Published: 2026-08-27 | Positioning: decision-oriented practical advice, serving ordinary people who want to make money with AI
Differentiation: no phenomenon reporting (already over-written by Jiemian and CVInfo); focused on three real paths plus a pitfall-avoidance checklist, as executable advice
Opening hook
2026 is being called the first year of the one-person company (OPC).
In the same month, two stories went viral side by side:
Peng Qingyun in Hangzhou, a former brand designer optimised away by AI, now makes short dramas alone with AI, with annual income reaching the million level; another founder, A Yuan, quit a big company to do Latin American cross-border e-commerce, and his income shrank by 90% compared with his salary.
Same wave, one person earns a million a year, another loses everything. Where exactly is the difference?
This is not another bowl of AI wealth soup, nor a lament about an AI bubble. As a practitioner who works with AI every day, I want to open it up for you: the wave is real, but how you get on matters.
One: the data first — this is not a bubble, it is a real explosion
Do not rush to a conclusion; look at a few hard data points (cross-verified from multiple sources):
- The stock of one-person companies nationwide has passed 16 million, 27.4% of all enterprises (as of mid-2025)
- The share of companies founded by solo founders rose from 23.7% in 2019 to 36.3% in the first half of 2025, a 53% increase in six years
- One quantitative indicator: every 1 yuan of AI cost is equivalent to replacing about 72 yuan of development labour (the HACR human-machine cost ratio)
- 92% of highly profitable one-person companies use AI tools deeply
- Global view: the United States has nearly 30 million solo founders, creating nearly 2 trillion US dollars of economic value; Gallagher, aged 41, used 20000 US dollars plus a dozen-odd AI tools to build Medvi, with first-year revenue of 401 million US dollars
A clear signal: the era of individuals amplified by tools has arrived; this is a real structural change, not hype.
Two: four real cases — where the winners won, where the loser lost
Put these cases together and the pattern becomes very clear.
✅ Winner 1 | Xiao Tao (biotechnology) — customers first, company second
Working in biological cell culture and development services, mainly using AI to predict culture medium formulas. DeepSeek token cost is under 30 yuan a month; the hardware is one computer. AI takes on 80% of basic repetitive work; income doubled, time is free.
✅ Winner 2 | October (Japanese materials processing) — treating AI as an all-purpose employee
Custom processing plus retail in Japan, using AI for translation, customer service, graphics, programming and legal advice. Monthly fee about 3400 yen (about 160 yuan), income three times that of white-collar peers. His own words: 90% of problems can be solved through AI, and an ordinary individual, backed by AI, now has the ability to start a business independently.
✅ Winner 3 | Li Yunfan (intelligent essay grading) — grinding one extremely narrow track
An AI essay grading tool for Chinese and English teachers, passing 15000 users in four months. AI takes on 80% of the programming, computing power costs just over 5000 yuan a month, one person completed development on six platforms, stable monthly income close to 50000.
❌ The loser | A Yuan (cross-border e-commerce) — technical enthusiasm, no business logic
He quit a big company to work on the Latin American market, and AI replaced 60% of manual work. But the result was income 90% below his big-company salary.
Three: the root cause of the two extremes — AI levelled skills and exposed scarcity
Look closely and you find: winners and loser use roughly the same AI tools.
So where is the difference?
When everyone can generate content with one click of AI, output itself is worth nothing.
You can use AI, and so can your customers; you write 10 articles a minute, and so can others.
What the AI era lacks least is skill.
Once skills are levelled, three things become genuinely scarce:
1. Trust — why should a customer choose you rather than the person using the same tools as you?
2. Personal brand — who you are matters more than what you can do.
3. Judgement — AI gives 100 options, and you can tell which is right and which makes money.
People who treat AI as a tool can only take low-priced orders on platforms; people who treat themselves as the core asset are the ones who can hold up a company earning a million a year.
Four: getting ordinary people onboard — 3 real paths plus a pitfall checklist
If you are tempted too, do not rush to quit your job. Here is practical advice for ordinary people (combined with my own experience moving from operations in a traditional industry into AI):
Path A: monetising skills (fastest, suits people with a professional background)
Take the professional judgement you have built in an industry and amplify it into a service with AI. Xiao Tao, for instance: he had a biotechnology background plus customer resources, and AI saved him 80% of repetitive work.
Key: customers and business logic first, AI second. Do not start a company and then look for work.
Path B: narrow products (medium, suits people who can grind)
Like Li Yunfan, find a need that is extremely narrow and not yet noticed by the giants, and build a product quickly with AI. Essay grading, industry-specific reports, niche tools — all work.
Key: prefer a needle market 1 centimetre wide and 1000 metres deep.
Path C: personal IP (slowest, but the steadiest and most worth doing long term)
This is the path A Yuan should have learnt — build the IP first, then the product. Let who you are, your judgement and your taste settle into a personal brand. It takes time, but once it stands, it is the deepest moat.
⚠️ Pitfall checklist (summarised the hard way)
- Do not quit your job to start a business. Protect cash flow first and validate in your spare time
- Do not chase waves. Others do cross-border e-commerce so you do too, but you do not understand that market
- Introverted personalities have a natural disadvantage in customer acquisition and pitching; work out first who acquires customers for you
- There is a lot AI cannot handle: deep judgement, complex system architecture, genuine human trust — these are your value
- Start-up cost is actually low (a few hundred yuan a month in AI fees); what is really expensive is the time spent on trial and error, so direction matters more than effort
Five: my judgement
The one-person company is not a false proposition, but it is absolutely not lying down and earning money.
Its greatest value is compressing a business loop that used to need a whole team down to something one person can start. But one person being able to start does not mean one person can succeed — the ones who succeed rely on what AI cannot replace: judgement, trust, persistence.
The biggest risk in the AI era is not being laid off, but burning through the window in anxiety without building anything.
The best time to plant a tree was ten years ago; the next best time is today.
Closing question
What are you using AI to do at the moment? Where are you stuck?
Talk in the comments and we will break down your one-person company path together.
(Data sources: Jiemian News, CVInfo / Tech Planet, Tianxia Wangshang, Zhihu columns, Goldman Sachs 2026 Global AI and Employment Report; cross-verified from multiple sources.)
#AI second brain #one-person company #AI startup #super individual #side income
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