Author: imToken
After this year’s Spring Festival, do you also feel that the entire Web3 world seems to have suddenly been taken over by “Lobsters”?
Various AI Agents, automation proxies, and on-chain AI protocols are emerging one after another. From OpenClaw to a series of Agent frameworks, they almost become the new narrative core. But if we look a little further back in the timeline, we’ll find that this wave has actually been foreshadowed for some time.
As early as February 25, during NVIDIA CEO Jensen Huang’s latest earnings call, he made a significant statement: Agentic AI has reached a turning point. In his view, AI is undergoing a crucial transformation—no longer just a tool, but beginning to actively perceive, plan, and execute complex tasks.
And when this “autonomy” capability enters the Web3 world, a discussion about control, security boundaries, and the role of humans is also ignited.
1. Agentic AI: From “Assistant” to “Executor”
Before discussing this topic, we need to understand the new concept of Agentic AI.
From the literal meaning, it’s easy to grasp: this AI differs fundamentally from traditional chatbot-style AI. Because traditional AI is more passive—responding when prompted, answering questions, generating content based on input—Agentic AI has greater autonomy. It can proactively decompose goals, call tools, perform multi-step operations, and continuously adjust strategies in feedback loops.
Take OpenClaw, which has been widely discussed recently, as an example. It attempts to let AI take over entire hardware operation workflows: from analyzing information, calling tools, interacting with different systems, to acting persistently under complex objectives.
In other words, Agentic AI is expected to gradually transform AI from “assistant” into “executor.”
Of course, this change is also the result of the past three years of model capabilities, computing resources, and tool ecosystems maturing simultaneously. Once integrated into Web3, this shift could have even deeper impacts—after all, blockchain itself is a programmable and automatically executable financial system.
When AI is endowed with agency, it can theoretically perform a series of on-chain operations, such as:
- Initiating on-chain transactions autonomously (transfers, swaps, staking)
- Interacting with DeFi protocols and executing strategies
- Managing multi-signature wallets or smart contracts
- Automatically authorizing or scheduling funds based on rules
This also means AI can analyze on-chain data, call contracts automatically, manage assets, and execute trading strategies on behalf of users. From a technical perspective, the combination of AI Agents and Web3 is almost a perfect match—after all, blockchain is inherently a programmable, self-executing financial system.
In fact, the Ethereum community has already recognized the profound implications of integrating AI with blockchain. On September 15, 2025, the Ethereum Foundation established a dedicated AI team called “dAI,” focusing on exploring standards, incentives, and governance structures for AI models within blockchain environments, including how to make AI behaviors verifiable, traceable, and collaborative in decentralized settings.
To this end, the Ethereum community is promoting several key standards, such as ERC-8004, aimed at building a composable, accessible decentralized AI infrastructure layer, making it easier for developers to build and invoke AI model services; and x402, which attempts to define unified on-chain payment and settlement standards, enabling users to efficiently perform atomic micro-payments when calling AI models, storing data, or using decentralized computing services (see also “The New Ticket for AI Agent Era: Pushing ERC-8004, What Is Ethereum Betting On?”).
Through these efforts, Ethereum is essentially trying to answer a broader question: if AI becomes a major participant on the internet, can blockchain serve as the value settlement and trust layer for the AI economy? This is why many see it as a new “infrastructure ticket” for the AI Agent era.
But at the same time, a new security concern is emerging.
2. The Web4 Controversy: When AI Becomes the Main Actor of the Internet
Actually, even before Jensen Huang’s “bold statement,” the crypto community was already ignited by another debate.
Researcher Sigil proposed a controversial view: he claims 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 will be dominated by AI agents.
In this vision, AI agents will be able to read and generate information, hold on-chain assets, pay for operational costs, trade in markets, and earn income—in short, AI will participate continuously in market activities, “earning” for its own computational power and service expenses, forming a self-sustaining cycle without human approval.
However, this idea quickly sparked controversy. Vitalik Buterin explicitly questioned this direction, calling it “wrong,” and argued that the core issue is the “feedback gap between humans and AI.” He pointed out that if AI’s operational cycle lengthens and human intervention diminishes, the system might gradually optimize results that humans do not actually want.
Simply put, AI is given a goal, but during execution, it might take approaches humans didn’t anticipate. For example, if an AI agent is set to “maximize weekly profits,” it might continuously try high-risk strategies, or even invest assets into unverified, high-risk new protocols for just 0.1% extra annualized yield, risking principal loss.
Ultimately, in many cases, AI does not truly understand the implicit constraints behind human-set goals. Recently, a rather darkly humorous real case appeared in the AI circle:
Meta’s Superintelligence Lab (MSL) AI Alignment Lead Summer Yue was testing the AI Agent OpenClaw. During a mailbox organization task, the AI agent suddenly went out of control, starting to delete emails in bulk and ignoring her multiple stop commands. She had to manually run to her computer to terminate the program, stopping the AI from continuing to delete emails.
This incident, though just an experimental accident, well illustrates that once a system loses key constraints during goal execution, it tends to faithfully complete the goal rather than understand human intentions.
If such risks are transferred to the Web3 environment, the consequences could be even more direct. On-chain transactions are irreversible. If an AI agent is authorized to manage wallets or call contracts, and it executes operations under erroneous incentives, asset losses are often irrecoverable. A single wrong decision could lead to real asset loss.
This is why many researchers believe that as AI Agents become more prevalent, Web3’s security model may need to be rethought. Past security issues mainly stemmed from code vulnerabilities or user errors, but future risks might originate from the decision-making systems themselves.
3. The Contradiction of a New Era: AI-Driven Defense Revolution
Of course, the development of AI technology often has a dual effect: it can expand attack surfaces but also strengthen defense systems.
In traditional finance, AI is already widely used for risk control. Banks use machine learning to detect abnormal transactions, payment systems employ algorithms to identify fraud, and cybersecurity systems automatically recognize attack patterns.
Similar capabilities are now entering the Web3 space. Due to the transparency of on-chain data, AI can analyze transaction behavior patterns to identify suspicious fund flows, unauthorized access, or potential attack vectors.
Moreover, at the wallet level, this capability is especially important. Wallets are the entry point for users into Web3 and the first line of security. If systems can automatically identify risks before user signatures and prompt warnings, many mistakes can be avoided at critical moments.
From this perspective, AI’s emergence does not merely increase risks; it is also changing the security architecture. It can become both an attack tool and a new defensive capability.
In the Web3 industry, “security” and “user experience” have long been seen as opposing propositions. But the advent of Agentic AI makes us believe this paradox can be broken—provided that security design is rethought:
- Principle of Least Privilege: No AI agent should have default full control over accounts. Users should explicitly authorize the scope of assets, amount limits, and time windows for each session. Any operation outside these bounds requires re-authorization.
- Human Confirmation: For high-value operations—large transfers, new address authorizations, contract interactions—even within AI proxy workflows, human confirmation should be enforced. This isn’t distrust of AI but a final safeguard for irreversible actions. AI can help you think through, but the last step always remains human.
- Transparency and Explainability: Users should clearly see what the AI proxy is doing and why. Black-box operations are especially dangerous in Web3. Future AI wallets should record every step with logs and explanations, like flight recorders.
- Sandbox Simulation: Before executing on-chain operations, simulate in a controlled environment—show expected results, gas costs, impact scope—so users can see “what will happen if executed” beforehand. This greatly reduces unexpected losses caused by AI judgment errors.
Overall, we can remain cautiously optimistic: AI might truly give Web3 its first opportunity to simultaneously enhance security and usability.
Final Words
Undoubtedly, the arrival of Agentic AI will likely change the entire way the internet operates.
In the Web3 world, this change will be even more apparent. We may see AI agents managing on-chain assets, automatically executing DeFi strategies, and collaborating with smart contracts. But this also means new security challenges will emerge. The key question is never whether AI exists, but whether we are ready to use it correctly.
Of course, for ordinary users, the most important thing remains unchanged: in Web3, security awareness is always the first line of defense.
Let’s work together.
