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New Trends in the Integration of AI and encryption Technology: Intelligent Agents, Development Efficiency, and Open Technology Stacks
Three Strategic Directions for the Integration of AI and Encryption Technology
The combination of artificial intelligence and encryption technology is entering a period of explosive growth. This article will explore three key development directions in this field in detail.
Summary
The operation of intelligent agents on the blockchain has been proven to be feasible. This field is full of potential and innovative space, and is becoming one of the most groundbreaking directions in the AI and encryption sectors.
Large language models excel in code writing and are expected to improve further in the future. This is likely to increase developer efficiency by 2 to 10 times. Establishing high-quality benchmarks to evaluate the relevant capabilities of AI models will help understand their potential impact on the ecosystem.
Key elements include training data acquisition, computing power, model weight sharing, and output validation capabilities. The importance of this open technology stack lies in accelerating innovation and providing users with alternative choices.
1. Build an Active Smart Agent Economic Ecosystem
When AI agents begin to participate in blockchain activities, a new world full of possibilities has unfolded. Although it is currently impossible to accurately predict future developments, by observing existing innovations, we can glimpse the vast prospects of this field:
Future Development Direction
In the future, intelligent agents may manage complex projects that require multi-party economic coordination. For example, in the field of scientific research, agents can be responsible for finding therapeutic compounds for specific diseases:
In addition, agents can also perform simple tasks such as building personal websites and creating artworks, with unlimited possibilities for application scenarios.
Blockchain Advantages
Although agents can use both traditional financial channels and encryption systems simultaneously, encryption has unique advantages in certain areas:
From the perspective of technological development patterns, path dependency plays a key role. As more and more agents gain profits through encryption, encrypted connections are likely to become a core capability of agents.
Focus on key directions
Risk control mechanism: agents should not be given complete unconstrained freedom of action.
Promote non-speculative use cases: such as purchasing tickets, optimizing stablecoin investment portfolio returns, etc.
Development progress requirements: At least reach the prototype stage of the test network, preferably already running on the mainnet.
2. Enhance the capabilities of AI models in development
The advancements of large language models in the field of code writing may be particularly rapid, as this is a task that can be objectively assessed. Currently, AI-assisted programming tools have fundamentally changed software development. Given the anticipated rapid rate of progress, these models are likely to completely transform the software development process.
However, there are still some challenges present:
expected progress
The final major achievement will be: a completely new, high-quality, differentiated validator node client created entirely by AI.
3. Support Open and Decentralized AI Technology Stack
In the field of AI, the long-term power balance between open-source and closed-source models remains unclear. The simplest expectation at present is to maintain the status quo—large tech companies drive cutting-edge development, while open-source models quickly follow and gain unique advantages in specific application scenarios.
The importance of supporting the open AI technology stack is reflected in:
Open-source models accelerate innovation and iteration: The rapid improvements and fine-tuning of models by the open-source community demonstrate how the community can effectively complement the work of large AI companies.
Provide users with options: AI may be used as a control tool, and supporting an open-source AI technology stack can offer alternatives for users who do not trust centralized AI.
Currently, there are multiple projects in the ecosystem supporting the open AI technology stack, including data collection, decentralized computing power, decentralized training frameworks, etc.
expected progress
Hope to build more products at all levels of the open source AI technology stack:
Through these efforts, we hope to promote the deep integration of AI and encryption technology, opening up new possibilities for industry development.