Revenue-Sharing as Infrastructure: A Distributed Business Model for Generative AI Platforms
Organizations: University of Yaoundé I, Yaoundé, Cameroon
Abstract
Generative AI platforms (Google AI Studio, OpenAI, Anthropic) provide infrastructures (APIs, models) that are transforming the application development ecosystem. Recent literature distinguishes three generations of business models: a first generation modeled on cloud computing (pay-per-use), a second characterized by diversification (freemium, subscriptions), and a third, emerging generation exploring multi-layer market architectures with revenue-sharing mechanisms. Despite these advances, current models impose a financial barrier to entry for developers, limiting innovation and excluding actors from emerging economies. This paper proposes and analyzes an original model, "Revenue-Sharing as Infrastructure" (RSI), where the platform offers its AI infrastructure for free and takes a percentage of the revenues generated by developers applications. This model reverses the traditional upstream payment logic and mobilizes concepts of value co-creation, incentive mechanisms, and multi-layer market architecture to build an original theoretical framework. A detailed comparative analysis shows that the RSI model lowers entry barriers for developers, aligns stakeholder interests, and could stimulate innovation in the ecosystem. Beyond its economic relevance, RSI has a major societal dimension: by enabling developers without initial capital to participate in the digital economy, it could unlock the "latent jobs dividend" in low-income countries, where mobile penetration reaches 84%, and help address local challenges in health, agriculture, and services. Finally, we discuss the conditions of feasibility and strategic implications for platforms and developers.
Figures & tables
| Dimension | First generation (2022-2023) | Second generation (2024-2025) | Third generation (emerging) |
|---|---|---|---|
| Dominant model | Pay-per-use (pay-per-token) | Freemium + differentiation | Multi-layer marketplaces |
| Flow direction | Upstream (developer → platform) | Mixed | Downstream potential |
| Risk | Entirely on developer | Partially mutualized | To be defined |
| Incentives | Linear (consumption) | Non-linear (quality, exclusivity) | Complex (revenue sharing) |
| Complexity | Low | Medium | High |
| Key citations | Wang et al. (2025) | Yu et al. (2025) | Ai et al. (2026), Keinan (2025) |
| Dimension | Pay-per-token | Freemium | Subscription | Marketplace | Hybrid | RSI (proposed) |
|---|---|---|---|---|---|---|
| Entry barrier for developers | High (variable costs) | Medium (quotas) | High (fixed cost) | Low | Medium | None |
| Risk borne by developer | High | Medium | High | Low | Medium | Low |
| Risk borne by platform | Low | Low | Low | High | Medium | High |
| Alignment of interests | Low | Low | Medium | High | Medium | Very high |
| Incentive for innovation | Constrained by costs | Medium | Medium | High | High | Maximized |
| Technical complexity | Low | Low | Low | High | Medium | Medium |
| Indicator | Value | Source |
|---|---|---|
| Adults owning a mobile (low/middle-income countries) | 84% | World Bank [ 13 ] |
| Mobile connections in Africa | 1.2 billion | WEF [ 15 ] |
| Share of temporary employment in Africa | up to 65% | African Development Bank [ 16 ] |
| Temporary work market in Africa | $100 billion | African Development Bank [ 16 ] |
| Adults earning money online (East Asia/Pacific) | >10% | World Bank [ 18 ] |
| Adults using the internet to learn | 26% | World Bank [ 18 ] |