Yao Shunyu said at Tencent Cloud’s AI industry application conference that Hunyuan 3 rebuilt pretraining and reinforcement-learning infrastructure, changed data and evaluation, and assigned its strongest post-training staff to improve Yuanbao first; he named coding agents, multimodality, and embodied AI as Tencent’s next focus areas.

Ruan Yifeng excerpts observations from U.S. analysts who visited 14 Chinese AI and robotics companies in early May: the article estimates U.S. AI compute at about 8 times China’s by the end of 2025, while Chinese firms’ intelligence output per unit of compute is estimated at 4-7 times naive scaling.

Alex Imas and Phil Trammell argue that robots can be copied and scaled after AGI, while scarce human skills such as ballet performance remain fixed; the post does not disclose a quantitative model or timeline.

Yi-Ling Liu compares U.S. and Chinese AI anxiety through labor, companionship, and agency: over 70% of U.S. teenagers report using chatbots as companions, while China is projected to reach 200 million single-person households by 2030.

Latent Space interviewed The Fourth Law founder Yaroslav Azhnyuk for a two-hour episode covering FPV drones, five levels of autonomy, eight dimensions of the autonomous battlefield, and China’s manufacturing advantage; the transcript claims Ukraine produced 4 million FPV drones last year and discusses a hypothetical Chinese capacity of 4 billion.

Jiqizhixin translated a MoE Capital blog tracing two world-model lineages. The article says more than $10 billion entered the category over 18 months, and cites DreamDojo as using 44,711 hours of first-person video pretraining to reach r=0.995 correlation with real-world robot policy outcomes.

Jensen Huang told Carnegie Mellon’s 2026 CS graduates that skilled trades have better prospects; Randstad says trade demand is growing three times faster than white-collar roles, with robotics technician jobs up 107%.

Yann LeCun discussed LLM limitations on the Unsupervised Learning podcast, covering his 2027 forecast, AMI’s bet on world models, his reasons for leaving Meta, and major disagreements with Geoffrey Hinton and Yoshua Bengio over Turing Award-era views.

Ted Xiao divides nearly a decade of robot learning into three eras: Google’s team trained RT-1 on 87,000 teleoperation trajectories, then adapted 5B to 55B VLMs into VLA policies for RT-2.

The speaker presented a physical AGI roadmap with six named components: video world models, WAM, EgoScale, dexterity scaling laws, physical reinforcement learning, and DreamDojo; the snippet also mentions a 2016 OpenAI DGX-1 signing story with Jensen and Elon.

Applied Intuition’s founders reviewed a 10-year physical AI path, with the company valued at $15B. The post cites 30+ products, 18 of the top 20 non-Chinese automakers as customers, and L4 driverless trucks in Japan. The key constraint is onboard deployment: millisecond latency, low power, small models, and safety validation.

Honor's humanoid robot Lightning ran 50:26 at the 2026 Beijing Yizhuang half marathon, faster than the men's human world record of 57:20; the post also says Unitree H1 did a 1.9 km winding course in 4:13. The post cites nearly 200 embodied-AI financings and over RMB 30 billion in Q1 2026, plus Spirit AI's $455 million Pre-A on April 16. The real signal is capital shifting from robot hardware to model-centric 'brains.'

Companies and investors put $6.1 billion into humanoid robots in 2025, 4x 2024, and MIT Technology Review attributes the surge to a shift in how robots learn. The piece highlights two mechanisms: around 2015, simulation plus reward signals enabled millions of trial-and-error runs; after ChatGPT in 2022, robotics models took images, sensors, and joint states to predict dozens of motor commands per second. The key change is data-driven learning over hand-written rules; the provided text is truncated, so later examples are not fully disclosed.

Micro1 hires thousands of contractors across 50+ countries to film chores at home with iPhones and sell that real-world data to humanoid robotics companies. The piece cites $15/hour pay for one worker, says robotics firms spend over $100 million a year on such data, and notes $6 billion+ went into humanoids in 2025. The real issue is data governance: workers know the footage trains robots, but the post shows they often do not know how it is stored, shared, or deleted.

Waymo placed a $6.25 task on a delivery platform to send a rider 1 km away to close a robotaxi door, with another $5 after completion. The post frames this as software dispatching human labor, not a one-off gig, and argues platform workers are becoming a human API inside automated workflows. The point to watch is the AI-plus-labor loop; the post does not disclose Waymo's scale, frequency, or formal product design.

The piece argues China has embedded AI into manufacturing, with 30,000+ smart factories, and over half of all industrial robots installed globally in 2024 going to Chinese plants. It cites shop-floor data: Zeekr's Ningbo plant uses 800+ robots, Xiaomi says its Beijing factory produces one car every 76 seconds, while only 18% of U.S. manufacturers report a formal AI strategy and two-thirds struggle to scale pilots. The real point is not frontier models but AI deployment in factory automation, scheduling, and inspection.

Jensen Huang said at Davos that global VC investment topped $100 billion in 2025, with most capital going to AI-native startups building the AI stack’s application and infrastructure layers. He described AI as a five-layer stack: energy, chips and computing infrastructure, cloud data centers, models, and applications, and cited a US nursing shortage of about 5 million where AI can handle charting and transcription. The key point for practitioners is that the bottleneck is not just models, but the full infrastructure and labor chain.

At CES, Silicon Valley 101 discussed humanoid robot deployment and cited official figures: 21 of 38 humanoid exhibitors were Chinese companies. Guests noted Boston Dynamics plans Atlas deliveries in 2026 and 30,000 annual capacity by 2028, but argued scale claims do not prove product-market fit; in warehouses, wheeled bases plus arms often beat humanoids on ROI.

CES 2026 drew 148,000+ attendees and 4,100+ exhibitors, with Chinese companies making up nearly a quarter and standing out in AI hardware and robotics. The post ties their optimism to manufacturing-led iteration speed, not one breakthrough; Lenovo Qira, Nvidia Vera Rubin, and AMD Helios show the race is shifting to cloud and hybrid AI.