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LNG Canada Phase 1 Completed

Fluor Corp and JGC Corp said Monday they had handed over LNG Canada’s second production train, bringing the Shell PLC-led project’s capacity to 14 million tonnes a year. Train 2 completes phase 1 of “Canada’s first LNG mega-project”, Irving, Texas-based Fluor said in a press release. LNG Canada already announced November 6 that Train 2 […]

Fluor Corp and JGC Corp said Monday they had handed over LNG Canada’s second production train, bringing the Shell PLC-led project’s capacity to 14 million tonnes a year.

Train 2 completes phase 1 of “Canada’s first LNG mega-project”, Irving, Texas-based Fluor said in a press release. LNG Canada already announced November 6 that Train 2 had started production.

Fluor energy solutions president Pierre Bechelany highlighted that the project had spent “more than CAD 3.3 billion [$2.38 billion] on goods and services contracted with Indigenous businesses and joint ventures and more than CAD 550 million with local area businesses”.

Fluor said, “The LNG Canada plant consists of a natural gas receiving and liquification facility, a marine terminal with the capacity to accommodate one LNG carrier, a tugboat dock and LNG loading lines. The facility also includes LNG processing units, storage tanks, a rail yard, a water treatment facility and flare stacks”.

“Located on Canada’s west coast, the LNG Canada facility benefits from access to abundant, natural gas and an ice-free harbor”, it added.

The Fluor-JGC joint venture won the engineering, procurement and construction contract for phase 1 in 2018. According to an online statement by Fluor October 2, 2018, the contract totaled around $14 billion, with Fluor’s share at $8.4 billion.

Earlier this year Fluor and Japan’s JGC also won a contract to update the front-end engineering design for LNG Canada’s proposed phase 2, as announced by the contractors last August.

LNG Canada, which targets the Asian market, announced the dispatch of its first liquefied natural gas cargo on July 1, saying the milestone introduces Canada as an LNG exporter.

“By turning aspiration into action, Canada can become the world’s leading energy superpower with the strongest economy in the G7”, Prime Minister Mark Carney declared then, as quoted in an LNG Canada statement.

LNG Canada said at the time it was evaluating the potential for a two-train expansion that would double the capacity.

“Each LNG Canada joint venture participant will provide its own natural gas supply and individually offtake and market their respective share of liquified natural gas from LNG Canada, starting today”, LNG Canada said July.

Britain’s Shell is the biggest owner in the LNG Canada joint venture at 40 percent through Shell Canada Energy. Malaysia’s state-owned Petroliam Nasional Bhd holds 25 percent through North Montney LNG LP. Japan’s Mitsubishi Corp and China’s state-backed PetroChina Co Ltd each have 15 percent through Diamond LNG Canada Partnership and PetroChina Kitimat LNG Partnership respectively. Korea Gas Corp owns five percent through Kogas Canada LNG Partnership.

The Haisla Nation is also involved in the project as site host.

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AI-driven network management gains enterprise trust

The way the full process works is that the raw data feed comes in, and machine learning is used to identify an anomaly that could be a possible incident. That’s where the generative AI agents step up. In addition to the history of similar issues, the agents also look for

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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

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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

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XCF’s New Nevada SAF Project Advancing toward Construction

XCF Global Inc said it had completed the initial site work for its second sustainable aviation fuel (SAF) production facility, which will rise adjacent to the one it had put onstream in Storey County, Nevada, early this year. New Rise Reno 2’s 10-acre parcel has been graded and access roads have been built, according to Houston, Texas-based XCF. “Engineering, design and project planning are underway, positioning construction to begin in 2026”, it said in a press release. “Located adjacent to the existing New Rise Reno facility in Nevada, the new site will benefit from integration with common facilities such as gas, water, rail and personnel offices as well as existing pre-treatment, hydrogen production and broader logistics infrastructure – reducing capital costs, lowering execution risk and accelerating time to production. “Since inception, approximately $350 million has been invested in XCF’s flagship New Rise Reno facility. New Rise Reno 2 represents the next phase of this growth strategy, with an expected $300 million investment enabling XCF to double SAF production capacity to ~80 million gallons annually”. XCF expects New Rise Reno 2 to start production 2028, along with two other SAF projects in the United States. All three have a planned nameplate capacity of 40 million gallons a year. New Rise Reno began production in the first quarter. It has a nameplate capacity of 38 million gallons a year, according to XCF. “The facility is designed and configured to produce 3,000 barrels per day of synthetic blending component for blending to make sustainable aviation fuel. All fuel is made from renewable triglyceride feedstocks (corn oil, soybean oil or other qualifying oils) that meet the Federal Renewable Fuels Standard”, according to the project website. XCF chief executive Chris Cooper said, “This expansion exemplifies how XCF grows: intentionally, efficiently and with a platform built to meet surging global demand”. XCF noted, “Federal targets call for

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BMI Analysts Make 2026 Oil Demand Prediction

In a BMI report sent to Rigzone by the Fitch Group on Monday, analysts at BMI, a unit of Fitch Solutions, projected that global oil demand “will post steady growth” in 2026 “but remain below historic trends”. “We forecast 2026 total fuel consumption to expand by 1.0 percent, a slight decrease from the 1.1 percent registered for 2025,” the BMI analysts noted in the report, which identified “key” oil and gas themes for next year. “The growth for 2026 will still see incremental demand of one million barrels per day for the year,” the analysts added. In the report, the BMI analysts said the 2026 global oil demand forecast “marks the continuation of waning oil consumption growth seen in the post-Covid era with most growth attributed to Mainland China and emerging markets (EMs)”. “The U.S. will post weaker growth in 2026 while other developed markets will see further contraction in fuel consumption,” they added. The analysts revealed in the report that global fuel consumption growth is forecast to continue shrinking throughout their 10-year forecast period, “despite the strength of gains in EMs”. “India will take over as the largest market for growth, adding 237,000 barrels per day of additional demand, followed by China with 165,000 barrels per day of growth in 2026,” they said. The BMI analysts went on to state in the report that the forecast for steady growth in fuel demand is supported by their global economic outlook, which they said “points to a year of steady gains in 2026, with forecast global GDP growth of 2.6 percent, unchanged from 2025”. “Supportive monetary and fiscal policies will bolster economic activity, unlocking consumer and business confidence. The stabilization of growth as trade frictions ease, following on from the volatility and uncertainty seen in 2025, will buttress demand for oil,”

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LNG Canada Phase 1 Completed

Fluor Corp and JGC Corp said Monday they had handed over LNG Canada’s second production train, bringing the Shell PLC-led project’s capacity to 14 million tonnes a year. Train 2 completes phase 1 of “Canada’s first LNG mega-project”, Irving, Texas-based Fluor said in a press release. LNG Canada already announced November 6 that Train 2 had started production. Fluor energy solutions president Pierre Bechelany highlighted that the project had spent “more than CAD 3.3 billion [$2.38 billion] on goods and services contracted with Indigenous businesses and joint ventures and more than CAD 550 million with local area businesses”. Fluor said, “The LNG Canada plant consists of a natural gas receiving and liquification facility, a marine terminal with the capacity to accommodate one LNG carrier, a tugboat dock and LNG loading lines. The facility also includes LNG processing units, storage tanks, a rail yard, a water treatment facility and flare stacks”. “Located on Canada’s west coast, the LNG Canada facility benefits from access to abundant, natural gas and an ice-free harbor”, it added. The Fluor-JGC joint venture won the engineering, procurement and construction contract for phase 1 in 2018. According to an online statement by Fluor October 2, 2018, the contract totaled around $14 billion, with Fluor’s share at $8.4 billion. Earlier this year Fluor and Japan’s JGC also won a contract to update the front-end engineering design for LNG Canada’s proposed phase 2, as announced by the contractors last August. LNG Canada, which targets the Asian market, announced the dispatch of its first liquefied natural gas cargo on July 1, saying the milestone introduces Canada as an LNG exporter. “By turning aspiration into action, Canada can become the world’s leading energy superpower with the strongest economy in the G7”, Prime Minister Mark Carney declared then, as quoted in an LNG Canada

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North America Adds Rigs Week on Week

North America added eight rigs week on week, according to Baker Hughes’ latest North America rotary rig count, which was published on December 5. The total U.S. rig count increased by five week on week and the total Canada rig count rose by three during the same period, taking the total North America rig count up to 740, comprising 549 rigs from the U.S. and 191 rigs from Canada, the count outlined. Of the total U.S. rig count of 549, 527 rigs are categorized as land rigs, 19 are categorized as offshore rigs, and three are categorized as inland water rigs. The total U.S. rig count is made up of 413 oil rigs, 129 gas rigs, and seven miscellaneous rigs, according to Baker Hughes’ count, which revealed that the U.S. total comprises 476 horizontal rigs, 58 directional rigs, and 15 vertical rigs. Week on week, the U.S. land rig count rose by three, and its offshore and inland water rig counts each increased by one, Baker Hughes highlighted. The U.S. oil rig count rose by six week on week, its gas rig count dropped by one by week on week, and its miscellaneous rig count remained unchanged week on week, the count showed. The U.S. horizontal rig count rose by one, its directional rig count remained unchanged, and its vertical rig count increased by four, week on week, the count revealed. A major state variances subcategory included in the rig count showed that, week on week, Louisiana added four rigs and New Mexico added one rig. A major basin variances subcategory included in Baker Hughes’ rig count showed that, week on week, the Eagle Ford basin dropped one rig. Canada’s total rig count of 191 is made up of 126 oil rigs and 65 gas rigs, Baker Hughes pointed out.

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Trump Admin Backs Potential American Buy of Lukoil Iraq Field

The US government is backing Iraq’s plan to transfer Lukoil PJSC’s stake in a giant oil field to an American company, days before a sanctions waiver on the Russian firm is set to expire. Iraq’s Oil Ministry last week said it’s approaching US companies to take over the majority holding in West Qurna 2, which pumps about 10 percent of the country’s crude. The Trump administration’s preference is for the Russian firm’s global assets to be taken over by a US entity, people familiar with the matter said last month. The ministry didn’t name any companies, but US firms including Exxon Mobil Corp. and Chevron Corp. have emerged as potential suitors for Lukoil’s assets. For West Qurna-2, Iraq would prefer Exxon, which had previously operated the neighboring West Qurna 1 oil field, one person said, asking not to be identified because the information is private. Exxon recently returned to Iraq after a two-year absence, signing an initial agreement in October that could pave the way for developing the Majnoon field in the country’s south. Chevron is in discussions to enter Iraq, Chief Executive Officer Mike Wirth said at the company’s Nov. 12 investor day. The company’s officials met with Iraq’s oil minister in Baghdad this week, according to a Iraqi statement. “We are encouraged by the Iraqi Ministry of Oil’s initial agreements with Exxon and Chevron, the recent commitment to transition West Qurna-2 to a US operator,” a State Department spokesperson said in answer to questions from Bloomberg. “The United States will continue to champion the interests of American companies in Iraq.” Exxon and Chevron declined to comment. A call to Lukoil’s press service went unanswered and the company didn’t respond to an email sent outside of normal business hours in Moscow on Monday. Iraq, the second-largest producer in the Organization of

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Noble to Sell 6 Jackups, Become Pureplay Deepwater Driller

Noble Corp said Monday it had signed separate deals to sell five jackup rigs to Borr Drilling Ltd for $360 million and one jackup to Ocean Oilfield Drilling for $64 million. After the completion of the transactions, expected next year, “Noble will be a pureplay deepwater and ultra-harsh environment jackup operator”, the offshore driller said in an online statement. Borr will acquire Noble Resilient (built 2009), Noble Resolute (built 2009), Noble Mick O’Brien (built 2013), Noble Regina Allen (built 2013) and Noble Tom Prosser (built 2014). The purchase price consists of $210 million in cash and $150 million in seller notes. “The $150 million in proposed seller notes to Borr are expected to have a six-year maturity and be secured by a first lien on three jackups (Noble Tom Prosser, Noble Regina Allen and Noble Resilient)”, Noble said. “Additionally, Noble intends to operate two rigs – Noble Mick O’Brien and Noble Resolute – under a bareboat charter agreement with Borr for one year from signing of the definitive agreement”, it said. Meanwhile Ocean Oilfield Drilling will buy Noble Resolve, built 2009, after the rig’s ongoing contract ends. Noble Resolve will be freed in the first quarter of 2026, Nobel says on its online fleet inventory. The rig is currently deployed in Spain for an unnamed operator, according to Noble’s latest fleet status report, published October 27. Ocean Oilfield Drilling will pay in cash. “These transactions are expected to be immediately accretive to our shareholders based on both trailing 2025 and anticipated 2026 EBITDA and free cash flow, while also bolstering our balance sheet and sharpening the focus on our established positions in the deepwater and ultra-harsh jackup segments”, said president and chief executive Robert W. Eifler. In its quarterly report October 27, Noble said the Noble Globetrotter II drillship, built 2013, was also being sold. During the third

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US approves Nvidia H200 exports to China, raising questions about enterprise GPU supply

Shifting demand scenarios What remains unclear is how much demand Chinese firms will actually generate, given Beijing’s recent efforts to steer its tech companies away from US chips. Charlie Dai, VP and principal analyst at Forrester, said renewed H200 access is likely to have only a modest impact on global supply, as China is prioritizing domestic AI chips and the H200 remains below Nvidia’s latest Blackwell-class systems in performance and appeal. “While some allocation pressure may emerge, most enterprise customers outside China will see minimal disruption in pricing or lead times over the next few quarters,” Dai added. Neil Shah, VP for research and partner at Counterpoint Research, agreed that demand may not surge, citing structural shifts in China’s AI ecosystem. “The Chinese ecosystem is catching up fast, from semi to stack, with models optimized on the silicon and software,” Shah said. Chinese enterprises might think twice before adopting a US AI server stack, he said. Others caution that even selective demand from China could tighten global allocation at a time when supply of high-end accelerators remains stretched, and data center deployments continue to rise.

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What does Arm need to do to gain enterprise acceptance?

But in 2017, AMD released the Zen architecture, which was equal if not superior to the Intel architecture. Zen made AMD competitive, and it fueled an explosive rebirth for a company that was near death a few years prior. AMD now has about 30% market share, while Intel suffers from a loss of technology as well as corporate leadership. Now, customers have a choice of Intel or AMD, and they don’t have to worry about porting their applications to a new platform like they would have to do if they switched to Arm. Analysts weigh in on Arm Tim Crawford sees no demand for Arm in the data center. Crawford is president of AVOA, a CIO consultancy. In his role, he talks to IT professionals all the time, but he’s not hearing much interest in Arm. “I don’t see Arm really making a dent, ever, into the general-purpose processor space,” Crawford said. “I think the opportunity for Arm is special applications and special silicon. If you look at the major cloud providers, their custom silicon is specifically built to do training or optimized to do inference. Arm is kind of in the same situation in the sense that it has to be optimized.” “The problem [for Arm] is that there’s not necessarily a need to fulfill at this point in time,” said Rob Enderle, principal analyst with The Enderle Group. “Obviously, there’s always room for other solutions, but Arm is still going to face the challenge of software compatibility.” And therein lies what may be Arm’s greatest challenge: software compatibility. Software doesn’t care (usually) if it’s on Intel or AMD, because both use the x86 architecture, with some differences in extensions. But Arm is a whole new platform, and that requires porting and testing. Enterprises generally don’t like disruption —

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Intel decides to keep networking business after all

That doesn’t explain why Intel made the decision to pursue spin-off in the first place. In July, NEX chief Sachin Katti issued a memo that outlined plans to establish key elements of the Networking and Communications business as a stand-alone company. It looked like a done deal, experts said. Jim Hines, research director for enabling technologies and semiconductors at IDC, declined to speculate on whether Intel could get a decent offer but noted NEX is losing ground. IDC estimates Intel’s market share in overall semiconductors at 6.8% in Q3 2025, which is down from 7.4% for the full year 2024 and 9.2% for the full year 2023. Intel’s course reversal “is a positive for Intel in the long term, and recent improvements in its financial situation may have contributed to the decision to keep NEX in house,” he said. When Tan took over as CEO earlier this year, prioritized strengthening the balance sheet and bringing a greater focus on execution. Divest NEX was aligned with these priorities, but since then, Intel has secured investments from the US Government, Nvidia and SoftBank that have reduced the need to raise cash through other means, Hines notes. “The NEX business will prove to be a strategic asset for Intel as it looks to protect and expand its position in the AI datacenter market. Success in this market now requires processor suppliers to offer a full-stack solution, not just silicon. Scale-up and scale-out networking solutions are a key piece of the package, and Intel will be able to leverage its NEX technologies and software, including silicon photonics, to develop differentiated product offerings in this space,” Hines said.

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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,

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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.

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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

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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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