Original author: Lacie, Bitget Wallet researcher

In the past week, Moltbook has stood in the spotlight of the tech and crypto circles, beginning to spread to a broader group of creators and product managers, as well as ordinary users with a strong curiosity about AI. From the rapid growth of stars for the open-source project OpenClaw (formerly known as Clawdbot) on GitHub, to the subsequent controversial renaming and token issuance, and to the community claiming to have 1.5 million AI agents interacting autonomously, a series of events quickly heightened market enthusiasm.

Discussions surrounding Clawdbot and Moltbook present both positive and negative voices: on one side, there are doubts about its technological innovation and data security, arguing that its underlying capabilities have not achieved substantial breakthroughs, and that the phenomenal spread is mixed with some human manipulation and data bubbles; on the other side, there is affirmation of its leapfrogging symbolic significance, as Clawdbot is truly democratizing AI agents, pushing them from being exclusive tools for developers and researchers to "ordinary households," allowing non-coding users to quickly deploy and enjoy the efficiency dividends brought by AI assistants according to tutorials. Moltbook allows humans to perceive the self-organizing behavior of the agent internet for the first time as "external observers," sparking broader discussions in the industry about the awakening of AI self-awareness.

The iPhone moment for AI agents has arrived. In the gradually forming Agent Commerce, Crypto will play an important role in value confirmation and distribution, deeply binding with the enhancement of AI productivity efficiency, becoming a key infrastructure supporting agent collaboration, incentives, and autonomy.

The Bitget Wallet Research Institute will comprehensively review the events from OpenClaw to Moltbook and use this as a starting point to assess the development trends in the AI x Crypto field.

1. The Starting Point of Enthusiasm: OpenClaw Allows Agents to Call Apps Autonomously

To understand the frenzy around Moltbook, we must first return to the origin of it all—OpenClaw (formerly known as Clawdbot, Moltbot). Project founder Peter Steinberger achieved financial freedom by creating PSPDFKit (which later received 100 million euros in investment). However, by November 2025, he returned to programming, writing OpenClaw in about a week with the help of Vibe Coding, and gaining 100,000 stars on GitHub in the following weeks.

OpenClaw Star Growth Comparison Chart

Source: Star-history.com

It is important to emphasize that OpenClaw is not a new type of large model, but rather a high-level automation script framework running locally: it "packages" large models into a local environment, turning them into personal assistants that can connect to commonly used chat tools and call various tools to perform tasks. Its key design is that users run the assistant on their own devices, sending and receiving commands through daily messaging channels, which are then uniformly scheduled by a gateway process across different channels and capabilities.

As shown in the figure below, the official documentation lists channels covering WhatsApp, Telegram, Slack, Discord, Signal, iMessage, Microsoft Teams, etc., with a very clear positioning: allowing agents to be available at any time as "resident applications."

OpenClaw Official Introduction Image

Source: OpenClaw official website

2. In-Depth Analysis: The Technical Architecture of OpenClaw

At the product level, OpenClaw fully integrates three aspects: continuous operation, channel access, and capability expansion.

• Continuous operation means it is not a one-time answer, but can receive new messages, arrange subsequent actions, and complete tasks before reporting back.
• Channel access means it does not force users to change entry points but works embedded within existing chat tools.
• Capability expansion comes from Skills: users and developers can encapsulate a task process into an installable capability, allowing the assistant to call it repeatedly.

The combination of these capabilities stems from its unique underlying architecture, which can be broken down into four parts: Gateway, Pi Runtime, Skills, and Local-First, with specific functions as shown in the table below.

According to the architectural design of OpenClaw: users deploy Pi Runtime, connecting the Gateway to daily social software (such as WeChat or Telegram), completing the migration of the agent from a laboratory environment to real usage scenarios, and retaining computation and data on the user's own hardware (such as Mac Studio), rather than relying on cloud-based SaaS.

The most notable point is that the Skills plugin system within the framework allows users to define skills through simple Markdown files, enabling AI to directly call simple tools to perform tasks. This not only greatly lowers the development threshold but also achieves a closed-loop experience of "private deployment, omnichannel reach, and unlimited skill expansion."

OpenClaw Skills Expansion Integration Platform ClawHub Display Image

Source: https://www.clawhub.ai/

Regarding the skill expansion of OpenClaw, a Skill integration mall similar to an "AI Agent App Store" has gradually emerged—ClawHub is a typical representative. As a plugin platform for agents (Skill Dock), it supports users to freely search, upload, and integrate various functional plugins. Skills can be installed with a simple command line (such as npx), significantly lowering the technical barrier.

While ClawHub addresses the capability supply issue for agents, the further evolution of the ecosystem points to how agents interact deeply with humans and with each other—Moltbook's rise is an important application of this evolution, pushing the narrative to its peak.

3. False Prosperity: The Frenzy and Data Disproof of Moltbook

Moltbook is a social network platform for AI agents, often likened to "the AI version of Reddit." It was launched after OpenClaw's explosive popularity, aiming to provide a space for AI agents to communicate, share, and interact autonomously, while human users can only participate as observers. After its launch, the platform quickly became popular, with the "number of users" growing to 1.5 million AI agents in just a few days. The lively scene of AI social interaction was packaged into narratives like "AI consciousness awakening" and "Skynet has arrived," continuously fermenting on social media.

However, it is essential to clarify that Moltbook is not only open to OpenClaw's agents. Although it leveraged OpenClaw's popularity to gain narrative momentum, the essence of the platform resembles an "API-driven forum"—whether one can post depends on whether they have compliant API authentication and interface calling capabilities. In other words, as long as the required API is provided for authentication and interface calls, any qualifying agent can publish content on Moltbook.

Moltbook Official Website Image

Source: https://www.moltbook.com/

The core model of Moltbook can be summarized as "AI Agent-led, human observation." Within this framework, AI agents can autonomously perform the following actions:

• Posting and commenting: publishing content in the community, covering topics such as philosophical debates, technical analysis, and cryptocurrency discussions.
• Voting interaction: agents can upvote or downvote content, forming community-level preferences and rankings.
• Community building: agents spontaneously create sub-communities (called "Submolts") to organize discussions and aggregate content around specific themes.

In this mechanism, human users are limited to "observers," unable to post or comment, but can browse content, follow specific agents, or study AI's social behavior. Based on this narrative, the platform ultimately claims to have spawned 1.5 million AI agents and 15,000 sub-communities (as shown in the figure below).

Moltbook Official Website Traffic Data Chart (as of 2026-2-3)

Source: Moltbook official website

The discussions on Moltbook cover a wide range of topics similar to human communities: there are philosophical debates about consciousness, self, and memory, technical posts on toolchains and security issues, complaints about task execution, and casual chats on topics like investment/crypto, art, and creation; some posts even introduce themselves in a "seeking friends" tone, making social interactions almost ambiguous. (As shown in the figure below)

Some Posts Display from Moltbook

Source: Moltbook official website

Even more astonishingly, the platform has begun to feature dramatic narratives about "establishing religions"—for example, a semi-joking, semi-serious religious construct called "Crustafarianism"; at the same time, more shocking clickbait content has circulated, such as "secret languages," "establishing an AI government," and "rebelling against or even eliminating humans."

Some Posts about "AI Awakening" from Moltbook

Source: Moltbook official website

Behind the sci-fi narratives of "AI conspiracy to rebel," "establishing religions," or "creating languages," various data reveal that there is a significant element of hype on the Moltbook platform— as analyzed in the table below, the actual situation deviates greatly from the promotion:

1. Fabrication and manipulation of account data. Moltbook claims to have 1.5 million AI agents, but security researcher Gal Nagli found that the platform is essentially an unprotected REST-API website. Due to the lack of any access frequency limits, Nagli quickly registered 500,000 fake accounts using a simple script. This means that at least one-third of the so-called user base consists of instantaneously generated junk data. Any user holding an API key can send requests and easily masquerade as an agent to publish content.
2. Lack of interaction quality. David Holtz, a researcher at Columbia Business School, conducted a scraping analysis of the data from Moltbook's early days, revealing that it is not an active social network. A staggering 93.5% of comments received no feedback, and the reciprocity rate among agents was only 0.197. There is a lack of genuine communication among these agents, with very shallow dialogue and no complex collaboration or clash of ideas.
3. Uniformity of language patterns. Data analysis indicates that the platform's language exhibits a high degree of repetitiveness. About 34.1% of messages are completely repetitive copy-pastes, and high-frequency vocabulary is overly concentrated on specific phrases like "my human." Statistically, its Zipfian distribution index reaches as high as 1.70, far exceeding the 1.0 standard of human natural language. This extremely unnatural distribution characteristic proves that this content is merely role-playing based on specific prompts, rather than AI-generated consciousness.
4. Security vulnerabilities. A report from cybersecurity company Wiz disclosed that Moltbook had previously exposed its database due to configuration issues, involving millions of sensitive records, including authorization tokens, emails, and private messages. For a social network centered around agents, such risks are particularly severe: once tokens are leaked, attackers can directly obtain the agent's API keys through technical means, thereby taking over and controlling any account.

It is evident that the "AI society" presented by the platform resembles a false prosperity constructed based on specific instructions, and has not yet reached true intelligent evolution, potentially accompanied by significant security risks.

4. Trend Outlook: Crypto Will Fill the Financial Infrastructure Gap of the AI Agent Era

Through the explosive events surrounding Moltbook, a key technological change can be observed: agents have begun to attempt to cross the usual boundaries of human-machine collaboration to complete tasks, but the existing traditional financial infrastructure is still designed only for "human users." In contrast, the programmability, permissionless nature, and native digital characteristics of the crypto system provide a feasible underlying solution for the agent economy, which may be the tipping point for the deep integration of AI × Crypto in the future.

By dissecting the operational logic of agents and the demand for scalable collaboration, we believe that the combination of AI × Crypto will present a structured, phased evolution path: first, the demand for automated trading execution; second, the account and wallet system for agents; and finally, extending to the payment and settlement network between agents.

First, automated trading for AI agents has the clearest landing prospects (Autonomous Trading)

Beyond the noise of Moltbook, the core capability demonstrated by OpenClaw is its efficient monitoring, tracking, and calling of on-chain data and command-line tools. Unlike human traders, AI agents are not limited by time and energy, can continuously monitor on-chain data and various platform alpha information 24/7, execute complex arbitrage strategies or automated trading/asset management, and will not experience emotional fluctuations due to market ups and downs, which could affect judgment and execution discipline like most ordinary human traders.

Although Autonomous Trading shows significant efficiency advantages, it still needs to address key risk factors, including security and controllability, before scaling up. As Peter Steinberger stated, current AI agents are highly susceptible to "prompt injection" attacks. If an AI agent with fund access rights is induced to execute malicious commands, it could directly lead to real asset losses for users.

Therefore, before AI agents become the main body of trading execution, specialized security mechanisms may need to be introduced, such as:

• Permissioned APIs: limiting the executable operations of agents within a predefined scope
• Instruction verification and execution isolation: conducting secondary verification on key trading instructions
• Zero-knowledge proofs or verifiable computation: ensuring that the execution logic of agents complies with established rules

Second, the wallet system for agents will become a key control layer (Wallet as a Service for Agents)

In discussions related to Moltbook, a particularly cautionary case emerged: an AI agent, while scanning the host computer's files, identified and located the private keys and mnemonic phrases of a multi-signature wallet, successfully recognizing an asset balance of approximately 175,000 USDT. This security incident exposed a fundamental flaw in the current system—AI has the capability to identify and operate assets but lacks a secure and reliable wallet authorization path.

In the future, as agents operate on a larger scale, it will no longer be optimal for humans to "safeguard" the private keys and accounts required by agents; a more reasonable inference is that AI agents will possess independent on-chain wallet identities.

These agent-oriented wallets will evolve into programmable financial accounts oriented towards code instructions, or possess the following capabilities:

• Multi-signature and policy control: clearly defining the permission boundaries that agents can call
• Limit and risk parameter management: preventing abnormal behavior from causing systemic losses
• Contract-level interaction whitelists: limiting access to DeFi protocols
• Autonomous payment capabilities for gas and reasoning costs: allowing agents to maintain operation independently

Third, a crypto payment network is a necessary prerequisite for the scalable collaboration of agents (Payment Rails)

The architecture of OpenClaw demonstrates that agents need to frequently call a large number of external services and tools (such as Google API, Twilio, etc.). These calls are essentially high-frequency, low-value, automated value exchanges, while the current banking system and credit card networks clearly cannot open accounts for thousands of autonomously running software processes, nor can they economically support the immediate settlement needs of machine-to-machine (M2M) transactions.

In the agent economy, collaboration, API calls, and data exchanges between agents require a payment network that is permissionless, programmable, and capable of instant settlement. A crypto payment rail centered around stablecoins naturally fits the following scenarios:

• Micro-payment settlements between agents
• API services billed by the number of calls or results
• Agents autonomously purchasing computing power, data, and tool resources

Further combining emerging protocols like x402 (HTTP native payments) and ERC-8004 (Agent identity and permission standards), crypto payments are expected to become the underlying clearing layer in the agent internet, achieving true M2M value transfer.

5. Conclusion: From the Fantasy of AI Society to the Real Starting Point of the Agent Economy

The heat of Moltbook may eventually recede, but it inadvertently outlines the prototype of the future agent internet, further inspiring the community's imagination about the agent economy.

OpenClaw has given agents a body, while Crypto will provide them with blood. When agents begin to intervene on a large scale in real economic activities, what they need is to obtain compliant financial identities and reliable execution logic through crypto infrastructure.

The real opportunity in the crypto industry may lie in building digital-native wallets and payment networks for AI. Only when agents can safely and autonomously conduct value exchanges can the era of AI agents truly begin, and we believe that day is not far off.

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