Disappointing release from Deepseek. V4 is even further behind US frontier models despite distilling from those models and training on Blackwell chips. It’s further behind US models than when R1 released.
"I don't even agree with the assumption that Chinese models are catching up — I believe we're still far behind," a former ByteDance engineer said.
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A former ByteDance engineer says China's AI industry is 'far behind' the US — and the gap is getting worse
- A former ByteDance engineer says China's AI industry is still far behind the US.
- Zhang Chi said China's model benchmarks perform well but don't translate into real-world results.
- Tech leaders like Elon Musk and Jensen Huang believe China is catching up and could even pull ahead.
For all the talk that China is catching up to the US in AI, a former ByteDance engineer believes it's actually falling further behind.
"I don't even agree with the assumption that Chinese models are catching up — I believe we're still far behind," Zhang Chi, a research scientist and assistant professor at Peking University, said on an episode of the "Into Asia" podcast. "I guess the gap is getting larger, very sadly."
Zhang, who said he spent about a year at ByteDance working on
AI models before returning to academia, said the differences go beyond the perceived rapid progress of Chinese startups.
While models from companies like ByteDance,
TikTok's parent company, and Alibaba may score well on benchmarks, he said that doesn't mean they work as well in the real world.
"On paper, every big tech company in China has a good model," Zhang said. "But I don't think they're good enough." He added that many teams are focused on "benchmaxxing" — optimizing for test scores rather than practical performance.
ByteDance and Alibaba have rolled out high-profile AI models — from
video generators like Seedance to open-source systems like Qwen — but they've also faced backlash over deepfakes, copyright disputes, and whether those models hold up in real-world use.
A key issue, Zhang said, is speed. He said top US companies can iterate on models far more quickly.
"Google can train or perform a full round of LLM training, both pre-training and post-training, in three months," he said. "But ByteDance — probably we can only do one iteration in half a year."
Zhang also pointed to China's structural disadvantages, including access to advanced chips, weaker infrastructure, and lower-quality training data.
"There's a huge difference between the infrastructure at Google and ByteDance," he said. "I don't think we're getting high-quality data."
He added that some companies rely on distilling outputs from leading US models rather than building their own data pipelines — a shortcut that may limit long-term progress.
Zhang said US firms also benefit from stronger user feedback loops. Products like
ChatGPT, Claude, and Gemini improve through constant interaction with users, which helps refine their models over time.
Chinese models, by contrast, risk falling into a negative cycle. "Chinese models started not as good, so no one really uses them for really important things," Zhang said. "And the models continue to be not that good."
Others say China is catching up
Zhang's view contrasts sharply with some of the most prominent voices in tech, many of whom say China is rapidly closing the gap and could even pull ahead.
Nvidia CEO Jensen Huang has warned that the
US risks falling behind, while Elon Musk has said that
China's advantage in energy and compute could help it overtake rivals.
AI pioneer Geoffrey Hinton has also said the
US lead may be narrower than it appears, cautioning it could erode over time.
Others take a more nuanced view. Alibaba chairman Joe Tsai has said the race will be decided less by
model strength and more by how quickly AI is deployed.
Still, Zhang's assessment reflects a more pessimistic perspective from inside China's AI ecosystem — one that suggests the gap with the US may be widening.
"To be fair, I don't think any Chinese company can catch up with them soon," he said.
