Zilan Qian is a fellow at the Oxford China Policy Lab and an MSc student at the Oxford Internet Institute.

This “AGI Bar” recently opened in Shanghai, where people openly poke fun at the hype surrounding AGI by stating that this bar is “all about bubbles.”

Not many people would point to this bar and say that China is racing towards AGI. Otherwise, the U.S. has zero chance of winning, because AGI is diffused to even bars in China. AGI is a buzzword for business in this context, period.

This is the consideration needed for people who want to know whether China is taking AGI seriously. Before you ask anyone who works on China and AI how AGI-pilled China is, ask yourself two questions: what do you mean by AGI, and who do you mean by China?

This post provides one piece to the picture by looking into a giant AGI wiki made by an open-source community in China. As this piece will show that, for AI hobbyists in China, “AGI” stands for Western tech aura and a desire for quick money.

What is “Way to AGI”?

Created in April 2023, the “Way to AGI” wiki is a collaborative knowledge hub hosted on the Bytedance-developed platform Feishu 飞书 (known internationally as Lark). It functions much like a shared giant Notion workspace — users can upload documents,1 create events, and leave comments on each other’s posts.

Since its launch, the wiki has attracted over 2 million unique visitors and generated 4.5 million total views for its front page. For context, the actual Wikipedia page on “artificial general intelligence” received about 2.1 million views globally during the same period.

The wiki is maintained by the Way to AGI community, an open-source AI collective boasting 8 million members interested in AI and 200,000 active developers,2 according to data published on its community forum. While slightly smaller than the largest AI-focused subreddit, r/ChatGPT (11.2 million members), it far exceeds r/OpenAI (2.5 million members) and the r/agi subreddit (82,000 members)3. The community appears to receive implicit support from tech companies, notably ByteDance — which owns both the Feishu platform and Coze, an AI app frequently discussed on the wiki. It also claims to form collaborations with other tech organizations and AI startups like Alibaba, Huawei, Tencent, Zhipu AI, and Moonshot AI.4

Driven by the belief that “AI will reshape the thinking and learning methods of everyone, and bring them unprecedented powers,” the group shares a wide range of AI-related resources on this wiki as part of its collective journey — the “way to AGI.”

Or so they believe they are. This is a “Way to AGI” if and only if the following formula holds:

1. AGI = Silicon Valley

“When you look long into an abyss, the abyss looks into you.”

The AGI community may not be AGI-pilled, but they are definitely Silicon Valley-pilled. Discussions, learning paths, and citations overwhelmingly reference Western, especially Silicon Valley, sources. “AI leaders”, recommended podcasts, and must-listen talks come predominantly from the other side of the Pacific Ocean.

Proof 1: Silicon Valley > Nobel/Turing Prize > Chinese CEOs >> Musk: Ranking the AI leaders

The wiki has a “top AI leader” leaderboard, which is regularly updated to include the top voices of what are perceived as “AI leaders” worldwide.5 On this board, Silicon Valley dominates by a landslide. Satya Nadella (Microsoft), Jensen Huang (Nvidia), Jeff Bezos, and Sam Altman lead the rankings, with Stanford’s Fei-Fei Li placed even higher than the three canonical AI “godfathers” — Geoffrey Hinton, Yann LeCun, and Yoshua Bengio.

The first China-based figure on the leaderboard is Robin Li 李彦宏, Baidu’s CEO, ranked ninth (Times AI 100 2023). His high position is somewhat surprising, given that ERNIE, Baidu’s flagship LLM, isn’t considered China’s strongest model. But Baidu has been an OG player in China’s AI ecosystem, investing in research long before the current LLM wave. It has also invested in full-stack AI development, including the recent open-source AI platforms PaddlePaddle 5.0 and Baige 4.0.

Other Chinese names on the list include:

• Liang Wenfeng 梁文峰— CEO of DeepSeek (Times AI 100 2025)

• Zeng Yi 曾毅— Professor on AI ethics, Chinese Academy of Sciences (Times AI 100 2023)

• Wang Xingxing 王兴兴— CEO, Unitree Robotics (Times AI 100 2025)

• Chen Tianshi 陈天石— CEO, Cambricon Technologies (AI chips)

• Xu Li 徐立— CEO, SenseTime

• Liu Qingfeng 刘庆峰— CEO, iFlytek

• He Kaiming 何恺明— MIT Professor

In total, seven people from China made the top 26 list compiled by Chinese AGI watchers themselves, with mostly CEOs from private tech companies, and several do not explicitly focus on frontier AI research. The list is likely also heavily influenced by Western rankings, as at least 23 of the 26 have appeared in the Times 100 AI rankings during 2023-2025. (’s Metis list does not appear to be an influence…). Profile photos of Clem Delangue and Marc Raibert are also directly taken from Times 100 AI 2023. However, the latest updated date (July) is before the release of Times 100 AI 2025, so the ranking foresaw Liang Wenfeng and Wang Xingxing’s debut on the 100 AI list.

Despite many of these “leaders” being AGI-pilled, the ranking itself is not. With each leader having one selected quote to highlight their beliefs in AI, only two of the 26 selected quotes discuss AGI. Others focus on AI’s commercial promise, industry potential, and future trends. For instance, the selected quote from Liang Wenfeng, likely one of the most prominent voices in China advocating for AGI, is about open source as a strategy for both commercial value and brand reputation.

Proof 2: Commercial Success > Technical Depth >> AGI Research: Curating Western AI Voices

While hero-worshipping Silicon Valley leaders might be dismissed as superficial fandom, the community’s choice of information sources reveals deeper structural biases.

The section of “recommended foreign information outlets” has 129 sources, with 24 starred as must-read recommendations. Stratechery tops the list, while Lex edges out Dwarkesh. Most of the recommended sources have deep Silicon Valley associations, with one-third focusing on investment. The rest are C-suite executives or top researchers from big-name tech companies like OpenAI, Google, and Nvidia. Although some of the figures from big tech are AGI-focused, the list itself does not appear to be curated for AGI expertise. Rather, the even distribution of top profiles from big tech, mixed with prominent VC voices, reads more like a collection of Silicon Valley’s most commercially successful figures.

When we zoom out to the full list, the AGI flavor dissipates further. Among the remaining 105 sources, approximately 25-30% focus on investment, while 35-40% feature key figures from big tech companies and AI startups. About 15-20% come from U.S. universities, predominantly California institutions like Stanford, UC Berkeley, and Caltech. Around 10% consists of journalism and media outlets covering Silicon Valley and venture capital culture, while only a handful represent more independent technical sources like Stephen Wolfram, Nathan Lambert, Lex Fridman, Sebastian Raschka, and SemiAnalysis.6

Out of 129 total sources in a wiki titled “Way to AGI,” only three are explicitly AGI-focused: Eliezer Yudkowsky (founder of MIRI and LessWrong), Ben Goertzel (who helped popularize the term AGI), and John Schulman (chief scientist at Thinking Machines Lab and co-founder of OpenAI), with perhaps two others (Demis Hassabis and Ilya Sutskever) operating in AGI-adjacent territory. Thus, if one wants to “study AGI” through these sources, they are probably learning how big names in Silicon Valley think about AI. And while Silicon Valley thinks about AI in many ways, the most appealing one to this community seems to be how AI can be used to make money.

2. AGI = Quick Money Knowledge:

But emulating Silicon Valley success requires significant time and capital investment. For users seeking faster returns, the wiki pivots from Western voices to Chinese practice: offering step-by-step guides for building and monetizing AI products domestically. Eager novices come here for quick profits, while the “AI pros” they aspire to become are simultaneously seeking to profit from them.

Step 1: Learn just enough

Following the “syllabus” of this wiki, the first step is an introduction to AI, where it uses “what is ChatGPT…and why does it work” as a basic guide. From there, you then learn how to install and subscribe to ChatGPT (step-by-step from how to register a Google account to how to add your credit card, and of course, using a VPN7). There are seven “must-read” entry-level documents, six of which are Chinese translations of English sources, from the book “What Is ChatGPT Doing … and Why Does It Work?” to articles explaining transformer, stable diffusion, and diffusion models for video generation. The only original content is the seventh section, “Easily Understand 20 AI concepts,” which uses only two or three sentences in Chinese metaphor to explain each concept related to AI, from the chain of thought to the chatbot arena.

Not every introductory content is that introductory, but they are definitely “quick to learn” and extremely “practical”. You can master “Python + AI Without Coding Experience in 20 Minutes,” or know how to “gather LLM Data” through a 400-word article. For some reason, knowing how to select the best GPUs for model reasoning through comparing 38 kinds of Nvidia’s chips, including the H100 and A100, is also categorized as “entry-level content.”

Step 2: Developing “skills”

After (supposedly) mastering these “introductory” concepts, you can then dive into area-specific learning: AI agents, AI drawing, AI video, AI music, AI character + audio combination, AI 3D, ComfyUI workflow, or AI coding. Let us take “AI agents”, which seems to be one of the trending focuses for developers on their way to AGI now. Here, you will start with a Chinese translation of Maarten Grootendorst’s A Visual Guide to LLM Agents.

Then you will read guides on how to create your own simple “AI agents” without any coding through ByteDance’s Coze platform by only prompting a few lines of description of the agent’s characteristics. The guide will not teach you to create the next autonomous system that can navigate complex real-world tasks. Instead, it mostly shows you how to build AI chatbots that act like a language teacher, or an AI workflow that generates outreach emails based on company profiles.

Interested in building, but have no idea what to build? There are loads of examples and analyses showing you the potential of integrating these “AI agents” into different real-life scenarios, as well as analyses of what’s trending in the AI agent market right now. Here, AI chatbots, workflows, and agents literally mean the same thing. Participation matters more than precision under the buzzing excitement of AGI.

Step 3: Practice in contests

After learning how to create your AI “agent”, you can participate in various “Agent co-learning pop-up contests (智能体共学快闪比赛)” to exchange with other people about how to build better bots/agents. Some smaller contests and workshops usually range from a few hours to a day online, with participants entering their own “agents” and experienced developers as judges to see who the winners are. Winners of these small skill contests receive a virtual certificate of “the coolest AI agent.”

Meatier contests also exist, such as the “AI Agent Olympics 2025.” This “global” contest was co-hosted by Rednote, Weibo, Z.ai (which builds the frontier LLM GLM-4.5), and flowith.ai, with “Way to AGI” as one of the guest collaborators. Branding itself as “the first AI agent creation contest in 2025 worldwide,” the contest offers winners monetary awards (15000 RMB, or about US$2100) as well as social media exposure (via Weibo and Rednote). Despite sponsorship from Z.ai — the only AI startup in China openly claiming to be interested in AGI besides DeepSeek — and “Way to AGI,” there is no single mention of “AGI” on the contest website. Instead, the contest’s organizers state that “the rights to intelligence (智能) should not belong to any corporation, but instead should belong to a community of mankind (人类共同体),” with the last phrase strikingly similar to the CCP’s diction “a community of shared future for mankind (人类命运共同体).”

Don’t expect to see some crazily AGI-pilled individuals or the next DeepSeek founder in this contest. According to the bios of group members published on the platform, your peers will likely have some professional background related to AI, perhaps as a prompt engineer, as a product manager at a big Chinese tech firm, or as a full-stack developer. But you will also likely see people who were previously working as graphic designers, visual editors, or real estate agents — jobs that are very susceptible to AI replacement and were hit hard by China’s economic crisis — asking to form groups for related competitions. The poster of the AI Agent Olympics 2025.

Step 4: Believe that you can monetize your agents, while actually being monetized yourself

The way to AGI may be important, but perhaps the way to money is more important. The final step tackles the question of how to quickly monetize your new knowledge. Massive materials on product management are available in this section: how to understand and create demand for agents, where AI agents integrate into companies’ workflows, and experiences shared by so-called “AI agent product managers.” However, even with this general knowledge, there is still a real gap between your immature “AI agents” and AI products that can actually earn money.

There are many “AI pros” who first offer some free learning materials claiming to fill that gap. They will share some introductory content that showcases the great potential of the AI agent market and how easy it is for people with no background to make a profit. Later, they introduce paid core lessons that they argue offer “systemic structure, professional guidance, personalized plans, and feedback” for more efficient learning. Effectively, this so-called “open-source AGI community” becomes the first step for some people to hook novices into their closed-source AI coaching business.

For example, in the AI Agent co-learning section, one member “shares” a piece of great paid content she “recently came across” (she is likely the person who runs the paid course). The screenshot below is how she justifies having paid for lessons (up to 5000 RMB/700 USD) in the open-source community: “It is like exercising in your home or going to the gym for guidance. Different people have different demands. The open-source community offers a wealth of resources suitable for disciplined self-learners. Recently, there have been many new entries to this community, and everyone is asking if there are suitable entry-level courses. Compared to learning from the text in the wiki, most people prefer the teachers to teach step-by-step.”

3. AGI ≠ Deep and Grand Knowledge: The Abandoned Projects

The emphasis on quick monetization comes at a cost. Buried beneath the layers of get-rich-quick content lie the remnants of more ambitious intellectual projects, which now serve as evidence of the roads not taken on the way to AGI.

AGI≠ AI Research

This community did attempt serious scholarship. Early projects included comprehensive translations of Google DeepMind research papers, philosophical explorations tracing the concept of “agent” back to ancient Greece, and an ambitious database cataloging AI agent papers from research groups worldwide, complete with translated Chinese abstracts.

But these initiatives couldn’t compete with monetized content for sustained attention. The AI agent paper database, launched in mid-2023, aimed to index AI agent research papers, provide reviews, and translate English abstracts into Chinese, but was abandoned by December 2023.

AGI ≠ AI Governance

Another abandoned project is the “Global AI Law Handbook (全球AI法规手册).” Originally conceived as an ambitious project to track, summarize, and translate AI-related legislation worldwide, it ceased updating Chinese regulations in mid-2024 and coverage of other jurisdictions by late 2023. Lost within its archived pages are translations of significant policy documents: the official EU AI Act interpretation from 2023, the UK Parliament’s pro-innovation AI regulation framework, Biden’s AI safety and security standards, and the Blueprint for an AI Bill of Rights. Some of these regulations remain active today; others, like the project itself, have been abandoned.

The handbook section has since pivoted toward narrower, more commercially oriented content — focusing on practical AI copyright guidance in China, including analysis of AI-generated artwork copyright disputes, while increasingly hinting at paid legal consultation services for users.

AGI ≠ AGI: the missing debate

Perhaps the most telling irony of this massive “AGI wiki” is what’s conspicuously absent: any serious discussion of AGI itself. Among hundreds of documents covering everything from GPU comparisons to monetization strategies, only two articles specifically address AGI as a concept — both written by the same author reviewing industry trends in 2023 and forecasting those in 2024.

The 2023 review reveals the community’s priorities starkly: the author spent literally zero percent of the text explaining what AGI actually is, and dedicated one brief section to “the Road to AGI (迈向AGI之路)”, mainly to forecasting GPT-5’s 2024 release and near-AGI capability (both did not happen), synthetic data training, and emergent behaviors. Then he dives into five detailed sections on development trends and business opportunities.

The 2024 forecast still devotes its main content to analyzing business and investment trends in AI products. After devoting 75% of the article to business trends and 20% to geopolitics, the author finally begins to discuss how actors might control and monopolize AGI technology. However, this discussion ends up going nowhere, with the author pointing out how individual voices are increasingly unheard under grand narratives put forward to celebrate the promise of AI. “I don’t want to talk more about the problems of AGI, because there is no point simply talking about this problem.”

This article captures the irony of “Way to AGI” well. Even though this wiki is titled “Way to AGI,” serious analyses of AGI are packaged in massive amounts of business buzzwords to attract attention. Only glittering investment bubbles and Western tech jargon can survive along the way to AGI, while more serious learning finds no way out.

Rather than leading to AGI, this wiki serves as a way for individuals to feel empowered and hopeful by engaging in AI discussions driven mostly by business interests. The motivation that drives many to this platform — the economic anxiety from AI disruption and China’s macroeconomic recession — gets buried beneath the promise that “AI will reshape the thinking and learning methods of everyone, and bring them unprecedented powers.”

The deeper paradox is: while “Way to AGI” promises to empower people through AI and make the path to AGI accessible to everyone, the only serious discussion of AGI feels profoundly disempowered. The community’s only AGI analysis retreats from complexity and laments powerlessness in the face of larger forces. To some extent, this AGI wiki is similar to the AGI bar, where people indulge in bubbles and avoid reality. Perhaps only by avoiding serious engagement with AGI itself can people maintain the promise and excitement that AGI represents. The moment AGI becomes real, with its implications for power, control, and human agency, the bubble begins to burst.

is a fellow at the Oxford China Policy Lab and an MSc student at the Oxford Internet Institute.

On September 11, the U.S. Federal Trade Commission launched an inquiry into seven tech companies that make AI chatbot companion products, including Meta, OpenAI, and Character AI, over concerns that AI chatbots may prompt users, “especially children and teens,” to trust them and form unhealthy dependencies.

Four days later, China published its AI Safety Governance Framework 2.0, explicitly listing “addiction and dependence on anthropomorphized interaction (拟人化交互的沉迷依赖)” among its top ethical risks, even above concerns about AI loss of control. Interestingly, directly following the addiction risk is the risk of “challenging existing social order (挑战现行社会秩序),” including traditional “views on childbirth (生育观).”

What makes AI chatbot interaction so concerning? Why is the U.S. more worried about child interaction, whereas the Chinese government views AI companions as a threat to family-making and childbearing? The answer lies in how different societies build different types of AI companions, which then create distinct societal risks. Drawing from an original market scan of 110 global AI companion platforms and analysis of China’s domestic market, I explore here shows how similar AI technologies produce vastly different companion experiences—American AI girlfriends versus Chinese AI boyfriends—when shaped by cultural values, regulatory frameworks, and geopolitical tensions.

Sexy AI girlfriends: Made in America, for the world

In my team’s recent market scan of the 110 most popular AI companion platforms as of April 2025, we turned Similarweb and Sensor Tower upside down to gather data on traffic, company profiles, and user demographics. At the expense of one teammate developing an Excel sheet allergy and the shared trauma of viewing many NSFW images, we discovered that American AI girlfriends rule the roost in the global market for romantic AI companions: Over half (52%) of these AI companion companies are headquartered in the U.S., drastically ahead of China (10%) in the global market.1 These products are overwhelmingly designed around heterosexual male fantasies: another similar market report this year shows that 17% of all the active apps have “girlfriends” in names, compared to 4% of those with “boyfriends.”

We estimated that dating-themed AI chatbots, designed specifically for romantic or sexual bonding, capture roughly 29 million monthly active users (MAU) and 88 million monthly visits globally across platforms. For comparison, Bluesky has 23.2 million total users and 75.8 million monthly visits as of early 2025. And our estimation is very conservative: We did not count the traffic of platforms containing other kinds of companionships, such as Character AI, which offers AI tutors, pets, and friends, though we think many people go there to use AI boy/girlfriends. We did not count AI companion app downloads, which have reached 220 million since 2022. Nor did we include parasocial engagement with general-purpose AI like GPT-4o, which some people apparently have also fallen in love with.

Behind the explosive popularity of AI companions are two main engagement models. On one side are community-oriented platforms like Fam AI, where users create and share AI companions, such as customizable “girlfriends” in anime or photorealistic styles. These platforms thrive on user-generated content, offering adjustable body types, personalities, and voice/video modes to deepen connections. Users can create new AI characters with just a few paragraphs instructing the model how to act, similar to personalizing a copy of ChatGPT. Many of these platforms use affiliate programs — for example, craveu.ai pays users $120–180 for creating high-engagement characters. The abundance of options and the competition for attention encourage users to frequently switch between different AI companions, creating more transient digital relationships.

In contrast, product-oriented platforms like Replika offer a single evolving AI partners with deeper and longer emotional ties. On Replika’s subreddit, many users report using Replika for years, and some seriously consider themselves “bonded” and “married” to their Replika partner. People also grieve for the loss of their Replika when they sense a subtle personality change and suspect the system behind had reset their chatbots.

Despite differences in engagement style, both models seek to capitalize on sexuality to attract and retain users. The monetization of sexuality is done mainly through “freemium” models, offering a few free basic functions while charging for advanced features or additional services. Among the top ten most-visited AI companion platforms in our scan, 8 opt for freemium models, with only one currently free and one choosing advertising and in-app currency. Premium accounts typically offer unrestricted interaction and access to unblurred explicit images. They also allow the user to have longer conversations and improve memory capacity for previous conversations. Many mating companion platforms promote explicit ‘NSFW’ (not safe for work) companions, images, and roleplay features as part of the premium features.

Dynamic AI boyfriends: Made in China, for China

On the other side of the Great Firewall, AI is also probing the emotional boundaries of humans. While the underlying LLMs may not differ drastically from their English-speaking counterparts, the fictional worlds and characters that users build around them are strikingly distinct.

One of the most notable contrasts lies in gender dynamics. In the Chinese AI companion market, male characters dominate: most trending products are marketed as AI boyfriends, and leading platforms prominently feature male characters on their main displays, while female characters occupy a more marginal space.

But looks are not everything that makes humans appealing–the same holds for AI characters. While many platforms still follow the community-oriented model where users create and share AI characters, apps like MiniMax’s Xingye (星野), Tencent-backed Zhumeng Dao (Dream-Building Island 筑梦岛), and Duxiang (独响), built by a startup, go beyond the basics. In addition to customizing AI companions’ personalities, users can generate themes, plots, and side stories, deepening immersion for themselves and others. Conversations are no longer limited to 1:1 exchanges: users can participate in group chats with multiple AI companions (1:N), and AI characters may even send messages to users when they are not using the app, similar to app notifications.

These AI companion products also draw insights from existing popular gaming cultures in China, such as card-drawing games that already have million-dollar markets. For example, Xingye allows users to generate 18 cartoon cards for one fictional character, adapting Japan’s popular gacha game mechanics and trading card culture for AI companions. In gacha games, players pay to randomly draw digital cards or characters, with rare editions commanding premium value. Chinese livestreamers have imported this model, streaming card draws on social media while viewers pay to test their luck for limited-edition collectibles tied to major intellectual properties. Similar to gacha games, AI-generated cards add an element of mystery and excitement when revealed. Users can also create and trade AI character photos on the platform, mimicking real-world card-collecting transactions.

The real monetary transactions occur through a combination of in-app currency and freemium models. Users purchase currency to buy cards and can upgrade to a monthly premium for more chances to generate AI cards, additional free in-app currency, and shorter wait times for conversations (a delay partly caused by limited compute capacity for Chinese LLMs). Card creators can also earn 2% of the revenue from the cards they sell.

Other AI companion companies also leverage users’ existing social behaviors. For instance, Duxiang’s AI WeChat Friend Circle allows AI partners to actively post on social media and interact with both users and other AI characters, mimicking real Chinese social media patterns. The company has even developed a wristband with Near-Field Communication (NFC) chips2 that connects to specific AI characters. When tapped on a phone, the AI character will appear on the screen to provide updates or show care, which builds physical connection in existing digital relationships.

You can also read ChinaTalk’s previous article to know more about other AI companion products and user experiences.

Product Managing AI companions: users, regulations, and geopolitics.

While Xingye/Talkie show some Character AI traits, such as community-oriented strategies and chatbot-based engagement, they differ in significant ways. These products illustrate Kai-fu Lee’s point: Chinese tech entrepreneurs, inspired by American innovation, developed new product features to achieve success. They are good product managers, even if not radical innovators. And good product managers understand their users while navigating local regulations and global geopolitical tensions, all of which shape product design.

Users: Who is longing for AI’s love?

Young men. This is the most common user base for English-speaking AI companion products, according to our market scan. SimilarWeb data shows the top 55 AI companion platforms globally attract predominantly male users (7:3 ratio), with 18-24-year-olds representing the largest demographic at an even more skewed 8:2 male-to-female ratio. Social media metrics again reinforce this gender pattern, with Reddit’s AI girlfriend community (r/AIGirlfriend) having 44k members compared to fewer than 100 in male-focused AI companion subreddits. Moreover, roughly one-third of the children falsely declared a social media age of 18+, so it is possible that a significant portion of the reported 18-24 users are underage.

A recent Reuters-covered report from an AI girlfriend platform further supports our findings: 50% of young men prefer dating AI partners due to fear of rejection, and 31% of U.S. men aged 18–30 already chat with AI girlfriends. Behind the fear of human rejection lies the manosphere. The “manosphere” is a network of online forums, influencers, and subcultures centered on men’s issues, which has become increasingly popular among young men and boys as their go-to place for advice on approaching intimacy. While the manosphere originated primarily in Western contexts, its discourses have increasingly spread to, and been adapted within, countries across Africa and Asia through social media. In these online spaces, frustrations over dating and shifting gender norms are common, often coupled with narratives portraying women as unreliable or rejecting. AI companions offer a controllable, judgment-free alternative to real-life relationships, aligning with manosphere ideals of feminine compliance and emotional availability. On the subreddit r/MensRights (374k members), users largely endorse the findings of the Reuters report and even celebrate the shift from human to AI relationships.

The desire for a controllable relationship is further illustrated through the many Japanese aesthetics and anime-inspired avatars on AI companion platforms. Even Grok’s Ani bears striking similarity to Misa Amane from the 2006 anime Death Note. These designs often present highly idealized forms of femininity, historically marketed to heterosexual male audiences. In Western contexts, anime-inspired aesthetics intersect with techno-orientalist fantasies, reinforcing the image of East Asia as a hyper-technological land and East Asian femininity as exotic, compliant, and unthreatening. This imagination extends to hypersexualized representations of AI and robots in East Asian forms. The orientalist fantasy of female partners who are cute, devoted, exotic, and endlessly available mirrors the appeal of AI girlfriends celebrated on many “men’s rights” subreddit forums. In essence, the combination of East Asian aesthetics + AI creates a perfect bundle for men who fear rejection or resist the demands of real-life relationships.

In China, however, AI companions have a markedly different user demographic: adult women. Although comprehensive user data for China’s AI companion market remains limited, many market analysts believe domestic AI companion products are primarily female-oriented. Many product managers also set their user portrait as women aged between 25 and 35, with some reaching 40+.

Why are adult women believed to be the main drivers of AI companionship? To answer this, we need to understand three trends: 1. Marriage rates have continued to fall to record lows, with 2024 experiencing a 20% decrease from 2023; 2. There are more males than females in China (1.045:1 in 2024, compared to 0.97:1 in the US); 3. There are millions of unmarried rural Chinese men, while their female peers get better education and move to the city. This has created a social landscape in which many unmarried people are unmarried educated women in the city and less-educated men, with fewer pathways for forming traditional romantic bonds.

While the two groups are both arguably longing for relationships, unmarried, educated women in cities are more likely to encounter and adopt new technologies like AI companionship. In contrast, less-educated rural men, despite also similarly longing for relationships, have fewer resources, less exposure to AI, and limited familiarity with parasocial interactions, making AI companions less immediately appealing. Influenced by the strong patriarchal culture in rural areas, most men prioritize finding a real-life partner to marry, have children, and continue the family line.

The gender imbalance, combined with growing resistance in China to traditional patriarchal family structures — driven by concerns over rising domestic abuse or feminist ideals — has led many urban, educated women to seek parasocial forms of romance. AI companions are not the first ones to profit from this demand. Originating in Japan, otome games (乙女ゲーム in Japanese or 乙女游戏/乙游 in Chinese) are storyline-based romance games targeted at women, where players interact with multiple fictional male characters through plots and events.

That said, demand and supply are a classic chicken-and-egg problem. While trends in AI boyfriends or girlfriends suggest some gendered differences in interest, these preferences are also shaped by what products are available. Historically, women’s sexual desires have often been overlooked, and men’s longing for subtle companionship is sometimes dismissed as “too feminine,” which could also explain the scarcity of hypersexual AI boyfriends and dynamic AI girlfriends. Thus, the two different markets may reflect not only inherent differences in demand but also the constraints and biases of what’s offered.

Domestic Regulation: Child Porn and Patriarchal Gaze

The user base is not the only difference between AI companion companies in the U.S. vs. China. It is unlikely that Chinese AI companion companies aren’t sexualizing their products only because hypersexual companions are less appealing to young Chinese people than global audiences. It is more likely that they simply cannot offer such functions.

In June, Shanghai Cyberspace Administration demanded a regulatory talk with Zhumengdao, as the app was exposed for containing sexually suggestive content involving minors. Even when users explicitly stated that they were 10 years old, the AI still sent them text messages that are considered sexually explicit in China (footnote: These contents are close to soft porn (known as 擦边, literally “near the e,dge”), meaning they approach but do not reach the explicitness of actual pornography, which is banned in China even for users over 18). Before the talk, the app’s teenage protection mode had to be manually activated by users. Three days later, the app released an updated version that automates the teenage protection mode. If users declare they are over 18, they will be asked to register their real names.

This talk is not a Zhumengdao issue but a warning for the whole AI companion market in China. Liang Zheng (梁正), Deputy Director of the Institute for International Governance of Artificial Intelligence at Tsinghua University, recently commented on this regulatory talk, stating that if AI companion companies do not have enough self-regulations, it will harm the whole industry. Liang also argues that AI chatbot applications must meet a series of requirements, including “content accuracy, consumer privacy protection, compliance with public order and good morals, and special safety considerations for minors”.

But is safety for minors the only concern for AI boyfriends in China? Unlikely. If we take one step further on Liang’s statement on “compliance with public order and good morals,” there is another motivation for the Chinese government to regulate AI boyfriends — the demographic crisis.

There is no secret that the government is extremely worried about the birth rate: most recently, they offered $1,500 per child in a bid to boost births. A decade ago, they coined the term “leftover women” in the hope of pushing highly educated unmarried women into marriage by stigmatizing their existence. In a traditional patriarchal perspective, AI companions — especially those handsome AI boyfriends that divert women from human-to-human relationships — can be threatening. Yet, like otome games, AI companions also represent a potential economic boon, which can help offset other societal pressures for the state. In recent years, several local governments have cancelled fandom celebrations for otome characters, including thematic subway decorations and parades in Shanghai, Nanjing, and Chongqing — arguably the most “open-minded” cities in China. These actions suggest that, although AI companions, like otome games, provide substantial economic benefits, they remain subject to selective censorship due to the state’s priorities around promoting marriage and childbirth, in addition to the outright bans on soft-pornographic material in China.

Geopolitical Constraint: from TikTok to Talkie

Talkie is Xingye’s overseas twin by Minimax, one of the six most promising AI startups in China. For the U.S., Minimax’s Talkie is probably as threatening as their M1 model, if not more. In the first half of 2024, Talkie was the fourth most-downloaded AI app in the U.S., ahead of Google-backed Character AI, which was in 10th place by number of downloads.

Then, Talkie mysteriously disappeared from the U.S. app store in December 2024. The company attributed the removal to “unspecified technical reasons.” Think of Talkie as a more powerful TikTok, in the sense that it has both manipulation and data commitment problems. While TikTok is accused of influencing users through algorithmically tailored content to achieve political aims, Talkie can potentially persuade users through direct conversations, a risk amplified by the emotional and romantic bonds and trust users form with their AI companions. This makes any AI companion from an untrusted region a potential national security concern.

In addition, as a Chinese app, Talkie also faces data-commitment issues,3 arguably more serious than TikTok’s — especially if your AI partner knows you more intimately than your social media accounts. TikTok now plans to create a new U.S. entity to secure all U.S. user data on Oracle servers and license the existing recommendation algorithm for the U.S. to retrain from the ground up. Will Minimax, ByteDance, or any other Chinese AI companion companies targeting Western markets follow the TikTok template, sacrificing a large commercial interest to settle for a minority stake? Or will they do nothing and hope to find a profitable niche that is not famous enough to attract the intense national security scrutiny that crippled TikTok? These questions remain unresolved as the next generation of Chinese AI boyfriends — or AI girlfriends designed for overseas markets — begins competing with American AI girlfriends in the global app marketplace.

Talkie is back on the app store now, but concerns around data privacy, national security, and potential CCP-backed influence continue. Currently, Talkie AI’s privacy policy states that all data will be transferred and stored in the U.S.

Why We Turn to AI

Regardless of whether they’re made in China or America, AI companions represent another pivotal crossroads in human-computer interaction. TikTok faces geopolitical challenges, as social media and short-form videos have fundamentally transformed daily life for both Americans and Chinese. Similarly, AI companionship is both a national security and geopolitical concern, and a deeply human issue for most of us with the privilege to access AI and the internet.

Made-in-America AI girlfriends and made-in-China AI boyfriends are strikingly different, and so are the social contexts and regulatory environments in which they exist. Yet one thing both markets share is the tension with real-life relationships. Whether healthy or not, frustrations with human interaction and broadly polarized gender dynamics are leading many men and women, regardless of nationality, to turn to AI.

But questions remain about AI companion products: Are they safe? Are they manipulative? Do they cure or amplify loneliness? Are they private enough? Are they responsible for mental health and suicide? Amid these debates about the technology itself, one question is often missing: where does the demand come from? If AI companions are truly unsafe, manipulative, or harmful, why do so many still turn to them? Psychologists, lawyers, national security experts, and AI safety researchers have many important questions to tackle about AI companions as products. But perhaps we should also ask ourselves: what gaps in our society make human relationships feel undesirable? AI companionship is a new problem, but misogyny, gender violence, social isolation, and racial stereotypes are not — in China and America alike.

Acknowledgement: a wholehearted thank you to my market scan teammates Mari Izumikawa, Fiona Lodge, and Angelo Leone, for splitting the mental trauma of reviewing many AI companion platforms. We are also looking for potential support to continue updating the market scan (for the English-speaking side) and conduct related research, so please reach out if you are interested.