Problems
Centralization: The majority of AI models are stored on centralized servers that are managed by large organizations. This gives rise to worries over the privacy and security of the data, as well as the possibility of bias within the models themselves.
High Costs: Accessing powerful GPUs for AI modeling is expensive and requires significant investment. Projects may incur monthly expenses in the hundreds of thousands of dollars for training and inference.
Dispersed Data: Users must pay for various crypto premium tools to aggregate, analyze, and evaluate projects, complicating the process and collecting data from disparate sources requires the use of multiple tools and processes, often leading to inconsistencies and errors.
Information Overload and Reliability: Investors are often caught in a double bind when it comes to information. On one hand, they grapple with an overwhelming volume of data from various sources, making it difficult to discern what is truly relevant and actionable. On the other, critical information may be scarce or inaccessible, hindering informed decision-making.
Knowledge and Analytical Skills Gap: The cryptocurrency market, characterized by its volatility and complexity, demands a high level of financial acumen and analytical ability. However, many investors lack the necessary knowledge and skills to navigate this challenging landscape effectively including the skills needed to analyze data and formulate effective trading and investment strategies
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