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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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NextDecade names Mott as interim CFO

NextDecade Corp. has appointed company senior vice-president Michael (Mike) Mott as interim chief financial officer, effective Oct. 20, 2025. Mott will take over from Brent Wahl, who resigns from the company as chief financial officer, effective Oct. 20. Wahl was named chief financial officer of NextDecade in 2021 after having served

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Analysts Talk Oil and Gas Bust Cycle

Is the oil and gas market currently in a bust cycle? That’s the question Rigzone asked James Davis, Director of Short-Term Global Oil Service and Head of Upstream Oil at FGE London, in an exclusive interview recently. In response, Davis told Rigzone, “crude oil prices have fallen year on year, and as the supply surplus continues, as evidenced by reported stockbuilds, prices will fall further”. “We’re already seeing evidence of oil companies cutting investment as a result of lower prices,” he added. “If these are the qualities of what you want to call a bust cycle, then, we’re in a bust cycle,” he said. In the interview, Davis highlighted to Rigzone that, for producers, $60 per barrel oil today doesn’t go as far as it did back in 2019. “For the average tight oil producer, $60 per barrel today gives you very little free cash flow,” he said. “However, in 2019, the average tight oil producer might have realized $10-15 per barrel of free cash flow at $60 per barrel,” he added. “While operating expenditure and capital expenditure have crept up, it is weaker gas prices that have had the biggest impact on cost exposure,” Davis pointed out. “Nonetheless operating margins are not as good at the current price deck as they would have been six years ago,” he noted. When asked if this bust cycle is going to negatively affect future production, Davis told Rigzone that FGE is already seeing evidence that the low oil price environment is impacting oil output, particularly in the United States. “While low cost producers (oil majors and international oil companies) have managed to grow output this year, the smaller, high cost producers have seen their output slump by around 200-300,000 barrels per day,” he said. “We expect more declines from high cost producers

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JERA, Hawai’i Partner for Energy Transition

JERA Co Inc and the Hawaiian government have signed a collaboration agreement “focusing on fuel diversity and developing pathways toward decarbonization”, the Japanese integrated power company said. The “strategic partnering agreement” between JERA and the governor’s office “is designed to help realize the Hawai’i State Energy Office’s ‘Alternative Fuels, Repowering and Energy Transition Study’, published in January 2025, which concluded in the short term that the state should accelerate its shift away from oil by using affordable and reliable alternative fuels, including natural gas”, JERA said in a statement on its website. Governor Josh Green said in the statement, “By collaborating with JERA – Japan’s largest power producer and a recognized global leader in energy transition – we are gaining access to valuable expertise and experience that will help accelerate our decarbonization journey while improving reliability and affordability for our residents”. JERA global chief executive Yukio Kani said, “As island communities, Japan and Hawai’i share similar challenges and opportunities in pursuing affordability, stability and sustainability. By working together, we aim to develop practical, innovative solutions that strengthen energy resilience and reduce costs for the people of Hawai’i”. The company added, “JERA brings extensive experience in the development and operation of large-scale, reliable energy infrastructure worldwide, with a growing focus on low-carbon fuels, hydrogen, ammonia and renewable energy integration”. In the study by the Hawai’i State Energy Office (HSEO), the agency proposed a new power plant that would run on natural gas supplied by a floating storage regasification unit. “LNG emerged as the near-term fuel with the potential to cost-effectively reduce the state’s greenhouse gas emissions during the transition to economywide decarbonization in 2045, but more analysis is needed to quantify a range of potential benefits and to identify how those benefits can be maximized to residents at the appropriate level of infrastructure buildout”,

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AEP Secures $1.6B DOE Loan Guarantee for Grid Upgrades

The United States Department of Energy (DOE) has awarded a $1.6-billion loan guarantee for projects by American Electric Power Co Inc (AEP) to upgrade nearly 5,000 miles of transmission lines across five states. The projects cover Indiana, Michigan, Ohio, Oklahoma, and West Virginia. “The upgrades supported by this financing will replace existing transmission lines in existing rights-of-way with new lines capable of carrying more energy”, AEP said in a press release. “Energy demand is increasing across AEP’s footprint. Customers have committed to business expansions or additions that will require an additional 24 gigawatts of electricity demand by the end of the decade”, the Columbus, Ohio-based company said. “The upgrades have primarily been identified to support data center, artificial intelligence and manufacturing development and represent generational load growth on the electric system. “Seeking federal funding opportunities and implementing rate structures that ensure new large customers are supporting infrastructure investment are some of the ways AEP is working to reduce rate impacts for customers”. It estimates the projects to save customers $275 million in financing costs over the life of the loan through lower bills. “Approximately 100 miles of transmission lines across Ohio and Oklahoma are the first projects to be supported by the loan guarantee”, AEP said. This is “the first closed loan guarantee under the Energy Dominance Financing (EDF) Program created by the Working Families Tax Cut, also known as the One Big Beautiful Bill Act”, DOE said separately. “All electric utilities receiving an EDF loan must provide assurance to DOE that financial benefits from the financing will be passed onto the customers of that utility”, DOE added. The AEP guarantee “was carefully evaluated under the new LPO [Loan Programs Office] guidance directed by Secretary Wright”, DOE added. Funding Cancelations Days earlier, DOE announced it had terminated nearly 350 financial

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Brent Set to Dip Its Feet into High $50ies This Week

Brent crude is set to dip its feet into the high $50ies per barrel this week, Skandinaviska Enskilda Banken AB (SEB) Chief Commodities Analyst Bjarne Schieldrop warned in a SEB report sent to Rigzone on Monday.   “Brent crude fell 2.3 percent over the week to Friday,” Schieldrop highlighted in the report. “It closed the week at $61.29 per barrel, a slight gain on the day, but also traded to a low of $60.14 per barrel that same day and just barely avoided trading into the $50ies per barrel,” he added. “This week looks set for Brent crude to dip its feet in the $50ies per barrel,” he continued. In the report, Schieldrop said front-end backwardation has been on a weakening foot and warned that “it is now about to fully disappear”.  “The lowest point of the crude oil curve has also moved steadily lower and lower and its discount to the five year contract is now $6.8 per barrel – a solid contango,” he noted. “The Brent three month contract did actually dip into the $50ies per barrel intraday on Friday when it traded to a low point of $59.93 per barrel,” he added. Schieldrop went on to warn in the report that more weakness is to come “as lots of oil at sea comes to ports”. “Mid-East OPEC countries have boosted exports along with lower post summer consumption and higher production,” he said. “The result is highly visibly in oil at sea which increased by 17 million barrels to 1,311 million barrels over the week to Sunday. Up 185 million barrels since mid-August. On its way to discharge at a port somewhere over the coming month or two,” he added. Schieldrop noted in the report that the oil market path ahead “is all down to OPEC+”. “Remember that what is playing

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For electric utilities to win with AI, focus on the 3 C’s

A recent gathering of electric industry leaders in Washington, DC, hosted by the Edison Electric Institute (EEI), underscored a critical truth: artificial intelligence is no longer a futuristic concept but a present reality shaping the electric utility landscape. This includes everything from data center power needs to customer outreach efficiencies. As the issue of AI and its possibilities becomes more complex, electric utilities must cut through the competing applications and focus their AI investments on the most pressing challenges. To truly deliver on AI’s promise of enhanced reliability, efficiency and security, the industry can focus on three core areas: cybersecurity, climate-stressed grids due to extreme weather, and wildfire risk and customer load growth. These are the “Three C’s” that will determine the industry’s success in this new era of AI. AI as a Shield Against Growing Cyber Threats For electric utilities, cybersecurity is a foundational pillar of grid resilience, with threats surfacing from geopolitical adversaries to typical cybercrime found in other industries. As the grid becomes more interconnected, the attack surface expands exponentially. For electric utility security teams, AI is a force multiplier for adversaries and it must become a force multiplier for defense as well. It can help with everything from triaging vulnerabilities and prioritizing high-risk assets, to detecting sophisticated digital attacks on infrastructure. It can also enhance the physical security of critical infrastructure, using data to recognize and alert human operators to potential threats. The sheer volume of data involved in monitoring a modern utility grid is too great for humans alone to process effectively. By deploying AI as a vigilant and intelligent partner, electric utilities improve their anticipatory security posture, shrinking the time between threat detection and response and significantly reducing the potential for damage. Electric utilities that succeed start by identifying and protecting their highest-value assets

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The race to pack more megawatt-hours into fewer boxes is a density trap

In the energy storage industry, we often see a race to pack more megawatt-hours into the same enclosure. The strategy of more battery capacity in fewer boxes seems like it would be efficient and less costly. But in practice, it’s a density trap. On paper, higher nameplate energy density makes promises of increased energy throughput and cost savings. But those metrics don’t always translate to operational value once systems are deployed in the field. Think of a battery storage system’s density like a car fuel tank. The size of the gas tank is important, but without an accurate gauge, you can’t be certain how many miles you can drive. You may never use the full capacity of the tank if you’re unsure when you will hit empty. The same is true of batteries. What matters isn’t simply how much energy you can store; it’s how much usable energy you know you have and can confidently, consistently deliver when it counts. That’s why usable energy is more critical than just energy density to generate revenue and support the grid. Higher density doesn’t guarantee a competitive advantage Asset owners often assume that higher energy density cells translate to fewer battery enclosures, smaller sites, and lower capital costs. In reality, that’s often not the case. Some ultra-dense units are too heavy or complex to transport easily, adding shipping and on-site integration challenges before the first kilowatt-hour is even dispatched. Combining multiple lower-weight, smaller units with those high density cells can complicate installation and raise integration costs. And once the system is online, performance doesn’t always match expectations. If batteries can’t reliably discharge at full-rated power, asset owners may need to oversize their installations just to meet contractual or expected energy commitments, erasing any cost or site density advantage. In other words, density is

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Roundup: Digital Realty Marks Major Milestones in AI, Quantum Computing, Data Center Development

Key features of the DRIL include: • High-Density AI and HPC Testing. The DRIL supports AI and high-performance computing (HPC) workloads with high-density colocation, accommodating workloads up to 150 kW per cabinet. • AI Infrastructure Optimization. The ePlus AI Experience Center lets businesses explore AI-specific power, cooling, and GPU resource requirements in an environment optimized for AI infrastructure. • Hybrid Cloud Validation. With direct cloud connectivity, users can refine hybrid strategies and onboard through cross connects. • AI Workload Orchestration. Customers can orchestrate AI workloads across Digital Realty’s Private AI Exchange (AIPx) for seamless integration and performance. • Latency Testing Across Locations. Enterprises can test latency scenarios for seamless performance across multiple locations and cloud destinations. The firm’s Northern Virginia campus is the primary DRIL location, but companies can also test latency scenarios between there and other remote locations. DRIL rollout to other global locations is already in progress, and London is scheduled to go live in early 2026. Digital Realty, Redeployable Launch Pathway for Veteran Technical Careers As new data centers are created, they need talented workers. To that end, Digital Realty has partnered with Redeployable, an AI-powered career platform for veterans, to expand access to technical careers in the United Kingdom and United States. The collaboration launched a Site Engineer Pathway, now live on the Redeployable platform. It helps veterans explore, prepare for, and transition into roles at Digital Realty. Nearly half of veterans leave their first civilian role within a year, often due to unclear expectations, poor skill translation, and limited support, according to Redeployable. The Site Engineer Pathway uses real-world relevance and replaces vague job descriptions with an experience-based view of technical careers. Veterans can engage in scenario-based “job drops” simulating real facility and system challenges so they can assess their fit for the role before applying. They

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BlackRock’s $40B data center deal opens a new infrastructure battle for CIOs

Everest Group partner Yugal Joshi said, “CIOs are under significant pressure to clearly define their data center strategy beyond traditional one-off leases. Given most of the capacity is built and delivered by fewer players, CIOs need to prepare for a higher-price market with limited negotiation power.” The numbers bear this out. Global data center costs rose to $217.30 per kilowatt per month in the first quarter of 2025, with major markets seeing increases of 17-18% year-over-year, according to CBRE. Those prices are at levels last seen in 2011-2012, and analysts expect them to remain elevated. Gogia said, “The combination of AI demand, energy scarcity, and environmental regulation has permanently rewritten the economics of running workloads. Prices that once looked extraordinary have now become baseline.” Hyperscalers get first dibs The consolidation problem is compounded by the way capacity is being allocated. North America’s data center vacancy rate fell to 1.6% in the first half of 2025, with Northern Virginia posting just 0.76%, according to CBRE Research. More troubling for enterprises: 74.3% of capacity currently under construction is already preleased, primarily to cloud and AI providers. “The global compute market is no longer governed by open supply and demand,” Gogia said. “It is increasingly shaped by pre-emptive control. Hyperscalers and AI majors are reserving capacity years in advance, often before the first trench for power is dug. This has quietly created a two-tier world: one in which large players guarantee their future and everyone else competes for what remains.” That dynamic forces enterprises into longer planning cycles. “CIOs must forecast their infrastructure requirements with the same precision they apply to financial budgets and talent pipelines,” Gogia said. “The planning horizon must stretch to three or even five years.”

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Nvidia, Infineon partner for AI data center power overhaul

The solution is to convert power right at the GPU on the server board and to upgrade the backbone to 800 volts. That should squeeze more reliability and efficiency out of the system while dealing with the heat, Infineon stated.   Nvidia announced the 800 Volt direct current (VDC) power architecture at Computex 2025 as a much-needed replacement for the 54 Volt backbone currently in use, which is overwhelmed by the demand of AI processors and increasingly prone to failure. “This makes sense with the power needs of AI and how it is growing,” said Alvin Nguyen, senior analyst with Forrester Research. “This helps mitigate power losses seen from lower voltage and AC systems, reduces the need for materials like copper for wiring/bus bars, better reliability, and better serviceability.” Infineon says a shift to a centralized 800 VDC architecture allows for reduced power losses, higher efficiency and reliability. However, the new architecture requires new power conversion solutions and safety mechanisms to prevent potential hazards and costly server downtimes such as service and maintenance.

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Meta details cutting-edge networking technologies for AI infrastructure

ESUN initiative As part of its standardization efforts, Meta said it would be a key player in the new Ethernet for Scale-Up Networking (ESUN) initiative that brings together AMD, Arista, ARM, Broadcom, Cisco, HPE Networking, Marvell, Microsoft, NVIDIA, OpenAI and Oracle to advance the networking technology to handle the growing scale-up domain for AI systems. ESUN will focus solely on open, standards-based Ethernet switching and framing for scale-up networking—excluding host-side stacks, non-Ethernet protocols, application-layer solutions, and proprietary technologies. The group will focus on the development and interoperability of XPU network interfaces and Ethernet switch ASICs for scale-up networks, the OCP wrote in a blog. ESUN will actively engage with other organizations such as Ultra-Ethernet Consortium (UEC) and long-standing IEEE 802.3 Ethernet to align open standards, incorporate best practices, and accelerate innovation, the OCP stated. Data center networking milestones The launch of ESUN is just one of the AI networking developments Meta shared at the event. Meta engineers also announced three data center networking innovations aimed at making its infrastructure more flexible, scalable, and efficient: The evolution of Meta’s Disaggregated Scheduled Fabric (DSF) to support scale-out interconnect for large AI clusters that span entire data center buildings. A new Non-Scheduled Fabric (NSF) architecture based entirely on shallow-buffer, disaggregated Ethernet switches that will support our largest AI clusters like Prometheus. The addition of Minipack3N, based on Nvidia’s Ethernet Spectrum-4 ASIC, to Meta’s portfolio of 51Tbps OCP switches that use OCP’s Switch Abstraction Interface and Meta’s Facebook Open Switching System (FBOSS) software stack. DSF is Meta’s open networking fabric that completely separates switch hardware, NICs, endpoints, and other networking components from the underlying network and uses OCP-SAI and FBOSS to achieve that, according to Meta. It supports Ethernet-based RoCE RDMA over Converged Ethernet (RoCE/RDMA)) to endpoints, accelerators and NICs from multiple vendors, such as Nvidia,

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Arm joins Open Compute Project to build next-generation AI data center silicon

Keeping up with the demand comes down to performance, and more specifically, performance per watt. With power limited, OEMs have become much more involved in all aspects of the system design, rather than pulling silicon off the shelf or pulling servers or racks off the shelf. “They’re getting much more specific about what that silicon looks like, which is a big departure from where the data center was ten or 15 years ago. The point here being is that they look to create a more optimized system design to bring the acceleration closer to the compute, and get much better performance per watt,” said Awad. The Open Compute Project is a global industry organization dedicated to designing and sharing open-source hardware configurations for data center technologies and infrastructure. It covers everything from silicon products to rack and tray design.  It is hosting its 2025 OCP Global Summit this week in San Jose, Calif. Arm also was part of the Ethernet for Scale-Up Networking (ESUN) initiative announced this week at the Summit that included AMD, Arista, Broadcom, Cisco, HPE Networking, Marvell, Meta, Microsoft, and Nvidia. ESUN promises to advance Ethernet networking technology to handle scale-up connectivity across accelerated AI infrastructures. Arm’s goal by joining OCP is to encourage knowledge sharing and collaboration between companies and users to share ideas, specifications and intellectual property. It is known for focusing on modular rather than monolithic designs, which is where chiplets come in. For example, customers might have multiple different companies building a 64-core CPU and then choose IO to pair it with, whether like PCIe or an NVLink. They then choose their own memory subsystem, deciding whether to go HBM, LPDDR, or DDR. It’s all mix and match like Legos, Awad said.

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BlackRock-Led Consortium to Acquire Aligned Data Centers in $40 Billion AI Infrastructure Deal

Capital Strategy and Infrastructure Readiness The AIP consortium has outlined an initial $30 billion in equity, with potential to scale toward $100 billion including debt over time as part of a broader AI infrastructure buildout. The Aligned acquisition represents a cornerstone investment within that capital roadmap. Aligned’s “ready-to-scale” platform – encompassing land, permits, interconnects, and power roadmaps – is far more valuable today than a patchwork of single-site developments. The consortium framed the transaction as a direct response to the global AI buildout crunch, targeting critical land, energy, and equipment bottlenecks that continue to constrain hyperscale expansion. Platform Overview: Aligned’s Evolution and Strategic Fit Aligned Data Centers has rapidly emerged as a scale developer and operator purpose-built for high-density, quick-turn capacity demanded by hyperscalers and AI platforms. Beyond the U.S., Aligned extended its reach across the Americas through its acquisition of ODATA in Latin America, creating a Pan-American presence that now spans more than 50 campuses and over 5 GW of capacity. The company has repeatedly accessed both public and private capital markets, most recently securing more than $12 billion in new equity and debt financing to accelerate expansion. Aligned’s U.S.–LATAM footprint provides geographic diversification and proximity to fast-growing AI regions. The buyer consortium’s global relationships – spanning utilities, OEMs, and sovereign-fund partners – help address power, interconnect, and supply-chain constraints, all of which are critical to sustaining growth in the AI data-center ecosystem. Macquarie Asset Management built Aligned from a niche U.S. operator into a 5 GW-plus, multi-market platform, the kind of asset infrastructure investors covet as AI demand outpaces grid and supply-chain capacity. Its sale at this stage reflects a broader wave of industry consolidation among large-scale digital-infrastructure owners. Since its own acquisition by BlackRock in early 2024, GIP has strengthened its position as one of the world’s top owners

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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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