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Nvidia open-sources Run:ai, the software it acquired for $700M to help companies manage GPUs for AI

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Nvidia has completed its acquisition of Run:ai, a software company that makes it easier for customers to orchestrate GPU clouds for AI, and said that it would open-source the software. The purchase price wasn’t disclosed, but […]

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Nvidia has completed its acquisition of Run:ai, a software company that makes it easier for customers to orchestrate GPU clouds for AI, and said that it would open-source the software.

The purchase price wasn’t disclosed, but was pegged by reports at $700 million when Nvidia first reported its intent to close the deal in April. Run:ai posted the deal news on its website today and the company said Nvidia additionally plans to open source its software. The company’s software remotely schedules Nvidia GPU resources for AI in the cloud.

Neither company explained why Run:ai will open source its platform, but it’s probably not hard to figure out. Since Nvidia has grown to be the No. 1 maker of AI chips, its stock price has soared to $3.56 trillion, making it the most valuable company in the world. That’s great for Nvidia, but it makes it hard for it to acquire companies because of antitrust oversight.

A spokesperson for Nvidia said only in a statement, “We’re delighted to welcome the Run:ai team to Nvidia.”

When Microsoft acquired Activision Blizzard for $68.7 billion, it appeased antitrust regulators by licensing Activision’s Call of Duty game to other platforms for a decade to address worries that the company would become too powerful in gaming. The same might be happening here.

Run:ai founders Omri Geller and Ronen Dar said in a press release that open sourcing its software will help the community build better AI, faster.

“While Run:ai currently supports only Nvidia GPUs, open sourcing the software will enable it to extend its availability to the entire AI ecosystem,” Geller and Dar said.

They said they will continue to help our customers to get the most out of their AI Infrastructure and offer the ecosystem maximum flexibility, efficiency and utilization for GPU systems, wherever they are: On-Prem, in the cloud through native solutions, or on Nvidia DGX Cloud, co-engineered with leading CSPs.

The founders also said, “True to our open-platform philosophy, as part of Nvidia, we will keep empowering AI teams with the freedom to choose the tools, platforms, and frameworks that best suit their needs. We will continue to strengthen our partnerships and work alongside the ecosystem to
deliver a wide variety of AI solutions and platform choices.”

The Israel-based company said its goal when it was founded in 2018 was to be a driving force in the AI
revolution and empower organizations to unlock the full potential of their AI infrastructure.

“Over the years, our world-class team has achieved milestones that we could only dream of back then. Together, we’ve built innovative technology, an amazing product, and an incredible go-to-market engine,” the founders said.

Run:ai helps customers to orchestrate their AI Infrastructure, increase efficiency and utilization, and boost the productivity of their AI teams.

“We are thrilled to build on this momentum, now as part of Nvidia. AI and accelerated computing are transforming the world at an unprecedented pace, and we believe this is just the beginning,” the Run:ai founders said. “GPUs and AI infrastructure will remain at the forefront of driving these transformative innovations and joining Nvidia provides us an extraordinary opportunity to carry forward a joint mission of helping humanity solve the world’s greatest challenges.”

Nvidia has been a longtime maker of graphics chips, and those chips have become a lot more useful in recent years in running AI software. Now the company is also emphasizing software and this acquisition is related to giving customers maximum choice, efficiency and flexibility for GPU orchestration software. Nvidia and Run:ai have been working together since 2020 and they have joint customers.

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Four new vulnerabilities found in Ingress NGINX

NGINX is a reverse proxy/load balancer that generally acts as the front-end web traffic receiver and directs it to the application service for data transformation. Ingress NGINX is a version used in Kubernetes as the controller for traffic coming into the infrastructure. It takes care of mapping traffic to pods

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Tankers With Russian Oil Flock to East Asia

More than a dozen tankers loaded with Russian Urals oil are sailing toward Asia or idling along the route, a sign of producers racing to get cargoes closer to China as India pulls back from the trade.  These vessels — carrying a combined 10 million to 12 million barrels of oil — are spread across the Indian Ocean, and off the coasts of Malaysia, China and Russia. Five of them are indicating ‘for orders’ or ‘China for orders’ as their status, according to data intelligence firm Kpler, a category that usually means they don’t yet have a specific buyer or discharge port. Another six are signaling Singapore and Malaysia, and are likely heading to a popular spot for ship-to-ship transfers in the South China Sea where they can wait until the crude is bought. Four are floating off Malaysia, China and Russia’s Far East, without indicating a clear destination. Urals — Russia’s flagship crude grade, which is loaded from ports in the Baltic Sea — has become the variety of choice for Indian refiners since the invasion of Ukraine in early 2022 saw it become heavily discounted. But pressure from Washington has pushed imports lower, reaching an average of 1.2 million barrels a day in January compared with a peak of more than 2 million barrels a day in mid-2024. Indian imports of the crude could be trimmed further after President Donald Trump said on Monday the country would stop buying Russian oil as part of deal to cut trade tariffs. Prime Minister Narendra Modi confirmed the agreement but didn’t comment on oil. Some refiners are holding off purchases while they seek clarification from New Delhi.  The big question is where the surplus cargoes of Urals — the bulk of which have gone to India over the last few years — will now end up. China’s

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BP, KOC Sign ETSA Extension

In a statement sent to Rigzone on Thursday, BP announced that it and Kuwait Oil Company have signed an extension of the Enhanced Technical Services Agreement (ETSA) between the companies. The agreement “paves the way for both companies to collaboratively progress Kuwait’s most strategic asset fields”, BP noted in the statement. BP added that the deal enables it to “bring expertise in enhanced oil recovery to the Greater Burgan oil field and develop local capabilities with Kuwait Oil Company to manage the development of South and East Kuwait fields through 50 secondment opportunities of BP’s technical experts”. Rigzone asked BP to disclose the deal’s value. A BP spokesperson was unable to do so. The ETSA was originally signed in 2016 for a period of 10 years, the statement highlighted, adding that it will now extend through to March 2029. BP Executive Vice President, Gas & Low Carbon Energy, William Lin, noted in the statement, “BP’s commitment to Kuwait dates back to our participation in the discovery of the Greater Burgan oil field in the 1930s, and we appreciate the trust placed in our expertise in giant oil and gas fields to continue to help develop this important strategic asset”. “This is another example of the deep relationships we’ve formed across governments, partners, and supply chains in the regions where we operate. We look forward to continuing our strong collaboration with Kuwait and to working with KOC to help support the country’s long-term energy resilience,” he added. BP notes on its website that it was one of the founders of the original Kuwait Oil Company, which it highlighted first discovered oil at Burgan in 1938. “Exportation of KOC began in 1946, in which the first export of Kuwait crude was loaded on to the bp vessel ‘Fusilier’,” BP’s site adds. BP

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IPAA Promotes Naatz to Chief Policy Officer

In a statement sent to Rigzone on Thursday, the Independent Petroleum Association of America (IPAA) announced that Dan Naatz has been promoted to Executive Vice President and Chief Policy Officer. As Chief Policy Officer, Naatz will lead IPAA’s policy priorities and oversee all government relations and advocacy efforts, the organization said in the statement, adding that he will focus “on the issues most critical to independent producers across regulatory, legislative, and permitting environments”. “He will also continue to build consensus across IPAA’s diverse membership and strengthen partnerships with aligned organizations,” the IPAA noted. Naatz also serves as Corporate Secretary on the IPAA board of directors, the statement highlighted. Prior to joining the IPAA in 2003, Naatz spent 12 years on Capitol Hill working for the late Senator Craig Thomas in various capacities, the IPAA pointed out in its statement. “Dan has led IPAA’s advocacy efforts on Capitol Hill, securing meaningful wins for independent oil and natural gas producers on issues including methane regulation, federal leasing, and permitting reform,” the statement said. “His decades of leadership and judgment have strengthened IPAA’s voice in Washington at a critical time for IPAA members,” it added. A bio on Naatz hosted on the IPAA website states that, “for more than two decades, Dan has been the public face for independent oil and natural gas producers in Washington, representing the industry in congressional hearings on Capitol Hill, roundtables, coalition meetings, and with federal regulating agencies”. “IPAA actively engages with agencies including the Environmental Protection Agency (EPA), Department of Interior (DOI), Bureau of Land Management (BLM) in support of rules and timelines that are technically feasible and cost-effective, so producers can do what they do best: provide reliable and affordable energy for Americans,” the bio page adds. IPAA President and CEO Edith Naegele said in the IPAA statement, “IPAA

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Tankers Speed Through Hormuz Chokepoint

Some supertanker operators, nervous about rising US-Iran tensions and potential risks to shipping in the Strait of Hormuz, are speeding their vessels through the chokepoint.  Very Large Crude Carriers are traveling around the narrow, congested waterway — through which about a quarter of the world’s seaborne oil trade travels — at as much as 17 knots, ship-tracking data show. About 13 knots is the usual top speed for a fully laden VLCC, which is typically about 330 meters (1,080 feet) long and cumbersome to maneuver. Some operators began the measures after Iran said it would conduct live-firing drills last week, according to shipowners and brokers that dispatch vessels through the waterway. The OPEC producer this week issued at least two warnings that they would commence live-firing exercises around the region, although none were observed, according to people with knowledge of the matter.  The development adds more uncertainty to volatile freight markets, which have spiked after tensions over Iran combined with tight vessel supply. The US continues to build up its military presence in the region as talks between Washington and Tehran kick off in Oman later on Friday. However, Iran has suggested the discussions won’t yield a quick resolution to tensions between the two countries. “Even when headline geopolitical risk cools, Hormuz remains a complex operating environment,” said Angelica Kemene, head of Optima Shipping Services’ market strategy team in Athens. “Shipowners are exercising caution over journeys that may take days to complete in the corridor.” Iran has previously threatened to lock down the Strait of Hormuz, though it has never actually followed through. Earlier this week, the Islamic Republic “harassed” a US-flagged oil tanker as it was transiting through the waterway. Meanwhile, some operators are making vessels wait off the United Arab Emirates port of Fujairah ahead of traveling through the chokepoint as they finalize

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Energy Complex Not Immune to Broad Commodities Sell-Off

The energy complex was not immune to the broad sell-off in commodities over the past week, Standard Chartered Bank Energy Research Head Emily Ashford said in a report sent to Rigzone by the Standard Chartered team late Wednesday. “The selection of Kevin Warsh as the next U.S. Federal Reserve chair, the notable ratcheting down of rhetoric between the U.S. and Iran, a business as usual OPEC+ meeting, and reduction in the U.S. tariff rates on India all acted against oil prices,” Ashford added. In the report, Ashford highlighted that oil prices “fell from their peak of $71.89 per barrel on 29 January (a six-month high), back towards $66.00 per barrel on 2 February, settling just $0.38 per barrel higher than our machine learning model SCORPIO’s forecast”. The Standard Chartered Bank analyst pointed out in the report that OPEC+ met virtually on February 1, adding that the meeting proceeded as Standard Chartered had expected. “The November 2025 decision to pause production increments for Q1-2026 was upheld, with the communique suggesting this was due to seasonality,” Ashford said in the report. “The commitment to market stability was reaffirmed, with the group retaining the optionality over returning barrels to the market, and flexibility to continue pausing or even reverse the adjustments,” Ashford added. “The four countries with additional compensation cuts for past overproduction submitted updated schedules. Kazakhstan increased its cuts for January, up to 503,000 barrels per day, from a 279,000 barrel per day target set last month,” the analyst continued. “February cuts have also been increased, to 629,000 barrels per day from 569,000 barrels per day. These adjustments are unsurprising, given the slew of issues affecting Kazakh production and exports over the last month. Iraq has also made small adjustments to its schedule, increasing its cuts by 20,000 barrels per day in

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Oil Settles Lower as Iran-US Talks Ease Risk

Oil fell for the first time in three days after Iran confirmed it would hold negotiations with the US, easing the immediate risk of military conflict and supply disruptions from the OPEC producer. West Texas Intermediate dropped to settle near $63 a barrel, after adding 4.8% over the previous two sessions, while Brent was below $68 a barrel. Iranian Foreign Minister Abbas Araghchi confirmed in a social media post that the talks will be held in Oman on Friday, clarifying the location of the encounter. Futures also extended declines after private jobs data revived worries about an economic slowdown in the US and a potential slowdown in oil demand. The commodity pared some losses after Saudi Arabia dropped the price of its main oil grade for buyers in Asia to the lowest in years, though by less than many in the industry had anticipated. That is offering the market a sign that the kingdom has faith in demand for its barrels. Differing positions over the parameters of US-Iran negotiations mean it remains unclear whether the two sides can realistically bridge major differences at a time of heightened tensions in the region, which supplies about a third of the world’s crude. That has reinserted some risk premium into oil prices, which have rebounded this year after slumping in the second half of 2025 on signs of a growing global glut. “We see that there is indeed a bit of oversupply at the moment, but that I would say is balanced with the significant uncertainty that we are seeing because of the geopolitical challenges,” Wael Sawan, chief executive officer of Shell Plc, said in a Bloomberg TV interview. “There is a premium with that uncertainty and volatility.” The added volatility is bolstering market gauges aside from benchmark futures prices. Bullish WTI call

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What hyperscalers’ hyper-spending on data centers tells us

Three hyperscalers, three capacity strategies Amazon Web Services, Google Cloud, and Microsoft Azure have each committed markedly different levels of capital for the next 12 months in their latest filings and earnings call. While Amazon CEO Andy Jassy spoke of spending $200 billion in 2026 on AI, chips, and possibly low-orbit satellites as data centers, Google CFO Anat Ashkenazi said during an earnings call that the company will commit around $180 billion to replacing aging servers and building new data centers. Microsoft, which operates on a July–June fiscal year, has yet to formally disclose its total capital expenditure plans through June 30, 2026. The company reported capex of $34.9 billion in its first fiscal quarter, and $37.5 billion in second. Its CFO, Amy Hood, has indicated that capital spending is expected to moderate in the coming quarters, leading industry analysts to revise their estimates for Microsoft’s full-year capex to around $100 billion. Each of these figures reflect distinct priorities of hyperscalers, especially how they are preparing their cloud platforms for the next phase of AI-driven demand. AWS is using capital expenditure to lock down the physical constraints that will shape future cloud capacity, including power, silicon, land, and water, Gogia said, adding that this signals a move beyond incremental expansion toward utility-scale infrastructure, signaling a strategy aimed at institutionalizing AI demand rather than simply responding to it. Microsoft and Google, however, are taking more targeted approaches.

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Super Bowl LX raises network expectations

And there’s a deeper business lesson here for IT leaders. The stadium didn’t just add more access points. The team performed a total technology overhaul. They combined their sound systems, LED screens (including the world’s largest outdoor 4K video boards), and production systems into a single, brand-new Cisco data center. This is the “One Cisco” approach I’ve written about before—breaking down silos to create a unified, observable environment. While there are many vendors that offer products to stadiums, Cisco’s platform approach addresses all aspects of stadium operations. Security and networking in tandem One of the most impressive aspects of the Levi’s Stadium tour was seeing how the Secure Networking vision has come together. George Griesler, senior director of cybersecurity for the NFL, provided some specifics of the deployment while giving a tour of the security operations center. The Super Bowl is considered a Tier 1 event, meaning it’s a high-value target for threat actors. To get ready for this, the NFL and Cisco have deployed a massive overlay network that acts as a frontline defense for the Super Bowl. A Joint Operation Center (JOC) is active 24/7, combining NFL security teams, stadium IT, and Cisco experts to monitor for incidents. Security teams are seeing a ramp-up in attacks, primarily consisting of short-lived malicious domains, credential compromises, and phishing attempts using the NFL logo. The team uses a blocking attack philosophy—proactively stopping threats via firewalls, Cisco Umbrella (DNS), and XDR (extended detection and response) before they can disrupt game operations or human safety. Griesler shared these statistics as a snapshot of activity over a 7-day period leading up to the game: 27,000+ active clients on the network 400,000+ firewall connection attempts blocked 47,000+ malicious DNS queries blocked via Cisco Umbrella The stadium network runs ticketing, medical services, and millions of dollars in

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Azure outage disrupts VMs and identity services for over 10 hours

After multiple infrastructure scale-up attempts failed to handle the backlog and retry volumes, Microsoft ultimately removed traffic from the affected service to repair the underlying infrastructure without load. “The outage didn’t just take websites offline, but it halted development workflows and disrupted real-world operations,” said Pareekh Jain, CEO at EIIRTrend & Pareekh Consulting. Cloud outages on the rise Cloud outages have become more frequent in recent years, with major providers such as AWS, Google Cloud, and IBM all experiencing high-profile disruptions. AWS services were severely impacted for more than 15 hours when a DNS problem rendered the DynamoDB API unreliable. In November, a bad configuration file in Cloudflare’s Bot Management system led to intermittent service disruptions across several online platforms. In June, an invalid automated update disrupted the company’s identity and access management (IAM) system, resulting in users being unable to use Google to authenticate on third-party apps. “The evolving data center architecture is shaped by the shift to more demanding, intricate workloads driven by the new velocity and variability of AI. This rapid expansion is not only introducing complexities but also challenging existing dependencies. So any misconfiguration or mismanagement at the control layer can disrupt the environment,” said Neil Shah, co-founder and VP at Counterpoint Research. Preparing for the next cloud incident This is not an isolated incident. For CIOs, the event only reinforces the need to rethink resilience strategies. In the immediate aftermath when a hyperscale dependency fails, waiting is not a recommended strategy for CIOs, and they should focus on a strategy of stabilize, prioritize, and communicate, stated Jain. “First, stabilize by declaring a formal cloud incident with a single incident commander, quickly determining whether the issue affects control-plane operations or running workloads, and freezing all non-essential changes such as deployments and infrastructure updates.”

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Intel sets sights on data center GPUs amid AI-driven infrastructure shifts

Supply chain reliability is another underappreciated advantage. Hyperscalers want a credible second source, but only if Intel can offer stable, predictable roadmaps across multiple product generations. However, the company runs into a major constraint at the software layer. “The decisive bottleneck is software,” Rawat said. “CUDA functions as an industry operating standard, embedded across models, pipelines, and DevOps. Intel’s challenge is to prove that migration costs are low, and that ongoing optimization does not become a hidden engineering tax.” For enterprise buyers, that software gap translates directly into switching risk. Tighter integration of Intel CPUs, GPUs, and networking could improve system-level efficiency for enterprises and cloud providers, but the dominance of the CUDA ecosystem remains the primary barrier to switching, said Charlie Dai, VP and principal analyst at Forrester. “Even with strong hardware integration, buyers will hesitate without seamless compatibility with mainstream ML/DL frameworks and tooling,” Dai added.

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8 hot networking trends for 2026

Recurring license fees may have dissuaded enterprises from adopting AIOps in the past, but that’s changing, Morgan adds: “Over the past few years, vendors have added features and increased the value of those licenses, including 24×7 support. Now, by paying the equivalent of a fraction of a network engineer’s salary in license fees, a mid-sized enterprise can reduce hours spent on operations and level-one support in order to allocate more of their valuable networking experts’ time to AI projects. Every enterprise’s business case will be different, but with networking expertise in high demand, we predict that in 2026, the labor savings will outweigh the additional license costs for the majority of mid-to-large sized enterprises.” 2. AI boosts data center networking investments Enterprise data centers, which not so long ago were on the endangered species list, have made a remarkable comeback, driven by the reality that many AI workloads need to be hosted on premises, either for privacy, security, regulatory, latency or cost considerations. The global market for data center networking technologies was estimated at around $46 billion in 2025 and is projected to reach $103 billion by the end of 2030, a growth rate of nearly 18%, according to BCC Research: “The data center networking technologies market is rapidly changing due to increasing use of AI-powered solutions across data centers and sectors like telecom, IT, banking, financial services, insurance, government and commercial industries.” McKinsey predicts that global demand for data center capacity could nearly triple by 2030, with about 70% of that demand coming from AI workloads. McKinsey says both training and inference workloads are contributing to data center growth, with inference expected to become the dominant workload by 2030. 3. Private clouds roll in Clearly, the hyperscalers are driving most of the new data center construction, but enterprises are

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Cisco: Infrastructure, trust, model development are key AI challenges

“The G200 chip was for the scale out, because what’s happening now is these models are getting bigger where they don’t just fit within a single data center. You don’t have enough power to just pull into a single data center,” Patel said. “So now you need to have data centers that might be hundreds of kilometers apart, that operate like an ultra-cluster that are coherent. And so that requires a completely different chip architecture to make sure that you have capabilities like deep buffering and so on and so forth… You need to make sure that these data centers can be scaled across physical boundaries.”  “In addition, we are reaching the physical limits of copper and optics, and coherent optics especially are going to be extremely important as we go start building out this data center infrastructure. So that’s an area that you’re starting to see a tremendous amount of progress being made,” Patel said. The second constraint is the AI trust deficit, Patel said. “We currently need to make sure that these systems are trusted by the people that are using them, because if you don’t trust these systems, you’ll never use them,” Patel said. “This is the first time that security is actually becoming a prerequisite for adoption. In the past, you always ask the question whether you want to be secure, or you want to be productive. And those were kind of needs that offset each other,” Patel said. “We need to make sure that we trust not just using AI for cyber defense, but we trust AI itself,” Patel said. The third constraint is the notion of a data gap. AI models get trained on human-generated data that’s publicly available on the Internet, but “we’re running out,” Patel said. “And what you’re starting to see happen

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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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