（SINGAPORE 2026.1.2） Chinese AI engineers are often recognized for their strength in turning AI into large-scale, deployable systems, whereas their American peers are typically seen as advancing the technological frontier. But what if some Chinese engineers excel at both? This may help explain why leading AI companies worldwide are increasingly snapping up Chinese talent.
A prime example is Meta’s June 2025 move: the company invested around US$14 billion (S$18 billion) to acquire Scale AI and brought its founder and CEO, Alexandr Wang, on board. Wang, a Chinese American born in 1997 who dropped out of MIT, founded Scale AI to provide AI data and evaluation infrastructure that helps major tech firms train cutting-edge models.
Mark Zuckerberg, founder of Meta, appointed Wang, alongside former GitHub CEO Nat Friedman, to lead the newly established Meta Superintelligence Labs (MSL). MSL is no ordinary unit—according to OpenAI CEO Sam Altman, Meta is offering signing bonuses up to US$100 million to attract top talent to join it.
Unsurprisingly, Altman noted that Zuckerberg has aggressively “raided” OpenAI for recruitment. An internal Meta memo highlighted 11 hires, at least six of whom are Chinese and seven of whom came from OpenAI. Key MSL members such as Yu Jiahui (余家辉), Zhao Shengjia (赵晟佳), Bi Shuchao (毕树超), Huiwen Chang (常慧文), Ji Lin (林纪), and Ren Hongyu (任泓宇) previously held leadership roles in core models or teams at OpenAI, according to Ai-Front in 36Kr.
At OpenAI, Yu—considered one of the world’s leading Chinese mathematicians and AI engineers—played a central role in developing models like GPT-4o, GPT-4.1, and the o3 and o4-mini series. The other five hires are also top Chinese AI researchers, with expertise ranging from agents, multi-step reasoning, and execution research to multimodality, speech and vision understanding, post-training, and interactive systems.
In practice, agents, multi-step reasoning, and execution explore how AI acts and plans, while multimodality, speech and vision, post-training, and interactive systems focus on what AI perceives, processes, and refines after training.
Why the emphasis on Chinese experts? By 2025, model capability still matters, but it is no longer the sole determinant of a company’s market position. The focus has shifted from “model parameters and benchmark scores” to who can embed models into products and systems that work reliably in the real world.
Tech giants are loudly promoting ambitious hiring campaigns to attract leaders in agents, systems, and infrastructure, while simultaneously restructuring internal AI research divisions—prompting many mid- to senior-level leaders to step away from the spotlight. In a recent high-profile move, Meta reportedly spent US$2 billion to acquire AI agent startup Manus, bringing its founder Xiao Hong (肖弘）onboard as part of the deal.
From Meta to OpenAI, from Google to Apple, from titles like “chief scientist” to “head of research,” one thing is clear: the AI R&D priorities of US tech giants are undergoing a deep structural transformation.
Research itself has not lost its importance—model training remains the foundation of the field. What’s increasingly valued, however, is the ability to transform models into deployable systems that deliver lasting impact in real-world applications. Notably, in the midst of this shift, a significant number of Chinese engineers are rising to key positions.
The utopian goal of artificial general intelligence (AGI) is fading, replaced by domain-specific, executable superintelligence (ASI). Anthropic executive Jack Clark has warned that “a massive upheaval is coming—AI will tear the world into two parallel universes.”
At the center of this shift is the platformization of large language models (LLMs). For years, the simple equation— “more parameters, more data, more compute”—drove technological excitement and soaring valuations. By 2025, however, returns from scaling alone have diminished sharply. Leading models are approaching performance ceilings, while compute costs continue to rise exponentially. Companies increasingly recognize that simply making models bigger no longer guarantees high returns.
As the space for pure technical exploration narrows, corporate focus now revolves around three questions: Can it be applied? Can it generate revenue? Can it scale? This shift is reshaping the hierarchy of AI talent.
Meta stands out as one of the most disruptive players. It offers astronomical compensation to attract top global talent while key senior veteran research leaders are leaving. Collectively, these moves signal a strategic pivot: Meta is moving from dual-track research and product development toward a fully centralized, product-focused R&D structure.
The 2025 hiring and acquisition trends among Silicon Valley giants illustrate a clear focus on three areas: agents, multimodal and real-time interactive systems, and reasoning integrated with AI infrastructure.
In 2025, leading AI talent has not exited the stage —it has shifted focus from papers and demos to building systems, platforms, and real-world applications. Meanwhile, Silicon Valley is quietly undergoing a new course correction in response to this talent migration.
Chinese researchers, who are prominent in applied AI and systems work—spanning AI agents, model deployment, and large-scale AI infrastructure—are among the main beneficiaries of this trend.
