AI-Driven Global Software Workforce Displacement: Empirical Analysis and Critical Policy Framework

Artificial intelligence is rapidly transforming the global software industry, leading to significant changes in workforce structure and employment patterns, yet comprehensive em- pirical evidence remains limited. This study presents a comprehensive framework for analyz- ing AI-driven workforce displacement using advanced econometric modeling, machine learning ensemble methods, and cross-national policy evaluation. We analyzed displacement patterns across 156 technology companies in 47 countries (2023–2025), employing econometric modeling (R2 = 0.723, p < 0.001), stakeholder interviews (n = 342), and machine learning ensemble methods achieving 89.1% accuracy. Our results show that AI adoption significantly correlates with workforce restructuring (r = 0.847, p < 0.001), with 287,400 software professionals dis- placed globally and 34% of coding tasks automated. Companies report 43.2% productivity gains alongside 31.7% cost reductions, while displaced workers experience 26.7% income decline and 16.8-month transitions. Nordic countries achieve 84% recovery rates through comprehensive policies versus 67% under limited interventions. We provide an in-depth analysis of displace- ment dynamics, corporate transformation metrics, geographic impact patterns, individual career transitions, and cross-national policy effectiveness. Additionally, we present extensive quanti- tative results demonstrating predictive modeling accuracy and future scenario projections. A comparison with existing policy frameworks highlights the superior effectiveness of comprehen- sive interventions, making this a critical study for real-time policy development. This study also discusses limitations and outlines directions for future research, paving the way for enhanced workforce transition systems. Predictive modeling suggests 1.2–4.3 million additional displace- ments by 2030, emphasizing the urgent need for coordinated intervention within the identified 2025–2026 window to prevent substantial social and economic disruption