Stop counting GPUs. Start reading leases.

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Reader Outcome: After reading this edition, the reader will understand why AI compute is restructuring into a landlord-and-tenant economy and think differently about who actually carries the risk, and who captures the value, in the trillion-dollar buildout.

1. A four-role lens (landlord, tenant, lender, developer) for decoding any AI infrastructure deal in under a minute.

2. Why an asset-light frontier lab can hold a structurally stronger position than the companies buying the chips.

3. The mismatch at the center of the boom: assets with 2-4 years of economic life financed on 5-6 year books.

4. How to read compute markets the way property analysts read office markets: lease tenor, pre-let coverage, tenant credit.

5. Where the tail risk actually sits if demand disappoints, and why it will surface in debt before it surfaces in models.

1. Executive Summary

2. Deep Dive in One Sentence

3. Why This Topic Matters Now

4. The Big Question

5. The Conventional Narrative

6. What’s Really Happening

7. The Economics Behind the Shift

8. Winners and Losers

9. Second-Order Effects

10. Strategic Implications

11. Mental Model of the Week

12. Key Takeaways

13. Closing Thought

• The AI buildout is being financed and monetized like commercial property: multi-year leases, pre-let capacity, anchor-tenant contracts, and datacenter debt that trades on rental data.

• Owning compute means owning depreciation. Hardware becomes economically obsolete in two to four years but sits on five-to-six-year books, so the owner, not the renter, holds the obsolescence clock.

• In today’s shortage, rents are surging and landlords look brilliant. The structure being built now is what matters when scarcity ends.

• Durable advantage flows to whoever controls appreciating scarcity (power, memory, credible demand), not whoever holds the most silicon.