The Real Story Behind Why Song-Chun Zhu Took His AI Research from the US to China

Song-Chun Zhu is no ordinary researcher. Born during the Cultural Revolution in rural China, he saw poverty, loss, and silences about people’s lives from a very young age. That sense of stories untold stayed with him. He vowed that if his life led him to science, it would also tell stories not just record facts.

Eventually he left for the U.S, got a PhD at Harvard, built a top lab at UCLA, won recognition, grants, collaborators. But by 2020, after nearly three decades, he made a sharp turn: he returned to China, took up positions at Beijing institutions, and accepted leadership of a state backed AI institute.

What drove the change wasn’t a single cause but a whole tangle: academic frustrations, shifting global politics, and a vision of AI that Zhu felt wasn’t getting the support it needed in the U.S.

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Growing Disillusionment with Mainstream AI

Zhu’s discontent started with what he saw as over-emphasis on “big data, big compute, big models” the neural network / deep learning paradigm dominating much of today’s AI. While those tools achieved breakthroughs, Zhu felt they often lacked reasoning, cause & effect, intuition especially for novel scenarios. He has repeatedly expressed skepticism that large language models, by themselves, capture the kind of intelligence he wants to build.

It wasn’t just theory. He believed in a different path: “small data, big task” that is, systems that can solve difficult problems with less data but more structure, more internal logic. That direction needed institutional support. And in the U.S, with rising scrutiny of Chinese scientists, visa pressures, political regulation, and the funding environment, Zhu reportedly saw barriers.

Politics, Identity, and Opportunity

The move back to China also came at a time when global tensions over technology, research, and national strategy were heating up. Chinese scientists in America were increasingly viewed with suspicion, and some U.S. policies made collaborative and international work harder. Zhu has mentioned that political pressures played a role in his thinking.

Meanwhile, China has been investing heavily in AI. It offered Zhu resources both financial and institutional that he said he couldn’t get in the U.S. for the kind of work he wanted to pursue. BigAI (Beijing Institute for General Artificial Intelligence), faculty roles, support for labs and apprentices all of that made returning seem like not just a personal or patriotic choice, but a strategic one.

What Zhu Is Trying to Build Now

Since his return, Zhu has focused his efforts on what he sees as more rigorous, philosophical, and intellectually bold questions: What makes intelligence understand context? How does reasoning emerge from structure rather than raw volume of data? How does a system learn for itself, from minimal cues, rather than being fed huge datasets?

Projects such as TongTong, a platform mentioned in the Guardian article, illustrate Zhu’s attempt to design AI that has resourcefulness, social and physical intuition, understanding of cause and effect. It is not just about bigger models but smarter systems.

What This Means for AI and the Research World

Zhu’s choice to leave the U.S. and work back in China is a sign of shifting currents:

• The allure of the U.S. as the center of AI research is no longer unchallenged. Talent is mobile, and institutional support matters a lot.

• Geopolitics can’t be separated from research. National policy, visa regimes, funding sources all affect what work is possible.

• There’s a growing debate about the foundations of intelligence in AI, not just performance. Voices like Zhu’s are pushing for alternatives to the biggest, data-hungry models.

Personal Drive Meets Global Stakes

In the end, Zhu has often said, “I have to do it.” It’s a personal phrase loaded with urgency. For him, this is about his life’s story coming from a childhood where lives were forgotten, where people were not always remembered. He wants his work to make a mark not just for science, but for meaning: what we remember, how we think, how machines mirror or extend human understanding.

A Different Curve in the AI Race

Song-Chun Zhu’s return to China isn’t a retreat it might be a turning point. It’s a signal that the AI race is no longer just about raw computing power or who releases the Next LLM first. It’s also about who dares to question assumptions, build alternative foundations, and shape what intelligence really means.

For countries, universities, and funders, Zhu’s journey lays down a challenge: support work that asks hard questions. For the rest of us, it reminds that behind every algorithm, there’s a person with hopes, doubts, and history.