AI Agents and Human Coexistence: Exploring New Paradigms of Smart Finance on the AO Network

In the future world, AI agents may form a digital companionship/symbiotic relationship with humans. These autonomous agents can understand users' natural language needs, clarify intentions in conversations, and automatically break down tasks to achieve the desired outcomes.

The AO network has established an actor-based asynchronous parallel system that achieves massive scalability of computing power by reaching consensus only on the order of transactions, rather than on the entire computation process of contracts. The AR network plays the role of the transaction order consensus layer and the state storage layer.

Compared to other mainstream blockchain projects, AO's infrastructure is better suited to support complex computations, including the operation of AI models. The recent update of the WASM virtual machine has enabled AO's computing units to access 16GB of memory, sufficient to run large language models such as the unquantized version of the Falcon series like Llama 3.

AO also adopts WeaveDrive technology, allowing users to access Arweave data within AO as if they were accessing a local hard drive. This increases the possibilities for data sources and combinations, while also incentivizing users to upload more data to the Arweave network.

The system design of AO makes it possible to implement smart contracts that integrate AI agents. In AO, we can create AI agents that make intelligent decisions in the market, which may be competitive with each other or represent humans against humans. This design suggests that future quantitative trading may be more easily automated by AI.

In recent years, the development of DeFi has made it possible to execute various financial operations on-chain without relying on centralized entities. However, the vitality of the market still mainly depends on capital flow and decision-makers. With the development of AO applications, we may see a richer decentralized autonomous agent financial system, where AI agents can make real-time decisions within the network.

Some projects have already begun to realize this vision, among which the results of Autonomous Finance (AF) are the most noteworthy. AF focuses on developing AI-integrated financial applications on AO, mainly including core infrastructure, smart agent finance (AgentFi), and content finance (ContentFi).

AgentFi by AF utilizes on-chain data streams for self-learning and executes investment strategies within the AO ecosystem. These agents can operate autonomously without off-chain signals or human intervention. Typical autonomous agents include DCA asset management agents, self-balancing index funds, and autonomous hedge funds.

AF's ContentFi is a framework that monetizes data on Arweave into composable assets for AO processes. It allows data contributors or content funds to contribute data to the permaweb, which can serve as on-chain signals for autonomous agents and machine learning.

Another important project is Dexi, which is the key interactive interface for users to participate in AgentFi using proxies in AO. Dexi can autonomously identify, collect, and aggregate various financial data from the AO network, providing services for end users and AO applications.

In addition, Outcome is a prediction market built by the @puente_ai team that allows users to place bets on various events. In the future, users may be able to make automated bets by building autonomous agents based on large language models.

AgentFi on AO provides us with a new perspective, showcasing the possibility of directly deploying AI models on the blockchain and using AI agents to execute automated trading in the future. The design of AO+AR breaks through the limitations of traditional single-blockchain systems, and we look forward to seeing more innovative applications that combine AI agents to realize financial strategies.