21+ research reports from leading institutions. Each entry includes a summary, the original source, and how we use the findings in our methodology.
We synthesize findings from these sources to weight our four risk factors: task automation potential, AI capability trajectory, economic incentives, and barriers.
Timeline estimates and industry outlooks are informed by the consensus across these studies, adjusted for recent AI developments.
Key statistics cited on our platform come directly from these peer-reviewed sources, ensuring accuracy and credibility.
We review new research monthly to refine our methodology and ensure our risk assessments reflect the latest findings.
The IMF provides macroeconomic analysis of AI's impact on global labor markets, with a focus on economic exposure and cross-country differences.
Foundational report estimating 40% of global jobs exposed to AI, with 60% in advanced economies. Explores both displacement and productivity enhancement potential.
How we use it: Primary source for global exposure statistics and advanced economy impact figures.
Examines how AI and IT skills are reshaping labor markets, creating new roles while demanding supply-side adjustments.
How we use it: Informs our understanding of emerging job categories and skill requirements.
Quantifies AI exposure across dozens of countries, distinguishing between automation and augmentation potential.
How we use it: Provides geographic context for our risk assessments.
The WEF's Future of Jobs reports are the definitive source for global employment projections and skill requirements.
Projects 92 million jobs displaced and 170 million created by 2030, with detailed breakdowns by sector and region.
How we use it: Primary source for job creation/destruction projections and reskilling statistics.
Deep dive into how LLMs affect different job categories, with granular analysis of tasks within occupations.
How we use it: Informs our task-level automation potential assessments.
Maps out four scenarios for global labor markets by 2030, depending on AI innovation pace and reskilling effectiveness.
How we use it: Provides scenario planning context for timeline estimates.
The OECD provides detailed analysis of AI's impact on developed economies, with a focus on skills and policy responses.
Estimates 27% of OECD jobs at high risk of automation. Comprehensive analysis of job quality and quantity impacts.
How we use it: Key source for OECD high-risk job statistics and policy recommendations.
Identifies demographic and occupational groups most vulnerable to AI-driven task automation.
How we use it: Informs our understanding of vulnerable worker populations.
Analyzes the link between AI exposure and wage growth, finding AI may widen the gap between top and bottom earners.
How we use it: Provides economic context for displacement impacts.
The ILO focuses on worker rights, social dialogue, and ensuring AI benefits are broadly shared.
Refines measurement of occupational exposure to generative AI, estimating both automation and augmentation potential.
How we use it: Informs our generative AI-specific risk assessments.
Policy brief on AI's potential to enhance job quality if governed well, while warning of digital divide risks.
How we use it: Provides policy context for our recommendations.
Examines AI in recruitment and management, arguing for human-in-the-loop systems to protect worker rights.
How we use it: Informs our understanding of AI in HR decisions.
McKinsey provides business-focused analysis of automation potential and productivity impacts.
Estimates up to 30% of US work hours could be automated by 2030, with generative AI accelerating the timeline.
How we use it: Key source for US-specific automation estimates and timeline projections.
Argues Europe must accelerate AI adoption, requiring massive investment in worker retraining.
How we use it: Provides European context and regional comparison.
Latest survey on AI adoption, revealing how organizations are moving from experimental to integrated AI agents.
How we use it: Informs our AI capability trajectory assessments.
Brookings provides policy-focused analysis with emphasis on worker retraining and social safety nets.
Critical examination of retraining feasibility. Workers with graduate degrees face 4x higher AI exposure.
How we use it: Key source for education-level exposure differentials and retraining limitations.
Stanford's Human-Centered AI Institute provides comprehensive annual assessments of AI progress.
Definitive annual report on AI state, with dedicated chapter on economic and labor market impacts.
How we use it: Informs our AI capability trajectory and adoption rate assessments.
PwC's AI Jobs Barometer tracks real-time labor market shifts through job posting analysis.
Analysis of 1 billion job ads showing AI-exposed sectors have 5x higher productivity growth.
How we use it: Provides real-time labor market validation of AI impact.
BCG research focuses on organizational AI adoption and worker experience.
Survey of 10,000+ workers revealing 70% of frontline workers lack AI tool access (silicon ceiling).
How we use it: Informs our understanding of AI adoption barriers.
World Bank focuses on AI impacts in developing economies and digital infrastructure gaps.
High-frequency job posting data shows AI-exposed occupations seeing relative decline in openings.
How we use it: Provides labor demand trend validation.
While AI exposure is lower in developing nations, lack of infrastructure makes workers more vulnerable.
How we use it: Provides developing economy context.
See how we synthesize these sources into our risk scoring methodology.
View Methodology