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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 $67 billion in contracted revenue from hyperscalers. The market values them at $20-25 billion combined while valuing speculative AI plays with zero revenue at similar or higher multiples. Either the contracts are worthless, or the market is mispricing who actually benefits from AI infrastructure buildout. This thesis argues it's the latter, 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.
Contents
5 Parts · 14 Sections
II
The Lock-In
III
The Mispricing
IV
The Operators
V
What Breaks It
About This Thesis
What this is
An investment thesis arguing that five specific companies are undervalued. I'm making a case for APLD, GLXY, IREN, CIFR, and WULF based on their contracted revenue, power positions, and what I see as market mispricing. This is a stock pitch. I have positions in these names.
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 also biased toward my own positions.
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 January 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 →