Harvard Meets Bairong: Chinese AI Firm Bets Big on Silicon-Based Employees

Harvard Business School’s Tsedal Neeley visited Bairong Inc. in March 2026 to explore how AI agents are replacing roles inside real enterprises and what comes next.

Tsedal Neeley, Senior Associate Dean and Chair of the MBA Program at Harvard Business School, paid an in-person visit to Bairong Inc. at its Beijing headquarters. The agenda was not a routine corporate tour. Instead, it was a focused exploration of a question that is rapidly reshaping business strategy worldwide: can artificial intelligence truly function as an employee not just a tool?

Rather than starting with slides, Bairong began with a live demonstration. Professor Neeley was first introduced to Bairong’s VoiceAgent, a voice-based AI agent built on the company’s proprietary BR-Voice end-to-end large model. Unlike conventional voice assistants that answer isolated queries, VoiceAgent handled multi-turn conversations across multiple scenarios and languages, responding to complex business questions with a naturalness and professionalism that, by all accounts, drew repeated expressions of surprise from Professor Neeley.

From there, the visit moved on to WiseNote, an AI agent designed for knowledge work. Under natural language instructions, WiseNote autonomously pulled together data from multiple sources, identified core trends, constructed analytical frameworks, and produced comprehensive reports all within a short timeframe. In practice, this goes far beyond summarization. Professor Neeley reportedly described the output as closer to the work of an independent AI expert than a writing assistant.

However, it was the third demonstration that triggered the deepest organizational reflection. Bairong’s Home for Silicon-Based Employees is, in essence, a full human-resources management system but built entirely for AI agents.

Each silicon-based employee within the platform carries an exclusive employee number, a designated email address, a formal job title, training records, performance ratings, and even transfer and promotion histories. Furthermore, every agent reports to a clearly identified human supervisor. Onboarding, position assignment, performance review, and role changes all follow standardized processes backed by dedicated system infrastructure.

To fully appreciate what Bairong presented, it helps to understand how long the company has been building toward this moment. During the closed-door exchange that followed the live demonstrations, CEO Zhang Shaofeng walked Professor Neeley through a timeline that began not in the era of ChatGPT, but all the way back in 2014 when Bairong first deployed what it calls silicon-based risk control employees.

Subsequently, in 2017, following the emergence of the Transformer architecture that underpins today’s large language models, Bairong deployed voice-based language models and AI-driven customer service and sales agents. Over the years that followed, the company evolved through successive service models: from MaaS (Model as a Service) and BaaS (Business as a Service), and eventually to its current approach where clients pay not for access to AI features, but for measurable business outcomes.

After 2023, this evolution accelerated. Bairong extended its silicon-based employee coverage across multiple industries and roles, ultimately arriving at a position matrix that today spans nearly 200 distinct job functions across finance, telecommunications, retail, and enterprise services.

This trajectory is significant. Whereas many AI companies today are still at the stage of demonstrating capabilities, Bairong arrived at the Harvard meeting with more than a decade of enterprise deployment experience and data to match.

Arguably the most philosophically interesting part of the exchange centered on a concept Bairong calls Silicon-Carbon Co-Governance. The premise is straightforward, even if its implications are not: as AI agents take over standardized, repetitive, and process-driven tasks including, increasingly, specialized professional work such as deep analysis and report generation human workers are freed to focus on strategy, creativity, risk judgment, and emotional value delivery.

Professor Neeley noted that most academic research on enterprise AI has, to date, remained at the level of AI as an assistant to humans. What she observed at Bairong, however, represented a meaningful leap: AI operating not as an add-on, but as a participant in organizational structure. Consequently, she described the model not as replacement, but as a new division of labor and a new governance framework worthy of sustained attention from global business researchers.

This, of course, is precisely the distinction Bairong has been drawing for years. As Zhang Shaofeng put it, the transformation they are driving is not about software upgrades or efficiency tools it is about the deep restructuring of organizational models, where AI agents function as legitimate collaborators, not peripheral utilities.

This was not the first time Bairong has attracted attention from Cambridge, Massachusetts. Previously, the firm was selected as one of the flagship Chinese corporate case studies in Harvard Business School’s case library, recognized specifically for its pioneering work in machine learning and decision intelligence.

Nevertheless, the March 2026 visit marked a new chapter. Where the earlier HBS case focused on Bairong’s technical foundation, the current engagement centered on something far more forward-looking: the organizational and managerial implications of treating AI as workforce, not infrastructure.

On the financial side, the company’s non-IFRS profit reached RMB 376 million in FY2024, while its H1 2025 non-IFRS profit margin climbed to 16%, up from 14% a year earlier. Research and development spending rose by 33% during this period yet gross margin held steady at 73%, a combination that analysts have pointed to as evidence of disciplined scaling.

Additionally, Bairong has been recognized by IDC as a consistent leader across multiple dimensions for large language models and intelligent agents, and several of its RaaS deployments have been incorporated into Gartner-related research. The company also co-leads an AI Agent ecosystem working group, further cementing its position as a standard-setter rather than a follower.

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Looking ahead, Bairong has outlined a phased expansion strategy. Through 2026, the focus remains on strengthening its domain large model capabilities and refining the silicon-based employee standard framework. From 2027 to 2028, the company plans to extend into vertical fields such as healthcare and education, while building a global partner ecosystem. By 2029 to 2030, the stated ambition is to become a global leader in silicon-based productivity and to drive the setting of industry standards.

Whether or not those milestones are met on schedule, the broader direction is clear. As Professor Neeley’s visit underscores, the questions Bairong is grappling with how do you manage AI as a workforce? How do you govern a hybrid organization of human and machine agents? are no longer hypothetical. They are, increasingly, operational.

Bairong Inc. is a Beijing-based enterprise AI company that delivers services through a Results as a Service (RaaS) model. Its proprietary technology stack includes the BR-LLM large language model family and the CybotStar enterprise AI agent operating system. The company’s clients span internet, retail, communications, education, and healthcare sectors, among others.

Disclaimer: This reporting is based on publicly available sources. Nervnow has not independently verified the claims.

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