Digital Strategy Review | 2026
If You Feel Nothing About the OpenClaw Hype, You Are Definitely Not Average
By Uncle Guo · Reading Time / 8 Min
Foreword
Recently, a business owner friend asked me whether “OpenClaw” (the “Little Lobster”) is actually any good. His meaning was straightforward: almost everyone is talking about OpenClaw right now—in WeChat Moments, group chats, executive meetings, and training sessions. It’s as if the whole country has suddenly started “farming lobsters.” To keep the conversation going, I mentioned that I was currently rushing to finish a book on OpenClaw and had put everything else on hold. Since I didn’t offer immediate praise, he followed up with, “Then can you help me install one?”
The first reaction that popped into my head wasn’t excitement, but confusion.
For me, the explosion of OpenClaw feels incredibly dramatic. For a long time now, I have already delegated about 80% of my work to agents like Claude Code and Codex. Writing code, modifying scripts, organizing structures, researching information, and drafting content—these are actions I’ve long been accustomed to. So, when a new agent application is suddenly packaged as a “must-have productivity artifact for everyone,” I naturally take a closer look: What exactly has it added? Or is it just wrapping existing capabilities in a shell that’s easier to market?
If you feel similarly unmoved by this wave of hype, I actually think it suggests you are likely not an average person.
01
Why Did OpenClaw Become So Popular?
Let’s start with the conclusion: Its popularity is perfectly normal.
According to public data, OpenClaw’s GitHub repository has surged to the 300,000-star level. Official and community documentation repeatedly emphasize a few things: it runs 24/7, connects to multiple channels like WhatsApp, Telegram, Discord, and Slack, supports various skills, and integrates with Claude, GPT, Grok, and local models. For the average user, these claims are highly attractive because they point to a very concrete vision: “I can finally have my own AI assistant, and it doesn’t just chat in a browser—it can actually do work for me.”
This narrative is even more incendiary in the domestic market. It’s open-source, the deployment seems controllable, it supports domestic models, and service providers can set it up for you. Add a few screenshots in WeChat groups showing “I let it automatically post content, reply to messages, and manage accounts,” and the spread becomes terrifyingly fast.
Many people are moved not because they have seriously compared the technical details of OpenClaw, Codex, and Claude Code. They are simply seeing something intuitively for the first time: AI can stay online like a living account, connect to chat software, work across platforms, and perform actions automatically. If you are a professional in AI, the internet, or software, you are likely already very familiar with the concept of “agents.” This concept has existed for years, and products have been landing on a large scale since last year. However, for most people, this idea of an AI that “can do work” is not yet a familiar concept. If you search for keywords like “DeepSeek free download” on Taobao, you might be surprised. For most, AI is still stuck in the “it can chat with me” phase. In this environment, an AI product form like OpenClaw is naturally suited for a viral explosion. Most importantly, it has almost no barrier to entry—no need to fiddle with virtual cards or cross-border payments; just install it locally, connect it to MiniMax, and it runs.
02
If You Feel Nothing, It Usually Means You’ve Passed the “Novelty” Phase
When I say “you are not average,” I’m not posturing. There are usually two reasons for this.
First, you already clearly understand that installing a tool and running a business process are two very different things.
Many people talk about agents now as if they were talking about air fryers—as if buying one, plugging it in, and watching a tutorial will result in a steady stream of meals. Reality, of course, is not that simple. Whether an agent can save you time depends on whether you have thought through your business chain: What problem are you solving? Which step is the most time-consuming? Which node requires human judgment? Which step, if done wrong, will directly affect the results? What data proves it has actually improved your efficiency?
If these questions aren’t thought through, even if someone comes to your door and deploys it beautifully, you are just looking at a system that can talk, click tools, and run processes. You still won’t know what to have it do, let alone at what point its output becomes valuable.
Second, you have already lived in a higher-level workflow.
When someone has been using tools like Codex or Claude Code for a long time, their criteria for judging agents naturally rise. They aren’t easily excited by superficial capabilities like “can execute commands automatically,” “can connect to chat software,” or “can install skills.” They’ve seen these things before and have already integrated them into their daily lives. The entire digital world is made of code; to a developer, Claude Code and Codex, which can run everything within a terminal, might actually be closer to the form of a “universal agent.”
They care more about other things: Is the context management stable? Is the task decomposition reliable? Is the integration with existing workflows smooth? Can it roll back quickly after an error? Is the model’s judgment at critical nodes sufficient? Have the cost and security boundaries been calculated?
At this stage, no matter how “lively” a new tool is, it’s hard to convince you just by “looking impressive.”
I think the following comic illustrates the point well:
03
Many Think the Barrier is Deployment; I Worry More About the Barrier of Cognition
This is my strongest feeling about this wave of “lobster farming.”
Everyone is discussing how to install it, how to connect it, how to integrate domestic models, and how to get the service running. But the factors that truly determine success often lie nowhere near these things.
You spend 500 yuan to have someone install it remotely, or have a service provider set it up for you, or use one of the “one-click deployment” SaaS tools that have recently started popping up to harvest the hype. This action has value—it helps you cross the “I don’t know how to deploy” hurdle. But what happens after you cross it? Do you know which process to have it intervene in? Do you know which steps must be monitored by humans? Do you know the difference between effective efficiency improvement and “busy work” that looks lively but is actually just ineffective automation?
In most companies, few people can think these questions through. If a department has one or two people who can clearly see the job goals, business bottlenecks, and value nodes, that business line is usually very competitive. In the AI era, this will only become more obvious. Because tools are rapidly becoming cheaper, the truly scarce part is gradually becoming “who can define the problem, who can design the process, and who can judge the results.”
So, when you see a bunch of people lining up to install OpenClaw, you don’t feel much excitement. Because you understand that the key to the problem is: Which chain are you planning to connect it to? What metrics will you use to judge whether it’s doing a good job? If none of these exist, installing it is likely just a more expensive form of self-comfort.
04
OpenClaw Itself Isn’t as “Brainless and Local” as Everyone Thinks
This is not to say that OpenClaw has no value. On the contrary, it is very communicable in its product form and is very suitable as an entry point for the public to understand agents.
But if you look at the official documentation carefully, you will find many conditions that are downplayed. The official local model documentation clearly states that systems like OpenClaw expect larger context and stronger prompt injection defenses. The hardware recommendation is to go straight to “two top-tier Mac Studios or equivalent GPU machines.” Smaller cards can only run lighter tasks and have higher latency. The security documentation is also quite blunt: prompt injection issues have not been solved, weaker models are more easily hijacked, and boundaries like tool permissions, sandboxes, and whitelists must be configured by yourself.
In other words, the ideal version that everyone talks about—“local, cheap, private, fully automatic”—is still a long way from reality. If you have used enough AI tools, your emotions will naturally be much calmer than the average bystander when you see these conditions. Because you know that “being able to run” and “being able to use with confidence” have never been the same thing.
05
This Doesn’t Prevent OpenClaw from Being Important
The fact that OpenClaw can go viral shows that it has captured a major, real demand: people want an agent that can stay resident, work across platforms, connect to tools, and belong to them. This desire is very strong and will not disappear in the short term.
In this sense, OpenClaw is like a bridge. It brings a large number of people who were previously just watching AI from the sidelines to the door of “I also want AI to take over some work” for the first time. Open source, low barriers, multi-model support, and assisted installation—these are all helping it spread. Its greatest contribution to the industry may not be pushing capabilities to new heights, but making more people seriously consider for the first time: “Which tasks can I actually delegate to an agent?”
That in itself is very valuable.
06
Final Thoughts
So, if you feel nothing about the OpenClaw hype, I really think you are likely not an average person.
You may already be ahead of the curve, having long ago integrated more mature agents into your daily work. Or you may simply be more clear-headed than those around you, knowing that installation is only the starting point, and that things like workflows, business logic, judgment nodes, and security boundaries are what determine whether a tool can eventually become productivity.
Being unmoved does not mean being slow. Sometimes it means you have already passed through the noise and seen the challenges behind it.
And those challenges are precisely what is most valuable in the AI era.
Finally, I’d like to take this opportunity to promote my book. This book was co-created with my partner, senior algorithm engineer Tam, who was responsible for the security and technical principles sections. While the content is not aimed at absolute beginners and leans more towards engineering and technical aspects, it may be of great reference value for those researching agent development, or for general enthusiasts who wish to understand the advanced usage strategies of such agents at a deeper level.