---
title: "Interview with Meitu CEO Wu Xinhong: Meitu is an AI Beneficiary, Product Growth Follows a Methodology"
type: "News"
locale: "en"
url: "https://longbridge.com/en/news/280933140.md"
description: "AI compresses production but cannot compress demand discovery and user reach"
datetime: "2026-03-30T01:03:05.000Z"
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---

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# Interview with Meitu CEO Wu Xinhong: Meitu is an AI Beneficiary, Product Growth Follows a Methodology

Author | Xiao Mao

Editor | Ying AI

After Meitu's 2025 financial report was released, market divergence about the company still exists. Is AI weakening Meitu or strengthening it? As large model multimodal capabilities continue to evolve, is the survival space for image processing applications narrowing or expanding?

In an exclusive conversation with Wallstreet Insights · Ying AI after the earnings call, Meitu CEO Wu Xinhong provided an extremely direct response: **If only one operational metric can be chosen to answer this question, he would choose profit growth. Users vote with real money, the majority from subscriptions, and a portion from token consumption**.

Both point to the same conclusion: **The product is recognized**.

But the logic behind profit growth needs to be deconstructed.

The impact of AI on Meitu's value chain is uneven: **The upstream data and image middle platform, built by thousands of engineers over years, cannot be easily replicated. The downstream channel reach capability, with a distribution advantage formed by 280 million monthly active users, is also difficult for AI to replace**.

What has been **significantly changed by AI is the middle segment**, the production process of the product itself.

AI, on one hand, reduces costs, and on the other hand, provides Meitu with more supply capacity. Previously, users faced high learning costs in certain scenarios. After AI emerged, the threshold was greatly lowered, allowing Meitu to offer more products to the market, expanding market potential. (**NVIDIA CEO Jensen Huang recently expressed a view that the AI era has significantly lowered the learning threshold for certain complex software, triggering a broader demand explosion for such software**)

Wu Xinhong cited an internal case: A designer can now independently complete a full AI product, which previously required a team of twenty to thirty people.

But this fact simultaneously raises a more pointed question: If one person can create a product with an annual revenue in the millions, why would they stay with the company?

Wu Xinhong's answer points to **scale**. The reuse of middle platform capabilities and the low-cost customer acquisition brought by channel advantages are things an individual cannot replicate from scratch externally.

This combination of upstream middle platform and downstream channels also dictates how Meitu develops new products.

Every new Meitu product's demand grows from the previous one: Meitu Design Studio originated from the demand of Meitu Xiuxiu users for e-commerce material creation, and Kai Pai originated from the spoken-word video needs of users of the selfie camera's teleprompter function. **Gaining insight into new demands from existing users, and then acquiring customers at low cost through channel advantages, is the growth methodology that Wu Xinhong repeatedly emphasizes**.

Nearly 80% of Meitu's users are lifestyle scenario users, forming the entire user base. The company aims to extend upwards from this base to reach professional consumers and small and medium-sized businesses (SMBs) with stronger willingness to pay and higher ARPU.

Wu Xinhong explicitly stated that Meitu will not venture into the professional user and large enterprise market where Adobe operates, as there is a gap, high customer acquisition costs, and completely different product requirements.

In terms of overseas growth, the explosion of RoboNeo in 15 countries, including Brazil, has attracted market attention.

Wu Xinhong stated that the company has formed a replicable methodology. The judgment criteria are also straightforward: whether user growth and revenue are synchronized, and whether retention rates are sustained.

If only users grow without revenue, it is unhealthy. The European and American markets themselves have a strong willingness to pay, so as long as user growth is achieved, revenue can generally keep pace.

In the commercialization path of productivity tools, the e-commerce design scenario was the first to succeed. The e-commerce industry is highly competitive, making the demand for cost reduction and efficiency improvement urgent. The workflow of e-commerce design is highly digitized, which can be completely replicated by AI teams.

Wu Xinhong particularly emphasized that standardization and digitization are indispensable. If a task is standardized but relies on offline labor, AI can hardly intervene. Meitu Design Studio is far from reaching its user growth ceiling, and there is still considerable room in the global market. In productivity scenarios, user ARPU is rapidly increasing, and the evolution of model capabilities is improving output quality and stability, turning what was previously conceptual into truly usable.

The industry's revenue model is undergoing structural changes. From the earliest advertising referrals to the addition of subscriptions in 2018, and now the rapid expansion of token consumption.

Wu Xinhong expressed in the conversation that token consumption is becoming one of Meitu's main future revenue sources.

This trend is even clearer in the layout of OpenClaw. Meitu has launched Meitu CLI, with eight AI Skills integrated into OpenClaw. The company positions itself as imaging infrastructure in the AI era. Wu Xinhong defines OpenClaw as a capability distribution channel and revenue source.

Market concerns about the proliferation of external interfaces weakening Meitu's own product entry mindshare are addressed by Wu Xinhong's analogy to mobile phone manufacturers: No matter how much mobile phone manufacturers invest in imaging, they can only meet general needs, and product depth is limited. Meitu Xiuxiu's market share has steadily increased over the past decade of smartphone proliferation. OpenClaw provides general capabilities, while proprietary products offer deep capabilities, complementing each other.

When discussing the next two years, **Wu Xinhong believes the biggest uncertainty comes from agile startup teams, not large corporations**.

In the AI era, the biggest bottleneck facing all companies may lie within the organization itself. Technology and computing power are not the primary issues, but if the organization becomes redundant, inefficient, and bureaucratic, no strategy can be effectively implemented. **When AI significantly compresses the intermediate links of product production, those who can continuously discover demand and continuously reach users at low cost will gain the upper hand in the next round of competition.**

## The following is the transcript of the dialogue between Meitu CEO Wu Xinhong and Wallstreet Insights · Ying AI, **focusing on core issues such as AI's enhancement and weakening of Meitu, product methodology, productivity commercialization path, token consumption and revenue structure changes, OpenClaw ecosystem positioning, and the competitive landscape**.

**Ying AI: Meitu has recently attracted a lot of market attention. The company has seen many positive changes in both AI product iteration and productivity tool expansion, but on the other hand, market divergence is also significant, especially with the increasing power of AI. Is a company like Meitu being weakened or strengthened by AI? Today, we'd like to have a more in-depth discussion on this topic.**

**Let's start with a major point of divergence: Is AI weakening or strengthening Meitu? A core voice in the market is that as large models' multimodal Agent capabilities become stronger, will products like Meitu be replaced or strengthened? From the company's perspective, what has been the biggest internal change felt over the past 6 to 12 months, and which demands have indeed been covered by AI, while which have become stronger?**

Wu Xinhong: First of all, we believe Meitu is a beneficiary of AI, not a company being eliminated by AI. Looking at our own products, for example, in lifestyle scenario products, the rapid advancement of AI or model capabilities has contributed to our overall user experience and rapid growth in overseas markets. It has significantly shortened our R&D cycles for new features and effects, allowing us to quickly translate these model capabilities into user-friendly features and effects. Therefore, over the past year, many of Meitu's products have repeatedly ranked high on app store charts globally, also with the help of AI development.

Although models themselves have increasingly strong multimodal capabilities, they struggle to deeply explore certain vertical scenarios in productivity tools, which provides opportunities for application development companies like Meitu. This is because we understand that models are more capable of solving various general needs, but once they delve into vertical scenarios or industries, they find it difficult to fully satisfy those needs. Thus, our productivity tools have also seen rapid growth following the rapid improvement of model capabilities, such as Meitu Design Studio, Kai Pai, and RoboNeo.

Our internal perception is that AI has greatly helped Meitu, and from our understanding, models and applications are not in an adversarial relationship; it's not that models will devour applications, but rather they occupy different ecological niches and are complementary.

**Ying AI: If management were to choose just one most important operational metric to prove to the market that Meitu has been strengthened rather than weakened by AI, what would that metric be?**

Wu Xinhong: I think if we look at the core metrics, it would still be profit growth. Why? Because this indicates that users are voting with real money to support a product. This profit growth might largely come from subscriptions, and partly from token consumption, both of which stem from recognition of the product, leading them to spend money on it.

Furthermore, profit growth actually reflects our economies of scale. Only with a sufficiently large user base and sufficient technological investment can we achieve economies of scale and thus have better product competitiveness.

**Ying AI: Can it be understood that AI, on one hand, reduces costs, and on the other hand, the development of AI technology provides Meitu with more supply capacity? Previously, users faced high learning costs in certain scenarios, but with AI, the threshold has been greatly lowered, allowing companies like Meitu to offer more products to the market, leading to a greater explosion of user needs and thus more revenue, subscriptions, or token usage for Meitu. Is this understanding correct?**

Wu Xinhong: That understanding is correct.

**Ying AI: So, over the past 6 to 12 months, could you specifically discuss the quantifiable efficiency improvements in Meitu's content work due to AI, such as R&D cycles, launch speed, or trial-and-error costs?**

Wu Xinhong: First, let's talk about internal AI usage scenarios, such as AI programming, AI marketing, AI testing, and AI translation. Many of our workflows are being AI-driven, which allows us to allocate more resources to verifying innovative projects. Perhaps our headcount has decreased, but the products we are developing have increased. This is essentially the significant efficiency improvement brought by AI across various work segments.

For example, AI programming allows many engineers to become full-stack engineers. Previously, an engineer might specialize in C++, with expertise in various programming languages or front-end development. Now, they can use AI programming to master the writing of various scripts, transitioning from writing code themselves to directing AI to quickly complete code writing and verification in different domains.

Designers are also experiencing this. Previously, many designers focused solely on design. Now, many designers have become super-individuals because they can also use AI programming to independently build a product.

Therefore, we are currently conducting a Meitu Product Challenge, where some designers have created stunning AI products single-handedly, which was unimaginable before.

Previously, achieving product completeness and product strength required a team of at least twenty to thirty people, whereas now, a single designer can independently complete all the work. I use engineers and designers as examples because many companies have these two roles.

**Ying AI: Perhaps people say that with the advent of AI, there are more "one-person companies." In fact, internal feedback has also indicated this issue. Let me ask a rather sharp question: if one person can now do what previously required a team of twenty to thirty people, how should the relationship between individuals and companies be handled? If one person can create a product with an annual revenue in the millions, there's no need for them to stay within a company structure, as their individual income would be higher. How should this relationship be balanced?**

Wu Xinhong: It seems that way, but in reality, Meitu encourages many people to create AI-native applications through internal incubation.

This is because we have significant advantages and investments at the company level, such as our thousands of engineers working on the construction of the entire image middle platform. This allows our colleagues, even if they are working alone to build a product, to fully utilize the middle platform capabilities provided by the company.

However, if they were to start their own venture externally, the challenges would be much greater. Firstly, the middle platform capabilities, accumulated over years by a team of thousands of engineers, can be rapidly reused in innovative imaging products, whereas externally, everything has to start from scratch.

Secondly, we have certain channel advantages. If they build within the company, for instance, we have 280 million monthly active users, allowing us to reach the target user group for the product with relative precision and achieve lower customer acquisition costs. So, although one person can accomplish the task, it's entirely different to do it externally versus within the company, because the scaled investment and channel advantages within the company are the foundation for the success of many products.

**Ying AI: The data accumulated by the company over the years is its moat in the AI era. Additionally, the accumulation in user reach over these years also constitutes an advantage. Therefore, AI cannot easily replace the company in the upstream data and downstream reach segments, while the middle part, product production, can be significantly improved by AI. Is this understanding correct?**

Wu Xinhong: Yes.

**Ying AI: Moving on to overseas growth, Meitu's performance overseas, whether with AI photo collage or RoboNeo, has been very impressive recently. Are these due to individual feature-driven temporary hits, or has the company developed a replicable overseas methodology that can consistently produce hit products?**

Wu Xinhong: We believe it's the latter – the company has a progressively refined methodology to support us in consistently creating hit products or features.

For example, RoboNeo's popularity in countries like Brazil and others recently is not the first time it has seen such success. This is actually built upon the continuous iteration of these product capabilities. We have also invested significantly in global social media marketing, developing our own methodology.

**Ying AI: How does the company internally determine if it's a one-time traffic bonus or if the user quality is genuinely improving?**

Wu Xinhong: It's simple. For example, if a product explodes in popularity, and if only user growth occurs without revenue growth, it might not be a very healthy state. However, if you find that after the explosion, users and revenue grow in tandem, then it's very healthy, indicating that people are using real money to purchase memberships or consume tokens on the platform.

Another factor is retention. If you create a hit product, are these users still using and paying for it after one month, two months? This also reflects whether you truly have product strength or if it's just a fleeting success.

**Ying AI: In the European and American markets, does Meitu focus more on user scale or on higher willingness to pay?**

Wu Xinhong: The European and American markets inherently have a strong willingness to pay. Therefore, we focus on both. In other words, in the European and American markets, as long as there is good user growth, it generally brings corresponding revenue growth. So, basically, as long as you can achieve user growth in areas with a high willingness to pay, revenue is generally not an issue.

**Ying AI: Regarding the productivity business, people are interested in which scenario will be the first to succeed. Among Meitu Design Studio, Kai Pai, and e-commerce material related capabilities, which type of scenario has formed a commercial closed loop first?**

Wu Xinhong: The first to form a commercial closed loop is the e-commerce design scenario, represented by Meitu Design Studio. Why? Firstly, the e-commerce industry is highly competitive, which squeezes profits, thus making e-commerce sellers more eager for tools that can help them reduce costs and increase efficiency.

Secondly, the e-commerce design scenario is highly digitized. In traditional e-commerce design teams, most tasks can be replicated in a digital manner. This allows AI teams to replicate the work of traditional teams. Because it is highly digitized, AI teams can achieve automation, further helping e-commerce sellers reduce costs and increase efficiency. Of course, this is just an example, and there are likely many other scenarios that meet these criteria.

**Ying AI: This means that, similar to other scenarios, if it can be standardized, B2B scenarios are very easily replaced by AI?**

Wu Xinhong: It's not just standardization, but also digitization. Digitization is crucial. Even if a task is standardized, if it relies on offline labor, such non-digitized aspects are difficult to turn into automated products.

Many tasks might require intensive human intervention, making them more challenging.

**Ying AI: If we break down the revenue structure further, will the growth of Meitu Design Studio in the next one to two years come from new users, increased usage frequency, more computing power consumption, or higher average revenue per user (ARPU)?**

Wu Xinhong: Meitu Design Studio is far from reaching its user growth ceiling, meaning there is still significant growth potential in the global market. Therefore, I believe the primary driver will be user growth.

**Ying AI: Has the consumption per user significantly increased recently?**

Wu Xinhong: We are observing a rapid increase in token consumption from users in productivity scenarios, meaning user ARPU is rapidly increasing. This is because the rapid evolution of current model capabilities has led to better output quality and stability, turning what was once conceptual into increasingly usable and widely applied in their work domains.

**Ying AI: What are the typical usage scenarios for these high ARPU values?**

Wu Xinhong: For example, Meitu Design Studio is a typical scenario. E-commerce users' design needs generally lead to higher ARPU.

For instance, as an e-commerce seller, we must distribute our products across all channels. We don't just sell on one platform; we definitely deploy on platforms like Shopee for cross-border sellers, which means creating materials adapted for various platform requirements. The volume is considerable.

**Ying AI: If we take a broader perspective, say looking ahead to 2030, what do you anticipate to be the biggest challenges for Meitu? Would it be supply bottlenecks, computing costs, or something else?**

Wu Xinhong: I believe the biggest bottleneck for a company is likely to be its organization. Other factors like technology and computing power are less likely to be bottlenecks. However, if the organization becomes redundant, inefficient, bureaucratic, complacent, or arrogant, it will severely impact the company's competitiveness. Therefore, when embracing AI, the first thing we must reform is the organization, otherwise, even with set goals, the organization's misalignment will prevent execution.

**Ying AI: Meitu has launched Meitu CLI, with eight AI Skills integrated into OpenClaw. What is the company's vision for OpenClaw?**

Wu Xinhong: Our positioning is to become a provider of fundamental image capabilities or infrastructure in the AI era. Therefore, in addition to developing our own standalone products, we also export our capabilities.

Since token consumption is gradually becoming an important revenue source for many companies, our goal is to act as an infrastructure provider for AI-era imaging capabilities. We hope that OpenClaw, and other products, will serve as our capability distribution channels and revenue streams.

**Ying AI: As OpenClaw becomes a unified way to call Meitu's capabilities, will this reduce the reliance on Meitu's own web pages and potentially weaken the company's entry mindshare and subscription relationships?**

Wu Xinhong: Yes, we provide more general capabilities. When these general capabilities are called upon, they might be combined with other capabilities. However, our own products offer deeper capabilities.

For example, mobile phone manufacturers highlight imaging as a product selling point. Theoretically, with the numerous features already provided by phone manufacturers for photos and videos, is an independent imaging product like Meitu Xiuxiu still needed? Yes, it is. Because no matter how much phone manufacturers invest, they cannot satisfy all imaging needs; they are destined to focus on general public demands, thus limiting their product depth. Therefore, we complement the foundational capabilities of phone manufacturers by offering more in-depth and innovative features. Over the past decade of smartphone proliferation, Meitu's market share has steadily increased, demonstrating the effectiveness of this complementary strategy.

Similarly, in the AI era, while people can call upon these foundational capabilities, our existence as an independent product has its value. Therefore, we don't foresee a significant impact on entry mindshare or revenue.

**Ying AI: Will the expansion of the OpenClaw ecosystem accelerate Meitu's transition from a purely subscription-based model to a hybrid revenue model?**

Wu Xinhong: Yes, our business model has always been hybrid. For instance, early on, Meitu only had referral models, such as advertising and e-commerce referrals. In 2018, we experimented with subscription models, so it's an accumulation: referrals plus subscriptions.

Now, it has extended to token consumption. Increasingly, companies are considering token consumption as a significant metric. We ourselves are observing a rapid increase in token consumption for some productivity products and a quick rise in ARPU. Clearly, this will become a major revenue source for us in the future.

**Ying AI: Within OpenClaw, other imaging companies will also offer related Skills. What is Meitu's competitive advantage in these external interfaces?**

Wu Xinhong: Meitu has been developing imaging products for 18 years, with large-scale engineering investment from thousands of engineers. Therefore, even if other options exist in the market, we are confident that our capabilities, especially in areas where we excel, will surpass those of comparable products. We are indeed confident about this.

**Ying AI: The market is very interested in Meitu's collaboration with Alibaba. To date, in which areas has this partnership initially yielded quantifiable benefits?**

Wu Xinhong: You've essentially mentioned our collaborative examples, but as both companies wish to maintain confidentiality regarding specific partnerships, we don't have many external details to share at this time. However, current observations indicate complementary and synergistic effects.

**Ying AI: Finally, from a longer-term perspective, which areas are you willing to invest more heavily in to proactively gain market share, and which areas are you less interested in participating in?**

Wu Xinhong: First, let's discuss our target user groups and corresponding growth strategies. Nearly 80% of Meitu's users are in lifestyle scenarios, such as with Meitu Xiuxiu, Meiyan Camera, and Wink. They form Meitu's user base and provide a significant channel advantage. On this foundation, Meitu has the opportunity to extend upwards, for example, from serving the general public in lifestyle scenarios to reaching intermediate professional consumers or SMBs (Small and Medium-sized Businesses).

Why do we choose to extend to intermediate user groups? Because we have the opportunity to reach these user groups from our existing base of 280 million monthly active users, as they might also be users of other Meitu scenarios.

Their demand for productivity tools is very strong, and their willingness to pay is relatively high. Therefore, we observe that products serving these professional consumers and SMBs generally have a higher subscription penetration rate and ARPU compared to lifestyle scenarios. These are excellent demographics, with a sufficiently large base and easier to reach from existing users, leading to lower customer acquisition costs. Their demand is inherently strong, and many of their needs are not yet well-met. Thus, I would say Meitu will focus more on this demographic in the future.

As for Adobe, they serve professional users and large enterprises. If we were to directly target professional users or large enterprises, there would clearly be a gap, making it difficult to acquire customers at a low cost from our existing user base. Furthermore, these professional users or large enterprises have very high product requirements, as they have used many top-tier products and possess strong professional skills, which might make it challenging for us to serve them. Therefore, choosing the intermediate demographic is a relatively rational consideration. As our capabilities accumulate over time, perhaps one day we will have the capacity to serve these professional users and large enterprises, which would be a natural progression.

**Ying AI: So, Meitu still aims to continuously explore more niche markets within its existing user base, especially high-ARPU markets for customer acquisition. Is that correct?**

Wu Xinhong: Yes. Moreover, in recent years, most of Meitu's new products have emerged from insights derived from existing products. For example, Meitu Design Studio originated from Meitu Xiuxiu. Through user interviews, we found that some users were using Meitu Xiuxiu for processing product images. Further interviews revealed a significant demand for e-commerce material design and a strong willingness to pay. Similarly, we observed that the teleprompter feature became the third most subscribed function in Meiyan Camera. Interviewing typical teleprompter users revealed they were primarily vloggers who desired an independent product to facilitate video creation, significantly lowering the barrier.

I'm using these examples to illustrate that we gain insights from existing products to identify opportunities for new products, and then leverage our established channel advantages for low-cost customer acquisition. I believe this is where Meitu has a significant edge.

**Ying AI: I personally used to wonder why there were so many sub-products under one Meitu webpage. Now that you've explained it, I understand. In essence, each new Meitu product acts as a discoverer of the demand for the next product, continuously identifying niche user needs within the product, defining the next product accordingly, and iterating constantly.**

Wu Xinhong: Yes. Regarding new products, we developed many products in the early stages of the mobile internet era, but only a few have survived to be seen today, such as Meitu Xiuxiu and Meiyan Camera. These are the ones that ultimately emerged from numerous product endeavors. Therefore, with the advent of a new era like AI, it's difficult to definitively say that one product will serve all needs and become the ultimate entry-level AI application. Thus, we can only leverage our existing advantages to validate AI application opportunities. In the future, it's not impossible that an entry-level AI application product will emerge, although this is purely speculative.

**Ying AI: If we look ahead to the next two years, specifically 2026 to 2027, which indicator does management have the most confidence in exceeding expectations, and conversely, which indicator do you worry might fall short of market expectations?**

Wu Xinhong: Firstly, we are confident in improving the subscription penetration rate and ARPU for our productivity tools. We have already observed significantly higher subscription rates and ARPU in several products compared to lifestyle scenarios. This is a very important indicator, signifying that users highly recognize the value and results delivered by the product, leading them to subscribe and consume tokens. Therefore, we will pay close attention to these two metrics, as our productivity tools are currently goal-oriented towards delivering results. We do not currently have any metrics that we are worried about falling short of expectations.

**Ying AI: For ToB services or those directly tied to customer business, as long as the ROI is justifiable, it's quite easy to exceed expectations or monetize effectively.**

Wu Xinhong: Yes, we must genuinely deliver high-quality results to users and significantly reduce their costs before they develop a strong willingness to pay.

**Ying AI: What is the biggest uncertainty management is concerned about for the next two years?**

Wu Xinhong: We believe that large corporations may not necessarily be the most concerning competitors. Instead, it is likely to be many very agile and highly competitive startup teams. The products they create might closely resemble what we are currently doing. We have observed that the market competitive landscape is largely driven by external startup teams.

**Ying AI: Meitu's strengths lie in the upstream and downstream segments. Will the middle part become more fiercely competitive in the next two years due to enhanced AI capabilities?**

Wu Xinhong: Competition may be both fierce and not fierce. Why? Because we see that some sectors are very crowded, with everyone rushing to create tools for short video dramas or comic creation using AI, or for e-commerce. Our Meitu Design Studio is also in the e-commerce design sector, and it must be admitted that this sector is quite crowded.

Instead, we feel that many vertical scenarios are not well-served, or perhaps there are no corresponding products at all. Therefore, the current crowding is only localized. Looking at the broader AI ecosystem, there are still numerous opportunities to establish a foothold by finding relatively less competitive vertical scenarios.

**Ying AI: Thank you very much for your sharing today. I believe there are several key takeaways from this discussion: how the company defines the boundaries of AI's impact on Meitu's core needs, how OpenClaw is reshaping the company's long-term business model, how to discover needs in the AI era, and Meitu's position, landscape, and advantages in the upstream, midstream, and downstream competition. I am confident that these issues will continue to be important observation points for the market following Meitu.**

This article is from the WeChat Official Account "Ying AI". For more AI frontier information, please click here.

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