A comprehensive guide to OPC technology, applications, ecosystem, and governance.

Author: Zhang Feng

In early 2026, with the explosive popularity of AI intelligent agents such as OpenClaw, a lightweight entrepreneurial model centered on "single-person drive + AI collaboration"—OPC (One-Person Company)—began to enter the public eye. On the internet, people paint an alluring picture: sipping coffee, simply inputting a few commands, and a large number of "digital employees" diligently make money for you. In reality, this picture is becoming a reality at an astonishing speed. According to industry ecosystem statistics, as of 2025, more than 12 million individual entrepreneurs nationwide exhibited OPC model characteristics; within the Greater Bay Area, the "Moli Camp" ecosystem community in Shenzhen alone has gathered nearly 200 OPC-related companies.

I. What is OPC: Redefining the "One-Person Company"

OPC is a new type of one-person organization in the era of the intelligent economy.

OPC is a new type of one-person organization in the era of the intelligent economy. In the context of this article, OPC (One-Person Company) is not a legal concept, but a business term describing the development trend of the intelligent economy. It refers to a new organizational form centered around a core founder, utilizing a cluster of AI agents to complete the entire workflow from product development and operation to customer service, achieving "one-person command." He Minhua, head of the Qianhai OPC International Community, has repeatedly defined OPC as a "new paradigm for entrepreneurship in the AI ​​era": "Traditional entrepreneurship relies on collaboration between people, while the core collaboration object of an OPC team is AI." This judgment captures the essence of OPC—the smallest production unit of an organization is shifting from "people + people" to "people + AI systems." In OPC practice, a single core operator serves as the main entity, relying on an AI Agent cluster as a virtual auxiliary force to complete the entire workflow from requirements understanding and product design to development and delivery. A single core founder can efficiently accomplish tasks that previously required multiple collaborations by using AI to manage multiple agents. The participants in OPC have also expanded from experienced developers to academics with professional knowledge, and even ordinary people with no coding experience. Five core characteristics. Data-driven: The core production factor of OPC is data. Entrepreneurs rely on the data processing, analysis, and generation capabilities of AI models to directly transform data into products or services. Intelligent Development: AI Agents possess autonomous perception, reasoning, decision-making, and execution capabilities. They can understand and break down complex goals, autonomously invoke tools and systems, and gradually complete multi-step tasks, forming the intelligent foundation of OPC. New Assets: OPC's asset structure is primarily composed of intellectual capital and digital assets. Computing power costs have replaced traditional fixed costs such as salaries and facilities, and the marginal cost of business expansion has gradually shifted from labor expenditures to computing power utilization. Some high-quality OPC projects can achieve high profit margins; some entrepreneurs have publicly stated that their project profit margins can exceed 90%. Open Ecosystem: OPC relies on open AI infrastructure, open-source models, and an out-of-the-box intelligent agent ecosystem. Open-source projects like The Agency claim to provide multi-functional AI agent support, enabling developers to quickly build digital teams and complete the business loop from development to delivery. Digital Governance: OPC faces new governance needs—entrepreneurs need to simultaneously coordinate multiple dimensions such as technology, law, and data compliance. Understanding models, data, and compliance has become a must for OPC entrepreneurs. Differences and Connections with Traditional One-Person Companies OPCs are related to, yet fundamentally different from, traditional one-person limited liability companies and sole proprietorships. In terms of legal status, a one-person limited liability company has legal person status, and the shareholder's liability is limited to the amount of their subscribed capital; a sole proprietorship does not have legal person status, and the investor bears unlimited joint and several liability with their personal assets. However, OPCs are not currently an independent legal entity type; their registration form can be either a limited liability company or a sole proprietorship—the difference lies in the fundamental change in their organizational core. At the level of liability assumption, the connection is reflected in the fact that, regardless of the legal organizational form chosen, OPC entrepreneurs may ultimately face a new chain of liability arising from the behavior of AI agents—when an AI agent oversteps its authority and causes losses to a third party, how liability should be allocated has not yet reached a legal consensus. At the level of efficiency logic, the difference is even more significant: the efficiency of traditional one-person businesses is limited by individual time and energy, while the efficiency of OPC depends on the scheduling efficiency between the founder and the AI ​​system. Professor Zhou Guangsu of the School of Labor and Human Resources at Renmin University of China summarized this leap forward with three "transformations": the source of productivity has shifted from humans themselves to AI technology, greatly improving production efficiency; the enterprise model has shifted from "human + human" to "human + AI," significantly reducing production costs; and returns have shifted from linear growth to exponential growth, significantly raising the ceiling of enterprise value. Taking the human resources service sector as an example, traditional teams need several years to complete large-scale services, while some OPC entrepreneurs, leveraging AI tools, can achieve the same business scale and higher returns in a shorter time. Why has OPC emerged? The concentrated emergence of OPC is not accidental, but rather the result of the resonance of technological, economic, and social forces. From a technical perspective, large-scale model capabilities are rapidly evolving. In early 2025, leading models such as GPT and Claude completed key capability iterations, significantly compressing the entire chain from requirement understanding to product development. AI agents are rapidly evolving from "auxiliary tools" to "executing entities." The open-source project OpenClaw, launched by the MIT team, allows ordinary users to use natural language descriptions to instruct AI to schedule a series of third-party tools to complete complex tasks—application subscriptions, web page operations, API calls, e-commerce order placement, etc.—all intelligently. At the same time, the unit computing power cost on the inference side is decreasing, significantly improving the economics of AI invocation. From an economic perspective, the cost function of entrepreneurship has been fundamentally restructured. Traditional entrepreneurship relies on both human resources and capital investment, while under the OPC model, entrepreneurs can directly purchase computing power and AI capabilities, reducing team management costs. As feedback from OPC entrepreneurs has shown: "Previously, starting a business required building a team for repeated communication and verification; now, with the help of AI, ideas can be iterated quickly, significantly reducing trial-and-error costs." From a social perspective, the policy ecosystem is rapidly taking shape. Shenzhen issued the "Action Plan for Building a Leading Hub for the AI ​​OPC Entrepreneurship Ecosystem," and Guangdong introduced the nation's first provincial-level OPC-specific policy, aiming to cultivate 1,000 benchmark enterprises by 2028—all of which constitute the institutional foundation for the rapid growth of OPC. II. General Development Model and Cases of OPC (I) Basic Development Path The general development path of OPC can be summarized into five stages: Proof of Concept → MVP Development → Market Validation → Expansion and Replication → Ecosystem Building. In the first stage, entrepreneurs use AI Agents to complete their initial ideas. In the second stage, they quickly build a minimum viable product using AI programming tools and large language models. In the third stage, they conduct market testing through AI-driven marketing systems. In the fourth stage, they achieve sustainable business expansion by deploying a multi-agent matrix. The fifth stage involves joining an OPC community or platform to further expand capabilities by leveraging industry resources. (II) Domestic Success Stories Case 1: Li Jiehui – OPC Validation for AI Service Providers. Former game operations professional Li Jiehui transitioned to AI entrepreneurship, focusing on providing AI solutions for law firms, consulting companies, and other organizations. With AI assistance, the intelligent contract review system integrated multi-person collaborative workflows, significantly reducing delivery cycles; on the cost side, it achieved high profitability with relatively low investment. Case 2: Wupai Technology – Efficiency Improvement for Small Core Teams. Founder Sun Hongliang adopted an AI cluster-assisted model, streamlining the core team and pairing it with AI assistants to streamline the entire product process, achieving positive cash flow in a short period and validating the efficient operational capabilities of a lightweight team. Case 3: Guangzhou Haizhu Intelligent Agent Case. In the outstanding AI Agent cases released by Haizhu District, Guangzhou, several companies were listed as typical OPC or micro-teams. These companies applied AI to repetitive tasks, effectively improving risk identification efficiency. Haizhu District accelerated the commercialization process of OPC through an industrial closed loop of "technology + capital + scenario". Case 4: Shenzhen Nuoyin Intelligent – ​​Expert-Driven OPC. Engaged in the research and development of intelligent household robots, the team uses AI as a technical research and development assistant to help complete design, technical verification, and other stages, achieving lightweight operation through "expert research and development + AI collaboration". Shenzhen's well-developed supply chain system helped the company achieve mass production while streamlining its core R&D personnel. (III) International Benchmarking Cases Case 1: Midjourney – A Pioneer of Streamlined Teams and High Value. Midjourney, an AI image generation platform, achieved rapid user growth with a very small core team in its early stages, providing a reference case for lightweight startup models. Case 2: Swan AI – A Practice of Efficient Operations. Israel's Swan AI, with its streamlined team and multiple AI Agents, has planned high revenue targets, demonstrating the efficiency potential of the "small team + multiple agents" model (Source: Company Public Planning). Case Study 3: LaunchLemonade – AI Agent Empowering SMEs and Entrepreneurs. LaunchLemonade in the UK has built a low-code platform, lowering the technical barrier for SMEs and entrepreneurs to use AI agents and providing infrastructure support for the large-scale development of OPC. III. Considerations and Challenges Facing OPC While OPC shows great promise, various parties face many deep-seated challenges in practice. Technology Supply Risks OPC relies on large-model-driven agent workflows, consuming significant computing power. High-frequency agent interactions generate pressure on data throughput and token costs. Currently, OPC faces three major technical limitations: output quality depends on the capabilities of the underlying model, it cannot completely replace human decision-making, and multi-agent collaboration drives up computing costs. Industry observations show that the number of high-value OPC companies is currently limited, and many AI startups are still in the exploratory stage. Legal Compliance Risks: First, AI agents lack legal standing, and the current legal system's authentication mechanisms, built around natural persons and legal entities, are ill-suited for multi-agent interaction scenarios. Second, agentic AI can autonomously call APIs, read files, update databases, and perform cross-system operations; if it oversteps its authority, the attribution of responsibility remains unclear. Furthermore, the "Interim Measures for the Administration of Humanized Interactive Services of Artificial Intelligence," released in April 2026, will officially come into effect on July 15th, further clarifying the compliance obligations of OPC entrepreneurs regarding humanized interaction. Order and Market Risks: While OPC entrepreneurs generally possess the ability to implement their technology, they still need to invest significant effort in customer acquisition, marketing, and business negotiations. Order channels and brand credibility remain important constraints on the current development of OPC. Data and Privacy Risks: Frequent API calls and cross-system operations by intelligent agents may lead to data leaks. While persistent memory improves task continuity, it also brings information security challenges. Data corruption can affect the stability of agent decision-making. Funding and Development Risks: OPC entrepreneurs can validate their business concepts at a lower cost in the early stages, but they still face funding challenges during the scaling phase. Investment institutions are observing the stability of lean teams, competitive barriers, and long-term governance efficiency. IV. Current Development Status and Future Prospects (I) Domestic and International Development Status Domestically, OPC has entered the substantive incubation stage in many places. The Shenzhen "Moli Camp" ecosystem community has attracted nearly 200 OPC companies, with a total estimated value of over 20 billion yuan (data source: public promotion of the ecosystem community). Guangdong has released the first provincial-level OPC special policy in China, and Shenzhen has issued an action plan. At the technical level, the domestic large-scale model ecosystem is becoming increasingly rich, and AI Agents are moving from "single capabilities" to a systematic approach of "multi-model collaboration." Internationally, Singapore released the world's first national governance framework for autonomous intelligent agent systems. British authorities released compliance guidelines for AI applications, providing a reference for the commercial use of Agentic AI. Anthropic CEO Dario Amodi publicly predicted: "The first company operated by a human employee in collaboration with AI, with a valuation of $1 billion, is expected to emerge in 2026" (Source: Public industry speech). (II) Approximate Stages of Future Development From a technological perspective: From "multi-step execution" to "autonomous intelligent agents". Currently, Agentic AI has entered a stage where it can independently execute multi-step tasks, but still requires human supervision. In the next three to five years, higher-level autonomous agents will gradually be implemented, OPC-schedulable intelligent agent systems will achieve capability upgrades, multi-agent collaborative frameworks will mature, and swarm intelligence will compensate for the limitations of single intelligent agents. From an application perspective: from "single-point breakthroughs" to "full-industry applications." Currently, OPC is mainly concentrated in vertical fields such as software development, content creation, and segmented AI services. In the medium to long term, scenarios such as consulting, finance, education, and cross-border services are expected to gradually become more widespread, and "AI + industry experts" will become a common model for lightweight entrepreneurship. From an industry perspective: From "spontaneous emergence" to "sustainable ecological cycle." The emergence of the OPC community is essentially a proactive layout of the industrial ecosystem. The Qianhai OPC International Community provides space and computing power support, while Shenzhen Moli Camp gathers diverse support platforms—all of which are lowering the survival threshold for OPC. In the future, the OPC industry will gradually form a multi-center collaborative industrial ecosystem from scattered startups. From a governance perspective: From "rule vacuum" to "refined supervision." 2025-2026 is a critical window period for the initial establishment of the Agentic AI governance framework. China's algorithm registration, content labeling, and security assessment systems have already formed constraints. In the future, new regulations may emerge regarding AI Agent system qualification certification, liability insurance, and human oversight mechanisms. The improved governance system will increase OPC compliance costs and provide institutional guarantees for the orderly development of the industry. OPC is a microcosm of organizational transformation in the AI ​​era. It is neither purely legal innovation nor a complete technological disruption, but rather a redefinition of the "smallest entrepreneurial unit" driven by the combined forces of technology, economy, and society. Ma Yide, Dean of the School of Intellectual Property at the University of Chinese Academy of Sciences, pointed out that AI large-scale models and automated systems have, for the first time, turned "single-person success" from imagination into practice, "something unparalleled by any previous entrepreneurial model." In the future, as more industries' long-tail demands are met by lightweight entrepreneurial entities, and as more ordinary people leverage AI to start businesses, OPC will not only be an organizational form but may also become a significant force in unleashing social innovation. However, the development of OPC requires a mature governance framework, lowered technological barriers, and a well-developed business ecosystem to transform entrepreneurial enthusiasm into sustainable productivity. The AI-powered OPC model is becoming an important direction for industrial innovation and deserves continued attention and investment from all parties.