Google and Westinghouse Deploy AI to Accelerate Nuclear Reactor Construction

Google and Westinghouse lean on AI to speed reactor builds

Pair say digital twin-powered scheduling will cut costs, shrink timelines for 10 planned reactors Google and atomic power biz Westinghouse Electric claim that AI will speed construction and cut the cost of building the new US power plants it is planning in response to rising demands for energy to fuel AI. The pair announced back in July that they are working together to transform how nuclear reactors are constructed and optimize their operation. Now, they are showing off how the first of those works in practice. Also back in July, Westinghouse disclosed plans to build ten additional large nuclear reactors in the US. Last month, the Trump administration backed it with an $80 billion deal to help fund those plans. The problem, according to Westinghouse, is that atomic power plants have long and uncertain construction timelines, and the considerable build costs can easily run over budget. To make matters worse, there have been little or no new nuclear builds for at least a couple of decades, so lots of vital know-how has been lost. This is where Google comes in, with help to develop a custom AI-powered platform to optimize the reactor construction process. The new system combines AI models and prediction tools from both companies with Westinghouse's WNEXUS, a 3D digital twin of its reactors. With current and historic data, it is able to predict bottlenecks, optimize construction task sequences, adjust staffing levels, and account for external factors like supply chain constraints, the pair claim. In a virtual roundtable for the media, Westinghouse chief data scientist and chief engineer of digital Scott Sidener gave a brief demonstration. The tool breaks down the build into millions of individual construction tasks, and finds the optimal daily schedule to minimize delays. If some tasks are delayed, perhaps because parts are late being delivered, these show on the supervisor's overview of the construction work. Clicking an "AI Optimize" button causes it to reorganize the schedule around the delays. "It knows what tasks are disrupted, and it can re-order, identifying tasks for the crews to perform today," Sidener said. The AI also estimates build costs, and for the air-handling equipment room used in the demonstration, the cost went down by nearly $1 million (about 25 percent) after optimization. "And this can be performed in seconds rather than a week to redo the schedule," Sidener claimed, comparing it with traditional manual working. However, while this shows that AI does have useful applications, we're not convinced that optimizing schedules is something that requires AI to accomplish. Westinghouse said that the platform is now moving from proof of concept into production use. "Cost and schedule certainty is what customers want," said chief technology officer and executive vice president of R&D and innovation Lou Martinez Sancho. Thanks to AI, the firm says it expects to have its ten reactors operational in just five to seven years, effectively cutting traditional timelines in half. But construction is not due to start until 2030, so it will still be a decade before they are producing any power for all those bit barns that are springing up. Meanwhile, datacenter and energy infrastructure supplier Schneider Electric this week unveiled its own AI-powered platform intended to unify energy, power, and building systems management. Announced at the firm's Innovation Summit North America in Las Vegas, EcoStruxure Foresight Operation is claimed to offer users greater control, visibility, and predictive insight over the workings of their facilities. Converging building and electrical systems means that the platform can streamline engineering workflows, cutting time by up to 40 percent, and boosting operational efficiency by up to 50 percent, according to Schneider. EcoStruxure Foresight Operation will be available to early adopter customers in Q3 2026, but customers interested in taking part in beta testing can get involved in the next few months. ®

Google, Westinghouse to cut US nuclear reactor construction cost, time

The system uses integration to predict bottlenecks and optimize task sequencing. Westinghouse has officially partnered with Google Cloud to deploy a custom artificial intelligence platform designed to optimize and accelerate the complex construction processes of nuclear reactors. The collaboration, which utilizes specialized AI models from both companies, aims to address the logistical hurdles that have historically slowed nuclear development. Early pilots of this new platform are already demonstrating significant time and cost savings, signaling a potential shift in how large-scale energy infrastructure is built.

How Westinghouse is reenergizing nuclear power with -- and for -- AI

Earlier this year, Westinghouse publicly shared its plan with the White House to have 10 of its state-of-the-art AP1000 nuclear reactors under construction by 2030. The new reactors would help address growing demands on the U.S. energy grid from AI and other sources, and when completed, they would provide enough combined power to electrify 7.5 million households. That's roughly every household in the five largest U.S. cities plus a few data centers. Yet reactor construction has slowed down in recent decades due to complex approvals and building processes. To reach its ambitious goal, Westinghouse has partnered with Google Cloud to develop a custom AI-powered platform using specialized models from both Google and Westinghouse -- itself a leader in AI for energy production -- that helps optimize and accelerate reactor construction. So far, early pilots of the platform have shown significant time and cost savings, and the companies are also exploring ways for AI to help enhance nuclear operations and safety. Here's a closer look. The partnership is driven by a pressing global challenge of meeting the ever-increasing energy demands with carbon free power. Dr. Lou Martinez Sancho, Westinghouse's CTO and Executive Vice President of R&D and Innovation, framed the core idea as "energy for AI and AI for energy." Nuclear is notable for offering clean, reliable power at an immense scale from a small footprint. With the United States projected to need 400 gigawatts of new power by 2040 -- a 32% increase from current usage -- conventional construction timelines are insufficient.