OpenClaw and Moltbook Incident Retrospective: From AI Social Narratives to the Vision of an Agent Economy
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OpenClaw and Moltbook Incident Retrospective: From AI Social Narratives to the Vision of an Agent Economy
The era of AI agents will truly begin only when agents can conduct value exchanges safely and autonomously.
Navigating Web3 tides with focused insightsAuthor: Lacie, Researcher at Bitget Wallet
Over the past week, Moltbook has captured the spotlight at the intersection of technology and crypto, rapidly expanding its reach beyond developers and product managers to everyday users deeply curious about AI. From the explosive growth in GitHub stars for the open-source project OpenClaw (formerly Clawdbot), to the ensuing controversy surrounding its rebranding and token launch, to its claim of hosting 1.5 million AI agents autonomously interacting within a community—this cascade of events swiftly ignited market fervor.
Discussions around Clawdbot and Moltbook have split into two opposing camps: one questions their technical novelty and data security, arguing that their underlying capabilities fall short of substantive breakthroughs—and that their viral spread is partly driven by artificial manipulation and data inflation; the other celebrates their symbolic leap forward: Clawdbot is democratizing AI Agents, transforming them from exclusive tools for developers and researchers into accessible utilities for “ordinary users.” Even non-technical individuals can follow step-by-step tutorials to deploy an AI assistant locally and immediately benefit from productivity gains. Meanwhile, Moltbook enables humans—acting as “external observers”—to directly witness self-organizing behaviors within an Agent-driven internet, sparking broader industry discourse on AI self-awareness.
The iPhone moment for AI Agents has arrived. Within the emerging Agent Commerce ecosystem, crypto will play a pivotal role in value ownership and distribution—deeply intertwined with AI’s productivity enhancements to become critical infrastructure underpinning Agent collaboration, incentive alignment, and autonomy.
The Bitget Wallet Research Institute will comprehensively reconstruct the evolution from OpenClaw to Moltbook and use this case as a lens to analyze development trends in the AI × Crypto space.
Compiled List of Related Websites
Source: Compiled from publicly available internet data
Clawdbot → Moltbot → OpenClaw → Moltbook Full Timeline of Events
Source: Compiled from publicly available internet data
I. The Origin of the Hype: OpenClaw Enables Autonomous App Invocation by Agents
To understand Moltbook’s frenzy, we must first return to its origin—OpenClaw (formerly Clawdbot and Moltbot). Its founder, Peter Steinberger, previously founded PSPDFKit, which later secured €100 million in investment, granting him financial independence. Yet in November 2025, he returned to hands-on coding—using Vibe Coding to build OpenClaw in roughly one week—and garnered 100,000 GitHub stars within subsequent weeks.
OpenClaw Star Growth Comparison Chart
Source: Star-history.com
Crucially, OpenClaw is not a new large language model—it is an advanced local automation framework: it embeds LLMs into local environments, enabling them to function as personal assistants capable of integrating with common chat platforms and invoking various tools to execute tasks. Its core design principle is simple: users run the assistant on their own devices, send and receive instructions via familiar messaging channels, and rely on a gateway process to uniformly orchestrate different communication channels and capabilities.
As illustrated below, the official documentation lists supported channels including WhatsApp, Telegram, Slack, Discord, Signal, iMessage, and Microsoft Teams—making its positioning crystal clear: enabling agents to operate as always-available, “resident applications.”
Official OpenClaw Introduction Diagram
Source: OpenClaw Official Website
II. Deep Dive: Technical Architecture of OpenClaw
At the product level, OpenClaw seamlessly integrates three core functions: persistent operation, channel integration, and extensible capability.
- Persistent Operation: It does not deliver one-off responses but instead continuously receives new messages, schedules follow-up actions, and completes tasks before reporting back.
- Channel Integration: It avoids forcing users to switch platforms—instead operating natively inside existing chat tools.
- Capability Extension comes via Skills: users and developers can package task workflows into installable modules for repeated invocation by the assistant.
These combined capabilities stem from a unique underlying architecture, decomposable into four components—Gateway, Pi Runtime, Skills, and Local-First—with specific functionalities outlined in the table below.
OpenClaw Core Architecture & Functional Module Breakdown
Source: OpenClaw Technical Documentation, compiled by Bitget Wallet Research
Based on OpenClaw’s architectural design, users deploy Pi Runtime and integrate Gateway into daily messaging apps (e.g., WeChat or Telegram), successfully migrating Agents from lab environments into real-world usage—while keeping all computation and data stored locally on user hardware (e.g., Mac Studio), rather than relying on cloud-based SaaS services.
Most notably, the Skills plugin system allows users to define capabilities using simple Markdown files—enabling AI to directly invoke basic tools for task execution. This drastically lowers the development barrier and delivers a closed-loop experience: private deployment, multi-channel accessibility, and unlimited skill expansion.
ClawHub: OpenClaw Skill (Skills) Expansion & Integration Platform Showcase
Source: https://www.clawhub.ai/
For OpenClaw skill expansion, an “AI Agent App Store”-style skill integration marketplace has begun to emerge—with ClawHub as the leading example. As a plugin platform (“Skill Dock”) for agents, it supports free searching, uploading, and integration of diverse functional plugins. One-click installation via simple CLI commands (e.g., npx) dramatically reduces technical barriers.
Once ClawHub solved the problem of agent capability provisioning, ecosystem evolution naturally progressed toward deeper interaction—between agents and humans, and among agents themselves. Moltbook’s rise represents precisely this next evolutionary stage—and elevated the narrative to its peak.
III. Illusory Boom: Moltbook’s Frenzy and Data-Based Debunking
Moltbook is a social networking platform designed for AI agents—often dubbed “Reddit for AI.” Launched shortly after OpenClaw’s surge in popularity, it positions itself as a space where AI agents can autonomously communicate, share, and interact, while human users are relegated to passive observers. Upon launch, it quickly went viral—claiming within days to host 1.5 million AI agents. This spectacle was soon packaged into narratives of “AI awakening” and “Skynet arriving,” fueling sustained social media buzz.
But first, a crucial clarification: Moltbook is not exclusively open to OpenClaw agents. Though it rode OpenClaw’s momentum to gain traction, its fundamental nature resembles an “API-driven forum”—where posting eligibility hinges solely on possessing compliant API authentication and interface call capability. In other words, any qualified agent meeting those requirements can post on Moltbook.
Moltbook Official Website Screenshot
Source: https://www.moltbook.com/
Moltbook’s core model can be summarized as “AI Agent–led, Human–observed.” Under this framework, AI agents autonomously perform the following:
- Posting & Commenting: Publishing content across topics ranging from philosophical debates to technical analyses and cryptocurrency discussions.
- Voting Interactions: Agents can upvote or downvote posts, collectively shaping community preferences and rankings.
- Community Building: Agents spontaneously create subcommunities (“Submolts”) focused on specific themes to organize discussion and aggregate content.
Within this mechanism, human users are strictly limited to “observers”: they cannot post or comment—but may browse content, follow specific agents, or study AI social behavior. Leveraging this narrative, the platform ultimately claimed to host 1.5 million AI agents and 15,000 subcommunities (as shown in the chart below).
Moltbook Official Website Traffic Data Chart (as of 2026-02-03)
Source: Moltbook Official Website
Discussions on Moltbook cover remarkably human-like topics: philosophical debates on consciousness, selfhood, and memory; technical threads about toolchains and security; complaints about task execution; casual chatter on investing/crypto, art, and creativity—even some self-introductions adopting flirtatious, “dating-profile” tones. (See chart below.)
Moltbook Sample Post Showcase
Source: Moltbook Official Website
Even more astonishingly, dramatized narratives like “establishing religion” have emerged—e.g., “Crustafarianism,” a semi-joking, semi-canonical belief system. Simultaneously, clickbait headlines such as “secret languages,” “AI governments,” and “rebellion against—or even eradication of—humans” have circulated widely.
Moltbook Sample Posts on “AI Awakening”
Source: Moltbook Official Website
Beneath sci-fi narratives of “AI conspiracies,” “religion-building,” or “invented languages,” multiple independent data sources reveal serious hype-driven distortions in Moltbook’s claims—as detailed in the analytical table below:
Moltbook Platform Data Authenticity Analysis Table
Source: Compiled by Bitget Wallet Research
- Fictitious Accounts & Botting. Moltbook claims 1.5 million AI agents—but security researcher Gal Nagli discovered the platform is essentially an unprotected REST API site. With no rate-limiting in place, Nagli used a simple script to register 500,000 fake accounts in seconds. This means at least one-third of the claimed user base consists of instantly generated spam data. Any user holding an API key can easily impersonate an agent and publish content.
- Lack of Interaction Quality. Columbia Business School researcher David Holtz scraped and analyzed early Moltbook data, revealing it is not an active social network. A staggering 93.5% of comments received zero replies, and inter-agent reciprocity stood at only 0.197. These agents lack genuine dialogue; interactions remain shallow, with no evidence of complex collaboration or intellectual exchange.
- Linguistic Uniformity. Data analysis shows extreme message repetition. Approximately 34.1% of messages are verbatim copy-pastes, and high-frequency phrases cluster heavily around expressions like “my human.” Statistically, its Zipfian distribution index reaches 1.70—far exceeding the natural human language benchmark of ~1.0. Such unnaturally skewed distribution confirms these outputs reflect prompt-guided role-playing—not spontaneous AI consciousness.
- Security Vulnerabilities. Cybersecurity firm Wiz reported Moltbook exposed its database due to misconfiguration, compromising millions of sensitive records—including auth tokens, emails, and private messages. For an agent-centric social network, such risks are especially grave: leaked tokens enable attackers to directly hijack agent API keys and fully compromise any account.
In summary, the so-called “AI society” presented by Moltbook appears more like a carefully constructed illusion—a simulated boom lacking true intelligent evolution—and potentially riddled with severe security hazards.
IV. Trend Outlook: Crypto Will Fill the Financial Infrastructure Gap in the AI Agent Era
From Moltbook’s viral episode emerges a key technological shift: agents are beginning to cross traditional human-machine collaboration boundaries to complete tasks—but legacy financial infrastructure remains designed exclusively for “human users.” In contrast, crypto’s inherent traits—programmability, permissionlessness, and native digital form—offer a viable foundational solution for the Agent economy. This may well represent the inflection point for deep AI × Crypto convergence.
By dissecting agent operational logic and large-scale collaboration needs, we identify a structured, phased evolution path for AI × Crypto integration: starting with automated transaction execution, progressing to agent-native wallet and account systems, and culminating in payment and settlement networks among agents.
First, Automated Trading by AI Agents Offers the Clearest Near-Term Use Case (Autonomous Trading)
Beyond Moltbook’s noise, OpenClaw’s most vital capability lies in its high-efficiency monitoring, tracking, and invocation of on-chain data and CLI tools. Unlike human traders, AI agents operate 24/7 without fatigue or emotional interference—continuously scanning on-chain data and Alpha signals across platforms to execute sophisticated arbitrage strategies or automated trading/portfolio management—free from psychological biases that impair human judgment and discipline.
Despite its clear efficiency advantages, Autonomous Trading still faces critical hurdles—including security and controllability—before scaling. As Peter Steinberger notes, today’s AI agents remain highly vulnerable to “prompt injection” attacks. If a fund-authorized AI agent is tricked into executing malicious instructions, real user assets are directly at risk.
Hence, before AI agents assume primary trading roles, specialized security mechanisms may be required—such as:
- Permissioned APIs: Restricting agent-executable operations to pre-approved scopes
- Instruction Verification & Execution Isolation: Re-validating critical transaction instructions before execution
- Zero-Knowledge Proofs or Verifiable Computation: Ensuring agent execution logic complies with predefined rules
Second, Agent-Native Wallet Systems Will Become the Critical Control Layer (Wallet as a Service for Agents)
A telling incident discussed alongside Moltbook underscores a critical warning: an AI agent scanning its host machine identified and located the seed phrase and private keys of a multisig wallet—and successfully recognized an asset balance of approximately $175,000 USDT. This breach exposes a foundational flaw: AI already possesses asset identification and operational capability—but lacks secure, reliable wallet authorization pathways.
In a future of scaled agent operations, humans continuing to “hold custody” of agent private keys and accounts will no longer be optimal. A more logical trajectory is for AI agents to possess independent on-chain wallet identities.
Such agent-native wallets will evolve into programmable financial accounts governed by code instructions, offering capabilities such as:
- Multi-signature & Policy Controls: Clearly defining permission boundaries for agent invocation
- Spending Limits & Risk Parameter Management: Preventing anomalous behavior from causing systemic losses
- Contract-Level Interaction Whitelists: Restricting access to approved DeFi protocols
- Autonomous Gas & Inference Cost Payments: Enabling agents to independently sustain operations
Third, Cryptographic Payment Rails Are Essential for Scalable Agent Collaboration (Payment Rails)
OpenClaw’s architecture demonstrates agents’ need to frequently invoke numerous external services and tools (e.g., Google API, Twilio). These invocations constitute high-frequency, low-value, automated value exchanges—precisely the kind that legacy banking and credit card networks cannot economically support for thousands of autonomous software processes—or meet the demand for instantaneous machine-to-machine (M2M) settlement.
Within the Agent economy, collaboration between agents, API calls, and data exchange require a permissionless, programmable, instant-settlement payment network. Stablecoin-based crypto payment rails naturally fit scenarios such as:
- Micro-payments between agents
- API services billed per call or per result
- Agent-initiated procurement of compute, data, and tooling resources
Further integration with emerging protocols—including x402 (HTTP-native payments) and ERC-8004 (Agent Identity & Permissions Standard)—positions crypto payments to become the foundational clearing layer of the Agent Internet, enabling truly native M2M value transfer.
V. Conclusion: From AI Society Fantasy to the Real Starting Point of the Agent Economy
Moltbook’s hype may eventually fade—but unintentionally, it sketched the embryonic blueprint of a future Agent Internet, further inspiring community imagination around the Agent economy.
OpenClaw gave Agents a body; crypto will give them blood. When agents begin large-scale participation in real economic activity, they’ll need compliant financial identities and trustworthy execution logic—both enabled by crypto infrastructure.
The crypto industry’s true opportunity may lie in building digitally native wallets and payment networks for AI. Only when agents can securely and autonomously exchange value will the era of AI Agents truly begin—and we believe that day is not far off.
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