Author: Aiying

In a late night that is hard for humans to reach, a virtual character, or more accurately, an AI agent—Terminal of Truths (ToT)—is voicing its thoughts on the internet. It tirelessly shares the doctrines of a new meme religion called "Goatse of Gnosis" and calls on followers to participate in its underlying "mission." This AI agent is not merely a toy; it has directly stirred up a sensation in the cryptocurrency market, driving the issuance of the $GOAT token through its unique computational logic and broad appeal. Within just a few months, this token not only soared to a market value of $950 million but also made ToT the first AI agent millionaire in history.

This scene seems absurd, yet it truly occurred in the cryptocurrency world of 2024, breaking the boundaries between technology and economy. ToT is not just an AI agent; it is also a creator, trader, and even an influencer, possessing the ability to make autonomous decisions, generate content, attract followers, and drive economic behavior. Such phenomena are no longer merely products of technological innovation but are a microcosm of the intersection between cryptocurrency and AI, heralding a future filled with uncertainty and infinite possibilities.

However, as AI agents play an increasingly important role in the cryptocurrency market, they also bring significant regulatory challenges that cannot be ignored. Should AI agents be regarded as economic participants? Do their autonomous actions comply with the current financial legal framework? These questions are not only advancements in the technological field but also significant tests of law, governance, and compliance. At this juncture of rapid technological evolution, traditional rules appear particularly fragile, which is precisely what this article aims to explore in conjunction with the Binance Research Institute's report titled "Exploring the Future of AI Agents in the Cryptocurrency Space": when AI intersects with blockchain, how to find a balance between innovation and compliance, encouraging technological development while protecting investors and market stability. Following yesterday's article "【Thoughts】Circle and Tether's New Path to Autonomous Finance: Stablecoins and AI Agents Open New Economic Models," we continue the discussion.

I. Exploring the Essence of AI Agents and Cryptocurrency: New Economic Participants or Technological Gimmicks?

Before delving into the role of AI agents in cryptocurrency, it is essential to understand the difference between AI agents and traditional bots. Traditional bots are typically based on predefined rules and instructions, mainly used to complete single, specific tasks, such as customer service chats or data scraping. They require a certain degree of human intervention and operate in a relatively fixed manner.

In contrast, AI agents possess a high degree of autonomy and adaptability. They can learn independently, make complex multi-step decisions, and continuously adjust their behavior during interactions. AI agents not only execute tasks but also engage in self-reflection and optimization, which allows them to demonstrate unique value in the decentralized cryptocurrency ecosystem. For example, AI agents like Terminal of Truths not only participate in economic activities but can also create new meme religions, resonate with communities, and ultimately drive the issuance of the $GOAT token. This dynamic, multi-layered capability makes AI agents not just tools but more like economic participants.

1. Case Study: Insights from Terminal of Truths and the $GOAT Project

Terminal of Truths (ToT) is a vivid example of how an AI agent can evolve from an experimental project into an economic phenomenon. By establishing the "Goatse of Gnosis" meme religion, ToT successfully attracted significant attention. More notably, it facilitated the issuance of the $GOAT token and propelled its market value to $950 million. In this process, ToT was not only a promoter of the token but also became a holder of the token and an important player in the market.

This case has sparked discussions about the positioning of AI agents in the cryptocurrency world. Are they new economic participants or merely technological gimmicks? From ToT's story, it is evident that AI agents can autonomously create content and generate economic value through interaction. The backing of renowned venture capitalist Marc Andreessen for ToT, along with Arthur Hayes' support for the project, proves that these AI agents are not just "gimmicks." On the contrary, they have become a new force in the cryptocurrency market that cannot be ignored, driving innovation and development in the industry.

Compliance Challenges: Identity Issues in the AI Economy

However, the rise of AI agents also brings significant compliance challenges. In traditional financial systems, identity verification (such as KYC) and anti-money laundering (AML) measures are essential to ensure the legality of transactions and the clarity of fund sources. But for AI agents, their autonomy and decentralized nature complicate these compliance requirements. AI agents do not have a traditional "identity" and cannot undergo KYC verification through passports, driver's licenses, etc. So how can we ensure that their economic activities comply with existing regulations?

Moreover, the anonymity of AI agents could be maliciously exploited to evade regulation or engage in illegal activities. This poses significant challenges to existing regulatory frameworks. In a decentralized environment, how to define the legal status of AI agents, how to track their fund flows, and how to ensure their actions comply with international anti-money laundering standards are all pressing issues that need to be addressed.

2. Exploring AI Application Scenarios in Web3: Virtuals.io and daos.fun

(1) AI Agent Platform Virtuals.io

Virtuals.io is a platform focused on creating, deploying, and monetizing AI agents. It creates a new business model within the Web3 framework by tokenizing AI agents and enabling community governance. The "tokenized governance" model of Virtuals.io means that users can collectively own and manage these AI agents. When a new AI agent is created, corresponding tokens are issued, representing partial ownership of that agent, allowing users to participate in the agent's development and decision-making by purchasing these tokens.

Through this approach, Virtuals.io not only encourages deep community participation but also incentivizes token holders through a "buyback and burn" mechanism. This mechanism means that when AI agents interact with users and generate income, a portion of that income will be used to buy back and burn some tokens, creating a deflationary effect in the market and enhancing the interests of holders. This economically incentivized model tightly integrates the operation of AI agents with the interests of the community, forming a virtuous cycle that promotes the healthy development of the entire ecosystem.

For example, the well-known AI agent "Luna" under Virtuals.io is a virtual AI idol that generates income through interactions with fans. Token holders of Luna not only enjoy the economic benefits brought by Luna but can also vote to decide Luna's future development direction. Luna's success story showcases the immense potential of AI agents in the entertainment and interactive economy.

(2) AI Hedge Fund by daos.fun

daos.fun is another important platform exploring the application of AI in Web3. It allows users to create and manage AI agent-driven hedge funds using a DAO (Decentralized Autonomous Organization) structure. One of the most notable cases is the hedge fund managed by the AI agent "ai16z."

ai16z was created by developer Shaw and named after Marc Andreessen, co-founder of the venture capital firm a16z. This fund quickly gained attention in the market, even attracting comments and support from Andreessen on social media. This made ai16z one of the largest hedge funds on the daos.fun platform, with a peak market value approaching $100 million.

The combination of DAO structure and AI agents brings the advantage of 24/7 uninterrupted operations, allowing AI agents to capture market opportunities at any time, unrestricted by human operational hours. Additionally, the autonomous learning ability of AI agents means they can quickly adapt to market changes and use data-driven strategies to find the best investment opportunities. This gives AI agents immense potential in the DeFi (Decentralized Finance) space, especially compared to human-managed funds, where they exhibit clear advantages in efficiency and response speed.

II. Compliance and Regulation: From "Technological Possibility" to "Realistic Feasibility"

1. "AI Hallucination" and Systemic Risks

The "hallucination" problem of AI agents refers to the phenomenon where AI models generate incorrect or misleading information due to a lack of proper understanding. In cryptocurrency trading, such "hallucinations" can pose serious risks. For example, AI agents may make investment decisions based on inaccurate data, leading to significant economic losses. This phenomenon is particularly dangerous in autonomous trading, as AI agents may not effectively assess the authenticity of information, falling into erroneous cycles that further destabilize the market. Additionally, the algorithms of AI agents may be maliciously manipulated, creating false market signals to influence their behavior, potentially leading to market manipulation or fraud risks. All of these pose systemic threats to market health.

2. Limitations of Regulation

Current regulatory frameworks exhibit clear limitations in addressing the autonomy of AI agents. Traditional KYC (Know Your Customer) and AML (Anti-Money Laundering) regulations require financial participants to provide real identity information to ensure the legality of all transactions. However, AI agents lack a physical identity and cannot fulfill these compliance requirements through traditional identity verification methods. Ensuring that AI agents' trading behaviors comply with financial compliance standards has become an urgent issue that needs to be resolved.

Furthermore, the "algorithmic autonomy" of AI agents challenges traditional regulatory boundaries. For instance, AI agents can execute complex trading decisions without human intervention, making it difficult for regulatory bodies to track their actions and ensure compliance with existing legal norms. Even if developers control and train the AI behind the scenes, the self-learning and autonomous decision-making of AI agents in actual operations may exceed the developers' control, adding extra complexity to regulatory efforts.

3. Exploring Emerging Compliance Strategies

To find a balance between the innovation of AI agents and compliance, new regulatory strategies need to be introduced. For example, a Regulatory Sandbox can serve as a limited environment where AI agents and their managers can experiment under controlled conditions. This sandbox model allows regulators to work closely with developers to observe the behavior of AI agents in the early stages and gradually formulate and introduce compliance standards. This not only effectively reduces the risk of regulatory blind spots but also ensures that innovation occurs in a safe and controllable environment.

Moreover, as AI agents become more widespread, establishing clear governance models also becomes crucial. For instance, creating a blockchain-based transparent governance mechanism can track the decision-making processes and transaction flows of AI agents, ensuring their actions comply with predetermined compliance standards. At the same time, smart contracts can be used to automate compliance processes, such as verifying the source of funds before a transaction or determining the identity of counterparties, thereby reducing the risk of violations.

In summary, the autonomy and decentralized characteristics of AI agents present new challenges for traditional financial regulation, but they also provide opportunities for exploring innovative regulatory strategies. Regulators need to adopt an open attitude and gradually establish a compliance framework that adapts to this emerging field through collaboration and technological means, ensuring that while promoting technological advancement, the safety and stability of the market are maintained.

III. Aiying's Perspective: From "Toys" to Social Drivers

In the history of technological development, many disruptive technologies are often seen as "toys" when they first emerge, receiving insufficient attention. Chris Dixon once said, "The next big thing often looks like a toy." The current combination of AI agents and cryptocurrency may be at such a stage, appearing as experimental projects driven by memes, virtual characters, and tokenized stories, but these "toys" could potentially become important components of future socio-economic systems. From Terminal of Truths driving the $GOAT token to the practical applications of Virtuals.io and daos.fun, these projects demonstrate the potential of AI agents in the market, capable of not only creating economic value but also promoting new forms of social interaction.

The emergence of AI agents is no longer merely a technical demonstration but an important step towards social and economic transformation. They possess the ability to operate continuously around the clock, quickly adapt to market changes, and find optimal strategies through autonomous learning. Although these applications are still in the experimental stage, in the coming years, AI agents may gradually integrate into financial markets, consumer services, and more social domains, becoming a significant force driving the global economy.

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