Palanor: AI Margin Compression

Overview

When the cost of intelligence falls faster than the price of software, margins compress. The AI Margin Compression Index reads the macro thesis Palanor has been writing into — that the software-sector margin structure of the 2010s and early 2020s is unstable in the AI era. v2 (this version) adds quarterly hyperscaler AI capex to the composition; v1 relied entirely on proxy signals.

What changed in v2

v1 had no direct read on hyperscaler AI capex — the most important input to the compression thesis. v2 adds four manual-entry components — Microsoft, Alphabet, Meta, and Amazon AWS AI capex per quarter, sourced from public 10-Q filings. These now carry 50 percent of the index's total weight; the v1 proxy components (Nasdaq, ISM Services, GitHub trending, wage growth, PPI, industrial production) retain the other 50 percent. Expect v2 readings to diverge from v1 — that divergence is itself informative.

Why these components

AI capex by the top four hyperscalers is the closest public-data proxy for the cost of building intelligence at scale. Rising capex sustains the supply build that ultimately compresses unit cost of inference — and therefore compresses software pricing power. The v1 proxies remain because they capture the demand side: if Nasdaq is strong and ISM Services is strong, the compression pressure is being absorbed; if both weaken alongside accelerating capex, the thesis is materializing.

Methodology

Capex components transform via 12-month percentage change — the level grows over time, so we read acceleration rather than level. v1 proxies transform via 24-month z-score (level-based, mean-reverting). Weighted sum, sign-flips on negative-direction components, logistic scaling to 0-100. Thesis bands: 0-35 weak, 35-65 baseline, 65-100 accelerating. Manual-entry values refresh quarterly — see the manual entry surface in the admin panel (planned).

Interpretation

A reading above 65 with rising trend is the macro environment Palanor has been warning about — capex accelerating into a demand stack that cannot price the new supply. A reading below 35 with stable trend is the opposite — the thesis is being absorbed by services strength and the proxy market reading is holding.

Caveats

Manual-entry data drifts when admins do not refresh. Future versions will automate ingestion via SEC filing parsers. Capex categorization is also imperfect — hyperscalers do not always cleanly separate AI capex from broader infrastructure capex; we use the most generous bucket each company reports and document the source quarter-by-quarter in the admin panel.

Components & Weights

Every component, every weight. The 30-day spark shows how the underlying series has moved; the transform column shows how it converts to a comparable scale before weighting. Direction indicates whether rising values raise or lower the index.

Awaiting data means the most recent compute did not have a fresh value for that component. The composite re-normalizes around what is present, so the index reading remains meaningful. The component returns the moment its source publishes.

Historical

24-month history of this index. The composite is recomputed daily; values are stored with timestamps.

References

• FRED — Federal Reserve Economic Data, St. Louis Fed. https://fred.stlouisfed.org/
• Atlanta Fed — Wage Growth Tracker and Sticky-Price CPI. https://www.atlantafed.org/research
• Cleveland Fed — Model-based inflation expectations. https://www.clevelandfed.org/our-research/indicators-and-data
• OECD — Composite Consumer Confidence Indicator. https://data.oecd.org/leadind/consumer-confidence-index-cci.htm
• CNN Business — Fear & Greed Index. https://www.cnn.com/markets/fear-and-greed
• Google Trends — interest over time. https://trends.google.com/