New Cognitive Automation Index (CAI): Monitoring AI Displacement & Service Sector Deflation—6-Month Component Scores & Methodology

[rastogi]

Hi all,
I've built a real-time “Cognitive Automation Index” (CAI) to track macro impacts of AI on routine cognitive/service jobs, margin effects, and incipient service sector deflation. Would greatly value this community’s review of scoring logic, evidence, and suggestions for methodological enhancement!
Framework (Brief):

• Tier 1 (Leading, 40%):
  • AI infra revenue, Corporate AI adoption, Pro services margins, Tech diffusion
• Tier 2 (Coincident, 35%):
  • Service employment (risk split), Service sector pricing
• Tier 3 (Lagging, 25%):
  • Productivity, Consumer price response
• Score: +2 = maximum signal, +1 = strong, 0 = neutral, -1 = contradictory

Calculation:
CAI = (Tier 1 × 0.40) + (Tier 2 × 0.35) + (Tier 3 × 0.25)
Interpretation:

• +1.4+: “Strong displacement, margin compression beginning”

Monthly Scoring: Full Details & Evidence (Mar 2025–Aug 2025)​

[th]

Month​

[/th][th]

Tier 1​

[/th][th]

Tier 2​

[/th][th]

Tier 3​

[/th][th]

CAI​

[/th][th]

Comment​

[/th]​

[td]

Mar 2025​

[/td][td]

1.1​

[/td][td]

1.0​

[/td][td]

0.7​

[/td][td]

0.98​

[/td][td]

Early infra growth, AI adoption signals up, jobs flat, minor productivity uptick​

[/td]​

[td]

Apr 2025​

[/td][td]

1.3​

[/td][td]

1.0​

[/td][td]

0.7​

[/td][td]

1.06​

[/td][td]

Service margins up, infra accel, service jobs start declining​

[/td]​

[td]

May 2025​

[/td][td]

1.8​

[/td][td]

1.25​

[/td][td]

0.7​

[/td][td]

1.32​

[/td][td]

Big AI infra jump (Nvidia/MSFT/Salesforce QoQ >50%), >2% annualized service job drop, pro services margins +200bp vs prior yr​

[/td]​

[td]

Jun 2025​

[/td][td]

2.0​

[/td][td]

1.35​

[/td][td]

0.8​

[/td][td]

1.48​

[/td][td]

CAI peaks: AI mentions in >25% of large cap calls, BLS confirms >2% annualized admin/customer services decline; CPI flat​

[/td]​

[td]

Jul 2025​

[/td][td]

2.0​

[/td][td]

1.35​

[/td][td]

0.8​

[/td][td]

1.48​

[/td][td]

Sustained: AI infra and service software growth steady, margins/declines persist​

[/td]​

[td]

Aug 2025​

[/td][td]

2.0​

[/td][td]

1.35​

[/td][td]

0.8​

[/td][td]

1.48​

[/td][td]

Trends continue: No reversal across any tracked indicators​

[/td]​

Component Scoring Evidence by Month​

Tier 1: Leading Indicators​

• AI Infrastructure Revenue (18%)
  • May–Aug: +2 (NVIDIA/Salesforce Q2/Q3: >50% QoQ growth in AI/data center, Salesforce AI ARR up 120%)
  • Mar/Apr: +1 (growth 25–40%)
• Corporate Adoption (12%)
  • May–Aug: +2 (>25% of S&P 500 calls mention “AI-driven headcount optimization/productivity gains;” surge in job postings for AI ops)
  • Mar/Apr: +1 (10–20% companies, rising trend)
• Professional Service Margins (10%)
  • May–Aug: +2 (major consulting/call center firms show margin expansion >200bp YoY, forward guidance upbeat)
  • Mar/Apr: +1 (early signals, margin expansion 100–200bp)
• Tech Diffusion (5%)
  • May–Aug: +2 (Copilot/AI automation seat deployment accelerating, API call volumes up)
  • Mar/Apr: +1 (steady rise, not explosive yet)

Tier 2: Coincident Indicators​

• Service Sector Employment (20% High/8% Med Risk)
  • May–Aug: +2 (BLS/LinkedIn: >2% annualized YoY declines in high-risk service categories; declines pronounced in admin and customer service)
  • Mar/Apr: +1 (declines start to appear; <2% annualized)
• Service Sector Pricing (15%)
  • Mar–Aug: +1 (CPI flat or mild disinflation for professional/financial services; no inflation acceleration)

Tier 3: Lagging Indicators​

• Productivity (15%)
  • Mar–Aug: +1 (Service sector productivity up 2.4–2.5% YoY)
• Consumer Price Response (10%)
  • Mar–Aug: 0–+1 (CPI for services broadly stable, some mild disinflation but not universal)

Request for Feedback​

• Validation: Does this weighting/scoring structure seem robust to you? Capturing key regime shifts?
• Enhancement: What quant or macro techniques would tighten this? Any adaptive scoring precedents (i.e., dynamic thresholds)?
• Bias/Risk: Other ways to guard against overfitting or confirmation bias? Worth adding an “alternative explanations index”?
• Data Sources: Any recs for higher-frequency or more granular real-time proxies (especially for employment and AI adoption)?
• Backtesting: Best practices for validating this type of composite macro indicator against actual displacement or deflation events?

Happy to share methodology docs, R code, or scoring sheets to encourage critique or replication!
Thanks for your thoughts—open to any level of feedback, methodological or practical, on the CAI!