Jack Ma invested hundreds of millions in three post-90s geniuses at one go.

Alibaba has invested in an embodied intelligence enterprise.

According to Enterprise Early Warning System, Yuanli Lingji has completed hundreds of millions of yuan in Series A+ financing, with Alibaba (China) Network Technology Co., Ltd. as the sole lead investor.

In February this year, Yuanli Lingji just completed a 200 million yuan angel round of financing, with investors including Legend Capital, Jiukun Venture Capital, and Qiming Venture Partners.

The Series A financing was led by NIO Capital, followed by Hongtai Fund, Lenovo Capital and Incubator Group, Wuxi Venture Capital, and Zhengjing Fund. Old shareholders Legend Capital made an oversubscribed follow - on investment, and Qiming Venture Partners and Jiukun Venture Capital also made follow - on investments.

It is understood that the total amount of the Series A and A+ financings is nearly 1 billion yuan.

Founded in 2024, Yuanli Lingji's core technology is an end - to - end embodied intelligence large model. It can directly go from perception input to action output without intermediate links, achieving true end - to - end intelligent control.

It is understood that the core founding team of Yuanli Lingji comes from Megvii Technology, a well - known Chinese artificial intelligence company. The members include Fan Haoqiang, Zhou Erjin, and Wang Tiancai.

In July 2011, as one of the four contestants of the Chinese team, Fan Haoqiang, who was a high - school freshman at the time, won the second - place gold medal at the 23rd International Olympiad in Informatics (IOI) in Pattaya, Thailand, with a score of 599 out of 600. In the same year, as a high - school student, he joined Megvii and became the 6th employee.

Fan Haoqiang was recommended to Tsinghua University and was admitted to the "Yao Class" - the Experimental Class of Computer Science at Tsinghua University.

He studied at the "Yao Class" while working at Megvii. Despite the part - time work and study, he always ranked first in the class. During his freshman military training, he completed an ICCV paper (one of the top international conferences in computer vision).

Zhou Erjin is from Shaoxing. His parents are both teachers, and he studied at Shaoxing No. 1 High School. He represented China at the 2009 International Olympiad in Informatics in Plovdiv, Bulgaria, and won a silver medal. In 2010, as a sophomore in high school, Zhou Erjin won a gold medal at the National Olympiad in Informatics for Teenagers. In 2011, he won a gold medal at the International Olympiad in Informatics.

Zhou Erjin entered the Department of Electronic Engineering at Tsinghua University in 2011.

Wang Tiancai entered the School of Electronic and Information Engineering at Southwest University in 2013 and later joined Megvii as a senior researcher.

Behind them stands Tang Wenbin, the co - founder of Megvii Technology.

Tang Wenbin is from Shaoxing, Zhejiang. He has won many awards, including the first - prize in the National Informatics Olympiad League and the gold medal in the National Olympiad in Informatics. In his sophomore year of high school, he was recommended to Tsinghua University (Class of 2006, Department of Computer Science). Later, he became the first - ever winner of the "Yao Award" and served as the head coach of the Tsinghua University Informatics Olympiad for seven consecutive years.

In October 2011, Tang Wenbin co - founded Beijing Megvii Technology Co., Ltd. with his classmates Yin Qi and Yang Mu from the "Yao Class". Tang Wenbin invited high - school student Fan Haoqiang, and Fan Haoqiang, who hadn't entered college yet, became the 6th employee of Megvii. He also invited sophomore Zhou Erjin to intern at Megvii, and Zhou Erjin became the 12th employee.

What does Yuanli Lingji want to do?

It wants to do something very simple but remarkable - to turn robots into "intelligent colleagues" in your work and life, rather than a machine that you have to worry about.

First, it will install a "super brain" in the robot. This brain doesn't rely on rigid programs but learns to recognize the world, understand scenarios, and make decisions on its own through a large model. It can see, hear, understand the environment, and then do things autonomously. It's a bit like putting the intelligence of GPT into a body that can walk, move, and grab things.

Second, this robot doesn't need you to "teach it step by step". You don't have to tell it every time: "First take a step to the left, then grab something, and then put it there."

Imagine you walk out of the office and say "Help me organize the warehouse" before going home. The robot will automatically execute the task when it comes the next day, and you don't need to monitor every step.

You can tell it in everyday language: "It's a bit messy here. First clean this up, and then move that over." It can not only understand but also know what you really want it to do, not just follow the literal commands.

What has been mentioned above are all the long - term goals of Yuanli Lingji. After all, end - to - end large - model robots are "one of the most difficult directions" in the field of AI + hardware combination, because it requires the machine to "understand the world and act like a human" rather than run tasks according to programs.

The industry self - media Embodied Intelligence Lecture Hall predicts that Yuanli Lingji's future market opportunities will be more concentrated in two major scenarios: logistics robots and industrial manufacturing upgrades. Relying on the logistics scenario advantages accumulated by the founding team at Megvii and the breakthrough in end - to - end embodied algorithms, the technology can be quickly implemented in rigid - demand scenarios such as warehouse automation and flexible manufacturing. For example, through end - to - end algorithm optimization, better cluster scheduling of four - way vehicles can be achieved, and problems such as high - density storage and dynamic picking can be solved.

This article does not constitute any investment advice. This article also refers to reports from newspapers such as China Youth Daily. Thanks are extended to them all.

This article is from the WeChat public account "Pencil News" (ID: pencilnews). The author is Zhu Zhishan, and it is published by 36Kr with authorization.