Coinbase Says AI Represents 'Important Opportunity' for Crypto

The tokens classified under the AI category by crypto data platform CoinGecko have seen large inflows this year as ChatGPT became one of the fastest applications and its owner OpenAI attracted billions of dollars in investment.

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)

The largest US digital asset exchange Coinbase Global Inc. said the intersection of artificial intelligence and crypto represents an important opportunity for entrepreneurs, including helping to prevent some of the excesses cited by critics of the technology.

“As applications within AI and blockchain mature, the disruptions these technologies represent may lead to areas of collaboration and the emergence of new use cases for crypto to help address specific societal challenges posed by AI,” David Duong, head of research at Coinbase, said in a June 1 research report.

However, crypto projects that are directly developing AI models on blockchain, applying AI to decentralized applications or solving for AI-related issues are still low. The total market cap of crypto projects that are directly involved in AI is around 0.07% of the total crypto market cap with $772 million, citing data from research firm Messari. The majority of crypto assets are for Bitcoin, smart contract platforms and stablecoins, according to the report.

The tokens classified under the AI category by crypto data platform CoinGecko have seen large inflows this year as ChatGPT became one of the fastest applications and its owner OpenAI attracted billions of dollars in investment.

Duong listed a range of potential use cases that combine both of the technologies. For example, decentralized data marketplaces may help generative AI meet its demand for verified and diverse set of data to train its model. A token-based incentive mechanism could improve quality of data sourced from those marketplaces, according to the report.

Other use cases include providing computational power from decentralized networks that utilize graphics processing units for AI projects to train their models, improving data authenticity and making AI algorithm's opaque decision-making process more auditable, the report said.