# Survey of Artificial Intelligence Model Marketplace
## 1 Introduction  
Artificial intelligence (AI) has become rapidly ubiquitous in various industries and has opened new business opportunities, affecting various smart-world systems [1, 2]. As the adoption of AI continues to accelerate, companies and individuals face challenges in sourcing, integrating, and customizing AI solutions that meet their unique needs. In the recent past, just like the case of big data marketplace [1] that trades data as commodities, the rise of AI marketplaces aims to solve these challenges by providing centralized and decentralized platforms where businesses or individuals can access a wide range of AI models. These platforms act as a bridge to establish relationships between AI developers,businesses, and researchers for AI development and deployment. The demand for the AI marketplace has increased significantly; this includes marketplaces capable of trading both predictive and generative AI models [3; 4; 5]. According to the Precedence Research report, the AI market's average Compound Annual Growth Rate (CAGR) is projected to increase by 19.1 % from 2024 to 2034 [6; 7; 8]. Like traditional markets, the AI marketplace, which is a good example of digital commodity-based markets, helps to control and manage the distribution and utilization of the AI model. An ideal AI marketplace also offers scalability and flexibility, enabling organizations to explore existing and customizable models for their use cases. This type of marketplace will revolutionize e-commerce, cloud computing, and software development. However, the design and development of AI marketplaces are still in the infancy stage [9]. On the other hand, it is trivial that new technologies always give rise to corresponding marketplaces for innovation and growth. For example, the development of the MPEG audio layer revolutionized traditional music by converting it into digital formats, bringing about a digital music marketplace like Spotify. Spotify allows artists to be compensated based on streams instead of distribution channels [10]. Similarly, global positioning system (GPS) technology enables the establishment of Uber, turning people with cars into service providers, thus monetizing personal transportation [5; 11; 12]. With the rapid expansion of AI-based technology in recent years, innovators and researchers have begun exploring the establishment of AI marketplaces to promote the development and sharing of AI models. Unlike traditional physical products or bonds, AI model markets do not transfer ownership to the buyer upon sale. Instead, AI model trainers often seek to maintain control over their models' distribution and post-sale performance. Thus, an ideal AI marketplace should focus on the growing demand for flexible, customizable solutions and shared platforms accessible to various stakeholders. Currently, there exist several challenges that are militating against the development of an effective AI marketplace, including:  
* _Model Distribution Management_: Unauthorized reselling of AI models might be possible in the AI marketplace. For example, a consumer might purchase an AI model and then resell it without the original developer's awareness, which violates intellectual property rights and diminishes the value of the original product [13]. * _Standard Governing Policies:_ Lack of universally accepted policies or regulations to govern the AI marketplace. Using the California Consumer Privacy Act (CCPA) as an example [14; 15]. Under CCPA, AI developers and sellers are required to ensure that sensitive personal data is protected before sale or distribution. However, this level of regulation is not required in other states or countries. An AI model trained on sensitive data might be sold outside California, where the privacy regulations are less stringent, but buyers might use it in California. * _Customer Feedback System:_ The lack of an effective customer feedback mechanism significantly hinders AI developers from improving their models.