# Survey of Artificial Intelligence Model Marketplace
## 1 Introduction
For example, a customer using a predictive analytics tool might experience substantial performance issues. However, without a structured feedback system integrated into the AI marketplace, developers are left unaware of these problems and are unable to address or resolve them. This disconnection impacts the customer experience and limits the developer's ability to refine and enhance their AI solutions. * _Low Efficiency:_ Multiple AI models have highly similar feature functionalities. Rather than leveraging existing models, significant labor and resources are often spent on redeveloping the same functionalities from scratch. If there is an AI platform that can collect all existing AI models to be utilized by developers in building AI models upon pre-existing models, organizations could significantly reduce development costs and accelerate deployment timelines while avoiding redundancy in feature development [13; 16]. * _High Energy Consumption with Low Productivity:_ According to the bank company PCBB, the electricity required to train a single AI model exceeds the annual consumption of approximately 130 U.S. households [17]. According to ClearML 3 rd global AI research survey, 15 % used less than 50 % of their GPU utilization, 53 % used 50 % to 70 %, while only 7 % believed their GPU utilization is over 85 %. Optimizing GPU utilization to reduce the overall energy consumption will be a big challenge for the current AI development [18]. * _High Cost:_ Stanford researchers replicated models such as GPT-3, OPT-175B, GPT-4, and Gemini Ultra. According to their testing result, training ChatGPT-4 incurred a cost of $40 million, while the R&D expenses for Gemini Ultra accounted for 49 % of the total cost [19]. How to promote cooperation between developers to reduce overall costs is a challenge for the AI marketplace. * _Imprecise Pricing Strategy:_ Lack of an effective pricing strategy guiding AI developers and model buyers creates significant challenges in AI marketplaces. Relying solely on sellers to determine prices can lead to biased or unfair pricing strategies, potentially alienating buyers and undermining trust in the marketplace. This imbalance highlights the need for mechanisms that improve pricing efficiency by identifying a fair and balanced price point that aligns the interests of both sellers and buyers. Developing such mechanisms will be a critical task for ensuring the long-term sustainability and fairness of AI marketplaces [20]. * _AI Ethics, Security and Privacy Issues:_ The Lack of a robust guiding framework on AI ethics, privacy, and security issues militates against the establishment of an ideal AI marketplace. For example, an AI model might produce biased or discriminatory results or operate as a \"black box\" with little transparency, raising ethical concerns. Privacy issues also arise, as AI models are often trained on sensitive data (i.e., healthcare, etc.) that, if mishandled, could lead to breaches or misuse. Security risks further complicate the landscape, with vulnerabilities in AI (a double-edged sword) potentially exposing users to harm or exploitation. Without addressing these critical areas, trust in the marketplace is eroded, limiting its ability to foster responsible innovation and deliver reliable, ethical, and secure AI solutions [9; 15; 21; 22; 23]. Based on the identified challenges above and the existing efforts summarized by Table 1, there is room for more efforts guiding the end-to-end design of an effective marketplace for AI models. To address these issues, this article aims to examine the unique characteristics of an effective AI marketplace, reviews existing AI marketplaces to identify areas that need improvement, and proposes a three-dimensional problem space to evaluate AI marketplaces from its efficient, secure, and user-centric perspective.