Section 1 →