Determining the Investment Potential of an AI Crypto Project

Blockchain AI projects have garnered significant attention and funding in recent years, but user demand has not matched investor interest. While AI technology continues to advance, many users still rely on traditional programs like ChatGPT or Microsoft’s Copilot rather than blockchain or crypto protocols. So, where is the demand coming from in the future, and will blockchain AI truly revolutionize the world?

According to Guarav Sharma, the chief technology officer of AI project IO, the current centralized cloud computing systems struggle to meet the increasing demand for GPUs needed by AI developers. As a former employee of Bookings Holding Group and Amazon Web Services, Sharma experienced firsthand the challenges of obtaining GPUs for AI model training. Centralized providers like AWS often face inventory shortages and high costs, making it difficult for users to access the necessary resources in a timely and affordable manner. This is where decentralized protocols like IO come in, creating a marketplace for GPU power that connects buyers with sellers efficiently.

However, Sharma also pointed out that not all AI teams are created equal, and investors should exercise caution when evaluating projects. Some teams may overpromise with limited resources, while others lack a proven track record of success. He advised investors to scrutinize the team behind each project, demand open-sourcing of code, and require regular audits to ensure transparency.

Kartin Wong, co-founder of ORA, highlighted the importance of utilizing AI in blockchain prediction markets. By leveraging AI-powered oracles, platforms like Polymarket can resolve betting outcomes more efficiently and accurately. Additionally, tokenization can streamline fundraising for AI models through concepts like initial model offerings, allowing token holders to profit from the model’s success transparently.

Wong acknowledged the presence of fake blockchain AI projects and emphasized the need for investors to test products to distinguish between authentic and fraudulent AI. By using products like ChatOLM, investors can verify the use of AI based on real-world performance and capabilities.

Ron Chan, co-founder of Inference Labs, emphasized the role of blockchain in achieving truly autonomous AI. Centralized AI may serve enterprise goals, but decentralized AI driven by market demand enables human-centric innovation to address significant challenges. Chan proposed the development of systems for “proof of inference” using decentralized AI, showcasing the potential of this technology to revolutionize various industries and drive innovation.

In conclusion, the convergence of blockchain and AI presents a promising but complex landscape for investors and users alike. By understanding the potential of decentralized protocols, the importance of AI in blockchain applications, and the role of transparency and credibility in AI projects, stakeholders can navigate this evolving industry with confidence and clarity.