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)
Table 1: Evolution of Business Models for GenAI Platforms
Figure 1: Proposed RSI model: architecture of technical (blue) and financial (red) flows. Percentages illustrate revenue distribution.
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
Table 2: Multi-dimensional Comparative Analysis of Business Models
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 ]
Table 3: Key Indicators for the Societal Impact of the RSI Model
Generative AI enables value creation through multi-stage collaboration among heterogeneous contributors, including training data, base models, fine-tuning behaviors, and prompts. However, how to fairly allocate the data value remains largely unexplored. This paper formulates multi-stage generative AI value allocation as a new research problem and identifies three core challenges: heterogeneous data contribution valuation, data rights mapping, and trustworthy execution. We propose AME (Attribution-Mapping-Execution) framework, a unified framework that integrates data contribution valuation, data rights mapping, and trustworthy execution into a single workflow. Experimental results demonstrate that AME framework achieves data value allocation outcomes more consistent with human reference judgments while maintaining low-cost trustworthy execution. Our work provides an initial foundation for value assessment and revenue allocation in generative AI data markets.
Yang Shi, Songwen Pei, Yang Gao +1
University of Shanghai for Science and Technology, Shanghai, China · Shanghai Institute of Technology, Shanghai, China
Generative model ecosystems increasingly operate as competitive multi-platform markets, where platforms strategically select models from a shared pool and users with heterogeneous preferences choose among them. Understanding how platforms interact, when market equilibria exist, how outcomes are shaped by model-providers, platforms, and user behavior, and how social welfare is affected is critical for fostering a beneficial market environment. In this paper, we formalize a three-layer model-platform-user market game and identify conditions for the existence of pure Nash equilibrium. Our analysis shows that market structure, whether platforms converge on similar models or differentiate by selecting distinct ones, depends not only on models' global average performance but also on their localized attraction to user groups. We further examine welfare outcomes and show that expanding the model pool does not necessarily increase user welfare or market diversity. Finally, we design novel best-response training schemes that allow model providers to strategically introduce new models into competitive markets.
Recent advances in generative AI systems have dramatically reduced the cost of digital production, fueling narratives that widespread participation in software creation will yield a proliferation of viable companies. This paper challenges that assumption. We introduce the Builder Saturation Effect, formalizing a model in which production scales elastically but human attention remains finite. In markets with near-zero marginal costs and free entry, increases in the number of producers dilute average attention and returns per producer, even as total output expands. Extending the framework to incorporate quality heterogeneity and reinforcement dynamics, we show that equilibrium outcomes exhibit declining average payoffs and increasing concentration, consistent with power-law-like distributions. These results suggest that AI-enabled, democratised production is more likely to intensify competition and produce winner-take-most outcomes than to generate broadly distributed entrepreneurial success. Contribution type: This paper is primarily a work of synthesis and applied formalisation. The individual theoretical ingredients - attention scarcity, free-entry dilution, superstar effects, preferential attachment - are well established in their respective literatures. The contribution is to combine them into a unified framework and direct the resulting predictions at a specific contemporary claim about AI-enabled entrepreneurship.