He Xiaopeng launches Xpeng's next-gen Iron humanoid robot during a press conference at the company's headquarters in Guangzhou on November 5, 2025. He Xiaopeng launches Xpeng's next-gen Iron humanoid robot during a press conference at the company's headquarters in Guangzhou on November 5, 2025. It was a controversy laced with pride for He Xiaopeng. In November, He, the founder and CEO of Chinese physical AI firm XPeng, had just debuted his new humanoid robot, IRON, whose balance, posture shifts, and coquettish swagger mirrored human motion with such eerie precision that a slew of netizens accused him of faking the demonstration by putting a human in a bodysuit. To silence the naysayers, He boldly cut open the robot's leg live on stage to reveal the intricate mechanical systems that allow it to adapt to uneven surfaces and maintain stability just like the human body. "At first, it made me sad," He tells TIME in his Guangzhou headquarters.

As the data center backlash grows, support is growing for server factories and the hundreds of jobs they're expected to bring. Last month, Pamela Griffin and two other residents of Taylor, Texas, took to the lectern at a city council meeting to object to a data center project. But later, they sat back as council members discussed a proposed tech factory. Griffin didn't speak up against that development. A similar contrast is repeating in communities across the US.

Shipping experts say the big winter storm across a wide swath of the country should be business as usual--if their safeguards hold. Up to two-thirds of the US is facing down the threat of serious snow, cold, and ice this weekend, with the potential to snarl roads (and the businesses that depend on them) from Texas up to New York City . At this point, grocery stores, logistics experts, warehouse operators, and trucking companies have been prepping for days. Still, the effects on the supply chain--and the retail store shelves that depend on them--are yet to be determined. On one hand, this is winter business as usual.

No one can truly have it. The world is too interconnected for nations to go it alone. Governments plan to pour $1.3 trillion into AI infrastructure by 2030 to invest in "sovereign AI," with the premise being that countries should be in control of their own AI capabilities. The funds include financing for domestic data centers, locally trained models, independent supply chains, and national talent pipelines. This is a response to real shocks: covid-era supply chain breakdowns, rising geopolitical tensions, and the war in Ukraine. But the pursuit of absolute autonomy is running into reality.

Plus: why it's so hard to achieve AI sovereignty. At Davos this year Trump is dominating all the side conversations. There are lots of little jokes. The US president is due to speak here today, amid threats of seizing Greenland and fears that he's about to permanently fracture the NATO alliance. Read Mat's story to find out more . This subscriber-only story appeared first in The Debrief, Mat's weekly newsletter about the biggest stories in tech.

China's Renewable Energy Revolution Is a Huge Mess That Might Save the World A global onslaught of cheap Chinese green power is upending everything in its path. No one is ready for its repercussions. There's a particular kind of sci-fi nerd who equates fusion tech with utopia. If we could only harness the engine of the stars, it would uncork near limitless energy and neatly sweep away a whole mess of humanity's problems. But how would that work exactly? What would the transition look like?

Supply chains are integral to global economic stability, yet disruptions can swiftly propagate through interconnected networks, resulting in substantial economic impacts. Accurate and timely inference of supply chain resilience--the capability to maintain core functions during disruptions--is crucial for proactive risk mitigation and robust network design. However, existing approaches lack effective mechanisms to infer supply chain resilience without explicit system dynamics and struggle to represent the higher-order, multi-entity dependencies inherent in supply chain networks. These limitations motivate the definition of a novel problem and the development of targeted modeling solutions. To address these challenges, we formalize a novel problem: Supply Chain Resilience Inference (SCRI), defined as predicting supply chain resilience using hypergraph topology and observed inventory trajectories without explicit dynamic equations. To solve this problem, we propose the Supply Chain Resilience Inference Hypergraph Network (SC-RIHN), a novel hypergraph-based model leveraging set-based encoding and hypergraph message passing to capture multi-party firm-product interactions. Comprehensive experiments demonstrate that SC-RIHN significantly outperforms traditional MLP, representative graph neural network variants, and ResInf baselines across synthetic benchmarks, underscoring its potential for practical, early-warning risk assessment in complex supply chain systems.

In contemporary retail, the variety of products available (e.g. clothing, groceries, cosmetics, frozen goods) make it difficult to predict the demand, prevent stockouts, and find high-potential products. We suggest an agentic AI model that will be used to monitor the inventory, initiate purchase attempts to the appropriate suppliers, and scan for trending or high-margin products to incorporate. The system applies demand forecasting, supplier selection optimization, multi-agent negotiation and continuous learning. We apply a prototype to a setting in the store of a middle scale mart, test its performance on three conventional and artificial data tables, and compare the results to the base heuristics. Our findings indicate that there is a decrease in stockouts, a reduction of inventory holding costs, and an improvement in product mix turnover. We address constraints, scalability as well as improvement prospect.

Yttrium plays a critical role in everything from aircraft engines to semiconductors. China controls the vast majority of the market--and that's not changing anytime soon. The alarm hasn't yet reached the general public, but tension is beginning to build in the corridors of the aerospace industry, in microchip laboratories, and in government offices. For months, an element almost invisible to the world--yttrium--has become the silent center of a new global dispute. Supplies are thinning, prices are skyrocketing, deliveries are stalling.

Drug shortages pose critical risks to patient care and healthcare systems worldwide, yet the effectiveness of regulatory interventions remains poorly understood due to information asymmetries in pharmaceutical supply chains. We propose \textbf{ShortageSim}, addresses this challenge by providing the first simulation framework that evaluates the impact of regulatory interventions on competition dynamics under information asymmetry. Using Large Language Model (LLM)-based agents, the framework models the strategic decisions of drug manufacturers and institutional buyers, in response to shortage alerts given by the regulatory agency. Unlike traditional game theory models that assume perfect rationality and complete information, ShortageSim simulates heterogeneous interpretations on regulatory announcements and the resulting decisions. Experiments on self-processed dataset of historical shortage events show that ShortageSim reduces the resolution lag for production disruption cases by up to 84\%, achieving closer alignment to real-world trajectories than the zero-shot baseline. Our framework confirms the effect of regulatory alert in addressing shortages and introduces a new method for understanding competition in multi-stage environments under uncertainty. We open-source ShortageSim and a dataset of 2,925 FDA shortage events, providing a novel framework for future research on policy design and testing in supply chains under information asymmetry.