AI Disruption at Scale: DeepSeek’s Open-Source Model and Its Macroeconomic Impact on Markets, Labor, and Global Growth

Valdemar Tamez () and Gbolahan Solomon Osho ()

International Journal of Business Management and Finance Research, 2025, vol. 8, issue 3, 1-11

Abstract: This study explores the macroeconomic implications of DeepSeek, a Chinese artificial intelligence (AI) startup that disrupted the global AI landscape through the release of DeepSeek-R1, an open-source, cost-efficient large language model. Developed with significantly lower training costs and released under an MIT license, DeepSeek’s model rivaled proprietary offerings from Western firms like OpenAI and Google, triggering financial market turbulence and investor realignment. The study analyzes how DeepSeek’s innovation challenged prevailing norms in AI infrastructure, democratized access to advanced models, and altered investment strategies, particularly within AI-focused ETFs. The authors investigate labor market disruptions, productivity enhancements, and shifts in economic power resulting from DeepSeek’s model. With projections of up to 300 million jobs impacted globally, the paper weighs the trade-offs between displacement and productivity gains, especially for emerging economies and small and medium-sized enterprises (SMEs). Geopolitically, DeepSeek's success amidst U.S. chip export bans highlights China's growing technological autonomy and inspires policy debates around global AI governance, ethical use, and export regulations. The research further evaluates AI's role in economic forecasting, regulation, and development strategy. As DeepSeek redefines what is possible with limited resources, it serves as a case study in innovation-led disruption with far-reaching socioeconomic consequences. The findings underscore the need for inclusive policies, ethical oversight, and international cooperation in navigating AI’s transformative potential.

Keywords: AI governance and regulation; Artificial intelligence disruption; DeepSeek-R1; Global technological competition; Labor market transformation; Macroeconomic impact; Open-source AI models. (search for similar items in EconPapers)
Date: 2025
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