As AI service ecosystems continue to evolve, competition in end-user markets increasingly shapes both AI service providers’technology licensing strategies and traditional service firms’ AI adoption decisions, with important implications for ecosystem stability and resilience. This study develops a game-theoretic framework to investigate how economies of scale, ecological feedback effects, and AI technology conversion rates jointly influence licensing arrangements, competitive positioning, and social welfare within the ecosystem. The results highlight three key insights. First, economies of scale and ecological feedback mechanisms significantly affect firms’ strategic incentives to license and adopt AI technologies. Second, while higher AI technology conversion rates facilitate licensing equilibria, they may also generate misalignments between private incentives and social efficiency. Third, the entry of Third-party AI providers can alter established patterns of technological collaboration and fundamentally reshape competitive relationships among ecosystem participants. By integrating market competition with technology licensing decisions, this study advances understanding of AI-enabled service ecosystems and offers actionable managerial insights for designing sustainable AI strategies and governance mechanisms.