The widespread diffusion of Artificial Intelligence (AI) is fundamentally reconfiguring how knowledge is created, recombined, and distributed throughout the startup development process. In line with the overarching theme of the conference, this track focuses on the AI Entrepreneurial Ecosystem (AIEE) through the lens of knowledge ecology. It investigates how algorithmic tools, data-driven systems, and predictive technologies alter the mechanisms of startup creation, development, and scale-up, including interconnected actors, entrepreneurs, venture capitalists, universities, incubators, and local policymakers.

Adopting a holistic knowledge ecology perspective is essential to understanding how knowledge assets flow, evolve, or degrade across digitally augmented environments. The integration of AI introduces novel mechanisms, such as automated ideation, data-driven spillovers, and algorithmically mediated matchmaking, that redefine the spatial and relational boundaries of startup communities. Within this framework, knowledge ecology serves as the common thread connecting the entire system: it illuminates how AI-driven spillovers originate and circulate through shifting actor relationships, how adaptive governance structures must regulate these dynamic interactions, and ultimately how these combined forces generate sustainable ecosystem outcomes.

This track invites theoretical, empirical, and policy-oriented contributions addressing key dynamics across multiple levels of analysis in different ecosystem settings (including, not limited to, resource-constrained regions):

• Knowledge Spillover Mechanisms & Dynamics: How AI diffusion alters traditional channels, velocity, and absorptive capacity during venture creation.
• Actor Behavior, Relational Dynamics, & Cultural Lenses: How AI reshapes the cognition, strategic actions, and risk assessment of entrepreneurs, mentors, and investors, considering socio-cultural norms variation within and across the ecosystems.
• Startup Growth & Ecosystem Outcomes: How AI impacts startup survival rates, scale-up capabilities, and the long-term ecosystem outcomes.
• Ecosystem Sustainability, Inclusivity, & Governance: Whether and how AI affect ESG pillars within the EE, intellectual property ownership and inclusivity.

Scholarly and Managerial Relevance From a scholarly perspective, this track bridges knowledge management, entrepreneurial ecosystem, digital entrepreneurship, and regional economics, offering new theoretical frameworks to explain how AI acts simultaneously as an enabler and a disruptor of knowledge ecologies.  From a managerial and policy perspective, understanding these dynamics provides actionable insights for incubator directors, venture capitalists, and policymakers seeking to build resilient, inclusive, and effective innovative entrepreneurial ecosystems capable of thriving in an increasingly algorithm-driven landscape.