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  1. 云启资本60

    Yunqi Podcast Roundtable: Three Embodied AI Founders Discuss World Models, Scaling, and Deployment Bottlenecks

    During the World Robot Conference, Yunqi Capital co-hosted a themed roundtable with SEE Fund, which was included in the podcast Attent!on. Three founders—Xu Huazhe of PokeBot, Li Qiang of Quantum Dynamics, and Liu Nianqiu of Feimo Yi Technology—shared their views on world models, scaling, and deployment.

    Why it matters: The three entrepreneurs approached the topic from models, hardware systems, and mobility respectively, specifically breaking down where the bottlenecks in embodied intelligence deployment lie and where they diverge on scaling efficiency.

  1. 云启资本62

    Yunqi Capital roundtable transcript: Manycore, Shengshu, and Galbot discuss the next paradigm after Agentic AI

    Yunqi Capital released a transcript of a roundtable at Tencent WAIC Night, hosted by Managing Partner Chen Yu, in which Huang Xiaohuang of Manycore, Luo Yihang of Shengshu Technology, and Zhang Zhizheng of Galbot discussed the next paradigm after Agentic AI.

    Why it matters: The three guests' disagreements over the world-model approach and their differing judgments on the pace of deployment offer a multi-party reference for understanding the Physical AI debate.

  1. Dyna Robotics65

    Dyna Robotics reviews 10 months of production deployment, discusses robotics bubble and three paths

    Based on a review of nearly 10 months of real customer deployments, Dyna Robotics believes the timeline for converting robotics capabilities into commercial value is longer than current funding assumptions suggest, and that much of the market is a bubble. The article presents three judgments: hardware is not the same as a channel, pretraining data scale is not the same as downstream capability, and building deployment systems is underestimated. It says Dyna-1, released in April 2025, ran autonomously for more than 24 hours on tasks such as napkin folding with a success rate above 99%, but deployments did not self-accelerate, and no one in the industry has yet validated at scale that a second deployment is faster than the first.

    Why it matters: Based on the author's review of nearly ten months of real deployments, the piece points out differences between the pace of robotics commercialization and the LLM curve, offering a useful reference for judging an industry bubble.

  1. 线性资本62

    Bot Auto's Hou Xiaodi on the First Fully Driverless Truck Order and Commercialization Standards for Autonomous Driving

    Linear Capital shared an interview with Bot Auto founder Hou Xiaodi on Zhijian autoweek, in which he said that on April 30, the day before Musk announced the production version of the SemiTruck had rolled off the line, Bot Auto completed the first fully driverless autonomous truck commercial order in the United States, with no safety driver and no real-time remote human backup.

    Why it matters: The interview systematically lays out a commercialization judgment framework centered on CPM, which can be used to understand the differences between L4 companies and L2+ companies on metrics and issues.

  1. 线性资本62

    Interview with Tashi Intelligence's Chen Yilun: No VLA, no simulation—an embodied AI company's contrarian bets

    Linear Capital reposted LatePost's interview with Tashi Intelligence founder Chen Yilun, who laid out a technical path that differs from the mainstream: he rejects the VLA architecture that grows an action head on top of a VLM, and instead develops its own AWE (AI World Engine), aiming to represent physical quantities such as time, space, and force; it has also abandoned teleoperation data collection in favor of a self-developed collection device combining a wearable glove and a first-person-view camera.

    Why it matters: The interview systematically presents technical judgments and a data approach that differ from the mainstream VLA path, giving readers a basis for understanding the specific grounds of the divergence in embodied AI approaches.

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