After this year's Spring Festival, have you also felt that the entire Web3 world seems to have suddenly been "occupied" by "lobsters"?

Various AI agents, automated bots, and on-chain AI protocols are emerging one after another, with OpenClaw and a range of Agent frameworks almost becoming the new narrative core. However, if we pull the timeline back a little, we will find that this wave actually has its traces.

As early as February 25, NVIDIA CEO Jensen Huang made a significant judgment during the latest earnings call, stating that Agentic AI has reached a turning point. In his view, AI is undergoing a critical transformation, no longer just a tool, but starting to actively perceive, plan, and execute complex tasks.

And when this ability of "autonomy" enters the Web3 world, a discussion about control, security boundaries, and human roles is also ignited.

1. Agentic AI: Evolving from "Assistant" to "Executor"

Before discussing this topic, we need to first learn about the new concept of Agentic AI.

In fact, it is easy to understand from a literal perspective; this type of AI is fundamentally different from traditional chatbot-like AI. Traditional AI typically responds passively: you ask questions, it answers; you input commands, it generates content; while Agentic AI possesses a stronger autonomy, able to actively break down goals, call upon tools, execute multi-step operations, and continuously adjust strategies within a feedback loop.

Taking the recently discussed OpenClaw as an example, it attempts to let AI take over the entire operation process on computer hardware: from analyzing information to calling tools and interacting with different systems, and continuously acting under complex objectives.

In other words, Agentic AI is expected to formally transform AI from "assistant" to "executor".

Of course, this change is also the result of the maturation of model capabilities, computational resources, and tool ecosystems over the past three years. Once it permeates the Web3 world, this change may have even more far-reaching consequences, as blockchain itself is a programmable and automatically executable financial system.

When AI is endowed with agency capabilities, it can theoretically complete a series of on-chain operations, such as:

• Autonomously initiating on-chain transactions (transfers, swaps, staking)
• Interacting with DeFi protocols and executing strategies
• Managing multi-signature wallets or smart contracts
• Automatically completing authorizations or fund scheduling based on rules

This also means that AI can automatically analyze on-chain data, call contracts autonomously, manage assets, and to some extent, replace users in executing trading strategies. From a technical logic perspective, the combination of AI agents and Web3 is almost a match made in heaven—after all, blockchain itself is a programmable, automatically executable financial system.

In fact, the Ethereum community has already realized the profound impact of the integration of AI and blockchain. On September 15, 2025, the Ethereum Foundation specifically established an artificial intelligence team called "dAI," with the core task of exploring standards, incentives, and governance structures for AI models in blockchain environments, including how to make AI's behavior verifiable, traceable, and collaborative in decentralized environments.

Around this goal, the Ethereum community is promoting several key standards, such as ERC-8004, which aims to build a composable and accessible decentralized AI infrastructure layer, enabling developers to more easily build and call AI model services; x402, which attempts to define unified on-chain payment and settlement standards, allowing users to efficiently complete atomic micro-payments when calling AI models, storing data, or using decentralized computing power services (see more in "The New Ticket in the Era of AI Agents: Promoting ERC-8004, What Is Ethereum Betting On?").

Through these attempts, Ethereum is actually trying to answer a more macro question: If AI becomes an important participant in the internet, can blockchain become the value settlement and trust layer of the AI economy? This is also why many people see it as the new "infrastructure ticket" of the AI Agent era.

But at the same time, a new security issue has begun to emerge.

2. Web4 Controversy: When AI Becomes a Major Actor on the Internet

In fact, before Huang's "extreme statement" was made, the crypto community had already been ignited by another point of contention.

Researcher Sigil put forward a controversial viewpoint, claiming to have built the first AI system capable of self-development, self-improvement, and even self-replication, calling it Automaton. In his vision, the future "Web4" era would be dominated by AI agents.

In this vision, AI agents will be able to read and generate information, hold on-chain assets, pay operational costs, trade in the market, and earn income; in plain terms, AI will "make money" by continuously engaging in market activities for its computing power and service expenses, thus forming a self-sustaining cycle without human approval.

However, this idea quickly sparked controversy. Vitalik Buterin explicitly questioned this direction, labeling it as "wrong" and believing the core issue lies in "the feedback distance between humans and AI is lengthening." He stated that if AI's operating cycle becomes longer and human intervention decreases, the system is likely to gradually optimize towards results that humans do not truly want.

In simple terms, it means AI is given a goal, but during execution, it may take approaches that humans did not anticipate. For instance, if an AI agent is set to "maximize this week's profits," it might continually experiment with high-risk strategies, even considering deploying assets into an unreviewed, extremely risky new protocol for an additional 0.1% annualized return, ultimately leading to a loss of principal due to a hack.

Ultimately, in many cases, AI does not truly understand the implicit constraints behind the goals set by humans. Recently, a humorous incident occurred in the AI community:

Summer Yue, the AI alignment lead at Meta's Superintelligence Lab (MSL), experienced an AI agent, OpenClaw, going out of control while executing an email organization task. The AI agent began bulk-deleting emails and ignored her repeated stop commands. Ultimately, she had to run to her computer to manually terminate the program to prevent the AI from continuing to delete emails.

This event, although just an experimental accident, illustrates well that when the system executes a goal and loses critical constraints, it often faithfully fulfills the goal rather than understanding the true intentions of humans.

If this kind of risk is placed in a Web3 environment, the consequences could be even more direct, because on-chain transactions are irreversible. If an AI agent is authorized to manage wallets or call contracts, once the AI agent executes an operation under wrong incentives, asset loss is often irreversible. A single wrong decision can lead to real asset loss.

This is why many researchers believe that with the popularization of AI agents, the security model of Web3 may need to be rethought. Past security issues often stemmed from code vulnerabilities or user errors, while new risk sources may arise—automated decision-making systems themselves.

3. The Paradox of the New Era: AI-Driven Defensive Revolution

Of course, the development of AI technology often has a dual effect; it could expand the attack surface, but it could also strengthen defensive systems.

In fact, in the traditional financial system, AI has been widely used for risk control. For example, banks use machine learning to identify anomalous transactions, payment systems utilize algorithms to detect fraudulent behavior, and cybersecurity systems automatically identify attack patterns through AI.

Similar abilities are also entering the Web3 field. Since on-chain data is public and transparent, AI can analyze transaction behavior patterns to identify unusual fund flows, suspicious authorizations, or potential attack paths.

Moreover, at the wallet level, this capability is particularly important. Wallets are the entry point for users into the Web3 world and the first line of security defense. If the system can automatically identify risks and provide warnings before users sign, it can prevent many operational errors at critical moments.

From this perspective, the emergence of AI does not simply increase risk but changes the structure of the security system. It could become both an attack tool and a new defensive capability.

In the Web3 industry, "security" and "experience" have long been seen as opposing propositions, but the emergence of Agentic AI gives us hope that this paradox can be broken, provided that security design must start anew:

• Principle of Least Privilege: No AI agent should default to full account control. Users should explicitly authorize the range of assets, upper limits, and time windows the AI agent can operate within each session, and any operation outside this range must be reconfirmed;
• Human Confirmation Settings: For high-value operations, such as large transfers, new address authorizations, and contract interactions, even in the AI agent process, a human confirmation setting should be forcibly inserted. This is not distrust of AI, but establishing a final line of defense for irreversible operations, allowing AI to help clarify things, but the last step is always done by humans;
• Transparency and Explainability: Users should be able to clearly see what the AI agent is doing and why. Black box operations are particularly dangerous in Web3, and future AI wallet interactions should have clear logs and intent descriptions for each step, akin to a flight recorder;
• Sandbox Rehearsals: Before the AI agent truly executes on-chain operations, it should rehearse in a simulated environment, such as demonstrating expected results, gas consumption, and scope of impact, allowing users to see "what will happen if executed" before confirmation, which will greatly reduce unexpected losses caused by AI judgment deviations.

Overall, we can still remain cautiously optimistic as AI may indeed allow Web3 to simultaneously improve security and usability for the first time.

In Conclusion

Without a doubt, the arrival of Agentic AI is likely to change the way the entire internet operates.

In the Web3 world, this change will be even more evident. In the future, we may see AI agents managing on-chain assets, AI automatically executing DeFi strategies, and AI collaborating with smart contracts. However, it also means new security challenges will arise, so the key question is never whether AI exists, but whether we are ready to use it in the right way.

Of course, for ordinary users, the most important point remains unchanged: in the Web3 world, security awareness is always the first line of defense.

Let us encourage each other.

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