At the Eye of the OpenClaw Storm: Agents Are the New Demographic Dividend, Context Is Humanity's Moat | Ronghui

Rising from zero to become the most-starred open-source project in GitHub history in just four months, OpenClaw🦞's explosive growth was no accident. Over the past twelve months, AI coding tools led by Claude Code have pushed large-model engineering in programming scenarios to unprecedented heights, achieving a leap from code generation to autonomously completing complex engineering tasks.

At the eye of the storm stirred up by OpenClaw and Claude Code, how do we understand the technological evolution behind it, re-examine our spatiotemporal coordinates, and discover entirely new entrepreneurial opportunities?

Recently, Gaorong Ventures and Amazon Web Services organized a closed-door session. Four frontline experts from Amazon Web Services, SiliconFlow, Moonshot AI, and Pine AI offered in-depth analysis of the transformative opportunities brought by Claude Code and OpenClaw across three dimensions: technical deconstruction, cognitive reframing, and ecosystem practice.

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I. Technical Deconstruction: OpenClaw, the First "AI Operating System" That Grants Models Full Machine Permissions

"Unlike other agent frameworks, OpenClaw essentially provides an 'operating system' for AI — the first framework to hand over complete real-machine permissions to models." Jiade Wu, Senior Specialist at Amazon Web Services Solutions, led with a technical architecture perspective to parse OpenClaw's core differentiators. He noted that OpenClaw fundamentally does the engineering work above the model layer, making a breakthrough, unlimited amplification of the underlying model's potential for the first time.

Four-Layer Architecture: Enabling Personalized Task Execution

• L1 — LLM Abstraction Layer: Unified API integration for various large models, providing standardized inference interfaces. This is OpenClaw's foundational layer for calling underlying model capabilities.
• L2 — Agent Loop Engine: Bears core responsibility for agent looping and scheduling, encompassing four key steps: message reception, context assembly, model inference, and tool execution.
• L3 — Code Generation & Customization Layer: Capable of writing code on-site and completing personalized custom tasks, allowing OpenClaw to address diverse scenario requirements.
• L4 — Session Routing & Access Layer: Handles session routing, forwarding, and exchange, supporting multi-terminal, multi-channel access so users can invoke OpenClaw from different devices anytime.

Three-Layer Context Injection Mechanism: The More You Use It, the More It Knows You

Through an elegant three-layer context injection mechanism, OpenClaw gives AI agents "long-term memory," achieving "the more it does, the more it remembers, the more默契 it becomes." Wu pointed out that so-called "memory," from an engineering standpoint, means finding the corresponding context and loading it in for the large model to understand.

• Soul File (Soul.md): This is your preset persona document in front of OpenClaw, pre-packaged into context every time OpenClaw awakens, so the "lobster" can grow increasingly默契 with you.
• Long-Term Memory File (Memory.md): This is one of OpenClaw's most ingenious memory system designs. Think of it as a long-term memory summary file, automatically injected every session; simultaneously, it retrieves relevant fragments from historical logs through semantic search to load related context.
• Daily Log (YYYY-MM-DD.MD): Retrieves recent context on demand through semantic search, ensuring continuity with recent interaction scenarios.

Signature Mechanisms: Enabling Autonomous Execution and Multi-Task Coordination

OpenClaw also designed multiple differentiated mechanisms to push beyond agent capability boundaries.

• Concurrent Task Queue (Lane Queue): Effectively addresses the need for simultaneous execution of different tasks.
• Heartbeat Mechanism 💓 (Heartbeat): Periodically autonomously awakens the agent to perform inspection tasks (such as system status checks, to-do reminders, etc.), allowing OpenClaw to automatically wake up at fixed times and execute preset tasks.
• Three-Tier Model Routing Strategy: Automatically matches different large models based on task complexity (simple, medium, complex), achieving optimal resource allocation.
• Skills Ecosystem: A Skill is essentially a "tool instruction manual" describing how to enable large models to call tools more efficiently and with higher quality to complete tasks. OpenClaw's Skills ecosystem is rapidly expanding, providing rich tool support for diverse scenario needs.

Wu noted that Amazon Web Services is also actively exploring OpenClaw's commercial future with partners. Drawing from industry practice, Wu shared several commercialization directions: OpenClaw managed services; Skills Marketplace; dual-track interfaces — beyond human-to-agent interaction interfaces, building agent-to-agent interaction interfaces; and a team version of OpenClaw oriented toward enterprise team collaboration scenarios.

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II. Cognitive Reframing: In the Agent Era, Redrawing the Time/Resource Coordinate System

"The intensity of change in January 2026 roughly equals six months of the past 25 years." Amidst massive change and technological torrents, Pan Yang, Co-founder of SiliconFlow, shared his thinking on future agent-era transformations from industry trends and cognitive dimensions.

Large Models and Agents Are Shattering Software

Yang proposed that in the evolution of the technology world, there were originally only programs, no software. Bill Gates's invention of the software sales model drove software commercialization. To enable distribution, delivery, and charging, all functionality had to attach itself to the "software" container.

Large models and agents are shattering software: In the past, functionality was packaged into software and sold wholesale to everyone; today, large models possess all capabilities and can generate on-demand, instantly, for each individual.

He further predicted that we are currently in a transitional state: For now, Skills are an excellent way to deliver functionality and results; but when token costs drop 100x and generation speed increases 100x, entering a stage of "instant function generation → use-as-feedback → infinite iteration," Skills may be rapidly displaced. The endgame of AI coding may not be "generating code" but "abandoning code" — we need something, so we have the model generate it.

Redrawing the Coordinate System in the Agent Era

Facing agent-driven transformation, we need to redraw our coordinate system to better comprehend the new world.

📍 Time Coordinate: 1 Human Day = 100 Agent Days

Everyone must recognize that the time scale of the digital world where agents reside differs fundamentally from the physical world where humans exist. What humans accomplish in one hour, agents complete in 100 hours; one human day corresponds to 100 agent days. This means agents can complete complex tasks with efficiency far exceeding human capability — for example, rebuilding society after human migration to Mars might take a century for humans, while agents could complete all infrastructure in just 1-2 years.

📍 Resource Coordinate: Forget DAU, Focus on TPD

In 2025 and earlier, the dilemma of building AI applications was: more users meant more tokens burned, meant greater losses; in February 2026, the industry reached a critical inflection point — the value and returns brought by consuming $100 of tokens with SOTA large models already exceeded $100. This means that once the right product and commercialization direction is found, token consumption equals value creation. Therefore, Yang believes the most direct definition of an "AI-native product" today is: anything that achieves results by burning tokens is an AI-native program. "We should forget old metrics (DAU) and focus on new metrics (TPD, Tokens Per Day)."

Agents Are the New Demographic Dividend; Stop Developing Software for Humans

Yang offered a striking perspective: Agents are the new demographic dividend. China's internet and mobile internet development previously relied on demographic dividend; in the future, agents can both consume and create value, becoming new "consumers" and "producers," containing enormous commercial opportunity. There are 6 billion mobile internet users globally; if each person has 100 agents, that's 600 billion agents, and the actual number far exceeds this.

This means entrepreneurship needs to shift from "developing software for humans" to "building infrastructure for agents." This has two meanings: 1) Infrastructure of Agent — infrastructure for building agents; 2) Infrastructure for Agent — infrastructure to be called by agents, which harbors massive opportunity.

In closing, Yang offered several pieces of advice for today's entrepreneurs:

1. Stop developing software for humans; your next major customer may be an agent;
2. Redefine your North Star metric — look at TPD, not DAU;
3. Deliver results, not tools;
4. Be a "builder," "make your hands dirty."

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III. Ecosystem Practice: KimiClaw and PineClaw, From Cloud Deployment to Letting Agents Reach the Real World

OpenClaw's viral success has also spawned rich industry practice, with representatives from two Gaorong portfolio companies sharing their latest explorations.

KimiClaw: One-Click Cloud Deployment

Feihu Tang, Developer Relations Lead at Moonshot AI, introduced that Kimi rapidly launched KimiClaw during the Spring Festival, enabling one-click deployment. Users need not purchase hardware or servers, nor input code, to quickly access OpenClaw-related functionality.

The one-click-deployed KimiClaw possesses comprehensive capabilities including web search, scheduled tasks, file processing, multi-platform access, data analysis, and code generation; connected to the ClawHub community, it enables one-click installation of thousands of Skills. Users have explored various use cases with KimiClaw, such as investment research, information monitoring, and knowledge management.

The ability to be the fastest domestically to launch cloud-based rapid OpenClaw deployment stems from Kimi's long-term technical accumulation in the agent field — from Kimi Explorer Edition in November 2024, to the Kimi Deep Research model in June 2025, the K2 model in July, OK Computer in September, and the K2.5 model released and open-sourced in January 2026, Kimi has consistently深耕 agent technology. The K2.5 model introduced Agent swarm functionality, scheduling up to 100 agent分身 to process 1,500 steps in parallel and complete more complex tasks.

At the end of January, OpenClaw officially supported the Kimi K2.5 model. According to OpenRouter rankings, K2.5 ranks first in OpenClaw's model call leaderboard, becoming many users' preferred choice for "raising shrimp" — the K2.5 model achieves a perfect balance between cost and capability.

Tang also shared an increasingly common phenomenon: today when users "raise shrimp," the "Three Provinces and Six Ministries system" is popular, meaning different models execute different tasks. For example, using the highest-tier "Zhongshu Province" (SOTA model) for review环节, while other tasks or环节 can be completed with mainstream models or even local models.

Regarding OpenClaw's viral success, Tang believes the core reason lies in its "unlimited" design — allowing developers and users to fully unleash imagination and maximize models' strength areas. He also summarized several characteristics of OpenClaw: more focused functionality; stronger environmental awareness, with permissions to basically operate all computer systems; unlimited Skills and tool combinations; and the proactive heartbeat mechanism.

OpenClaw's emergence also benefited from three industry shifts: continuous improvement in model agentic capabilities; enhanced competitiveness of open-source models; and network effects of mainstream models continuously driving down token costs. Tang further advised entrepreneurs that as models continue evolving, we can continuously "find product opportunities from model capability overflow."

PineClaw: Letting Agents Touch the Real World

Bojie Li, Chief Scientist at Pine AI, similarly shared his thinking on OpenClaw's essence, and how PineClaw extends OpenClaw's capability boundaries to let AI agents truly reach the real world.

📞 OpenClaw's Breakthroughs and Limitations

He believes that as a "sovereign agent," OpenClaw's core proposition is giving agents full autonomy, with corresponding self-responsibility. OpenClaw brings four design inspirations to the agent field:

1) Pure Natural Language Interface: Abandoning traditional GUI operations, using natural language as the sole interaction method;

2) Self-Chat Connecting Multiple Messaging Platforms: Achieving connection with multiple messaging platforms through Self-Chat;

3) No-Session Design: Supporting unlimited context like a real person, as if hiring a real human secretary;

4) Skills + CLI Accessing Third-Party Services: Efficiently calling third-party services through Skills and command-line interfaces.

Meanwhile, Li also pointed out AI's limitations: AI has no "mind-reading" ability, cannot understand implicit constraints, historical reasons, and unexpressed human thoughts — "Context is humanity's moat"; Moravec's paradox is AI coding's biggest bottleneck — writing code, which is hard for humans, AI does quickly, but GUI operations simple for humans (such as applying for accounts, receiving verification codes) are difficult for AI, requiring humans to serve as "secretaries and testers" for agents.

Additionally, OpenClaw's limitation lies in its design paradigm of "one user + multiple agents" personal assistant mode, not supporting multiple people simultaneously using a given agent; in the real world, many scenarios involve multiple participants with misaligned interests, requiring agents to actively coordinate各方诉求, which is precisely the problem Pine AI aims to solve.

📞 PineClaw: Opening Voice Capabilities to the OpenClaw Ecosystem

Pine AI is an end-to-end AI agent for resolving real-world affairs, with core capability in helping users complete complex negotiations in daily life through voice calls, such as consumer bill negotiations, hospital bill negotiations, and hotel hygiene complaints. The core difficulty lies in maintaining prolonged "IQ online" during negotiations, simulating real-person communication logic, while proactively gathering sufficient information before negotiations to ensure success rate. Currently Pine AI has achieved sustained calls lasting several hours, verified by tens of thousands of users, with a 93% negotiation success rate, having cumulatively helped users save tens of millions of dollars.

Worth emphasizing, Pine AI adopts a "pay-for-results" model — not selling tokens, but only taking commission from saved amounts after successful negotiations.

Following OpenClaw's viral success, Pine AI moved quickly to open its voice capabilities (Pine-Voice) to the OpenClaw ecosystem. Li vividly比喻: "OpenClaw gave agents hands — controlling computers, reading and writing files, executing code; Pine AI gave agents a mouth — making calls, negotiating, coordinating multi-party interests. PineClaw opens this mouth to the entire agent ecosystem, letting agents no longer be confined within screens, truly touching the real world."

In early 2026, the paradigm leap of AI agents has become an irreversible trend. If agents have given each of us a magic wand, "some use it to conjure a hamburger, some a Ferrari, or even create a new world of their own." May we all better wield the magic wands in our hands, welcoming the arrival of the Personal Intelligence era.