Are AI Applications Collapsing in China? An Analysis of the 60% User Loss Report

🚀 Introduction: Between Growth and Contraction

Amid the global race to adopt artificial intelligence technologies, China has consistently led the charge—backed by massive investments and a wave of innovative apps targeting hundreds of millions of users. But behind this technological momentum, troubling signs are emerging that threaten the sustainability of this growth. A recent report revealed that 60% of native AI applications in China lost a significant portion of their users during Q3 of 2025—despite the overall rise in active users.

This apparent contradiction between total growth and individual decline raises a fundamental question: Are we witnessing the deflation of an AI bubble? Or is the market undergoing a natural selection process between those offering real value and those merely riding the hype?

In this article, we’ll dive into the details of this shocking report, unpack its causes, and analyze its impact on both startups and tech giants. We’ll compare the apps that survived with those that collapsed, and explore how specialized models are beginning to regain user trust. We’ll also examine how China’s market compares to global counterparts, to determine whether this is a local anomaly or a global signal of a new phase in AI’s lifecycle.

If you’re interested in understanding what lies behind the numbers—and forecasting the future of smart applications in the world’s largest digital market—keep reading. What you discover may completely reshape your view of the so-called AI revolution.

* The Shocking Numbers: What Does the Report Say?

In September 2025, the number of monthly active users for AI applications in China reached 729 million, up from 680 million in June of the same year. This jump of nearly 49 million new users initially suggests that the market is expanding. But the reality is more complex than it appears.

According to Quest Mobile, 60% of native AI-powered apps lost users during the same period. This means that overall growth doesn’t reflect collective success—it masks a redistribution of users across apps, where major platforms capture the lion’s share while others fade into obscurity or exit the market entirely.

To understand this contradiction, we must distinguish between two types of apps:

Native AI apps: Those that rely on AI as their core functionality, such as chatbots, auto-generation tools, or smart interaction platforms.
Enhanced traditional apps: Existing apps that have added smart features like recommendations or auto-translation.

The report indicates that native apps suffered the most, unable to maintain user momentum after the initial wave of curiosity. In contrast, traditional apps benefited from integrating AI more seamlessly into daily user experiences, boosting their retention.

This performance gap raises deep questions about the true sustainability of AI applications, revealing that numerical growth alone doesn’t guarantee success. Instead, success must be measured by an app’s ability to deliver consistent, compelling value to users.

📌 Possible Reasons Behind User Loss

Although AI is one of the most captivating technologies of the digital age, Chinese apps built around it have struggled to retain their user base. The report reveals that 60% of native AI apps lost users in a short span, prompting a closer look at the underlying causes. Here’s a detailed breakdown of the key factors:

🥊 Fierce Competition Among Apps

China’s market has exploded with AI apps, as startups and tech giants race to attract users. This has led to market saturation, making it difficult for smaller or undifferentiated apps to hold user attention for long.

🧩 Weak User Experience or Value Proposition

Many apps focused on delivering smart features without integrating them smoothly into the user journey. For example, chatbots that offer shallow or inaccurate answers quickly lose appeal. Users aren’t just looking for “intelligence”—they want real, personalized solutions.

🧪 Overreliance on Immature Technologies

Some apps relied on incomplete language models or underdeveloped algorithms, resulting in unreliable outputs or unnatural interactions. This kind of performance creates a negative first impression that’s hard to recover from.

🔁 Redundant Features Across Apps

Dozens of apps offer nearly identical functions: text generation, translation, summarization, or recommendations. This redundancy causes users to hop between apps without forming real loyalty, leading to erosion of competitive value.

📉 Lack of User Retention Strategy

Many apps focused on attracting users through ads or promotions but neglected to build lasting relationships. There were no effective mechanisms for personalization, fresh content, or rewards for repeat usage—making abandonment easy.

Together, these factors reveal that AI alone isn’t enough to make an app succeed. It must be embedded within a complete experience that delivers tangible value and fosters long-term engagement. The market doesn’t reward those who ride the wave—it rewards those who create it.

🔗 Winners and Losers

In a market with over 700 million active users, being “smart” isn’t enough to survive. The report shows that survival in China’s AI landscape depends not just on technology, but on adaptability, specialization, and long-term investment. Here, the gap between thriving and declining apps becomes clear.

🏆 Winners: Tech Giants Take the Lead

Companies like Baidu, Alibaba, Huawei, and Tencent managed to maintain—and even grow—their user base thanks to:

Investment in large language models like Ernie and Tongyi Qianwen, which offer more accurate and interactive performance.
Integration of AI into comprehensive service ecosystems spanning e-commerce, search, education, and even government services.
Personalized experiences powered by massive user data, boosting loyalty and reducing churn.

🧨 Losers: Startups Collapse Under Pressure

Conversely, many startups that emerged during the 2023–2024 AI boom are losing momentum. Reasons include:

Limited funding compared to tech giants
Lack of clear user retention strategies
Dependence on off-the-shelf models without internal development

Some of these apps were forced to shut down or merge with larger platforms in a bid to stay relevant.

📉 Brief Economic Impact

User loss wasn’t just an operational issue—it directly affected startup valuations, with many dropping over 30% in Q3. This decline pushed investors to redirect capital toward more specialized and sustainable apps, especially those serving education, healthcare, and financial services.

In this context, models like DeepSeek and Baichuan began regaining momentum, thanks to technical updates and a shift toward professional, niche use cases—proving that the market remains open, but under stricter conditions.

🔐 The Shift Toward Specialization: Is It the Solution?

As general-purpose AI apps decline across the board, a new trend is emerging in China’s market: specialized applications—those that focus on a specific domain and offer deep solutions rather than broad features. This shift isn’t just a technical choice—it’s becoming a survival strategy in an increasingly mature and demanding market.

🎓 Education: AI as a Personal Tutor

Apps like Zhipu AI now offer interactive learning models that help students understand subjects, solve exercises, and prepare for exams. These apps don’t just answer—they explain and guide, making them more engaging.

🏥 Healthcare: Decision Support and Health Literacy

Some platforms use AI to analyze symptoms, offer health advice, or assist doctors in diagnostic decisions. This type of usage delivers immediate, tangible value, boosting user trust.

💰 Financial Services: Data Analysis and Personalized Recommendations

AI apps in finance offer investment tips, market analysis, or personal budgeting tools. Here, users aren’t seeking novelty—they want tools that help them make better financial decisions.

📈 Why Specialization Works

Clear user need: Users know exactly what they want from the app and get direct answers.
Measurable value: App performance can be judged by real outcomes (better grades, savings, improved health).
Higher loyalty: Specialized apps build long-term relationships because they serve vital user needs.

This shift toward specialization doesn’t mean the end of general apps—but it shows that the next phase of AI will be deeper and less flashy. Success will come not from offering everything to everyone, but from offering the right thing to the right person.

📊 Global Comparison: Is China a Unique Case?

global comparaison . is china a unique case

Although the report focuses on the Chinese market, the phenomenon it reveals doesn’t appear to be geographically isolated. Many global markets—including the United States and Europe—have begun to face similar challenges in retaining users within AI applications, especially those launched during the initial wave of excitement in 2023 and 2024.

🇺🇸 United States: From Mass Adoption to Specialization

Apps like ChatGPT, Copilot, and Claude achieved widespread adoption, but reports indicate that daily usage rates have started to decline—particularly among non-professional users. Why? Feature redundancy, lack of personalization, and repetitive content. In contrast, specialized apps focused on legal writing, education, or data analysis are showing higher retention rates.

🇪🇺 Europe: Privacy Shapes the Path

In Europe, strict regulations around privacy and personal data have pushed developers to design more conservative apps with less reliance on open data. This has slowed development, but created a more stable environment in terms of user trust and retention.

🌍 Cultural and Behavioral Differences

User behavior varies dramatically across cultures:

In China, users tend to try new apps quickly—but abandon them just as quickly if they don’t deliver clear value.

In Western markets, there’s a stronger inclination toward specialization and professionalism, where AI is used more as a productivity tool than a novelty experience.

📊 Is China a Unique Case?

The answer isn’t a simple yes or no. China represents an extreme version of a global trend. The market size, speed of innovation, and intensity of competition make China a magnified mirror of what could unfold in other regions. What’s happening there today may be a preview of what’s to come in India, Brazil, or even the Middle East over the next two years.

This comparison reveals that user retention has become the biggest challenge for AI applications worldwide, and that success is no longer measured by download numbers—but by how deeply users remain engaged with the app over time.

📌 Read also : How Alibaba Is Redrawing the Map of Consumer AI in 2025

🎯 Conclusion: Between Collapse and Transformation

What the numbers reveal is not merely a decline in user count, but a fundamental shift in the nature of the relationship between users and AI applications. It’s no longer enough to be “AI-powered” to attract attention or retain an audience. Today’s user is more aware, more demanding, and less tolerant of shallow or repetitive experiences.

The Chinese market experience shows that artificial intelligence is transitioning from a phase of mass fascination to a phase of selective maturity. Applications that fail to deliver real value or build a personalized, sustainable experience will decline—no matter how advanced their technology. Conversely, those that succeed in understanding user needs and offering precise solutions in critical domains will have the opportunity not just to survive, but to lead.

This is not the end of artificial intelligence—it’s the end of its chaotic phase. What we’re witnessing today is the beginning of a more conscious, more specialized, and more reality-grounded era. It’s a call to rethink how apps are designed, how success is measured, and how long-term relationships with users are built.

In the end, the ones who survive in this market are not those who shout “I’m smart,” but those who whisper to the user: “I understand you.”

❓ Frequently Asked Questions

At the end of this analysis, it’s helpful to pause and address the key questions that may arise in the reader’s mind—especially given the complexity of the landscape and the many influencing factors. Here’s a set of frequently asked questions, answered with clarity and precision:

➊ What are “native AI applications”?

These are apps that rely on artificial intelligence as their core functionality—not just as an added feature. Examples include chatbots, auto-generation tools for text or images, or smart data analysis platforms. These apps cannot function without AI, unlike traditional apps that integrate it partially.

➋ Does losing users mean total failure?

Not necessarily. Losing users is a sign of weak retention or declining value, but it doesn’t mean the app is finished. Some platforms restructure, specialize further, or merge with other companies to regain momentum.

➌ What’s the difference between general and specialized apps?

General apps offer multiple functions to a broad audience—like translation, generation, or recommendations. Specialized apps focus on a specific domain such as education or healthcare, delivering deeper and more relevant solutions tailored to user needs in that field.

➍ Can this phenomenon happen in other markets?

Yes—especially in markets that experienced a rapid surge in app launches without clear differentiation or structure. The United States, Europe, and even India are beginning to face similar challenges in user retention, making this a global trend rather than a local anomaly.

➎ How does this phenomenon affect investors?

It pushes them to reassess their strategies—moving away from general apps that lack loyalty, and toward platforms that offer sustainable value in critical sectors. It also impacts startup valuations and reshapes funding priorities across the AI industry.