The Impact of Data Factors and Algorithms on AI Business Model Innovation
Abstract
driving AI business model innovation. Research indicates that data factors facilitate the transition from resource-dependent to innovationdriven models through scale accumulation, circulation activation, and analytical depth, algorithms reshape value creation via efficiency
revolution, personalization, and explainability, computing power redefines infrastructure rules through elastic supply, scenario-based demand,
and shared synergy. These elements form a dynamic data foundation-algorithm drive-computing support triangle, collectively addressing
bottlenecks (e.g., a chip dependency, <50% computing utilization), ethical risks, and talent gaps. Integrating empirical data (e.g., Chinas intelligent computing power reaching 1, 037.3 EFLOPS by 2025) and industry cases (e.g., East Data West Computing reducing PUE to 1.15),
we propose stratified implementation pathways and innovation strategies emphasizing the systemic balance of technical feasibility, market
adaptability, and ecosystem synergy, providing a framework for sustainable AI commercialization.
Keywords
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DOI: http://dx.doi.org/10.70711/aitr.v2i10.7138
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