Solo Company Craze: Some Earn Millions Annually, Others See Incomes Shrink by 90%

marsbit2026-06-01 tarihinde yayınlandı2026-06-01 tarihinde güncellendi

Özet

The Rise of the "One-Person Company" (OPC): AI Fuels a Solo Entrepreneurship Wave The concept of the "One-Person Company" (OPC)—where an individual leverages AI tools to start and run a business—is gaining significant traction, hailed by some as ushering in a "golden age" for solo entrepreneurship. While success stories abound, the reality is a mixed picture of high earnings and significant struggles. The article profiles several OPC founders across different industries: * A game developer created 6 bullet-chat (danmaku) games in a year using an AI-powered workflow, earning approximately 1 million RMB. AI handled around 70% of art and 99% of coding tasks, slashing development cycles from months to about 15 days per game. * A materials researcher in Japan, using AI for tasks from translation to legal advice, earns roughly triple the salary of a local white-collar worker. * A biotech entrepreneur uses AI Agents to automate 80% of repetitive work like data analysis, doubling their previous income while gaining time freedom. * Conversely, a former tech executive turned cross-border e-commerce founder in Latin America reports a 90% drop in income compared to their previous corporate job, cautioning against blindly following the trend. Key insights from these cases include: AI dramatically lowers barriers to entry and operational costs, but does not guarantee success. It excels at automating repetitive tasks but cannot replace core human skills like creativity, project m...

The "Solo Company," a model where individuals leverage tools for independent entrepreneurship (OPC) in the AI era, has gone viral.

The year 2026 is hailed as the "Year One of Solo Companies." With AI empowerment, ordinary individuals have entered a golden era for starting businesses. One person + AI enables minimal-cost entrepreneurship: using large language models to write code, GPT to generate content, and AI for monetization. AI is no longer just a tech concept; it's genuinely creating commercial value for individuals.

Success stories of "Solo Companies" are emerging everywhere. Dozens of cities across the country have introduced policies encouraging and supporting "Solo Company" entrepreneurship. According to public data, by mid-2025, the national stock of "Solo Companies" had exceeded 16 million, accounting for 27.4% of the total number of enterprises in China.

Of course, behind the hype lies much controversy. Reports claim that "the first batch of solo company founders have already exited." Some criticize that "Solo Companies" are not fundamentally different from previous entrepreneurial models like "sole proprietorships" or "founder-led businesses." There are even statistics suggesting that amidst the OPC solo company frenzy, the reality is that 52.7% earn less than 7,000 yuan per month.

In fact, starting a business always carries risk; successful entrepreneurs are always a minority. AI lowers the barrier to entry, reduces costs, and increases efficiency, but it doesn't guarantee success. What is the real survival situation of "Solo Companies," and who is reaping the benefits? We spoke with several entrepreneurs.

Developing 6 Bullet Screen Games, Earning 1 Million Annually

Entrepreneur: Mr. Zhang, Game Development Sector

Since resigning to start my business in March 2025, I have developed six bullet screen games in one year, with total revenue nearing 20 million yuan and personal net income around 800,000 to 1 million yuan. Among them, the most successful game has accumulated over 8 million yuan in revenue.

Platforms like Douyin take a high commission rate: 50% platform fee, 42% to the streamer, leaving the developer with only about 8% of the total revenue.

The game industry commonly suffers from "high costs and long cycles." The "Solo Company" model perfectly addresses these pain points. The production cost for a single game can be controlled between 1,000 and 1,500 yuan, mainly covering AI tool subscriptions and asset pack purchases. Compared to the traditional team cost of often 2 million yuan, this achieves extreme cost compression.

Compared to traditional mobile or Steam games, bullet screen interactive games rely on platform traffic, requiring almost no additional marketing investment. This zero-marketing advantage lowers the entrepreneurial barrier.

AI tools replace traditional art and programming teams, enabling highly efficient production. In game development, AI handles about 70% of the art workload. I've established a complete pipeline: "Doubao (Keywords) -> Banana (Concept Art) -> Triple3D/Premium 3D (Modeling) -> Blender (Optimization) -> Mixamo (Rigging)."

AI also assists in code development. Using AI to write Java code achieves about 99% accuracy, significantly lowering the barrier to programming. Besides AI, I am personally responsible for final assembly, UI interface adjustments, and engine adaptation.

Regarding development cycles, AI effectively compresses the production timeline. A traditional team (around 10 people) would need 2-4 months to develop a similar game. Using the AI toolchain, a solo developer can shorten the cycle to around 15 days. Moreover, the final product quality is nearly comparable to that of large studios, indistinguishable from the output of traditional game company teams; average players and streamers can hardly tell if it's AI-made.

Of course, solo game development entrepreneurship requires certain core competencies: the solo company model suits practitioners who have "channels (client resources)" or "understand the process and have ideas." AI cannot replace creativity and project management skills. Experience is the best entrepreneurial barrier. While AI lowers the technical threshold, those with "zero foundation" and lacking industry experience struggle because they cannot judge the correctness of AI-generated content for effective correction.

Currently, layoffs in the game industry are severe, making traditional job hunting difficult. The application of AI tools empowers individuals with productivity comparable to teams,反而 providing a window for experienced practitioners to start independent businesses.

Personally, besides self-developed games, I also undertake outsourcing projects using the mature AI production process. Single orders are quoted at 30,000-50,000 yuan with a timeline of about one week, mainly to achieve stable cash flow for "drought and flood protection."

Work Inseparable from AI, Income 3 Times That of Peers in White-Collar Jobs

Entrepreneur: October, Materials R&D Sector

I am an entrepreneur running a solo company in Japan, holding Japanese permanent residency. I previously worked in the traditional materials R&D industry. I established my solo company last July, engaged in deep processing + retail (custom services). The company has been operating for less than a year.

My current monthly income is about three times the average for Japanese white-collar workers of the same age, with an annual income of around 15 million yen, which is much higher than before.

Regarding the entrepreneurial契机, it was the emergence of generative AI that prompted my decision to start a business. Ordinary individuals, empowered by AI, gained the ability to start independent businesses; 90% of problems can be solved by querying AI tools. Now, in my daily work, I almost constantly rely on AI tools: customer service translation, chatting, image creation, order processing, programming, legal consultation, etc.

Currently, my costs for AI tools are not high, requiring only about 3,400 yen per month in subscription fees. My work doesn't involve high-cost API calls; the main use cases are:

1. Language Localization: Using ChatGPT for proofreading, polishing, and localized translation of professional vocabulary to address differences between Chinese and Japanese.

2. Knowledge Acquisition and Learning: For new domain knowledge encountered in entrepreneurship (e.g., tax, regulations, processing knowledge, business decisions), quickly querying and summarizing information via AI to辅助决策.

Compared to the "Solo Company" trend in China, there aren't many people starting solo companies in Japan, but the number is increasing compared to before; the number of Japanese starting solo companies is multiplying. The门槛 for foreigners starting businesses in Japan is relatively high: they need to apply for a business management visa, requiring an initial investment of at least 30 million yen; without permanent residency or a stable work visa, the创业门槛 is extremely high. Qualification requirements vary greatly across industries in Japan. Sectors like餐饮,医美, and二手买卖 all require specific business licenses. My industry has relatively宽松资质要求.

Industry barriers are high in Japan. In the Japanese market, establishing personal or corporate credit rating is a key prerequisite for obtaining supplier resources. The Japanese market is extremely exclusionary. Entering an industry usually requires an internal introduction from a "guide," otherwise, it's difficult to build trust. Obtaining supplier resources requires going through strict信用审查 procedures. Business推进 was very slow in the initial stages. Through口碑 accumulation over the past six months, I achieved exponential growth, and operations are now趋于稳定.

Compared to China, the Japanese consumer market is slower to adopt new things like smart tools and technology. Scanning codes for ordering only became popular in recent years. This provides opportunities for Chinese entrepreneurs to fill market gaps. There are also differences in competitive environments. The Japanese market isn't as "inwardly卷" as China's. The population base is smaller, and industry competition is relatively缓和, allowing some生存空间 for solo companies. Low-price competition doesn't work well in Japan. Japanese consumers are skeptical of低价 products; adopting a high客单价 strategy反而更容易建立信任.

AI Handles 80% of Repetitive Work, Income Doubles

Entrepreneur: Xiao Tao, Biotechnology Sector

I started my business last August. A solo company doesn't create clients; I was introduced to client needs in the biotech field through former colleagues. I had the clients first, then decided to establish a solo company to take on the business.

The industry has certain barriers. I mainly provide biotech cell culture and development services for B2B clients. The core is using algorithms to optimize biological culture media formulations, improving prediction efficiency. I previously accumulated some AI algorithm experience at a biotech company. That company focused on the niche field of biological culture media development, which only a few global institutions can do. Using AI to predict formulas created a high technical threshold.

Compared to full-time employment, my solo company model has achieved doubled income growth (about twice) and significantly improved time freedom, realizing a higher return on investment.

My daily work核心 uses the DeepSeek model, combined with the Claude model to build an AI Agent. The development environment is based on VS Code, achieving automated tasks through the Agent.

Actually, computing power costs aren't high. Currently, the Token fees consumed by using DeepSeek are less than 30 yuan. Hardware投入 is just a personal computer, so the overall startup cost is extremely low. The AI Agent handles about 80% of the basic repetitive work (e.g., data analysis, report generation). The remaining 20% of core judgment, framework design, and client communication are handled manually.

20% of the work cannot be replaced by AI. Although AI handles a lot of work, the entrepreneur needs to monitor the Agent's progress and output direction in real-time to prevent deviation from goals and ensure delivery quality.

Personally, I think positions like data analysts, outsourced product managers, and administrative roles that rely on电子化重复劳动 face a relatively high risk of being directly replaced by AI Agents. Simultaneously, these groups are also suitable for entrepreneurship: professionals with technical backgrounds, business understanding, and client resources are the best candidates for transitioning to solo companies. If they can master AI technology and have client resources, they can尝试一人创业.

Having been in business for less than a year, I've also faced many challenges and have certain operational短板. Entrepreneurs need to补足 non-technical短板, such as interpreting government policies, company registration procedures, and financial/tax compliance. These通常需借助 external resources. I advise against blindly jumping into "solo company" entrepreneurship. Introverted personalities (I-types) have天然劣势 in client acquisition and funding pitches. Before starting, one must ensure stable client resources.

Income Shrinks 90% Compared to Big Tech Job, Advises Against Blindly Following the Trend

Entrepreneur: AYuan's Free Voyage, Cross-border E-commerce Sector

I previously worked in operations at a large internet company. With slowing industry growth and facing an invisible career ceiling, I decided to resign and start a business. My创业方向 is cross-border e-commerce targeting the Latin American market.

Due to previous work assignments, I was stationed in Latin America and am familiar with the market. Also, current trade往来 with Latin America is growing rapidly with巨大潜力, so I chose this market. Initially, due to lack of experience, I joined a startup team. I officially went independent this April, adopting the "solo company"轻资产 model, controlling all aspects from product selection to after-sales.

For me, the startup capital门槛 for a solo company isn't high. Company registration is free, accounting services cost around 2,000 yuan/year, e-commerce platform入驻 is basically free. Depending on the chosen business model, purchasing a Mexican local tax ID costs under 10,000 yuan.

Currently, I focus on the Mexican market, adopting the "overseas warehouse" model—pre-stocking goods in the platform's official warehouse to reduce logistics costs and improve fulfillment efficiency.

AI replaces about 60% of manual workload. For轻体量 cross-border e-commerce, product selection and product page optimization/display are most important. And this part of the work can almost all be done by AI. AI tools cover market selection, graphic/video content generation, product detail page optimization, and financial data analysis.

Personally, I mainly use GPT, Gemini, and Doubao video generation tools. The average monthly subscription cost is around 200+ yuan, significantly reducing operational costs compared to traditional manual design. Typically, manual design for a single product's images alone can cost up to 500 yuan.

Of course, I don't建议盲目辞职创业. There's excessive hype about "solo companies" online; one shouldn't blindly follow the trend. The性价比 of working at a big tech company is still very high. Previously, my annual salary at a big tech company was around 600,000 yuan. Currently, after two months of entrepreneurship, sales are just over 10,000 yuan, profit over 1,000 yuan. Even with part-time代运营, there's a huge income落差,直接缩水90%.

However, the成长价值 brought by entrepreneurship indeed far exceeds the experience of working for years. There's a Western saying: "Get your hands dirty." Entrepreneurship has allowed me to truly learn and understand the business from the ground up, shifting from a platform perspective to hands-on operations. I deeply appreciate the hardships of entrepreneurship, gained a more down-to-earth understanding of business essence, and have become more grounded as a person.

This article is from WeChat public account "Tech Planet" (ID: tech618), author: Zhai Yuanyuan

İlgili Sorular

QWhat is an 'OPC' or 'one-person company' and why has it gained popularity recently?

AAn 'OPC' or 'one-person company' refers to an AI-era model where an individual starts a business independently using tools. It has gained popularity because AI has lowered the barrier to entry, enabling individuals to perform tasks like coding, content generation, and monetization with minimal cost, marking a 'golden age' for solo entrepreneurship.

QAccording to the article, what are some key advantages of the one-person company model in game development?

AIn game development, key advantages include extreme cost compression (e.g., development costs of 1000-1500 yuan per game vs. 2 million yuan for traditional teams), near-zero marketing expenses by leveraging platform traffic, and significantly reduced production cycles (15 days vs. 2-4 months for a team) while maintaining quality comparable to large studios.

QHow does the article describe the role of AI in a one-person company operating in the biotechnology field?

AIn biotechnology, AI (specifically DeepSeek and Claude models used to build AI Agents) automates around 80% of repetitive tasks like data analysis and report generation. This allows the entrepreneur to focus on core judgment, framework design, and client communication, achieving higher income and time freedom while keeping computational costs low.

QWhat challenges and risks associated with the one-person company trend does the article highlight?

AThe article highlights that success is not guaranteed; over half of OPCs reportedly earn less than 7000 yuan monthly. Challenges include the need for prior industry experience and client resources, the difficulty for introverts in customer acquisition, operational hurdles like understanding regulations and taxes, and the potential for significant income reduction compared to traditional employment.

QWhat contrasting experiences regarding income are presented by the different entrepreneurs profiled in the article?

AThe article presents a stark contrast: some entrepreneurs report high success, like a game developer earning 800,000-1,000,000 yuan annually and a materials researcher in Japan tripling the average white-collar income. Conversely, a former big-tech employee turned cross-border e-commerce founder reports a 90% income reduction initially, cautioning against blindly following the trend.

İlgili Okumalar

Deconstructing the U.S. Stock Quantum Computing Sector: IonQ, Rigetti, D-Wave, Which of These Concept Stocks is Worth Betting On?

**Title:** Analyzing the US Quantum Computing Race: IonQ, Rigetti, D-Wave – Which Concept Stock is Worth Betting On? **Summary:** The podcast discusses the resurgence of quantum computing as a national priority for both the US and China, driven by its potential to break current encryption, revolutionize drug discovery, finance, and logistics. The core challenge is commercializing the technology, which is hampered by high error rates in quantum bits (qubits). Quantum error correction, requiring thousands of physical qubits per reliable logical qubit, is key but years away. The analysis compares three main publicly traded US quantum computing firms: * **IonQ (Ion Trap):** Considered the most financially stable with the fastest commercial progress (2025 revenue: $130M, +202%) and high-quality clients. Its valuation is very high, pricing in significant future growth. * **Rigetti (Superconducting):** Seen as the highest-risk, highest-potential-reward bet. It has the smallest revenue but recently launched a 108-qubit system. Its valuation multiples are extreme, making it highly sensitive to news. * **D-Wave (Quantum Annealing):** Has the most unique positioning with real-world enterprise clients today (e.g., Mastercard, Volkswagen) solving optimization problems. Its recent acquisition moves it into general-purpose quantum computing ("dual-platform"), adding execution risk. Major tech giants like Google, IBM, and Microsoft are also heavily invested, pursuing various technical approaches. Nvidia is positioning itself as the essential bridge between classical and quantum computing. The investment phase is likened to AI in 2018-2020: promising underlying technology with accelerating breakthroughs but a commercial inflection point still 3-7 years away, suggesting potential for a market correction ("bubble washout"). For investors, suggested approaches include gaining exposure through tech giants with quantum divisions (e.g., Google, IBM) or using niche ETFs like WQTM for pure-play quantum exposure, rather than direct stock picks in the highly volatile pure-play companies at this early stage.

marsbit2 dk önce

Deconstructing the U.S. Stock Quantum Computing Sector: IonQ, Rigetti, D-Wave, Which of These Concept Stocks is Worth Betting On?

marsbit2 dk önce

From Parallel Finance to Mainstream Finance: The On-Chain Securities Era Ushers in a Historic Window

From Parallel Finance to Mainstream: The Dawn of On-Chain Securities For over a decade, the crypto industry has operated as a parallel financial system with its own currencies, markets, and assets—from Bitcoin and ICOs to DeFi, NFTs, and memecoins. Despite building a robust internal ecosystem, a wall has separated it from the traditional financial world. That barrier is now crumbling. The industry's first act was one of internal evolution: ICOs streamlined fundraising, DeFi recreated financial services on-chain, and layer-2 networks competed for scalability—all within the crypto bubble. While innovative, this cycle remained closed, with capital and users circulating internally, leading to volatile boom-bust cycles. Even Bitcoin ETFs, while attracting Wall Street capital, merely provided a channel to buy crypto assets without bridging the systems. The next, larger narrative is Real-World Assets (RWA) moving on-chain. This involves tokenizing stocks, bonds, funds, and future cash flows. Blockchain can compress the complex traditional processes of trading, settlement, clearing, and custody into a seamless, automated network operating in seconds. This shift is creating a new financial gateway: the native crypto securities broker. This entity will combine functions of an exchange, broker, bank, and custodian into a unified global financial operating system. Consequently, the next major battleground won't be the "public chain wars" focused on speed and cost, but the competition to build the financial infrastructure capable of hosting high-quality, liquid real-world assets. Access to global equities, index funds, or stakes in companies like SpaceX could erase the boundary between crypto and traditional finance, unlocking a market orders of magnitude larger than crypto's current valuation. In summary, after years of creating a separate financial world, crypto's next decade will be defined by its integration into the existing global financial system, marking the true beginning of its largest growth story.

marsbit23 dk önce

From Parallel Finance to Mainstream Finance: The On-Chain Securities Era Ushers in a Historic Window

marsbit23 dk önce

Wang Chuan: When the Neighbor Old Wang Made 30x on Memory Stocks, How to Avoid Anxiety (Part Six) - The Trap of Commoditized Goods

Wang Chuan: When the Neighbor Lao Wang Made 30x on Storage Stocks, How to Stay Anxiety-Free (Part 6) - The Trap of Commoditized Goods. This essay uses historical and current examples to analyze the cyclical and high-risk nature of the data storage industry. It begins with the 1990s rise and dramatic fall of Iomega, whose stock soared over 160x in 18 months before collapsing 97% from its peak, illustrating the fleeting success of storage "meme stocks." The core problem is that storage products, like DRAM and flash memory, are highly commoditized. This leads to extreme volatility: prices have plummeted over 80% multiple times, and company stocks often crash 95% or go bankrupt. The industry's dynamic is defined by "elastic demand facing heavy-asset, long-cycle, rigid supply." When demand spikes and supply is fixed, prices skyrocket, as seen recently with AI-driven demand for High Bandwidth Memory (HBM). Companies like Sandisk and Micron have reported massive revenue and gross margin jumps (e.g., Sandisk's gross margin rising from 22.5% to 78.3%) despite minimal increases in production volume. However, these high margins are self-defeating. They incentivize massive new capacity investments (hundreds of billions planned from 2026), with supply expected to surge by late 2027. Once new supply meets demand, prices and profits will crash, potentially leading to a scenario where "selling more results in earning less." The article debunks the safety of long-term supply agreements, comparing them to fragile non-aggression pacts easily broken when market conditions shift. It warns that when an industry is highly profitable but trades at low P/E ratios, the risk is greatest, as plummeting prices quickly erase those earnings. Multiple asymmetric risks loom, including economic recession, reduced AI spending, faster-than-expected capacity expansion (especially from Chinese firms), and technological innovations that reduce memory requirements. In conclusion, the storage sector is a cyclical trap where periods of euphoric profits are often precursors to devastating downturns, luring unprepared investors into a "wealth incinerator."

marsbit33 dk önce

Wang Chuan: When the Neighbor Old Wang Made 30x on Memory Stocks, How to Avoid Anxiety (Part Six) - The Trap of Commoditized Goods

marsbit33 dk önce

Wang Chuan: When the neighbor Lao Wang earned thirty times from investing in memory storage stocks, how can you still avoid anxiety (6) - The trap of homogeneous products

The article, "Wang Chuan: How to Remain Unanxious After Neighbor Lao Wang's Thirty-Fold Gain on Storage Stocks (Part 6) - The Trap of Commoditized Goods," analyzes the cyclical and perilous nature of the data storage industry through historical and current case studies. It begins with the example of Iomega, whose Zip drives led to a stock surge of over 160x in the mid-1990s before collapsing over 97% from its peak due to competition from cheaper CD-R technology. This pattern is characteristic of storage, where products like DRAM are highly commoditized, leading to extreme price volatility. The sector has seen prices crash over 80% multiple times, with companies often facing bankruptcy. The core dynamic is "elastic demand facing heavy-asset, long-cycle, rigid supply." High prices attract new capacity, but the long lead time means supply eventually overshoots, causing sharp price corrections. The current AI-driven boom, exemplified by surging demand for High-Bandwidth Memory (HBM), has led to skyrocketing prices and profit margins for companies like SanDisk and Micron, despite relatively flat production volumes. However, the author warns this high-margin environment is self-defeating. The high profits are already triggering massive new capacity investments (hundreds of billions starting 2026), with supply expected to ramp up by late 2027. When supply catches up, total revenue and profits may fall even as more units are sold. Long-term supply agreements offer little protection, as buyers can find ways to renegotiate if market prices drop, similar to fragile political treaties. Key risks include economic downturns, cuts in AI spending, faster-than-expected capacity expansion (especially from Chinese firms), and innovations in chip/algorithm design that reduce memory needs. A critical trap is that at the cycle's peak, storage stocks often appear cheap with low P/E ratios, luring value investors just before an impending downturn where profits evaporate. The conclusion cautions that for commoditized goods like storage, high margins inevitably destroy themselves, and the current asymmetry favors downside risk over further upside. The neighbor's dream of easy wealth from storage stocks is portrayed as a precarious illusion.

链捕手50 dk önce

Wang Chuan: When the neighbor Lao Wang earned thirty times from investing in memory storage stocks, how can you still avoid anxiety (6) - The trap of homogeneous products

链捕手50 dk önce

İşlemler

Spot
Futures
活动图片