Crypto projects that are directly developing AI models on blockchain, applying AI to decentralized applications or solving for AI-related issues are still low. (REUTERS)

Coinbase Highlights Potential of Artificial Intelligence for Cryptocurrencies

Adarsh V.KJune 3, 23 AI, Artificial Intelligence, cryptocurrency

Coinbase Global Inc., the biggest digital asset exchange in the US, has stated that the convergence of artificial intelligence and cryptocurrency presents a significant chance for entrepreneurs. This includes the potential to address some of the criticisms aimed at the technology by preventing certain excesses.

“As AI and blockchain applications mature, the disruption caused by these technologies could lead to areas of collaboration and the emergence of new use cases for cryptocurrencies that help address the specific societal challenges posed by AI,” said David Duong, director of research at Coinbase. In a research report on June 1.

However, cryptocurrency projects that develop AI models directly on the blockchain, apply AI in decentralized applications, or solve AI-related problems are still few and far between. The market capitalization of crypto projects directly involved in artificial intelligence is about 0.07% of the $772 million market capitalization of cryptocurrencies, according to data from research firm Messari. According to the report, the majority of crypto assets are dedicated to Bitcoin, smart contract platforms and stablecoins.

Crypto data platform CoinGecko’s AI graded tokens have seen a significant influx this year as ChatGPT becomes one of the fastest growing apps and owner OpenAI attracts billions of dollars in investment.

Duong listed a number of possible use cases that combine both technologies. For example, a decentralized data marketplace can help generative AI satisfy the demand for a variety of validated data to train its models. A token-based incentive mechanism could improve the quality of information received from those marketplaces, according to the report.

According to the report, other use cases include providing computing power from distributed networks using GPUs to AI projects to train their models, improving data credibility, and auditability of an AI algorithm’s opaque decision-making process.