Over the past two years, the AI conversation has been dominated by scale, with model size and benchmark results being the key metrics for progress, but a quieter trend has emerged, shifting focus from power to practicality.
While large model capabilities still matter, the frontier is increasingly defined by how and where these models are used, giving rise to a new class of players whose strengths lie in productization, integration, and user-centered adaptability. Tencent’s growing body of AI work is drawing quiet but serious attention, with its progress unfolding in a more iterative, behind-the-scenes manner. Its Hunyuan Turbo S model recently broke into the top 10 on Chatbot Arena, joining the likes of GPT-4 and Claude 3 Opus, and became one of only two Chinese models to reach that tier.
Hunyuan Turbo S introduces a hybrid architecture combining Mamba’s efficient long-sequence processing with Transformer-style contextual reasoning, built on a Mixture-of-Experts (MoE) structure, and is optimized not for demos, but for deployment, powering actual tools used by millions of people every day. The model follows the “Apple of AI” approach, letting the user experience speak rather than shouting about features. One of the most compelling implementations is Hunyuan’s 3D generation system, which has gained traction among digital designers, gaming studios, and industrial prototyping teams, and has been downloaded over 1.6 million times via open platforms like Hugging Face.
At CVPR 2025, Tencent open-sourced Hunyuan 3D 2.1, described as the first end-to-end open industrial-grade 3D generation model, improving geometric generation quality and introducing support for PBR material generation. Tencent’s AI ambition also shows in its handling of scale, supporting multi-model orchestration, with products like Yuanbao routing user queries to different models based on complexity, task type, or latency requirements. This orchestration approach also underpins Tencent IMA, an internal productivity platform that integrates document ingestion, search, and AI-assisted drafting features into a single workspace.
Tencent’s emphasis on usability over novelty has become a hallmark of its AI deployment strategy, with the company reaffirming AI as a long-term infrastructure priority, not just a layer for innovation, but a foundational component across its cloud, enterprise, and consumer portfolios. The company’s practical ethos stands in contrast to the theatricality surrounding much of the AI sector, with users outside Silicon Valley asking not if a model can do 32K context windows, but if it can summarize a report, rewrite a résumé, or design a product prototype.
Other companies, like South Korea’s Naver, France’s Mistral, and mid-sized players like Cohere and Inflection, are also pivoting to infrastructure-level partnerships, moving from chatbot demos to developer platforms, sharing a shift from AI as a spectacle to AI as a service. Challenges remain, including localized AI success not always translating to global relevance, and Chinese models facing hurdles around developer adoption and regulatory skepticism in overseas markets.
If AI’s next chapter is about application more than architecture, then companies that have mastered infrastructure and integration may hold the real keys to impact, and not everyone needs to be first in the model race, as that may not be the race that matters most.
