---
title: "Unique Friend | Lessie AI's Yu Beichuan: Using AI to Accelerate Human Connections"
author: "Unique Research"
sourcePublication: "Unique Research Substack"
originalPublishedAt: "2025-11-13T08:00:58+00:00"
canonical: "https://ffcap.cn/en/research/src-20251113-03html"
source: "https://uniqueresearch.substack.com/p/src-20251113-03html"
language: "en"
---

# Unique Friend | Lessie AI's Yu Beichuan: Using AI to Accelerate Human Connections

_Original · Unique Research · 2025-11-13_

Editorial note: This complete English edition preserves the source’s historical article, analysis and all 15 interview questions and answers. Product capabilities, performance examples, compliance practices and market assessments reflect the source or interviewee’s claims, not independently verified current results. The reference to the technological capabilities of 2024 is retained from the source despite its November 2025 publication date. The source describes Yu Beichuan’s Tencent experience with the abbreviation “chanpei” (产培), without defining the role; this edition does not expand it into an unverified job title. Douyin and TikTok references are retained where each appears in the source.

If the large models of the past two years have shattered the barriers to writing and image creation, Yu Beichuan is working on something less visible but even more important: reinventing how people find one another.

In most organisations, finding people has long been an opaque, inefficient black box governed largely by luck. You may want to hire an operator who understands both TikTok and cross-border e-commerce; find a group of fitness KOLs willing to try a new product; or sell a product in the United States without knowing whom to approach first. These needs disappear into endless searches, connection requests, private messages, Excel sheets and follow-ups—consuming human patience and time instead of machine compute.

Yu Beichuan describes the purpose of Lessie AI in one simple sentence: use AI to redesign the process of finding people so that it finally matches the technological capabilities available in 2024.

From searching for people to understanding people

Lessie AI positions itself as a People Search AI Agent. It is neither a new social network nor a smarter recruitment website, but a layer of infrastructure for human connections.

In conventional products, “finding people” essentially requires users to adapt to the system's language. You must break a need into a string of keywords—location, industry, job title, years of experience, follower count and so on—then move between platforms to search, copy, paste and filter. Inefficiency is only one problem; more seriously, the entire process has an extremely crude understanding of people.

In Lessie AI's vision, the order should be reversed: instead of people having to explain themselves to a system, AI should learn to understand people.

Users need only speak to it as they would to a capable assistant: “Find me TikTok creators in the United States who focus on fitness, have more than 100,000 followers and are open to product-trial collaborations.” The Agent handles almost everything that follows: interpreting the request, searching multiple data sources, filtering by intent, estimating price ranges and even initiating the first connection.

Moving from keywords to natural language, and from displaying a list to advancing an entire process, is more than an improvement in experience. It is a change of paradigm: AI is no longer merely a tool, but an execution partner that can be entrusted with an objective.

An Agent is not simply a smarter ChatGPT; it is a colleague that can finish the job

To many people, Agentic AI remains a rather abstract term. Yu Beichuan makes it easy to understand: instead of merely asking a model, “What do you think?”, you tell it, “Please get this done for me.”

Behind Lessie AI is an Agent system built around the task of finding people. It does not rely on one large model working alone, but on a group of specialised agents collaborating: one interprets the need, another searches and compares results, another scores and filters them, and another develops the communication strategy.

From the user's perspective, this feels like one thing: a chain of tedious actions that previously required constant personal attention becomes a closed loop of expressing an intent and waiting for the result.

This is why Yu Beichuan believes the true breakthrough in generative AI is not that it writes ever more like a person, but that it can organise actions around a goal. Once AI spans understanding, decision-making and execution, it stops being a feature and becomes infrastructure to which tasks can be entrusted.

Under this paradigm, we may need to reconsider a seemingly simple question: should future AI products be organised around functions or around tasks? Lessie's answer is the latter. Users do not want a more powerful search feature; they want the right people found and contacted.

To build infrastructure for human connections, respect the rules first

Unlike many products that begin in China and only later test overseas markets, Lessie made the United States the focus of its growth from the outset.

This is not an easy choice. Using AI to find people naturally brings a product close to the most sensitive boundaries around data compliance, privacy protection and regional regulation. Yu Beichuan's previous experience at TikTok made one thing clear from the beginning: these are not issues to patch later, but pressures the product must bear from birth.

Many aspects of Lessie's operating rhythm are therefore counterintuitive. Every major feature undergoes rigorous legal review before launch. Before entering a new market, the first question is not how to grow, but which boundaries must not be crossed. The team also began bringing in local talent very early—not to add a translation layer, but to build an international perspective into the company itself.

In the prevailing story of AI entrepreneurship, speed is treated almost as the only virtue. But when you are building infrastructure that connects people, crossing the line even a few times can destroy trust. Connections without trust become nothing more than cold, impersonal harassment.

For Yu Beichuan, globalisation is not about selling a Chinese product overseas. It is about rebuilding, within global rules, a more efficient and transparent way for talent and opportunities to circulate. What looks like a technical question is fundamentally a question of values.

Chinese AI founders are earning respect through the answers they build

When asked about the role Chinese AI companies play globally, Yu Beichuan offered an interesting description: they are moving from followers to practice-driven innovators.

There is indeed a gap between China and the United States in foundational research on large models, and no serious discussion should avoid that reality. But when the question becomes how to turn technology into products that can be replicated at scale—and how engineering capability and execution speed can close the gap—the experience of Chinese teams is in scarce supply globally.

Faster iteration, a sharper instinct for real-world scenarios and stronger engineering execution are allowing Chinese founders to move ahead in many AI application categories. This is not because China's theory is more advanced, but because its teams are willing to test, refine and repeatedly prove narrow yet genuine commercial paths at greater intensity.

Lessie is a representative example: it applies the distinctive engineering and scenario expertise of a Chinese team to a universal need that is difficult to reduce to a single algorithmic problem—finding people efficiently, accurately and at scale.

Yu Beichuan believes Chinese AI companies must keep doing three practical and straightforward things to gain a stronger voice in global markets: build products whose value global users can perceive directly; treat localisation and brand building as long-term programmes rather than marketing campaigns; and remain transparent and explainable on data compliance, partnership models and values.

None of this sounds novel, but few companies sustain the effort over the long term.

The individual era is not a slogan, but a real career choice

This conversation took place against the backdrop of a conference themed “Pioneering Intelligence | The Individual Era.” It is easy to treat the phrase as polished promotional language, but for Yu Beichuan it refers to a real group of users: overseas creators, small brand owners and founders of small teams.

In the past, completing something that appeared professionally demanding—running a full influencer campaign, building a multi-channel partnership network, or establishing an end-to-end cross-border sales loop—often required an agency or a large intermediary network. Limited budgets, information asymmetry and high trial-and-error costs were the norm.

When Lessie turns finding people into a one-sentence request, it expands what these individuals can do.

A team of two or three can complete in days work that once took a marketing agency weeks or longer. A creator can proactively find better commercial partners for their content and personal brand. A product professional based in China can use a contact network automatically extended by AI to reach opportunity nodes thousands of kilometres away.

If the previous generation of the internet dramatically reduced the cost of being seen, the task of this generation of AI is to reduce further the barriers to being connected and trusted.

This is why Yu Beichuan emphasises that AI is not merely restructuring how we work, but who is qualified to participate in collaboration at a given level. Methods once available only to large organisations are gradually being unlocked for small teams and one-person companies.

The real challenge has never been generation, but understanding

One idea in Lessie's narrative deserves repeated consideration: generating content has become easy enough; what remains truly difficult is understanding people.

Technically, generating an email, a list or a report is no longer much of a challenge. The real obstacle is whether a system can understand the unspoken preferences, concerns, opportunity costs and practical constraints behind a vague sentence such as, “What I roughly want is…”

A good interpersonal Agent does not help you send hundreds more cold emails. It helps you avoid unproductive connections and concentrate limited attention on the small group of people with whom trust and value may genuinely emerge.

That sounds profoundly human, yet it is precisely one of today's large models' weakest capabilities. A model can write a polished template, but it struggles to make quiet trade-offs in context as an experienced business leader would—to know when to step back and when to persist a little longer.

By focusing on the difficult problem of understanding people, Yu Beichuan is also sounding a warning for AI founders: if we pursue only generation that sounds more like human language without confronting how systems might understand one another more as humans do, we will ultimately stall halfway up the hill.

Several tough but constructive suggestions for individuals and small teams

When asked how individuals and small teams should position themselves over the next 1–3 years, Yu Beichuan did not provide a list of tools. He began with mindset and ways of working.

First, mindset: shift from an executor to a collaborative creator. If you still define yourself only as the person who takes an assignment and completes a task, AI will probably be no more than a faster set of automation tools. Once you begin to see yourself as someone who creates together with AI, you will proactively ask: what must I judge personally? What can I assign to an Agent? How should I design an automated workflow from idea to implementation?

Second, tools and ecosystems. ChatGPT, Cursor and various Agent platforms all matter, but the more important question is whether you can build an integrated workflow for yourself—from information intake and structured thinking to execution, follow-through and review. If you still use AI to solve only one or two isolated points instead of connecting an entire process, an invisible productivity gulf will separate you from true one-person companies.

Third, choose the right arena. Instead of worrying, “Will AI replace me?”, ask honestly whether your field contains a critical action that AI could greatly amplify but that no one has yet properly redesigned. For Yu Beichuan, that action is finding people. For you, it might be signing contracts, selecting topics, writing cold-start emails, conducting user interviews or scanning an industry. The real opportunities are often hidden in the tasks you perform—and complain about—every day.

No one can yet say how far Lessie AI will ultimately go. But Yu Beichuan's path offers a clear reference point: instead of building yet another general-purpose AI tool, choose an apparently small but indispensable action and use an Agent to take it as far as possible—until individuals and small teams suddenly discover that the range of what they can accomplish has quietly expanded.

When more products like this emerge, “Pioneering Intelligence | The Individual Era” will stop being merely a conference theme and become a reality ordinary people can feel.

Selected Interview Q&A

Q1: Please briefly introduce yourself and Lessie AI.

Yu Beichuan: I am Yu Beichuan, founder of Lessie AI. I was an early member of the Douyin team and later worked at Tencent in a role the source calls “chanpei” (产培). Lessie AI was founded in 2024. We position it as a People Search AI Agent and want to use AI to reinvent how people find one another, enabling anyone to find and connect within minutes with the person they want to work with or hire.

Q2: How exactly does Lessie AI find people?

Yu Beichuan: Users need only state their need in one sentence—for example, the region, field and scale of the person they want to find. AI automates everything else, from understanding the request and searching to filtering and initiating a connection. We want to turn what was once a fragmented, manual process into an intelligent, closed-loop Agent experience.

Q3: Which industry pain points are you primarily addressing?

Yu Beichuan: Traditional approaches to finding people have three problems: they are inefficient, imprecise and impossible to scale. HR teams, BD teams and brands must search back and forth across several platforms, compile spreadsheets and initiate cold outreach, which consumes time and depends heavily on luck. Lessie AI aims to identify genuinely suitable people within minutes in scenarios such as recruitment, business partnerships and creator marketing, and then reach out to them directly.

Q4: What originally motivated you to start the company, and what is your vision?

Yu Beichuan: The original motivation is simple: use AI to make person-to-person connections more efficient, so finding the right person is no longer so difficult. My vision is to make Lessie AI infrastructure that connects talent and opportunities worldwide. Whatever kind of person someone wants to find, they should no longer have to navigate multiple layers; they can simply give the task to AI.

Q5: In your view, what has been the most important breakthrough in generative AI over the past two years?

Yu Beichuan: I believe the real breakthrough is not how humanlike a model's writing has become, but the move from large-model capabilities to Agent systems. AI no longer merely generates a piece of content; it can understand a goal, break down tasks, act proactively and keep learning. This is the leap from answering questions to helping you finish the job.

Q6: How is Lessie AI applying Agents in its product?

Yu Beichuan: Our entire system is built around Agents. One Agent interprets your people-search request, others search multiple data sources, others evaluate and filter the results, and still others design the connection method and messaging. The user perceives only one thing: state the need and wait for the result, while the entire search process runs automatically.

Q7: With technology evolving so quickly, how do you balance technical innovation with commercial execution?

Yu Beichuan: Our approach is outcome-oriented technical innovation. Roughly half our resources go into core AI capabilities such as understanding, matching and execution; the other half goes into validating specific business scenarios, including recruitment, business development and creator partnerships. Every technical investment must ultimately produce quantifiable results on the business side, or it is not a good technology investment.

Q8: How do you see the new Agentic AI paradigm affecting the future application ecosystem?

Yu Beichuan: Agentic AI will transform AI from a point solution into an end-to-end execution partner. Products used to be divided into functional modules; increasingly, they will be organised around tasks and objectives. For users, the experience will no longer be, “I combine more than 10 tools,” but, “I tell one Agent what to achieve, and it calls the tools and connects the process itself.”

Q9: What opportunities and challenges do you see in globalisation, and which markets are your current priority?

Yu Beichuan: Demand for efficient people discovery and cross-border connections is surging globally. Whether in recruitment, creator partnerships or BD, everyone is looking for a more efficient approach. The main challenges are data compliance and understanding local contexts. We are currently focusing growth on the United States, where we are working to validate the product and business model.

Q10: How do you handle privacy, compliance and cultural differences when building a cross-border product that connects people?

Yu Beichuan: Our past experience at TikTok has made us particularly sensitive to compliance. Every major feature undergoes strict legal review before launch, and we continue bringing in local talent to participate in product design and decision-making. Our goal is not merely to build a usable tool, but infrastructure that can establish itself in different markets.

Q11: How do you see the position of Chinese AI companies in the global market, and what capabilities do they still need to build?

Yu Beichuan: I believe Chinese AI companies are moving from followers to practice-driven innovators. We have advantages in engineering efficiency, product iteration speed and implementation in real-world scenarios. Next, we need to build products whose value is more immediately evident to global users; invest in localisation and brand building for the long term; and create a genuinely trusted global network through transparent compliance and partnership models.

Q12: How do you interpret the theme “Pioneering Intelligence | The Individual Era”?

Yu Beichuan: Intelligence used to serve organisations above all; now AI is being distributed to every person. It gives individuals superpowers, enabling them to complete independently work that once required a small team or even an entire department. Creativity will be amplified, productivity automated, and significant commercial value will flow back from large organisations to individuals and small teams.

Q13: Can you give a specific example of Lessie AI empowering an individual or small team?

Yu Beichuan: Consider our core users—overseas creators and small brand owners. In the past, finding suitable brand partners or other creators could require weeks of manual searching, price comparison and communication. Now they enter a single request—for example, TikTok creators in the United States who produce fitness content, have more than 100,000 followers and are open to partnerships—and the system can complete the search, screening and initial connection within minutes. A small team of two or three can finish in days what used to take an agency several weeks.

Q14: For AI creators in your field, what are the greatest opportunity and the greatest challenge today?

Yu Beichuan: The greatest opportunity is that AI makes creativity and collaborative capability reproducible at scale. Individuals and small teams have the chance to accomplish things once possible only for large companies. The greatest challenge is enabling AI to understand people genuinely. Generating content is no longer difficult, but understanding intent, emotion and context—and forming real connections on that basis—remains extremely hard. That is the problem we are working to solve.

Q15: Looking ahead 1–3 years, what advice would you give individuals and small teams?

Yu Beichuan: First, change your mindset: move from executor to creator collaborating with AI. Do not see yourself merely as someone who uses tools. Second, build your own AI workflow as early as possible, using Agents and automation to connect ideation, execution and distribution so that even a one-person team can achieve the output of a small company. Third, ask which critical action in your field is best suited to amplification by AI. The more grounded and painful the problem, the more likely it is to be your opportunity.

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Original publication: https://uniqueresearch.substack.com/p/src-20251113-03html
On-site reading page: https://ffcap.cn/en/research/src-20251113-03html