Co-innovation between digital platforms and complementors is motivated by their interactions, especially on content creation platforms that emphasise creativity. With the platform monopoly, creators are increasingly dependent on the platform thus making the interaction directional. As the long-term effect of the dependency effect on co-innovation under multi-agent networks is currently under-researched, a novel asymmetric NK model is proposed in this paper to evaluate creators’ dependence on the platform through agent-based simulation. The results show that the internal interaction of creators has an inverted U-shaped effect on co-innovation, and the external dependency effect has a negative effect on co-innovation. Further results considering global complexity constraints show that there is a substitution effect between internal interaction and external dependency and that relying on a platform can facilitate co-innovation by reducing potential external risks under high environmental complexity. Moreover, exploratory innovation is equally conducive to co-innovation and enables creators to be less dependent. This study extends a new model for digital platform research and responds to discussions between interaction, exploration, and innovation in the literature.

Generative AI and agentic systems are reshaping platform competition and its long-run welfare consequences. Existing research, however, does not explain why similar AI interventions can produce concentration, costly quality races, cyclical repricing, and different welfare outcomes. A dynamic model distinguishes traditional consumers from AI-mediated consumers and characterizes adoption thresholds, recommendation-driven quality boundaries, price-feedback bifurcations, and welfare across long-run regimes. AI adoption interacts with network effects to narrow the range of stable symmetric competition. Recommendation amplification lowers the threshold for asymmetric quality states and causes profit-eroding quality competition to arise earlier. Price feedback can generate stable local cycles around the symmetric state and resilience thresholds around asymmetric leadership states. When quality and price feedback coexist, more dispersed or more active competition need not improve the model-implied welfare measure. Subject to the focal-episode assumptions, the model identifies adoption, recommendation amplification, and price feedback as distinct sources of regime divergence. It thereby explains why otherwise comparable AI-platform environments can develop different market structures and welfare rankings.