How AI Career Moat Score Works

A comprehensive scoring system that analyzes any career across 21 factors and 5 pillars to determine its resistance to AI displacement.

The Scoring System

AI Moat Score evaluates careers on a 0–100 scale, where higher scores indicate greater resistance to AI displacement. The score is calculated by analyzing 21 specific factors grouped into 5 pillars.

Four pillars add to the score (representing moat strength), while the fifth pillar (AI Exposure Risk) subtracts from it. Five conditional modifiers can further adjust the score based on factor combinations.

Grade Bands

FORTRESS(80–100)
FORTRESS
80–100 points
Near-immune to displacement
RESILIENT(60–79)
RESILIENT
60–79 points
Strong moats, augment to thrive
EXPOSED(40–59)
EXPOSED
40–59 points
Partial automation risk in 5–10 yrs
VULNERABLE(20–39)
VULNERABLE
20–39 points
Likely role consolidation
ENDANGERED(0–19)
ENDANGERED
0–19 points
Automation highly probable

How Scores Are Calculated

Factor Scoring

  • Pillars 1-4 factors: Scored 1-5 (1 = weakest moat, 5 = strongest moat)
  • Pillar 5 factors: Scored -1 to -5 (-1 = low AI exposure, -5 = maximum AI exposure)

Pillar Calculation

Each pillar uses weighted factors that are normalized to their maximum values:

  • Human Cognitive Moat: 5 factors weighted 3.0, 2.5, 2.5, 2.5, 2.5 → normalized to 35 max
  • Social & Institutional Moat: 5 factors weighted 3.0, 2.5, 2.5, 2.0, 2.0 → normalized to 28 max
  • Physical Reality Moat: 5 factors weighted 3.0, 3.0, 2.5, 2.0, 2.0 → normalized to 21 max
  • Economic & Demand Moat: 5 factors weighted 3.0, 2.5, 2.5, 2.5, 2.5 → normalized to 21 max
  • AI Exposure Risk: 6 factors weighted 4.0, 4.0, 3.5, 3.5, 3.0, 3.0 → normalized to -35 max

Total Score Formula

Total Score = (P1 + P2 + P3 + P4 + P5) + Conditional Modifiers
Where P1-P4 are positive scores (0-35, 0-28, 0-21, 0-21) and P5 is negative (0 to -35). The result is clamped between 0-100.

The 5 Pillars

Human Cognitive Moat

Max: 35 points

Measures the uniquely human cognitive abilities required for the role.

Factors:
  • Judgment Stakes: Consequences of wrong decisions
  • Creative Synthesis: Novel problem-solving and idea generation
  • Relational Depth: Emotional intelligence and interpersonal skills
  • Human Preference: Social and cultural factors favoring humans
  • Contextual Reasoning: Understanding complex, ambiguous situations

Social & Institutional Moat

Max: 28 points

Evaluates legal, regulatory, and social barriers to automation.

Factors:
  • Regulatory Mandate: Legal requirements for human oversight
  • Guild Density: Professional associations and licensing
  • Institutional Protection: Unionization and labor protections
  • Proprietary Data Access: Restricted access to sensitive information
  • Trust Certification: Credentials and trust-based relationships

Physical Reality Moat

Max: 21 points

Assesses physical and environmental constraints on automation.

Factors:
  • Physical Dexterity: Fine motor skills and manual manipulation
  • GeoTethering: Location-specific requirements
  • Environment Variability: Unpredictable or hazardous conditions
  • Physical Exertion: Strength and endurance requirements
  • Sensory Integration: Multi-sensory perception and integration

Economic & Demand Moat

Max: 21 points

Examines market forces and economic barriers to displacement.

Factors:
  • Demand Trajectory: Future growth prospects
  • Training Duration: Time and cost to become proficient
  • Entry Cost: Capital and credential requirements
  • Polymathy Index: Need for diverse, integrated skills
  • Knowledge Stability: Rate of knowledge obsolescence

AI Exposure Risk

Max: 35 points

Penalty for factors that make the role vulnerable to AI displacement.

Factors:
  • AI Penetration: Current AI adoption in the field
  • Task Routineness: Predictability and standardization of tasks
  • Data Availability: Digital data for training AI models
  • Output Measurability: Ease of quantifying performance
  • Remote Deliverability: Ability to work remotely
  • Substitution History: Past automation of similar roles

Conditional Modifiers

These special rules adjust the final score based on specific factor combinations, reflecting real-world interactions between different moat components.

ConditionEffectExplanation
Physical Dexterity ≥4 AND GeoTethering ≥4
+5 points
Roles requiring both fine motor skills and location-specific presence are highly resistant to automation.
AI Penetration ≤-4 AND Task Routineness ≤-4
-6 points
Roles already experiencing high AI adoption with routine tasks face significant displacement risk.
Regulatory Mandate ≥4 AND Guild Density ≥4
+5 points
Strong legal and professional barriers provide robust protection against automation.
Judgment Stakes ≥4 AND Human Preference ≥4
+4 points
High-stakes decisions combined with human preference create a strong cognitive moat.
Demand Trajectory ≥4 AND Knowledge Stability ≤2
-4 points
Growing demand but rapidly changing knowledge creates vulnerability to AI disruption.

Frequently Asked Questions

How often are careers re-scored?

Each career is scored once and cached in our database. If you believe a score needs updating, you can request a re-score through the admin panel. The scoring system is designed to be stable, as the fundamental characteristics of most careers change slowly over time.

Can I request a career to be scored?

Yes! Simply enter any job title on the home page. If the career hasn't been scored before, our system will analyze it using Google Gemini models via OpenRouter. The result will be cached for future users.

How accurate is the scoring?

The scoring system uses a comprehensive 21-factor model developed by labour economists and AI researchers. While no system can predict the future with 100% accuracy, our model provides a data-driven assessment based on current understanding of AI capabilities and labour market dynamics.

What does the AI Exposure Risk penalty mean?

AI Exposure Risk is a penalty score (negative points) that reduces the total moat score. It represents factors that make a career vulnerable to AI displacement. A higher negative score indicates greater exposure to automation risk.

Why does my career score seem low or high?

Scores are relative and based on the 21-factor model. Some careers naturally have stronger moats due to regulatory protection, physical requirements, or cognitive complexity. Remember that a "low" score doesn't mean immediate job loss—it indicates areas where AI could potentially augment or automate certain tasks over time.

What is OpenRouter and why is it the default provider?

OpenRouter is a unified API that provides access to multiple AI models, including Google Gemini. We use it as our default provider because it offers reliable access to state-of-the-art models with consistent performance. We specifically use Google Gemini 2.5 Flash for scoring and Gemini 2.5 Flash Lite for validation.

Ready to Discover Your AI Moat?

Enter any job title to get a detailed analysis of its AI displacement risk, complete with pillar scores, strengths, vulnerabilities, and actionable insights.

Score Your Career Now