Insider Brief
- QCraft has raised $100 million in a Series D round to expand its autonomous driving and physical AI capabilities, including work in world models and reinforcement learning.
- The company’s QPilot system is deployed in more than 1 million vehicles across nearly 30 models, with plans to expand urban autonomous driving features to over 50 additional models in 2026.
- QCraft is advancing L4 autonomous logistics deployments and preparing a robotaxi pilot in 2026, while developing a broader physical AI platform for real-world applications.
QCraft has raised $100 million in a Series D funding round to advance its autonomous driving and physical AI efforts.
According to the China-based company, the round included a mix of financial and strategic investors, including Ningbo Ninghai Xingtaihe Fund, Wonderland Capital and Liangxi Science and Innovation Industry Investment Fund, along with backing from an automotive OEM and an automotive electronics supplier.
“2026 marks a critical inflection point in AI development. We are transitioning from ‘human-like’ intelligence to superhuman intelligence,” Chairman and CEO Dr. James Yu said in a statement. “Over the next five to ten years, the greatest opportunities in AI will emerge in the physical world — and that is precisely what makes autonomous driving so exciting. It is the best and most direct gateway into physical-world AI.”
What will QCraft Do With the New Capital?
QCraft said the proceeds will be used to expand research in world models and reinforcement learning, while also strengthening its global talent pipeline and organizational capabilities.
The funding comes as the company scales deployment of its QPilot intelligent driving system, which it said is now installed in more than 1 million vehicles across nearly 30 production models through partnerships with about 10 automakers. The company said it expects to expand its urban Navigate on Autopilot capabilities to more than 50 additional vehicle models in 2026.
QCraft is also developing a world model and reinforcement learning platform aimed at advancing broader physical AI applications, with a public debut expected in the near term.
In parallel, the company is expanding its presence in autonomous logistics, where it has deployed L4 systems in multiple Chinese cities, and is preparing a robotaxi pilot program planned for 2026, with broader deployment targeted for 2027.
The company also said it is focusing its strategy on scaling L4 autonomy and extending its technology into more generalized physical-world AI systems as it continues international expansion.
Featured image: QCraft
