> For the complete documentation index, see [llms.txt](https://applix-network.gitbook.io/applix-network-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://applix-network.gitbook.io/applix-network-documentation/problem-statement.md). # Problem Statement The rapid advancement of artificial intelligence (AI) and machine learning has created an unprecedented demand for high-performance computing resources. However, several significant challenges impede the progress and accessibility of AI development: **High Costs of Centralized Computing** Traditional centralized data centers require substantial capital investment for setup and maintenance. The cost of acquiring and operating high-performance GPUs, coupled with the expenses associated with cooling, electricity, and physical space, makes it prohibitively expensive for many researchers, developers, and small businesses. These costs create a barrier to entry, limiting access to essential computational resources. **Scalability Issues** Centralized computing models struggle with scalability. As AI models grow in complexity and data requirements increase, the demand for computational power intensifies. Scaling up centralized infrastructure to meet this demand is not only costly but also logistically challenging. Bottlenecks in data processing and resource allocation can lead to inefficiencies and slow down innovation. **Limited Accessibility** Access to high-performance computing resources is often restricted to well-funded organizations and institutions. Smaller entities, independent researchers, and developers frequently find themselves at a disadvantage, unable to compete due to a lack of affordable and available computational power. This disparity hinders the democratization of AI and stifles the potential for groundbreaking discoveries and developments. **Resource Underutilization** Many GPU resources remain underutilized when confined to specific tasks or organizations. During periods of low demand, these powerful resources sit idle, leading to inefficiencies and wasted potential. Optimizing the utilization of existing GPU resources is crucial for maximizing the value and impact of high-performance computing infrastructure. **Security and Trust Concerns** Centralized systems are vulnerable to security breaches, data theft, and cyber-attacks. Ensuring the integrity and confidentiality of sensitive data is a major concern for organizations leveraging AI. Additionally, trust issues arise when relying on third-party service providers for computational resources, as users must place significant trust in the provider's security measures and operational integrity. **Payment and Transaction Inefficiencies** The current methods of payment and transaction processing for renting computational power are often cumbersome and inefficient. Traditional payment systems involve multiple intermediaries, leading to delays and additional costs. There is a need for a more streamlined, transparent, and efficient transaction mechanism that can facilitate quick and secure payments between resource providers and users. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://applix-network.gitbook.io/applix-network-documentation/problem-statement.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.