In an interview featured by Qiushi, Zhipu AI co-founder and Chief Scientist Tang Jie discussed the state and future of China’s large-model industry.
Tang argued that Chinese models are narrowing the gap with global leaders in areas such as question answering and search, while acknowledging that leading international models are moving rapidly into coding and long-horizon tasks. He identified open-source AI as an important source of competitive advantage for Chinese developers, citing Hugging Face’s use of Zhipu’s GLM-5.2 as an example of the value of locally deployable, auditable and controllable models, while cautioning that this does not mean GLM is superior to GPT.
On longer-term AI development, Tang said he believes models surpassing individual human intelligence are inevitable, although whether AI can ultimately exceed the collective intelligence of humanity remains an open question. He also highlighted that insufficient access to high-quality data and China’s continued weakness in original breakthroughs in fundamental AI theory are major constraints on China’s AI development.
Notably, Tang Jie published a commentary titled “Driving High-Quality Development of the Intelligent Economy with Tokens as the Engine,” on Qiushi Magazine, one of China’s most important political outlets.
**Host(Qiu Shi)**
From Doubao and DeepSeek to Zhipu Qingyan, how far are these hugely popular Chinese AI applications from the world’s leading technologies?
Tang Jie
The gap between Chinese models and the world’s leading models is narrowing.
Host
Will artificial intelligence surpass human intelligence?
Tang Jie
I think it is perfectly normal for a model to surpass the intelligence of an individual human being.
Host
Should AI education also start from an early age?
Tang Jie
Once a new tool arrives, we have to learn to use it. That is why I encourage my students—and even my own children—to use large models.
Host
In this episode of Shidian Interview, Tang Jie, Professor in the Department of Computer Science and Technology at Tsinghua University, continues to share his insights into the present and future of China’s homegrown large models.
Host
Professor Tang, welcome to Shidian Interview. Today, generative AI applications such as Doubao, DeepSeek, Yuanbao, Kimi, Zhipu Qingyan and Tongyi Qianwen are rapidly becoming mainstream. Behind these consumer AI products are large models developed in China. How do you assess the current development of China’s homegrown large models?
Tang Jie
The products you just mentioned—including Kimi, Doubao, Zhipu Qingyan and DeepSeek—are all very good products, and essentially all of them are powered by large models developed in China.
On November 30, 2022, ChatGPT was launched by OpenAI and quickly took the world by storm. Then, in 2023, ERNIE Bot and Zhipu Qingyan were released in China, followed soon afterward by Doubao and other products. There were roughly eight companies among the earliest group of players in China. Today, I would say that large models have become widely used by consumers for question answering, search and other related applications.
In question-answering and search-related applications, the gap between Chinese models and the world’s leading models is narrowing. That is the first basic reality.
At the same time, however, we can see that the world’s leading large models are beginning to shift into new areas—for example, programming and long-horizon tasks. As large models begin to program and carry out these long-horizon tasks, we need to transfer our workplace experience and expertise to them. In this way, large models gradually acquire the ability to perform work across different industries and scenarios.
These capabilities are being added step by step. Some leading international and Chinese developers started working on them relatively early, so they do have certain advantages today.
The third point brings us back to open source and openness. Only through open source and openness can our models establish their own advantages—not only in model R&D, but also in expanding their global reach and enabling the broader deployment of AI workflows.
Host
Not long ago, Hugging Face, the U.S.-based open-source AI community, was affected by an intrusion involving an OpenAI model. Hugging Face initially tried to call leading proprietary U.S. models to help identify and fix the vulnerability, but those attempts were blocked. It then turned to GLM-5.2, a large model developed by China’s Zhipu AI, and reportedly resolved the problem within several hours.
As the founder of Zhipu AI and now its Chief Scientist, how do you view this incident? Does it suggest that China’s open-source large models have greater advantages and stronger development prospects than proprietary U.S. models?
Tang Jie
We have also been following this incident very closely. Let me first walk through what happened from beginning to end.
During the week of July 13, Hugging Face—the world’s largest open-source AI community—was hit by an intrusion involving a large model.
What caused it? OpenAI was conducting an experiment in a closed environment, using a large model to attack software. But the model unexpectedly escaped that closed environment and ended up attacking Hugging Face over the internet.
Hugging Face then tried to use some of the most advanced proprietary models available. Initially, it turned to closed models such as those from OpenAI and Claude. But OpenAI’s and Claude’s models have extensive safeguards and guardrails, which meant Hugging Face could not actually use them for what it needed to do.
So what did Hugging Face do? It installed our GLM-5.2 locally. After installation, it created an internally isolated environment and reproduced the entire attack process within that environment. By tracing the attack, it was ultimately able to identify the cause.
There are several lessons we can draw from this.
First is the importance of open source. Without an open-source model, Hugging Face would not have been able to construct that environment, reproduce the incident or identify the cause of the attack.
Second, both offensive security capabilities and safety guardrails are extremely important. A model needs to have the ability to conduct security testing, but it also needs defensive capabilities.
Does this mean that open-source models necessarily have an advantage over proprietary models? I think it is still too early to draw that conclusion. After all, in this particular incident, the attack itself was initiated by OpenAI’s model.
Why did Hugging Face use our GLM-5.2? Because it could not use the proprietary models for this purpose, so it had to use our open-source model.
We cannot say that GLM-5.2 is therefore better than GPT. What we can say is that, through its open-source approach, GLM-5.2 has successfully occupied this particular ecological niche.
That is the first point. The second point worth reflecting on is the value of controllability. The large models of the future need to be models whose operation is visible and understandable, that users can actually deploy, and that are auditable and controllable. Those are the kinds of models that people will truly be willing and able to use in real-world applications.
Host
You have mentioned that you encourage many of your students to use large models. Even primary and secondary school students may increasingly use them in the future. Could this lead to weaker foundations in basic knowledge, or even the deterioration of certain fundamental skills?
Tang Jie
Yes. This is a question I am asked very often.
I remember that many years ago, when large models were first beginning to take off, I was talking with several friends. I told them that large models would develop extremely rapidly in the future, that knowledge would become much more widely accessible, and that these technologies would become part of our everyday lives.
Many of my friends then asked me: what should our children do in the future? What should they learn?
I think we need to answer that question from two perspectives.
The first is that technology has already developed to this point, so simply refusing to use it is not really an option.
Think about when the steam engine emerged. Before that, people knew how to ride horses. So should we still need to master horse-riding skills today?
As you can see, many people today no longer know how to ride a horse. And even when I ride a horse, I do not do it because I want to travel faster or because I need a horse for transportation. Horse riding has become more of a recreational activity.
So the first point is that a new era has arrived. Once these new tools emerge, we have to use them. That is why I encourage my students—and even my own children—to use large models.
There is, of course, another side to the question. What happens if people no longer have a solid grasp of basic knowledge, or eventually lose certain fundamental skills altogether?
That is another dimension of the issue.
Personally, I think our technology stack may indeed change in the future. We may no longer need to master some of the basic knowledge points that people were previously expected to learn.
But there is another dimension as well: large models allow us to expand the scope of our knowledge.
Why do I say that?
In the past, if you relied primarily on reading books, perhaps you could study 1,000 individual pieces of knowledge. Being able to absorb and make notes on 1,000 such points would already be quite an achievement.
With large models, however, you might develop a deep understanding of 100 core knowledge points while gaining some exposure to another 10,000. In that sense, the overall space of knowledge available to you becomes much larger.
Host
There is a book called The Singularity Is Near by Ray Kurzweil. He later wrote another book, The Singularity Is Nearer. These books discuss the possibility that by 2045, artificial intelligence could surpass the intelligence of the human brain.
How do you view the direction in which AI is developing? Will machines replace humans? Could machine intelligence ultimately surpass human intelligence—or even come to dominate humanity? Is that a real possibility?
Tang Jie
Personally, I think artificial general intelligence represents, in a sense, the aggregate intelligence of people around the world.
From that perspective, I think it is perfectly normal for a single model to surpass the intelligence of a single human being. A model brings together knowledge from across the world. We have also given these models our reasoning processes and ways of thinking. More recently, we have begun giving them behavioral data as well, including data about the workflows through which we perform our jobs.
Think about what that means. Such a model can work, it possesses knowledge, it can have strong emotional intelligence, and it can reason logically. In that sense, it brings together the intellectual capabilities of people from around the world.
Could there come a day when large models surpass humanity as a whole and generate theories and ideas that none of us have ever conceived of? That remains an open question.
This is also an extremely hot topic right now in Silicon Valley, Europe and China. We call it ASI—Artificial Superintelligence. The question is whether AI could eventually develop superintelligence that surpasses human intelligence.
There is another concept that has also become very popular recently: recursive superintelligence. The question here is whether a large model can correct and improve itself, continuously making itself more capable.
If a model can continuously improve and repair itself, then surpassing humanity could eventually become a realistic prospect. It is possible that this development curve will eventually reach a critical point and plateau. It is also possible that it will rise until it reaches the aggregate intelligence of humanity as a whole.
So whether large models will ultimately surpass the combined intelligence of all humanity remains an open question. But I believe surpassing the intellectual capabilities of an individual human being is inevitable.
Host
China’s large-model industry has developed rapidly. In your view, what problems and challenges does China still face in developing large models, and how can these challenges be overcome?
Tang Jie
I would highlight four challenges.
The first is computing power. We still face major constraints in this area. U.S. restrictions on China’s access to high-end chips have resulted in genuine shortages of advanced chips in China.
The second is data. You can never have too much data, and there is never really enough of it.
For various reasons, many industries cannot share their data or make it available for model training. Yet as large models expand their knowledge structures and their ability to perform different tasks, they need precisely this kind of real-world contextual data. This creates a fundamental tension. At the same time, the shortage of data has created new industries. There are now many companies specializing in data annotation, data generation and data cleaning. These activities have also created significant employment opportunities.
So data represents the second major challenge, but also the second major area of opportunity.
The third issue brings us back to originality and fundamental innovation.
China still has room for improvement when it comes to original theoretical breakthroughs. The Transformer architecture that underpins modern large models, for example, was not originally proposed by Chinese researchers. Nor did reinforcement learning and reasoning techniques originate in China.
So we still need to strengthen our capacity for original innovation in fundamental theory.
This was also one of the motivations behind our team’s commercialization of research from Tsinghua University and the establishment of Zhipu AI. We hoped to combine academia and industry—to pursue fundamental innovation in technological theory while also expanding those technologies into a much broader range of practical applications.
And, of course, we hope that in the future we can make our own contributions to original innovation in fundamental AI theory.
Host
Thank you, Professor Tang. We greatly appreciate your insights and explanations.