Episode summary: In this episode of My Weird Prompts, Corn and Herman dive into the "geographic soul" of artificial intelligence, using a sloth in a supermarket as a lens to explore the cultural divide between Western and Chinese models. They discuss how training data—from the open-web scrapes of Common Crawl to the walled gardens of WeChat—creates fundamentally different worldviews, contrasting the analytic individualism of the West with the holistic, community-focused orientation of the East. The duo also explores how hardware constraints have forced Chinese labs like DeepSeek and Alibaba to innovate in efficiency, leading to a future where "multi-model systems" might be the key to finding cross-cultural truth in an increasingly fragmented digital landscape. Show Notes In the rapidly evolving landscape of 2026, the conversation around artificial intelligence has shifted from mere processing power to a more nuanced exploration of "digital culture." In a recent episode of *My Weird Prompts*, hosts Herman Poppleberry and Corn explored this phenomenon through a seemingly simple image prompt: a sloth in a supermarket. While the prompt sounds whimsical, the results revealed a profound truth about the "geographic soul" of modern AI models. ### The Supermarket Mirror The discussion began with an observation by their housemate, Daniel, who tested the new Alibaba Wan 2.1 model. When prompted to generate a sloth in a supermarket, the AI did not produce the wide, fluorescent-lit aisles of a typical American grocery store. Instead, it rendered a scene filled with live seafood tanks, stacks of durian, and red-and-yellow promotional banners—a quintessentially Chinese shopping environment. Herman and Corn argued that these models act as mirrors of the digital cultures that raised them. A model is not a neutral observer; it is a product of its training data. For Western models, that data is largely sourced from Common Crawl, a massive repository that is nearly 43% English. For Chinese models like those from Alibaba or Tencent, the data comes from a different ecosystem—one that is often a "walled garden" of integrated apps like WeChat and Douyin, supplemented by high-quality synthetic data and internal repositories. ### Analytic vs. Holistic Logic The hosts pointed to a fascinating study from MIT that analyzed how models respond to translated prompts. The findings suggested that Western models tend to prioritize independent, analytic patterns—focusing on the individual and the future. In contrast, Chinese models often reflect an "interdependent social orientation," prioritizing family, community, and collective harmony. This cultural leaning manifests in practical ways. Herman noted that a Western AI might generate a life insurance slogan focused on "your peace of mind," while a Chinese AI would likely emphasize "your family's future." This isn't just a matter of translation; it is a fundamental difference in how the models are "socialized" during the Reinforcement Learning from Human Feedback (RLHF) stage. The human trainers in Hangzhou and San Francisco have different definitions of what constitutes a "helpful" or "polite" response, leading to models with distinct social etiquettes. ### Innovation Born of Constraint One of the most compelling segments of the discussion centered on how hardware limitations have actually spurred innovation in the East. Due to export controls on high-end chips, Chinese labs have been forced to become masters of efficiency. Herman explained that models like the Qwen series and the upcoming DeepSeek V4 have had to evolve more "elegant" architectures to achieve state-of-the-art performance with less compute. Specifically, the hosts discussed the use of Mixture of Experts (MoE) architectures and "Manifold-Constrained Hyper-Connections." These technical advancements allow neural networks to be denser where it matters most, avoiding wasted energy on irrelevant calculations. Corn compared this to a chef who, having fewer ingredients, must be more precise with their seasoning. This efficiency has allowed Chinese models to lead in benchmarks for coding and mathematics, often outperforming Western counterparts that have access to more raw computing power. ### The Library vs. The Street Herman and Corn also highlighted a functional difference in how these AIs operate. Western models, they argued, are still very much "in the library"—excellent at research, text generation, and academic synthesis. Chinese models, however, are "out on the street." Because the Chinese tech ecosystem is so integrated (apps-within-apps), their AI models are trained on sequences of actions rather than just sequences of words. This makes Chinese models feel more "agentic." They are designed to navigate the real world—ordering coffee, paying bills, and managing schedules—because their training data reflects a world where digital interaction is a seamless, all-in-one experience. ### The Future: A Council of AI As the episode drew to a close, the hosts addressed the fear of a "splinternet" of intelligence—a world where different AIs provide different versions of the truth. While there is a risk of geographic siloing, Herman offered an optimistic alternative: the rise of multi-model systems. By using aggregators to consult a "council" of AIs—one Western, one Chinese, one European—users can gain a more complete perspective on any given topic. If multiple models from different cultural backgrounds agree on a fact, it provides a higher level of certainty. If they disagree, it highlights a cultural or political nuance that warrants further human investigation. Ultimately, the episode concluded that the geographic soul of AI is not a bug, but a feature. As models like DeepSeek implement "Engram conditional memory" to switch between cultural reasoning frameworks, the goal is not to find one single, neutral AI, but to leverage the diverse worldviews that these digital mirrors provide. The sloth in the supermarket is just the beginning; the real journey is understanding the world through a multitude of digital eyes. Listen online: https://myweirdprompts.com/episode/geographic-soul-ai-models