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Enverus Warns of ‘Contrasting Market Dynamics’ in 2026

In its 2026 energy outlook report, which was published recently, Enverus warned that this year is “likely to be characterized by contrasting market dynamics”. “In the first half, notwithstanding upside risk from increasing geopolitical tensions, oil prices are expected to decline further as global inventories swell to levels not observed since the Covid-19 era and the U.S. […]

In its 2026 energy outlook report, which was published recently, Enverus warned that this year is “likely to be characterized by contrasting market dynamics”.

“In the first half, notwithstanding upside risk from increasing geopolitical tensions, oil prices are expected to decline further as global inventories swell to levels not observed since the Covid-19 era and the U.S. shale price war,” Enverus said in the report.

“Global demand in the second half is projected to outpace supply, initiating stock draws and supporting a price recovery,” it added.

“Against this backdrop, Brent crude is forecast to average $55 per barrel for 2026, reflecting the combined effects of early-year weakness and late-year stabilization,” the report went on to state.

In a statement posted on Enverus’ website in November 2025, Al Salazar, Senior Vice President at Enverus subisidiary Enverus Intelligence Research (EIR), noted that “the current oil market forecast indicates significant oversupply to come as roughly 2.9 billion barrels of crude and petroleum products are stored in OECD tanks today, up from the typical total of 2.7-2.8 billion”.

“At EIR, our oil price outlook foresees a potential drop in early 2026 to levels reminiscent of the pandemic in 2020 or the OPEC-U.S. shale war back in 2015,” Salazar warned in that statement.

“This sobering forecast has traders and energy experts reevaluating their oil trading strategies,” he added.

“The typical seasonal demand dip from Q4 to Q1, reducing global oil consumption by about 1-2 million barrels per day, coincides with a steady supply. This mismatch could exacerbate today’s surplus, possibly pushing Brent crude into the $40-$50 per barrel range in the first half of the coming year,” Salazar highlighted.

In a statement sent to Rigzone by the Enverus team announcing the release of Enverus’ 2026 energy outlook report, Dane Gregoris, managing director at EIR, said, “our work shows oil prices will reset lower in 2026 without signaling long-term scarcity”.

“Upstream operators will continue to push for efficiency gains while capital stays highly selective,” he added.

Henry Hub

In its report, Enverus projected that Henry Hub prices will average $3.80 per million British thermal units (MMBtu) “through the winter months” and $3.60 per MMBtu “during the summer, before gradually increasing to $4.00-$4.50 per MMBtu by the end of the decade”.  

Enverus noted in its report that this outlook “reflects several structural factors expected to limit the sustainability of recent price strength”.

“Although prices at the trading hub surged by about $1.00 per MMBtu in late October, driven by colder weather forecasts and record LNG export volumes, multiple dynamics point to ongoing market softness,” Enverus added.

“L48 production remains highly elastic, with output growth exceeding expectations in response to price signals. At the same time, gas-fired power generation is losing market share to coal at current elevated price levels, indicating demand-side weakness,” it continued.  

“Additionally, weekly storage withdrawals have been lighter than weather patterns would suggest, and the current storage surplus of roughly 150 billion cubic feet versus the five-year average is unlikely to be materially reduced given robust supply and muted demand,” Enverus went on to state.

In the statement sent to Rigzone, Enverus describes itself as “the most trusted energy-dedicated SaaS company, with a platform built to maximize value from generative AI, offering anytime, anywhere access to analytics and insights”.

“These include benchmark cost and revenue data sourced from more than 95 percent of U.S. energy producers and more than 40,000 suppliers,” Enverus adds.

Enverus outlined in the statement that its report pulled from a variety of products, including Enverus PRISM, Enverus FOUNDATIONS – Carbon Innovation, Short‑Term Grid Analytics & Forecasting Solutions, Oil & Gas Production Forecast Solutions (Forecast Analytics), Enverus CORE, and Enverus AI. 

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IP Fabric 7.9 boosts visibility across hybrid environments

Multicloud and hybrid network viability has also been extended to include IPv6 path analysis, helping teams reason about connectivity in dual-stack and hybrid environments. This capability addresses a practical challenge for enterprises deploying IPv6 alongside existing IPv4 infrastructure. Network teams can now validate that applications can reach IPv6 endpoints and

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Azerbaijan Starts Sending Gas to Austria, Germany via TAP Pipeline

Azerbaijan this month began supplying natural gas to Germany and Austria via Italy using the Trans-Adriatic Pipeline, the State Oil Company of Azerbaijan Republic (SOCAR) said. “Consequently, the number of countries buying Azerbaijani gas has reached 16”, SOCAR said in a brief statement on its website. “SOCAR in accordance with the gas strategy of the Republic of Azerbaijan established under the leadership of the President Ilham Aliyev continues to consistently expand its gas marketing activities across Europe and the Middle East to broaden its portfolio of cooperation with buyers from various countries and to further strengthen Azerbaijan’s position as a reliable energy supplier”. Part of the Southern Gas Corridor, TAP carries gas from the Shah Deniz field on Azerbaijan’s side of the Caspian Sea to Europe. The 877-kilometer (544.94 miles) pipeline connects with the Trans Anatolian Pipeline on the Turkiye-Greece border, then crosses Northern Greece, Albania and the Adriatic Sea before landing in Southern Italy. Deliveries to European markets are via exit points in Greece and Italy, as well as interconnectors, according to TAP, which has become a fully fledged transmission system operator after launching into commercial operation December 2020. Last year Germany’s state-owned SEFE Securing Energy for Europe GmbH signed up for a 10-year gas supply from SOCAR. The first delivery was scheduled for that same year. “The annual quantity will gradually increase to 15 terawatt hours, which is approximately 1.5 billion cubic meters [52.97 billion cubic feet]”, SEFE said in a press release June 10, 2025. “This partnership will support investments in production and infrastructure such as gas compressors, increasing the amount of pipeline gas coming to Europe and thus ensuring the continent’s security of supply”. About four years ago Azerbaijan and the European Union agreed to double the capacity of the Southern Gas Corridor to supply the

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Enverus Warns of ‘Contrasting Market Dynamics’ in 2026

In its 2026 energy outlook report, which was published recently, Enverus warned that this year is “likely to be characterized by contrasting market dynamics”. “In the first half, notwithstanding upside risk from increasing geopolitical tensions, oil prices are expected to decline further as global inventories swell to levels not observed since the Covid-19 era and the U.S. shale price war,” Enverus said in the report. “Global demand in the second half is projected to outpace supply, initiating stock draws and supporting a price recovery,” it added. “Against this backdrop, Brent crude is forecast to average $55 per barrel for 2026, reflecting the combined effects of early-year weakness and late-year stabilization,” the report went on to state. In a statement posted on Enverus’ website in November 2025, Al Salazar, Senior Vice President at Enverus subisidiary Enverus Intelligence Research (EIR), noted that “the current oil market forecast indicates significant oversupply to come as roughly 2.9 billion barrels of crude and petroleum products are stored in OECD tanks today, up from the typical total of 2.7-2.8 billion”. “At EIR, our oil price outlook foresees a potential drop in early 2026 to levels reminiscent of the pandemic in 2020 or the OPEC-U.S. shale war back in 2015,” Salazar warned in that statement. “This sobering forecast has traders and energy experts reevaluating their oil trading strategies,” he added. “The typical seasonal demand dip from Q4 to Q1, reducing global oil consumption by about 1-2 million barrels per day, coincides with a steady supply. This mismatch could exacerbate today’s surplus, possibly pushing Brent crude into the $40-$50 per barrel range in the first half of the coming year,” Salazar highlighted. In a statement sent to Rigzone by the Enverus team announcing the release of Enverus’ 2026 energy outlook report, Dane Gregoris, managing director at EIR, said, “our work

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Kolibri Reports Increased Flows at New Oklahoma Wells

Kolibri Global Energy Inc has reported improved flow rates at the Barnes and Velin wells in Oklahoma’s Tishomingo field. The 1.5-mile lateral Barnes 6-31-2H well posted a 30-day average of 529 barrels of oil equivalent per day (boepd). The one-mile lateral Barnes 6-4H well averaged 452 boepd in 30 days, Thousand Oaks, California-based Kolibri said in a press release. The company said it owns 100 percent of the wells. “The Barnes wells continue to produce high percentages of oil (~83 percent), much like the Lovina wells the company drilled earlier this year” in the same field, Kolibri said. “On a comparable lateral length basis, these wells are producing at a boepd rate that is 22 percent higher than the Lovina wells were producing at the same time in their production life”, it added. Meanwhile the one-mile lateral wells Velin 12-9H and Velin 12-10H yielded 30-day averages of 257 boepd and 176 boepd respectively. Kolibri said it holds stakes of 97 percent in the wells. “The Velin wells are still improving and continue to act differently than the typical wells in the field”, Kolibri said. “There are some differences between these wells and the company’s offsetting wells. “One is that the Velin wells were shut in longer than our normal time after fracture stimulations were complete. This was due to the close proximity of all four wellbores, and as a result, the wells needed to stay shut-in while the Barnes wells were being fracture-stimulated. While that is standard industry practice, it may be a contributing factor to the lower early production rates and the slower cleanup. “Additionally, while the formation analysis of these wells is comparable to that of the offsetting wells, there is the presence of increased natural healed fractures and small-scale faulting, which appears unique to this location, potentially

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Veteran Gas Executive Leaving Mercuria

Steve Hill, who was hired by Mercuria Energy Group in 2024 to build out its liquefied natural gas business, is leaving the trading house. Hill was part of the company’s efforts to expand into the fast-growing global LNG market. Before joining, he was responsible for the vast LNG, gas and power marketing and trading business at energy giant Shell Plc. He was one of a trio of heavyweight hires Mercuria made after reaping bumper profits, setting off a renewed push into trading physical commodities, along with Kostas Bintas in metals and Nick O’Kane in gas and power. Known as one of the world’s biggest traders of oil and gas, the firm has been a relative latecomer behind other trading house rivals in building out a large-scale physical trading business for LNG. During Hill’s relatively brief tenure, Mercuria signed deals to offtake LNG from Oman, as well as supply Turkey and China. He also hired several of his former colleagues from Shell, though one — Singapore-based Dong Yuan — recently left the company. A spokesperson for Mercuria confirmed Hill is leaving the company. Hill didn’t immediately respond to a request for comment. WHAT DO YOU THINK? Generated by readers, the comments included herein do not reflect the views and opinions of Rigzone. All comments are subject to editorial review. Off-topic, inappropriate or insulting comments will be removed.

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Crude Settles Higher After Volatile Week

Oil edged higher at the end of a volatile week, as traders weighed tensions in Iran and positive sentiment in wider markets. West Texas Intermediate settled near $60 a barrel after plunging 4.6% on Thursday, the most since June. President Donald Trump said in a social media post that he “greatly” respects Iran’s decision to cancel scheduled hangings of protesters. His rhetoric over recent days has reduced expectations of an immediate US response to violent protests in the Islamic Republic, which could have led to disruptions to the country’s roughly 3.3 million barrel-per-day oil production, as well as shipping. Nevertheless, Washington is boosting its military presence in the Middle East. At least one aircraft carrier is moving into the region and other military assets are expected to be shifted there in the coming days and weeks, Fox News reported, citing military sources. Traders have in the past covered bearish wagers ahead of the weekend in periods of heightened geopolitical risks. “While the risk of imminent intervention from the US against Iran has subsided, it’s pretty clear that the risk is still present, which should keep the market on its toes in the short term,” said Warren Patterson, head of commodities strategy at ING Groep NV. “However, the longer this goes on without a US response, the risk premium will continue to evaporate, allowing more bearish fundamentals to take center stage.” Disruption to Kazakh exports from the Black Sea, short-term tightness in the North Sea and a host of financial flows from options markets to commodity index rebalancing have also helped lift an oil market coming off its biggest drop since 2020 on rising supplies. In a sign that lower prices are starting to bite, Harold Hamm, the billionaire wildcatter who helped kick off the US shale revolution, said his firm

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U.S. Energy Secretary and Slovakia’s Prime Minister Sign Agreement to Advance U.S.-Slovakia Civil Nuclear Program

WASHINGTON—U.S. Secretary of Energy Chris Wright and Slovak Prime Minister Robert Fico today signed an Intergovernmental Agreement (IGA) to advance cooperation on Slovakia’s civil nuclear power program. This landmark agreement includes the development of a new, state-owned American 1,200 MWe nuclear unit at the Jaslovské Bohunice Nuclear Power Plant, deepening the U.S.-Slovakia strategic partnership and strengthening European energy security. The agreement builds on President Trump’s commitment to advancing American energy leadership. A project of this scale is expected to create thousands of American jobs across engineering, advanced manufacturing, construction, nuclear fuel services, and project management, while reinforcing U.S. supply chains and expanding access to global markets for American-made nuclear technology. These efforts lay the foundation for sustained U.S. engagement in Slovakia’s nuclear energy program and support future civil nuclear projects across the region. It also supports Slovakia’s efforts to diversify its energy supply, strengthen long-term energy security, and integrate advanced American nuclear technology into Central Europe’s energy infrastructure. “The United States is proud to partner with Slovakia as a trusted ally as we expand cooperation across the energy sector,” said Energy Secretary Chris Wright. “Today’s civil nuclear agreement reflects our shared commitment to strengthening European energy security and sovereignty for decades to come. By deploying America’s leading nuclear technology, we are creating thousands of good-paying American jobs, expanding global markets for U.S. nuclear companies, and driving economic growth at home”. “I see this moment as a significant milestone in our bilateral relations, but also as a clear signal that Slovakia and the United States are united by a common strategic thinking about the future of energy – about its safety, sustainability, and technological maturity,” said the Prime Minister of the Slovak Republic Robert Fico. The planned nuclear unit represents a multibillion-dollar energy infrastructure investment and one of the largest in

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NVIDIA’s Rubin Redefines the AI Factory

The Architecture Shift: From “GPU Server” to “Rack-Scale Supercomputer” NVIDIA’s Rubin architecture is built around a single design thesis: “extreme co-design.” In practice, that means GPUs, CPUs, networking, security, software, power delivery, and cooling are architected together; treating the data center as the compute unit, not the individual server. That logic shows up most clearly in the NVL72 system. NVLink 6 serves as the scale-up spine, designed to let 72 GPUs communicate all-to-all with predictable latency, something NVIDIA argues is essential for mixture-of-experts routing and synchronization-heavy inference paths. NVIDIA is not vague about what this requires. Its technical materials describe the Rubin GPU as delivering 50 PFLOPS of NVFP4 inference and 35 PFLOPS of NVFP4 training, with 22 TB/s of HBM4 bandwidth and 3.6 TB/s of NVLink bandwidth per GPU. The point of that bandwidth is not headline-chasing. It is to prevent a rack from behaving like 72 loosely connected accelerators that stall on communication. NVIDIA wants the rack to function as a single engine because that is what it will take to drive down cost per token at scale. The New Idea NVIDIA Is Elevating: Inference Context Memory as Infrastructure If there is one genuinely new concept in the Rubin announcements, it is the elevation of context memory, and the admission that GPU memory alone will not carry the next wave of inference. NVIDIA describes a new tier called NVIDIA Inference Context Memory Storage, powered by BlueField-4, designed to persist and share inference state (such as KV caches) across requests and nodes for long-context and agentic workloads. NVIDIA says this AI-native context tier can boost tokens per second by up to 5× and improve power efficiency by up to 5× compared with traditional storage approaches. The implication is clear: the path to cheaper inference is not just faster GPUs.

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Power shortages, carbon capture, and AI automation: What’s ahead for data centers in 2026

“Despite a broader use of AI tools in enterprises and by consumers, that does not mean that AI compute, AI infrastructure in general, will be more evenly spread out,” said Daniel Bizo, research director at Uptime Institute, during the webinar. “The concentration of AI compute infrastructure is only increasing in the coming years.” For enterprises, the infrastructure investment remains relatively modest, Uptime Institute found. Enterprises will limit investment to inference and only some training, and inference workloads don’t require dramatic capacity increases. “Our prediction, our observation, was that the concentration of AI compute infrastructure is only increasing in the coming years by a couple of points. By the end of this year, 2026, we are projecting that around 10 gigawatts of new IT load will have been added to the global data center world, specifically to run generative AI workloads and adjacent workloads, but definitely centered on generative AI,” Bizo said. “This means these 10 gigawatts or so load, we are talking about anywhere between 13 to 15 million GPUs and accelerators deployed globally. We are anticipating that a majority of these are and will be deployed in supercomputing style.” 2. Developers will not outrun the power shortage The most pressing challenge facing the industry, according to Uptime, is that data centers can be built in less than three years, but power generation takes much longer. “It takes three to six years to deploy a solar or wind farm, around six years for a combined-cycle gas turbine plant, and even optimistically, it probably takes more than 10 years to deploy a conventional nuclear power plant,” said Max Smolaks, research analyst at Uptime Institute. This mismatch was manageable when data centers were smaller and growth was predictable, the report notes. But with projects now measured in tens and sometimes hundreds of

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Google warns transmission delays are now the biggest threat to data center expansion

The delays stem from aging transmission infrastructure unable to handle concentrated power demands. Building regional transmission lines currently takes seven to eleven years just for permitting, Hanna told the gathering. Southwest Power Pool has projected 115 days of potential loss of load if transmission infrastructure isn’t built to match demand growth, he added. These systemic delays are forcing enterprises to reconsider fundamental assumptions about cloud capacity. Regions including Northern Virginia and Santa Clara that were prime locations for hyperscale builds are running out of power capacity. The infrastructure constraints are also reshaping cloud competition around power access rather than technical capabilities. “This is no longer about who gets to market with the most GPU instances,” Gogia said. “It’s about who gets to the grid first.” Co-location emerges as a faster alternative to grid delays Unable to wait years for traditional grid connections, hyperscalers are pursuing co-location arrangements that place data centers directly adjacent to power plants, bypassing the transmission system entirely. Pricing for these arrangements has jumped 20% in power-constrained markets as demand outstrips availability, with costs flowing through to cloud customers via regional pricing differences, Gogia said. Google is exploring such arrangements, though Hanna said the company’s “strong preference is grid-connected load.” “This is a speed to power play for us,” he said, noting Google wants facilities to remain “front of the meter” to serve the broader grid rather than operating as isolated power sources. Other hyperscalers are negotiating directly with utilities, acquiring land near power plants, and exploring ownership stakes in power infrastructure from batteries to small modular nuclear reactors, Hanna said.

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OpenAI turns to Cerebras in a mega deal to scale AI inference infrastructure

Analysts expect AI workloads to grow more varied and more demanding in the coming years, driving the need for architectures tuned for inference performance and putting added pressure on data center networks. “This is prompting hyperscalers to diversify their computing systems, using Nvidia GPUs for general-purpose AI workloads, in-house AI accelerators for highly optimized tasks, and systems such as Cerebras for specialized low-latency workloads,” said Neil Shah, vice president for research at Counterpoint Research. As a result, AI platforms operating at hyperscale are pushing infrastructure providers away from monolithic, general-purpose clusters toward more tiered and heterogeneous infrastructure strategies. “OpenAI’s move toward Cerebras inference capacity reflects a broader shift in how AI data centers are being designed,” said Prabhu Ram, VP of the industry research group at Cybermedia Research. “This move is less about replacing Nvidia and more about diversification as inference scales.” At this level, infrastructure begins to resemble an AI factory, where city-scale power delivery, dense east–west networking, and low-latency interconnects matter more than peak FLOPS, Ram added. “At this magnitude, conventional rack density, cooling models, and hierarchical networks become impractical,” said Manish Rawat, semiconductor analyst at TechInsights. “Inference workloads generate continuous, latency-sensitive traffic rather than episodic training bursts, pushing architectures toward flatter network topologies, higher-radix switching, and tighter integration of compute, memory, and interconnect.”

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Cisco’s 2026 agenda prioritizes AI-ready infrastructure, connectivity

While most of the demand for AI data center capacity today comes from hyperscalers and neocloud providers, that will change as enterprise customers delve more into the AI networking world. “The other ecosystem members and enterprises themselves are becoming responsible for an increasing proportion of the AI infrastructure buildout as inferencing and agentic AI, sovereign cloud, and edge AI become more mainstream,” Katz wrote. More enterprises will move to host AI on premises via the introduction of AI agents that are designed to inject intelligent insight into applications and help improve operations. That’s where the AI impact on enterprise network traffic will appear, suggests Nolle. “Enterprises need to host AI to create AI network impact. Just accessing it doesn’t do much to traffic. Having cloud agents access local data center resources (RAG etc.) creates a governance issue for most corporate data, so that won’t go too far either,” Nolle said.  “Enterprises are looking at AI agents, not the way hyperscalers tout agentic AI, but agents running on small models, often open-source, and are locally hosted. This is where real AI traffic will develop, and Cisco could be vulnerable if they don’t understand this point and at least raise it in dialogs where AI hosting comes up,” Nolle said. “I don’t expect they’d go too far, because the real market for enterprise AI networking is probably a couple years out.” Meanwhile, observers expect Cisco to continue bolstering AI networking capabilities for enterprise branch, campus and data centers as well as hyperscalers, including through optical support and other gear.

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Microsoft tells communities it will ‘pay its way’ as AI data center resource usage sparks backlash

It will work with utilities and public commissions to set the rates it pays high enough to cover data center electricity costs (including build-outs, additions, and active use). “Our goal is straightforward: To ensure that the electricity cost of serving our data centers is not passed on to residential customers,” Smith emphasized. For example, the company is supporting a new rate structure Wisconsin that would charge a class of “very large customers,” including data centers, the true cost of the electricity required to serve them. It will collaborate “early, closely, and transparently” with local utilities to add electricity and supporting infrastructure to existing grids when needed. For instance, Microsoft has contracted with the Midcontinent Independent System Operator (MISO) to add 7.9GW of new electricity generation to the grid, “more than double our current consumption,” Smith noted. It will pursue ways to make data centers more efficient. For example, it is already experimenting with AI to improve planning, extract more electricity from existing infrastructure, improve system resilience, and speed development of new infrastructure and technologies (like nuclear energy). It will advocate for state and national public policies that ensure electricity access that is affordable, reliable, and sustainable in neighboring communities. Microsoft previously established priorities for electricity policy advocacy, Smith noted, but “progress has been uneven. This needs to change.” Microsoft is similarly committed when it comes to data center water use, promising four actions: Reducing the overall amount of water its data centers use, initially improving it by 40% by 2030. The company is exploring innovations in cooling, including closed-loop systems that recirculate cooling liquids. It will collaborate with local utilities to map out water, wastewater, and pressure needs, and will “fully fund” infrastructure required for growth. For instance, in Quincy, Washington, Microsoft helped construct a water reuse utility that recirculates

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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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