Designing the AI Marketplace: Applying Matching Theory to Generative AI Adoption for Knowledge Equity and Human–AI Collaboration

The proliferation of generative AI tools such as ChatGPT, Claude, Gemini, and Perplexity has created an allocation challenge: how can heterogeneous users—students, researchers, and professionals—be systematically matched with the AI systems best suited to their needs? Existing approaches rely on benchmarking or trial-and-error adoption, which often leads to mismatches, inefficiencies, and inequities in access. This paper applies the framework of matching theory, particularly the Gale–Shapley Deferred Acceptance Algorithm, to conceptualize AI adoption as a two-sided market. Users express preferences over tools based on functionality, affordability, and usability, while tools (or their providers) exhibit implicit preferences over user categories aligned with intended use cases and market strategies. We formalize these preferences using matrices, demonstrate stable matchings through illustrative examples, and provide a proof sketch of algorithmic stability. The model ensures allocations that are stable, Pareto efficient, and strategy-proof, minimizing churn and enhancing long-term engagement. Beyond theory, we highlight practical implications for AI governance, equitable access, and dynamic coalition-based tool integration. By framing AI adoption as a market design problem rather than a simple consumer choice, this study provides a novel mechanism to optimize knowledge generation, fairness, and human–AI collaboration in the evolving digital ecosystem.