IBM targets mainframe customers with prebuilt AI training modules

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IBM targets mainframe customers with prebuilt AI training modules

If a customer has an existing model or LLM, synthetic data provides additional data that is rich, labeled, and diverse to fine-tune the AI model. If a client does not have a model, the Synthetic Data Sets are designed to offer quick and privacy-compliant training data to create models from scratch, the vendor stated. Customers […]

Customers can deploy models on IBM Z and IBM LinuxONE with AI Toolkit for IBM Z and IBM LinuxONE, Cloud Pak for Data on Z, or Machine Learning for z/OS, wrote Elpida Tzortzatos, an IBM Fellow and Z architect, and Tina Tarquinio, IBM vice president, in a blog about the news. They can “perform inference on IBM z16 and IBM LinuxONE 4, leveraging hardware acceleration investments and data gravity to dramatically enhance AI inferencing speed and scale,” the authors wrote.

“In addition customers can enhance predictive AI models and fine-tune LLMs with additional rich and broad data, leading to significant cost savings in areas such as fraud detection and money laundering prevention,” the authors wrote.

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Chinese cyberspies target VMware vSphere for long-term persistence

Designed to work in virtualized environments The CISA, NSA, and Canadian Cyber Center analysts note that some of the BRICKSTORM samples are virtualization-aware and they create a virtual socket (VSOCK) interface that enables inter-VM communication and data exfiltration. The malware also checks the environment upon execution to ensure it’s running

IBM boosts DNS protection for multicloud operations

“In addition to this DNS synchronization, you can publish DNS configurations to your Amazon Simple Storage Service (S3) bucket. As you implement DNS changes, the S3 bucket will automatically update. The ability to store multiple configurations in your S3 bucket allows you to choose the most appropriate restore point if

Andrew Wheeler of HPE Labs: Being a constant learner is key to being a good technologist

Andrew Wheeler, director of HPE Labs, speaking at the Discover 2025 event in Barcelona, Spain.

Cloudflare firewall reacts badly to React exploit mitigation

During the same window, Downdetector saw a spike in problem reports for enterprise services including Shopify, Zoom, Claude AI, and Amazon Web Services, and a host of consumer services from games to dating apps. Cloudflare explained the outage on its service status page: “A change made to how Cloudflare’s Web

WoodMac Flags ‘Key Themes’ Shaping Lower 48 in 2026

Wood Mackenzie (WoodMac) identified several “key themes shaping the U.S. Lower 48 landscape” next year in a statement sent to Rigzone recently. Among these was a projection that the horizontal rig count will fall below 500. “Oil focused activity levels will decline as operators face macro headwinds, particularly in H1 2026,” WoodMac said in the statement. “This sits below the $60 per barrel threshold that sparks questions around investment strategy,” it added. WoodMac said in the statement, however, that declining rig count is no longer the needle mover it once was. “Major strides in operational efficiency have reduced the number of active rigs required to maintain base business,” the company stated. “Operators are drilling faster, and cycle times are improving,” it added. WoodMac went on to note in the statement that “the activity taper will create deflationary pressures on costs”. “Wood Mackenzie expects to see a modest reduction in drilling and completion costs across the Lower 48 in 2026, including tariffs,” it said. “Lower costs help protect most of the new drill supply curve. Even at $60 per barrel Brent, more than 90 percent of U.S. Lower 48 assets can cover their capex requirements, with all assets covering operating costs,” it continued. Another theme was a projection that core Permian plays produce more than 50 percent of U.S. onshore liquids next year. “Lower 48 oil production will stall in 2026 for the first time since the pandemic,” WoodMac warned. “Rigs falling throughout 2025 and less activity in the year create this culmination. The Permian remains resilient and the powerhouse of U.S. oil supply,” it added. “Combined 2026 production from the Delaware Wolfcamp, Bone Spring, Midland Wolfcamp, and Midland Spraberry will account for more than 50 percent of onshore U.S. oil output for the first time ever,” it continued. “Delaware Wolfcamp

DTECH 2026: 5 observations ahead of the biggest grid event of the year

The energy transition isn’t coming — it’s here. Across North America, utilities are navigating an unprecedented convergence of challenges: exponential load growth from data centers and electrification, rising reliability expectations and an accelerating influx of distributed energy resources (DERs). At DTECH 2026, Feb. 2-5 in San Diego, OATI will showcase how utilities can turn these pressures into opportunities through a simple, unifying concept: flexibility. “Flexibility is no longer optional — it’s the operating principle of the modern grid,” says Sasan Mokhtari, OATI’s president & CEO. “The future belongs to the utilities that can orchestrate DERs, manage load growth intelligently and connect operations from meters to markets.” 1. From DER chaos to DERMS clarity Distributed Energy Resource Management Systems (DERMS) have evolved from niche pilots to mission-critical platforms. OATI would know—we deployed the first DERMS in North America in 2009 and created what would define a generation of grid modernization. What was once an experiment in aggregation is now the foundation of reliable, data-driven grid management. Modern DERMS platforms, like OATI DERMS, enable utilities to see, forecast and control a complex web of rooftop solar, battery storage, EV chargers and flexible loads in real time. They bring together three critical capabilities: Visibility — a unified picture of all DERs across the grid Optimization — dispatch decisions that balance economics, carbon and reliability Market Integration — the ability to monetize flexibility through participation in wholesale markets At DTECH 2026, OATI will demonstrate how its DERMS platform bridges the divide between bulk power and distribution operations, uniting IT and OT operations and helping utilities truly manage the grid from meters to markets. 2. The new face of load growth: Data centers, EVs and electrification The growth of data centers and electric transportation is transforming grid demand faster than many planners ever imagined. In 2024 alone, U.S. data

Solving the AI power puzzle: Taming data center demand with flexible grid-scale storage

Data centers – the vast, physical warehouses where IT servers and systems are kept – are experiencing a boom in demand, particularly across the USA. Driven by the rapid ascent of AI, analysts project that the global electricity demand for data centers is expected to double by 2030, reaching consumption levels that rival entire developed nations. Still, the challenge goes beyond the sheer volume of power needed. Data centers operate 24/7 and experience pronounced swings in demand which legacy grids simply aren’t engineered to handle. Luckily, answers are emerging. Grid-scale batteries can respond quickly enough to tame this volatile demand, and when properly coordinated by a sophisticated operating system – like Kraken – they can contribute to building a healthier, better-balanced grid overall. Data centers have uniquely volatile demand profiles The fundamental nature of data center electrical loads distinguishes them from traditional industrial consumption, being not just especially large, but unusually “spiky” and unpredictable. When tech companies launch AI training algorithms or massive computing clusters activate, the resulting power draw is instantaneous and intense. This poses a pressing stability problem. The grid’s legacy generators, such as slow-ramping gas-fired peaker plants, aren’t merely relatively expensive and slow to build, but are ultimately technically incapable of matching huge demand spikes that occur in milliseconds. This critical mismatch between fast demand and slow supply results in immediate frequency instability, severe stress on local transmission and distribution networks and significantly higher balancing costs for grid operators (if they can meet that demand at all). And this volatility is only compounded as clusters of data centers concentrate in particular geographical regions. A faster, smarter solution is clearly needed. Coordinating grid-scale storage to tame demand Fortunately, the flexibility afforded by large batteries is well-suited to addressing pronounced immediate swings, charging up whenever energy is cheapest and cleanest

How flexible loads are revolutionizing grid capacity

It’s well known that the electric grid faces a capacity crisis. Energy-intensive operations such as data centers, commercial and industrial electrification and bitcoin mining are driving unprecedented demand while transmission constraints and permitting timelines limit infrastructure expansion. For power suppliers, a critical solution is transforming energy-intensive loads from liabilities into assets through intelligent control. Understanding Controllable Loads Flexible, controllable loads represent a fundamental shift in capacity management. Rather than viewing large industrial customers solely as demand to be met, controllable load technology allows these operations to be part of the larger Distributed Energy Resource Management System (DERMS) ecosystem, effectively increasing generation capacity without building new plants. A controllable load gives grid operators or power suppliers the ability to dynamically adjust consumption in real-time. This capability becomes essential as the grid integrates more intermittent renewable generation and faces tightening capacity margins. The Market Context: Why Now? Several converging forces are driving unprecedented growth in power demand. The number of AI data centers is growing and bitcoin mining continues to scale in deregulated markets. Commercial and industrial (C&I) electrification is accelerating across sectors—from manufacturing facilities converting to electric processes to transportation fleets transitioning to EVs. While surging demand presents the primary challenge, other factors make controllable loads essential for modern grid management: Renewable Integration Volatility: Renewable energy production such as solar and wind fluctuates throughout the day, while transmission constraints prevent power from moving freely between regions, creating price volatility and reliability challenges. Outdated Grid Infrastructure: Traditional infrastructure operates with binary logic—generators on or off, loads consuming or not—clashing with dynamic grid requirements. Evolving Market Structures: Real-time pricing and ancillary service markets reward flexibility. ERCOT’s Controllable Load Resource (CLR) program integrates flexible loads directly into grid dispatch. New Regulatory Requirements: Texas Senate Bill 6 (SB6) is likely to mandate that loads above

Halliburton Promotes Slocum to COO Role

Halliburton revealed, in a statement posted on its website recently, that Shannon Slocum has been promoted to Executive Vice President and Chief Operating Officer and been appointed to the board of directors, effective January 1, 2026. Slocum will report to Jeff Miller, Halliburton Chairman, President, and CEO, the statement highlighted. It noted that Slocum will be responsible for Halliburton’s global operations, as well as business development, health, safety and environment, and global technology. In the statement, Halliburton said Slocum “has demonstrated excellence in a variety of roles of increasing responsibility at Halliburton, including most recently as President, Eastern Hemisphere”. The company highlighted in the statement that Slocum previously served in leadership positions around the world, including Senior Vice President, Global Business Development and Marketing, Senior Vice President for the Eurasia, Europe, And Sub-Saharan Africa Region, and Vice President of Cementing. Slocum joined Halliburton in 2005 as Senior Manager of Innovation and Marketing and has served in multiple technology, operations, product service, and business development roles, the statement noted. A bio page on Slocum on Halliburton’s website points out that he holds a Bachelor of Science in Industrial Technology from Lamar University. It adds that, in 2012, he was named Eastern Hemisphere Country Manager of the Year “for his outstanding work in Azerbaijan”. In the statement posted on Halliburton’s site, Miller said, “our business strategy demands execution, and now is the right time to transfer operations to Shannon while I focus on the company’s long-term strategic advancement and execution”. “Shannon brings global operations experience and proven leadership that strengthen our ability to maximize asset value for our customers,” he added. The statement also revealed that, effective January 1, 2026, Rami Yassine will succeed Slocum as President, Eastern Hemisphere, after serving as Senior Vice President, Middle East North Africa region. Previously, Yassine

Eni to Supply LNG to Thailand for 10 Years

Eni SpA has signed a 10-year deal to supply Thailand’s Gulf Development Co 800,000 metric tons a year of liquefied natural gas (LNG). “The LNG will be delivered at regasification terminals located in the country starting from 2027”, the Italian state-backed integrated energy company said in an online statement. The new contract is on top of a two-year agreement signed 2024 under which Eni is to supply Gulf, a multi-sector company, about 500,000 metric tons per annum of LNG starting 2025. “The agreement is Eni’s first long-term LNG supply to Thailand, in a move designed to strengthen its presence in Asia”, Eni said. A day earlier Eni said it had won a deal to supply Turkiye’s state-owned Boru Hatlari ile Petrol Tasima AS (BOTAS) around 400,000 metric tons per year of LNG for 10 years. The deal is on top of an earlier one signed September under which BOTAS committed to buying 400,0000 metric tons a year for three years from Eni, Eni said in a press release. BOTAS said September 12 it had signed agreements with Eni, BP PLC, Cheniere Energy Inc, Equinor ASA, Hartree Partners LP, JERA Co Inc, SEFE Securing Energy for Europe GmbH and Shell PLC for around 15 billion cubic meters (529.72 billion cubic feet) of LNG. The volumes are to be delivered to Turkiye in 2025-28. Eni said of the 10-year contract, “The agreement is Eni’s first long-term LNG sale to Turkiye, confirming the growing role of LNG in supporting the country’s energy needs, and is in line with Eni’s strategy to diversify its global LNG footprint, expanding its customer base in markets with high potential and growing its LNG portfolio to approximately 20 million metric tons per annum [MMtpa]”. On December 2 Eni said the second phase of Congo LNG in the Republic of the Congo had started up. The project now

At the Crossroads of AI and the Edge: Inside 1623 Farnam’s Rising Role as a Midwest Interconnection Powerhouse

That was the thread that carried through our recent conversation for the DCF Show podcast, where Severn walked through the role Farnam now plays in AI-driven networking, multi-cloud connectivity, and the resurgence of regional interconnection as a core part of U.S. digital infrastructure. Aggregation, Not Proximity: The Practical Edge Severn is clear-eyed about what makes the edge work and what doesn’t. The idea that real content delivery could aggregate at the base of cell towers, he noted, has never been realistic. The traffic simply isn’t there. Content goes where the network already concentrates, and the network concentrates where carriers, broadband providers, cloud onramps, and CDNs have amassed critical mass. In Farnam’s case, that density has grown steadily since the building changed hands in 2018. At the time an “underappreciated asset,” the facility has since become a meeting point for more than 40 broadband providers and over 60 carriers, with major content operators and hyperscale platforms routing traffic directly through its MMRs. That aggregation effect feeds on itself; as more carrier and content traffic converges, more participants anchor themselves to the hub, increasing its gravitational pull. Geography only reinforces that position. Located on the 41st parallel, the building sits at the historical shortest-distance path for early transcontinental fiber routes. It also lies at the crossroads of major east–west and north–south paths that have made Omaha a natural meeting point for backhaul routes and hyperscale expansions across the Midwest. AI and the New Interconnection Economy Perhaps the clearest sign of Farnam’s changing role is the sheer volume of fiber entering the building. More than 5,000 new strands are being brought into the property, with another 5,000 strands being added internally within the Meet-Me Rooms in 2025 alone. These are not incremental upgrades—they are hyperscale-grade expansions driven by the demands of AI traffic,

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

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

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

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

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

Neoverse’s Expanding Footprint and the Power-Efficiency Imperative With Neoverse deployments now approaching roughly 50% of all compute shipped into top hyperscalers in 2025 (representing more than a billion Arm cores) and with nation-scale AI campuses such as the Stargate project already anchored on Arm compute, the addition of NVLink Fusion becomes a pivotal extension of the Neoverse roadmap. Partners can now connect custom Arm CPUs to their preferred NVIDIA accelerators across a coherent, high-bandwidth, rack-scale fabric. Arm characterized the shift as a generational inflection point in data-center architecture, noting that “power—not FLOPs—is the bottleneck,” and that future design priorities hinge on maximizing “intelligence per watt.” Ian Buck, vice president and general manager of accelerated computing at NVIDIA, underscored the practical impact: “Folks building their own Arm CPU, or using an Arm IP, can actually have access to NVLink Fusion—be able to connect that Arm CPU to an NVIDIA GPU or to the rest of the NVLink ecosystem—and that’s happening at the racks and scale-up infrastructure.” Despite the expanded design flexibility, this is not being positioned as an open interconnect ecosystem. NVIDIA continues to control the NVLink Fusion fabric, and all connections ultimately run through NVIDIA’s architecture. For data-center planners, the SC25 announcement translates into several concrete implications: 1. NVIDIA “Grace-style” Racks Without Buying Grace With NVLink Fusion now baked into Neoverse, hyperscalers and sovereign operators can design their own Arm-based control-plane or pre-processing CPUs that attach coherently to NVIDIA GPU domains—such as NVL72 racks or HGX B200/B300 systems—without relying on Grace CPUs. A rack-level architecture might now resemble: Custom Neoverse SoC for ingest, orchestration, agent logic, and pre/post-processing NVLink Fusion fabric Blackwell GPU islands and/or NVLink-attached custom accelerators (Marvell, MediaTek, others) This decouples CPU choice from NVIDIA’s GPU roadmap while retaining the full NVLink fabric. In practice, it also opens

Flex’s Integrated Data Center Bet: How a Manufacturing Giant Plans to Reshape AI-Scale Infrastructure

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

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

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist Data Center Facility Technician (All Shifts Available) Impact, TX This position is also available in: Ashburn, VA; Abilene, TX; Needham, MA and New York, NY. Navy Nuke / Military Vets leaving service accepted! This opportunity is working with a leading mission-critical data center provider. This firm provides data center solutions custom-fit to the requirements of their client’s mission-critical operational facilities. They provide reliability of mission-critical facilities for many of the world’s largest organizations facilities supporting enterprise clients, colo providers and hyperscale companies. This opportunity provides a career-growth minded role with exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer Montvale, NJ This traveling position is also available in: New York, NY; White Plains, NY; Richmond, VA; Ashburn, VA; Charlotte, NC; Atlanta, GA; Hampton, GA; Fayetteville, GA; New Albany, OH; Cedar Rapids, IA; Phoenix, AZ; Salt Lake City, UT; Dallas, TX or Chicago, IL. *** ALSO looking for a LEAD EE and ME CxA Agents and CxA PMs. *** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and

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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