AI has become a focal point within the Trump administration, often framed as a two-player race between the U.S. and China. And while U.S. companies like OpenAI, Google, and Anthropic may have developed some of the world’s most advanced AI models, they are among the priciest. As costs associated with token and AI usage rise, now some consumer-facing companies are turning to China’s cheaper, open-source models.
Take for example DoorDash, which, according to a post on X on Wednesday by co-founder and CTO Andy Fang, will be launching DoorDash CLI, an experimental tool in limited beta that will allow users to order DoorDash through an AI agent, or even directly from the terminal. Earlier this month, Fang said using a model from Chinese startup Moonshot AI is “better quality” and comes at a “cheaper cost.”
DoorDash is far from the first to turn to Chinese AI companies, or Moonshot for that matter. Cursor, the AI coding startup, used Moonshot’s Kimi to help build its Composer 2 coding agent, while fellow startup Lindy has reportedly dropped Anthropic’s tools altogether in favor of DeepSeek’s V4 models, according to the FT.
These companies is joining the likes of Airbnb and Siemens—both of which are experimenting with moving their daily operations to Chinese AI companies like Alibaba and DeepSeek—to save on rising AI costs.
For Yasir Atalan, deputy director and data fellow in the Futures Lab at the Center for Strategic and International Studies, the shift comes down to three factors: cost, capability and the availability of open-source models.
“What we’re seeing right now is that it seems like the recent high-quality, high-performance models by U.S. companies seem expensive compared to Chinese models,” Atalan told Fortune. “The idea of open-source models is much more exciting for some people, specifically countries other than the U.S. for the reason that people don’t want to share their enterprise data.”
As excitement builds around open-source AI, companies looking for more control over their data are embracing Chinese open-source models. Running these models locally can give companies more control over how sensitive information is handled and reduce the need to send proprietary data to outside providers.
“It’s better for you to host a local model instead of just a closer model because that means everything will stay in that computer and will not go to any company,” said Atalan. “Open-source models give that sort of relief to those people who want to keep their data.”
The approach comes with tradeoffs. “You need to have a very high-level computer in your company, like you paid $30,000 for GPUs, RAM, storage, etc,” he said.
Cheap at the cost of secure
Others in the industry are more skeptical. While some startups turn to cheaper Chinese AI models to cut costs, experts warn they may be overlooking key security risks.
Snehal Antani, co-founder and CEO of Horizon3.ai, said in a statement to Fortune that startups adopting such models “risk severe data sovereignty violations by exposing proprietary code and user data to foreign surveillance,” while also overlooking “critical vulnerabilities in model integrity and reasoning.”
Still, Atalan cautioned against viewing the trend as a wholesale migration to Chinese AI models. Rather than replacing U.S. models outright, he said companies are experimenting with alternative models for different tasks.
“A company could try to use one of those open-source models for one task and use Claude for something else. That’s very plausible,” he said.
Few companies have publicly disclosed using Chinese AI models, but they are widely available through platforms like code-hosting site GitHub and AI-model hub Hugging Face, where developers can upload, download and run open-source models. A March 16, 2026 study from Hugging Face found that Chinese open-source models accounted for 41% of downloads.
However, lower cost doesn’t eliminate risk. Even as companies experiment with cheaper models, questions remain around security, data control and how these systems perform in higher-stakes use cases.
For many companies, the decision may come down to cost and capability, not country of origin. As Atalan suggests, if a model is “cheap and capable enough” and can be run locally, businesses are likely to use it regardless of whether it came from the U.S. or China.
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