Knowledge base · canonical index
Knowledge base
The canonical index of frameworks, named theses, topic hubs, and comparison references on adikumar.co. Written for practitioners, sponsors and analysts working on AI infrastructure. All content is by Aditya (Adi) Kumar , an independent analyst covering AI data centre power, industrial technology, and commercial + technical due diligence.
How the site is organised
Everything on adikumar.co maps onto three canonical frameworks that compose with each other, eight series that instantiate them, and a reference layer of topic hubs, comparison hubs, living indexes, glossary and companion reference assets. The diagram below is the single-page map.
Author entity
Aditya (Adi) Kumar
Frameworks (canonical)
Physical AI Power Chain 6 engineering layers from grid interconnection to the die Capital Investment Layer 6 pools of capital, 5 sponsor playbooks, 3 downside cases Operational Due Diligence 14 workstreams in 4 stages, 3 companion reference assets Series that instantiate the frameworks
DC-DC Transition 19 essays (13 core + 6 supplements) on 800V DC AI Power Chain series 15 essays (6 stacks + 6 tech companions + 3 cross) Investment Layer series 8 essays on capital allocation + sponsor econ. Due Diligence series 17 essays (14 workstreams + 3 companion assets) Financing 10 essays on capital structures Regulatory Layer 10-essay field guide on rules shaping AI infra Case Studies Landmark AI infra deal retrospectives Tech Spotlights 15 vendor + technology deep-dives Topic hubs + comparison hubs (reference surfaces)
Topic hubs (8) 800V DC, cooling, semis, interconnection, nuclear, M&A, financing, regulation Comparison hubs (5) 800V vs 48V, SiC vs GaN, DLC vs immersion, nuclear vs SMR, NVIDIA vs OCP Living indexes (3) Rack power by GPU generation, MVDC/LVDC map, Vendor Screen Glossary + reference 57 defined terms with structured definitions across the corpus Companion reference assets (DD framework)
Question Banks 250+ questions, WS-indexed Seller-side workbook 14 workstreams inverted for seller DD Evidence Matrix Tracker with P/D/I provenance Every essay is tagged to its series. Every framework composes with the others.
Figure 1. Site architecture: frameworks, series, reference surfaces, companion assets.
The three frameworks compose as physical infrastructure (AI Power Chain ) plus capital allocation (Investment Layer ) plus operational underwriting (Due Diligence ). Each essay in the corpus sits inside a series that instantiates one of those three frameworks. Reference surfaces (topic hubs, comparison hubs, living indexes, glossary) are where practitioners look for specific answers rather than reading through long-form arguments. The three companion reference assets (Question Banks , Seller-side workbook , DD Evidence Matrix ) are proprietary tools that ship alongside the frameworks.
01 Adi's Theses (the practitioner view)The canonical statement of the five core theses that run through every essay on adikumar.co. Reference material for practitioners + sponsors.
02 Named frameworksSix-layer market + engineering map from grid interconnect to on-package delivery. 15 essays: 6 stacks + 6 technical companions + 3 cross-cutting.
13 core essays + 6 supplements on the 800V DC data centre transition. Architecture, standards, retrofit vs greenfield, vendor economics.
8 essays on AI infrastructure capital allocation, sponsor economics, M&A + debt underwriting.
16-essay DD framework covering commercial, technical, financial, organisational, legal, regulatory + closing workstreams.
10 essays on the rules shaping AI infrastructure buildout: FERC + ISO reform, state PUCs, EPA, CFIUS, export controls, EU AI Act, standards, labour.
10 essays on capital structures financing AI infrastructure: DDTL, PE equity, ABS, nuclear PPAs, sale-leaseback, green bonds, sovereign wealth, REITs.
15 vendor + technology deep-dives across the AI Power Chain: Vicor, Menlo Micro, Skeleton, Navitas, Wolfspeed, CoolIT, Submer, Kairos, Fervo, Bloom, Cerebras, Groq, Ayar, Lightmatter, Compass.
8 landmark AI infrastructure deal retrospectives: Blackstone/QTS, Microsoft/TMI, Amazon/Talen, CoreWeave, Iron Mountain, Digital Realty/Teraco, FTC/Nvidia-ARM, CMA cloud.
03 Canonical topic pages (direct answers)What thermal management architectures work above 100 kW per rack. DLC + immersion + CDU + chemistry supply chain.
The 800V DC transition: architecture, standards, retrofit vs greenfield, vendor ecosystem.
SiC + GaN vendor map. Wolfspeed, Infineon, Navitas, EPC, ST, onsemi. Design-ins, capex, supply chain.
Every material AI infrastructure deal 2023-2026, four through-lines, fifteen forward hypotheses.
The capital-structure map: DDTL, PE equity, ABS, nuclear PPAs, sale-leaseback, sovereign wealth.
FERC + state PUCs, EPA permitting, CFIUS, export controls, EU AI Act, DORA.
Nuclear restart PPAs, SMR project readiness, traditional PPA structures, grid interconnection.
FERC queue, hyperscaler substation lead times, ISO reform, grid connection economics.
04 Comparison hubs (decision reference)Where each wins, vendor maps for both, and the 800V DC transition that reshapes volume distribution.
Direct-to-chip vs single-phase vs two-phase immersion. Vendor concentration by tier.
Economics, permitting timeline, technology risk, grid interconnection implications.
Comparison of the two 2026 hyperscaler DC power specifications and what standardises when.
Architecture, efficiency, capex, standards, vendors. Where each fits the AI GPU envelope through 2030.
Living index of 58 vendors across DC voltage classes for AI data centres. Updated monthly.
Living index of AI rack power across 11 accelerator generations, H100 to Rubin Ultra.
05 Reference infrastructure57 defined terms across AI infrastructure, power engineering, and due diligence. Bookmark reference for reading the essays.
Author bio, background, professional experience at Eaton, Honeywell, Cisco, BCG. Advisory relationships + disclosures.
Tag-based topic index across the site.
