<![CDATA[This study aims to model the economic impact of human-AI collaboration using the "Global_AI_Content_Impact_Dataset.csv" spanning 2020–2025 across various countries and industries. The research employed regression models, including XGBoost, to assess the influence of the Human-AI Collaboration Rate (%) on Revenue Increase Due to AI (%) and Job Loss Due to AI (%). A key finding was the poor predictive performance of the models, evidenced by negative R² values, indicating that the selected features and models struggled to explain the variance in the economic outcomes. Consequently, interpretations of feature importance and observed sectoral paradoxes—such as high collaboration with low revenue increase in Marketing and Retail, or high collaboration with high job loss in Automotive and Manufacturing—are approached with extreme caution. These results are contextualized within the AI Productivity Paradox, suggesting that the observed period may represent an early phase where substantial complementary investments and organizational adjustments are still underway, masking immediate, quantifiable economic gains. The study underscores the limitations of current data in capturing the multifaceted nature of AI's economic integration and highlights the complex, evolving relationship between human-AI collaboration and economic performance, pointing towards the necessity for further research incorporating richer datasets, longitudinal analyses, and a deeper understanding of complementary organizational factors]]>
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