The success or failure of AI applications often lies not in the technology itself, but in the ability to scale delivery and create a closed loop.
When “Those Who Discuss It” Are Not “Those Who Pay for It”
On December 30, 2025, a piece of news went viral: Manus was acquired by Meta for billions of dollars (Manus Joins Meta for Next Era of Innovation). This startup, founded in China and under pressure from tech giants since its inception, completed a whirlwind journey in less than a year—from explosive growth, relocating to Singapore, to being acquired by a global giant.
According to Manus’s official statement, its products and subscriptions will continue to be available via the app and website, and the company will remain operational in Singapore. The team will join Meta to provide general Agent capabilities for Meta’s consumer and enterprise products (including Meta AI).
Rather than focusing on “who won,” I’m more interested in the chain reaction this event triggered: it activated completely opposite judgment systems among different groups, and this split is reshaping the growth paths and strategies for AI applications and startups.
Two Public Opinion Arenas: Blessings and Doubts Coexist
After Manus was acquired, the mainstream sentiment in social circles was one of congratulations and excitement. Many saw it as a stellar example of a Chinese team going global—achieving remarkable results in the most competitive field in a very short time.
Meanwhile, the comment sections of public accounts became “venting valves for counter-narratives,” with skepticism centering on three main points:
- Whether the technology has real barriers (e.g., “there are countless similar products,” “it’s not hard for big companies to build their own”).
- Valuation and bubble concerns (e.g., “another case of the AI bubble”).
- Distrust in the buyer’s judgment (e.g., “giants making desperate bets,” “history repeating itself”).
This divergence isn’t about who understands AI better, but about different evaluation frameworks: social circles focus on “trajectory and outcome,” while comment sections focus on “legitimacy and worthiness.”
Where Does the $100M ARR Come From: The Target Users Aren’t in Our Social Circles
Many people are impressed by Manus’s marketing buzz and controversies, which can lead to skepticism. But if it achieved a “strict $100M ARR” in 10 months, one fact is clear: its revenue doesn’t depend on broad consensus, but comes from a highly concentrated group of global users with strong willingness to pay.
Manus’s core user profile is closer to “individuals as production units,” including freelancers, indie developers, independent researchers, and key deliverers in small and medium businesses. They don’t care about debates over “wrapping” or not; they care about “can I deliver end-to-end tasks,” and “can this help me hire one less person, work fewer late nights, or avoid juggling ten tools.”
This leads to a counterintuitive phenomenon: those who discuss the most may not pay, while those who pay steadily are often silent.
For these users, tools are not identity badges—they are profit levers.
Three Lessons for Entrepreneurs: The Growth Paradigm in the AI Application Era Has Changed
Based on the above, the Manus case offers three lessons for entrepreneurs:
Growth No Longer Equals Positive Reviews
AI applications can commercialize first and build consensus later. Public opinion can remain divided for a long time, but cash flow doesn’t wait for unified recognition.
“Heavy Marketing” Is Becoming a Capability, Not a Stigma
As foundational models and capabilities spread rapidly, differentiation is quickly erased. Being seen, understood, and paid for is itself part of the moat. Not all marketing deserves respect, but “distribution and mindshare” have become unavoidable battlegrounds for AI applications.
Globalization Is No Longer a Bonus, but May Be a Survival Strategy
From payment willingness, compliance boundaries, talent density to valuation systems, market structure means many teams “can only complete the loop overseas.” It’s not romantic, but it’s reality.
A Personal Reflection
As someone long engaged in cloud native and AI infrastructure, I’m used to evaluating products by their “technical barriers.” But cases like Manus remind me: at the AI application layer, barriers may not first appear in models or code, but often in organizational speed, productization capability, delivery loop, and distribution efficiency.
When a system can reliably turn “capability” into “results,” it has built a commercial moat—even if its tech stack doesn’t meet outsiders’ ideals of “purity.”
The biggest butterfly effect of Manus being acquired by Meta may not be the deal itself, but making more entrepreneurs realize: in the AI era, the winning move is shifting from “what model you use” to “whether you can deliver results at scale.”
Summary
The acquisition of Manus by Meta is not just a convergence of capital and technology, but also a microcosm of the changing growth paradigm in the AI application era. For entrepreneurs, understanding and mastering “user structure,” “distribution capability,” and “global closed loops” will be key to future competition.
