The first batch of people to 'eat lobster' have made money.

Since its release in November last year, the intelligent agent framework OpenClaw (commonly known as Lobster) has completely transformed AI gameplay, enabling AI to transition from 'just talking' to 'getting things done.'

OpenClaw itself does not directly generate revenue, but when it starts 'working,' it consumes a massive amount of tokens. Any large model that integrates with it gains access to exponential growth opportunities.

Cost-effective domestic large model manufacturers have been the first to reap the benefits.

In early February, OpenClaw announced that Kimi K2.5 would be its official free main force (main) model. Within 20 days of its release, Kimi K2.5's revenue surpassed its total income for 2025. Overseas revenue exceeded domestic revenue for the first time, and the company's valuation doubled to over $10 billion.

Meanwhile, listed companies MiniMax and Zhipu AI have seen their stock prices surge.

MiniMax is the biggest beneficiary of the lobster harvest season.

As one of the officially supported large model providers, MiniMax achieved an annualized revenue of over $150 million in February based on the OpenClaw ecosystem. Its market value skyrocketed 6.4 times in two months. Particularly on March 9 and 10, its stock price rose by over 51% in just two days. By the close on March 10, MiniMax's market value reached HK$382.6 billion, surpassing established internet giants Baidu and JD.com.

Zhipu AI launched AutoClaw (Australian Lobster), an exclusive adapted version of OpenClaw, causing its stock price to surge by 12.87% in a single day. By March 10, Zhipu AI's market value briefly exceeded HK$300 billion during trading.

The 'National Installation of Lobster' craze spread rapidly, with companies like ByteDance, Alibaba, Tencent, Baidu, JD.com, and Xiaomi entering the fray by launching one-click deployment services for OpenClaw, aiming to attract users to their cloud platforms.

Locally installing OpenClaw caused the long-stagnant sales of Apple's Mac mini computers to surge.

Many people are reluctant to install OpenClaw on their primary computers due to its high permissions and potential security risks. As a result, an affordably priced Mac mini has become a popular 'intelligent agent host'—commonly known as a 'lobster nest.'

Li Yachong, CEO of Putao Technology, only took notice of 'lobster' after hearing about the soaring prices of Mac minis. He decided to create a cheaper alternative hardware. After the New Year, he launched a mini PC pre-installed with OpenClaw, priced at a minimum of 1,800 yuan. Li Mingshun, founder of Shunfu Capital, also quickly invested in a company making 'lobster nests' called MoltyBox.

Teaching lobsters to work (professionally known as training skills) can also be profitable. The official skills marketplace ClawHub saw the number of skills skyrocket from 700 to 5,705 within just one month of its launch.

Some individuals created e-commerce automation skills, earning 22,400 yuan in a month with the paid version. Office workers developed office automation plugins, charging 70 to 350 yuan per use, while enterprise customizations sold for 3,500 to 14,000 yuan.

Tian Jia, a Tsinghua University computer science alumnus who was previously well-known in the cryptocurrency circle, also joined the OpenClaw Skill trend. He told Pencil News, 'OpenClaw has revived AI.'

Below are exchanges between Pencil News, entrepreneurs, and investors about money-making opportunities with lobster.

- 01 - Sold 100 Lobster Nests in a Few Days, Earning 300 Yuan Each

Li Yachong

CEO, Putao Technology

Before the New Year, I noticed people saying that Mac mini prices were skyrocketing. I was puzzled and only then paid attention to OpenClaw.

After trying it out with friends, we found it quite useful. We brainstormed two ideas: First, since many people abroad use Mac minis, could we create a cheaper alternative? Second, if we made our own hardware, could we lower the barriers for pre-installation and use?

We immediately got to work, first testing OpenClaw in various application scenarios while experimenting with backend integrations for different models and coding solutions to determine the most cost-effective and high-performance option.

After the New Year, we started creating our own 'Little Lobster Box.' We purchased some mini PCs to rival the Mac mini. We offered two models: a lower-configured version priced around 1,500 yuan called 'White Dragon,' and a higher-configured version priced between 3,500 and 4,000 yuan called 'Green Dragon.' We then installed OpenClaw on them.

The mini PCs sold by Li Yachong's team, pre-installed with OpenClaw, start at 1,800 yuan.

After using them continuously for two or three days, we concluded that many companies would likely adopt a 'lobster nest' to allow employees to delegate some tasks to it.

Later, we verified that the 1,500-yuan machine could meet most people's needs.

We chose to develop hardware rather than just software for several reasons regarding Little Lobster.

First, it has stability and security issues, such as the potential for accidental file deletion. The simplest solution is to give it an independent system rather than installing it on a primary computer.

Second, Little Lobster has three crucial characteristics. First, it can directly operate computers, including local hardware. Second, it has a degree of self-looping and learning capability. Third, it can work continuously 24/7. These traits make it unsuitable for laptops.

Moreover, future interactions with such small boxes may not rely on traditional keyboards and monitors. One approach is using mobile chat apps like Feishu, WeChat, or even QQ for dialogue-based communication. Another is voice or video interaction in home or office settings.

After receiving inventory, we quickly started selling the devices, adding about 300 yuan per unit primarily for the pre-installed OpenClaw system and basic configurations. Through offline events and mini-programs, we've sold nearly 100 units so far.

However, demand for mini PCs has surged recently, and our purchase price for the 1,500-yuan model has risen to 2,000 yuan in the last two days.

Currently, three types of people are buying lobster nests:

First, tech enthusiasts curious about what it can do.

Second, business owners more concerned with whether it can improve company efficiency or help their teams complete tasks.

Third, professionals in fast-information-acquisition industries, such as lawyers, psychologists, and course trainers, who hope AI can enhance their work efficiency.

A small number of college students have also purchased them.

Now that so many are selling lobster nests with little differentiation, we're developing a next-generation product with a QR code. Users can scan it, create an instance via Feishu, and run OpenClaw on their phones. The small box stays at home, and you chat with it on Feishu to generate images or videos. Current boxes require a monitor and keyboard, which many people, including non-IT professionals, lack.

From my discussions with the tech community, everyone is acting quickly. Some are addressing security issues, others are creating lighter versions, and some are experimenting with one-click deployment. Many large model companies and startups are entering the space, with directions likely to become clearer in the next six months to a year.

- 02 - After Robots Integrate with OpenClaw

Tian Jia

Tsinghua Alumnus, Blockchain Entrepreneur, OpenClaw Skill Project Creator

I encountered OpenClaw early on when it was still called ClawBot. I used it to write a small project and test its capabilities.

What impressed me most was its ability to fix its own bugs—a rare occurrence with previous software.

I've also used Copilot before. Its core strength is code completion, continuing where you left off. However, this mode still centers on human-written code, requiring people to plan the code structure first, with AI only assisting.

The change with OpenClaw is that humans don't need to think at the code level. Instead, they directly describe the goal, letting the system plan the task path itself.

This suggests that many business models previously designed for humans may shift toward agents. Examples include e-commerce, advertising, social platforms, and even some B2B services. If agents make many future decisions, numerous commercial systems will need redesigning.

Recently, local deployment of OpenClaw has become popular, but the real opportunity lies in localizing all three parts of the system:

First, the 'brain'—the large model. Second, the 'limbs'—skills and various external tools. Third, the 'nervous system'—the service orchestration system responsible for task planning and scheduling.

If all three parts can run locally, data remains entirely in the hands of individuals or enterprises, improving privacy, security, and response speed.

The software industry faces significant disruption from OpenClaw, particularly:

First, programming, as code is a structured task AI can easily participate in. Second, operations and promotion, where many repetitive tasks can be automated.

A friend of mine works in operations at a company going global. They now assign several OpenClaw agents per person to manage multiple accounts on X and promote their business.

Recently, when meeting with entrepreneurial friends, we've discussed three main topics:

First, when OpenClaw will release a new version.

Second, whether new skills or gameplay mechanics will emerge.

Third, potential new application scenarios, such as integrating with robots or other devices.

Our company now focuses on OpenClaw skill development while exploring combinations of computing power, blockchain, and agents.

One interesting idea is using blockchain to record agent usage, such as task execution, computing power consumption, or service calls, and linking it to a point system or incentive mechanism.

Big players in this sector are moving quickly, but some startups remain cautious due to the rapid pace of change. Many teams worry their newly developed products might soon be replaced by newer versions.

However, everyone acknowledges that OpenClaw has revived AI.

Agents consume large amounts of tokens when executing tasks, driving growth in model API calls. Many model companies can gain new revenue streams through API usage.

Beyond model calls, computing power demand will also increase, as agents often need to continuously invoke different models and tools for complex tasks.

Some teams are discussing mapping computing resources via RWA (Real World Assets) to enable registration and circulation of real-world computing assets on the blockchain.

These models are still exploratory, but as the agent ecosystem expands, new business forms may emerge around computing power, call volume, and resource scheduling.

I'm particularly optimistic about combining OpenClaw with hardware. I've seen an experimental case where someone integrated OpenClaw into a robot system. Originally lying down, the robot stood up under system control. Though just an experiment, the visual impact was strong.

If integrated into robotic dog systems, OpenClaw could perform specific tasks like patrolling, delivery, yard maintenance, or companionship. Many robotic dogs now cost around $1,000, with simple structures and 3D-printable components. Large-scale deployment of robotic dogs would also create additional value through data collection.

Once agents can automatically execute tasks, security becomes critical. Future third-party security companies may specialize in agent system security testing and monitoring.

I also hope future systems will be more transparent in security design, letting users know how risks are controlled.

- 03 - Software May Disappear, Skills Are Rising

Li Mingshun

Chairman of Xingxing AI, Founder of Shunfu Capital

In my view, OpenClaw's high-intensity popularity stems from two key capabilities.

First, continuous memory.

Many agents previously had short-term memory or existed only within a single model. OpenClaw can connect contexts across multiple tasks, agents, and different models, forming long-term memory.

Second, the skills system.

Human experience and methods can be distilled into various skills, allowing personal capabilities to be continuously reused and amplified.

For the past two years, everyone has talked about the 'first year of agents,' but many agents were designed for Q&A. Now, agents can manage other agents and plan/execute tasks without continuous human operation.

This model will also transform AI's economic framework. As agents execute tasks, they continuously invoke models and APIs, significantly increasing token consumption. This wave of OpenClaw's popularity has boosted API call volumes for many large model companies.

From an entrepreneurial perspective, I see this as an opportunity.

This paradigm will unlock massive productivity. Future vertical industries may see industry-specific versions of OpenClaw, such as in healthcare, law, finance, or manufacturing, each combining their skills to form solutions.

Why haven't many agent startups from the past two years truly succeeded?

One reason is that many previous products focused on single capabilities. Now, the ecosystem has accumulated numerous skills, each functioning like a small software or agent. Future users won't need to search for tools themselves; they'll simply state their goals, and agents will invoke the right skills to complete tasks. This will transform how software is used.

After OpenClaw gained popularity, we made some attempts, such as incubating a company that makes OpenClaw hardware boxes. Many teams now pre-install OpenClaw on small computers or home workstations, creating products similar to 'Little Lobster Boxes' that users can immediately use.

Hardware has become an opportunity for a simple reason: many OpenClaw instances currently run in the cloud, while local deployment can preserve long-term memories for individuals or enterprises. This data may become a vital asset in the future.

Local deployment offers several advantages: First, greater data security. Second, long-term preservation of continuous memory. Third, cross-platform and cross-model accumulation of experience.

If all AI services remain cloud-based, data will scatter across different systems. However, a local AI box could unify these memories, enabling AI to better understand users.

Creating such boxes also has barriers. One lies in software architecture, such as multi-model scheduling, cross-platform capabilities, and memory systems. The other involves hardware-software integration, including stability, security, and future skills markets.

From an investment perspective, this wave of changes will also impact the software industry.

Today, when we use Word, PPT, or various office software, manual operations are required. However, in the future, it may only be necessary to describe requirements in natural language, and AI will call on skills to complete the tasks. The same goes for website development. In the past, programming or using complex tools was necessary, but in the future, simply stating the requirements will suffice for the system to generate the desired outcome.

In the long run, many software applications may be replaced by the skills ecosystem. This change will also drive new infrastructure demands, such as increased requirements for computing power and Token consumption.

Another area is hardware devices. OpenClaw may become a significant gateway between humans and AI, such as connecting smart devices, robots, or embodied intelligence.

This article does not constitute any investment advice.