Original · Unique Research · 2025-11-08
Editor’s note: This complete English edition preserves the original commentary and Jiamu’s statements as of November 8, 2025. Revenue figures in the source do not specify a currency or reporting period. Course participation, rankings, startup counts and productivity claims remain source-attributed, not independently audited findings. The source prints ‘A’ in Q1; this edition reads it as ‘AI’ in context. ‘Con-funder’ is retained as the source’s wording rather than used to establish a formal founding title. Yuanzhi Turing and the English book title are renderings of Chinese names, not assertions of official English branding. No publication location is inferred.
If we were to give Jiamu three labels, they would be Prompt pioneer, AI Agent innovator and practitioner, and AI empowerment consultant. They sound like three separate careers, but they are more like three frames on a timeline: the same person, riding the same wave, repeatedly upgrading himself into a new kind of professional. At heart, this journey is not about how many tools he has learned. It is about his continuing effort to answer one question: in a world whose rules are being rewritten by AI, what can one person ultimately become?
I. Beginning with a Single Prompt: Reinventing “the Person Who Asks”
Many people think Prompt engineering is simply a collection of tricks for writing incantations: add a role, specify a format, and you can “squeeze everything” out of a large model. For Jiamu, however, a Prompt is more like an explicit framework for thinking. It breaks the fuzzy ideas in a person’s mind into a structure a machine can understand.
Early on, he worked with Jiangshu on the LangGPT community. As the community’s founder, Jiangshu open-sourced a structured prompting method that broke everything into modules: who the role is, what the task is, what the output format should be, and what boundary conditions apply. Using the same methodology, Jiamu also open-sourced many scenario-specific applications of structured prompts. That may sound highly engineered, but it actually corrects human thinking in reverse. Often, the problem is not that the model is incapable; it is that we ourselves have never clearly worked out what we want.
More interestingly, as large models become more capable and their context windows grow longer, Prompt engineering has not become obsolete. Instead, it has evolved into context engineering and information-ecosystem design. You no longer give the model a single instruction. You design an entire information field: which materials to feed it first, which examples to provide next, and finally how an Agent should act autonomously within that field.
In other words, Prompt engineering has evolved from writing one sentence to directing a play. What you write is not only a prompt for AI; it is also a productivity script for yourself.
That is why Prompt pioneers such as Jiamu are, in essence, staking out a position in a new layer of infrastructure. By learning earlier than others how to speak with agents, he also learned earlier how to organize the work of the future.
II. The Agent Era: an “Invisible Team” Behind One Person
Once Jiamu had refined Prompt engineering to an advanced level, he naturally moved to the next step: enabling AI not merely to respond, but to act.
Agent is an overused term, but stripped down, it means one thing: packaging the ability to complete tasks into a continuously operating system rather than a sequence of question-and-answer exchanges.
Jiamu and his collaborators wrote The Guide to Agent Design, implemented a range of Agent projects in companies, and advocated for the field at conferences. Books, courses, and consulting projects may look like different products, but they all serve the same purpose: he is teaching people how to turn “someone who knows what to do” into “a system that can actually do it on its own.”
There is a subtle shift behind this. In the traditional world, we extend an individual’s capabilities through a team; in the agent world, you can extend them through an Agent matrix.
Imagine a typical workday. You give one Agent a fuzzy objective: help me map the AI application opportunities in a certain industry. It searches public sources, reads papers, and organizes competitors on its own, then hands the material to another Agent to create a PPT draft, while a third Agent checks the data and reviews the logic. You step in only at critical junctures to exercise judgment and make trade-offs.
This is the way Jiamu already works: he is one person connected to a virtual team. Different models perform different tasks. LangGPT’s structured prompts provide a common language for the conversations; Kimi conducts research; Tongyi handles creative work; and the multi-model collaboration resembles a small company with a well-designed division of labor.
A “super individual” is not someone who becomes omnipotent. It is someone who learns how to command a fleet made up of AI.
III. The One-Person Company: Redefining the Unit of Organization
Interestingly, Jiamu has not rushed to establish another company in the traditional sense. His choice is to make himself the hub, connect a highly streamlined small team around him, and use AI to extend his reach as far as possible.
In the past, we judged an organization’s strength by its headcount, funding, and office space. In his model, three other dimensions matter more:
The first is cost and efficiency.
When AI takes over large volumes of basic work, one person can handle what once required a small team, and “fixed costs” suddenly become much less burdensome. Some of Jiamu’s work—refining courses, delivering corporate training, and developing AI consulting proposals—is fundamentally high-density creative labor. By handing research, formatting, polishing, and other routine work to AI, he can devote more time to the 30% that truly warrants his personal attention, including viewpoints and original thinking.
The second is response speed.
Large models evolve every day and new tools arrive in waves. Organizations with long decision chains often need to hold meetings merely to understand what has changed. Individuals and small teams can see a change, test it, and incorporate it into their workflows on the same day. In a fast-moving field such as AI, response speed is often more valuable than resources.
The third is how value is distributed.
In a large company, a person with insight and execution ability can easily have their impact diluted by processes and hierarchy. In the one-person-company model, individual judgment directly determines the direction of the business, and personal reputation converts directly into commercial opportunities. Jiamu’s courses, books, public-account articles, and advisory projects all revolve around the “Jiamu” IP. In the AI era, that tight association is actually an advantage, because users are increasingly willing to pay for identifiable individuals.
So when we talk about a one-person company, we are not talking about fighting alone. We are talking about an entirely new organizational theory:
The basic organizational unit is no longer department + role, but individual + AI tool stack. Scale is no longer expanded by adding people, but by adding agents and application scenarios.
IV. From Selling Time to Selling Systems: the Super Individual’s Business Flywheel
Many people envy the freedom of super individuals without seeing their business model clearly. Jiamu’s path follows a distinct flywheel:
First, use public content to build awareness.
Public-account posts, open courses, and open-source projects are all, in essence, free or low-cost mechanisms for building trust. He lays out his understanding of Prompt and Agent work without reservation, accumulating an audience while continually testing his methodology.
Next, monetize that awareness through education and training.
Whether he teaches on a knowledge platform such as Dedao or runs training camps for listed companies and universities, he is packaging what he knows into a deliverable product. A course is not a one-off traffic business; it is a filter. People willing to pay for knowledge are seed users for every kind of future relationship.
From there, move toward deeper consulting and advisory relationships.
Once companies discover that the approach can genuinely be implemented in their businesses, deeper collaboration follows naturally: strategic planning, scenario design, and project consulting. These engagements command higher fees and involve deeper trust, while also helping him accumulate more cases and build a stronger reputation.
Finally, systematize those experiences once again.
A book is one systematized version; a cognitive framework is another. In the future, it could readily become an AI consulting assistant or an AI training-camp SaaS product.
Every answer and every proposal is, in effect, training data for a future AI version of Jiamu. Truly sophisticated super individuals do not see themselves merely as people; they see themselves as models still being trained—iterating continuously in the real world and then turning those iterations into reusable systems.
Look at it this way, and one point becomes clear:
AI is not merely helping you make a little more money. It is helping you grow a second business curve: a digital counterpart that can work 24 hours a day at near-zero marginal cost.
V. AI’s Real Leverage Is Not 10x Efficiency, but Rewriting the “Upper Limit of the Individual”
When discussing AI’s leverage, it is easy to reduce the idea to crude multipliers: 3x, 10x, or 100x improvements in efficiency. Jiamu’s practice reminds us that the real leverage lies not in the numbers, but in shifting boundaries.
First, it pushes outward the boundary of how much one person can do.
In the past, even the most efficient individual was limited by time, energy, and coordination costs. Now that research, writing, formatting, and design can be partly delegated to AI, you can advance more projects at once, tolerate more experimentation, and test more ideas in less time. For the first time, scale is becoming an individual attribute rather than an organizational one.
Second, it pushes outward the boundary of how far one person can think.
AI is not simply a tool that follows orders; it is an always-available co-creation partner. You can ask it to write a proposal you completely disagree with just to see an alternative narrative, or have it criticize your product from a customer’s perspective just to force yourself into a different point of view. This ever-present dialogue continually stretches the boundaries of your thinking.
Third, it pushes outward the boundary of what one person can be worth.
When the market sees that one person plus an AI tool stack can truly accomplish work that once required a small team, it begins to reprice the individual. Jiamu says that many small teams or one-person companies with revenue at the ten-million level had already appeared in 2025. This is not motivational rhetoric, but a straightforward logic:
When you can create value consistently, and that value is no longer linearly tied to your time, the market will naturally pay a premium for a highly leveraged individual.
Of course, every form of leverage comes at a price.
Efficiency is amplified, but so is anxiety; capability is amplified, but so are mistakes. AI enables one person to move a larger world, but it also means one person’s choices can affect a larger group. That is why Jiamu repeatedly stresses that you may have many arrows, but a human must determine where their arrowheads point.
VI. If You Also Want to Become an “AI Super Individual,” Where Should You Begin?
Jiamu’s advice is not mysterious at all: strengthen foundational skills, choose a tool stack, and change your way of thinking.
Foundational skills mean becoming capable first, and becoming capable with AI second.
Whatever your original field—writing, design, programming, marketing, or organizational management—go deep in it. AI can amplify abilities you already possess; it cannot manufacture a craft from nothing. Jiamu’s ability to move quickly and creatively with Prompt and Agent systems rests on his years of experience in full-stack development and product management: he understands systems and users alike.
A tool stack means choosing depth over quantity.
Select a few models and tools closely aligned with your business, study them until you are highly proficient, and embed them into your daily workflow rather than opening them only when they happen to come to mind. The real gap is not how many tools you have tried, but how much of your work automatically benefits from those tools.
The mindset is the most difficult—and most critical—part.
You must learn to manage AI as you would manage a subordinate: state the goal clearly, define the boundaries, and inspect the result. You must also learn to develop yourself as you would develop a product: abstract your experience into templates, codify processes as SOPs, and package services as replicable products.
When you begin asking yourself:
“How can I use AI to complete this semi-automatically next time?”
“Can the solution to this problem become a reusable plan that my future digital counterpart can execute?”
you have already started moving toward selling systems instead of selling time.
One final requirement may sound like motivational rhetoric, but it is profoundly practical:
Above all these technologies, tools, and models, you must preserve a clear sense of “self.”
Jiamu stresses again and again that no matter how powerful AI becomes, people must always make the final decisions.
You can let AI produce a foundation and polish the language, but only you can decide which sentence remains and which point expresses the position you are prepared to uphold in life.
AI can make you faster, stronger, and larger, but what you use that power for will always be written into your choices.
VII. The Era Has Placed a Lever in Your Hands—First Decide What You Want to Move
“Pioneering Intelligence | The Age of the Individual” can easily sound like a marketing slogan if it is only a conference theme. Put someone like Jiamu onstage, however, and you see that the phrase is quietly becoming reality.
One person begins by writing a good Prompt,
turns conversations with AI into a methodology,
turns that methodology into a community, courses, and a book,
turns the book and courses into corporate cases,
then installs those cases in agents and systems,
and ultimately becomes a hybrid professional who is at once a consultant, creator, and product manager.
The journey is not easy, but it brings unprecedented good news:
You no longer necessarily need a large team to take part in a technological paradigm shift.
If you are willing to learn, iterate, and refine your experience until a system can reuse it, the era will place the same lever in your hands.
What truly determines the gap from here will no longer be whether an opportunity exists, but a more basic question:
When you, too, have the opportunity to become an AI super individual,
what exactly do you want to move?
Only a higher income and a more impressive title,
or a set of values and responsibilities you are willing to stand on for the long term?
In that sense, Jiamu’s story is not an answer but a mirror.
Through his own practice, he has already shown that one person can indeed be amplified far beyond the scale once possible.
You must decide for yourself which version of you will be amplified.
Selected Interview Q&A
Q1: Please introduce yourself and use three “labels” to define who you are. How did you find and establish your position in the AI field, and what is the most significant trend in that field today?
Jiamu: Hello, everyone. I’m Jiamu. If I had to define myself with three labels, I think they would be the following.
Prompt Pioneer
I am among the earliest practitioners in China to research a “structured prompting” methodology for Prompt Engineering, and I also run the LangGPT prompting community with Jiangshu. This field remains red-hot in 2025. As large models become more capable, prompt engineering has not become obsolete; on the contrary, it has grown even more important. Put simply, by optimizing prompts, we can make models easier to use and more general-purpose without modifying the models themselves, enabling ordinary people to command AI more effectively. That is precisely the value of prompt engineering. New concepts such as “context engineering” have also emerged, extending prompt design across the entire information ecosystem. This is another trend in the evolution of Prompt engineering: at the level of drawing out the capabilities of large models, the importance of a prompt remains.
AI Agent Innovation Practitioner
I focus on the design and practical implementation of artificial-intelligence agents (Agent), and I actively advocate for and practice in the AI Agent field. In simple terms, an AI Agent is a system that allows AI to execute tasks autonomously like an agent acting on someone’s behalf. The field has grown explosively over the past two years: in less than one year, the number of AI Agent startups worldwide surged from about 300 to several thousand, and agents are gradually becoming part of workflows across sectors including e-commerce and industry. Every leap in the capabilities of underlying large models produces a corresponding leap in the functionality and business value of Agent products. We are also among the co-initiators of the Asia-Pacific Prompt Engineering Conference (PEC), where we share frontier ideas about AI Agent systems. Through consulting projects, we also bring agents into real corporate scenarios. Positioning myself as a pioneer in the AI Agent field and turning the latest agent technology into practical productivity is a highly important trend in AI today.
AI Empowerment Consultant
In addition to development and content creation, I sometimes work as an AI strategy consultant, providing “AI+” empowerment consulting to companies and individuals. I have a background spanning 7 years of full-stack development and large-project management, along with a keen understanding of business. This enables me, as a consultant, to implement AI in specific operations. In recent years, almost every industry has sought an AI transformation, causing demand for AI consulting to rise sharply. I have provided AI strategic planning and scenario-solution design to clients ranging from state-owned enterprises and listed companies to small and medium-sized businesses. The most conspicuous trend today is that companies want to “reduce costs and increase efficiency.” AI is rapidly replacing parts of some basic roles—including customer service, design, and data analysis—cutting labor costs and improving efficiency. As a result, the role of an “AI consultant” has become highly sought after and requires people who understand both technology and business. I position myself as both a technical expert in AI and an enterprise-empowerment consultant, following this trend closely and helping organizations across industries plan AI strategies and implement AI solutions.
Q2: What opportunity—or which work or viewpoint—first brought you recognition in the AI creator community? What key decisions do you believe you or your team got right?
Jiamu: After Yunzhong Jiangshu founded the LangGPT community and launched its open-source structured prompt-engineering project, I later joined and helped operate the LangGPT community in a role described in the source as “Con-funder.” I am very grateful to Jiangshu. I also published many prompts of my own that attracted broad attention in the industry. That was a starting point for me: I seized the early opportunity created by the explosion of large models, focused on Prompt engineering, and chose open source and sharing. That decision allowed me to build recognition and relationships quickly.
Another representative achievement was launching an AI prompting course. The prompt-engineering course I lead on the Dedao knowledge platform attracted more than 10,000 learners in its first week, setting a record for an AI learning course. To date, it has accumulated more than 40,000 enrollments. That remarkable response made me realize that consistently publishing high-quality content is a highly important decision. Through the course and public-account articles, I shared my views and methodology on AI with a broader audience and built a personal IP. The course’s success greatly expanded my influence in media and education, while also validating my original decision to focus on sharing knowledge.
Publishing the best-selling book The Guide to Agent Design was another important milestone. I completed the book in 2024 with Jiangshu, Brother Gang, and Sister Xiaoqi, and it was released in March 2025. On its first day, it climbed to number 2 on JD.com’s bestseller list, and many peers called it a “red book” for the AI field. The process of writing it systematized my practical experience with AI Agent systems, and the feedback at the time was quite positive.
In AI communities and events, I have also met excellent friends including Kazike, Guizang, and Dacongming. Their distinctive insights and creative experiences have been tremendously inspiring, and I have benefited enormously from every exchange. There are, of course, many other interesting people in the AI community who have helped me along my AI-creation journey.
In 2024, some of my photographs and Prompt works were fortunate enough to appear on the large screen at Mr. Liu Run’s annual address. That appearance expanded my influence among corporate audiences, helped more entrepreneurs and business leaders understand and recognize the value of AI, and created opportunities for deeper collaboration with them. Meeting Mr. Liu Run and having my work presented on his stage was also a landmark moment, giving me a chance to bring my ideas and achievements to a much broader business arena.
I would not say I am famous yet, but I believe the most important formula in the AI era is “seize the initiative + keep doing the work.” Early in the AI wave, I chose the right direction—focusing on Prompt engineering and Agent systems—and had the courage to share results openly through open-source projects and public courses, thereby building a professional reputation. I also place great importance on cross-disciplinary collaboration and practical cases. My collaboration with Mr. Liu Run, for example, brought me onto the platform of a well-known business adviser, while my collaboration with Alibaba’s Tongyi team included creating an AI calendar. Behind these representative works and collaborations is a commitment to continually delivering value and embracing opportunities to work with others. That is a major reason creators stand out in the AI community in the AI era.
Q3: Do you currently operate as a “super individual,” a “one-person company,” or a lean small team of perhaps 2-3 people? Why did you choose this model, and what distinctive advantages does it offer in the AI era?
Jiamu: I would call it a lean small team, although I also assemble very small collaborative teams or project alliances when needed.
I personally lead the core work, including content creation, course development, and consulting delivery. Why choose this model?
One reason is my own expertise and brand. I have a clearly defined personal IP and professional positioning, and many clients come specifically for the “Jiamu” personal brand, so operating around the individual is more efficient.
The other is the opportunity created by our era. Empowered by AI technology, one person with the right tools can now complete work that previously required a team, truly realizing “one person as a company.”
The spread of AI large models, intelligent assistants, and automation tools has broken through the productivity bottleneck of the individual.
This model’s distinctive advantages in the AI era can be understood in several ways:
Cost and efficiency. A super individual does not bear the heavy management costs of a traditional company. AI assistants can replace many basic job functions, so my operating costs are extremely low without reducing output. Automation tools also minimize repetitive labor, allowing me to focus my energy on creative, high-value work.
Agility and flexibility. Small-team companies have short decision chains, allowing me to respond quickly to market and technological change. When new AI functions and trends constantly emerge, I can learn and apply them immediately without layer upon layer of internal coordination. This flexibility is extremely valuable in the fast-changing AI industry.
Maximizing personal value. The super-individual model allows me to convert personal influence and professional expertise directly into business value, without having them “diluted” by the structure of a large organization.
Technological leverage gives a “small unit” enormous power. I can create the greatest possible value with the smallest organization. This “travel light” model itself represents a shift from traditional thinking about scale to a mindset of precise empowerment. I believe we will see more and more similarly small but excellent teams in the future; it is a natural result of the era’s evolution.
Q4: This conference is themed “Pioneering Intelligence | The Age of the Individual.” As a leading practitioner of this era, what role does AI play in your daily workflow, and to what extent has it “liberated” you?
Jiamu: For me, AI permeates nearly every part of my daily work. You could call it my “second brain” and an “invisible member of the team.”
AI has dramatically improved my efficiency. Tasks that once took people a great deal of time can now be completed in minutes with an AI assistant.
In content creation, for example, I often ask large models to brainstorm outlines and generate first drafts. Generative AI such as ChatGPT can polish and revise my ideas and drafts while also suggesting changes and avenues for improvement. Human-AI collaboration dramatically accelerates the production of a high-quality article. In creative work, when I need a brand slogan or marketing idea, I also ask AI to offer alternatives in different styles to spark my inspiration. AI lowers the threshold for turning creativity into reality: it takes over a great deal of repetitive manual work, freeing me to focus on the most creative parts.
There are many other examples, including:
During research, I use AI tools with web-search capabilities to find information quickly.
During solution planning, I use AI to generate mind maps and PPT drafts.
In design, I can even use AI to create initial versions of posters and illustrations.
In terms of efficiency, AI therefore gives me multiplying leverage. In the past, I might have completed 1 task in a day; with AI, I can complete 3-5 without sacrificing quality.
More importantly, AI expands the boundaries of my creativity. Many people fear that AI will stifle human creativity, but my experience is precisely the opposite: AI provides a partner for “co-creation.” I can test all kinds of unconstrained ideas because AI helps simulate and validate them. If I want to write advertising copy in the style of classical Chinese poetry, I can first ask AI to draft several lines, draw inspiration from them, and then improve them. If I am designing a business process, I ask AI to simulate different scenarios. Through this interaction, people and AI form a creative feedback loop: AI’s output stimulates human inspiration, people respond by adjusting AI’s direction, and the final result goes far beyond what either the human mind or the machine could produce alone. In this “rise of the individual,” using AI well is like equipping yourself with a formidable team of assistants. Greater efficiency and amplified creativity are the leverage AI provides, enabling ordinary individuals to create intelligent results that once required a large company.
Q5: Could you share your current “AI tool matrix,” or your team’s? For example, which AI tools do you rely on most heavily for content ideation, asset generation, operations, and promotion?
Jiamu: In my daily work, I primarily use Gemini 2.5 Pro and GPT-4.5 for content ideation and asset creation, especially in individual use cases.
At the prompt layer, there are LangGPT’s GPTs, the community and toolset we created, which can be seen as a framework for structured prompt engineering. Put simply, LangGPT helps us design and reuse high-quality Prompts. When I write prompts, I follow LangGPT’s methodology, breaking them into structured modules such as role, task, and format to ensure the large model understands them correctly. This greatly improves the controllability and stability of our use of AI. LangGPT’s open-source project has also had considerable influence and has even been adopted as a prompting standard by agent platforms from China’s leading large-model providers. Internally, therefore, LangGPT is not only a methodology but also a knowledge base and tool. It lets team members quickly retrieve previously validated Prompt templates, greatly reducing duplicated effort. Externally, we share these experiences through the LangGPT community and advance the broader field of Prompt engineering.
Kimi serves as a knowledge assistant. When I need to search the web for information, Kimi can search and organize an answer at the same time. This saves substantial time compared with manual searching and subsequent processing, while also providing broader coverage.
During some development projects, I also use AI programming tools such as Trae, Cursor, and Claude Code, embracing vibe coding to improve efficiency dramatically. I use Doubao, Qwen, Zhipu, and others in different ways as well; in practice, I use many of them and flexibly draw on both open-source and commercial large models.
For example, Zhipu AI’s GLM family performs very well in Chinese-language processing, while Alibaba’s Tongyi Qianwen is strong in creative copy and multimodal generation. We call on these models as each project requires.
In practice, we often coordinate multiple models. When developing a marketing plan, for example, I might first use the LangGPT framework to design the Prompt, use Kimi to gather market information, turn to Tongyi Qianwen to generate varied copy, and finally ask DeepSeek to check the logic and reliability of the data across the plan.
Through this “matrix” collaboration, we effectively assemble a virtual team of AIs with different specialties, each playing to its strengths and filling the others’ gaps. It is precisely because I have this AI tool matrix that I can, as an individual, produce far more content and proposals than I could in the past. The matrix is not static: we continue to introduce new tools as AI technology advances. But the core principle remains the same—use the right AI for the right job, deploy each AI in the part of the process it handles best, and ultimately improve the efficiency and quality of the overall workflow substantially.
Q6: How does a “super individual” create a complete business flywheel? Could you describe your main revenue models today—for example, paid content, IP licensing, consulting, advertising partnerships, or tool development? What new monetization opportunities do you believe AI creates for individual creators?
Jiamu:
Corporate training and events: I regularly conduct workshops and training programs for companies and universities, which are one source of income. Many companies are willing to pay for AI-related training. For example, I explain “large-model applications” to the management teams of listed companies and run workshops for university faculty and students. I also speak at some paid events and conferences and sometimes receive a speaker’s fee. These engagements are valuable because they expand my network and brand influence, while corporate training often leads to further consulting work. They are therefore an indispensable part of the flywheel.
Consulting and services: I directly provide AI consulting services to companies and institutions as an independent adviser, and this is another major source of income. Many companies need outside expertise during digital transformation. Through retained-adviser arrangements and project consulting, I provide solutions and charge service fees. I am also invited to provide internal training and lectures for government agencies, universities, and other organizations, usually for a fee per session. Consulting not only generates direct revenue; it also gives me a rich body of cases and a strong reputation, further increasing the commercial value of my personal IP.
IP licensing and partnerships: This category includes content licensing and brand partnerships. My Prompt methodology and course content, for example, can be licensed to platforms or for use in corporate training, generating licensing fees. Some corporate talent-development programs purchase my courses or ask me to customize a course. Publishing is another form of IP monetization. My books generate royalties, and their best-selling status has also brought more corporate opportunities: many companies that read them invite me to consult or provide training. Overall, there are many possible models in this category. The central idea is to extend a personal IP commercially through content rights, co-productions, endorsements, knowledge-platform partnerships, and more—“licensing” my influence to create value for both the partner and myself.
As for new forms of monetization, the AI era has truly created many opportunities that were previously unimaginable. I believe the most disruptive is the ability to productize and scale an individual’s professional capabilities.
Traditionally, a consultant’s income is constrained by time and energy: consultants can only sell their own time. With AI, however, I can try to “sell a system” instead of “selling time.”
Could I, for example, develop an “AI consulting assistant” product and train a conversational AI on the typical questions and solutions from previous consulting engagements, enabling it to serve more small and medium-sized clients? That would effectively allow the consultant’s digital counterparts to advise hundreds or thousands of people simultaneously, while I would only need to maintain the AI’s quality. Packaging knowledge as a digital product in this way has extremely low marginal costs but the potential to generate recurring revenue.
Personalized AI content subscriptions are another new opportunity. I can use generative AI to mass-produce customized content—AI briefings for different industries, for example—and charge a subscription fee. In the past, customized one-to-many services were difficult to provide. With AI-based bulk generation and automated distribution, they are entirely feasible.
The new opportunities center on using AI to innovate the business model: developing AI-powered SaaS services, AI consultant Bots, and bundled AI-tool training, among other possibilities. Each model turns my expertise into components and uses technology to deliver them at scale. From the perspective of a complete business flywheel, this is an exciting field. As AI technology matures, I believe super individuals will be able to build their own “small business empires”: attract users through content at the front end, monetize through AI products and services in the middle, and use feedback from satisfied users to reinforce the personal brand at the back end, creating a virtuous cycle. AI has shown me that this flywheel is possible.
Q7: Where does AI’s greatest “leverage effect” appear for you? Is it a 10x increase in efficiency, or the ability to realize ideas that were previously unimaginable? How do you think AI technology will redefine the creativity, productivity, and commercial value of the “individual”?
Jiamu: The leverage AI creates is revolutionary. In the past, one person was an arrow; with AI, one person can become a “multiplier” for an entire flight of arrows. More specifically:
In productive efficiency, AI multiplies an individual’s output. A single idea or project might take me a week on my own; with AI assistance, I may complete it in 1-2 days. Tasks that once required team collaboration can now be handled by one person working with multiple AI tools. We can also see an industry-wide trend: many highly capable people choose to become freelancers or one-person companies because, armed with AI, their output is no lower than that of teams in large companies. This efficiency gain redefines “scale.” In the past, scale determined output; now an individual can achieve similarly scaled production.
In creativity, AI broadens the boundaries of individual imagination. Human sources of inspiration were once limited, but AI now lets us experiment and iterate rapidly across many ideas. This human-machine co-creation model enables individuals to attempt bold concepts they once would not have considered. A designer can instantly generate 100 design drafts with AI, then select and improve them; a screenwriter can have AI expand multiple plot branches to find the best direction for a story. AI has not replaced human creativity—it has sparked more of the creative ideas that were previously hidden. Creativity in the AI era is therefore no longer confined to personal experience and talent; it also includes your ability to mobilize AI. For someone who knows how to use it, creativity equals their own creativity multiplied by AI’s creativity.
In commercial value, AI changes how individual value is measured. In the past, companies valued teams and capital. Now they are discovering that an individual who understands AI and knows how to integrate resources may be worth as much as a small company. We saw many one-person companies—meaning small teams—with revenue above 10 million emerge in 2025, which is compelling evidence. AI lets individuals generate stable revenue at low cost and high efficiency: automated customer service enables one person to serve huge numbers of customers, while intelligent marketing tools allow a personal brand to reach an audience of one million. These possibilities were unimaginable before. The market is therefore beginning to recognize and seek out “super individuals,” and capital and partnership opportunities are flowing toward them. AI enables me as an individual to create value that once only organizations could create.
It is also worth noting that AI’s leverage changes the individual’s mindset. More people realize that instead of being a cog in a large institution, they can use AI to equip themselves and become versatile professionals capable of operating independently. AI lowers the barriers to entrepreneurship and creation, enabling people with ideas and ability to put them into practice more boldly. As this trend develops, individual autonomy and agency expand, and the innovative energy of society as a whole increases.
Of course, I also remind myself and my peers that however powerful the leverage, people still control the direction of the arrowhead. No matter how capable AI becomes, human beings must always make decisions; we cannot lose ourselves. Precisely because AI is redefining individual value, we must think more carefully about how to use that power responsibly and sustainably. This is a new question the AI era has given us—and another meaning of the leverage effect: ordinary people can now move the bigger picture, but they must develop the wisdom and responsibility to match that power.
Q8: For people who want to become AI creators, operate AI-focused media, or build a “one-person company,” what should an “individual” or “small team” prepare in advance—in skills, tools, mindset, or other areas?
Jiamu: Based on my own experience, I would offer several suggestions:
Build strong foundational skills. No matter how powerful AI tools become, hard skills will always be the basis of your professional standing. For AI content creators or solo entrepreneurs, I recommend first mastering your own professional field and then expanding into new AI-related skills. Specifically, you should become proficient at writing Prompts and understand basic model principles; these capabilities will help you direct AI tools more efficiently. Depending on your direction, you may also need adjacent skills. People building AI media should learn more about writing and product thinking, for example, while AI application developers would benefit from foundations in programming and data analysis. In my own case, I spent many years doing full-stack development and product management at internet companies. That experience helps me in the AI era: I can solve technical problems myself, and when I want to realize an idea, I can quickly build a prototype. I strongly encourage newcomers to develop interdisciplinary capabilities: understand content and some technology; know creativity and business logic. A super individual often wears many hats, and the breadth and depth of your skills directly determine how far you can go.
Select and master your tools. There is a bewildering range of AI tools, but more is not always better. The key is to choose the right weapons for your needs and use them to their fullest potential. I recommend building your own AI toolbox. Begin by trying the leading AI tools and learning what each does best: text generation such as ChatGPT and Claude, image generation such as Midjourney, search assistants, and so on. Then select a few that best match your business and study and use them deeply. Do not pursue quantity; pursue mastery. For a content creator, ChatGPT+Midjourney may already be a powerful combination. A data-analysis consultant might pair a language model with several data-processing tools. The important point is to embed these tools in your daily processes and create a standardized workflow, so you know which AI to use for ideation and which to use for proofreading. You should also remain alert to new tools. AI evolves quickly, and a revolutionary tool may appear every few months. The industry’s recent discussion of a shift from Prompt engineering to “Context engineering,” for example, tells us to follow new capabilities in large-model context management and Agent systems. At the tool level, therefore, lifelong learning and regular iteration are essential.
The overarching principle remains unchanged: make tools serve you rather than letting tools lead you. Choose those that genuinely improve your productivity, invest the time to understand them thoroughly, and you will build greater technological potential than others.
Upgrade your mindset. I believe this is the most important point. The AI era demands a change in how we think—put simply, “human-machine collaboration thinking” and “productization thinking.” Human-machine collaboration means treating AI as a partner and learning to direct it as you would lead a subordinate. When many newcomers first use AI, they either fail to ask effectively and get poor results, or trust AI too much and neglect to verify the output. The right approach is to state your needs clearly—which requires good communication and Prompt skills—and then review and recreate the AI’s output. You should trust AI to handle 90% of the routine labor while taking responsibility for the final 10% of quality control. This closely resembles managing a team, so you must cultivate both a manager’s mindset and critical thinking. Productization thinking means treating your service or content as a product and focusing on replicability and scale. The greatest mistake for a one-person company is to do everything personally in a way that cannot be replicated. With AI support, you should consider how to codify experience into templates, processes into standards, and services into packaged products. If you often write business-analysis reports, for example, you can develop an AI report-generation template for automatic reuse next time. If you operate a community, you can train an agent to answer FAQ automatically. This shift lets your business move beyond direct exchanges of time and gives it room to expand. Put plainly, stop relying solely on selling time and learn to use AI to build systemic income.
One final mindset recommendation is to remain curious and patient. The AI field changes so quickly that newcomers inevitably feel pressure, but that also means opportunities are everywhere. Do not be afraid to learn a new tool from scratch, and do not reject disruptive change. Treat each change instead as an opportunity to upgrade yourself. Believe that “the first people to master AI will have an advantage,” just as the first people to master the internet gained opportunities in an earlier era. At the same time, patiently refine your content and services. However extraordinary AI may be, producing high-quality results still requires your effort and guidance. You may encounter pitfalls and take wrong turns at first, but if you continue improving, you will soon find the best way for AI and you to work together.
Preserve human agency and creativity. As I often say, no matter how powerful AI becomes, we must always make the final decisions. We cannot and will not lose ourselves before AI.
Always put the self first and AI in a supporting role. Only then can you create works or products with a distinctive style and intellectual depth and develop irreplaceable personal value.
I hope everyone aspiring to AI creation and entrepreneurship prepares in both skills and mindset, and uses the lever of AI to build a more remarkable individual career. May everyone ride the wind and rise in the AI age of the individual!