Public Research System · Current topic: AI Infrastructure Economics · Live

Critical Mass

If an AI story is becoming real, where will the real world feel pressure first?

Critical Mass (cm-term.com) studies the layer between a technology narrative and its financial outcome. It traces where the system tightens first, whether that pressure becomes measurable resource allocation, and how the resulting demand changes who captures value, carries capital, controls bottlenecks, and absorbs risk.

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Filing-based analytics Framework essays Systems reasoning Web build

01 · Research question

Where does a technology story begin to press against reality?

The useful question is not simply whether a story sounds credible. It is what must be built, supplied, financed, approved, or operated if the story is becoming real — and where capacity, power, supply, capital, or deployment begins to tighten first.

Those pressure points are early evidence, not proof. Critical Mass follows the next transition: whether pressure pulls real money, contracts, capacity, and operating effort into the system.

02 · Research path

From narrative to economic consequence

Story

State the claim

Separate the promise, its assumptions, and the future already being priced or funded from the evidence used to test it.

Pressure

Find where reality tightens

Identify what must become scarce, expensive, delayed, regulated, built, or financed for the claim to move forward.

Demand

Test whether resources move

Look for committed capital, signed contracts, expanded capacity, procurement changes, and operating resources reallocated around the technology.

Distribution

Read the economic result

Trace who captures value, who bears capital and cost, who controls the bottleneck, and who remains exposed to execution, cycle, regulatory, or financing risk.

03 · Current investigation

AI infrastructure is one demand story with several economic forms

The first completed topic applies the method to eight public companies across compute, foundry, memory, equipment, networking, power, and cooling.

Filing-based analytics

Test the narrative against issuer economics

Issuer-level metrics compare revenue, margin, reinvestment, cyclicality, and value-chain position. The goal is not to isolate “pure AI revenue,” but to identify the financial signature through which AI demand reaches each company.

Framework research

Explain why the same demand lands differently

Essays connect public evidence with technical constraints to explain the system behind the numbers. They are explanatory research, not valuation work or investment coverage.

Platform margin capture

Demand can concentrate profit where a platform controls the most valuable layer of the system.

Manufacturing capital burden

The companies enabling the buildout may have to spend first and most heavily, even when they retain pricing power.

Bottleneck economics

Scarce equipment, manufacturing capacity, or infrastructure can control the pace of the entire chain.

Cycle and transmission risk

Memory, networking, power, and cooling may receive the demand while retaining very different cyclicality, margins, and execution exposure.

04 · Evidence system

The website is part of the research method

Systems

Map dependencies

Model the assets, relationships, and constraints that determine whether a technology can scale.

Evidence

Keep claims checkable

Connect companies, filings, metrics, sources, dates, assumptions, and known limitations.

Interpretation

Publish the reasoning

Turn evidence into a navigable argument without treating market attention as proof.

05 · My contribution

Research, product, and publication built as one system

I defined the analytical method, built the evidence structure, designed and developed the site, and wrote the research published through it.

Research design

Defined the story → pressure → demand → distribution method and applied it across the AI infrastructure value chain.

Public-company analysis

Built issuer comparisons from public filings, including metric construction, source mapping, and limitations.

Product and web build

Designed the information architecture, interface, archive, reading flow, and implementation of the live site.

Research communication

Wrote framework essays and analytical notes that connect technical constraints to business and economic outcomes.

Next Step

Explore the live research system

cm-term.com publishes the evidence, comparisons, and framework research behind this method — from the first pressure point to the economic result.

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