Original · Unique Research · 2025-11-11
Editorial note: This is a complete English edition of the historical article and interview. Revenue, margins, productivity figures, technology descriptions and commercial plans are retained as the source’s reporting and the interviewee’s claims, not independently verified current results. The interview states both a reduction from 12 to around 6 working hours per day and roughly 70% time saved. The former implies a 50% reduction; the source supplies no separate basis for the 70% figure, so both statements are preserved without silently reconciling them. “Symbol Interactive” is a descriptive English rendering of 符号互动, not a verified official English company name. ARR means annual recurring revenue; the source does not supply the company’s calculation method.
When people talk about the super individual, many imagine a fantasy: one person, one computer, and a stack of model APIs can generate a million in annual income and keep the passive revenue rolling in. Yet Yang Zehao, a founder born in or after 2000 who runs a digital marketing company with several million dollars in ARR, has followed a path that decisively dismantles that fantasy. AI is not a money-printing machine; it is an amplifier that gives an ordinary person the capabilities of an entire team.
I. A Founder Born in or After 2000: Not Trying to Look Cool, but Rewriting the Playbook
Yang Zehao gives himself three labels: serial entrepreneur born in or after 2000, practitioner of AI-empowered advertising and marketing, and Claude explorer. They may sound a little self-dramatizing, but on closer inspection they offer a representative glimpse of a new generation of entrepreneurs.
His post-00s identity has not made him immature; it has given him the capital to experiment and fail. This is not his first startup, and he has a natural tolerance for risk and uncertainty. While others calculate whether a step is completely foolproof, he is already thinking, ‘If it goes wrong, I can always start again.’ That mindset closely matches the pace of the AI era: technology changes so quickly that the only reliable approach is to keep testing and iterating.
His second label is practitioner of AI-empowered advertising and marketing. Rather than competing to build foundation models, he has kept his attention on an industry that may not look glamorous: advertising agencies. While others remain absorbed in discussing how elegant their prompts are, he has already embedded AI into project delivery, creative production, and client service, transforming a traditional creative company into marketing infrastructure that runs on AI. The company earns its money from advertising, but AI has raised its profit margin from 20% to 60%.
The third label is the most interesting: Claude explorer. For many people, a large model is simply a tool for writing code or copy; for him, Claude is a second brain. He uses Claude Code for all kinds of non-coding experiments: generating creative ideas, planning campaigns, developing brand proposals, and even simulating business models. Brainstorming that once required a team to shut itself in a room for two days can now be broken down into structured specs and handed to AI for a first pass. People no longer have to strain to produce ideas from 0 to 1; instead, they exercise judgment over a collection of workable first drafts.
More importantly, he has chosen neither a purely individual nor a purely corporate mode of operation. Instead, he has adopted an intriguing structure: by day, a lean team of dozens; by night, his own personal laboratory. His main business provides stable income and real-world scenarios, while his side projects let him push against every boundary of AI in a low-pressure environment. In essence, this dual-track model uses the stability of a team to enable the freedom of individual exploration.
II. From Fighting Fires to Preventing Them: How AI Rewrites an Advertising Agency's Workflow
When people ask how much AI can change work, they usually expect to hear about a 10-fold efficiency gain. Yang offers a more vivid set of figures: he reduced his workday from 12 hours to 6.
It is not because there is less work, but because concrete execution has been made entirely spec-driven. In the past, many advertising-agency processes depended on human memory and improvisation—writing briefs, breaking down tasks, reviewing materials, and responding to client feedback. Any one of these steps could turn into a fire drill. Now, he uses AI to break each step into structured instructions with clear inputs and outputs. Everything from objectives, audiences, and tone to the tacit lessons of past successes is translated into specifications that machines can understand. Execution is handed to AI and standardized processes, freeing people from the production line.
The deeper change is that he built an entire AI creative-agent system around this approach. It may sound abstract, but the idea is straightforward: the team took all the outstanding advertising cases from the past five years and assigned them millions of Chinese characters of structured tags. These are not crude categories such as automotive or beauty. They cover multidimensional semantics including creative techniques, shot-by-shot narrative, emotional arcs, copywriting rhythm, and brand personality.
The data is stored in both a vector database and a graph database. The former handles similarity, finding the closest creative concepts in semantic space; the latter maps relationships, connecting points that may appear unrelated. When a new campaign brief arrives, AI does more than write a little copy. It moves through a vast creative knowledge graph, retrieves past experience, recombines narrative structures, and proposes creative directions grounded in evidence and context.
Traditional advertising creativity is PGC—professionally generated content—driven by a creative director's personal experience and talent. At Symbol Interactive, however, this is quietly shifting toward a hybrid of AIGC plus professional review: AI produces a range of logically reasoned, traceable proposals, while the human creative director becomes the director who cuts, selects, and elevates them. What is truly being replaced is not creativity, but mechanical, repeatable knowledge work.
From fighting fires to preventing them, and from relying on intuition to relying on systems, AI is not introducing a new toy. It is introducing an entirely new mode of production.
III. The Real Leverage Is Not Time, but the Boundaries of Capability
Yang repeatedly emphasizes that AI's greatest leverage is not efficiency, but the boundaries of capability.
In his words, before AI he was a marketer and nothing more. After AI, he began writing code that actually runs, participating in product design, and conducting data analysis himself. This does not mean that he became an expert in every field. Rather, he evolved from a single-track professional into a multifaceted operator capable of directing several lines of expertise. One person is no longer a single role, but something closer to a small team.
This changed how he understands the individual.
At the level of creativity, we used to emphasize specialization: one person taking one skill to its limit. With AI, individuals are being pushed toward a combination of generalist ability and directional judgment. You do not need to master every detail of every domain, but you can mobilize the different modules needed to complete an entire loop. AI supplies detailed capabilities; people make directional judgments.
At the level of productivity, the change is more direct. Growth used to be linear: add one person and gain a little more output; add one hour and complete a little more work. Now it looks more like a curve. Once AI removes repetitive labor, the same 6 hours can unlock the volume of work that once required 12 hours or more, while leaving the mind clear enough to consider the longer term.
At the level of commercial value, he sees the shift clearly: AI moves people from selling time to selling capability. Whether it was consulting fees or service fees, pricing ultimately used to be based on person-days—if an hour was worth RMB 500, working harder still made it difficult to break through that ceiling. The logic has now changed. What you sell is a bundle of capabilities: understanding an industry, abstracting scenarios, orchestrating AI tools, and packaging all of that into a solution. The same 1 hour can therefore create far more value for a client than it once did, changing the logic of pricing along with it.
The so-called super individual should not be understood as someone who does everything alone. It describes one person whose ability to mobilize resources and capabilities extends far beyond their physical limits.
IV. Advice for Those Who Want In: Do Not Be Fooled by the Stories—First Use AI to Master the Work in Front of You
As someone using AI to make money on the front lines, Yang is direct about fantasies of getting rich through AI: stop relying on secondhand information and test things for yourself. Many success stories circulating on social media reflect sample bias and survivorship bias. If you look only at the story and not at the underlying structure, it is easy to be led astray.
He offers several simple but solid suggestions for people who want to become AI creators, build AI-focused media, or create one-person companies.
First, start with the repetitive work that annoys you most every day. Do not begin by imagining a grand product that will transform an industry. Ask yourself honestly: What consumes the most time, is the most mechanical, and is the task I least want to do each day? Is it writing reports, organizing data, replying to clients, or scheduling and alignment? Choose one task first and use AI to eliminate it. The first time you genuinely experience, ‘I am not doing this, yet it still gets done,’ your understanding of AI truly begins.
Second, do not chase the newest tool; pursue the best fit. New names appear every day, but what you actually need is the tool that is stable, useful, and easy to integrate into your current scenario. Rather than spending 10 hours reading reviews, spend 2 hours embedding an already mature model into your own workflow.
Third, moving fast in small steps matters more than anything else. One harsh reality of the AI world is that no one truly knows what it will look like 6 months from now. Instead of waiting for a perfect path, make one small iteration every month: make one process AI-enabled, standardize one service, or turn one tacit product into an explicit product. Over time, these small changes accumulate into a difference that anyone can see.
His final core view deserves a place on the desktop of everyone considering a move into AI: AI is not asking you to change tracks; it gives you an unfair advantage on the track you already know. The smart move is not to throw away everything you have accumulated and rush into a completely unfamiliar field. First use AI to take your existing work to its limit. Once your time is freed, you will naturally have the capacity to explore new possibilities.
Being a Super Individual Is Not About Carrying Everything Alone, but Learning to Dance with Intelligence
If the previous generation's startup stories were about the power of teams, Yang's story is about the power of collaboration between people and intelligence.
He does not mythologize himself as a boy genius. Instead, he candidly presents a realistic model: the advertising team steadily advances the main business, while his AI experiments continually push the boundaries on the side; he builds systems at the company during the day and breaks down problems with Claude at night. He neither mythologizes AI nor denies that it is reshaping the limits of individual capability.
In this sense, the super individual is no longer an exaggerated label, but a state that ordinary people can gradually approach—provided that they are willing to stop scrolling short videos and chasing trends, actually open a model, write down their first clear requirement, hand it over, and see what happens.
From that moment on, your relationship with this era quietly changes.
Selected Interview Q&A
Q1: Please introduce yourself. If you had to define yourself and your place in AI with three labels, what would they be? What industry trends do you see?
Yang Zehao: If I had to summarize myself with three labels, they would probably be these. First, a serial entrepreneur born in or after 2000. I may be young, but this is not my first startup. Youth has given me the capital to experiment and fail, while keeping me naturally sensitive to new developments. Second, a practitioner of AI-empowered advertising and marketing. I now run Symbol Interactive, a digital marketing company with several million dollars in ARR. My core interest has never been competing over large models; it is finding ways to bring AI into a specific industry and remake advertising from the ground up. Third, a Claude explorer. To me, Claude is not a coding tool but something more like a second brain. I use it to generate creative ideas, plan proposals, design business models, and conduct all kinds of non-coding experiments. The largest shift I see is that many people remain obsessed with pursuing technology itself, while real value is moving from technical showmanship to implementation—from ‘knowing how to use a model’ to ‘being able to reorganize an industry's workflow.’
Q2: Do you currently operate as a super-individual one-person company or as a lean small team? Why did you choose this ‘dual-track’ model?
Yang Zehao: I now operate through a classic dual-track model. On one side, a lean team of dozens handles the main business; on the other, I conduct personal AI explorations as side projects. The advertising company still needs a team to deliver projects and serve clients, and that is the foundation of our cash flow. AI's greatest dividend has been freeing me from large amounts of daily management and execution, giving me uninterrupted blocks of time to work with AI, run experiments, and build systems. The advantage of this model is that the main business provides stability while the side projects extend the boundaries. The team ensures that the company does not go off the rails because I enjoy experimenting, while I retain enough room to explore the future.
Q3: What specific role does AI play in your daily workflow? What exactly has it freed you from?
Yang Zehao: In one sentence, much of the execution work that people once had to carry has become spec-driven. I used to work 12 hours a day; now I generally keep it to around 6 hours, saving roughly 70% of my time. The release of time is one aspect, but freeing mental energy is even more important. Attendance, expense reimbursement, coordination, and other minutiae no longer pull me around by the nose. I can devote my attention to preventing fires rather than putting them out—for example, thinking about the company's AI transformation, exploring new product forms, and studying industry trends. These long-term priorities were constantly crowded out by daily work in the past; now I can invest in them consistently.
Q4: You mentioned building an AI creative-agent system. That sounds highly technical. Could you explain it in more detail?
Yang Zehao: Put simply, we structurally decomposed all the outstanding advertising cases from the past 5 years. We assigned millions of Chinese characters of tags to those cases—not crude categories such as automotive or beauty, but multidimensional semantic annotations covering creative techniques, narrative structure, emotional rhythm, visual style, and brand personality. The tagging system connects to both a vector database and a graph database: one handles retrieval by semantic similarity, while the other maps networks of relationships. When a new brief comes in, AI can quickly identify genuinely relevant experience within this vast case graph and recombine it. Traditional advertising creativity is PGC, in which creative directors rely on their personal experience to make intuitive calls. Our approach is closer to AIGC plus professional review: AI proposes candidate solutions, while people exercise judgment and elevate them. In essence, it turns creativity from a black box of human experience into an intelligent system that can be called on demand.
Q5: From a business perspective, what is your current monetization model? What has AI changed within it?
Yang Zehao: To be candid, monetization of my work as a super individual is still in the experimentation stage. Most of our cash flow currently comes from the advertising company's digital marketing services. But AI has already materially rewritten the profit-margin logic of this business: without raising prices, our profit margin rose from roughly 20% to nearly 60%. The reason is simple—many stages that previously required piling on labor have been automated and structured. We are also trying to productize the AI systems that work well internally and offer them to peers as SaaS products. Looking ahead, I believe AI creates new monetization paths through three main shifts: from selling time to selling capabilities, from purely bespoke services to standardized products, and from linear growth toward something closer to exponential growth.
Q6: What is AI's greatest ‘leverage effect’ for you? How is it reshaping individual creativity and commercial value?
Yang Zehao: For me, the greatest leverage is not efficiency but the boundaries of capability. Before AI, I was a typical marketer. After AI, I was pushed—and, in a sense, passively inspired—to learn programming, understand some design logic, interpret data, and build systems. I have not become an expert in every area, but I can combine these capabilities and use a one-person setup to accomplish work that once required a small team. In terms of creativity, the individual is shifting from specialist to generalist plus directional judgment. You do not need to spend 10,000 hours practicing in every field, but you can mobilize different capabilities to solve an entire problem. In terms of productivity, the old relationship was linear: work 1 more hour and produce a little more. Once key stages become AI-enabled, overall capacity makes a nonlinear leap. In terms of commercial value, you no longer sell only time by the hour; you price for results and combinations of capabilities. In the same 1 hour, the more resources and intelligence you can mobilize, the more valuable that hour becomes.
Q7: What practical advice would you give someone who wants to become an AI creator, build AI-focused media, or create a one-person company?
Yang Zehao: First, consume fewer secondhand feel-good success stories and run more small experiments of your own. Many narratives about getting rich through AI magnify a handful of lucky examples and can easily lead people astray. What you really need is to choose a real scenario already in your hands, try it yourself, and see what AI can actually solve. Second, start with the repetitive task that annoys you most each day, not with the flashiest technical term. Hand one task you hate to AI. Once you cross that threshold, your perception of AI will be completely different. Third, do not become infatuated with the latest tool. Choose the one that best fits your current situation and refine a workflow around real tasks. Finally, I always say this: AI is not asking you to change tracks; it gives you an unfair advantage on your existing track. First use AI to take your current work to its limit and free up time and mental capacity. Then consider side projects, media, or even a one-person company. At that point, you will find that the path becomes much clearer.