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Outdated billing systems are holding back the energy transition

Across the US, utilities are making significant advancements in the energy transition, one such being electrification: switching end-use devices like heating systems and vehicles away from fossil fuels and onto electric fuel. Because electrification – and the associated demand increase – could increase system costs if demand is left unmanaged, a subsequent trend has emerged: […]

Across the US, utilities are making significant advancements in the energy transition, one such being electrification: switching end-use devices like heating systems and vehicles away from fossil fuels and onto electric fuel. Because electrification – and the associated demand increase – could increase system costs if demand is left unmanaged, a subsequent trend has emerged: demand management at the grid-edge. Utilities are investing in new solutions to serve customer demand (particularly from new grid-edge devices like EVs and heat pumps) more efficiently. An important tool in a utility’s demand management toolbox is pricing innovation – that is, developing new rate designs that more accurately communicate to customers the costs and benefits of their use of the power system. With new time-of-use rate designs, dynamic pricing models, location-based incentives, utilities have a wealth of solutions at their disposal. But to implement these pricing innovations at scale, utilities will need modern billing technologies that can keep up.

Implementing rate design innovation requires more than just smart meters

Over the last decade, US utilities have invested billions of dollars into advanced metering infrastructure (AMI). The transition away from manually-read meters to communications-enabled smart meters has allowed utilities to collect more granular consumption data from end customers. The value of these AMI investments relates to the outcomes utilities are able to achieve with this new, granular meter data. Utilities across the US are seeking to deliver value from their AMI investments by implementing time-varying rates for customers, something they weren’t able to do without granular meter data. However, many utilities are finding that even with modernized metering equipment, the process of implementing new rate designs proves difficult and expensive if their customer IT systems (specifically, billing) have yet to be modernized. Coding a new rate design or product offering into a legacy billing system can take months to years and cost millions of dollars. This implementation bottleneck posed by legacy billing technology comes at the expense of market innovation and system efficiency – and ultimately borne by ratepayers.

What features should modern utility billing technology include?

Ability to utilize new types of data for billing purposes: utilities are collecting new types and formats of data that could be used for price-setting and billing, from increasingly-granular consumption data (collected by smart meters) to individual preference information (collected through customer surveys). Billing technology must be able to efficiently make use of those new data types as they become available.

Configurability for new rate designs and pricing:  as utilities explore new rate design offerings, they are moving from traditional flat volumetric rates to variable pricing structures that more accurately reflect marginal costs and benefits. Often implementing these has taken months or years to complete, costing millions of dollars in the process. Billing systems — and their necessary upgrades — should not serve as a bottleneck to rate design innovation.

Easily integrated with the full customer IT stack: designing new products and programs will only pay off if customers actually make use of those new offerings. To ensure robust uptake and enrollment, utilities will need to actively engage their customers regarding new products and programs, through tailored marketing and outreach, creative enrollment solutions, rate comparisons, etc. To do it well, this engagement will require a utility’s billing system to work with its other backend customer IT systems and seamlessly pass information between them.

Modern billing technology in action: Octopus Energy’s Fan Club and Agile Octopus Tariff

Octopus Energy, a retail energy supplier serving 7.95 million customers, is known for their innovative rate designs. In 2021, Octopus Energy’s British retail business launched a new product for electric customers called Fan Club. Fan Club is designed to increase popular support for onshore wind development by offering customers who live near certain onshore wind projects a discount off their electric supply rate in hours when those projects generate electricity.

Using Kraken’s billing platform, Octopus Energy cross-references a customer’s account-level zip code against the zip code of participating wind turbines (to check for participation eligibility) and then calculates and assigns bill credits to individual participants based on precise time-varying determinants (e.g. energy market prices, wind speed, etc.). Without Kraken, these billing processes may have otherwise been performed by different software programs, which could have been complicated and expensive to execute. With Kraken, Octopus Energy was able to implement Fan Club all from a single billing platform, in a matter of weeks.

Agile Octopus is another innovative smart rate that exposes customers directly to wholesale electricity market prices, using half-hourly market data. Customers enrolled on Agile Octopus are exposed to “plunge pricing,” which occurs in hours when more electricity is generated than consumed, resulting in negative price events. Customers receive alerts when wholesale prices drop below zero and can use an API from Kraken to program their smart devices (e.g. EVs) to charge when prices are negative. By consuming electricity when prices are negative, customers get paid for the electricity they use. This rate design is incredibly complex to bill because it relies on dynamic wholesale prices as key billing determinants. To implement this rate for customers, Octopus Energy required a modern billing system — which they found in Kraken.

Modern billing technology is foundational to the energy transition

To implement advanced rate designs that accelerate the energy transition and make full use of new data that is being collected at the grid edge, utilities will need to modernize their customer billing technology. Information technology vendors like Kraken are enabling this modernization by developing billing systems that allow for agility, scalability and innovation.

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Schneider Electric’s $2.3 Billion in AI Power and Cooling Deals Sends Message to Data Center Sector

When Schneider Electric emerged from its 2025 North American Innovation Summit in Las Vegas last week with nearly $2.3 billion in fresh U.S. data center commitments, it didn’t just notch a big sales win. It arguably put a stake in the ground about who controls the AI power-and-cooling stack over the rest of this decade. Within a single news cycle, Schneider announced: Together, the deals total about $2.27 billion in U.S. data center infrastructure, a number Schneider confirmed in background with multiple outlets and which Reuters highlighted as a bellwether for AI-driven demand.  For the AI data center ecosystem, these contracts function like early-stage fuel supply deals for the power and cooling systems that underpin the “AI factory.” Supply Capacity Agreements: Locking in the AI Supply Chain Significantly, both deals are structured as supply capacity agreements, not traditional one-off equipment purchase orders. Under the SCA model, Schneider is committing dedicated manufacturing lines and inventory to these customers, guaranteeing output of power and cooling systems over a multi-year horizon. In return, Switch and Digital Realty are providing Schneider with forecastable volume and visibility at the scale of gigawatt-class campus build-outs.  A Schneider spokesperson told Reuters that the two contracts are phased across 2025 and 2026, underscoring that this arrangement is about pipeline, as opposed to a one-time backlog spike.  That structure does three important things for the market: Signals confidence that AI demand is durable.You don’t ring-fence billions of dollars of factory output for two customers unless you’re highly confident the AI load curve runs beyond the current GPU cycle. Pre-allocates power & cooling the way the industry pre-allocated GPUs.Hyperscalers and neoclouds have already spent two years locking up Nvidia and AMD capacity. These SCAs suggest power trains and thermal systems are joining chips on the list of constrained strategic resources.

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The Data Center Power Squeeze: Mapping the Real Limits of AI-Scale Growth

As we all know, the data center industry is at a crossroads. As artificial intelligence reshapes the already insatiable digital landscape, the demand for computing power is surging at a pace that outstrips the growth of the US electric grid. As engines of the AI economy, an estimated 1,000 new data centers1 are needed to process, store, and analyze the vast datasets that run everything from generative models to autonomous systems. But this transformation comes with a steep price and the new defining criteria for real estate: power. Our appetite for electricity is now the single greatest constraint on our expansion, threatening to stall the very innovation we enable. In 2024, US data centers consumed roughly 4% of the nation’s total electricity, a figure that is projected to triple by 2030, reaching 12% or more.2 For AI-driven hyperscale facilities, the numbers are even more staggering. With the largest planned data centers requiring gigawatts of power, enough to supply entire cities, the cumulative demand from all data centers is expected to reach 134 gigawatts by 2030, nearly three times the current load.​3 This presents a systemic challenge. The U.S. power grid, built for a different era, is struggling to keep pace. Utilities are reporting record interconnection requests, with some regions seeing demand projections that exceed their total system capacity by fivefold.4 In Virginia and Texas, the epicenters of data center expansion, grid operators are warning of tight supply-demand balances and the risk of blackouts during peak periods.5 The problem is not just the sheer volume of power needed, but the speed at which it must be delivered. Data center operators are racing to secure power for projects that could be online in as little as 18 months, but grid upgrades and new generation can take years, if not decades. The result

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The Future of Hyperscale: Neoverse Joins NVLink Fusion as SC25 Accelerates Rack-Scale AI Architectures

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At this year’s OCP Global Summit, Flex made a declaration that resonated across the industry: the era of slow, bespoke data center construction is over. AI isn’t just stressing the grid or forcing new cooling techniques—it’s overwhelming the entire design-build process. To meet this moment, Flex introduced a globally manufactured, fully integrated data center platform aimed directly at multi-gigawatt AI campuses. The company claims it can cut deployment timelines by as much as 30 percent by shifting integration upstream into the factory and unifying power, cooling, compute, and lifecycle services into pre-engineered modules. This is not a repositioning on the margins. Flex is effectively asserting that the future hyperscale data center will be manufactured like a complex industrial system, not built like a construction project. On the latest episode of The Data Center Frontier Show, we spoke with Rob Campbell, President of Flex Communications, Enterprise & Cloud, and Chris Butler, President of Flex Power, about why Flex believes this new approach is not only viable but necessary in the age of AI. The discussion revealed a company leaning heavily on its global manufacturing footprint, its cross-industry experience, and its expanding cooling and power technology stack to redefine what deployment speed and integration can look like at scale. AI Has Broken the Old Data Center Model From the outset, Campbell and Butler made clear that Flex’s strategy is a response to a structural shift. AI workloads no longer allow power, cooling, and compute to evolve independently. Densities have jumped so quickly—and thermals have risen so sharply—that the white space, gray space, and power yard are now interdependent engineering challenges. Higher chip TDPs, liquid-cooled racks approaching one to two megawatts, and the need to assemble entire campuses in record time have revealed deep fragility in traditional workflows. As Butler put it, AI

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Data Center Jobs: Engineering, Construction, Commissioning, Sales, Field Service and Facility Tech Jobs Available in Major Data Center Hotspots

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Microsoft will invest $80B in AI data centers in fiscal 2025

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John Deere unveils more autonomous farm machines to address skill labor shortage

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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