Emerging AI-Enabled Business Models and Their Impact on Startup Ecosystem Dynamics

Abstract

Artificial Intelligence (AI) is quickly changing how startups create, capture, and grow value. This paper outlines a research program to study new AI-enabled business models (AIBMs) and their impacts on startup ecosystem dynamics, including venture formation, funding flows, talent mobility, competitive structure, and policy responses. We combine existing theories on business models, dynamic capabilities, and technological disruption to introduce a typology of AIBMs. The types include Productized AI, Platform-as-Data, AI-as-Service, Hybrid Human+AI Offerings, and Data-Network effect models. We propose a mixed-methods approach for gathering primary data, which includes a multi-country survey of startups, semi-structured interviews, and comparative case studies, along with an analysis plan that features multivariate modeling and qualitative thematic analysis. To clarify the empirical plan for conference reviewers, we present a synthetic illustrative analysis, clearly marked as simulated. This analysis shows how the proposed methods can reveal significant connections between the type of AIBM, investor interest, and ecosystem outcomes. Finally, the paper explores implications for managers, investors, and policymakers, and suggests a plan for primary empirical research. Key contributions include a new typology of AIBMs, a research design based on primary data, and preliminary simulated evidence showing potential ecosystem effects.