# Quadrant Classification Cubit classifies every task and job into one of four strategic zones based on AI Exposure Potential and Human Necessity. Understanding these quadrants is essential for interpreting Cubit data. ## The Four Quadrants ``` Human Necessity Low High +---------------+---------------+ High | AUTOMATION | AUGMENTATION | AI Exposure | ZONE | ZONE | Potential +---------------+---------------+ Low | TRANSITIONAL | PROTECTED | | ZONE | ZONE | +---------------+---------------+ ``` ## Automation Zone **High AI Exposure + Low Human Necessity** Work that AI can access AND where human presence isn't essential. These tasks face the highest displacement risk. ### Characteristics * Procedural and digital (easy for AI to access) * Minimal physical requirements * Transactional rather than relational * Clear success criteria ### Task Examples * Processing standardized forms * Routine data entry * Basic transaction processing * Scheduled report generation * Rule-based document classification ### Job Examples (high % of tasks in this zone) * Bookkeeping Clerks * Data Entry Keyers * Payroll Clerks * Title Examiners ### Strategic Response * **For workers**: Develop skills in adjacent zones, especially relational skills * **For employers**: Identify augmentation opportunities before full automation * **For educators**: Pivot training away from purely procedural skills *** ## Augmentation Zone **High AI Exposure + High Human Necessity** Work where AI can handle technical heavy lifting, but human presence remains essential for delivery. ### Characteristics * AI-accessible for technical components * Strong relational or physical requirements * Value tied to human judgment, trust, or presence * Likely to transform into human-AI collaboration ### Task Examples * Medical diagnosis (AI analyzes; physician delivers) * Legal research (AI surfaces precedents; attorney advocates) * Financial advisory (AI prepares analysis; advisor builds relationship) * Teaching (AI personalizes content; teacher mentors) ### Job Examples (balanced task distribution) * Physicians * Financial Advisors * Management Consultants * Credit Analysts ### Strategic Response * **For workers**: Learn to work effectively with AI tools * **For employers**: Invest in human-AI collaboration workflows * **For educators**: Teach AI literacy alongside domain expertise *** ## Protected Zone (Human-Centric) **Low AI Exposure + High Human Necessity** Work that AI struggles to access AND that fundamentally requires human presence. This is the durable core of human work. ### Characteristics * Unstructured environments or analog inputs * Complex physical requirements OR deep relational needs * Current AI has limited entry points * Value is inherently human ### Task Examples * Performing surgery * Crisis counseling * Skilled trades in unpredictable environments * Early childhood education * Executive negotiation * Pastoral care ### Job Examples (mostly protected tasks) * Surgeons * Psychologists * Skilled Tradespeople (Electricians, Plumbers) * Emergency Responders * Physical Therapists ### Strategic Response * **For workers**: Lean into distinctly human capabilities * **For employers**: Recognize these roles as strategic assets * **For educators**: Emphasize the irreducible human elements *** ## Transitional Zone **Low AI Exposure + Low Human Necessity** Work where neither AI nor humans have a decisive structural advantage. These roles will transform unpredictably as AI capabilities evolve. ### Why "Transitional"? Jobs in this zone often surprise people. Software developers and lawyers land here not because they're "safe" but because: * **Low AI Exposure**: The work is too varied and unstructured for current AI to cost-effectively automate * **Low Human Imperative**: The work doesn't require physical presence or deep interpersonal relationships This creates a **contested middle ground**. The positive balanced impact scores many of these jobs show come from dispositional factors (adaptability, judgment, creativity) rather than structural protection. ### Characteristics * Variable, unpredictable day-to-day demands * Complex judgment work that resists proceduralization * Neither strongly physical nor deeply relational * High adaptability requirements * Outcomes depend heavily on individual skill and context ### Task Examples * Complex problem-solving with ambiguous inputs * Strategic analysis and recommendation * Creative and exploratory work * Context-dependent decision making * Knowledge synthesis across domains ### Job Examples * Software Developers (varied technical judgment) * Lawyers (complex legal reasoning) * Management Consultants (strategic advisory) * General Operations Managers (diverse responsibilities) * Financial Analysts (judgment-heavy analysis) ### Strategic Response * **Build adaptability**: These roles reward those who can evolve with AI tools * **Develop judgment skills**: The human edge here is contextual reasoning, not routine execution * **Monitor AI progress**: Low exposure today may not hold as AI becomes more flexible * **Consider specialization**: Moving toward augmentation or protected zones reduces uncertainty *** ## How Quadrants Are Assigned ### Default Thresholds By default, Cubit uses a threshold of **0.5** for both dimensions: ```python # Default thresholds EXPOSURE_THRESHOLD = 0.5 # Scores above this = "High Exposure" IMPERATIVE_THRESHOLD = 0.5 # Scores above this = "High Imperative" ``` ### Custom Thresholds You can override the default thresholds in any API call that returns quadrant classifications. This allows you to adjust sensitivity based on your specific use case: ```python # Default behavior (threshold = 0.5) job = client.get_job("43-3031.00") print(job["classification"]["quadrant"]) # "transitional" # With custom threshold (more sensitive to automation risk) job = client.get_job("43-3031.00", exposure_threshold=0.45) print(job["classification"]["quadrant"]) # "automation" ``` Available threshold parameters: * `exposure_threshold`: Threshold for "high exposure" (0.0-1.0, default 0.5) * `imperative_threshold`: Threshold for "high imperative" (0.0-1.0, default 0.5) These parameters are available on: * `GET /jobs/search` * `GET /jobs/{soc_code}` * `GET /jobs/{soc_code}/tasks` * `GET /jobs/compare` ### Task Distribution Across 18,796 tasks: | Quadrant | Count | Percentage | | ------------------------- | ----- | ---------- | | Transitional | 8,036 | 43% | | Protected (Human-Centric) | 6,602 | 35% | | Automation | 3,467 | 18% | | Augmentation | 691 | 4% | *** ## Accessing Quadrant Data ### Job-Level ```python job = client.get_job("15-1252.00") print(f"Quadrant: {job['classification']['quadrant']}") # Output: "transitional" ``` ### Task-Level (Professional+) ```python tasks = client.get_job_tasks("15-1252.00") quadrant_counts = {} for task in tasks["tasks"]: q = task["quadrant"] quadrant_counts[q] = quadrant_counts.get(q, 0) + 1 print("Task distribution:") for quadrant, count in sorted(quadrant_counts.items(), key=lambda x: -x[1]): pct = count / len(tasks["tasks"]) * 100 print(f" {quadrant}: {count} tasks ({pct:.0f}%)") ``` **Output:** ``` Task distribution: augmentation: 12 tasks (52%) automation: 6 tasks (26%) protected: 3 tasks (13%) transitional: 2 tasks (9%) ``` *** ## Quadrant Transitions Understanding quadrant positioning helps identify career transitions: ### From Automation -> Protected Workers in automation-zone roles can transition to protected roles by: 1. Adding physical skills (trades, healthcare) 2. Deepening relational capabilities (counseling, teaching) ### From Automation -> Augmentation Workers can shift to augmentation by: 1. Moving to client-facing versions of their role 2. Adding judgment and advisory components 3. Specializing in edge cases that require human oversight *** ## Quadrant API Labels The API uses these exact string values: | Quadrant | API Value | | ------------ | ---------------- | | Automation | `"automation"` | | Augmentation | `"augmentation"` | | Protected | `"protected"` | | Transitional | `"transitional"` | *** ## Next Steps * [Understanding Scores](/core-concepts/understanding-scores.md) - How scores are calculated * [Career Transitions Guide](/guides/career-transitions.md) - Finding skill-adjacent paths * [Task-Level Analysis](/guides/task-analysis.md) - Working with task data --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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