陈冕

Low gross margin does not mean burning cash on subsidies. 陈冕 says the company has generated positive cash flow since May, with performance advertising contributing just 3%-4% of revenue and monthly spend below RMB1M; the host cautions that cash flow includes annual-plan prepayments and cannot by itself prove that growth is healthy. 陈冕’s target for early-stage AI applications is gross margin “above zero and no more than 30%.” The model depends on users not exhausting their full credit balances, controllable renewal rates, and falling token costs; if everyone used their full allowance, the current pricing would “definitely lose money.”

LiblibTV won on speed and attention, not because it was first to find PMF. 陈冕 acknowledges that competitors first validated the node-based video canvas and that LiblibTV’s differentiation was “thinner”; engineering innovations could be copied within a week. But once he realized the market’s revenue had exceeded Liblib’s daily revenue, the team changed course within just over a month and launched an approximately $1M, month-long “saturation attack” during the critical window. The product may have gone from zero to roughly $100,000 in daily revenue very quickly, with a one-day peak of $1M within its first month; using the host’s cited $300M annualized-revenue figure, 陈冕 only confirms that LiblibTV contributed more than half.

The applications era has not arrived; the first priority is to survive. 陈冕 believes profits are still concentrated upstream along the chip-model-application stack. For applications to target 70%-80% gross margins now would amount to voluntarily suppressing demand; the real opportunity comes when intelligence and tokens are cheap enough for the application layer to create incremental value. He estimates that global annual revenue from AI video creation could eventually reach $100B, because it will not only replace traditional software but also absorb parts of the production stack such as cameras and actors.

Dependence on model vendors is a real problem, but applications are not simply arbitrage middlemen. 陈冕 does not shy away from saying that applications are “leverage on top of models,” and concedes that the odds of surviving independently between the giants may be below 10%. His path is to “trade time for space, space for resources, and resources to build the moat.” For now, multimodal interfaces and user mindshare are only a “Shu Road”; ultimately, the company must build a “Yangtze defense” based on user scale, content supply, and a two-sided network, or it will remain subject to the model vendors’ “gravity.”

Being first to find PMF is still a world away from ultimately winning the market. 陈冕 accepts that LiblibTV is less original than Lovart and agrees that first movers deserve recognition. But swipe-based short video, the smartphone form factor, and node-based canvases will all converge, leaving the interface itself with little durable intellectual property. His response is that “innovation has a cost”; after inventing the airplane, one can study rockets, but if one cannot defend and operate the airplane business, there will be no resources for the next round of innovation.

Speed saved the company, but it also created organizational debt. After a battle with a better-funded rival, the company once had just RMB4,000 left, while GPT Image 1 suddenly erased a workflow direction the team had spent a year exploring; extreme anxiety became the engine for product pivots and timing. The cost was that roughly half of the early team left, including 4 technology heads who had reported directly to 陈冕, while Lovart at one point accumulated more than 2,000 unanswered customer-service emails. He admits he was good at removing people who did not fit, but failed to show those people enough respect or help them find a new role.

The ultimate bet is not on a particular interface, but on whether humans will still have value over the next decade. If AI concentrates both production and the feeding of humans, chips, top talent, and capital will absorb everything, leaving almost no room for independent applications. 陈冕 is betting that within 10 years people will still create, express themselves, and resist total centralization. He summarizes the company’s mission as “using AI to unleash imagination and make the inner world a little richer”—not merely replacing human production, but making human taste and nonstandard creativity part of the network effect.

🔗 Original source & video: 175: A Conversation with Liblib’s 陈冕: On Surviving—and Every Moment That Came Close to Death

陈冕’s core read on the Sora App is not “AI TikTok,” but an AI social product powered by Cameo. A nine-second video can now deliver shot language, Chinese voice, character consistency and audio-visual sync in one pass, but the real product flywheel comes from “shooting with friends and celebrities” and Remix via left-right swipes. His Aha moment was: “Isn’t this social? Wow, this is Instagram.”

Sora may be opening a virtual-social opportunity serving billions of users, while compressing the competitive window to three to six months. 陈冕 believes that if there is even a 10%–20% chance it becomes a Super App, model leaders and companies that already own Super Apps cannot afford to sit out. But even a mid-sized startup that gets there first would face facial-privacy, regulatory and enormous generation-cost pressures, followed by rapid replication from the giants; opportunity and capital are more likely to concentrate at the top.

Two months after its public launch, Lovart’s DAU had reached 150K–200K. During Beta, daily activity was roughly 10K–20K under invite-only access, although the original source does not specify the exact metric; after Beta, DAU first rose to 80K–100K, with Nano Banana driving another step-up. The US accounts for more than one-third of users, with a slightly higher but not dramatically higher share of revenue; on a full-year estimated-revenue basis, revenue has exceeded $30M. Usage costs are currently broadly covered, but the higher free-user mix and conversion lag mean the business will still lose money overall in its early phase.

Lovart’s vertical moat is not exclusive access to a foundation model, but a proprietary Interface, Context and industry experience. Chat Canvas simulates a client and designer pointing at visual work together around a table, while the product plans to accumulate enterprise historical assets, Reference and Preference; when every application can call similar models, “whoever collects more Context will deliver the better experience.”

陈冕 summarizes AI application growth as drawing the product that should exist after models mature, then waiting for the technology wave to catch up. Once models such as GPT Image 1 and Nano Banana delivered on complex instructions, editing and consistency, the pre-built Chat Canvas could amplify those capabilities immediately; the next phase is to grow the early Agent user base, deepen Context Engineering, and keep making the trade-off between waiting for models and filling today’s gaps with engineering.

Productivity applications have entered a knife-fight, and 陈冕 believes new entrants must “already be at the table this year,” while the next major opportunity is To C. Veo 3 once cut prices by 70%, Nano Banana is cheaper than GPT Image 1, and GPT-5 is cheaper than Claude; as creation costs continue to fall, the variable will shift from “who produces content” to “what new content people consume.” But startup To C Super Apps are more likely to grow gradually from an overlooked corner than explode at launch.

Speed is both Lovart’s organizational advantage and its most visible operating risk. 陈冕 believes an AI product’s effective window may last only two years, so the team prioritizes people who understand AI, learn at high frequency and accept constant adjustment. The pace is grounded not in blind optimism, but in two still-testable beliefs—that AI will continue to improve rapidly and that AI will replace fictional-content creation—along with a contradictory warning to himself: “I’m a little afraid I’m too anxious, and I’m also afraid I’m not anxious enough.”

🔗 Original source & video: 136: Sora’s New World & Lovart’s 4-Month Review | Talking with 陈冕 About Building a Vertical Agent | Agent #5

The core investment thesis: general-purpose models will not subsume verticals. 陈冕’s bet is that vertical data, interfaces, and use cases differ too widely; general-purpose agent entry points will converge around a few winner-take-all players, while vertical agents will proliferate, both called by general-purpose systems and accessed directly by users with their own mindshare. The implicit assumption is that “AGI will not arrive within five years—if AGI arrives tomorrow, quit; we’ll all be working for AI.”

The full numerical chain from despair to reversal unfolded within a year. In 2023, the first round raised $4M at an $18M valuation (“we priced it too low; we should have asked for $50M”); a subsidy war burned through roughly $3M; the product was taken offline in September for lacking a large-model filing; cash at one point fell to RMB4K, and “nobody wanted it even at a $30M valuation.” In 2024, 2–3 months after the license returned, the company closed three rounds totaling over $20M. By early 2025, ARR was close to, and even above, $10M; the current valuation is “probably a few hundred million dollars.”

Claude 3.5 unlocked Lovart’s product. The original plan was to have designers build workflow nodes on a canvas, but “designers don’t speak logic; designers speak in feelings.” In December 2024, after discovering the model’s stronger Agentic capabilities plus MCP, the team shifted to having AI plan workflows for designers, causing the barrier to entry to collapse; it took only 3–4 months from idea to execution. Manis’s lesson was that “people only remember the first”: leave general-purpose to Manis; “the true first vertical was Cursor—we want to build the Cursor of creation.”

The viral breakout proved that timing determines customer-acquisition cost. A post published at midnight drew 800K views overnight, Elon Musk liked it, and the waitlist reached 120K. Customer acquisition for the first-generation product cost just RMB3–4 per person; today it is around RMB20. “The core of product-market fit comes from timing, and timing comes from breakthrough innovation”—miss the window and paid acquisition becomes a bottomless pit; “you can never beat the big platforms.”

His decade in mobile internet became a library of failure cases. Mobike’s 40–50M rides were sustained by unlimited supply and ignoring supply costs; once it had to break even, “it might do only a few million orders a day,” making it “maybe acceptable as a cost center in a payment war, but unacceptable as a business in its own right.” Daily Fresh showed that “internet plus retail is a fake proposition.” The conclusion: “business model determines almost everything”; since then, he has only pursued pure-online, high-gross-margin businesses with scale effects.

The business model will move from subscriptions to paying by deliverable. In the short term, professional designers will use subscriptions; in the long term, if production relations change and clients use AI directly, “AI will calculate the tokens and electricity it expects to consume, then quote you a price.” To B “absolutely will not happen in the short term” because it is not the organization’s strength. The end state is “1–2 relative winner-take-most players,” while major ecosystems will still retain their own products, as Adobe coexists with Final Cut and 剪映.

AI has already deconstructed the organization. Product managers “no longer exist”; the core jobs are teaching AI industry knowledge and coding. His advice to founders: build vertical products, not general-purpose ones; “don’t believe in product managers”; prepare before model intelligence improves—and “the most important thing about getting out there is getting out there.”

🔗 Original source & video: 103. Lovart Founder 陈冕 Looks Back on Two Years of Building an App: This Moment Feels So Damn Good!! Hahahahahaha