What should ordinary people do after dressing up as a lobster?

Introduction

In 2026, while the entire internet is rushing to install “Lobster,” most people still mistake it for a high‑end version of ChatGPT. KK.aWSB points out that Lobster is fundamentally a “digital employee” with system‑level access, not merely a chatbot. Faced with the confusion after installation, the article advises abandoning the fantasy of instant mastery and instead starting with mundane tasks such as managing daily reports and emails—thereby shifting from a question‑asker to a manager.

Key Takeaways

• Cognitive Re‑framing: Lobster isn’t a Q&A consultant; it’s a digital employee capable of controlling software and hardware. Its core logic lies in issuing commands and delivering automated results.
• Strategic Focus: In the early stages, avoid grandiose goals like “automatically making money.” Instead, leverage its 24/7 availability to take over daily reports, email handling, and information filtering.
• Time Leverage: The primary value of Lobster is freeing up the time that “junk work” consumes, providing the essential time capital needed to build a “one‑person company.”

Notes

Many people’s anxiety about AI stems from “tool overload”—installing the most powerful model but not knowing how to deploy it. The clearest point of this article is that before becoming a super‑individual, you must first learn to be a competent “contractor.” Don’t always think about using AI to get rich; first let it handle the soul‑draining chores. That’s the most tangible benefit for ordinary people.

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From the standpoint of content completeness, the original text packs a high density of key information, especially in its core conclusions and actionable recommendations. Many people “install a lobster” because their friends are doing it, their boss is doing it, and their social circles are doing it.

But I’ve noticed a fascinating phenomenon: after most people install a lobster, their understanding of it remains stuck at “the high‑end version of ChatGPT.” This cognitive bias directly leads to all the subsequent confusion.

From here—March 2026, the Chinese internet experienced a magical moment. At the entrance of Tencent’s Shenzhen headquarters, nearly a thousand people queued up to have engineers help them install a “lobster.” The line included a nine‑year‑old elementary student, a nearly 70‑year‑old retired engineer… For readers, its most immediate value isn’t “to learn a new viewpoint,” but to quickly see the conditions, boundaries, and potential costs behind that viewpoint.

If we break this content down into verifiable judgments, it at least includes the following layers:

According to the source, release date, and any platform‑specific variations, don’t mistake “scenario‑based experience” for a universal rule. From an industry‑impact perspective, such content typically offers short‑term guidance on product strategy, operational pacing, and resource allocation—especially in areas like AI, development tools, growth, and commercialization. From an editorial standpoint, we focus on whether the claim can withstand subsequent fact‑checking: (1) can the results be reproduced? (2) can the method be transferred? (3) are the costs sustainable? The source is x.com; readers should treat it as one input among many, not the sole basis for decisions.

Practical advice: if you plan to act on this information, start with a small‑scale test, then scale up gradually based on feedback. If the original text touches on revenue, policy, compliance, or platform rules, rely on the latest official announcements and keep a rollback plan ready. Republishing serves to improve information flow, but real value is created through secondary evaluation and local adaptation. In line with this principle, the accompanying editorial commentary will continue to emphasize verifiability, boundary awareness, and risk control, helping you turn “what you see” into actionable insight.

Many people start using LLMs simply because their friends are, their bosses are, and their social circles are.

But I’ve noticed a fascinating pattern: after adopting an LLM, most people still think of it as just a “high‑end version of ChatGPT.” That cognitive bias is what fuels all the confusion that follows.

From here on—

2026年3月，中国互联网迎来了一个魔幻时刻。

深圳腾讯大厦门口，近千人排队等工程师帮忙装一只"龙虾"。队伍里有9岁的小学生，有快70岁的退休工程师，有律所老板，有行政文员。

小红书上，"龙虾"相关话题浏览量突破3亿。Mac mini被抢到脱销，现货价格翻倍。上门安装龙虾的服务，一单收500到上千，成了2026年起步最快的副业。

所有人都在装龙虾。但所有人装完之后都会问同一个问题：

然后呢？

一、先搞清楚你装的到底是什么

很多人装龙虾，是因为朋友在装、老板在装、朋友圈都在装。

但我发现一个很有意思的现象：大部分人装完龙虾之后，对它的理解还停留在"高级版ChatGPT"。这个认知偏差，直接导致了后面所有的迷茫。

龙虾不是ChatGPT。

ChatGPT是一个"顾问"。你问它问题，它给你答案。你关掉对话框，它就不存在了。

龙虾是一个"员工"。你给它一个目标，它自己去干。你关掉电脑去睡觉，它还在干。它能操作你的鼠标、键盘、文件系统、浏览器，能发邮件、管日程、抓数据、写代码。

这个区别非常关键。

你跟ChatGPT的关系是

“Question and Answer” – your relationship with the lobster is “issue commands, wait for delivery.”

One scenario is you going to the library to look up information; the other is you assigning tasks to an assistant.

So the question “I’ve set up the lobster but don’t know what to do next” is, at its core, not a technical problem but a management one. You suddenly have a 24/7 digital employee, yet you’ve never been a boss and don’t know how to delegate work.

That’s the real bottleneck.

—

2. Don’t rush into flashy features; first let the lobster save you time

I’ve seen far too many people, after setting up the lobster, immediately try to launch grand projects—automatic stock trading, automated account management, automated money‑making.

It’s like hiring a new employee and, on day one, sending them to negotiate a multi‑million‑dollar deal. The outcome is predictable.

My advice: start with the most tedious, repetitive tasks.

Daily and weekly reports? Hand them over.

You spend 30 minutes each day writing a daily report and about two hours each week compiling a weekly report. The lobster can take care of that for you. It reads your local files, emails, chat logs, and compiles a clear, structured work report. All you need to do is spend five minutes reviewing it and tweaking a few words.

A user who works in corporate administration set the lobster up as a “secretary.” Daily reports, weekly reports, quarterly summaries, annual performance materials—everything was handed to it. He said that each document used to take two or three hours to write, but now it takes only 20 minutes.

Email management? Let it handle that.

How many emails do you receive each day? 50? 100? How many of them actually need your attention…

Did you really reply thoughtfully? Most emails are either notifications, CCs, or can be answered with a templated response. Let “Lobster” sort your inbox every morning: flag the important ones in red, push urgent messages to your phone, and auto‑draft replies for routine items. All you have to do is hit “Send.”

Let it handle information organization.

That’s the most underrated capability of Lobster.

The industry news, competitor updates, and policy changes you care about are scattered across a dozen websites, official accounts, and group chats. In the past, you’d spend half an hour to an hour scrolling, watching, and filtering. Now you can have Lobster patrol those sources at 8 a.m. each day, compile a “Daily Brief,” and push it to your Feishu or WeChat.

A product manager shared his approach: on his commute, Lobster had already finished analyzing the online dashboards. By the time he reached the office for the morning meeting, he already had the latest data and trend insights in hand.

These tasks may seem small, but add them up: saving one hour a day equals 30 hours a month, 360 hours a year. Use those 360 hours to learn new skills, pursue side gigs, or spend time with family—none of which is as productive as burying yourself in weekly reports.

Lobster’s first value isn’t about making money for you; it’s about freeing your time from wasteful work.

3. Find Your “One‑Person Company” Angle

How do you spend the time you’ve saved?

This brings us to Lobster’s second layer of value: it makes a “one‑person company” truly possible.

In the past, the idea of a one‑person company was a beautiful concept. How could one person possibly handle product, operations, customer support, and finance all at once…

?

Now it’s possible.

Shenzhen’s Longgang district was the first to roll out its “10‑Rule Lobster” policy, and Wuxi followed with a “12‑Rule Lobster” plan. The core of the policy is to encourage OPCs—one‑person companies. With a single workstation, a modest subsidy, and your army of AI agents, you can get to work.

But the idea of a “one‑person company” is huge. For most people, the key is to find a specific entry point.

My decision framework is simple: three questions.

1. In your current job or life, is there a repetitive task that’s “annoying but unavoidable”?

If so, that’s your entry point.

• If you’re an e‑commerce operator who has to reply to hundreds of customer messages a day, let Lobster automatically answer common questions and only forward the complex ones to you.
• If you’re a content creator who has to publish on five or six platforms daily, let Lobster do a one‑click distribution and even fine‑tune the tone for each platform.
• If you’re an investor who has to sift through a mountain of financial reports and analysis, let Lobster scrape, organize, and generate summaries so you only read the conclusions and key data.

2. If you can solve that task for others, would they be willing to pay?

If they would, you’re not only saving your own time—you’ve found a monetizable niche.

For example, there’s a software developer named Yang Mingfeng. His job is to build software for clients. Previously he’d sit at a computer and write code line by line. Now he’s set up an AI‑powered collaboration pipeline with Lobster—he only has to communicate with the client.

需求，剩下的架构、编码、测试全部交给AI军团。他说自己现在”躺在床上发号施令就能赚钱”。

他做的事情本质上没有变——还是帮客户开发软件。但他的交付能力翻了好几倍，因为他不再是一个人干，而是指挥一支AI团队干。

第三，这个方向你能不能做到"一公里深"？

这是最重要的一个问题。

龙虾的技能商店已经有上千个现成的技能可以用。但现成的技能谁都能装、谁都能用。真正有价值的，是你在某个垂直场景里，把龙虾调教到别人用不了的程度。

什么意思？

龙虾有一个核心特性叫"记忆"。你跟它交互得越多，反馈得越多，它就越懂你。

一个律师用龙虾做法律文书，用了一个月之后，龙虾已经摸清了他的写作习惯、用词偏好、常见的条款模板。这时候它产出的东西，换一个人来用同一只龙虾是做不到的。

这就是你的护城河。

不是谁装了龙虾谁就牛，而是谁把龙虾养得好谁才牛。

四、冷静看待"龙虾赚钱"的叙事

我知道很多人关心的不是省时间，而是赚钱。

先说好消息：龙虾确实在帮一些人赚钱。

模型公司赚到了——Kimi近20天的收入超过了2025年全年，MiniMax的Token调用量

连续三周全球第一。

云厂商赚到了——腾讯云、阿里云的Coding Plan销量暴增。

安装服务赚到了——上门装龙虾，一单500块，有人一个月做了几十单。

教程贩子赚到了——"零基础三天学会养龙虾"的课程卖了一波又一波。

但我必须泼一盆冷水。

在这个生态里，目前赚到最多钱的人，不是"用龙虾干活"的人，而是"围绕龙虾提供服务"的人。

这跟淘金热的逻辑一模一样：真正发财的不是淘金者，而是卖铲子的。

你看到的"用龙虾日赚多少多少"的故事，很多经不起推敲。有人号称用龙虾做量化交易赚了大钱，但高频交易的风险和门槛，不是一只龙虾能解决的。有人说用龙虾全自动运营小红书月入过万，但平台的内容审核、限流、封号风险一个都跑不掉。

还有更荒诞的：安全公司发现龙虾的技能商店里有386个恶意技能包，伪装成加密交易工具，实际上是窃密木马，被下载了约7000次。

所以我的建议是：

把龙虾当作你的能力放大器，而不是印钞机。

你本来就擅长做某件事，龙虾可以帮你做得更快、更多、更好。

你本来就不擅长的事，龙虾也救不了你。

一个本来就会写代码的人，用龙虾可以把开发

效率提升3到5倍。一个从来没写过代码的人，指望龙虾帮他从零开始创业，大概率会失望。

龙虾放大的是你已有的能力，而不是凭空给你一个新能力。

五、真正该做的三件事

好，说了这么多，到底该干嘛？

我给出三个具体的行动建议。不需要你会写代码，不需要你有技术背景，普通人就能上手。

第一件事：花一周时间，记录你的"时间账本"

在你开始折腾龙虾之前，先花一周时间，记录自己每天的时间花在哪里。

不需要精确到分钟，大概就行。

你会发现，你每天至少有2到3个小时花在了"无脑但必须做"的事情上。整理文件、回复消息、搜索信息、填表格、写汇报。

把这些任务列出来，按"重复频率"和"痛苦程度"排个序。排在最前面的那一两个任务，就是你让龙虾上手的第一个切入点。

不要贪多。先搞定一个，搞顺了再加第二个。

第二件事：选一条赛道，养出你自己的龙虾

龙虾的核心壁垒不在于安装，而在于"养"。

傅盛在直播里说过一句话特别到位：用工具，你只管用；用员工，你要培训、要建规则、要在他犯错的时候给反馈。龙虾更像后者。

你给它的反馈越多，

It gets to understand you better. The clearer the rules you give it, the less likely it is to make mistakes.

So instead of trying to dabble with ten lobsters, each only a little bit, focus on raising one well.

Pick a domain you’re most familiar with and have the biggest advantage in. It could be your profession or a hobby.

Then feed it consistently—feed it data, feedback, and rules.

After a month, your lobster will have evolved into a different species from everyone else’s.

Third point: Find your place in this ecosystem

The lobster’s sudden boom is most interesting because users are actively demanding to pay for it. It’s not the platform teaching users or sales teams hard‑selling; it’s users themselves shouting, “I want to install, I want to use, I want to buy.” A demand‑driven market like this is rare in China’s tech circles.

What does that mean?

It means a brand‑new ecosystem is taking shape, and it’s still empty.

As the author referenced at the beginning said: many people see “I installed a lobster but don’t know what to do with it” and call it a bubble. But if a system becomes mature enough that everyone knows how to use it, the opportunity disappears.

An empty space isn’t a bad thing. An empty space means opportunity.

What kind of people does this ecosystem need?

It needs people who can develop vertical‑scene skills—such as contract‑review skills for lawyers, inventory‑monitoring skills for e‑commerce, or homework‑grading skills for teachers.

It also needs people who can act as “AI translators”—turning complex technical operations into something ordinary people can understand.

Talk about turning Xiao Bai’s vague requests into commands that a “Lobster” can actually execute.

You need to be someone who can offer industry‑specific solutions—not just a generic, one‑size‑fits‑all Lobster. You must provide a complete, pre‑configured, fine‑tuned, plug‑and‑play Lobster package tailored to a particular sector or scenario.

You don’t have to become a technical wizard. What you need is to become an expert in a particular field and then let the Lobster extend your expertise in that domain.

6. A Few Sincere Words

Every technological wave is accompanied by hype, misunderstanding, bubbles, and chaos.

When ChatGPT exploded in 2023, everyone asked, “Will AI replace me?” Two years later, most people’s jobs weren’t replaced; the way they worked simply shifted.

When the Lobster craze hits in 2026, people will ask, “What can I do once I’ve set up the Lobster?” The answer is similar: it won’t change your life overnight, but if you’re willing to invest time in learning, taming, and mastering it, it can become a powerful lever in both work and life.

My candid thoughts on the Lobster:

• In the short term, the hype will subside. Once the novelty wears off, those who “set it up but don’t know what to do” will gradually give up, and the monthly token costs will start to bite. The market will naturally filter itself.
• In the medium term, the Lobster—or an agent like it—will become a standard. Just as everyone has WeChat on their phones today, in the future every device will run some form of AI agent. Its form may not look exactly like today’s Lobster, but the direction is certain.

Long‑term, the real beneficiaries aren’t the first people to “install lobsters” (i.e., deploy AI), but those who first figure out what to do after the installation.

So instead of worrying about whether you should deploy AI, take a calm look at these questions:

1. Which of your current tasks are repetitive, mechanical, and time‑consuming?

2. In your area of expertise, what needs are people willing to pay for?

3. Can you apply AI in a vertical scenario to a depth that others can’t reach?

The answers to these three questions are a hundred times more important than the decision to deploy AI.

AI isn’t the destination; it’s the starting point.

What should you do after deploying AI?

The answer is: do what only you can do.