Artificial Intelligence has emerged as one of the biggest secular megatrends of our time. AI is powering the fourth industrial revolution and is increasingly being viewed as a key strategy for mastering some of the greatest challenges of our time, including climate change and pollution. Energy companies are employing AI tools to digitize records, analyze vast troves of data and geological maps, and potentially identify problems such as excessive equipment use or pipeline corrosion. AI is used to analyze seismic data, optimize drilling paths, and manage reservoirs more efficiently, maximizing extraction while minimizing environmental impact and human error. AI Driller employs AI to remotely manage drilling processes across multiple rigs; Petro AI and Tachyus deploy physics-informed AI models for production forecasting and reservoir management; OFS heavyweights Baker Hughes (NYSE:BKR) and C3.ai (NYSE:AI) utilize enterprise AI to predict failures across their assets while Buzz Solutions analyzes visual data for power line inspections.
Similarly, AI is reshaping the power sector by optimizing processes across the entire energy value chain, from generation to consumption, while simultaneously posing a significant challenge due to its own high energy demands.
AI is helping improve demand response and energy efficiency, with tools like Brainbox AI and Enerbrain helping to autonomously reduce energy drift while Uplight helps utilities to incentivize efficiency. AI is also facilitating renewable energy integration by analyzing vast datasets, including weather patterns, to accurately forecast the intermittent output of solar and wind energy sources. AI is used in renewable energy to improve grid management, optimize energy production, balancing supply and demand in real-time, and using machine learning to predict equipment failure, which reduces downtime and costs. For instance, Envision and PowerFactors provide integrated platforms that help manage vast renewable fleets; Clir and WindESCo employ AI to detect underperforming wind turbines, adjusting pitch and yaw to capture more energy; SkySpecs employs AI and autonomous drones to conduct automated inspections of wind turbines, while Form Energy is tackling the storage space.
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Meanwhile, AI is becoming integral in building smart grids by providing the visibility required to manage congestion and prevent blackouts. Kraken Technologies leverages artificial intelligence (AI) and machine learning (ML) as the “brain” of a modern energy grid to balance intermittent renewable supply with real-time demand, coordinate millions of decentralized energy assets, and automate operations for efficiency and stability.
WeaveGrid and Camus Energy use AI to help utilities integrate electric vehicles (EVs) and other distributed resources into the grid without causing overloads. WeaveGrid focuses on managing EV charging through software that optimizes it to align with grid capacity and renewable energy availability. Camus Energy uses AI, specifically machine learning, to create “copilot” systems that forecast electricity demand and power flow with high accuracy, which speeds up the grid’s complex physics calculations and improves stability during events like EV charging peaks.
Finally, AI is used in carbon emissions and ESG management to centralize data, optimize operations, monitor supply chains, and improve reporting. It helps companies with real-time tracking, predictive analytics for emissions, and real-time supply chain management. Additionally, AI automates tasks like ESG reporting, anomaly detection in emissions data, and helps navigate complex regulatory landscapes. Carbon Chain and Watershed use AI and machine learning (ML) to provide accurate, scalable, and granular carbon emissions measurement and management for businesses, particularly focusing on complex supply chain (Scope 3) emissions.
Carbon Chain helps enterprises account for their total carbon impact by automating the ingestion and analysis of large volumes of supply chain data to generate detailed, audit-ready reports. The platform uses machine learning to ingest data from diverse and often fragmented sources (ERP systems, supplier reports, etc.) to build a granular picture of emissions.
Meanwhile, Watershed utilizes AI extensively across its enterprise sustainability platform to automate data collection, improve data accuracy, and provide actionable decarbonization insight. Watershed’s key AI tool is “Product Footprints,” which uses advanced AI models to break down every purchased item into its constituent materials and processes, tracing upstream steps like raw material extraction, manufacturing, and transportation. This approach replaces slow, manual life-cycle assessments or imprecise spend-based estimates, producing detailed emissions profiles in minutes.
On the flip side, all these AI advancements have come at a price, with reports emerging that states and regions with a high concentration of AI data centers are seeing a much bigger surge in power bills compared to the rest of the country. Big Tech and AI labs are now building giant data centers that consume a gigawatt or more of electricity in some cases, enough to power more than 800,000 homes. It’s, therefore, hardly surprising that states with the highest number of data centers are also experiencing the biggest increase in electricity prices. With 666 data centers, Virginia has the largest number of these power-hungry facilities in the country. Interestingly, residential electricity prices in the state increased 13% in August compared with the same period in the previous year, the second-highest clip nationwide after Illinois’ 15.8%. Illinois has 244 data centers, the fourth highest amongst the 50 states.
Not surprisingly, there’s growing techlash, with various politicians criticizing the Trump administration for cutting sweetheart deals with Big Tech companies and forcing consumers to subsidize the cost of data centers. This means we are likely to see more states adopt the Oklo (NYSE:OKLO) model wherein data centers provide their own electricity supply to avoid burdening the consumer.
By Alex Kimani for Oilprice.com
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