What can the U.S. learn from China's open AI ecosystem?
Experts say China's competitive yet collaborative open-model developers offer lessons for strengthening open frontier AI research in the US.
Open-weight AI models — which allow developers to download, customize, and run the model on their own infrastructure — were back in the spotlight last week. Most recently, Palantir CEO Alex Karp said enterprise customers are increasingly concerned about giving AI companies access to their data and that some U.S. government customers have switched from closed AI models to open ones.
Those comments made me want to circle back to a conversation I heard last week at Open Frontier, a day-long meeting in San Francisco convened by Databricks and Perplexity co-founder Andy Konwinski’s Laude Institute. There was plenty of chatter about how Chinese open-weight models such as DeepSeek, Kimi, MiniMax, or GLM were at the leading edge, while American versions were not. And researchers kept asking: What can the United States learn from China about building an open AI research ecosystem? And how can it catch up?
There is reason to be concerned. Last week, I wrote about how researchers at the Open Frontier meeting warned that as frontier AI becomes increasingly concentrated in a handful of closed labs, the United States risks allowing the global open AI ecosystem to be shaped by China while advanced AI at home becomes dominated by proprietary systems that universities, startups, and independent developers cannot study, improve, and build upon.
One example came from Jennifer Chayes, dean of UC Berkeley’s College of Computing, Data Science, and Society. During a panel on funding the open research ecosystem, she said Berkeley researchers are “all building on Chinese models because we don’t have a Western open frontier model,” and argued that this represents a national security gap the U.S. government should address with greater investment in open frontier AI research.
But another theme emerged repeatedly throughout the day. Several AI leaders argued that one reason the U.S. has struggled to build a robust open frontier AI ecosystem is that Chinese companies have managed to be both fiercely competitive and unusually collaborative—sharing enough research and ideas for the broader ecosystem to benefit.
Competition and collaboration
“If you look at what’s happened in China, it’s really quite amazing,” said Bryan Catanzaro, vice president of applied deep learning research at Nvidia.
Chinese companies have produced so many leading open models, he argued, not because they have fundamentally different solutions to frontier AI, but because openness has allowed ideas, techniques, and research to compound across the ecosystem.
That doesn’t mean Chinese companies aren’t fiercely competitive, Catanzaro emphasized, pointing to his own experience working for two years at Baidu.
“These teams compete fiercely,” he said. “Being open doesn’t mean that we always agree with each other or that we’re not trying to win. But despite all of that competition, they’ve been able to build a self-sustaining community where information and ideas are able to compound and build an ecosystem that I think is much better than any other open ecosystem.”
The question, he said, is: “What can we learn from China about how to work together again? It doesn’t mean we all have to love each other all the time, or that we’re not competing with each other. But there is a synergy that comes from being open.”
