What Keeps Data Centers and Their Utility Partners Up at Night: The Power Problem

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What Keeps Data Centers and Their Utility Partners Up at Night: The Power Problem

AI has the potential to revolutionize how we manage the grid, marking a transformative shift in how utilities optimize operations, enhance reliability, and meet evolving consumer demands. Through the deployment of AI-driven algorithms and predictive analytics, utilities can anticipate grid dynamics, optimize energy flows, and proactively address challenges in real time. The integration of AI with cloud infrastructure further enhances efficiency and performance, enabling utilities to leverage vast amounts of data from diverse sources, including weather data, edge data, and advanced metering systems (AMS).

By leveraging machine learning and analytics to merge and assess data streams and sensored information, utilities can unlock new levels of efficiency and performance. The challenges of our power needs are so complex that a system will be best utilized to process the various permutations and uncertainties; this will need to be a highly sophisticated predictive tool, but if properly developed it can enhance grid equipment lifespans, apply data-driven decision making, identify issues quickly, and reduce unplanned downtime.

Utilities are increasingly recognizing the importance of leveraging AI to gain intimate insights into their customers’ energy needs and behaviors, allowing them to prepare for future power demands effectively. From improving customer experiences through innovative applications to reimagining day-to-day operations with self-healing grid technology, utilities are embracing AI to drive digital transformation and move beyond their traditional roles. This data-driven approach not only optimizes grid performance but also enhances customer experiences and drives digital transformation within the industry.

Strategic Grid Planning for Looming Demand

Part of the planning that worries them most is not just how to supply power to more data centers. At least data centers clue our local utilities in on our upcoming needs. Electric vehicles are altogether unpredictable, except for areas that have seen regulatory timelines enforced. They also tend to flock together, with charging stations handling many at a time. More than just consumer use, they have potential fleets being converted in bulk.

The proliferation of electric vehicles as well as data centers presents both challenges and opportunities for grid planners. Word on the street is that electrification of the transportation market will double energy usage in 10 years and lead to an 800% increase over the next 20 years. That’s the load that has them most worried, and calculating how many electric vehicles they can handle. They need to get uncomfortably close to what consumers and businesses are going to want in the future to predict and plan for this demand.

Strategic grid planning is essential to accommodate the surge in electricity demand while ensuring reliability and stability. Utilities are exploring innovative solutions such as smart charging infrastructure, vehicle-to-grid integration, and energy storage to manage peak demand and optimize resource utilization. With the exponential growth of EVs and data centers, grid planning has never been more critical. We must invest in scalable and resilient infrastructure to support this electrified future.

Embracing the Grid Edge and Prosumer Movement

The emergence of the prosumer movement and the evolution of the grid edge are reshaping the traditional utility-consumer relationship, transforming consumers from passive recipients to active participants in the energy transition. This shift is driven by the proliferation of rooftop solar, home energy storage, and distributed energy resources (DERs), highlighting the importance of grid-edge innovations and community energy initiatives.

Consumers are no longer merely consumers; they are prosumers actively shaping the energy landscape. Utilities must adapt to this transformation and empower consumers to become active stakeholders in the energy transition. At the grid edge, where consumers interact directly with energy systems, better data quality, validity, and granularity are achieved, leading to low latency, high reliability, and scalability. This proximity to data sources enables predictive infrastructure and empowers citizens to be part of the solution.

The path to edge intelligence involves various components, including metrology for energy, demand, and power quality, as well as anomaly detection for outage, temperature, loose neutral, and tampering. Despite existing limitations in edge technology, such as firmware-driven systems and communication bottlenecks, rapid advancements in hardware, communication protocols, and software are driving progress. Software deployed at the edge is customizable, agile, and driven by an application mindset, leveraging more advanced algorithms, especially in machine learning.

Overcoming challenges at the edge requires leveraging technologies that enable robust networks capable of making informed decisions and identifying various devices, such as EVs, solar panels, batteries, and pump controls. This necessitates funneling and utilizing data effectively to empower consumers to make informed energy decisions and optimize energy usage. Despite the complexities introduced by IP addresses and evolving technologies, the focus remains on enabling consumers to actively participate in the energy transition while ensuring the reliability and scalability of grid-edge solutions.

Renewable Energy Integration

Renewable energy integration is driving a significant transformation in the energy landscape, with solar and wind power playing increasingly prominent roles in the generation mix. Utilities are investing in renewable energy infrastructure, grid-scale energy storage, and innovative grid-edge technologies to maximize the potential of renewables and reduce carbon emissions.

With sustainability at the forefront of efforts, integrating renewable energy sources into the grid and leveraging advanced technologies are seen as crucial steps toward achieving environmental goals while ensuring reliability and affordability for customers. Last year, 84% of new installed capacity was renewables and storage, marking a substantial shift in the generation mix. Demand response, accounting for 60% of capacity, is becoming increasingly significant.

Orchestrating the energy transition requires flexible resources and demand-side capabilities, with virtual power plants (VPPs) emerging as cost-effective solutions. However, managing the transition poses challenges, particularly in forecasting net load, VPP capabilities, and battery capacity at scale. Artificial intelligence and machine learning are key applications that can help the industry navigate these transitions and keep moving forward.

Some companies are exploring off-grid solutions due to frustrations with traditional electricity networks. Off-grid technology, once frowned upon, is now considered a necessity for certain operations. Companies like Microsoft and Google are exploring options such as small nuclear plants and zero-emissions fusion power to power energy-intensive operations, although regulatory and land acquisition challenges remain significant hurdles in this endeavor.

Fostering Innovation and Scalability

In the midst of rapid change, utilities are recognizing the critical importance of innovation and scalability in navigating the evolving energy landscape. By fostering a culture of innovation, establishing strategic partnerships, and prioritizing scalability, utilities can unlock new opportunities for success and drive significant progress towards a smarter, more resilient grid.

To meet the challenges of tomorrow, it is essential to invest in cutting-edge technologies and scalable solutions. This proactive approach enables utilities to pioneer the power grid of the future while delivering tangible value to customers and communities alike.

As electrification continues to grow rapidly and new technologies emerge, such as nuclear energy, utilities are embracing innovative projects to enhance reliability and resiliency. For instance, there are some pretty cool utility-driven projects in my local area I’ve been following: Duke Energy’s floating solar project in South Florida and residential battery installations in neighborhoods like Hunter’s Creek exemplify the shift towards cleaner, more resilient energy solutions. Additionally, initiatives like the 100% green hydrogen project in DeBary, FL highlight the ongoing efforts to integrate renewable energy sources and drive sustainability forward.

Not Your Grandparents’ Power Grid

The pulse of energy shapes our present and affords our future. The job to be done itself has not changed over time: people need light and power. What has changed is the complexities that utility providers must navigate in the modern energy landscape: the convergence of AI, EV integration, grid-edge innovations, renewables, and scalable solutions are reshaping the trajectory of the power grid. By embracing these key themes and driving meaningful progress in each area, utilities can unlock new opportunities for growth, sustainability, and resilience, propelling the power grid into a new era of innovation and prosperity.

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Takeaways from Cisco’s AI Summit

Software development is in an absolute frenzy right now, Scott said. “You have very, very senior people, the best coders you’ve ever met in your life, who are just completely overwhelmed trying to keep up with the rate of progress that’s happening right now.” Optimizing AI development for agents or humans?

Eying AI factories, Nvidia buys bigger stake in CoreWeave

Nvidia continues to throw its sizable bank account around, this time making a $2 billion investment in GPU cloud service provider CoreWeave. The company says the investment reflects Nvidia’s “confidence in CoreWeave’s business, team and growth strategy as a cloud platform built on Nvidia infrastructure.” CoreWeave is not the only

AI, security tailwinds signal promising 2026 for Cisco

A big component of AI in communications is agentic agents talking to employees and customers, and bringing trust to the system is where Cisco should shine. It builds and runs its own infrastructure, which is secure by design. Cisco has relationships with governments all over the world, and between Webex

Quantum computing is getting closer, but quantum-proof encryption remains elusive

Meanwhile, some people are still unconvinced of the urgency. Somebody else’s problem Quantum computing has been ten to twenty years away for decades. Is today any different? I wouldn’t blame someone if they thought that way. And, according to ICASA’s recent survey of more than 2,600 security and privacy professionals,

Oil Ends Higher Amid Rising Middle East Risks

Oil edged higher as traders parsed conflicting reports on the status of nuclear talks between the US and Iran, clouding the outlook on whether Washington will proceed with military strikes against the major oil producer. West Texas Intermediate rose 3.1% to settle above $65 a barrel. Prices pared gains in post-settlement trading as Iranian Foreign Minister Abbas Araghchi confirmed in a social media post that negotiations will be held in Oman on Friday. The commodity surged earlier on reports that the US told Iran it will not agree to Tehran’s demands to change the location and format of talks planned for Friday, Axios said, citing two US officials. Adding to bullish momentum, US President Donald Trump said that Iran’s Supreme Leader Ayatollah Ali Khamenei “should be very worried” in an interview with NBC. Traders have been closely monitoring the risk of possible US military intervention in Iran, which could disrupt key shipping lanes as well as the country’s roughly 3.3 million barrels-per-day oil production. Doubts over whether talks surrounding Iran’s nuclear program would proceed as planned have intensified since Tuesday, when US and Iranian forces appeared to square off in the sea and air. An Iranian drone approached an American aircraft carrier in the Arabian Sea and was shot down just hours after a US-flagged oil tanker was hailed by small armed ships in the Strait of Hormuz off Iran’s coast. Concern over a potential conflict in the Middle East, a source of about a third of the world’s crude, helped lift prices last month despite signs of a growing oversupply. It has also kept the cost of bullish options high relative to bearish ones for the longest stretch in more than a year. “Geopolitical tensions are really driving it,” Equinor Chief Financial Officer Torgrim Reitan said in a Bloomberg

Holtum Says LNG Projects Need New Financing Playbook

Trafigura Group Chief Executive Officer Richard Holtum said the liquefied natural gas industry needs a “bit of innovation” when it comes to financing projects. “I feel sorry for LNG projects in the US,” Holtum said on a panel at the LNG 2026 conference in Doha. “They would only get bank financing when they show that they’ve sold 80%-90% of their volume on long-term projects.” LNG developers are scrambling to fully finance their projects to export more of the fuel, with the next wave of production from terminals under construction in the US and Qatar due to enter the market over the next decade. In the US, several projects including Delfin LNG off the coast of Louisiana, are working to finalize the debt and equity commitments. The current approach of LNG financing contrasts with oil, where banks are more comfortable with the inherent value of the commodity, the CEO said. “Whilst if your project financing some oil exploration, it’s simply the bank takes a view that, okay, oil is worth $40, $50, $60, $70, whatever it is, it doesn’t matter, they take a view, that’s what it’s worth in the long term,” he said. A similar model for LNG, where project financing is based on a long-term price forecast for the fuel, doesn’t seem to be developing, Holtum said. Still, global LNG capacity is set to rise by about 50% by the end of the decade — the biggest build-out in the industry’s history — led by the US. The current arrangement, where 90% of LNG is sold to utilities under long-term contracts, risks running into difficulties because many companies and countries have made net-zero commitments, according to the Trafigura CEO. “If they have made those commitments, signing a 20-year contract or a 25-year contract that starts in 2030 is inherently problematic,” Holtum

Russian Oil Revenues Plunge to 5 Year Low

The Russian government’s oil revenues collapsed to the lowest in more than five years in January as weaker global prices, steeper discounts for the nation’s barrels, and a stronger currency took a toll on the budget. Oil-related taxes halved to 281.7 billion rubles ($3.7 billion) last month from a year earlier, according to Bloomberg calculations based on finance ministry data published Wednesday. Combined oil and gas revenue also declined by 50%, to 393.3 billion rubles. Lower proceeds from the two industries, which between them contribute about a quarter of the budget, will put more strain on the nation’s coffers as the war in Ukraine drags toward a fifth year with little sign of ending. Brent oil futures were 15% lower year on year for the fiscal period, but US sanctions made the market downturn even worse for Russia. January’s oil revenue was the lowest since June 2020. The nation’s flagship grade Urals traded at about $26 a barrel below Dated Brent, a benchmark for physical oil trades, at the point of export. That compares with over $12 below the same marker a year earlier, data from Argus Media show. The discounts ballooned following the US blacklisting of Rosneft PJSC and Lukoil PJSC, Russia’s two largest producers, measures that were announced in October. This week, US President Donald Trump said the US would cut import tariffs for goods from India — a major buyer of Russian crude — in exchange for New Delhi halting purchases of oil from Moscow. It’s not clear the extent to which India will cut back in practice. Russia’s finance ministry calculated oil revenue based on the average price of Urals of $39.18 a barrel in December, a 38% drop from a year earlier. That’s much lower than the government assumed when planned nation’s budget for this year and expected crude

Eneos to Expand Oil Trading Portfolio Outside Japan

Eneos Holdings Inc. plans to expand its team to handle more oil-derivative trading at its overseas offices including Singapore, as Japan’s largest refiner looks to increase its presence at major trading hubs. The company intends to trade more oil derivatives, arbitrages and time spreads, as well as other paper market instruments, according to people familiar with the matter. They asked not to be named as they aren’t authorized to speak to the media. Eneos will hire traders, as well as other executives in middle and back office roles, said people with knowledge of those plans. Kenneth Quek, a former trader from Mercuria Energy Group, recently joined in Singapore to focus on crude and related derivatives. A company spokesperson didn’t respond to a request for comment during office hours. Some of these roles may be filled by internal candidates. The beefing up of its trading presence is part of a broader push to create more value across business sectors, including a bid for overseas assets such as Chevron Corp.’s stake in a Singapore oil refinery. Bloomberg previously reported that Eneos was a frontrunner in the process, ahead of rivals including trading houses Glencore Plc and Vitol Group. Oil markets have kicked off the year with a high level of volatility as geopolitical risks ran ahead of market glut concerns. India’s state-owned refiner Bharat Petroleum Corp. is also planning to set up a trading arm in Singapore this month. Eneos has a market capitalization of 3.6 trillion yen ($23 billion), making it Japan’s largest oil processor following years of consolidation in the country’s wider petroleum sector. It acquired renewable energy assets in recent years, and sold off its copper mining assets. WHAT DO YOU THINK? Generated by readers, the comments included herein do not reflect the views and opinions of Rigzone. All comments are subject to editorial review.

ADNOC, TAQA Pen 27 Year TA’ZIZ Deal

In a statement posted on ADNOC’s website recently, ADNOC and Abu Dhabi National Energy Company PJSC (TAQA) announced the signing of a 27 year utilities purchase agreement to supply “critical utilities” to the TA’ZIZ Industrial Chemicals Zone in Ruwais Industrial City, Abu Dhabi. The value of the deal was not disclosed in the statement, which noted that the duration of the agreement includes the offtake of the utilities and construction of the plant. Under the deal, ADNOC and TAQA will jointly develop the central utilities project, including the electricity grid connection, steam production, process cooling, and a range of water and wastewater utilities required to enable TA’ZIZ’s chemicals and transition-fuels projects, the statement revealed. The statement said TA’ZIZ, which is a joint venture between ADNOC and ADQ, will set up and own a service management company which will be the sole offtaker of the utilities, “providing a stable foundation for efficient industrial activity within the TA’ZIZ Industrial Chemicals Zone”. The statement noted that the agreement “marks a significant milestone in the development of the TA’ZIZ ecosystem”. “TA’ZIZ is set to accelerate the UAE’s industrial diversification and is set to produce 4.7 million tons per annum (MTPA) commencing in 2028. This will include methanol, low-carbon ammonia, polyvinyl chloride (PVC), ethylene dichloride (EDC), vinyl chloride monomer (VCM), and caustic soda,” it added. “TAQA’s Generation business continues to expand its regional portfolio with several major projects, including the 1-gigawatt Al Dhafra Gas Turbine project in the UAE and 3.6 GW new high-efficiency power plants – Rumah 2 IPP and Al Nairyah 2 IPP – in Saudi Arabia, being developed alongside partners JERA and AlBawani,” it continued. In the statement, Farid Al Awlaqi, Chief Executive Officer of TAQA’s Generation business, said, “this agreement strengthens TAQA’s role in enabling industrial growth in the UAE by

Texas Upstream Employment Rises

Employment in the Texas upstream sector increased between November and December 2025. That’s what the Texas Independent Producers and Royalty Owners Association (TIPRO) said in a statement sent to Rigzone on Friday, which cited the latest Current Employment Statistics (CES) report from the U.S. Bureau of Labor Statistics (BLS) at the time. TIPRO highlighted in the statement that oil and natural gas extraction jobs rose by 500, or 0.7 percent, month on month, to 70,200, and support activities employment grew by 1,500, or 1.1 percent month on month, to 133,200. TIPRO reported in the statement that combined upstream employment increased by 2,000 jobs, or 1.0 percent month on month, to 203,400. “From January to December 2025, employment in the Texas upstream sector showed early gains followed by later fluctuations,” TIPRO said in the statement. “Oil and Gas Extraction added a net 2,000 jobs (+2.9 percent), reaching a peak of 70,200 in June, July, and December, driven by robust Permian production despite market pressures,” it added. “Support Activities employment recorded a net loss of 2,100 jobs (-1.6 percent), with a February0May surge (+2,800) partially offset by mid-year declines (-3,400 in June-July) and subsequent volatility, reflecting rig count reductions and service sector adjustments,” it continued. “Combined, the sectors ended essentially flat, with a net change of -100 jobs (-0.05 percent), reaching 203,400 by December and underscoring the industry’s critical yet volatile role in sustaining Texas’ energy workforce,” TIPRO noted. In the statement, TIPRO said its workforce data “continues to indicate strong job postings for the Texas oil and natural gas industry in December” but added that analysis “revealed a continued decline in Q4 driven by lower oil prices, industry consolidation, and ongoing efficiency gains, which allow companies to maintain or increase production with reduced hiring activity”. There were 7,887 unique industry job postings in Texas during the

Azure outage disrupts VMs and identity services for over 10 hours

After multiple infrastructure scale-up attempts failed to handle the backlog and retry volumes, Microsoft ultimately removed traffic from the affected service to repair the underlying infrastructure without load. “The outage didn’t just take websites offline, but it halted development workflows and disrupted real-world operations,” said Pareekh Jain, CEO at EIIRTrend & Pareekh Consulting. Cloud outages on the rise Cloud outages have become more frequent in recent years, with major providers such as AWS, Google Cloud, and IBM all experiencing high-profile disruptions. AWS services were severely impacted for more than 15 hours when a DNS problem rendered the DynamoDB API unreliable. In November, a bad configuration file in Cloudflare’s Bot Management system led to intermittent service disruptions across several online platforms. In June, an invalid automated update disrupted the company’s identity and access management (IAM) system, resulting in users being unable to use Google to authenticate on third-party apps. “The evolving data center architecture is shaped by the shift to more demanding, intricate workloads driven by the new velocity and variability of AI. This rapid expansion is not only introducing complexities but also challenging existing dependencies. So any misconfiguration or mismanagement at the control layer can disrupt the environment,” said Neil Shah, co-founder and VP at Counterpoint Research. Preparing for the next cloud incident This is not an isolated incident. For CIOs, the event only reinforces the need to rethink resilience strategies. In the immediate aftermath when a hyperscale dependency fails, waiting is not a recommended strategy for CIOs, and they should focus on a strategy of stabilize, prioritize, and communicate, stated Jain. “First, stabilize by declaring a formal cloud incident with a single incident commander, quickly determining whether the issue affects control-plane operations or running workloads, and freezing all non-essential changes such as deployments and infrastructure updates.”

Intel sets sights on data center GPUs amid AI-driven infrastructure shifts

Supply chain reliability is another underappreciated advantage. Hyperscalers want a credible second source, but only if Intel can offer stable, predictable roadmaps across multiple product generations. However, the company runs into a major constraint at the software layer. “The decisive bottleneck is software,” Rawat said. “CUDA functions as an industry operating standard, embedded across models, pipelines, and DevOps. Intel’s challenge is to prove that migration costs are low, and that ongoing optimization does not become a hidden engineering tax.” For enterprise buyers, that software gap translates directly into switching risk. Tighter integration of Intel CPUs, GPUs, and networking could improve system-level efficiency for enterprises and cloud providers, but the dominance of the CUDA ecosystem remains the primary barrier to switching, said Charlie Dai, VP and principal analyst at Forrester. “Even with strong hardware integration, buyers will hesitate without seamless compatibility with mainstream ML/DL frameworks and tooling,” Dai added.

8 hot networking trends for 2026

Recurring license fees may have dissuaded enterprises from adopting AIOps in the past, but that’s changing, Morgan adds: “Over the past few years, vendors have added features and increased the value of those licenses, including 24×7 support. Now, by paying the equivalent of a fraction of a network engineer’s salary in license fees, a mid-sized enterprise can reduce hours spent on operations and level-one support in order to allocate more of their valuable networking experts’ time to AI projects. Every enterprise’s business case will be different, but with networking expertise in high demand, we predict that in 2026, the labor savings will outweigh the additional license costs for the majority of mid-to-large sized enterprises.” 2. AI boosts data center networking investments Enterprise data centers, which not so long ago were on the endangered species list, have made a remarkable comeback, driven by the reality that many AI workloads need to be hosted on premises, either for privacy, security, regulatory, latency or cost considerations. The global market for data center networking technologies was estimated at around $46 billion in 2025 and is projected to reach $103 billion by the end of 2030, a growth rate of nearly 18%, according to BCC Research: “The data center networking technologies market is rapidly changing due to increasing use of AI-powered solutions across data centers and sectors like telecom, IT, banking, financial services, insurance, government and commercial industries.” McKinsey predicts that global demand for data center capacity could nearly triple by 2030, with about 70% of that demand coming from AI workloads. McKinsey says both training and inference workloads are contributing to data center growth, with inference expected to become the dominant workload by 2030. 3. Private clouds roll in Clearly, the hyperscalers are driving most of the new data center construction, but enterprises are

Cisco: Infrastructure, trust, model development are key AI challenges

“The G200 chip was for the scale out, because what’s happening now is these models are getting bigger where they don’t just fit within a single data center. You don’t have enough power to just pull into a single data center,” Patel said. “So now you need to have data centers that might be hundreds of kilometers apart, that operate like an ultra-cluster that are coherent. And so that requires a completely different chip architecture to make sure that you have capabilities like deep buffering and so on and so forth… You need to make sure that these data centers can be scaled across physical boundaries.” “In addition, we are reaching the physical limits of copper and optics, and coherent optics especially are going to be extremely important as we go start building out this data center infrastructure. So that’s an area that you’re starting to see a tremendous amount of progress being made,” Patel said. The second constraint is the AI trust deficit, Patel said. “We currently need to make sure that these systems are trusted by the people that are using them, because if you don’t trust these systems, you’ll never use them,” Patel said. “This is the first time that security is actually becoming a prerequisite for adoption. In the past, you always ask the question whether you want to be secure, or you want to be productive. And those were kind of needs that offset each other,” Patel said. “We need to make sure that we trust not just using AI for cyber defense, but we trust AI itself,” Patel said. The third constraint is the notion of a data gap. AI models get trained on human-generated data that’s publicly available on the Internet, but “we’re running out,” Patel said. “And what you’re starting to see happen

How Robotics Is Re-Engineering Data Center Construction and Operations

Physical AI: A Reusable Robotics Stack for Data Center Operations This is where the recent collaboration between Multiply Labs and NVIDIA becomes relevant, even though the application is biomanufacturing rather than data centers. Multiply Labs has outlined a robotics approach built on three core elements: Digital twins using NVIDIA Isaac Sim to model hardware and validate changes in simulation before deployment. Foundation-model-based skill learning via NVIDIA Isaac GR00T, enabling robots to generalize tasks rather than rely on brittle, hard-coded behaviors. Perception pipelines including FoundationPose and FoundationStereo, that convert expert demonstrations into structured training data. Taken together, this represents a reusable blueprint for data center robotics. Applying the Lesson to Data Center Environments The same physical-AI techniques now being applied in lab and manufacturing environments map cleanly onto the realities of data center operations, particularly where safety, uptime, and variability intersect. Digital-twin-first deployment Before a robot ever enters a live data hall, it needs to be trained in simulation. That means modeling aisle geometry, obstacles, rack layouts, reflective surfaces, and lighting variation; along with “what if” scenarios such as blocked aisles, emergency egress conditions, ladders left in place, or spill events. Simulation-first workflows make it possible to validate behavior and edge cases before introducing any new system into a production environment. Skill learning beats hard-coded rules Data centers appear structured, but in practice they are full of variability: temporary cabling, staged parts, mixed-vendor racks, and countless human exceptions. Foundation-model approaches to manipulation are designed to generalize across that messiness far better than traditional rule-based automation, which tends to break when conditions drift even slightly from the expected state. Imitation learning captures tribal knowledge Many operational tasks rely on tacit expertise developed over years in the field, such as how to manage stiff patch cords, visually confirm latch engagement, or stage a

Applied Digital CEO Wes Cummins On the Hard Part of the AI Boom: Execution

Designing for What Comes After the Current AI Cycle Applied Digital’s design philosophy starts with a premise many developers still resist: today’s density assumptions may not hold. “We’re designing for maximum flexibility for the future—higher density power, lower density power, higher voltage delivery, and more floor space,” Cummins said. “It’s counterintuitive because densities are going up, but we don’t know what comes next.” That choice – to allocate more floor space even as rack densities climb – signals a long-view approach. Facilities are engineered to accommodate shifts in voltage, cooling topology, and customer requirements without forcing wholesale retrofits. Higher-voltage delivery, mixed cooling configurations, and adaptable data halls are baked in from the start. The goal is not to predict the future perfectly, Cummins stressed, but to avoid painting infrastructure into a corner. Supply Chain as Competitive Advantage If flexibility is the design thesis, supply chain control is the execution weapon. “It’s a huge advantage that we locked in our MEP supply chain 18 to 24 months ago,” Cummins said. “It’s a tight environment, and more timelines are going to get missed in 2026 because of it.” Applied Digital moved early to secure long-lead mechanical, electrical, and plumbing components; well before demand pressure fully rippled through transformers, switchgear, chillers, generators, and breakers. That foresight now underpins the company’s ability to make credible delivery commitments while competitors confront procurement bottlenecks. Cummins was blunt: many delays won’t stem from poor planning, but from simple unavailability. From 100 MW to 700 MW Without Losing Control The past year marked a structural pivot for Applied Digital. What began as a single, 100-megawatt “field of dreams” facility in North Dakota has become more than 700 MW under construction, with expansion still ahead. “A hundred megawatts used to be considered scale,” Cummins said. “Now we’re at 700

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

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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

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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