The surprises in the AI circle continue to emerge.
Recently, a product named OpenClaw (formerly known as Clawdbot/Moltbot) has rapidly gained popularity in both domestic and international tech communities and social media.
As a deep interactive agent that can run on its own computer and deeply access the user's computer system, files, applications, and chat records, users can issue commands and communicate with the AI in the most natural chat interface.
In the use cases shared by developers, this desktop agent can perform complex tasks such as comparing quotes from dozens of car dealerships, automatically sending emails, tracking replies, and organizing price differences. It can also handle daily tasks like bulk unsubscribing from emails, processing insurance claims, booking flights, and automatically checking in.
Importantly, it has long-term memory context; it can remember local projects, repetitive tasks, and personal preferences, and can proactively send briefings, reminders, or alerts without needing to be triggered, being described in the industry as a &#34;24/7 standby Jarvis.&#34;
Founders, developers, and tech enthusiasts are all trying it out, and overnight, the &#34;OpenClaw nanny-level deployment tutorial&#34; has become a traffic password on Xiaohongshu and Bilibili. Industry insiders say this is the ChatGPT moment for desktop agents.
With the help of network effects and word-of-mouth, more and more people are trying to build their own &#34;Jarvis,&#34; while beneath the surface, domestic model players and cloud vendors have quietly become the invisible winners behind desktop agents.
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<h2>The &#34;Shovel Seller&#34; of &#34;Jarvis&#34;</h2>
OpenClaw is not the first capable agent on the market, but it has reignited market sentiment after manus, Qianwen Assistant, and &#34;Doubao Phone.&#34;
With the explosive popularity of OpenClaw, MacMini has also suddenly become a &#34;financial product,&#34; with many early adopters in the community claiming to purchase large quantities of MacMini to run OpenClaw. Logan Kilpatrick, head of Google's AI products, is one of them.
The core design philosophy of this desktop agent is to run locally, so deploying it on a separate MacMini can avoid mixing with the main work computer, maximizing privacy and system security.
However, as more people learned about this project, another voice quickly emerged: under super high permissions, OpenClaw is more suitable to run in an isolated environment from the main computer, and cloud servers that can be deployed with one click soon became the &#34;chosen&#34; solution.
On January 28, sensing the opportunity, Alibaba Cloud quickly launched a full suite of dedicated cloud services and provided detailed deployment tutorials;
Tencent Cloud's lightweight application server Lighthouse has also launched an OpenClaw application template, pre-configured with the environment required to run OpenClaw.
Subsequently, JD Cloud, Mobile Cloud, and UCloud also joined the ranks
An AI application architect told Wall Street Insights that trying OpenClaw on a cloud server is a faster and more cost-effective choice, and cloud servers naturally support 24/7 operation, which aligns well with OpenClaw's positioning.
However, if &#34;Jarvis&#34; runs without using local open-source models, it inevitably needs to connect to model APIs, and OpenClaw's &#34;burn rate&#34; quickly becomes apparent.
&#34;OpenClaw has quite an appetite; I originally planned to use over a million tokens in ten days to half a month, but it was all gone in just half an hour.&#34; An independent developer from Shenzhen told Wall Street Insights that he recently used OpenClaw to clone a classic Snake game.
&#34;At first, I thought it was powerful; OpenClaw wrote code, ran it, and fixed bugs by itself, and I felt like a boss watching it work. But when I saw the API bill, my smile disappeared,&#34; the developer said.
Traditional chatbots operate on a &#34;you ask, I answer&#34; basis, where a single interaction often consumes only a few hundred tokens.
However, the Agent model represented by OpenClaw is &#34;self-looping.&#34; To fix a minor rendering error, OpenClaw engaged in over 40 self-dialogues and code attempts within half an hour.
An executive from a modeling company pointed out that applications like OpenClaw heavily rely on two core capabilities: ultra-long context and cost-effective reasoning ability. &#34;Agents need to have memory; the current mainstream approach is to store context in memory, bringing along previous questions and answers with each new question, which makes the agent's input grow larger and larger.&#34;
Therefore, to run this super-powered &#34;Jarvis,&#34; a highly responsive, performance-driven, and cost-effective large model is required.
Under the strong recommendation of project author Peter Steinberger, the M2.1 model from the domestic AI unicorn MiniMax, which excels in long text and logical reasoning, has been successfully popularized.
Peter Steinberger stated in an interview, &#34;Currently, I can run MiniMax M2.1 on it, and I believe it is the best model available right now. However, Kimi was just released recently, and I will also try it when I have the opportunity.&#34;
Moreover, for an Agent to think like a human, it cannot do without prompt engineering tools like LangChain, which define the logic for AI to mobilize tools; to remember thousands of file details and historical operations of users, vector databases like Pinecone or Weaviate become essential external &#34;hippocampi.&#34;
The key issue is that when AI has the authority to delete files and modify systems, security becomes the number one challenge. Therefore, Docker containers and various security sandbox technologies become necessities to ensure that AI does not accidentally delete your system disk while working. Although these middlewares are not directly user-facing, they are the invisible backbone supporting the stable operation of Agents.
Just like those in the gold rush, regardless of whether the applications developed by desktop Agents ultimately succeed, the profits of the &#34;shovel sellers&#34; are securely pocketed first
<h2>Agent Bonus Diffusion</h2>
The emergence of OpenClaw has brought the concept of AI &#34;working for people&#34; closer to reality. The appearance of this desktop agent has made the industry realize that future AI will no longer be just an app, but a shadow butler that transcends apps.
Following this pattern, the software landscape will shift from the previous &#34;battle of a thousand models&#34; to a &#34;battle of a thousand endpoints.&#34;
Currently, agent players are blooming everywhere. In addition to Manus, which addresses complex scenario needs, and the recently popular open-source OpenClaw, products like Coze Workflow, Flowith, CherryStudio, MiniMax Agent, and Step AI Desktop Partner are all rushing to launch.
It should be noted that agents and models typically achieve mutual success. Manus is backed by a multi-model architecture that includes Claude and Qianwen, while the construction of OpenClaw also requires the selection of models. This reveals a principle: the foundational capabilities of agents are still demonstrated by underlying large models.
When the interaction bonus of desktop agent applications explodes, competition will also partially return to the models themselves. Other foundational model startups, both domestically and internationally—whether OpenAI or domestic DeepSeek and Kimi—have already positioned agents as a focus: by directly &#34;internalizing&#34; agent capabilities into the models.
This means that within the next six months to a year, more &#34;Jarvis&#34; will emerge both domestically and internationally.
On the other hand, as the embedded system operators, giants like Apple, Android (Google), and Microsoft will not relinquish system entry to desktop robots.
Industry insiders believe that Apple's Apple Intelligence and Microsoft's Copilot are likely to evolve into comprehensive agents at the system level.
After all, they possess permissions that third parties cannot match: difficult-to-obtain screen recording permissions, no need to simulate mouse clicks, and direct calls to interfaces at the system kernel level.
Domestically, Huawei's HarmonyOS Next &#34;native intelligence,&#34; Doubao Assistant, and Alibaba's Qianwen Assistant are also working on this.
This is an AI defensive battle belonging to the system providers.
When the built-in assistants not only can converse but also help users with takeout and sending red envelopes, the penetration space for OpenClaw-like applications in the Chinese mobile and PC markets will also be significantly narrowed.
It can be said that both large model players and major endpoint manufacturers will join the competition for desktop agents.
At the same time, the hardware market as the terminal will also welcome a window of opportunity.
The Mac mini, which has been popularized by OpenClaw, is not due to Apple's hardware performance arrangements, but rather the advantageous settings of the Mac system and memory architecture, along with the convenience of the MacOS system and the power consumption advantages of its self-developed ARM architecture SOC.
However, the Mac mini is merely the optimal solution &#34;at this moment&#34; and not the endgame
Hardware manufacturers are sniffing out new opportunities. Right now, the fast-moving Huaqiangbei has entered the market with AI mini-hosts pre-installed with desktop Agents.
These small boxes, similar to NUC or Mac mini, run 24/7 and connect to your main machine via a local area network.
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Edge computing manufacturers are also preparing to &#34;intercept.&#34;
The &#34;cloud computer boxes&#34; being launched by Alibaba, Tencent, and China Mobile are essentially thin clients, with computing power in the cloud. For users who only need lightweight Agents, a cloud box costing a few dozen yuan per month may provide a better experience than buying a Mac mini.
From this perspective, as Cowork and OpenClaw gain traction, a large cake centered around desktop Agents is rapidly taking shape.
The future competitive landscape is gradually becoming clear. At the software layer: third-party open-source Agents will spring up like mushrooms after rain; at the model layer, companies like minimax and Kimi will become the behind-the-scenes shovel sellers; finally, at the hardware layer, a batch of more cost-effective, domestically designed &#34;large memory Mini hosts&#34; or &#34;cloud AI boxes&#34; specifically for AI Agents is on the way.
A battle for desktop control combining software and hardware seems to be about to begin

Finding Shovel Sellers Under the Desktop Agent Dividend