下芬新闻——王兴新搬走时间表。 8月，在Unitree的STAR市场首播的第二天，他将一个真正的“ChatGPT瞬间”放置在了几年到十年之间，在10月22日的杭州，在世界物理AI大会和Unitree伙伴峰会上，他更尖锐地指出：物理AI已经显示的迹象是"断开的，"在一到两年内就可能采取决定性的步骤，或者在外边有2到3个。
他命名的瓶颈没有改变。 模型必须把所预测的与真实世界中的马达、手指和不完善的传感器相配合。 这种调整问题——按厘米和毫秒漂移的投入和产出——仍然将舞蹈与家务分开。 改变的是他对曲线的读数。 边疆软件实验室，他称，在某种内部意义上已经达到了模型自我演化：系统可以写代码，收集数据，并且只用轻度人类引导来改进。 公众感到的滞后是传播，他仍将传播从实验室突破到日常使用，用到5到10年的时间。
Unitree想为机器人拥有更多的堆积物 Wang 称公司正在对物理AI的“自演V1.0”大型模型进行预先研究，并推进适合自身硬件的较小模型。 除了软件之外，他还列出了两个工业赌注：提高量产能力以达到量产，一个真正的量产市场所需要的量将远远超过他所列举的大约一万到二万个已经建成的G1单位，并且为自主开发、模拟、真实世界测试和反馈的闭环建立计算基础。 在他的框架设计中，下一个时代的稀缺投入是数据，计算，芯片供应，能量和矿物，就像聪明的演示。
部署命令与模型版本一样重要。 他认为，商业服务角色的移动速度可能快于家庭或重工业楼层。 这种等级制度与人类运输已经集中的地方相符：结构化的物流和厂房，而不是客厅。 2027-2028年关于家庭的意向语言就坐落在这条狭窄的近期道路旁边。
会议还为索赔提供了公开文书。 组织者宣布世界体能AI竞赛向世界各地的团队和个人开放，每年奖金为数千万元人民币和8个赛道——包括自由行动、运动、战斗、狡猾操纵、生产、体育活动、多式联运和老年人护理。 竞技并不发明概括。 它们确实强迫执行类似的任务，吸引外部人才，并将供应商日变成征聘漏斗。
阅读反对八月言论，十月信息不如在上市季监督下重新校正，自相矛盾。 然后，首要的是冷却幻想，Unitree已经可以大规模替代劳动力。 而Unitree打算持有硬件、模型环路和生态系统奖。 投资者和工厂买家仍然会问同样的无礼问题：场景转移时的成功率，完成的任务成本，以及显示在发货软件中是“自我演变”还是只在主旨名词中。
直到机器人在别人的大楼里完成任务，王先生把时间缩短了他愿意做那个测试接下来的两三年将显示他看到的裂缝是否变成一扇门。
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NextFin News — Wang Xingxing is moving his timeline. In August, speaking the day after Unitree’s STAR Market debut, he placed a true “ChatGPT moment” for embodied machines somewhere between a few years and a decade—defined as a robot that could enter an unfamiliar home and finish most daily tasks on voice or text alone. In Hangzhou on October 22, at the World Physical AI Conference and Unitree partner summit, he struck a sharper note: physical AI already shows signs of being “broken through,” with a decisive step possible in one to two years, or two to three at the outside.
The bottleneck he named did not change. Models must align what they predict with what motors, fingers and imperfect sensors actually do in the real world. That alignment problem—inputs and outputs that drift by centimeters and milliseconds—still separates choreography from chores. What shifted was his reading of the curve. Frontier software labs, he said, have in some internal sense already reached model self-evolution: systems that write code, gather data and improve with only light human steering. The lag the public feels is diffusion, which he still put at five to ten years from lab breakthrough to everyday use.
Unitree is trying to own more of that stack for robots. Wang said the company is pre-researching a “self-evolution V1.0” large model for physical AI and pushing smaller models fitted to its own hardware. Beside software, he listed two industrial bets: raising mass-production capacity toward the volumes a true mass market would demand—far above the roughly ten to twenty thousand G1 units he cited as already built—and building the compute base for a closed loop of autonomous development, simulation, real-world test and feedback. In his framing, the scarce inputs of the next era are data, compute, chip supply, energy and minerals as much as clever demos.
Deployment order matters as much as model version. Commercial service roles, he suggested, will likely move faster than either the household or the heavy industrial floor. That hierarchy matches where humanoid shipments have already concentrated: structured logistics and factory cells, not living rooms. Aspirational language about homes in 2027–2028 sits beside that narrower near-term path.
The conference also gave the claim a public instrument. Organizers announced a World Physical AI Competition open to teams and individuals worldwide, with annual prize money in the tens of millions of yuan and eight tracks—among them free movement, athletics, combat, dexterous manipulation, production, sports events, multimodal interaction and elder care. Contests do not invent generalization. They do force comparable tasks, attract outside talent and turn supplier days into a recruitment funnel.
Read against the August speech, the October message is less a contradiction than a recalibration under listing-season scrutiny. Then, the priority was cooling fantasies that Unitree could already replace labor at scale. Now, with partners in the room and a competition on the calendar, the priority is to argue that the technical wall is cracking—and that Unitree intends to hold the hardware, the model loop and the ecosystem prizes when it does. Investors and factory buyers will still ask the same rude questions: success rates when the scene shifts, cost per completed task, and whether “self-evolution” shows up in shipped software or only in keynote nouns.
Breakthrough talk is cheap until a robot finishes the job in someone else’s building. Wang has shortened the clock he is willing to put on that test. The next two or three years will show whether the crack he sees becomes a door.
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