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Why security stacks need to think like an attacker, and score every user in real time

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More More than 40% of corporate fraud is now AI-driven, designed to mimic real users, bypass traditional defenses and scale at speeds that overwhelm even the best-equipped SOCs. In 2024, nearly 90% of enterprises were targeted, and […]

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More than 40% of corporate fraud is now AI-driven, designed to mimic real users, bypass traditional defenses and scale at speeds that overwhelm even the best-equipped SOCs.

In 2024, nearly 90% of enterprises were targeted, and half of them lost $10 million or more.

Bots emulate human behavior and create entire emulation frameworks, synthetic identities, and behavioral spoofing to pull off account takeovers at scale while slipping past legacy firewalls, EDR tools, and siloed fraud detection systems.

Attackers weaponize AI to create bots that evade, mimic, and scale

Attackers aren’t wasting any time capitalizing on using AI to weaponize bots in new ways. Last year, malicious bots comprised 24% of all internet traffic, with 49% classified as ‘advanced bots’ designed to mimic human behavior and execute complex interactions, including account takeovers (ATO).

Over 60% of account takeover (ATO) attempts in 2024 were initiated by bots, capable of breaching a victim’s credentials in real time using emulation frameworks that mimic human behavior. Attacker’s tradecraft now reflects the ability to combine weaponized AI and behavioral attack techniques into a single bot strategy.

That’s proving to be a lethal combination for many enterprises already battling malicious bots whose intrusion attempts often aren’t captured by existing apps and tools in security operations centers (SOCs).

Malicious bot attacks force SOC teams into firefighting mode with little or no warning, depending on the legacy of their security tech stack.

“Once amassed by a threat actor, they can be weaponized,” Ken Dunham, director of the threat research unit at Qualys recently said. “Bots have incredible resources and capabilities to perform anonymous, distributed, asynchronous attacks against targets of choice, such as brute force credential attacks, distributed denial of service attacks, vulnerability scans, attempted exploitation and more.”

From fan frenzy to fraud surface: bots corner the market for Taylor Swift tickets  

Bots are the virtual version of attackers who can scale to millions of attempts per second to attack a targeted enterprise and increasingly high-profile events, including concerts of well-known entertainers, such as Taylor Swift.

Datadome observes that the worldwide popularity of Taylor Swift’s concerts creates the ROI attackers are looking for to build ticket bots that automate what scalpers do at scale. Ticket bots, as Datadome calls them, scoop up massive quantities of tickets at the world’s most popular events and then resell them at significant markups.

The bots flooded Ticketmaster and were a large part of a surge of 3.5 billion requests that hit the ticket site, causing it to crash repeatedly. Thousands of fans were unable to access the presale group, and ultimately, the general ticket sale had to be canceled.

Swarms of weaponized bots froze tens of thousands of Swifties from attending her last Eras concert tour. VentureBeat has learned of comparable attacks on the world’s leading brands on their online stores and presence globally. Dealing with bot attacks at that scale, powered by weaponized AI, is beyond the scope of an e-commerce tech stack to handle – they’re not built to deal with that level of security threat.  

“It’s not just about blocking bots—it’s about restoring fairness,” Benjamin Fabre, CEO of DataDome, told VentureBeat in a recent interview. The company helped See Tickets deflect similar scalping attacks in milliseconds, distinguishing fans from fraud using multi-modal AI and real-time session analysis.

Bot attacks weaponized with AI often start by targeting login and session flows, bypassing endpoints in an attempt not to be detected by standard web application firewalls (WAF) and endpoint detection and response (EDR) tools. Such sophisticated attacks must be tracked and contained in a business’s core security infrastructure, managed from its SOC.

Why SOC teams are now on the front line

Weaponized bots are now a key part of any attacker’s arsenal, capable of scaling beyond what fraud teams alone can contain during an attack. Bots have proven lethal, taking down enterprises’ e-commerce operations or, in the case of Ticketmaster, a best-selling concert tour worth billions in revenue.  

As a result, more enterprises are bolstering the tech stacks supporting their SOCs with online fraud detection (OFD) platforms. Gartner’s Dan Ayoub recently wrote in the firm’s research note Emerging Tech Impact Radar: Online Fraud Detection that “organizations are increasingly waking up to the understanding that ‘fraud is a security problem’ as is becoming evident in adoption of some of the emerging technologies being leveraged today”.

Gartner’s research and VentureBeat’s interviews with CISOs confirm that today’s malicious bot attacks are too fast, stealthy and capable of reconfiguring themselves on the fly for siloed fraud tools to handle. Weaponized bots have long been able to exploit gaps between WAFs, EDR tools and fraud scoring engines, while also evading static rules that are so prevalent in legacy fraud detection systems.

All these factors and more are why CISOs are bringing fraud telemetry into the SOC.

Journey-Time Orchestration is the next wave of online fraud detection (OFD)

AI-enabled bots are constantly learning how to bypass long-standing fraud detection platforms that rely on sporadic or single point-in-time checks. These checks include login validations, transaction scoring tracking over time, and a series of challenge-responses. While these were effective before the widespread weaponization of bots, botnets and networks, AI-literate adversaries now know how to exploit context switching and, as many deepfakes attacks have proven, know how to excel at behavioral mimicry.

Gartner’s research points to Journey Time Orchestration  (JTO) as the defining architecture for the next wave of OFD platforms that will help SOCs better contain the onslaught of AI-driven bot attacks. Core to JTO is embedding fraud defenses throughout each digital session being monitored and scoring risk continuously from login to checkout to post-transaction behavior.

Journey-Time Orchestration continuously scores risk across the entire user session—from login to post-transaction—to detect AI-driven bots. It replaces single-point fraud checks with real-time, session-wide monitoring to counter behavioral mimicry and context-switching attacks. Source: Gartner, Innovation Insight: IAM Journey-Time Orchestration, Feb. 2025

Who’s establishing an early lead in Journey Time Orchestration defense  

DataDome, Ivanti and Telesign are three companies whose approaches show the power of shifting security from static checkpoints to continuous, real-time assessments is paying off. Each also shows why the future of SOCs must be predicated on real-time data to succeed. All three of these companies’ platforms have progressed to delivering scoring for every user interaction down to the API call, delivering greater contextual insight across every behavior on every device, within each session.

What sets these three companies apart is how they’ve taken on the challenges of hardening fraud prevention, automating core security functions while continually improving user experiences. Each combines these strengths on real-time platforms that are also AI-driven and continually learn – two core requirements to keep up with weaponized AI arsenals that include botnets.

DataDome: Thinking Like an Attacker in Real Time

DataDome, A category leader in real-time bot defense, has extensive expertise in AI-intensive behavioral modeling and relies on a platform that includes over 85,000 machine learning models delivered simultaneously across 30+ global PoPs. Their global reach allows them to inspect more than 5 trillion data points daily. Every web, mobile and API request that their platform can identify is scored in real time (typically within 2 milliseconds) using multi-modal AI that correlates device fingerprinting, IP entropy, browser header consistency and behavior biometrics.

“Our philosophy is to think like an attacker,” Fabre told VentureBeat. “That means analyzing every request anew—without assuming trust—and continuously retraining our detection models to adapt to zero-day tactics”​.

Unlike legacy systems, which lean on static heuristics or CAPTCHAs, DataDome’s approach minimizes friction for verified, legitimate users. Its false-positive rate is under 0.01%, meaning fewer than 1 in 10,000 human visitors see a challenge screen. Even when challenged, the platform invisibly continues behavior analysis to verify the user’s legitimacy.

“Bots aren’t just solving CAPTCHAs now—they’re solving them faster than humans,” Fabre added. “That’s why we moved away from static challenges entirely. AI is the only way to beat AI-driven fraud at scale”​.

Case in point: See Tickets used DataDome to defend against the same bot-driven scalping wave that crashed Ticketmaster during the Taylor Swift Eras Tour. DataDome could distinguish bots from fans in milliseconds and prevent bulk buyouts, preserving ticket equity during peak load. In luxury retail, brands like Hermès deploy DataDome to protect high-demand drops (e.g., Birkin bags) from automated hoarding.

Ivanti Extends Zero Trust and exposure management into the SOC

Ivanti is redefining exposure management by integrating real-time fraud signals directly into SOC workflows through its Ivanti Neurons for Zero Trust Access and Ivanti Neurons for Patch Management platforms. “Zero trust doesn’t stop at logins,” Mike Riemer, Ivanti Field CISO told VentureBeat during a recent interview. “We’ve extended it to session behaviors including credential resets, payment submissions, and profile edits are all potential exploit paths.”

Ivanti Neurons continuously evaluates device posture and identity behavior, flagging anomalous activity and enforcing least-privilege access mid-session. “2025 will mark a turning point,” added Daren Goeson, SVP of product management at Ivanti. “Now defenders can use GenAI to correlate behavior across sessions and predict threats faster than any human team ever could.”

As attack surfaces expand, Ivanti’s platform helps SOC teams detect SIM swaps, mitigate lateral movement and automate dynamic microsegmentation. “What we currently call ‘patch management’ should more aptly be named exposure management or how long is your organization willing to be exposed to a specific vulnerability?” Chris Goettl, VP of product management for endpoint security at Ivanti told VentureBeat. “Risk-based algorithms help teams identify high-risk threats amid the noise of numerous updates.”

“Organizations should transition from reactive vulnerability management to a proactive exposure management approach,” added Goeson. “By adopting a continuous approach, they can effectively protect their digital infrastructure from modern cyber risks.”

Telesign’s AI-driven identity intelligence pushes fraud detection to session scale

Telesign is redefining digital trust by bringing identity intelligence at session scale to the front lines of fraud detection. By analyzing more than 2,200 digital identity signals ranging from phone number metadata to device hygiene and IP reputation, Telesign’s APIs deliver real-time risk scores that catch bots and synthetic identities before damage is done.

“AI is the best defense against AI-enabled fraud attacks,” said Telesign CEO Christophe Van de Weyer in a recent interview with VentureBeat. “At Telesign, we are committed to leveraging AI and ML technologies to combat digital fraud, ensuring a more secure and trustworthy digital environment for all.”

Rather than relying on static checkpoints at login or checkout, Telesign’s dynamic risk scoring continuously evaluates behavior throughout the session. “Machine learning has the power to constantly learn how fraudsters behave,” Van de Weyer told VentureBeat. “It can study typical user behaviors to create baselines and build risk models.”

Telesign’s Verify API underscores its omnichannel strategy, enabling identity verification across SMS, email, WhatsApp, and more, all through a single API. “Verifying customers is so important because many kinds of fraud can often be stopped at the ‘front door,’” Van de Weyer noted in a recent VentureBeat interview.

As generative AI accelerates attacker sophistication, Van de Weyer issued a clear call to action: “The emergence of AI has brought the importance of trust in the digital world to the forefront. Businesses that prioritize trust will emerge as leaders in the digital economy.” With AI as its backbone, Telesign looks to turn trust into a competitive advantage.

Why fraud prevention’s future belongs in the SOC

For fraud protection to scale, it must be integrated into the broader security infrastructure stack and owned by the SOC teams who use it to avert potential attacks. Online fraud detection platforms and apps are proving just as critical as APIs, Identity and Access Management (IAM), EDRs, SIEMs and XDRs. VentureBeat is seeing more security teams in SOCs take greater ownership of validating how consumer transactions are modeled, scored and challenged.

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Equinix launches AI platform to simplify control of distributed AI resources

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AI’s need for speed, optical connectivity in focus at OFC 2026

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Arm shifts course, moves into silicon business

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DOE and GSA Announce Collaborative Effort for a New Headquarters for the U.S. Department of Energy

WASHINGTON—The U.S. Department of Energy (DOE), in partnership with the U.S. General Services Administration (GSA), announced today that DOE’s headquarters will relocate from the James V. Forrestal Building to the Lyndon B. Johnson (LBJ) building. LBJ currently serves as the headquarters for the U.S. Department of Education (ED). The relocation to the LBJ building will save taxpayers over $350 million in deferred maintenance and modernization costs, advancing President Trump’s commitment to eliminating waste and promoting efficiency within government agencies.“Relocating to the LBJ building will deliver significant taxpayer savings and will ensure the Energy Department continues to deliver on its mission,” said Energy Secretary Chris Wright. “We look forward to working closely with the General Services Administration and the Education Department throughout this process.” The LBJ building has been modernized to a Class A building with minimal deferred maintenance. All DOE Forrestal staff will be reassigned to LBJ, DOE Germantown Campus, Portals, or 950 L’Enfant.  “GSA is partnering with the Department of Education and the Department of Energy to match their missions of tomorrow with ideal environments that powers their talented workforce, cuts waste, and lowers costs,” said GSA Administrator Edward C. Forst. “This is the government working smarter for the American people. I want to thank Secretary Wright and Secretary McMahon for their positive energy and collaboration in executing President Trump’s directive to strengthen the government’s real estate portfolio.” This effort aligns with the Trump Administration’s broader strategy to streamline the federal real estate footprint, reduce wasteful spending, and support a high-performing government workforce with facilities that reflect modern expectations for efficiency and accountability. For more information, please visit the U.S. General Services Administration (GSA), U.S. Department of Energy (DOE), and Accelerated Disposition Website.  ### 

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Energy Department Announces $50 Million Investment to Advance Affordable, Reliable, and Secure Energy for Tribes

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Trump Administration Keeps Indiana Coal Plants Open to Ensure Affordable, Reliable and Secure Power in the Midwest

Emergency orders address critical grid reliability issues, lowering risk of blackouts and ensuring affordable electricity access WASHINGTON—U.S. Secretary of Energy Chris Wright today issued emergency orders to keep two Indiana coal plants operational to ensure Americans in the Midwest region of the United States have continued access to affordable, reliable, and secure electricity. The orders direct the Northern Indiana Public Service Company (NIPSCO), CenterPoint Energy, and the Midcontinent Independent System Operator, Inc. (MISO) to take all measures necessary to ensure specified generation units at both the R.M. Schahfer and F.B. Culley generating stations in Indiana are available to operate. Certain generation units at the coal plants were scheduled to shut down at the end of 2025. The orders prioritize minimizing electricity costs for the American people and minimizing the risk and costs of blackouts. “The last administration’s energy subtraction policies had the United States on track to likely experience significantly more blackouts in the coming years—thankfully, President Trump won’t let that happen,” said Energy Secretary Wright. “The Trump Administration will continue taking action to keep America’s coal plants running to ensure we don’t lose critical generation sources. Americans deserve access to affordable, reliable, and secure energy to power their homes all the time, regardless of whether the wind is blowing or the sun is shining.” The reliable supply of power from these two coal plants was essential in powering the grid during recent extreme winter weather. From January 23–February 1, Schahfer operated at over 285 megawatts (MW) every day and Culley operated at approximately 30 MW almost every day. These operations serve as a reminder that allowing reliable generation to go offline would unnecessarily contribute to grid reliability risks. Since the Department of Energy’s (DOE) original orders were issued on December 23, 2025, the coal plants have proven critical to MISO’s operations, operating during periods of high energy demand and low levels of intermittent

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Energy Department Begins Delivering SPR Barrels at Record Speeds

WASHINGTON — The U.S. Department of Energy (DOE) today announced the award of contracts for the initial phase of the Strategic Petroleum Reserve (SPR) Emergency Exchange as directed by President Trump. The first oil shipments began today—just nine days after President Trump and the Department of Energy announced the United States would lead a coordinated release of emergency oil reserves among International Energy Agency (IEA) member nations to address short-term supply disruptions. Under these initial awards, DOE will move forward with an exchange of 45.2 million barrels of crude oil and receive 55 million barrels in return, all at no cost to the taxpayer. This represents the first tranche of the United States’ 172-million-barrel release. Companies will receive 10 million barrels from the Bayou Choctaw SPR site, 15.7 million barrels from Bryan Mound, and 19.5 million barrels from West Hackberry. “Thanks to President Trump, the Energy Department began this first exchange at record speeds to address short-term supply disruptions while also strengthening the Strategic Petroleum Reserve by returning additional barrels at no cost to taxpayers,” said Kyle Haustveit, Assistant Secretary of the Hydrocarbons and Geothermal Energy Office. “This exchange not only maintains reliability in the current market but will generate hundreds of millions of dollars in value in the form of additional barrels for the American people when the barrels are returned.” This initial action will ultimately add close to 10 million barrels to the SPR’s inventory when the barrels are returned. Taxpayers will benefit from both the short-term support for global supply and long-term growth of the SPR’s inventory. This helps protects U.S. and global energy security. The Trump Administration continues to pursue additional opportunities to strengthen the reserve and restore its long-term readiness as a cornerstone of American energy security. For more information on the Strategic Petroleum Reserve and DOE’s

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Then & Now: Oil prices, US shale, offshore, and AI—Deborah Byers on what changed since 2017

In this Then & Now episode of the Oil & Gas Journal ReEnterprised podcast, Managing Editor and Content Strategist Mikaila Adams reconnects with Deborah Byers, nonresident fellow at Rice University’s Baker Institute Center for Energy Studies and former EY Americas industry leader, to revisit a set of questions first posed in 2017. In 2017, the industry was emerging from a downturn and recalibrating strategy; today, it faces heightened geopolitical risk, market volatility, and a rapidly evolving technology landscape. The conversation examines how those earlier perspectives have aged—covering oil price bands and the speed of recovery from geopolitical shocks, the role of US shale relative to OPEC in balancing global supply, and the shift from scarcity to economic abundance driven by technology and capital discipline. Adams and Byers also compare the economics and risk profiles of shale and offshore development, including the growing role of Brazil, Guyana, and the Gulf of Mexico, and discuss how infrastructure and regulatory constraints shape market outcomes. The episode further explores where digital transformation—particularly artificial intelligence—is delivering tangible returns across upstream operations, from predictive maintenance and workforce planning to capital project execution. The discussion concludes with insights on consolidation and scale in the Permian basin, the strategic rationale behind recent megamergers, and the industry’s ongoing challenge to attract and retain next‑generation talent through flexibility, technical opportunity, and purpose‑driven work.

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Eni plans tieback of new gas discoveries offshore Libya

Eni North Africa, a unit of Eni SPA, together with Libya’s National Oil Corp., plans to develop two new gas discoveries offshore Libya as tiebacks to existing infrastructure. The gas discoveries were made offshore Libya, about 85 km off the coast in about 650 ft of water. Bahr Essalam South 2 (BESS 2) and Bahr Essalam South 3 (BESS 3), adjacent geological structures, were successfully drilled through the exploration well C1-16/4 and the appraisal well B2-16/4 about 16 km south of Bahr Essalam gas field, which lies about 110 km from the Tripoli coast. Gas-bearing intervals were encountered in both wells within the Metlaoui formation, the main productive reservoir of the area. The acquired data indicate the presence of a high-quality reservoir, with productive capacity confirmed by the well test already carried out on the first well. Preliminary volumetric estimates indicate that the BESS 2 and BESS 3 structures jointly contain more than 1 tcf of gas in place. Their proximity to Bahr Essalam field will enable rapid development through tie-back, the operator said. The gas produced will be supplied to the Libyan domestic market and for export to Italy. Bahr Essalam produces through the Sabratha platform to the Mellitah onshore treatment plant.

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Return of the PTT: Poste Italiane looks to snap up telco TIM

Poste Italiane sees opportunities in reuniting with the former state-owned telecommunications business: “The creation of an integrated group strategic pillar for the national economy, Italy’s largest connected infrastructure with leading positions in financial and insurance services,” it said in a news release. The company is looking to build some complementary services. “The transaction aims to scale and enhance Poste Italiane’s platform by adding three significant assets: a nationwide fixed and mobile network, a leading position in the country’s cloud and data center infrastructure and the ability to offer secure and seamless connectivity to all stakeholders,” it said. Poste Italiane was already the largest stakeholder in TIM and, as the government is the largest stakeholder in Poste Italiane, we’re getting back to the status quo of the 1980s. There is no sign, however, of other European governments following suit.

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Networking terms and definitions

Monitoring: DCIM tools provide real-time visibility into the data center environment, tracking metrics like power consumption, temperature, humidity, and equipment status.   Management: DCIM enables administrators to control and manage various aspects of the data center, including power distribution, cooling systems, and IT assets.  Planning: DCIM facilitates capacity planning, helping data center operators understand current resource utilization and forecast future needs.  Optimization: DCIM helps identify areas for improvement in energy efficiency, resource allocation, and overall operational efficiency.  Data center sustainability Data center sustainability is the practice of designing, building and operating data centers in a way that minimizes their environmental by reducing energy consumption, water usage and waste generation, while also promoting sustainable practices such as renewable energy and efficient resource management. Hyperconverged infrastructure (HCI) Hyperconverged infrastructure combines compute, storage and networking in a single system and is used frequently in data centers. Enterprises can choose an appliance from a single vendor or install hardware-agnostic hyperconvergence software on white-box servers. Edge computing Edge computing is a distributed computing architecture that brings computation and storage closer to the sources of data. That is, instead of sending all data to a centralized cloud or data center, processing occurs at or near the edge of the network, where devices like sensors, IoT devices, or local servers are located to process, analyze and retain the data.  In short, it’s about processing data closer to where it’s generated, which is designed to minimize latency, reduce bandwidth usage,and enable real-time responses. Edge AI Edge AI is the deployment and execution of artificial intelligence (AI) algorithms on edge devices or local servers, rather than relying solely on cloud-based, more centralized, AI processing. This involves running machine learning models and AI applications directly on devices at the edge of the network. Some key aspects of edge AI include the

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Data center poaching adds to staffing crisis

“You can’t just not have a pipeline and keep drawing from the same talent pool. It’s going to wane. It’s going to dwindle, and then eventually you’re going to be at a point where you are needing to upskill a bunch of people, rapidly all at once, and you don’t have enough senior experts to really pass on that information,” Weinschenk said. Shortages are shifting up the stack In 2023, Uptime data showed most staffing pain at junior and mid-level roles, particularly in facilities. Senior gaps were visible but less severe. By 2024, electrical expertise had become a pressure point, reflecting a broader trade shortage just as infrastructures densified and voltages increased. When asked which roles in the data center have the highest rates of staff turnover, respondents said: Operations junior/mid-level: 57% Operations management: 27% Electrical: 21% Cabling/IT: 20% Senior management/strategy: 12% Design: 7% None: 9% By 2025, a pattern emerged: Operations management roles overtook junior positions as shortage areas, Uptime reported, marking the arrival of the silver tsunami as highly experienced managers and engineers retire without enough trained successors to replace them. As more sites are built—often in regions with limited local expertise—operators are discovering they cannot simply hire experience indefinitely, Uptime said. The pool of ready-made experts is shrinking just as demand rises, according to its data. Poaching masks a deeper talent pipeline failure Uptime survey data revealed how heavily the sector leans on poaching. Roughly a quarter of staff departures are employees hired away by competitors; only a small amount of workers leave the industry entirely. Instead of investing in training and upskilling, many operators are rotating the same set of skilled people around the industry, hoping higher pay will keep them in place. Uptime said that this creates several long-term risks:

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Panasonic says datacenter batteries are selling out and AI is to blame

AI servers are rewriting the power rulebook The root cause, Panasonic noted in the statement, is the electrical behavior of AI workloads. Unlike conventional server applications, AI inference and training draw large amounts of electricity in short bursts to sustain GPU processing, causing peak power levels to spike rapidly and voltages to fluctuate. “Peak power levels for such servers can rise rapidly, and voltages can often become unstable,” the statement said. “Securing stable, highly reliable power supplies is an absolute necessity for AI datacenters.” Vertiv warned in its 2025 Data Center Trends predictions that AI racks must handle loads that “can fluctuate from a 10% idle to a 150% overload in a flash,” requiring UPS systems and batteries with significantly higher power densities than current infrastructure provides. Panasonic said the solution gaining traction among hyperscalers is to place a battery backup unit on each server rack rather than rely on centralized UPS infrastructure upstream, absorbing voltage instability at the source. The company said its systems also carry a peak shaving function that stores off-peak electricity and deploys it during demand spikes, reducing peak grid draw at a time when AI-driven consumption faces growing regulatory and utility scrutiny. Several independent research bodies have reached similar conclusions on the severity of the power challenge ahead. Uptime Institute, in its Five Data Center Predictions for 2026, said “developers will not outrun the power shortage,” with research analyst Max Smolaks warning the crisis “is likely to last many years.” The IEA projected global datacenter electricity consumption could exceed 1,000 TWh by 2026, more than double 2022 levels, while Gartner has warned that energy shortages could restrict 40% of AI datacenters by 2027. Gogia said the shift runs deeper than a hardware swap. “This is not backup in the traditional sense. This is active stabilisation,”

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Why AI rack densities make liquid cooling non-negotiable

Average rack power density has more than doubled in two years, from 8 kW to 17 kW, and is projected to reach 30 kW by 2027, according to anOctober 2024 McKinsey report, with AI training racks already well ahead of that average. Those limits show up in GPU clock speed. H100 GPUs under inadequate air cooling can throttle to a fraction of their rated clock speed within seconds of a sustained training run. In distributed jobs across thousands of GPUs, one throttled chip can stall the entire run. TheDOE estimates cooling accounts for up to 40% of data center energy use. JLL research establishes three density thresholds: Up to ~20 kW per rack: air cooling is adequate Up to ~100 kW: rear-door heat exchangers extend viability Above ~175 kW: immersion cooling is required Direct-to-chip cooling fills the middle band, handling densities between ~100 and ~175 kW where rear-door exchangers fall short and immersion is not yet warranted. Hot water changes the economics Mechanical chillers are one of the biggest energy draws in any liquid-cooled data center, and until recently there were an unavoidable cost of liquid cooling. Nvidia’s Vera Rubin processor is changing that. At CES in January 2026, Jensen Huang announced that Vera Rubin supports liquid cooling at 45 degrees Celsius, high enough for data centers to reject heat through dry coolers using ambient air rather than mechanical chillers.Nvidia’s CES press release confirmed Rubin is in full production, with customer availability in the second half of 2026. According toNvida’s product specifications, the Vera Rubin NVL72 uses warm-water, single-phase direct liquid cooling at a 45°C supply temperature, allowing data centers to reject heat through dry coolers using ambient air rather than energy-intensive chiller systems.

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Executive Roundtable: AI Infrastructure Enters Its Execution Era

Miranda Gardiner, iMasons Climate Accord:  Since 2023, the digital infrastructure industry has moved definitively from planning to execution in the AI infrastructure cycle. Industry analysts forecast continued exponential growth, with active capacity at least doubling between now and 2030 and total capacity potentially tripling, quintupling, or more. In practical terms, we’ll see more digital infrastructure capacity come online in the next five year than has been built in the past 30 years, representing a historic industrial transformation requiring trillions of dollars in capital expenditure and a workforce measured in the millions. Design and organizational flexibility, integrated execution of sustainable solutions, and community-centered workforce development will separate those that thrive from those that struggle. Effective organizations will pivot quickly under these constantly shifting conditions and the leaders will be those that build fast but build right, as strategic flexibility balances long-term performance, efficiency, and regulatory compliance. We already know the resource intensity required to bring AI resources online and are working diligently to ensure this short-term, delivering streamlined and optimized solutions for everything from site selection to cooling and power management while lower lifecycle emissions. Additionally, in some regions, grid interconnection timelines and power availability are already the pacing item for data center development. Organizations that align their sustainability targets and energy procurement strategies will have a clearer path to execution. An operational model capable of delivering multiple large-scale facilities simultaneously across regions is another key piece to successful outcomes. Standardized, repeatable frameworks that reduce engineering time and accelerate permitting. We hear often about collaboration and strong partnerships, and these will be critical with utilities, regulators, and equipment manufacturers to anticipate bottlenecks before they impact schedules. Execution discipline will increasingly determine competitive advantage as the industry scales. The world and, especially, our host communities, are watching closely. Projects that move forward

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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.

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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

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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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