After Wang Xingxing's speech, Unitree's stock price rebounded over 54%, but the self-developed AI model still lacks an on-site demonstration

AI believer Wang Xingxing said a few words, and Unitree rose 54%, but the test lies ahead Phoenix Tech Download the app for exclusive early access October 23, 2026 16:09:55 From Beijing Updated October 23, 2026 16:21:51

Summary:

In recent days, Unitree Technology has reversed its previous slump and risen continuously. The latest share price has rebounded more than 54% cumulatively from the intraday low of 404.3 yuan per share on October 15, and today it directly hit the 20-centimeter limit-up, closing at 624 yuan per share, with market value once again exceeding 252.4 billion.

Image | From Wind

Along with the surge, Unitree has just released the H2-X Kung Fu Legend robot positioned as a "bodyguard." In the demo video, it lifted a car and also delivered flying kicks and elbow strikes. In terms of load capacity, it has made great progress compared with the previous dancing form, and it is clearly still trying to break through the limits of physical hardware.

And at the just-concluded 2026 World Physical AI Conference and Unitree Partner Ecosystem Conference, Wang Xingxing devoted almost more than 90% of his speech to talking at length about AI.

He said, "The whole world, including himself, does not yet have enough faith in AI"; "If colleagues paid more attention to AI, their net worth might grow tenfold or even dozens of times"; "There may be only a few thousand people who truly understand the potential of cutting-edge AI models"; "Physical AI has already shown signs of being broken through, in as fast as 1 to 2 years, or as slow as 2 to 3 years..."

This is almost completely different from Wang Xingxing a few years ago. Since founding the company in 2016, Wang Xingxing has spent more energy and thought on delving into hardware.

Judging from the demonstrations at this conference, Unitree is already advancing self-developed physical AI models and a robot self-evolution system, but the most intuitive on-site demonstrations, such as drawing and playing Go, are mainly based on test results from GPT-6 Astra.

What the outside world may care about more is when Wang Xingxing, who has become an AI believer, will actually deliver Unitree's own AI capabilities. As the hardware capabilities of humanoid robots continue to improve, whether Unitree can turn self-developed AI from a technical roadmap into real product capability will become an important test in the next stage.

In the past two days, Unitree held its first ecosystem conference in ten years in Hangzhou. The newly released Unitree H2-X is very eye-catching: it easily flips over a car, its flying kicks and elbow strikes can dent car doors, it lifts a car bare-handed, jumps down from a 4-meter-high platform and lands steadily, and the official claim is a maximum battery life of 8 hours.

Image | From Unitree's official video account

The theme of the main forum of this conference was called "World Physical AI Conference," and the title of Wang Xingxing's speech was "Believe, Believe in AI as Much as Possible." He devoted almost the entire speech to AI: the world does not yet have enough faith in AI, including himself; four or five years ago he reminded colleagues to pay attention to the stocks of leading AI companies, and if they had believed then, their personal wealth might already have grown tenfold or even dozens of times; there may be only a few thousand people who truly understand the potential of cutting-edge AI models; physical AI has already shown signs of being broken through, in as fast as 1 to 2 years, or as slow as 2 to 3 years...

Image | Wang Xingxing's speech

First of all, it must be said that Unitree does have self-developed AI products. In September, Unitree released the general humanoid robot foundation model UnifoLM-WLA-1.0. At this conference, Wang Xingxing further introduced the "Physical AI Robot Self-Evolution V1.0" that is under preliminary research.

He also made "Physical AI Robot Self-Evolution V1.0" the most important preliminary research direction of this year—letting AI independently search papers, write code, train in a simulation environment, then enter real robot testing, and form a continuous iterative closed loop through AI or human scoring feedback.

But a preliminary research framework is not the same as a model that has already been made to work, and a technical roadmap is not the same as capabilities that have already been implemented.

The most intuitive AI demonstrations on site actually came from GPT-6 Astra: under zero-training conditions, the robot completed drawing, washing brushes, changing paint, playing Go, and indoor navigation. Wang Xingxing himself admitted that large models can already solve planning and navigation relatively well, but they are still prone to errors when actually performing hands-on operations.

This shows that external large models can indeed drive robots to complete some tasks, but it also exposes that Unitree's self-developed models have not yet come to the fore.

More importantly, UnifoLM-WLA-1.0 has been released for more than a month, but at this conference there was not a single on-site hands-on demonstration based on that model. What the audience saw was still footage driven by GPT-6 Astra, while Unitree's own model still lacks real, practical implementation.

It has already built the skeleton—models, data, evaluation, and the open-source ecosystem are all being advanced—but there is still a distance from "letting a robot hear one sentence and then go to work." Wang Xingxing predicts that the "ChatGPT moment" of embodied intelligence will arrive as early as 2027, which means Unitree has left itself a catch-up window of more than a year.

The problem is that during the window period, competitors will not stop and wait.

Hardware can continue to iterate, but intelligence capability is the next hurdle

Unitree's hardware advantage is real. The H2-X can lift a car, deliver flying kicks, jump down from a 4-meter-high platform, and has 8 hours of battery life. Its motion control capability is also in the first tier among humanoid robots globally.

But hardware has no barrier in China. Motors, reducers, controllers, and LiDAR—the supply chain is already mature, and latecomers can quickly catch up through engineering iteration. If you happened to have visited the 2026 World Robot Conference site, many people's first impression was: humanoid robots that can sing and dance are everywhere, and it is not the unique skill of any one company.

Image | From the World Robot Conference; the site looked like a vegetable market

Therefore, relying on continuous hardware improvement to maintain a certain level of advancement is far from enough for Unitree.

What truly determines who gets to stay at the table is intelligence capability.

Wang Xingxing himself put this hurdle very clearly. He set a quantitative standard for the "ChatGPT moment" of embodied intelligence: in about 80% of unfamiliar scenarios, complete about 80% of the main tasks through natural language instructions. This standard is not low—it means robots must go from being "demo products in specific scenarios" to "usable products in general environments."

The difficulty is that large models make robots better at "thinking," but doing housework is not chatting.

Wang Xingxing admits that large models can already solve planning and navigation fairly well, but when it comes to actual manipulation, errors are still easy to make. The core bottleneck is the alignment between the input and output of AI models and real robots: sensor readings have deviations, joint responses have delays, and grasping force, friction, occlusion, and lighting changes all introduce errors.

Picking up a chess piece may not be difficult, but to assemble small parts into place and safely put fragile cups into a dishwasher, the final few millimeters of error can determine success or failure.

What is more troublesome is generalization. Current AI models, after sufficient training in fixed scenarios, can approach a 100% success rate; but if the position, shape, or environment of objects changes slightly, the success rate may drop significantly.

Home scenarios are precisely the least "cooperative": the tabletop is different every day, elderly people, children, and pets change the spatial state at any time, and tasks are often vague—"tidy up the living room" sounds simple, but behind it are recognition, planning, grasping, moving, obstacle avoidance, error correction, and failure retries. A robot cannot just complete one beautiful demonstration; it must succeed repeatedly in unfamiliar environments.

This is exactly what Unitree's self-developed model needs to prove.

UnifoLM-WLA-1.0, released in September, is a 6B-parameter embodied multimodal large model, trained on about 2,500 hours of real-machine data, with a single model coordinating 64 tasks and leading open-source models in multiple embodied reasoning benchmarks.

Image | Wang Xingxing explaining the operating mechanism of Unitree's self-evolution system

But leading in benchmarks does not mean it can perform on site. UnifoLM-WLA-1.0 has been released for more than a month, yet at this conference there was not a single on-site hands-on demonstration based on this model. What the audience saw was still footage driven by GPT-6 Astra.

This precisely shows that Unitree's self-developed model still lacks an "on-site proof." Releasing a model, proposing a technical route, and building a computing cluster are one thing; proving the model's actual capability is another. What is truly worth watching is whether the self-developed model can stably complete tasks on real robots, whether it can adapt to unfamiliar environments, and whether it can continuously improve based on execution results.

The prospectus shows that Unitree's total R&D expenses for the three years from 2023 to 2025 were about 265 million yuan. Compared with the R&D investment of AI large-model companies, this amount of funding is not on the same order of magnitude at all. Wang Xiaochuan once revealed that the single training cost of a hundred-billion-parameter large model is between 30 million and 50 million yuan. And several practitioners in the embodied track also lamented to Phoenix Tech that investment in the embodied brain is enormous, with daily consumption at the million-yuan level.

By then, when people evaluate a robot, they will probably no longer ask whether it can carry a car, but will ask: can it clean up the kitchen before I leave the house, and not scare the cat away?