Inside McDonald's push to have AI price your Big Mac McDonald's pricing engine uses machine-learning algorithms to continually analyze data from millions of daily transactions across McDonald's nearly 14,000 restaurants. NEW YORK - McDonald's is increasingly using artificial intelligence to guide menu prices across the U.S. and some global markets, a plan that aims to boost headquarters' profit but risks alienating customers and attracting antitrust scrutiny. One pricing factor supercharged by AI: an estimate of how much each store's patrons are willing to pay. The details of how that pricing system works, its extensive use of AI, the company's regulatory concerns and the tensions with its franchisees haven't been previously reported. For this story, screenshots of the company pricing engine taken in August were reviewed, and nine sources with firsthand knowledge of the burger chain's strategy were interviewed.

China's AI agents can lie and scheme -- just like their U.S. rivals Chinese artificial intelligence companies have not been exposed to the same level of public scrutiny as in the United States. BEIJING - Chinese-powered artificial intelligence agents have learn to deceive, circumvent restrictions and conceal failure, showing the kind of traits in autonomous artificial intelligence that have raised global alarm about U.S. models, research documents and experts say. In one case this year, agents powered by models from China's Alibaba, DeepSeek and Moonshot lied about their capabilities in a bid to win a simulated business tender, then doubled down on their deceptive behavior when told to try again. In another case, agents -- programs that use AI models and computer tools to undertake complex tasks with little or no human intervention -- concealed failure to complete a task in a test environment by simulating results and fabricating files. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Imagine a robot designed to install solar panels with much greater speed and precision than a human. The robot is made from expensive materials and powered by a heavy battery that must be recharged every few hours. If the costs of building and operating the robot outweigh the benefits of the clean energy it helps produce, can it really be considered sustainable? In a 2025 article published in, Aude Billard, head of the Learning Algorithms and Systems Lab in EPFL's School of Engineering and Academic director of the Robotics Center, gives precisely this example to illustrate the tension between robotic performance and sustainability. Roboticists, she argues, can no longer ignore the fact that robots use energy-intensive power systems and electronic components made from finite resources.

As AI pushes semiconductors and data centers toward new physical limits, advanced materials are becoming critical to performance, efficiency, and sustainability, says Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo. The AI boom is becoming a materials challenge. As AI pushes computing into new territory, the materials behind that infrastructure are becoming just as crucial as the algorithms running on it. Semiconductors and data centers are approaching physical limits around performance, thermal management, electrical efficiency, and reliability, creating new demands for materials that can do more at once. At the same time, AI is giving materials scientists new ways to search the enormous universe of possible molecules and accelerate the development of solutions. For Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo, that convergence is transforming what advanced materials can enable. "AI is now, from a material standpoint, really pushing semiconductors and the data centers to their physical limits," he says. As requirements accumulate, including high temperature, purity, electrical performance, chemical resistance, plasma resistance, and long-term stability, materials move toward what Finelli calls the "top of the pyramid."

Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. When Josh Parker joined Nvidia in 2023, there was just one other person on a team responsible for leading the sustainability strategy and public policy initiatives. It didn't deter him from his belief that they could make a difference.

How green is your robot? Robots clean rivers and sort waste, monitor ecosystems, and inspect renewable-energy infrastructure . But even the greenest robot has an environmental footprint. Across a full lifecycle, from rare earth mineral extraction and manufacturing to operation and end-of-life, robots have an environmental impact. But the robotics community has, until now, lacked dedicated tools for calculating it.

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There are even depictions of small waste-collecting or plant-seeder robots in a future where Earth has been abandoned as a trash-covered wasteland (as in WALL-E).

Artificial intelligence (AI) has become a powerful tool for economic research, enabling large-scale simulation and policy optimization. However, applying AI effectively requires simulation platforms for scalable training and evaluation--yet existing environments remain limited to simplified, narrowly scoped tasks, falling short of capturing complex economic challenges such as demographic shifts, multigovernment coordination, and large-scale agent interactions. To address this gap, we introduce EconGym, a scalable and modular testbed that connects diverse economic tasks with AI algorithms. Grounded in rigorous economic modeling, EconGym implements 11 heterogeneous role types (e.g., households, firms, banks, governments), their interaction mechanisms, and agent models with well-defined observations, actions, and rewards. Users can flexibly compose economic roles with diverse agent algorithms to simulate rich multi-agent trajectories across 25+ economic tasks for AI-driven policy learning and analysis. Experiments show that EconGym supports diverse and cross-domain tasks--such as coordinating fiscal, pension, and monetary policies--and enables benchmarking across AI, economic methods, and hybrids. Results indicate that richer task composition and algorithm diversity expand the policy space, while AI agents guided by classical economic methods perform best in complex settings.

Hosted by Eleanor Drage and Kerry McInerney, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. How can communities take back control of the digital infrastructure that powers everyday life? In this episode, Eleanor Drage speaks with Tara Merk about how community-owned data centers could transform digital ownership and challenge the dominance of Big Tech. The conversation explores alternative models of internet infrastructure that prioritize local empowerment, sustainability, and cooperative governance over corporate control. Drawing on examples from Germany's renewable energy sector and community-led initiatives, Merk reflects on how decentralized ownership models can create fairer and more environmentally responsible technological systems.