Bottom-up models, technology primers, and investment memos covering the buildout of compute, networking, power, and the application layer that consumes them.
Working index — small audience, unlisted.
Working index of research outputs. Each card links to a self-contained piece. Models open as live-data summary pages; primers and memos open as the full reading text with inline citations.
Numbers throughout chain back to source models (the bottom-up token model and the DC-capex archetype model). Citations chain back to verbatim primary sources, verified line-by-line.
Bottom-up token-demand decomposition for the agentic-AI buildout, four demand chains, eleven workforce functions. Headline: $1.2T (2026) → $1.8T (2030). Anchors the gap thesis against consensus top-down forecasts.
Open model summaryFive datacenter archetypes (training-core, inference, agentic, edge, legacy) × five years of capex breakdown. Built bottom-up from public hyperscaler models. Summary extract page in build.
Extract page in buildThe unified AI-infrastructure market map. Vertical Compute column on the left, six horizontal supply-chain bands on the right. The base layer the archetype BOMs sit on top of.
Extract page in buildOptical and electrical interconnect economics through 2030. Goldman's $154B optical TAM unpacked, with services unbundled — network test, hyperscale fit-out, fiber MSPs all surface as exposed names.
HTML conversion pendingHow the AI datacenter buildout is being financed. Neocloud debt, hyperscaler bond issuance, ABS structures, Stargate financing — and where the credit cycle gets uncomfortable.
Read primerWhat "agentic AI" actually means in practice, and the workforce-replacement framing that underpins the token-demand model. Foundational primer for the bottom-up token model.
Read primerThe agent-to-agent communication layer — MCP, A2A protocols, orchestration patterns, and what changes when agents talk to other agents rather than to humans.
Read primerWhat's actually inside each datacenter archetype, line by line. Cumulative spend by category, 2026–2030. The investable-theme view sits on top of this: networking surfaces as the asymmetric exposure.
HTML conversion pendingStrategy memo on how a mid-market PE firm ($1–10B AUM) should approach AI tooling. The hybrid path wins on durability, not cost — recommends a four-tier stack.
Read memoWhere SemiAnalysis's value-capture framing in the AI infrastructure stack misses. The counter-read on who actually captures economics across the buildout.
Read memoWhat tokens actually cost in 2026, by vendor and model class. Validates the per-token price assumptions inside the bottom-up token model.
Read memoWhere AI-DC renewable power capacity is actually being built outside the United States. The gap between US deliverability headlines and the global picture.
Read memoSingle-name company research on TSS Inc. Datacenter services exposure analysis. HTML conversion pending before going live.
HTML conversion pending