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I’m a Chinese product manager who created 6 AI employees on OpenClaw. I’m working more than ever and am way more tired.

by with No Comment Xpandfy

This as-told-to essay is based on a conversation with Vivi Mengjie Xiao, an AI product manager and content creator on RedNote, China’s social media platform. The conversation has been edited for length and clarity.

I’m an AI product manager in China, and earlier this year, my CEO asked me to explore how AI could go beyond cost-cutting, to drive innovation.

Outside of work, I’m a content creator on RedNote, where I share AI tools, workflows, and insights with over 45,000 followers.

I used to spend about four hours a day gathering AI industry news: reading posts on X, newsletters, blog posts, and translating English sources into Chinese. I thought: “Can I automate this?” If AI can handle information gathering, what else can it do? If I’m doing something repetitive, I should automate it.

Each agent was born from a real problem I was experiencing. I created six AI employees, and they’re split between work and personal life.

My foray into OpenClaw

At first, I set up only one “lobster” — a nickname Chinese netizens use for deploying an OpenClaw agent — and tried to make it do everything.

I wanted it to manage my calendar, schedule, to-do list, and monitor my work. I get distracted easily, so I wanted it to help me focus on what I needed to do in the moment and help connect the main thread of my work.

I kept stuffing other tasks into it as well, such as assigning it to manage my finances.

The result of putting all of that on one lobster was that its context became long and messy. It basically became ADHD like me: jumping from one thing to another without helping me focus. It was running three work streams at once. That wasn’t going to work, so I split tasks up and assigned them to different lobsters.

Over time, the six AI employees naturally organized into personal vs. work, and within each category, into clear roles.

I have three work agents: the administrative assistant, the researcher, and the chief of staff. The chief of staff simulates my boss’s communication style, and I use it to practice and polish presentations. For personal agents, I have a life coach, a content and expression assistant, and a finance assistant.

It felt like building a real team. It makes sense — you don’t hire six people on day one. You start with one, and as the workload grows, you specialize.

The compound effect of having them connected surprised me. The life coach can read conversations from all five other agents. I use the life coach agent to help me journal daily, and now 70% of my journaling is automated. The agent knows everything — what I researched, what I invested in, and what I stressed about in my presentation rehearsal.

I’m more productive, but also more tired

About 60% to 70% of my daily operational work is handled by these AI agents, including information gathering, research, and content distribution.

However, my workday hasn’t gotten shorter. I’ve shifted from doing “grunt work” to doing more creative, strategic, and high-leverage work. The AI employees freed up capacity for significantly more output.

I’m more productive by any conventional metric. I publish podcast episodes daily, monitor financials in real time, run a knowledge management system, and create content for RedNote and X — all while working full-time.

Honestly, I’m also more tired. This is a paradox I’ve been thinking about: When your efficiency goes up, you don’t work less. You just attempt more.

My bedtime has shifted from midnight to 2 a.m. because there’s always one more thing I want to do, or one more agent I could spin up to solve a new problem.

The future of work

We’re witnessing a fundamental shift in what “work” means.

The Industrial Revolution standardized physical labor. The information revolution standardized knowledge work. And now, AI is standardizing execution work — the “how” of getting things done.

This means the premium is shifting from execution ability to three things: taste and judgment, ability to direct AI, and emotional intelligence.

The future of work is “one-person studios,” solo creators and operators who leverage AI to produce at team-level scale. For companies, the question becomes: do you need 10 junior analysts, or one senior thinker with 10 AI agents?

This isn’t about replacing humans. It’s about liberating humans to do more human work. The parts AI takes away were never the parts that made work meaningful. The parts that remain — creativity, judgment, connection, purpose — are what make us human.

Building a team of six AI agents feels like going from being a solo freelancer to being the CEO of a small company, except your team never sleeps, never complains, and works for the cost of API subscriptions.

I’ve become a more structured thinker, a clearer communicator, and a more ambitious creator. I now think in terms of “which agent should handle this?” for almost every task. AI expanded my sense of what’s possible for one person to build.

Do you have a story to share about tech in China? Contact this reporter at cmlee@businessinsider.com.




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Chong Ming Lee, Junior News Reporter at Business Insider's Singapore bureau.

I’ve been a product manager at one of China’s biggest tech firms. Here’s how Chinese AI products are built differently.

by with No Comment Xpandfy

This as-told-to essay is based on a conversation with Yilin Zhang, an AI product manager at AI startup Kuse who worked at Meituan for more than three years. It has been edited for length and clarity. Business Insider has verified his employment and academic history.

I graduated from Tsinghua University with a master’s degree in computer science in 2021 and then joined Meituan — one of China’s biggest tech firms — as a product manager.

At Meituan, China’s platform for local services, especially known for food delivery, I worked on two AI projects. One was a consumer-facing AI assistant that helps users complete various tasks, including ordering food. The other was a merchant-facing AI agent designed to help businesses manage their daily operations, including handling reservations, managing orders, and supporting routine operational tasks.

The main difference between how products are built in China and in the US comes down to the market.

Why Chinese tech companies are so cost-efficient

Across most large Chinese tech companies, AI product development accelerated more aggressively around 2025.

The AI initiatives I worked on at Meituan started around April or May of that year. It coincided with the surge of interest around DeepSeek, when attention around AI agents took off.

Large companies began racing to build AI projects, and almost every business unit launched its AI initiative.

For a long time, especially before 2021 or 2022, Chinese tech companies were primarily focused on domestic competition rather than overseas expansion. Because competition in China is intense, tech companies were forced to become extremely efficient. Their execution methods have been sharpened to an almost frightening degree.

Constraints have also pushed Chinese AI companies to pursue different paths, with a strong focus on open-source models and cost efficiency. These limitations forced exploration in new directions, and those paths have proven valuable in their own way.

DeepSeek is a good example. Because of international restrictions, it couldn’t access large numbers of GPUs and was forced to innovate around efficiency instead.

Why Chinese AI products differ from the West

Chinese and overseas markets are fundamentally different, leading to distinct user bases, expectations, and product designs.

Chinese users have a much lower willingness to pay for software; hence, many mass-market AI products, such as Doubao, tend to be free. The core objective is often to scale active usage.

Many capabilities are packaged into a single prompt you can ask, essentially a chatbox interface with a low barrier to entry.

International AI products target users doing high-value tasks. They are more often designed for desktops than for mobile devices, with interfaces better suited to work contexts. These products explore how AI and humans can collaborate and intersect across different work scenarios, helping users complete tasks more effectively and efficiently.

In China, that user group is relatively small. That makes it harder for its mainstream AI products to move beyond chat-based forms into more advanced products.

China’s internet success over the past decade has also largely come from consumer-facing apps. That environment forces product managers to obsess over user feedback and relentlessly polish even the smallest features.

Teams may spend enormous effort refining a tiny feature just to win over a small group of users. In markets with less competition, that level of detail isn’t always necessary.

The AI startup scene is growing in China

After three to four years at Meituan, I felt I had learned most of what I could from that environment. I left to join the AI startup Kuse in October.

AI is evolving extremely fast. In large companies, iteration speed can be slower. Many of my friends across different Big Tech companies share this same frustration. Smaller, more agile companies can adapt faster.

In the past, top graduates had basically two paths: becoming a civil servant or joining a Big Tech company.

That’s changing. Especially over the past year, many AI startups have emerged, and more young people are choosing entrepreneurship. AI has created a new path outside Big Tech.

By 2025, not being involved in AI at all will feel like staying in the PC internet era of 2010 instead of joining the mobile internet wave.

Do you have a story to share about working in a Chinese tech company? Contact this reporter at cmlee@insider.com.




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Microsoft manager explains how she pivoted from admin to AI — and doesn’t regret her English degree

by with No Comment Xpandfy

This as-told-to essay is based on a conversation with Brit Morenus, a 37-year-old senior AI gamification program manager, based in Charlotte, North Carolina. Her identity and employment have been verified by Business Insider. The following has been edited for length and clarity.

I’ve been at Microsoft for a total of 13 years, but for five and a half, I was a contract worker.

I graduated from college with a degree focused on English, communications, and marketing. I first landed a job at Microsoft as a contract executive assistant. I stayed in that role for about eight months, then joined the marketing team.

Eventually, I had the opportunity to take a really special position, but it required knowing gamification. Gamification is about integrating game mechanics and motivators, such as storytelling and reward systems, into learning. So I was going to teach people about our products and sell them in a gamified way.

I spent about a year getting certifications that taught me about gamification. I upskilled and learned how to create games, what game mechanics are, and what motivates someone when they’re learning.

That was the position where I was able to prove my impact, and they decided to bring me on full-time. I stayed in that role for another six years, training the frontline and customer service support to develop the right sales skills.

Eventually, I had the opportunity to start gamifying learning about AI. They wanted someone with gamification skills, and my certifications and experience made me the ideal candidate.

I didn’t know much about AI yet, aside from using it for personal reasons, but transitioning to an AI role was actually faster than pivoting to gamification. Since I held the gamification role for about six years, I became really good at it. It only took about three months for me to upskill in AI.

In my first three months on the team, I made myself knowledgeable about AI to the point where I could teach others about it. That’s when I got a certification in Azure AI Fundamentals. It was a certification specific to how Microsoft’s AI works.

I helped my entire team get it, and then I helped my entire organization start working on it. Then I helped the greater customer service support organization work toward getting it as well.

Get outside your comfort zone

My advice to those who want to transition would be: Don’t let fear keep you from stepping outside your comfort zone. There’s so much ambiguity about changing roles or companies, but there’s no time like the present.

With AI specifically, you just need to learn. Everyone already uses it, but you need to understand how it works, because that’s how you can understand what to do with it.

It’s also important to upskill yourself. You have to be willing to constantly move and learn more, because it’s going to keep changing — and faster than you can grasp it. Sometimes AI makes wrong predictions, but it is using words to make that prediction. So I absolutely need to use my English degree in order to figure out keywords and how to prompt it to do the right thing.

I don’t regret my English degree

Up until this Al role, I always joked that I wasn’t using my English degree. But now I use it everywhere, and it truly does help. It helps with things like talking to executives and also with the role itself.

It’s important to know the language of AI and how it operates. So now, more than ever, I am using every bit of my English degree and understanding English, grammar, and how it all functions.

For example, there’s a tagging process that happens behind the scenes with AI, just like on social media. Looking at an image, it might tag it as a woman, or a supermarket, and that gives it a confidence score and tells you if it’s relevant or not, and if it’s what we’re looking for.

A lot of it is more about understanding how to apply the English language than about AI — so, thanks, Mom and Dad, I am using the degree you paid for.

This is part of an ongoing series about workers who transitioned into AI roles. Did you pivot to AI? We want to hear from you. Reach out to the reporter via email at aaltchek@insider.com or secure-messaging platform Signal at aalt.19.




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