What if the key obstacle to addressing the rising energy demand lies not in generation capacity but rather in the lack of visibility into the electric grid?

In a special live episode of Where the Internet Lives, a podcast by Google and Latitude Studios, Tapestry—a project from Alphabet—offers insights into its mission to create the first unified model of the electric grid. Tapestry’s AI-driven tools provide essential visibility into the currently fragmented energy system, empowering grid planners and operators to integrate renewable energy sources, optimize energy distribution, and enhance system reliability.

During the episode, Latitude’s Stephen Lacey interviews Page Crahan, Tapestry’s general manager, who highlights four real-world case studies showcasing the transformative impact of AI on grid operations. These cases include PJM’s transmission planning, strategies to address curtailment in Chile, maintenance protocols in New Zealand, and Brazil’s efforts toward sustainable data center development. Each example demonstrates the practical application of Tapestry's insights and offers valuable guidance for improving grids globally.

Additionally, the event addresses the dual aspects of the AI-energy connection: the development of data centers necessary to support the AI-driven era and how AI technologies can facilitate the transition to cleaner energy solutions. By leveraging AI, Tapestry aims to tackle urgent energy challenges while promoting responsible and efficient energy practices.

Data centers driven by AI are significantly increasing electrical loads, with projections suggesting they may account for 12% of US electricity use by 2028. Conventional power plants struggle to meet these rapidly fluctuating demands, while renewable energy sources like solar, wind, and batteries offer more effective solutions.

As AI data centers grow, the nature of electricity demand is shifting. Traditional electric machines, which include motors and generators, operate with inertia that helps maintain grid stability. However, inverter-based resources (IBRs) such as solar panels and batteries, which have no moving parts, can respond to changes in demand almost instantaneously, making them better suited for the quick load variations seen in modern data centers.

Research by Microsoft, OpenAI, and NVIDIA has highlighted the advantages of utilizing battery energy storage systems (BESS) for managing power fluctuations during AI training cycles. Compared to other methods, energy storage ranks highest in reliability, performance, and integration ease, showcasing the potential of IBRs to stabilize the grid.

Moreover, IBRs can be deployed rapidly, making them a viable supply option as data centers expand. They are also more cost-effective and environmentally friendly than fossil gas alternatives, which face supply bottlenecks and contribute to pollution.

As the grid evolves with inverter-based loads and resources, stability will increasingly rely on advanced electronic control rather than traditional inertia. This shift suggests an urgent need to adapt our energy supply choices to align with the demands of the 21st-century grid, ensuring reliability and sustainability amid a burgeoning AI landscape.

March 3, 2026
Grid Assets: Refining Architecture’s Role in the Energy Puzzle
By Brendan Owens

As winter grips North America, the electricity grid faces immense strain due to high energy demands from buildings. However, advancements in AI and technology present concrete solutions. Buildings can now intelligently collaborate with the grid to optimize energy use, thereby alleviating peak demand and enhancing overall efficiency. This transformative approach not only makes electricity more affordable but also bolsters grid reliability, representing a significant opportunity for architects, engineers, and building owners.

Many essential services such as hospitals and data centers see a growing demand for electricity that the current grid infrastructure struggles to support. However, instead of costly new power plants, we can harness existing systems more efficiently. Modern building designs can turn structures into energy assets—dynamic participants in a smarter grid. For instance, using battery storage and distributed energy resources allows buildings to absorb excess energy during off-peak hours, functioning as shock absorbers that balance grid supply and demand.

Examples include schools and sports stadiums that can utilize their facilities' downtime to store energy. Similarly, data centers can enhance operations by employing on-site energy generation and demand-response technologies, creating synergies within their communities. By implementing smart energy systems, buildings can flexibly adjust their consumption patterns, significantly easing the load on the grid during peak periods.

The vision extends to integrating shared battery facilities with district energy systems to optimize energy distribution, as explored in a project in Dallas. As architectural professionals, the challenge lies in creatively deploying these solutions. The good news? The tools for grid-interactive buildings already exist. Collaborative efforts and innovative thinking are essential as we move toward building designs that contribute to a more resilient and efficient energy future.

The energy sector is undergoing a significant transformation, shaped by increasing demand from electrification and data center investments alongside pressure to decarbonize. Advanced technologies such as artificial intelligence (AI), machine learning (ML), and automation are proving to be vital in reducing carbon emissions and modernizing electricity systems.

AI applications are particularly influential in grid management. Innovative tools like smart meters, Internet of Things (IoT) devices, and digital twins enable operators to predict and mitigate disruptions, enhance maintenance routines, and plan for complex operational scenarios. This proactive approach not only secures grid reliability but also optimizes resource use.

On the demand side, industries and data centers are harnessing digitalization to improve efficiency and decrease emissions while transitioning to cleaner energy sources. AI-powered solutions in sectors like steelmaking and cement production are streamlining processes, minimizing downtime, and facilitating real-time monitoring, thereby maximizing operational efficiency.

By embracing advanced technologies, the energy sector can enhance the flexibility and reliability of power generation systems, facilitating the integration of renewable energies and energy storage. These groundbreaking innovations are not only helping to fulfill the growing energy demand but also propelling the shift toward net-zero emissions and fostering the development of more sustainable industrial practices. Ultimately, the strategic application of AI and related technologies represents a powerful pathway to a cleaner, more efficient energy future.