陈冕

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.”