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Petronas Forges AI, Digital Partnerships

Petroliam Nasional Berhad (Petronas) has formed a strategic partnership with Beicip-Franlab, a leading geoscience and reservoir technology company, and AFED Digital, a specialist in advanced AI and digital solutions. Also, through Malaysia Petroleum Management (MPM), it formalized MOUs with Amazon Web Services, SLB, Halliburton, Microsoft, Accenture, Iraya Energies, Rystad Energy Advisory Asia Pacific, and S&P […]

Petroliam Nasional Berhad (Petronas) has formed a strategic partnership with Beicip-Franlab, a leading geoscience and reservoir technology company, and AFED Digital, a specialist in advanced AI and digital solutions.

Also, through Malaysia Petroleum Management (MPM), it formalized MOUs with Amazon Web Services, SLB, Halliburton, Microsoft, Accenture, Iraya Energies, Rystad Energy Advisory Asia Pacific, and S&P Global Commodity Insights, Petronas said in a media release.

Petronas said its partnership with Beicip-Franlab and AFED Digital includes a Joint Development Agreement (JDA) with TriCripta AI, a venture aimed at accelerating hydrocarbon discovery, reducing uncertainties, and maximizing recovery sustainably across Petronas Carigali’s operations.

Petronas said the partnership will start by providing high-impact and practical AI solutions to tackle key challenges in exploration, development, and production.

“This partnership reflects Petronas’ firm commitment to collaborate for a future-ready Upstream digital and technology ecosystem. By integrating advanced AI and data-driven solutions across the value chain, Petronas is reinforcing our position as a resilient and adaptive upstream player”, Mohd Jukris Abdul Wahab, Petronas Executive Vice President and Chief Executive Officer of Upstream, said.

The MOUs meanwhile aim to speed up investor decision-making through technologies that harness agentic artificial intelligence and high-performance computing.

“At Petronas, we believe that by embracing digital intelligence and strategic partnerships, we are enhancing Malaysia’s exploration and production competitiveness, creating an investment environment anchored on pace, data reliability, and transparency. We are elevating our web-based exploration and production data platform, Petronas myPROdata to support future Malaysia Bid Rounds”, Bacho Pilong, Senior Vice President of MPM, said.

“These partnerships are also about reshaping the way we work, unlocking new value through technology, enabling better investment decisions, and strengthening Malaysia’s appeal to global energy players”, Bacho Pilong added.

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Essential commands for Linux server management

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JPM Analysts Look at Israel Military Conflicts and Oil Prices

In a research note sent to Rigzone on Wednesday by Natasha Kaneva, Head of Global Commodities Strategy at J.P. Morgan, analysts at the company, including Kaneva, stated that, since 1967, except for the Yom Kippur War in 1973, none of the 11 major military conflicts involving Israel have had a lasting impact on oil prices. “Examining Brent oil prices three months before and after the onset of each crisis, regional conflicts involving Israel often trigger a sharp increase in oil prices – even without immediate supply disruptions – likely due to apprehensions about potential future disruptions,” the analysts said in the note. “Over time, however, oil prices tend to gradually stabilize and decline, leading to Brent trading at a discount to its fair value,” they added. “Similarly, other military conflicts in the Middle East, such as the Syrian civil war in March 2011, the Yemeni civil war in September 2014, ISIS’s advance into Northern Iraq in June 2014, a U.S. drone strike on an Iranian Major General in January 2020, and an airstrike in Syria in April 2017, did not result in significant supply losses following the conflict, thereby having no lasting impact on oil prices,” they continued.   The analysts went on to state in the research note that, in contrast, events involving a major regional oil producer tend to have a material impact on oil prices. “The First Gulf War in August 1990, the Second Gulf War and the conflict in the Niger Delta both in March 2003, the 2011 Libyan civil war in February 2011, the fall of Mosul in June 2014, and sanctions on Iran in 2018 all directly affected oil supply, leading to increased oil prices,” the analysts highlighted. “During these episodes, we estimate that oil traded at a wide $7-14 per barrel premium to

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Renewables Share in EU Power Generation Down YoY in Q1

Renewable sources accounted for 42.5 percent of net electricity production in the European Union in the first quarter (Q1), down 4.3 percentage points from the same three-month period last year, the European Commission said. A fall in hydro and wind production from 260.5 terawatt hours (tWh) to 218.5 tWh offset an increase in solar from 40.9 tWh to 55 tWh. Denmark had the highest share of renewables in net power generation at 88.5 percent, followed by Portugal at 86.6 percent and Croatia at 77.3 percent. Nineteen EU countries saw a decline in the share of renewables in generation in the January-March 2025 period, driven by decreases in hydro and wind. The biggest drops were logged in Greece (-12.4 percentage points), Lithuania (-12 percentage points) and Slovakia (-10.6 percentage points). Wind led renewable power generation in the EU in Q1 at 42.5 percent, followed by hydro at 29.2 percent, solar at 18.1 percent, combustible renewable fuels at 9.8 percent and geothermal at 0.5 percent. According to a Commission analysis of National Energy and Climate Plans (NECPs), published May, most member states have declared national contributions that are in line with the Renewable Energy Directive’s target to raise the share of renewables in the overall regional energy mix to 42.5 percent by 2030. However, current national goals present a gap of 1.5 percentage points, according to the analysis. “Contributions put forward by Member States signal a strong commitment to renewables deployment but indicate a 41 percent renewable energy share in gross final energy consumption by 2030”, the analysis said. “At the same time, a more optimistic assessment based on Member State projections suggest that the EU could reach a figure of 42.6 percent, demonstrating the potential to go further”, it said, noting that between 2022 and 2024 EU nations installed about 205

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Oil Market on Edge Waiting for What Iran Will Do

The oil market looks relaxed, but it is still on edge waiting for what Iran will do. That’s what Bjarne Schieldrop, Chief Commodities Analyst at Skandinaviska Enskilda Banken AB (SEB), said in an oil report sent to Rigzone by the SEB team on Monday. In the report, Schieldrop highlighted that Brent crossed the $80 per barrel line this morning but noted that it “quickly fell back, assigning limited probability for Iran choosing to close the Strait of Hormuz”. “Brent traded in a range of $70.56-$79.04 per barrel last week as the market fluctuated between ‘Iran wants a deal’ and ‘U.S. is about to attack Iran’. At the end of the week though, Donald Trump managed to convince markets (and probably also Iran) that he would make a decision within two weeks. i.e. no imminent attack,” Schieldrop said in the report. “Previously when he has talked about ‘making a decision within two weeks’ he has often ended up doing nothing in the end. The oil market relaxed as a result and the week ended at $77.01 per barrel which is just $6 per barrel above the year to date average of $71 per barrel,” he added. “Brent jumped to $81.4 per barrel this morning, the highest since mid-January, but then quickly fell back to a current price of $78.2 per barrel which is only up 1.5 percent versus the close on Friday,” he continued. “As such the market is pricing a fairly low probability that Iran will actually close the Strait of Hormuz. Probably because it will hurt Iranian oil exports as well as the global oil market,” he went on to state. Schieldrop highlighted in the report that the U.S. attacked Iran on Saturday, noting that the attack involved 125 warplanes, submarines, and surface warships, and that 14 bunker buster

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7 ways fuel cells future-proof utility energy strategies

The acceleration of AI workloads — marked by their high power density, fluctuating demand and critical uptime requirements — is intensifying pressure on utilities to deliver more than just raw capacity. While traditional centralized generation and long-distance transmission remain essential, they are no longer sufficient because they can’t respond quickly enough to the rapid and distributed load growth driven by AI, electrification and evolving reliability demands.  Many recognize the need for flexible, resilient and low-emissions power that can be deployed quickly and operated with precision, especially as regulatory scrutiny increases and interconnection delays stretch for months or years.  Energy leaders are embracing the notion that fuel cell systems have become a strategic resource to expand their reach, enhance resilience and support large customers without burdening ratepayers or compromising decarbonization goals.  Among the available technologies, solid oxide fuel cells (SOFCs) stand out for their high efficiency, modular scalability, long operational life and competitive operating costs. These systems convert fuel — such as natural gas, bio gas and hydrogen — into electricity through a combustion-free high-temperature electrochemical process, making them uniquely suited to support both grid infrastructure and distributed energy applications. Fuel cell systems offer utilities a flexible tool to future-proof their energy strategies in an era of electrification, data center expansion and escalating climate and capacity challenges. Fuel cells are a hedge against market volatility Because fuel cells are highly efficient, they require less fuel to generate each kilowatt-hour of electricity than traditional combustion-based power sources, reducing exposure to fuel price volatility. SOFCs can achieve electrical efficiencies of up to 65% and when integrated with combined heat and power (CHP) configurations, the total system efficiency can exceed 90%. These systems can also be powered by renewable fuels such as biogas or hydrogen, enhancing long-term sustainability. By working with partners like Bloom

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ChargeScape’s EV Virtual Power Plant ensures summertime grid reliability

As we enter the summer months, America’s aging power grid faces a double-whammy of unprecedented, 24/7 electricity demand from AI data centers coupled with the prospect of temporary but severe peaks from air conditioning load during heat waves. Without smart energy management, parts of the country could face the rolling blackouts recently seen in California. Electric vehicles (EVs) have been painted by some as a liability to the power grid. But at ChargeScape, we are building a Virtual Power Plant of electric vehicles that will bolster the nation’s power grid using a distributed network of batteries. As a joint venture of BMW, Ford, Honda and Nissan, ChargeScape is on a mission to transform EVs from a grid liability into an asset. ChargeScape’s platform does this by: Balancing the complexity of fast-growing distributed energy resources (DERs) alongside intermittent renewable generation; Delivering real-time peak load reduction at the bulk system level; Preventing asset overload from localized demand on specific parts of the distribution network; and Meeting heightened customer expectations for energy management. Real-world-impact: enhancing grid resilience across the U.S. Our vision of a more resilient, reliable U.S. power grid isn’t just a dream: it’s a reality that we are actively building today with our utility partners across the country. Below are a few examples of how ChargeScape is leveraging EVs to deliver grid reliability: 1. PSEG Long Island’s System Peak Relief Program To support summer grid reliability, ChargeScape is participating in PSEG Long Island’s System Peak Relief Program. Through this program, EV charging is automatically paused during high-demand hours in the summer, helping to reduce system stress without disrupting driver routines. Enrolled customers receive $50 upfront and another $50 at the end of the season for staying enrolled and participating in demand response events. ChargeScape, through its direct partnerships with automakers, was

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Unpacking wildfire risk: A new imperative for every utility executive

For many electric utility executives, especially those outside the Western U.S., the escalating threat of wildfires, especially asset-caused wildfires, can seem removed from their daily lives. Historically, catastrophic power line fires were infrequent. A wire falling on healthy vegetation posed little threat, and even in dry conditions, severe impacts were rare. However, the conditions on the ground have shifted rapidly, and it’s time for every electric utility leader to confront this evolving reality with clear eyes. Predicting Today’s Changing Conditions and Tomorrow’s Risks The traditional playbook for assessing wildfire risk is no longer sufficient. Most electric utilities still rely on static risk assessments: snapshots in time based on historical data that become outdated the moment they’re completed. But wildfire risk changes hour by hour with weather conditions, fuel moisture, and equipment status. A circuit rated ‘low risk’ in the morning can become catastrophically dangerous by afternoon.  We have seen this level of change play out in recent years. What was once considered an anomalous event in wildfire, like a catastrophe so large that an electric utility faces damages exceeding their insurance coverage, has become a disturbing trend. Since 2017, catastrophic power line fires have occurred annually, and not just in California. Such catastrophic asset-caused events are spreading unexpectedly to places like Colorado with the 2021 Marshall Fire; Hawaii with the tragic 2023 Lahaina Fire; Texas with the 2024 Smokehouse Creek Fire, and even in Oklahoma, Pacific Northwest, and the East Coast. The recent Eaton Fire in a bone dry January in Los Angeles, and the forest fires in the Carolinas in March are a stark reminder: the threat is now universal and changing constantly. Several converging factors fuel this intensified risk. Persistent drought and a drier climate is a major driver, transforming previously benign landscapes into pending tinderboxes. Dry conditions

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Can Intel cut its way to profit with factory layoffs?

Matt Kimball, principal analyst at Moor Insights & Strategy, said, “While I’m sure tariffs have some impact on Intel’s layoffs, this is actually pretty simple — these layoffs are largely due to the financial challenges Intel is facing in terms of declining revenues.” The move, he said, “aligns with what the company had announced some time back, to bring expenses in line with revenues. While it is painful, I am confident that Intel will be able to meet these demands, as being able to produce quality chips in a timely fashion is critical to their comeback in the market.”  Intel, said Kimball, “started its turnaround a few years back when ex-CEO Pat Gelsinger announced its five nodes in four years plan. While this was an impressive vision to articulate, its purpose was to rebuild trust with customers, and to rebuild an execution discipline. I think the company has largely succeeded, but of course the results trail a bit.” Asked if a combination of layoffs and the moving around of jobs will affect the cost of importing chips, Kimball predicted it will likely not have an impact: “Intel (like any responsible company) is extremely focused on cost and supply chain management. They have this down to a science and it is so critical to margins. Also, while I don’t have insights, I would expect Intel is employing AI and/or analytics to help drive supply chain and manufacturing optimization.” The company’s number one job, he said, “is to deliver the highest quality chips to its customers — from the client to the data center. I have every confidence it will not put this mandate at risk as it considers where/how to make the appropriate resourcing decisions. I think everybody who has been through corporate restructuring (I’ve been through too many to count)

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Intel appears stuck between ‘a rock and a hard place’

Intel, said Kimball, “started its turnaround a few years back when ex-CEO Pat Gelsinger announced its five nodes in four years plan. While this was an impressive vision to articulate, its purpose was to rebuild trust with customers, and to rebuild an execution discipline. I think the company has largely succeeded, but of course the results trail a bit.” Asked if a combination of layoffs and the moving around of jobs will affect the cost of importing chips, Kimball predicted it will likely not have an impact: “Intel (like any responsible company) is extremely focused on cost and supply chain management. They have this down to a science and it is so critical to margins. Also, while I don’t have insights, I would expect Intel is employing AI and/or analytics to help drive supply chain and manufacturing optimization.” The company’s number one job, he said, “is to deliver the highest quality chips to its customers — from the client to the data center. I have every confidence it will not put this mandate at risk as it considers where/how to make the appropriate resourcing decisions. I think everybody who has been through corporate restructuring (I’ve been through too many to count) realizes that, when planning for these, ensuring the resilience of these mission critical functions is priority one.”  Added Bickley, “trimming the workforce, delaying construction of the US fab plants, and flattening the decision structure of the organization are prudent moves meant to buy time in the hopes that their new chip designs and foundry processes attract new business.”

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Next-gen AI chips will draw 15,000W each, redefining power, cooling, and data center design

“Dublin imposed a 2023 moratorium on new data centers, Frankfurt has no new capacity expected before 2030, and Singapore has just 7.2 MW available,” said Kasthuri Jagadeesan, Research Director at Everest Group, highlighting the dire situation. Electricity: the new bottleneck in AI RoI As AI modules push infrastructure to its limits, electricity is becoming a critical driver of return on investment. “Electricity has shifted from a line item in operational overhead to the defining factor in AI project feasibility,” Gogia noted. “Electricity costs now constitute between 40–60% of total Opex in modern AI infrastructure, both cloud and on-prem.” Enterprises are now forced to rethink deployment strategies—balancing control, compliance, and location-specific power rates. Cloud hyperscalers may gain further advantage due to better PUE, renewable access, and energy procurement models. “A single 15,000-watt module running continuously can cost up to $20,000 annually in electricity alone, excluding cooling,” said Manish Rawat, analyst at TechInsights. “That cost structure forces enterprises to evaluate location, usage models, and platform efficiency like never before.” The silicon arms race meets the power ceiling AI chip innovation is hitting new milestones, but the cost of that performance is no longer just measured in dollars or FLOPS — it’s in kilowatts. The KAIST TeraLab roadmap demonstrates that power and heat are becoming dominant factors in compute system design. The geography of AI, as several experts warn, is shifting. Power-abundant regions such as the Nordics, the Midwest US, and the Gulf states are becoming magnets for data center investments. Regions with limited grid capacity face a growing risk of becoming “AI deserts.”

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Edge reality check: What we’ve learned about scaling secure, smart infrastructure

Enterprises are pushing cloud resources back to the edge after years of centralization. Even as major incumbents such as Google, Microsoft, and AWS pull more enterprise workloads into massive, centralized hyperscalers, use cases at the edge increasingly require nearby infrastructure—not a long hop to a centralized data center—to take advantage of the torrents of real-time data generated by IoT devices, sensor networks, smart vehicles, and a panoply of newly connected hardware. Not long ago, the enterprise edge was a physical one. The central data center was typically located in or very near the organization’s headquarters. When organizations sought to expand their reach, they wanted to establish secure, speedy connections to other office locations, such as branches, providing them with fast and reliable access to centralized computing resources. Vendors initially sold MPLS, WAN optimization, and SD-WAN as “branch office solutions,” after all. Lesson one: Understand your legacy before locking in your future The networking model that connects centralized cloud resources to the edge via some combination of SD-WAN, MPLS, or 4G reflects a legacy HQ-branch design. However, for use cases such as facial recognition, gaming, or video streaming, old problems are new again. Latency, middle-mile congestion, and the high cost of bandwidth all undermine these real-time edge use cases.

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Cisco capitalizes on Isovalent buy, unveils new load balancer

The customer deploys the Isovalent Load Balancer control plane via automation and configures the desired number of virtual load-balancer appliances, Graf said. “The control plane automatically deploys virtual load-balancing appliances via the virtualization or Kubernetes platform. The load-balancing layer is self-healing and supports auto-scaling, which means that I can replace unhealthy instances and scale out as needed. The load balancer supports powerful L3-L7 load balancing with enterprise capabilities,” he said. Depending on the infrastructure the load balancer is deployed into, the operator will deploy the load balancer using familiar deployment methods. In a data center, this will be done using a standard virtualization automation installation such as Terraform or Ansible. In the public cloud, the load balancer is deployed as a public cloud service. In Kubernetes and OpenShift, the load balancer is deployed as a Kubernetes Deployment/Operator, Graf said.  “In the future, the Isovalent Load Balancer will also be able to run on top of Cisco Nexus smart switches,” Graf said. “This means that the Isovalent Load Balancer can run in any environment, from data center, public cloud, to Kubernetes while providing a consistent load-balancing layer with a frictionless cloud-native developer experience.” Cisco has announced a variety of smart switches over the past couple of months on the vendor’s 4.8T capacity Silicon One chip. But the N9300, where Isovalent would run, includes a built-in programmable data processing unit (DPU) from AMD to offload complex data processing work and free up the switches for AI and large workload processing. For customers, the Isovalent Load Balancer provides consistent load balancing across infrastructure while being aligned with Kubernetes as the future for infrastructure. “A single load-balancing solution that can run in the data center, in public cloud, and modern Kubernetes environments. This removes operational complexity, lowers cost, while modernizing the load-balancing infrastructure in preparation

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Oracle’s struggle with capacity meant they made the difficult but responsible decisions

IDC President Crawford Del Prete agreed, and said that Oracle senior management made the right move, despite how difficult the situation is today. “Oracle is being incredibly responsible here. They don’t want to have a lot of idle capacity. That capacity does have a shelf life,” Del Prete said. CEO Katz “is trying to be extremely precise about how much capacity she puts on.” Del Prete said that, for the moment, Oracle’s capacity situation is unique to the company, and has not been a factor with key rivals AWS, Microsoft, and Google. During the investor call, Katz said that her team “made engineering decisions that were much different from the other hyperscalers and that were better suited to the needs of enterprise customers, resulting in lower costs to them and giving them deployment flexibility.” Oracle management certainly anticipated a flurry of orders, but Katz said that she chose to not pay for expanded capacity until she saw finalized “contracted noncancelable bookings.” She pointed to a huge capex line of $9.1 billion and said, “the vast majority of our capex investments are for revenue generating equipment that is going into data centers and not for land or buildings.”

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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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