Hyundai bought control of Boston Dynamics at a $1.1B valuation in 2021. Today Unitree opened near $66B - 35x it's last VC round of $1.9B and ~7x its IPO price.

Unitree has real revenues and major brand presence, but the company is not very well understood by most investors.

In 2025, it generated $131m in Humanoid sales. 70-80% of its Humanoid sales were for research use cases, 20-30% for education and entertainment, and very little for genuine robotic use cases. Unitree's main product was a robot body that lacked a brain.

An affordable, programmable, at least semi-reliable brainless humanoid body however, is exactly what the research market demanded. Unitree G1s are ubiquitous across robotics research groups around the world. Just as AI research is dependent on physical computing hardware, robot hardware is indispensable for research in robot learning, control systems, simulation, etc.

While there are few Unitree humanoids currently deployed for real robotic work, the rise of the company has greatly accelerated global research progress. It optimized for an axis (cheap dynamic locomotion) that allowed it to capture the research market but is different from the requirements of of the deployment market (intelligence, durability, payload, safety certification). However, a lot of the engineering capability they've built as a company can and is starting to be used to develop more deployment optimized hardware models.

This is a fundamentally different approach from most American humanoid companies which are building towards operational products for consumers and businesses in a straight shot. Companies like Figure and Apptronik invest more resources in R&D and don't yet offer it to retail because they want to go direct to the larger deployment markets. They are building towards a highly functional polished product that can eventually become a development platform like Apple (as opposed to starting as a development platform). For AI, Anthropic took the mass market product capital intensive approach and it took a lot of dollars and time before lifting off on revenue. Cohere and AI21 Labs have existed for a similar amount of time, and took a more capital-light path. AI21 had to pivot, while Cohere has continued to grow, although significantly more slowly than Anthropic. Neither approach is right or wrong and history is filled with examples of successful parallels for both. You cannot compare companies taking different approaches solely on a revenue multiple basis.

The company's commercial approach is a byproduct of the Chinese private capital markets. A market where there are not as many venture dollars as the US that are willing to fund hundreds of millions to billions for R&D before any revenue is generated. Revenue growth is required to fund the next rung of capital even for potentially massive TAMs. Actuator scaling parlayed into quadrupeds, quadrupeds into the dominant robot hardware research platform. This IPO funds their transition to the most ambitious phase yet - a company building vertically integrated intelligent robots across a wide variety of form factors.

The current market valuation is suggesting that they will accomplish this transition, although it is not final yet. The outcome for Unitree differs dramatically based on if they can successfully move up market. Companies like DJI and Toyota have previously done so, while a failure to do so could have the company looking like Raspberry Pi. A company that cemented themselves within experimentalists and niche industrial markets.

However, it may not be necessary for Unitree to build SOTA research capabilities in order to scale robot sales into real deployments. If physical intelligence commoditizes, which we believe it does, then they could have plenty of externally produced models for their customers to choose from. The companies that can produce high quality hardware at scale stand to be large benefactors from the development of physical AGI.

The focus on hardware has enabled Unitree to raise a huge war chest and have access to thousands of robots that they can use for robot learning data collection and research. While various data types can be used in pretraining for robot foundation models, robot data is required for the models to become performant. To collect a large set of robot data, you will need a lot of robots. They may have actually created a stronger path for themselves to produce performant physical AI models than companies that have focused purely on robot model development years ago.

Wang Xingxing is famous for his technical chops, but he has also been an excellent business strategist.

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