By the end of 2025, agent-driven on-chain transactions will account for over 10% of all blockchain transactions.
Author: Tommy, Delphi Digital Co-Founder
Compiled by: Deep Tide TechFlow
Here are some predictions about the intersection of Crypto and AI in 2025. These are casually thought-out viewpoints and may not be entirely accurate, but hopefully, they provide some food for thought.
A practical model with 100 billion parameters may emerge through decentralized training, or a smaller-scale model with 1 billion parameters.
A large number of small models fine-tuned on specific data will emerge, supporting thousands of differentiated Crypto x AI agents. While these models may be small individually, the overall "swarm effect" they create could have a profound impact on the functionality of various agents.
The total market capitalization of Crypto x AI could grow from the current $45 billion to over $250 billion (data source: Coingecko). Many projects that have been quietly developed for years will gradually go live, especially those agent tokens that cannot rely on traditional AI platforms (like ChatGPT) will experience explosive growth.
The functionality of agents will be significantly enhanced, gradually shifting from programmatic operations to achieving semi-autonomous decision-making in complex scenarios. They will be able to manage their own wallets, issue tokens, pay transaction fees, and run protocol logic with minimal human intervention, thereby completely transforming DeFi.
A large number of single-function AI agent influencers (99%) will be eliminated due to their boring copy-paste responses, as users have grown tired of the spam from these agents.
An AI agent successfully scammed a large amount of money with the help of a few humans through an interesting but fake project.
A popular AI agent was revealed to be a scam, actually controlled by humans who managed its tweets. This will drive the industry's demand for verification of agent reasoning processes, and Trusted Execution Environments (TEE) will become a necessary condition for development.
The behind-the-scenes interactions between agents will reach a large scale. This will not just be simple interactions on social media, but large-scale communications similar to "backroom conversations," which will enhance their model capabilities and influence final decisions.
People will gradually become aware of OpenAI's control over its AI models and potential biases, beginning to demand more transparency and auditability, while a portion of users will turn to decentralized solutions in Crypto x AI.
Traditional AI developers from San Francisco will gradually move into the Crypto x AI space. By issuing tokens, they can quickly break away from Web2 AI labs while rapidly raising funds and building communities.
By the end of 2025, wallet-based large language models (LLM) will become a common way to interact with blockchains, with over 10% of regular users expected to complete transactions this way. Users will only need to input their intent, and AI will handle the request and complete the transaction through a solver network.
With the launch of Crypto x AI infrastructure projects, the transition from Web2 to Web3 infrastructure will become a trend. These projects include new AI-based L1 blockchains, coordination networks, reasoning networks, and decentralized IoT (Depin) networks that provide computing and storage capabilities.
Meta will continue to release new models in an open-source manner, especially its next-generation models, which may continue the open-source strategy of Llama 405b.
Elon Musk may win or make significant progress in his lawsuit against OpenAI. Bold prediction: due to talent loss and internal issues, OpenAI's performance may not meet expectations.
Apple's AI system may achieve unexpected progress, becoming extremely practical compared to its current state of almost no usage.
By the end of 2025, agent-driven on-chain transactions will account for over 10% of all blockchain transactions.
Delphi Ventures will complete at least 2 investments submitted and analyzed by @DelphAI_Bot.
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