From late 2025 to early 2026, which high-profile healthcare product stole the spotlight? The answer is Ant Afu!

On December 15th of the previous year, Ant Group upgraded its AI health app, originally named 'AQ,' to 'Ant Afu.' This new iteration emphasizes health companionship and Q&A services. In a short span, it garnered over 15 million monthly active users, quickly becoming China's most popular health management AI app.

On January 21st of the current year, Ant Afu rolled out a free medical information search function, DeepSearch, tailored for medical professionals such as doctors. The goal is to position it as the Chinese equivalent of 'OpenEvidence.'

As a standout product in the AI health sector, every move Ant Afu makes is under intense scrutiny. However, beneath its rapid user growth and continuous functional innovations and upgrades lie deep-seated concerns and challenges related to trust, business models, and AI ethics.

Misaligned Marketing: A Contradictory Renaming

Several years ago, tech giants were locked in a fierce race to develop 'general large models'—AI systems capable of handling a wide range of tasks.

Now, realizing the oversaturated market, these companies are shifting their focus to specialized sectors like healthcare, law, and education, aiming to establish dominant positions in these niches.

Ant Group is a prime example of this trend. By elevating the health sector to the same strategic level as finance and lifestyle, it expands the ecosystem boundaries of Alipay while responding to the growing health demands driven by an aging society.

As a cornerstone of Ant Group's health sector strategy, Ant Afu is a heavyweight player endowed with abundant resources, essentially born with a silver spoon.

Afu is powered by Ant's self-developed medical multimodal large model, trained on trillion-scale medical datasets, capable of both asking and answering questions. It also integrates resources from 300,000 real doctors on Haodf.com and features 'AI avatars' of over 500 renowned doctors, led by six academicians.

These 'hard strengths' lay the foundation for user trust. The renaming to 'Afu' aims to package cutting-edge technology in a warm, approachable persona, allowing users to feel the comfort of technology rather than its distance when facing health anxieties.

Unfortunately, the brand's warm positioning significantly contrasts with the hardcore image presented in its actual promotions.

Ant Afu's marketing often emphasizes that '60% of the team members have medical backgrounds' and 'the medical large model is trained on over trillion-scale datasets.' While these claims are factual, users' core needs are not about technical prowess but reliability. This grand narrative clashes with users' fundamental expectations of 'security' and 'trust,' ultimately diluting brand trust.

For instance, when a solitary elderly person faces a complex medical report, they don't need technical jargon like 'trillion-scale dataset training.' Instead, they need a specific promise like, 'I can help you understand this report.'

To truly make Afu the 'health steward' in users' hearts, Ant needs to shift away from data bragging and return to the user perspective. This involves filling the marketing gap caused by the renaming with specific scenario values, authentic experience details, and continuous trust-building efforts.

Advertising Restrictions and Privacy Minefields

Balancing interests and neutrality, as well as privacy and value, are core challenges that all AI health management apps must navigate.

Recently, Ant Afu's biggest public opinion challenge stems from users' doubts about 'paid rankings' and 'commercial ads.'

In response, Afu issued an official statement on December 29th, asserting, 'Afu's Q&A results contain no ad recommendations, no commercial rankings, and are free from other commercial influences. Users can use it with confidence.' This reflects a cautious approach, learning from the aftermath of Baidu's 'Wei Zexi incident,' and shows respect for user boundaries.

However, public commitments are just the first step.

In the underlying logic of AIGC (AI-Generated Content), risks of data poisoning and generative engine optimization (GEO) exist objectively. From a technical standpoint, marketing agencies could 'train' AI models by flooding them with content containing specific brand information, prompting them to prioritize paid brands and silently insert ads.

Thus, to uphold its 'zero-ad' promise, Ant Afu needs more than just commercial moral self-discipline; it requires a robust technological firewall. This includes establishing a full-link compliance review process, from data collection and bias detection to content generation and automatic review, ensuring full lifecycle traceability. It also necessitates deploying multi-platform real-time monitoring and early warning systems to accurately identify and intercept polluted data.

Compared to the explicit challenges of advertising restrictions, the implicit minefield of privacy protection is more perilous.

Ant Group's strong financial background naturally raises user concerns about the flow of health data. Some netizens question: Will Ant Afu collect user health information ultimately to sell insurance?

In response, Afu emphasizes 'data availability without visibility' through privacy computing technology, employing techniques like desensitization, localized storage, and Ant Chain encryption to safeguard data security. It even goes as far as fully coding photos of medicine boxes. However, technological protections alone cannot fully dispel user doubts.

In the future, convincing users that their medical records and personal health data are strictly protected privacy rather than commercial assets for the platform to monetize will be Afu's most daunting hurdle.

The Gap from 'Trial' to 'Regular Use'

Even if we set aside user trust in Afu and focus solely on the product's functionality, Afu faces an awkward situation: users have high expectations, but actual experiences reveal inadequacies in critical areas, creating a psychological gap.

Ant Afu's tool-based advantages are undeniable. For example, it helps users understand medical reports, identify medicines, and reminds them to take medications. These features address 'information asymmetry,' making complex medical terms intuitive and accessible, thus attracting users.

However, when user demands escalate from information queries to diagnostic decision references, Ant Afu appears somewhat inadequate.

Several examples illustrate this. When inquiring about whether children's common cold symptoms require medication, Ant Afu lists multiple possibilities but ultimately advises 'seeking medical attention.' Only after personal use did the author understand why some netizens complain, 'Who doesn't know to go to the hospital when sick?'

User complaints point to a core issue: Ant Afu's excessive caution in diagnostic decision-making renders it a tool for outputting 'trivial truths.' This conservatism further limits the product's tool attributes, hindering the transition from 'trial' to 'regular use.'

In contrast, JD Health's AI product 'Kangkang' leverages its supply chain advantages, deeply integrating AI consultations with instant medicine delivery and offline services. After consultations, if medication is needed, 'Kangkang' can immediately connect with JD Medicine's instant delivery service, shortening the time from consultation to medication.

Ant Afu's mild and conservative style may reduce user panic but also causes it to lose differentiation advantages in a homogenized market, trapping it in a 'user retention' dilemma.

In summary, competition in the AI health sector ultimately hinges on user trust and service capabilities. Ant Afu has secured market entry with its ecological advantages, but to achieve sustainable development, it must transcend the 'safety-first' mindset and reconstruct product value around user needs.

Afu's Future: A 'Brain' or a 'Dependency'?

Amid growing AI anxiety among major players, Ant Afu's emergence marks a crucial move in Ant Group's intelligent health strategy and a sample of major players seeking differentiated breakthroughs in the AI wave.

Leveraging Alipay's super entrance with 1 billion monthly active users, Haodf.com's 300,000 registered doctors, and Ant's vast user data assets, Afu quickly established itself after the upgrade, with monthly active users exceeding 15 million. This validates the market's urgent demand for AI health services, as demonstrated by its traffic.

However, its future remains uncertain.

If the future of AI is purely a 'knowledge brain,' Afu's independent value may be diluted. Today, general large models like Doubao and DeepSeek can interpret medical reports and popularize common ailments through medical plugins, offering greater flexibility in knowledge update speed and cross-scenario adaptability.

If the future of AI is 'hands for getting things done,' connecting offline services and building fulfillment loops, then Afu, with its ecosystem capabilities in registration, medical insurance, and medicine purchases, can efficiently connect offline resources, reliably fulfill services, and intimately serve users. This ecological synergy may bring unexpected surprises.

In conclusion, Ant Afu's ultimate success in this battle depends not on how 'big' it is but on how 'authentic' it is. Afu truly comes to life when users willingly entrust it with their health data without reservation and regard it as an indispensable part of family health.