Mian Chen

AI · AI creative tools
陈冕ChinaAge ~34

Founder and CEO of Yanyu Technology · Founder of Lovart / LiblibAI

From a campus product experiment, a Tencent internship, and product roles at major tech companies, he assembled an overseas-facing portfolio of an AI creator community and a design agent.

One-line read: Big-tech executive turned founder: reorganizing product, growth, and global commercialization experience into an AI creative product portfolio, then using new products to test whether that experience can translate into transactions.

Big-tech executive turned founderScaled companyVC-backedChina to globalFrom Mainland ChinaBorn 1992Top-school trainedBig-tech background

Key numbers

Each figure includes its definition, date, and evidence level.

1992Born · age ~34
Media report$300 million↗Company ARR · 2026-05
Media report200,000↗Lovart DAU · 2025-09
Media reportNearly $300 million↗Series B+ funding · 2026-06
Media reportMore than $2 billion↗Post-money valuation after Series B+ · 2026-06

Key stages

What he was doing at each age

~20

Campus venture

~32

¥4,000 left in the account

~33

Lovart launch

~34

Portfolio scale

Geographic moves

The city behind each change in direction

  1. Nanjing, China
    ~18 Undergraduate~20 Campus venture
  2. Nanjing
    ~22 Graduate → Product manager

Selected work

An end-to-end AI design agent for designers and marketers, with subscriptions and credits supporting multi-step creative tasks

LiblibAI

A model-creation community and online generation platform, with membership and credits as its publicly visible payment units

An AI video-creation platform using a membership and credit system

How the business works

Market
An AI design agent for designers and marketers worldwide; it handles the process from understanding a brief to delivering a complete design through multiple creative steps
Users and payers
Used by designers, marketers, and teams that need to produce brand assets quickly; individual subscribers and team customers pay, while public sources do not disclose the revenue split by customer type
Problem solved
Turns tasks such as brand visuals, posters, and marketing assets from manually stitching together multiple tools into reusable natural-language workflows; the designers page also highlights brand kits and local editing
Monetization
The pricing page uses subscriptions and credit packs; complex tasks typically consume more credits, measured by task and model usage, giving users a budget unit for inference and retries after failure. Exact prices change with the page and are not fixed here
Acquisition and timing
Observable entry points include a free trial, community and work showcases, designer/marketer landing pages, and a global launch; this is an analytical judgment based on public entry points. By 2025, generative AI had taught users to create with natural language, but end-to-end agents still had a product window
Main costs
Model inference and multi-step task calls, retries after failure, product and model R&D, global service, and acquisition; this is a cost-structure analysis, and the more complex tasks there are, the more the credit price must cover per-unit inference costs
Validation level
Scale validated, profit undisclosedA 2025-09 report put DAU at about 200,000 and projected annual subscription revenue at $30 million, while saying service costs were not yet covered; these are scale and run-rate figures, not realized revenue or profitability
Main risks
Rapid commoditization of foundation-model capabilities, inference costs and failure rates eroding gross margins, and rising professional-user demands for control and copyright protection; this is a risk analysis, and cheaper models or built-in big-tech capabilities could both compress the paid market
Growth flywheel · analysis

Free trials and work showcases bring in creators → works and templates lower the next user's experimentation cost → more tasks generate credit consumption and feedback → product improvements expand creator distribution again. This is an analytical judgment; public sources do not establish conversion rates at any step.

Lovart is the representative product of Mian Chen's shift from a creative community to a design agent. The billing unit is not simply a seat, but subscription allowance and credits; users spend credits by task and model usage, and the company also needs this measurement system to manage multi-step model calls, retries after failure, and service costs.Lovart pricing page

The opportunity at this point is that users have accepted generating visual content with natural language, but still need brand consistency, local edits, and delivery across steps. Lovart's designers and marketers pages show reusable workflows such as brand kits and local editing.Lovart Designers · Lovart Marketers This helps explain the product's value, but cannot by itself prove profitability. From a cost-structure perspective, inference costs and retries after failure may affect subscription gross margins; public sources do not disclose complete unit economics.

LiblibAI

Website ↗
Market
Model-creation community and online generation platform
Users and payers
Used by model authors, image creators, and users who need online generation; members pay through subscriptions and credits, while community content also serves for showcase and distribution
Monetization
Membership and credit system; see the membership page for exact plans and prices
Acquisition and main costs
Models, works, and the creator community form the content entry point; network effects and cost efficiency are analytical judgments, public sources contain no unit-economics data, and inference, storage, moderation, community operations, and R&D are potential cost items
Public verification
Product existence verifiedThe 2023-05 founding date comes from public reports; company ARR, users, and profit cannot be attributed separately to LiblibAI
Main risks
Model copyright and content governance, the quality of community supply, falling prices for general-purpose models, and retention on both the creator and generation-user sides

LiblibAI is more like a supply-side community for models and works: authors upload and share models, users create online, and community content in turn becomes an entry point for discovering models and works.LiblibAI · Membership and credits This is a different transaction logic from Lovart; without a revenue split, group revenue cannot be assigned to a single product, and user counts across products cannot be added without deduplication.

Market
AI video-creation platform
Users and payers
Used by video creators and users who need video content; public pages show that membership and credits are the main payment units
Monetization
Membership and credits; see the platform page for exact prices
Main costs and risks
Video generation typically requires heavier inference, storage, and bandwidth; this is a cost analysis. Content copyright, generation stability, and model iteration may also affect the profit logic, and no independent public profit data is available

LibTV fills out the video-creation use case, and public interviews list it alongside LiblibAI and Lovart as Yanyu Technology products. It can share models, creators, and brand awareness, but video tasks are heavier, so the cost assumptions of an image community cannot be applied directly.LibTV

Timeline

  1. Entered Southeast University School of Economics and Management

    About 18 years oldStarting outNanjing, China · Undergraduate

    Public university information identifies Mian Chen as an undergraduate in the School of Economics and Management, class of 2010, at Southeast University.

    Source ↗
  2. In his sophomore year, tried to build a Chinese version of GitHub with an upperclassman; the product did not continue after launch

    About 20 years oldFailureNanjing, China · University student · Campus project participant

    A transcript of his interview says the attempt failed after the product launched, after which he moved to an internship at Tencent. It establishes a campus entrepreneurship attempt, but cannot support the claim that a company was registered or that this was his first formal venture. A contemporaneous university news report lists him as part of the Jihe team whose Jihe Technology Co., Ltd. business plan won the national Challenge Cup gold award.

    Source ↗
  3. Graduated with a bachelor's degree and entered a product and internet career path

    About 22 years oldTurning pointNanjing · Graduate → Product manager

    The interview transcript says he graduated in 2014 with a major in information management and information systems; the major and graduation year currently rely on the transcript and have not been independently checked against the original program.

    Source ↗
  4. Joined ByteDance's education business, then moved to global commercialization for Jianying CapCut

    About 28 years oldTurning pointProduct and commercialization lead at ByteDance

    The interview transcript says he joined Guagualong in 2020-01 and moved to global commercialization for Jianying CapCut after the double-reduction policy; public career materials describe him as a former Mobike product director and former head of global commercialization for Jianying CapCut.

    Source ↗
  5. Founded LiblibAI and entered generative-AI creative platforms

    About 31 years oldStarting outFounder

    Public reports date LiblibAI's founding to May 2023. This does not describe 2023 as his first venture, since the earlier campus project was a clear prior attempt.

    Source ↗
  6. Experienced a cash crisis early in the venture

    About 32 years oldFailureFounder

    Mian Chen recalled in an interview with Zhang Xiaojun that at one point between 2023 and 2024, only about ¥4,000 remained in the account. This is his own recollection, not a LatePost report, and the amount and timing should not be extrapolated into a company financial statement.

    Source ↗
  7. Lovart launched globally and exited beta

    About 33 years oldMilestoneFounder and CEO

    The launch release says Lovart began internal testing in May 2025 and launched globally in July as an end-to-end design agent; the testing month also appears in a [LatePost podcast report](https://podcast.latepost.com/136?t=0).

    Source ↗
  8. Lovart reached about 200,000 DAU, with projected annual subscription revenue of about $30 million

    About 33 years oldMilestoneFounder and CEO

    The report used figures of 200,000 DAU and $30 million in projected annual subscription revenue, and said service costs were not yet covered; this is not realized annual revenue or profit.

    Source ↗
  9. Yanyu Technology completed a nearly $300 million Series B+ round at a post-money valuation of more than $2 billion

    About 34 years oldMilestoneFounder and CEO

    The report also said company ARR reached $300 million as of May 2026; the company's public product portfolio includes LiblibAI, Lovart, and LibTV. ARR is a company-level figure, unaudited, and should not be attributed to Lovart alone or interpreted as profit.

    Source ↗

From a Campus Product to an AI Creation Portfolio

Mian Chen's verifiable starting point is not the founding of LiblibAI in 2023, but an attempt to build a Chinese GitHub with an older student while at Southeast University. A transcript of his interview says that the product failed after launch and that he then went to Tencent for an internship; a Southeast University news item records the Jihe team's startup plan winning a national Challenge Cup gold award in 2012. These two sources establish an early attempt at product building and entrepreneurship. They do not show that a registered company existed, nor do they establish the age of his “first startup.”

Before and after graduation, he interned at Tencent, then worked in product at 360 and Baidu Zhidao, at Didi, at Mobike and in the organization after Meituan acquired it, followed by product growth operations at Daily Fresh. He later joined ByteDance's education business and moved to global commercialization for Jianying CapCut after the double-reduction policy. This sequence comes from the interview transcript and public career materials; the precise employment dates still need verification. It shows that his accumulated capabilities span product, growth, organizational change, and overseas commercialization, and that he did not start his company with a complete set of resources from day one.

Stage Verifiable identity / organization Evidence boundary
Early Tencent intern The interview transcript says he did not stay on
Product roles 360, Baidu Zhidao, Didi, Mobike Sequence follows the interview transcript; exact dates need verification
After acquisition Stayed after Meituan acquired Mobike This was not a separate job hop; see the interview transcript
Growth and overseas expansion Product growth operations at Daily Fresh; ByteDance education business → global commercialization for Jianying CapCut Public career materials and interview transcript; months need verification

Cash Pressure and Product Choices

After founding LiblibAI in May 2023, Mian Chen recalled the early cash pressure in an interview with Zhang Xiaojun: at one point during 2023–2024, only about 4,000 yuan remained in the account. This is his own statement, not a company financial disclosure, but it explains why later funding and ARR cannot be read backward as evidence of an uninterrupted rise. Lovart moved from an internal test in May 2025 to a global launch in July, extending the product focus from a model and work community into a deliverable design agent.

Decision Principles

The experiences above suggest several tendencies in his product choices; the following is an analytical interpretation. The repeated actions we can observe are continuing to redefine the problem after a product failure, moving into globally demanded product contexts under external constraints such as the double-reduction policy, and breaking complex creative work into measurable credit consumption. They support an analysis that he adjusts products to trading and technical conditions, but do not prove that every pivot was driven by a single principle.

Evidence Boundary

The 2014 graduation and major, as well as the sequence of early roles, come from the interview transcript and are explicitly not independently replayed or verified here; the cash crisis is self-reported; Lovart's 2025 scale and the 2026 ARR, funding, and valuation come from media or company disclosures and have not been audited. Where public evidence was unavailable, I did not add specific months to employment histories, and I did not turn company ARR into Lovart revenue, personal wealth, or profit.

Open verification items

Unknown numbers stay blank instead of pretending to be reliable.

  • The 2014 graduation and information management/information systems major currently rely on his interview transcript; the original program has not been independently replayed and no university graduation record has been found.
  • Public sources are insufficient to establish the exact months, role boundaries, and reasons for departure across his stints at Tencent, 360, Baidu, Didi, Mobike, Meituan, Daily Fresh, and ByteDance.
  • The 2026 ARR, funding, and valuation are based on reports or company disclosures; no audited financial statements were found, and the figures cannot be split among LiblibAI, Lovart, and LibTV.

My read

Mian Chen is easy to misread as someone who could build an AI unicorn simply because he had experience at large technology companies. The closer-to-fact throughline is this: after a failed campus project, he accumulated experience with complex organizations, growth, and global commercialization across multiple product roles; during the early startup period he also faced a cash crisis with about 4,000 yuan left in the account, and eventually put a model community, a design agent, and a video platform in the same company.

Three concrete things are worth copying. First, turn past product work into an understanding of user tasks: Lovart does not merely showcase model capabilities, but puts brand kits, local editing, and multi-step delivery into the design workflow. Second, bind user budgets to model calls through subscriptions and credits, at least leaving a unit for measuring inference and retry costs. Third, separate markets by product: LiblibAI handles the model and work community, Lovart focuses on end-to-end design tasks, and LibTV enters video creation. This is easier to validate than treating “AI platform” as one vague product.

The caution is equally clear. Company ARR, funding, and valuation do not prove profit; a September 2025 report on Lovart instead showed that its revenue had not yet covered service costs at that time. Individuals can learn from task decomposition, product feedback, and adjusting direction as technology changes, but cannot directly copy the funding, team, model supply, or distribution advantages of a large company. My view is that if this portfolio has a moat, it must come from continually lowering the inference cost of each complex task, reliably delivering usable results, and reusing community work to acquire the next user; there is currently no public evidence for the conversion rates or profit margins of these mechanisms.

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