Assessing Chinese GenAI Applications: Why Invest and What to Consider? (1 of 2)

Last year, when Sand Hill Road funds went all-in on GenAI, I started to feel somewhat anxious. I was previously focused on Deep Tech investment, and I had to push myself to pivot into GenAI investing. From paying attention to a new sector to deal sourcing and researching, and then to hands-on investing, there are significant gaps to fill in. This year, after DeepSeek and Manus showing their disruptive capabilities, almost every Chinese investor started looking into AI. There was no longer any diverse opinion, and my anxiety deepened.

When meeting with founders, I found out that many of them are Gen-Z people like me, but with more first-hand knowledge about GenAI than I have. This is a whole new founder persona, different from the Deep Tech founder that I am used to co-work with. I feel easy to empathize with these founders, since I have nearly the same age as them. But I still have encountered challenges as follows:

1. The industry is immense, spanning from AI infrastructure to all kinds of AI applications. From which sectors should I seek entry points?
2. Are there technological barriers inherent in AI applications? If technological barriers are absent, what alternative forms of competitive advantage should AI applications develop?
3. As large language models (LLMs) continue to advance in capability, will many currently popular products ultimately prove to be merely transitional in nature?
4. Should I invest in AI-Native applications or AI-Powered applications?

First question:

The industry is immense, spanning from AI infrastructure to all kinds of AI applications. From which sectors should I seek entry points?

• We are presently in the nascent stage of the GenAI era, presenting significant opportunities to invest in leading players. These prominent companies typically exhibit distinct characteristics, such as focusing on consumer-oriented products and targeting the largest possible user base. Notably, there is currently a divergence in value consensus between China and the United States regarding AI. While American technology giants concentrate primarily on foundational models, numerous teams in China have begun to develop applications and solutions.
• The underlying secret conferring an advantage to Chinese AI companies is frequently attributed to two key factors. First, China possesses an abundance of engineers, which enables rapid product development and iteration. Second, there is also a substantial pool of highly experienced product managers within the country. If one were to speculate whether the next historically viral app, akin to TikTok or Pinduoduo, would emerge in China or the United States, the prevailing consensus would likely favor China. This perspective is rooted in the extensive product development expertise accumulated during the PC Era and Post-PC Era.The rationale for referencing TikTok and Pinduoduo, as opposed to WeChat or Alipay, lies in the discernible distinction between what I called, Post-PC Era 1.0 and 2.0. A tangible manifestation of this difference is that TikTok and Pinduoduo(Temu) have demonstrated the capability to achieve dominance in overseas markets, whereas WeChat and Alipay have not. As representatives of Post-PC Era 2.0, products such as TikTok and Pinduoduo exemplify how quantitative accumulations in product design can ultimately lead to qualitative transformations.
• Thus, a third meaningful point is to focus on industries where China already has an advantage, such as e-commerce (especially short-form video commerce and live commerce), and creative industries (short-form video, novels, comics, animation, gaming and so on). Should AI applications achieve widespread adoption in these sectors in China, it is anticipated that their international adoption will be comparatively straightforward. Essentially, this is about learning from leading players.

Second question:

Are there technological barriers inherent in AI applications? If technological barriers are absent, what alternative forms of competitive advantage should AI applications develop?

• This has been a persistent confusion since I pivot into investing AI. Deep tech companies can establish technological barriers for a period, allowing first-mover to convert technological barriers into other advantages. In slower-iterating industries, technological barriers can be converted into economies of scale. And in faster-iterating industries, robust customer relationships and continuous product iteration can serve to sustain technological barriers.
• In the context of AI applications, there do not appear to be particularly evident technological barriers. This raises the question: what alternative forms of competitive advantage should AI applications develop? What core capabilities are companies fundamentally competing on?
• Companies differentiate themselves by the depth of their understanding of users. Achieving this requires first clearly identifying the core user group. Whether in software or hardware, great companies typically begin by developing a SOTA product tailored to a specific niche market, thereby capturing that segment comprehensively. As additional customers recognize the product’s applicability to adjacent niches, the market naturally expands and the product achieves broader adoption. Companies such as DJI and Shokz exemplify this approach. In the early stages, the core user base does not need to be large, but their persona must be clearly defined. Only with a precise user persona can a company distill the most streamlined product definition and deliver a best-fit, SOTA solution for its users.
• Once the user persona is established, the next thing is about finding user needs. Even with a clearly defined user profile, there will inevitably exist both urgent and less urgent user needs. A genuine and urgent need is often characterized by a misalignment between the tasks on which users spend most of their time and those that generate the greatest value—where substantial time is devoted to routine or menial activities, while comparatively little time is spent on value creation. If a product can enable users to focus their efforts entirely on value creation, delegating repetitive or low-value tasks to AI, the overall value generated by users will be significantly amplified. This principle is consistent with the philosophy behind Vibe Coding and can be extended to broader concepts such as Vibe Creating or even Vibe Anything, forming a fundamental logic for how AI is poised to reshape applications.
• Finding user needs is like cracking a safe—listening carefully, trying repeatedly, and finally unlocking it. Once needs are identified, These needs should be a guidance to product definition. Product definition involves determining what to prioritize and what to exclude. It becomes possible to streamline the product by eliminating unnecessary features and selectively introducing key enhancements, thereby leveraging critical advantages to expand the user base. Throughout this process, it is also essential to recognize the limitations of AI, understanding both its capabilities and constraints. By adopting a multidimensional approach to user interaction and incorporating diverse functionalities from an engineering perspective, one can effectively compensate for AI’s shortcomings and deliver a comprehensive product experience.
• If a company possesses the aforementioned capabilities and has established a system to ensure sustained excellence, it should be able to secure a distinct product-level advantage. AI applications iterate rapidly and are relatively difficult to replicate; without accurate insight into core user needs and continuous innovation, competitors cannot easily duplicate or deliver a comparable user experience. Beyond user insight, what additional qualities should startups possess? Can the success of TikTok and Pinduoduo be distilled into key characteristics of exemplary products?
• TikTok exemplifies strong user engagement, underpinned by two core features: personalized recommendations and a vibrant content community. Personalized recommendations continually enhance the user experience, making it increasingly enjoyable and precise, thereby creating a data-driven flywheel effect. The content community unites creators and consumers, establishing a dual-sided network effect in which growth on one side increases value for the other. This dynamic attracts additional users and generates a second reinforcing flywheel.
• Pinduoduo represents community marketing and viral growth. Campaigns like UGC content seeding and check-in marketing are also forms of community marketing. At a deeper level, these strategies are fundamentally about persona building and trust-based dissemination. For example, sharing one's running route image using Keep, a Chinese fitness tracking app similar to Strava, builds a persona of fit and disciplined, while also reinforcing the brand association with Keep, resulting in a win-win and encourage sharing. Platforms like Substack and Tencent IMA employ analogous strategies—paid knowledge sharing not only fosters a knowledgeable persona but also generates tangible income, further reinforcing user recognition of the platform.

To Be Continued...