At the beginning of 2026, a &#34;lobster&#34; stirred the entire tech circle.
Over the past two years, users have become accustomed to conversing with AI. Doubao, Qianwen, Kimi— the interaction modes of all AI assistants have essentially remained unchanged: you ask a question, it provides an answer, and you move on, resembling a smarter search engine.
OpenClaw (commonly known as &#34;lobster&#34;) has done something fundamentally different. You give it a task, and it independently breaks it down, plans, calls tools, executes, and returns results. In a chat interface similar to QQ, when you issue a command, it doesn't just reply with text; it directly &#34;gets to work,&#34; organizing files, sending emails, and posting on websites. It can remember user habits and continuously expand its capabilities through &#34;Skills.&#34;
This ability has created the deepest FOMO. &#34;Lobster helps me trade stocks,&#34; &#34;Lobster helps me buy a car,&#34; and even &#34;Lobster finds me a girlfriend&#34;—these narratives naturally carry a viral quality. Product managers have begun to let Lobster automatically gather data and generate analyses during their commutes, using the Agent's output to direct their teams in morning meetings.
The anxiety of not having a lobster is beginning to spread.
However, there is a huge engineering gap between deploying Lobster and being able to use it. OpenClaw is a prototype written in an hour by Austrian developer Peter Steinberger, with the code open-sourced on GitHub, allowing anyone to deploy it. The problem is that to run a Lobster locally, you need to configure the runtime environment, clone the repository, apply for a model API key, set up configuration files, connect to IM channels, handle sandbox permissions and network proxies, and then maintain it continuously.
Changing a model, adding a plugin, or building a Skill could lead to system crashes. For 99% of ordinary people without a technical background, the barrier is absurdly high. &#34;Helping people deploy OpenClaw&#34; has even become a business, charging anywhere from hundreds to thousands of yuan.
Peter's vision is to &#34;build an agent that even my mum can use.&#34; However, the current open-source form of OpenClaw is clearly far from this goal.
Then, tech giants collectively entered the fray. In March 2026, variants like KimiClaw, MaxClaw, and GLM-Claw were launched in quick succession, focusing on one-click deployment and accessibility for everyone.
Tech giants are rapidly completing a &#34;lobster equity&#34; movement.
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<h2>Technological equity, solving deployment challenges</h2>
Well-known blogger Simon Willison observed that &#34;Claw&#34; is becoming a new category term used to refer to these intelligent agents that can control devices and autonomously execute tasks
From February to March 2026, domestic technology companies are intensively launching variants or compatible solutions of OpenClaw, reminiscent of the cloud vendor deployment battle triggered by DeepSeek earlier. However, this time, the focus of competition has shifted from computing power to who can enable non-technical users to use the Agent the fastest and with the lowest barriers.
Kimi is one of the first companies to launch a &#34;claw&#34; product in this wave. Sources close to Kimi revealed that the release of Kimiclaw was not a result of long-term planning but rather driven by demand. &#34;After the release of Kimi K2.5, the Kimi team found that many people were integrating K2.5 with OpenClaw, and the API usage quickly surged. Meanwhile, many non-technical individuals around them wanted to use K2.5, but deploying OpenClaw on their own computers was very troublesome, taking a whole day to set up, and there was also a high risk in deploying it on their main devices. So the Kimi team decided to create a cloud version with the K2.5 model built-in, minimizing the deployment effort.&#34;
The original intention of various companies may simply be to lower the barriers and allow users to start using it.
What the future holds is uncertain. The source also stated, &#34;OpenClaw represents a direction of technological development. When AI has its own computer, online 24/7, learns to use software tools, and learns to create software tools to solve problems... What will happen in the future? This is just the beginning, and there is a lot of exploration space ahead.&#34;
These &#34;Claw&#34; products have almost all chosen the cloud route rather than the native local deployment model of OpenClaw. The main reason is that the cloud can achieve 24/7 online availability, and security protection is uniformly covered by the platform, eliminating the need for users to handle permissions and network configurations themselves. It's simple and secure.
They can generally be divided into two categories:
The first category consists of fully packaged independent products, as listed in the table below. Their common feature is an extremely low usage threshold, either zero deployment directly used in a web page or app, or one-click installation to run. Some products have been deeply optimized for specific scenarios, such as Tencent WorkBuddy, which is more focused on office automation and knowledge production, with over 20 built-in Skills and support for WeChat, Enterprise WeChat, QQ, and other IM entrances.
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Compared to the native open-source version, these products typically exchange preset models, skills, and interaction channels for lower barriers and stronger usability, but the degree of openness varies among different products. Although some products support multi-model switching, skill expansion, and custom integration, the personalization freedom of the products is evidently affected.
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The second category is one-click deployment services provided by cloud vendors. Tencent Cloud, Alibaba Cloud, Baidu Intelligent Cloud, Volcano Engine, JD Cloud, and Huawei Cloud have all launched related solutions. These services do not turn OpenClaw into a ready-to-use standalone product directly, but rather aim to compress the originally complex deployment process through pre-installed images, application templates, or resource stacks.
For users, it does lower the barrier to setup, but it hasn't reached &#34;zero configuration&#34;: aspects such as model API, channel access, port opening, and security policies often still need to be completed manually.
Its advantage lies in higher freedom; users can typically choose models, access channels, and expansion capabilities themselves, making it more suitable for developers and advanced users who wish to strike a balance between usability and controllability.
<h2>Cognitive Equality, the Interaction Entry of AI is Still IM</h2>
Large companies have lowered the deployment barriers that originally belonged to engineers through encapsulation, pre-setting, and one-click solutions. However, what truly determines whether ordinary people can use it may not necessarily be a technical issue. A deeper and more challenging barrier to overcome is the cognitive threshold.
For example, various Claws have achieved &#34;zero deployment,&#34; but users still need to know of its existence, find the entry point, understand the concept of &#34;Agent,&#34; and learn to collaborate with it. For a broader audience of non-technical users, such as retired parents, the term &#34;Agent&#34; itself is a significant barrier.
The solution to breaking down this wall may not be any single Claw product, but rather IM (Instant Messaging).
OpenClaw founder Peter Steinberger recalled his initial motivation in the Lex Fridman podcast: As early as April 2025, he wanted to create an AI assistant based on IM, but thought big companies would surely do it. After waiting for six months with no one taking action, he had to do it himself.
IM is the only &#34;always-online&#34; digital interface for humans, connecting the entirety of human digital life.
Agents reside here; users do not need to &#34;think&#34; to find them; they are right there in the chat list you open dozens of times, alongside friends and colleagues.
Behind this &#34;one-hour prototype&#34; is actually a powerful product insight: AI's capabilities are already strong enough, but it lacks a door that ordinary people can walk through. IM is that door; it does not require installing a new app, learning a new interface, or remembering any commands.
As the interface for Agents, IM has three structural advantages:
First, a massive user base means zero learning costs; an Agent is like adding another contact to your address book. You send it a message, and it helps you get things done.
Second, asynchronous interaction naturally fits the working mode of Agents. Chatbots are synchronous; you ask them a question, and they answer immediately. However, Agents executing complex tasks require time, ranging from a few seconds to several hours
IM's messaging mechanism naturally supports this asynchronous rhythm. You can give an instruction to the Agent in WeChat and go to a meeting, and by the time you return, the results have already been pushed to the chat box. There's no need to keep an eye on it while working.
Thirdly, IM encapsulates all complexity within a minimalist interface. Behind OpenClaw are file system permissions, API call chains, sandbox execution environments, and MCP protocols, which users do not need to know about at all. What they see is just a chat box; by inputting a natural language sentence, the task begins to be executed. This is very similar to the design philosophy of mobile payments: users do not need to understand the clearing system and bank interfaces; they just need to scan a code.
What IM achieves is not technical equality, but cognitive equality, allowing users to be completely unaware that they are &#34;using AI,&#34; just like you don't need to understand the HTTP protocol to watch short videos.
When technical equality and entry equality overlap, the threshold for lobster has been pushed to a historical low, becoming wildly popular among the masses.
<h2>After Equality, the Real War Begins</h2>
The story of lobster equality seems to have a happy ending here.
But this may not be the end; rather, it is a more distant beginning. After the intersection of &#34;silicon-based lobsters&#34; and &#34;carbon-based humans&#34; at the most familiar &#34;IM&#34; entry, will new business models emerge in the future? For example, on top of human social networks, there is another layer of silicon-based networks of Agents (the lobsters).
How will the entry point of this larger network evolve? Will it bring significant changes to the current business landscape?
This is a fast-paced war for ecological niches and user mindsets. If in the future, when users mention intelligent agents, they can never recall a particular company, that would be a disastrous outcome.
Currently, after various Claws have rapidly emerged and users have had their first taste, the wave of lobster uninstallation has already begun. According to publicly reported data, the actual usage rate of individual users is less than half of the download volume.
More than half of the lobsters are gobbling up huge amounts of tokens, but are just &#34;forecasting the weather.&#34;
The cases circulating on social media are always the best ones, where product managers use it to automatically prepare morning meeting materials, lawyers perform web scraping and simulate negotiations, and administrative staff hand over daily, weekly, and annual summaries entirely to the Agent.
&#34;Installing lobsters is easy, but raising them is hard! Everyone can install OpenClaw, but no one tells you how to use it.&#34; Various Claws are useful hammers, but the next crucial step is to help users understand &#34;where the nails are.&#34;
Additionally, there is a structural contradiction in the business model of lobsters: if companies providing products and services charge users through a subscription model, the current pricing ranges from 39 to 199 yuan. However, each time a lobster executes a task, it initiates an API call to the underlying model, consuming tokens.
A complete calendar organization and email reply may consume thousands of tokens, and if long-term memory, multi-Agent collaboration, and scheduled awakenings are enabled, daily consumption can easily exceed 100,000 tokens. In extreme cases, some users have bills exceeding a thousand yuan for just six hours
This calculation is difficult to balance for both ends. For the companies providing services, relying on subscription fees to cover the comprehensive costs of model inference, cloud computing power, skill maintenance, and customer support leaves very limited profit margins. For users, if they do not actively control Token consumption, it is easy to incur exorbitant bills.
As long as the value of the Agent's tasks does not significantly exceed the cost of Token consumption, the lobster's business model cannot stand.
This also confirms the importance of &#34;finding the nail&#34; from another perspective; only when the work done by the lobster for users is truly worth that much money will users continue to pay for it.
The day when product iteration, scenario exploration, and cost control are all solved is the day the lobster can transform from a geek toy into a truly popular product.
And perhaps the emergence of a new network blueprint that integrates &#34;silicon-based intelligences&#34; will allow AI-based new business models to flourish.
The lobster war is far from over; equality is just the beginning.
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In early 2026, OpenClaw ("Lobster") caused a sensation in the tech circle, allowing users to complete tasks and return results, surpassing the interaction model of traditional AI assistants. However, there are high barriers to deploying and using OpenClaw, making it difficult for ordinary users to operate. Although Peter Steinberger hopes to create an easy-to-use intelligent agent, the current open-source form has not yet reached that goal. Major tech companies have also begun to participate in this field

- In early 2026, a new AI assistant called OpenClaw, or "Lobster," differentiated itself by autonomously breaking down tasks and executing them beyond simple text responses, causing a stir in the tech community.  
- Despite its innovative capabilities, deploying OpenClaw remains challenging for non-technical users, leading to a surge in companies creating easier, cloud-based alternatives for broader accessibility.  
- The competition in this emerging "claw" category highlights a shift towards making AI more user-friendly, although significant hurdles remain in user comprehension and the effective monetization of these tools.

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The big companies' Claw have brought "Lobster Equality," but half of the shrimp are forecasting the weather