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DCF Poll: What Will Constrain Data Center Growth Next?

Matt Vincent is Editor in Chief of Data Center Frontier, where he leads editorial strategy and coverage focused on the infrastructure powering cloud computing, artificial intelligence, and the digital economy. A veteran B2B technology journalist with more than two decades of experience, Vincent specializes in the intersection of data centers, power, cooling, and emerging AI-era […]

Matt Vincent is Editor in Chief of Data Center Frontier, where he leads editorial strategy and coverage focused on the infrastructure powering cloud computing, artificial intelligence, and the digital economy. A veteran B2B technology journalist with more than two decades of experience, Vincent specializes in the intersection of data centers, power, cooling, and emerging AI-era infrastructure. Since assuming the EIC role in 2023, he has helped guide Data Center Frontier’s coverage of the industry’s transition into the gigawatt-scale AI era, with a focus on hyperscale development, behind-the-meter power strategies, liquid cooling architectures, and the evolving energy demands of high-density compute, while working closely with the Digital Infrastructure Group at Endeavor Business Media to expand the brand’s analytical and multimedia footprint. Vincent also hosts The Data Center Frontier Show podcast, where he interviews industry leaders across hyperscale, colocation, utilities, and the data center supply chain to examine the technologies and business models reshaping digital infrastructure. Since its inception he serves as Head of Content for the Data Center Frontier Trends Summit. Before becoming Editor in Chief, he served in multiple senior editorial roles across Endeavor Business Media’s digital infrastructure portfolio, with coverage spanning data centers and hyperscale infrastructure, structured cabling and networking, telecom and datacom, IP physical security, and wireless and Pro AV markets. He began his career in 2005 within PennWell’s Advanced Technology Division and later held senior editorial positions supporting brands such as Cabling Installation & Maintenance, Lightwave Online, Broadband Technology Report, and Smart Buildings Technology. Vincent is a frequent moderator, interviewer, and keynote speaker at industry events including the HPC Forum, where he delivers forward-looking analysis on how AI and high-performance computing are reshaping digital infrastructure. He graduated with honors from Indiana University Bloomington with a B.A. in English Literature and Creative Writing and lives in southern New Hampshire with his family, remaining an active musician in his spare time.

You can connect with Matt via LinkedIn or email.

You can connect with Matt via LinkedIn or email.

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Why 6 GHz Wi-Fi will make or break the modern enterprise

To date, 6 GHz Wi‑Fi deployments across enterprises remain in their early stages, with only a subset of organizations moving aggressively beyond Wi‑Fi 6 and legacy bands. Most corporate campuses, manufacturing plants, healthcare facilities, and retail environments continue to run primarily on 2.4 GHz and 5 GHz, even as their

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IBM partners with OpenAI to drive enterprise AI deployment

There will be three focus areas in the new partnership: helping organizations transform their businesses to integrate AI into their daily work, assisting clients in modernizing legacy applications through a combination of OpenAI products and IBM’s expertise, and an expansion of an existing cybersecurity collaboration combining OpenAI frontier AI capabilities

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Cisco rides ‘networking supercycle’ for strong Q4

Security revenue grew 14% year-over-year in Q4, with more than 1,500 customers adopting new products such as Secure Access, XDR, HyperShield, and AI Defense, bringing the total new customer count for these products to 6,400 since launch, Robbins noted. Firewall orders increased more than 30%, and AI security features like AI

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Equinor lets stimulation service contract for NCS assets

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Energy Department Modernizes National Laboratory Operations to Strengthen America’s Scientific, Energy, and National Security Missions

WASHINGTON—The U.S. Department of Energy (DOE) today announced updated operating directives for its National Laboratories, plants, and sites as part of a broader effort to modernize operations across DOE’s laboratory complex.  To advance President Trump’s commitment to Restoring Gold Standard Science, DOE is updating outdated and duplicative operating requirements to give its world-class scientific workforce more time to focus on critical science, energy, and national security missions. These reforms will improve efficiency, strengthen stewardship of taxpayer resources, and help DOE’s National Laboratories, plants, and sites operate with the speed, discipline, and agility their missions demand, while maintaining rigorous safety and security standards.  “America’s National Laboratories are among our nation’s greatest scientific assets and have powered generations of American discovery and innovation,” said U.S. Secretary of Energy Chris Wright. “President Trump has called on DOE to build on that legacy by restoring Gold Standard Science and unleashing the full potential of American ingenuity. By removing unnecessary barriers, we are giving our scientists, engineers, and technicians, more freedom to focus on the critical missions that matter most.” Working with laboratory leaders and subject matter experts, DOE reviewed a targeted set of directives governing day-to-day field operations. Its reforms build on more than three decades of recommendations from Congress, the Government Accountability Office, the National Academies, and other independent reviews that have identified unnecessary complexity in DOE’s directives framework.  DOE is acting on these longstanding recommendations while preserving strong oversight, accountability, and operational excellence—including strong protections for DOE workers, the public, the environment, and the Nation’s nuclear security enterprise.   DOE’s National Laboratories, plants, and sites carry out some of the nation’s most consequential scientific, engineering, and national security missions. Today’s action better aligns their operations with the pace and complexity of today’s missions, giving its scientific workforce more time to develop technologies, strengthen American

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Energy Secretary Announces Cancellation of Three Proposed National Interest Electric Transmission Corridors

WASHINGTON—U.S. Secretary of Energy Chris Wright today announced that the U.S. Department of Energy (DOE) will not move forward with designating the three proposed National Interest Electric Transmission Corridors (NIETCs) previously selected in December 2024 to advance in the review process. “Extensive review, including public feedback and stakeholder input, made clear that the current designation process for these three proposed transmission corridors should not continue,” said Secretary Wright. “Transmission policy must serve the American people—not special interests or a climate-alarmist agenda that drives up costs, worsens reliability, and disregards the concerns of local communities. The Trump Administration is committed to strengthening America’s electric grid with common-sense policies that prioritize delivering affordable, reliable, and secure electricity to American families and businesses.” The previous administration touted the Lake Erie–Canada Corridor, the Southwestern Grid Connector Corridor, and the Tribal Energy Access Corridor, as a means to advance their Green New Scam agenda and “accelerate decarbonization.” As the process unfolded, the current designation framework proved ineffective in strengthening grid reliability and reducing electricity costs. In some communities, it also contributed to confusion and concern about the scope and intent of NIETC authority. Thanks to President Trump and Secretary Wright, DOE has already taken numerous steps to build new transmission infrastructure and modernize existing infrastructure, including: In October 2025, DOE’s Office of Energy Dominance Financing (EDF) closed a $1.6 billion loan guarantee to AEP Transmission to reconductor and rebuild nearly 5,000 miles of transmission lines across five states.  In February 2026, DOE’s Office of Energy Dominance Financing (EDF) closed $26.5 billion in loans to Southern Company subsidiaries Georgia power and Alabama Power to support generation and grid investments, including more than 1,300 miles of transmission and grid enhancement projects.  In March 2026, DOE’s Office of Electricity (OE) announced the $1.9 billion SPARK funding opportunity to

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Permian Resources lifts forecast on working interest gains, acquisitions

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Canada rig count down 3 units

The rig count in Canada fell by 3 units to 216 rigs working for the week ended Aug. 7, according to data from Baker Hughes. A 4-rig drop in oil-directed rigs in Canada was partially offset by a 2-unit gain in gas-directed rigs. There were 146 oil-directed rigs working in Canada this week, while those drilling for gas ended the week at 65 units working. The overall US drilling rig count was unchanged this week at 588 rigs working. That number is up 49 units from this time last year. In the US, 3 additional rigs were drilling for oil, bringing the total count to 454. That number is up 43 units from this time last year. The number of gas-directed rigs fell by 3 to 124 working for the week. This time last year, 123 rigs were drilling for gas in the US. There were 572 rigs drilling on US land this week, unchanged from last week and up 48 from the year-ago period. A 1-rig increase in offshore rigs offset a 1-unit decrease in rigs drilling in inland waters. There were 14 rigs drilling offshore and 2 in inland waters this week. Leading the major oil-and gas-producing states was Texas with a 2-unit gain to end the week with 275 rigs working. The count is up 32 units from this time in 2025. Pennsylvania and Wyoming each dropped a rig to bring the respective rig counts to 16 and 15 for the week.

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Orlen’s Mažeikiai refinery to benefit from renewable electricity

Orlen SA has brought a 42.2-Mw solar photovoltaic (PV) farm online to supply renewable energy that will help to power operations at subsidiary Orlen Lietuva AB’s 10.4-million tonne/year refinery in Mažeikiai, Lithuania. Operable as of Aug. 11 and designed to generate about 45 gigawatt-hours (Gw-hr)/year of electricity, the Mažeikiai solar farm aims to reduce the refinery’s electricity procurement costs by about €4 million/year while supporting Orlen’s goal of increasing the share of renewables across its portfolio, the company said. Located on site across 60 hectares on the refinery’s grounds, the solar farm consists of about 68,000 bifacial photovoltaic modules. Each module is rated at 620 w, the bifacial design of the modules enabling the capture of sunlight on both sides to improve energy output during lower-light conditions on cloudy days, according to Orlen. The solar PV farm’s generation of about 45 Gw-hr of electricity will cover roughly 7% of the Mažeikiai manufacturing complex, where it will be dedicated to supplying power for day-to-day refinery operations, office buildings, and other critical infrastructure at the site. Completed at an overall investment of nearly €35 million, Orlen said the solar farm project received €2.5 million in support from the European Union’s Modernization Fund. Energy transition, efficiency Alongside strengthening the refinery’s energy security by providing an on-site source of reliable electricity, the new solar farm advances Orlen’s commitment to advancing regional energy transition initiatives. “This is an important step towards reducing the environmental impact of our operations and lowering the [Mažeikiai] refinery’s operating costs,” said Dariusz Zonenberg, Orlen Lietuva’s chief executive officer. “The project will increase the share of Orlen Lietuva’s electricity demand met by its own renewable generation, strengthening the company’s competitiveness and supporting the Orlen Group’s long-term strategy,” Zonenberg added. Orlen said the project supports its 2035 strategy to expand renewable energy

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ADNOC Gas advances its largest-ever gas processing expansion

Abu Dhabi National Oil Co. (ADNOC) subsidiary ADNOC Gas PLC has let a contract to Tecnimont SPA—a subsidiary of Maire SPA—to provide a suite of services for the third phase of the operator’s broader multibillion-dollar, multiphased Rich Gas Development (RGD) project that aims to expand the company’s natural gas processing capacity to meet rising energy demand and secure the United Arab Emirates’ (UAE) reliability as a global energy supplier. As part of the $4.3-billion contract officially revealed on Aug. 10 following intimations to the market in releases dated June 4 and May 20 that withheld the identity of the operator and project, Tecnimont will deliver engineering, procurement, and construction (EPC) services for ADNOC Gas’ RGD Phase 3 expansion involving the addition of a fifth NGL fractionation unit at the Ruwais NGL complex in Abu Dhabi, Maire said. Alongside the NGL fractionation unit designed to separate various hydrocarbon components, as well as treatment and sweetening systems to remove impurities and ensure product quality, Maire confirmed Tecnimont’s scope of work also will cover EPC for a new regeneration gas treatment unit, a propane refrigeration system, ancillary systems, and associated storage installations of the RGD Phase 3 project. Scheduled for completion in 2030, the Phase 3 plant will have an output capacity of 23,000 tonnes/day, equivalent to about 8 million tonnes/year (tpy), according to the service provider. Confirmation of the Phase 3 contract award follows ADNOC Gas’ announcement earlier on Aug. 10 that it had taken final investment decision on both Phase 2 and Phase 3 of the RGD project, including the operator’s separate and concurrent award to Wison Engineering Ltd. for the project’s second phase. As part of the $3.9-billion RGD Phase 3 contract, Wison Engineering will deliver EPC services for a new 670-MMcfd natural gas processing train at the operator’s Habshan

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Texas Tightens Oversight of Data Center Development

Texas has spent the past decade building one of the most data center-friendly policy environments in the United States. But the state’s political posture is tightening. The emerging message from Austin is that continued data center growth will face greater scrutiny over grid costs, water use, tax incentives and community impacts. What is interesting about this policy conversation is that the Texas Legislature is not in regular session. The 89th regular session ended June 2, 2025, and the 90th Legislature does not convene until January 12, 2027. What has occurred instead is a concentrated period of interim committee work, gubernatorial recommendations, implementation of Senate Bill 6, calls for a special session, and regulatory action by the Public Utility Commission of Texas and the Electric Reliability Council of Texas. Together, those efforts are creating the framework for a broader legislative debate in 2027 while already affecting projects seeking ERCOT interconnection, infrastructure costs and site-selection decisions. Abbott Sets Out a New Policy Framework The policy shift accelerated June 10, when Gov. Greg Abbott directed the PUCT to require data centers to fully fund the electric infrastructure needed to serve their operations and directed PUCT and ERCOT to identify additional actions available under existing authority. Separately, Abbott pledged to work with lawmakers in 2027 on legislation requiring data centers to add electric capacity, use water-efficient cooling systems, report electricity and water use, phase out outdated tax incentives and adopt additional protections for neighboring communities. The most consequential shift began June 10, when Gov. Greg Abbott sent state electricity regulators a sweeping list of data center policy priorities. Abbott called for future legislation requiring new facilities to add generation to the Texas grid, pay the full cost of their interconnection and related infrastructure, use closed-loop or similarly water-efficient cooling systems, and file annual reports

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NVIDIA Pushes the AI Factory From Rack to Asset Class

Making Compute Underwritable Huang expanded the argument a day later in an NVIDIA blog describing AI factory compute as an emerging investable asset class. NVIDIA’s case begins with a definition. The company does not describe its compute platform simply as a GPU. It includes accelerated computing, networking, systems software, AI frameworks and the CUDA software ecosystem surrounding the hardware. That wider platform matters to the financing thesis because NVIDIA argues it increases the number of potential users for an installed AI system. An NVIDIA DSX AI factory could support language models, vision, speech, biological computing, robotics, physical AI and other workloads. The same infrastructure could potentially move among customers, clouds or operators as demand changes. In financial terms, NVIDIA is arguing for fungibility. That could become particularly important to lenders and infrastructure investors trying to determine what happens if an original customer disappears, a contract expires or the economics of a particular workload change. A GPU cluster tied economically to one speculative tenant is one thing. Compute that can be redeployed across a large global market of clouds, enterprises, AI developers and model providers is a different risk proposition. NVIDIA contends that this breadth of potential offtakers helps protect residual value. Whether institutional markets ultimately price that risk the way NVIDIA hopes remains to be seen. But the company is now explicitly trying to establish a financial framework around that premise. Challenging the Traditional Depreciation Curve NVIDIA’s second argument is that software can extend the economic life of installed hardware. CUDA is central to that case. The company maintains that successive software improvements can increase the performance and efficiency of systems that have already been deployed, allowing the same hardware to produce more useful work at lower cost over time. That does not eliminate hardware obsolescence. New GPU generations continue

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The Next Data Center Constraint: Trust

When Facts Aren’t Enough Few places offer a more revealing test case than Loudoun County, Virginia. Data Center Alley has spent decades living with data center development at a scale most emerging markets will never approach. Rizer said Loudoun’s experience gives the county an unusually deep record with which to answer questions about environmental impacts, infrastructure and economic benefits. But those facts increasingly struggle to penetrate the broader public debate. Rizer said Loudoun today has more than 250 data centers, while the entire sector uses less than 10% of the county water system. He also pointed to improved air quality over the past decade and approximately $1.2 billion in tax revenue from the industry. Yet he acknowledged that simply producing another data point does little good when residents no longer trust the people presenting it. “I call it community concern whack-a-mole, because every time you address one thing, there are three others that pop up,” Rizer said. The problem, in his view, has become partly emotional rather than informational. “You can’t change how people think until you change how they feel,” he said. “And right now they feel angry, they feel confused, they are fearful, they are mistrustful, both of government and the big tech industry.” That distinction matters. The industry’s instinct has often been to counter criticism with facts: tax receipts, job numbers, water-use calculations, emissions data or explanations of how a particular cooling system works. Those facts remain important. But Rizer’s argument is that the industry must first rebuild enough credibility for communities to hear them. The Industry’s Unforced Errors Not all of the distrust has arrived from outside the industry. Rizer and Waitkunas were equally pointed about mistakes by developers and operators that have given opponents powerful examples to use against data center projects elsewhere. “The industry

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Reports: Data Center Expansion Finds Its Contours

AI Density Is Arriving Unevenly Inside the data center, the AI transition remains equally uneven. Uptime’s 2026 survey found the average modal, or most common, rack density across respondents exceeding 11 kW for the first time, up from 9 kW in 2025. But that number requires context. A relatively small group of very high-density facilities pulls the average upward. Without those facilities, Uptime puts average modal rack density at 7.8 kW, only modestly higher than 7.5 kW in 2025. The industry therefore continues to operate two realities at once: a vast installed base running conventional rack densities and a rapidly emerging class of AI facilities pushing far beyond them. The latter is becoming more visible. Some 24% of Uptime respondents now report racks at 30 kW or higher, up from 19% last year. Much of the increase occurred above 50 kW, and some operators reported deployments exceeding 100 kW. Still, most surveyed facilities have no racks at 30 kW or above. AI inference is also moving up the density curve. For the first time in Uptime’s survey, generative AI inference matched AI training as a driver of respondents’ highest-density deployments, with 21% citing each workload. That matters because inference potentially pushes AI infrastructure requirements beyond a relatively concentrated population of model-training campuses and into a broader set of facilities and markets. Power Is Both Constraint and Risk No issue connects the three reports more consistently than power. It limits new site availability. It redirects development toward emerging markets. It shapes community debates. It affects density and cooling architecture. And once a facility is operating, it remains the largest source of outage risk. Uptime says 56% of operators who experienced an impactful outage identified power as the primary cause of their most recent incident. The institute cautions against treating the increase

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DCF Poll: What Will Constrain Data Center Growth Next?

Matt Vincent is Editor in Chief of Data Center Frontier, where he leads editorial strategy and coverage focused on the infrastructure powering cloud computing, artificial intelligence, and the digital economy. A veteran B2B technology journalist with more than two decades of experience, Vincent specializes in the intersection of data centers, power, cooling, and emerging AI-era infrastructure. Since assuming the EIC role in 2023, he has helped guide Data Center Frontier’s coverage of the industry’s transition into the gigawatt-scale AI era, with a focus on hyperscale development, behind-the-meter power strategies, liquid cooling architectures, and the evolving energy demands of high-density compute, while working closely with the Digital Infrastructure Group at Endeavor Business Media to expand the brand’s analytical and multimedia footprint. Vincent also hosts The Data Center Frontier Show podcast, where he interviews industry leaders across hyperscale, colocation, utilities, and the data center supply chain to examine the technologies and business models reshaping digital infrastructure. Since its inception he serves as Head of Content for the Data Center Frontier Trends Summit. Before becoming Editor in Chief, he served in multiple senior editorial roles across Endeavor Business Media’s digital infrastructure portfolio, with coverage spanning data centers and hyperscale infrastructure, structured cabling and networking, telecom and datacom, IP physical security, and wireless and Pro AV markets. He began his career in 2005 within PennWell’s Advanced Technology Division and later held senior editorial positions supporting brands such as Cabling Installation & Maintenance, Lightwave Online, Broadband Technology Report, and Smart Buildings Technology. Vincent is a frequent moderator, interviewer, and keynote speaker at industry events including the HPC Forum, where he delivers forward-looking analysis on how AI and high-performance computing are reshaping digital infrastructure. He graduated with honors from Indiana University Bloomington with a B.A. in English Literature and Creative Writing and lives in southern New Hampshire with

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Is your networking built for AI’s traffic patterns and data volumes?

As data centers evolve into AI factories, compute has shifted from a cost center to a revenue driver. “Compute is revenue,” said Jensen Huang, co-founder and CEO of NVIDIA. “Without compute, there is no way to generate tokens. Without tokens, there’s no way to generate revenue. So, in this new world of AI, compute equals revenue.” This reframe changes an organizations’ calculus. If compute is revenue, what do you optimize for? Here are 5 questions to consider: Are you measuring what actually drives AI factory revenue? Most AI factories are power-constrained, so tokens per watt dictate how much revenue you can generate and the cost per token impacts the AI factory profit margin. But neither of these metrics should be evaluated at a single operating point. Batch jobs, real-time chat, and agentic workloads demand different points on the throughput-latency curve. AI chips that perform well at only a few points will underserve the full range of workloads. Additional key operational metrics like time to first token (TTFT), mean time between interruptions (MTBI), and platform useful life are the bedrock of AI factory efficiency. They dictate how quickly an AI factory comes online to generate tokens, the reliability of its revenue streams, and its long-term ability to remain productive as AI workloads evolve. How does agentic AI change what your CPU needs to deliver? Data center CPUs have historically been optimized for parallel throughput, where more cores improve aggregate capacity.  Agentic workloads run in loops and make different demands. The model reasons on the GPU, the CPU executes tool calls such as code compilation and data retrieval, and the result returns to the GPU so the model can reason again. Every step runs in sequence, gated by the one before it. Per-core performance and memory latency determine how fast each step

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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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