The Three-Dimensional Economics of AI - Rethinking returns when capital, revenue and technological velocity are endogenous

This essay examines whether conventional return analysis remains sufficient when AI capital requirements, revenue or economic value, and technological velocity become increasingly endogenous. It argues that rapid technological advancement can shorten the competitive economic life of AI infrastructure even while successful deployment attracts further investment and accelerates the frontier. The paper introduces TRACER — Technological Rate-Adjusted Capital Expected Return — as a conceptual framework for evaluating the sustainable expected return on marginal AI capital when these variables evolve together over time. It also considers the implications for capital allocation, infrastructure, labor, productivity and economy-wide value. TRACER is presented as a research hypothesis rather than a solved mathematical model; its next stage is empirical development across economics, mathematics, technology and capital markets.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-20
DOI
https://doi.org/10.5281/zenodo.22033871
Primary Topic
Capital Investment and Risk Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Three-Dimensional Economics of AI - Rethinking returns when capital, revenue and technological velocity are endogenous

Krishnan Vasudevan Anantha
Zenodo (CERN European Organization for Nuclear Research)
Capital Investment and Risk Analysis
article

The Three-Dimensional Economics of AI - Rethinking returns when capital, revenue and technological velocity are endogenous

Krishnan Vasudevan Anantha
article en

Abstract

This essay examines whether conventional return analysis remains sufficient when AI capital requirements, revenue or economic value, and technological velocity become increasingly endogenous. It argues that rapid technological advancement can shorten the competitive economic life of AI infrastructure even while successful deployment attracts further investment and accelerates the frontier. The paper introduces TRACER — Technological Rate-Adjusted Capital Expected Return — as a conceptual framework for evaluating the sustainable expected return on marginal AI capital when these variables evolve together over time. It also considers the implications for capital allocation, infrastructure, labor, productivity and economy-wide value. TRACER is presented as a research hypothesis rather than a solved mathematical model; its next stage is empirical development across economics, mathematics, technology and capital markets.

Zenodo (CERN European Organization for Nuclear Research)
TeraXion (Canada) (CA)
Industry, innovation and infrastructure, Decent work and economic growth
Openalex Percentile: Top 6%
Capital Investment and Risk Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.