Current Challenges in the Market ❗

💻 AI computing is currently dominated by a few tech giants (e.g., Google Cloud, AWS, OpenAI), leading to several problems:

Unfair resource allocation – Independent developers and small businesses struggle to access high-performance AI computing power.
Computing monopolization & high costs – AI training is expensive, making it inaccessible to the general public.
Single point of failure – Centralized AI computation is vulnerable to system outages, cyber-attacks, and censorship.

📊 AI training requires vast amounts of data, yet current data storage models face several issues:

User data exposure – Traditional AI platforms require users to upload data for training, raising privacy concerns.
Data monopolization – Large AI firms control most global AI training data, limiting diversity in AI development.
Data breach risks – Centralized storage is a prime target for hackers, leading to potential leaks of sensitive data.

⚡ Smart contracts currently operate on fixed logic, lacking adaptability, resulting in:

High Gas fees – Complex contract executions incur excessive costs, limiting DeFi and Web3 scalability.
Lack of smart optimization – DeFi trading strategies rely on static rules rather than AI-driven optimization.
Security vulnerabilities – Traditional contracts lack AI-based automated security audits, increasing attack risks.

🔗 AI computing tasks often rely on a single blockchain or computing platform, leading to:

Data silos across chains – AI requires data from multiple blockchains, but efficient cross-chain data sharing remains a challenge.
Incompatibility of protocols – Different blockchains use varying smart contract languages, making AI execution across chains inefficient.
Low execution efficiency – AI computation struggles with synchronization and consistency across multiple chains.

Last updated 3 months ago

💻 AI computing is currently dominated by a few tech giants (e.g., Google Cloud, AWS, OpenAI), leading to several problems:

Unfair resource allocation – Independent developers and small businesses struggle to access high-performance AI computing power.
Computing monopolization & high costs – AI training is expensive, making it inaccessible to the general public.
Single point of failure – Centralized AI computation is vulnerable to system outages, cyber-attacks, and censorship.

📊 AI training requires vast amounts of data, yet current data storage models face several issues:

User data exposure – Traditional AI platforms require users to upload data for training, raising privacy concerns.
Data monopolization – Large AI firms control most global AI training data, limiting diversity in AI development.
Data breach risks – Centralized storage is a prime target for hackers, leading to potential leaks of sensitive data.

⚡ Smart contracts currently operate on fixed logic, lacking adaptability, resulting in:

High Gas fees – Complex contract executions incur excessive costs, limiting DeFi and Web3 scalability.
Lack of smart optimization – DeFi trading strategies rely on static rules rather than AI-driven optimization.
Security vulnerabilities – Traditional contracts lack AI-based automated security audits, increasing attack risks.

🔗 AI computing tasks often rely on a single blockchain or computing platform, leading to:

Data silos across chains – AI requires data from multiple blockchains, but efficient cross-chain data sharing remains a challenge.
Incompatibility of protocols – Different blockchains use varying smart contract languages, making AI execution across chains inefficient.
Low execution efficiency – AI computation struggles with synchronization and consistency across multiple chains.

Last updated 3 months ago