An Ai Economy Beyond Big Tech Hyperscalers? A Taxonomy Of Machine Learning Platform Business Models

The AI economy is often viewed as dominated by big tech hyperscalers, who leverage their cloud infrastructures to deliver scalability, standardization, and tight integration. Yet in machine learning (ML), non-hyperscaler platform providers have emerged that specialize in data orchestration, third-party tool integration, and niche industry applications. This paper develops a taxonomy of ML platform business models to compare hyperscaler-based platforms with these alternatives in terms of value creation, delivery, and capture. We identify four archetypes (data orchestrators, aggregators, niche specialists, and cloud orchestrators) and examine boundary cases. Our findings show qualitative and enduring differences: cloud orchestrators follow efficiency-oriented logics of integration and standardization with limited openness, while aggregators and niche specialists employ more open governance and sourcing logics that foster innovation, specialization, and ecosystem diversity. This paper contributes by developing a taxonomy of ML platform business models and by showing how non-hyperscaler providers embody distinct value-creation logics beyond hyperscaler efficiency.