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AIMX — AI Margin Expansion Index

Tracking how artificial intelligence is reshaping profitability across the software industry.

AIMX quantifies the operating leverage unlocked by AI adoption across leading software companies. It continuously analyzes hiring velocity, layoff sentiment, AI-efficiency narratives, developer productivity, and management language to produce a real-time composite score that reflects margin expansion momentum.

In short: AIMX measures how much of a company's margin expansion is socially visible before it shows up in earnings.

What AIMX Measures

AIMX captures five signal dimensions that reflect early shifts in operating leverage:

Hiring Velocity (H)

25%
Source
LinkedIn, job boards
Implication
↓ hiring = cost discipline

Tracks net change in job postings for technical and sales roles. Declining hiring rates often precede cost discipline and operating margin expansion.

Layoff Intensity (L)

20%
Source
Blind, Reddit, Layoffs.fyi
Implication
↑ layoffs = near-term margin boost

Measures frequency and sentiment of layoff chatter across internal and social channels. Rising layoff intensity indicates near-term efficiency pushes.

AI Productivity Chatter (A)

30%
Source
X/Twitter, Reddit, Hacker News
Implication
↑ chatter = efficiency leverage

Detects internal and developer discussions around automation, copilots, and AI tooling. Higher chatter correlates with productivity improvements and margin expansion.

Efficiency Narrative Drift (E)

15%
Source
Exec posts, earnings calls
Implication
↑ frequency = strategic pivot to profit

Quantifies shifts in management vocabulary toward 'efficiency,' 'profitability,' and 'leverage.' Tracks strategic narrative changes before they manifest in metrics.

Sentiment Stability (S)

10%
Source
Social & internal forums
Implication
stable → sustainable margins

Tracks morale and confidence in social and internal forums. Sustained optimism during cost optimization phases signals durable margin expansion.

Calculation Methodology

AIMX operates as a composite normalized score for each company c at time t.

Step 1: Normalize Each Signal

For each metric x ∈ {H, L, A, E, S}:

Z_x(c,t) = (x(c,t) - μ_x) / σ_x

where μ_x and σ_x are rolling 90-day mean and standard deviation within the same peer group.

Step 2: Directional Adjustment

To align signals with margin implications:

H' = -Z_H (lower hiring = higher margin)
L' = +Z_L
A' = +Z_A
E' = +Z_E
S' = +Z_S

Step 3: Weighted Composite Score

Weights are empirically tuned via regression against gross-margin deltas:

AIMX(c,t) = 0.25H' + 0.20L' + 0.30A' + 0.15E' + 0.10S'

All values are scaled to a 0–100 index:

AIMX_scaled(c,t) = 50 + 10 × AIMX(c,t)

Step 4: Aggregate Index

The headline AIMX value at time t is a market-cap-weighted average across the tracked universe (top 150 software firms):

AIMX_t = Σ w_c × AIMX_scaled(c,t)
where w_c = MarketCap_c / Σ MarketCap_c

AIMX Score Interpretation

Understanding what AIMX values signal about market-wide margin trends:

> 60

Broad-based margin expansion signals visible. Expect earnings surprises or guidance upgrades.

50

Neutral operating environment.

< 40

Margin compression likely — rising hiring or falling AI efficiency signals.

Live AIMX Dashboard

Coming Soon

Top Gainers

Companies with fastest 30-day AIMX acceleration

Sector Breakdown

SaaS vs Infrastructure vs AI-native performance

Signal Attribution

Contribution breakdown (H, L, A, E, S) per company

Narrative Pulse

Live feed of AI efficiency mentions on X and Blind

Methodology Highlights

📊

Rolling Normalization

Eliminates noise from temporary spikes with 90-day rolling windows

🔄

Dynamic Updates

Signals updated daily; weights re-fit quarterly against reported margins

🛡️

Outlier Filtering

Robust z-score methods and sentiment smoothing for reliable signals

🔌

Public API

Access via /api/aimx?company=ticker

“Tracking profitability before the earnings call.”

AIMX is developed by StreetMining