Frameworks
The named analytical frameworks that structure the work on adikumar.co. Each framework has a canonical definition page covering what it decomposes, why it exists, how it maps, and where it has been applied across the corpus.
The AI Power Chain framework
The six engineering layers between grid interconnection and the accelerator die. Vendor concentration, standards state and lead-time exposure per layer. Composed with the Investment Layer framework beneath and the operational Due Diligence framework alongside.
The Investment Layer framework
Six pools of capital, five sponsor playbooks, four M&A through-lines, three downside cases. Maps how AI infrastructure gets financed, consolidated and stress-tested. Sits above the AI Power Chain framework as the capital-allocation layer.
The Due Diligence framework
The 14-workstream commercial and technical DD framework for AI infrastructure acquisitions, organized across four stages (scoping and setup, substantive analysis, structural and paper, post-close validation), with two companion reference assets (Question Banks and Seller-side workbook). Composes with the AI Power Chain and Investment Layer frameworks as the operational layer that applies them to live transactions.
Live-deal calibration
These are not additional frameworks; they are case studies applying the three frameworks above to 2024-2026 transactions. Each includes per-layer scoring, the 2×2 verdict visual, and the assessment basis.
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AThe clean-deal calibration anchor. Physical ~22% of 162 (LOW-MEDIUM), thesis ~28% of 105 (LOW), veto GREEN.
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BParent-guarantee absorbs an open tail that would fire the veto in a thin-equity SPV. Verdict: RESTRUCTURE with AMBER, not RED.
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CFramework flagged single-tenant concentration pre-close; the failure mode materialised in Mar 2026 when Oracle + OpenAI dropped the expansion. Microsoft + Meta stepped in.