Recently, the open-source AI agent OpenClaw — nicknamed "lobster" — went viral online. Netizens have joked about "raising lobster" as the latest tech hobby. Beneath the humor, however, lies a serious signal: when technology becomes accessible through open-source communities and anyone with curiosity and creativity can participate, the spread of innovation accelerates.

This time, the transformation is not abstract or confined to the cloud. It manifests on the ground in the form of "digital employees" entering workplaces across industries. This AI-driven productivity revolution is reshaping the way we work.

The year 2026 marks a turning point: artificial intelligence is moving from a "hype phase" into a period of tangible value creation. A few years ago, people were fascinated that large language models could "chat about anything". Today, the focus has shifted. AI agents are taking on real responsibilities.

These AI agents perceive, plan, decide and collaborate across systems. They are no longer passive tools waiting for instructions — they are digital employees capable of independently executing the full cycle from understanding tasks to delivering outcomes.

In finance, digital employees can perform risk assessments and credit analyses in hours instead of days. On factory floors, they monitor production lines in real time, predict equipment failures, and schedule maintenance, slashing downtime costs. In hospitals, they assist doctors in analyzing medical records and imaging, improving diagnostic accuracy while freeing scarce healthcare resources for where they are most needed. This is not science fiction — it is the new reality of AI-driven productivity.

The spread of digital employees also sparks new forms of employment and demands for higher skills. China's core AI industry has surpassed one trillion yuan ($145.3 billion) in scale, requiring talent capable of integrating AI with manufacturing, services and biotech. AI is no longer merely a technical tool; it is a driver of high-skilled jobs and industrial upgrading.

Underpinning this productivity shift is a fundamental change in AI's development logic: from a "model race" to ecosystem building, from isolated breakthroughs to system-wide coordination. The "raising lobster" craze demonstrates that when core technologies become accessible via open-source platforms, innovation is no longer the domain of the tech-savvy alone. Every individual, small business, university, and research institute can contribute and innovate collaboratively, forming cross-industry, cross-domain AI networks that magnify technology's impact.

China's 2026 Government Work Report emphasizes "creating new forms of smart economy" and calls for "promoting faster application of new-generation intelligent terminals and AI agents", while "supporting the development of open-source AI communities". This reflects a global shift in innovation paradigms: by rooting AI in open-source soil, developers worldwide are collectively nurturing core technologies, avoiding redundant "reinventing the wheel" and exploring solutions efficiently and safely.

This "more open, more intelligent" virtuous cycle exemplifies the era of model democratization. AI is no longer confined to tech giants or first-tier cities; it now reaches small businesses, universities, and even remote regions, delivering the inclusive benefits of "AI plus" across society.

What distinguishes autonomous agents from traditional AI is autonomy. When agents make independent decisions and data and code flow freely across borders, risks around data privacy, algorithmic bias and safety become more complex. Advanced technologies without adequate governance can amplify systemic risks.

Striking the right balance between innovation and security is critical. Regulatory "sandboxes" and tiered management frameworks allow high-risk applications — financial AI, autonomous vehicles — to be tested safely. At the same time, robust legal frameworks must define data ownership, algorithmic accountability, and product responsibility, backed by ethics guidelines covering the entire life cycle from research to deployment. Public education in digital literacy and AI awareness is essential to ensure society can understand and manage these technologies rationally.

The large-scale onboarding of digital employees signals more than efficiency gains. It heralds deep changes in production methods, organizational forms, and governance structures. China's dual-track approach — pushing technological innovation while reinforcing institutional safeguards — ensures that digital employees operate legally, efficiently, and responsibly, while providing a stable environment for continued AI advancement.

The OpenClaw phenomenon reminds us that technology alone is not enough. Fertile ecosystems and clear rules are equally vital. When the soil of innovation is rich and the boundaries are well-defined, autonomous agents can truly become engines driving high-quality economic and social development.

The author is an associate researcher at the Chongyang Institute for Financial Studies, Renmin University of China.

The views don't necessarily represent those of China Daily.