{ "generated_at": "2026-05-16T21:58:47Z", "delegation": { "composite": { "current": 45.8, "previous": 37.7, "delta": 8.1, "trend": [ 31.5, 37.7, 45.8 ], "last_updated": "2026-05-16", "data_year": 2026, "data_freshness": "2026 Q2" }, "domains": [ { "id": "content-mod", "name": "CONTENT-MOD", "full_name": "Content Moderation", "score": 96.1, "previous_score": 94.1, "trend": [ 89.6, 94.1, 96.1 ], "status": "autonomous", "weight": 0.1, "tier": 1, "description": "Share of platform content moderation detection, flagging, or enforcement actions handled by automated systems before or without human review.", "sub_indicators": [ { "name": "Meta Automated Detection", "value": 95.2, "unit": "%", "source": "Meta Transparency Report", "freshness": "Q3 2025" }, { "name": "YouTube Automated Flagging", "value": 99.5, "unit": "%", "source": "YouTube Transparency Report", "freshness": "Jul-Sep 2025" }, { "name": "TikTok Automated Enforcement", "value": 93.8, "unit": "%", "source": "TikTok DSA H2 2025", "freshness": "H2 2025" } ], "data_sources": [ { "name": "Meta Transparency Report", "cadence": "Quarterly", "type": "transparency-report" }, { "name": "YouTube Transparency Report", "cadence": "Quarterly", "type": "transparency-report" }, { "name": "TikTok DSA Transparency Report", "cadence": "Semi-annual", "type": "transparency-report" } ] }, { "id": "algo-trade", "name": "ALGO-TRADE", "full_name": "Algorithmic Trading", "score": 39.2, "previous_score": 39.2, "trend": [ 34.3, 39.2, 39.2 ], "status": "nominal", "weight": 0.15, "tier": 1, "description": "Financial-market automation score combining FX electronic trading share with current buy-side internal-AI trade-execution adoption.", "sub_indicators": [ { "name": "FX Electronic Trading Share", "value": 59, "unit": "%", "source": "BIS Triennial Survey", "freshness": "April 2025" }, { "name": "Buy-Side AI Trade Execution Adoption", "value": 15, "unit": "%", "source": "Coalition Greenwich", "freshness": "2025" } ], "data_sources": [ { "name": "BIS Triennial Survey", "cadence": "Triennial", "type": "survey" }, { "name": "Coalition Greenwich", "cadence": "Annual", "type": "survey" } ] }, { "id": "code-gen", "name": "CODE-GEN", "full_name": "Software Development", "score": 48.1, "previous_score": 32.6, "trend": [ 27, 32.6, 48.1 ], "status": "elevated", "weight": 0.15, "tier": 1, "description": "Share of software engineering output, workflow reliance, and agentic task delegation substantially influenced by AI coding systems.", "sub_indicators": [ { "name": "AI-Generated Code Output Share", "value": 39.2, "unit": "%", "source": "Sonar and GitLab", "freshness": "2026" }, { "name": "Technical Work AI Value Share", "value": 50, "unit": "%", "source": "METR AI Usage Survey", "freshness": "Mar 2026" }, { "name": "AI Workflow Reliance", "value": 67.7, "unit": "%", "source": "DORA and JetBrains", "freshness": "2025/2026" }, { "name": "Agentic Task Delegation", "value": 17.7, "unit": "%", "source": "Anthropic and arXiv", "freshness": "2025/2026" }, { "name": "Tool Ecosystem Reach", "value": 90.134995, "unit": "M", "source": "VS Code Marketplace", "freshness": "2026-05-16" } ], "data_sources": [ { "name": "Sonar and GitLab", "cadence": "Annual", "type": "survey" }, { "name": "METR AI Usage Survey", "cadence": "Annual", "type": "survey" }, { "name": "DORA and JetBrains", "cadence": "Annual", "type": "survey" }, { "name": "Anthropic and arXiv", "cadence": "Annual", "type": "research" }, { "name": "VS Code Marketplace", "cadence": "Continuous", "type": "api" } ] }, { "id": "support", "name": "SUPPORT", "full_name": "Customer Support", "score": 40.5, "previous_score": 37, "trend": [ 27.5, 37, 40.5 ], "status": "elevated", "weight": 0.15, "tier": 1, "description": "Share of customer support cases, deflection, production communications agents, and mature support operations involving AI systems.", "sub_indicators": [ { "name": "Cases Handled by AI", "value": 30, "unit": "%", "source": "Salesforce State of 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"description": "Degree of AI involvement in credit underwriting and lending decisions, with platform automation adjusted by product-market denominators.", "sub_indicators": [ { "name": "AI-Underwritten Personal Loan Proxy", "value": 38.2, "unit": "%", "source": "Upstart and TransUnion", "freshness": "2025" }, { "name": "AI Credit Decisioning (Banks)", "value": 20, "unit": "%", "source": "McKinsey Credit Risk Survey", "freshness": "H2 2024" } ], "data_sources": [ { "name": "Upstart and TransUnion", "cadence": "Quarterly", "type": "filing/report" }, { "name": "McKinsey Credit Risk Survey", "cadence": "Annual", "type": "survey" } ] }, { "id": "medical-dx", "name": "MEDICAL-DX", "full_name": "Medical Diagnosis", "score": 32.3, "previous_score": 21.7, "trend": [ 16.8, 21.7, 32.3 ], "status": "nominal", "weight": 0.12, "tier": 2, "description": "AI involvement in clinical diagnosis and imaging, combining FDA-cleared AI device capacity, imaging AI adoption, physician assistive-diagnosis use, and pathology adoption.", "sub_indicators": [ { "name": "FDA AI-Enabled Medical Devices", "value": 1430, "unit": "devices", "source": "FDA AI-Enabled Medical Devices", "freshness": "2026-05-16" }, { "name": "Radiology or Imaging AI Adoption", "value": 50, "unit": "%", "source": "KLAS Global Imaging AI", "freshness": "2025" }, { "name": "AI-Assisted Diagnosis Rate", "value": 17, "unit": "%", "source": "AMA Physician AI Sentiment Survey", "freshness": "2026" }, { "name": "Pathology AI Adoption", "value": 10, "unit": "%", "source": "Pathology AI adoption surveys", "freshness": "2025" } ], "data_sources": [ { "name": "FDA AI-Enabled Medical Devices", "cadence": "Continuous", "type": "database" }, { "name": "KLAS Global Imaging AI", "cadence": "Annual", "type": "report" }, { "name": "AMA Physician AI Sentiment Survey", "cadence": "Annual", "type": "survey" }, { "name": "Pathology AI adoption surveys", "cadence": "Annual", "type": "survey" } ] }, { "id": "legal-ai", "name": "LEGAL-AI", 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"freshness": "2025" } ], "data_sources": [ { "name": "ICIMS/Aptitude AI Adoption Report", "cadence": "Annual", "type": "survey" }, { "name": "SHRM State of AI in HR 2026", "cadence": "Annual", "type": "survey" }, { "name": "HireVue Platform Volume Update", "cadence": "Periodic", "type": "report" }, { "name": "HireVue 2026 Global AI in Hiring Report", "cadence": "Annual", "type": "report" } ] }, { "id": "education", "name": "EDUCATION", "full_name": "Education & Assessment", "score": 35.8, "previous_score": 18, "trend": [ 12.4, 18, 35.8 ], "status": "nominal", "weight": 0.07, "tier": 3, "description": "AI influence across student schoolwork, assessment and grading, teacher workflow, and AI-written student output.", "sub_indicators": [ { "name": "Students Using AI for Schoolwork", "value": 54, "unit": "%", "source": "Pew Research Center", "freshness": "2026" }, { "name": "AI-Graded Assessments", "value": 8, "unit": "%", "source": "Gradescope / Turnitin", "freshness": "2025" }, { "name": "Teachers Using AI for Work", "value": 37, "unit": "%", "source": "OECD Digital Education Outlook", "freshness": "2024" }, { "name": "Student Papers 80%+ AI-Written", "value": 15, "unit": "%", "source": "Turnitin AI Writing", "freshness": "2026" } ], "data_sources": [ { "name": "Pew Research Center", "cadence": "Annual", "type": "survey" }, { "name": "Gradescope / Turnitin", "cadence": "Annual", "type": "report" }, { "name": "OECD Digital Education Outlook", "cadence": "Triennial", "type": "report" }, { "name": "Turnitin AI Writing", "cadence": "Annual", "type": "report" } ] } ] }, "analysis_runs": [ { "run_id": "legacy-2024", "label": "2024 published baseline", "published_at": "", "measurement_period": "2024", "measurement_year": 2024, "methodology_version": "legacy-trend-v1", "composite_score": 39.7, "notes": "Archived published baseline from the original scoring series; retained for audit but not used in the headline comparison.", "is_public_series": false }, { "run_id": "current-method-2024", "label": "2024 recalculated history", "published_at": "2026-05-16", "measurement_period": "2024", "measurement_year": 2024, "methodology_version": "delegation-curve-v2", "composite_score": 31.5, "notes": "Backcast using the 2026 scoring frame. Source-level coverage is limited; 2024 values preserve the original 2024-to-2025 domain movement from the recalculated 2025 anchor.", "is_public_series": true }, { "run_id": "legacy-2025", "label": "2025 published baseline", "published_at": "2026-03-02", "measurement_period": "2025", "measurement_year": 2025, "methodology_version": "legacy-trend-v1", "composite_score": 46, "notes": "Archived March 2026 publication baseline from the original scoring series; retained for audit but not used as the headline prior after the 2026 source refresh.", "is_public_series": false }, { "run_id": "current-method-2025", "label": "2025 recalculated baseline", "published_at": "2026-05-16", "measurement_period": "2025", "measurement_year": 2025, "methodology_version": "delegation-curve-v2", "composite_score": 37.7, "notes": "Recalculated with the 2026 source and scoring rules so the 2026 update is compared using the same measuring stick. 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