Currently, many startups and academic research institutions are exploring the possibilities of combining blockchain and artificial intelligence (AI). The cryptocurrency exchange Bitfinex recently published an article discussing the potential of AI and blockchain. They believe that generative AI can be used to improve the exchange efficiency of main chain databases, autonomously improve workloads, or reduce the operating costs of blockchain protocols, helping to simplify operations and enhance efficiency, especially in the efficiency-focused DeFi sector.
More specifically, many companies are developing blockchain-customized AI models that businesses can use to automate trading, manage wallets, or enhance the efficiency and scalability of the main blockchain, thereby reducing the transaction costs of protocols. Bitfinex emphasizes that Stanford University is collaborating with Eliza Labs to research the progress of cryptocurrencies. They believe that AI applications on decentralized finance (DeFi) platforms (such as Ethereum and Solana platforms) may bring more capital and liquidity, thus increasing the market value of many competitive coins.
However, before commercially viable cryptocurrency AI models are truly launched, many AI and blockchain-related topics have already driven the rise of related cryptographic tokens, reaching a market value of billions of dollars. Projects like TAO and AIOZ plan to use AI models to improve blockchain protocols, and even some meme coins like GOAT have seen significant increases.
Statement: The article only represents the personal views and opinions of the author and does not represent the views and positions of BlockKe. All content and opinions are for reference only and do not constitute investment advice. Investors should make their own decisions and trades, and neither the author nor BlockKe will bear any responsibility for any direct or indirect losses incurred by the investor's trading.
"MICA Daily|Bitfinex Research: AI Models are Reshaping the Future of Blockchain DeFi Protocols" was first published on (BlockKe).