Chinese generative AI tools, led by DeepSeek, are highly favoured by overseas users.
The penetration rate of domestic generative AI tools is rising rapidly overseas, with products like DeepSeek, Talkie AI, and ByteDance’s Cici ranked as the top three in monthly active users (MAU) for Chinese generative AI tools in overseas markets as of May 2025.
Hybrid monetisation models can help generative AI companies achieve higher profitability.
Each individual monetisation model has pros and cons. Currently, monetisation is shifting from single-model subscriptions to hybrid models that not only enhance the lifetime value (LTV) of non-paying users but also preserve a premium experience for core users. This enables improved user retention while unlocking greater commercial value.
Europe and North America offer more mature payment environments, while emerging markets hold greater potential.
Mature digital economies, strong willingness to pay, and demand for cutting-edge technologies make Europe and North America top priorities for AI expansion. However, tech giants in these regions dominate the market with early-mover advantages and strong ecosystems, creating high barriers to entry. Meanwhile, emerging markets like the Middle East and Latin America are experiencing explosive growth in digital demand, making them high-potential growth markets for global expansion.
Compliance is foundational for global expansion—data and tax governance are critical.
Generative AI companies must treat compliance as a strategic prerequisite. For data compliance, companies must adapt to fragmented global regulations and establish a tiered governance framework. On the tax side, managing cross-border policy differences and proactively mitigating risk are essential to ensuring the legality and sustainability of global operations.
Generative AI, trained on deep learning and large-scale datasets, identifies and mimics patterns and features within data. It focuses on automating or assisting in the generation of creative, independently usable content that meets user needs (such as conversations, stories, images, videos, and music), rather than merely performing analysis or decision-making tasks based on preset rules.
Currently, most user-facing generative AI tools are concentrated in two areas: content creation and interactive experiences.
Content creation
Generative AI tools in the field of content creation primarily produce outputs such as articles, images, videos, etc. Their core goal is to enhance creative efficiency and quality by automating or assisting in the generation of independently usable creative content. The core capabilities of AI in this field are replacing repetitive labour, inspiring creative ideas, and enabling cross-media transformation. Users remain the “directors” of content creation, guiding the generative AI through instructions specifying output requirements and optimisation directions. High-quality output heavily relies on “precise input instructions.”
e.g., a basic instruction like “Write an article about climate change” yields generic results. An advanced instruction like “Using a science fiction approach, describe Shanghai’s technological solutions for rising sea levels in 2080, incorporating details on carbon capture technology” produces content with professionalism and narrative depth.
Interactive experience
The core output of generative AI tools in the field of interactive experiences is dynamic human-computer interaction, including virtual character generation, virtual conversations, AI companionship, etc., focusing on real-time interactive experiences. Their core function lies in building virtual environments and intelligent characters that respond to user behaviour, generating content that changes in real-time during the interaction. This creates immersive, personalised scenarios, where the AI acts as either an environment or character builder. Users in this process are both “consumers”, receiving real-time AI-generated feedback and “participants”, directly shaping the interaction through inputs like language or actions.