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Nvidia unveils Mega Omniverse blueprint for building industrial robot fleet digital twins

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Nvidia unveils Mega Omniverse blueprint for building industrial robot fleet digital twins, as part of a CES 2025 keynote speech by Nvidia CEO Jensen Huang. The new framework enables next era of industrial AI and robot […]

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Nvidia unveils Mega Omniverse blueprint for building industrial robot fleet digital twins, as part of a CES 2025 keynote speech by Nvidia CEO Jensen Huang.

The new framework enables next era of industrial AI and robot simulation through software-defined testing and optimization to factories and warehouses, the company said.

According to Gartner, the worldwide end-user spending on all IT products for 2024 was $5 trillion. This industry is built on a computing fabric of electrons, is fully software-defined, accelerated — and now generative AI-enabled. While huge, it’s a fraction of the larger physical industrial market that relies on the movement of atoms.

“In the future, every factory will have a digital twin,” Huang said.

Today’s 10 million factories, nearly 200,000 warehouses and 40 million miles of highways form the “computing” fabric of our physical world. But that vast network of production facilities and distribution centers is still laboriously and manually designed, operated and optimized.

In warehousing and distribution, operators face highly complex decision optimization problems — matrices of variables and interdependencies across human workers, robotic and agentic systems and equipment. Unlike the IT industry, the physical industrial market is still waiting for its own software-defined moment.

That moment is coming, Nvidia said.

Mega

The company today at CES announced “Mega,” an Omniverse Blueprint for developing, testing and optimizing physical AI and robot fleets at scale in a digital twin before deployment into real-world facilities.

Advanced warehouses and factories use fleets of hundreds of autonomous mobile robots, robotic arm manipulators and humanoids working alongside people. With implementations of increasingly complex systems of sensor and robot autonomy, it requires coordinated training in simulation to optimize operations, help ensure safety and avoid disruptions.

Mega offers enterprises a reference architecture of Nvidia accelerated computing, AI, Nvidia Isaac and Nvidia Omniverse technologies to develop and test digital twins for testing AI-powered robot brains that drive robots, video analytics AI agents, equipment and more for handling enormous complexity and scale. The new framework brings software-defined capabilities to physical facilities, enabling continuous development, testing, optimization and deployment.

Developing AI Brains With World Simulator for Autonomous Orchestration

With Mega-driven digital twins, including a world simulator that coordinates all robot activities and sensor data, enterprises can continuously update facility robot brains for intelligent routes and tasks for operational efficiencies.

The blueprint uses Omniverse Cloud Sensor RTX APIs that enable robotics developers to render sensor data from any type of intelligent machine in the factory, simultaneously, for high-fidelity large-scale sensor simulation. This allows robots to be tested in an infinite number of scenarios within the digital twin, using synthetic data in a software-in–the-loop pipeline with Nvidia Isaac ROS.

Supply chain solutions company Kion Group is collaborating with Accenture and Nvidia as the first to adopt Mega for optimizing operations in retail, consumer packaged goods, parcel services and more.

Huang offered a glimpse into the future of this collaboration on stage at CES, demonstrating how enterprises can navigate a complex web of decisions using the Mega Omniverse Blueprint.

“At Kion, we leverage AI-driven solutions as an integral part of our strategy to optimize our customers’ supply chains and increase their productivity,” said Rob Smith, CEO of Kion Group, in a statement. “With Nvidia’s AI leadership and Accenture’s expertise in digital technologies, we are reinventing warehouse automation. Bringing these strong partners together, we are creating a vision for future warehouses that are part of a smart agile system, evolve with the world around them and can handle nearly any supply chain challenge.”

Creating Operational Efficiencies With Mega Omniverse Blueprint

Kion is working with Mega

Creating operational efficiencies, Kion and Accenture are embracing the Mega Omniverse Blueprint to build next-generation supply chains for Kion and its customers. Kion can capture and digitalize a warehouse digital twin in Omniverse by using computer-aided design files, video, lidar, image and AI-generated data.

Kion uses the Omniverse digital twin as a virtual training and testing environment for its industrial AI’s robot brains, powered by Nvidia Isaac, tapping into smart cameras, forklifts, robotic equipment and digital humans. Integrating the Omniverse digital twin, Kion’s warehouse management software can create and assign missions for robot brains, like moving a load from one place to another.

These simulated robots can carry out tasks by perceiving and reasoning in environments, and they’re capable of planning next motions and then taking actions that are simulated in the digital twin. The robot brains perceive the results deciding the next action, and this cycle continues with Mega precisely tracking the state and position of all the assets in the digital twin.

Delivering Services With Mega for Facilities Everywhere

Accenture, global leader in professional services, is adopting Mega as part of its AI Refinery for Simulation and Robotics, built on NVIDIA AI and Omniverse, to help organizations use AI simulation to reinvent factory and warehouse design and ongoing operations.

With the blueprint, Accenture is delivering new services – including Custom Robotics and Manufacturing Foundation Model Training and Finetuning; Intelligent Humanoid Robotics; and AI-Powered Industrial Manufacturing and Logistics Simulation and Optimization — to expand the power of physical AI and simulation to the world’s factories and warehouse operators. Now, for example, an organization can explore numerous options for their warehouse before choosing and implementing the best one.

“As organizations enter the age of industrial AI, we are helping them use AI-powered simulation and autonomous robots to reinvent the process of designing new facilities and optimizing existing operations,” said Julie Sweet, chair and CEO, Accenture, in a statement. “Our collaboration with Nvidia and KION will help our clients plan their operations in digital twins, where they can run hundreds of options and quickly select the best for current or changing market conditions, such as seasonal market demand or workforce availability. This represents a new frontier of value for our clients to achieve using technology, data and AI.”

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TotalEnergies farms out 40% participating interest in certain licenses offshore Nigeria to Chevron

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AI-driven network management gains enterprise trust

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Naftogaz Seals New PrivatBank Loan for Winter Gas

Naftogaz Group has secured a fresh UAH 5 billion ($118.82 million) loan from local bank JSC CB PrivatBank to procure more natural gas for Ukraine. “To get through the winter steadily, Naftogaz needs to import 4.4 billion cubic meters of gas [155.38 billion cubic feet], with a total cost of about EUR 1.9 billion [$2.22 billion]”, the state-owned integrated energy company said in an online statement, noting local production has fallen due to war damage. Earlier Naftogaz sealed local loans totaling UAH 9.4 billion from PrivatBank and PJSC JSB Ukrgasbank, each committing UAH 4.7 billion, to buy gas for the heating season, as announced by Naftogaz in July. Chief executive Sergii Koretskyi said then, “At the same time, we continue to work with international financial institutions and partner countries”. Also in 2025 JSC State Saving Bank of Ukraine agreed to lend Naftogaz UAH 3 billion for gas import, as announced by Naftogaz October 9. On November 13 Naftogaz and the European Investment Bank (EIB) announced a grant of EUR 127 million for the procurement of gas for Ukraine, in addition to a EUR 300 million EIB loan to Naftogaz that was disbursed October for the same purpose. The loans are guaranteed under the Ukraine Investment Framework (UIF), part of the European Union’s Ukraine Facility. The facility aims to mobilize up to EUR 50 billion – EUR 33 billion in loans and EUR 17 billion in grants – from 2024 to 2027, according to the European Council, which approved the facility February 2024. The UIF aims to mobilize up to EUR 40 billion of investments for recovery, reconstruction and modernization, according to its implementer the European Commission. On August 13 Naftogaz and the European Bank for Reconstruction and Development (EBRD) said they had agreed on a EUR 270 million loan for gas purchases.

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CNOOC Ltd Announces 12th Startup in Chinese Waters in 2025

CNOOC Ltd has started production in the Weizhou 11-4 Oilfield Adjustment and Satellite Fields Development Project in the Beibu Gulf Basin of the South China Sea. This is the company’s 12th announced startup offshore China this year, and the sixth in the South China Sea. Including two projects in Brazilian waters and Guyana’s Yellowtail, the publicly listed arm of China National Offshore Oil Corp has now announced 15 upstream startups in 2025. CNOOC Ltd expects the newest startup to reach its capacity of about 16,900 barrels of oil equivalent a day in 2026. The oil is light crude, according to the company. The development plan eyes 35 wells: 28 for production and seven for water injection. “The main production facilities include a newly-built unmanned wellhead platform and a central processing platform, which are connected to an existing platform through a trestle bridge”, CNOOC Ltd said in a press release. “The project has adopted a coordinated development plan of ‘three offshore processing centers + one onshore terminal’, serving as a gathering and transportation hub to release the resource capacity and ensure stable energy supply in the region”, the sole owner said. The project has an average water depth of around 43 meters (141.08 feet), according to CNOOC Ltd. Earlier this year it put online five projects in the South China Sea: the Dongfang 29-1 field, the Panyu 11-12/10-1/10-2 Oilfield Adjustment Joint Development Project, the Weizhou 5-3 field, the Wenchang 16-2 field and phase II of the Wenchang 19-1 field. In the Bohai Sea, CNOOC Ltd put onstream four projects in 2025: phase I of the Bozhong 26-6 field, the Caofeidian 6-4 field adjustment, phase I of the Kenli 10-2 Oilfields Development Project and phase II of the Luda 5-2 North field. In the Yinggehai Basin, two came online this year: the Dongfang 1-1 Gas

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Crude rises after US seizes Venezuelan tanker

Oil futures erased earlier declines after US forces intercepted and seized a sanctioned oil tanker off the coast of Venezuela, a move that marks a major escalation of tensions between the two countries. West Texas Intermediate traded higher to settle above $58 after earlier dropping as much as 1%. Brent crude settled above $62. The seizure may make it much more difficult for Venezuela to send its oil overseas, as other shippers are now likely to be more reluctant to load its cargoes. Most Venezuelan oil heads to China, usually through intermediaries, at steep discounts due to sanctions risk. US President Donald Trump has suggested numerous times that the US could strike on land in Venezuela and that the country’s President Nicolas Maduro’s “days are numbered.” “Tensions are continuing to move up the escalation ladder and introduce some short-term supply risk,” said Rebecca Babin, a senior energy trader at CIBC Private Wealth Group. “That said, given the administration’s clear desire to keep oil and gasoline prices contained, the market is only assigning a small risk premium. Any potential disruption is still being viewed as short-lived.” Still, oversupply concerns continue to weigh on sentiment. The US said domestic crude production would hit a record 13.6 million barrels a day this year, adding to a flood of supply hitting the global market, while several of India’s largest refiners are buying sanctioned Russian oil, easing the worst fears of a supply threat. Ukraine carried out yet another attack on a Russian shadow-fleet oil tanker as it continues to target Moscow’s vital seaborne petroleum trade. Meantime, data from the US Energy Information Administration on Wednesday showed US inventories declined 1.8 million barrels, the first draw on stocks in around three weeks. Inventories at the Cushing, Oklahoma, storage hub rose. Data also showed a surge

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South Sudan Oil Exports at Risk

South Sudan’s oil exports faced a new threat after rebels in war-torn Sudan seized facilities key to transporting crude to the Red Sea. Workers fled and operations were halted at Heglig, an oil hub in southern Sudan, as the Rapid Support Forces paramilitary group approached the area, according to people familiar with the events. The RSF, which has been battling Sudan’s army since April 2023, said on Telegram on Monday it had taken control of the “strategic Heglig” area, and was committed to securing the oil facilities. The development raises the prospect of another halt in exports of South Sudan’s Dar Blend after a disruption in mid-November. While Sudan ships little crude of its own, it’s the sole conduit for oil from landlocked South Sudan. Heglig — which lies near their border — plays a crucial role in the pipeline network.  Sudan exported an average of 165,000 barrels a day of its neighbor’s crude in the past three months, according to tanker-tracking data compiled by Bloomberg.  The army-backed government in Sudan didn’t respond to requests for comment. Nor did South Sudanese authorities, who rely on oil exports for the vast majority of state revenue. The RSF’s seizure of Heglig caps a string of recent territorial gains in southern Sudan for the group that’s been accused by the US of genocide in a conflict in which hundreds of thousands of people are thought to have died. The World Health Organization on Monday said drone strikes on a kindergarten and a nearby hospital in South Kordofan on Dec. 4 had killed 114 people, including 63 children. WHAT DO YOU THINK? Generated by readers, the comments included herein do not reflect the views and opinions of Rigzone. All comments are subject to editorial review. Off-topic, inappropriate or insulting comments will be removed.

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Exxon CFO to Retire

Kathy Mikells, the first outsider to join Exxon Mobil Corp.’s inner circle of top executives, will retire next year as she battles a serious but non-life threatening health condition.  Mikells, 60, joined Exxon in 2021 when the Texas oil giant was under pressure from shareholders to improve financial performance, diversify its leadership and build a low-carbon business. She will retire on Feb. 1 to focus on her health, Chief Executive Officer Darren Woods said in a presentation to investors Tuesday. She will be replaced by Neil Hansen, a 25-year Exxon veteran who leads the company’s global business solutions division.  “In recent months Kathy has undergone a series of procedures and surgeries to address a debilitating but thankfully non-life threatening health issue,” Woods said. “While her condition has improved, it has become clear to her, and I think the rest of us, that she needs to focus fully on her recovery.” Mikells came to Exxon from beverage titan Diageo Plc and became the first female senior vice president on the company’s management committee, which oversees day-to-day operations and sets strategy. She was also the first executive to hold such a high position at Exxon without a background in oil, natural gas or chemicals. “Things are absolutely getting better, but it has been slow going, and I still have a lot of work to do with my doctors to one day get back to my usual self,” Mikells said. “I know my colleagues on the call today will recognize the sincerity of my disappointment in needing to leave this great company.” Mikells modernized Exxon’s finance function and was the first manager to hold the formal title of chief financial officer. She overhauled investor communications and provided more granular, forward-looking information to analysts and the market in general.  Hansen has been prepared for

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Eni Announces ‘Significant’ Find Offshore Indonesia

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Arista goes big with campus wireless tech

In a white paper describing how VESPA works, Arista wrote: The first component of VESPA involves Arista access points creating VXLAN tunnels to Arista switches serving as WLAN Gateways…. Second, as device packets arrive via the AP, it dynamically creates an Ethernet Segment Identifier (Type 6 ESI) based on the AP’s VTEP IP address. These dynamically created tunnels can scale to 30K ESI’s spread across paired switches in the cluster which provide active/active load sharing (performance+HA) to the APs. Third, the gateway switches use Type 2 EVPN NLRI (Network Layer Reachability Information) to learn and exchange end point MAC addresses across the cluster. … With this architecture, adding more EVPN WLAN gateways scales both AP and user connections, to tens of thousands of end points. To manage the forwarding information for hundreds of thousands of clients (e.g: FIB next hop and rewrite) would prove very complex and expensive if using conventional networking solutions. Arista’s innovation is to distribute this function across the WiFi access points with a unique MAC Rewrite Offload feature (MRO). With MRO, the access point is responsible for servicing mobile client ARP requests (using its own mac address), building a localized MAC-IP binding table, and forwarding client IP addresses to the WLAN gateways with the APs MAC address. The WLAN Gateways therefore only learns one (MAC) address for all the clients associated with the AP. This improves the gateway’s scaling from 10X to 100X, allowing these cost effective gateways to support hundreds of thousands of clients attached to the APs. AVA system gets a boost In addition to the new wireless technology, Arista is also bolstering the capabilities of its natural-language, generative AI-based Autonomous Virtual Assist (AVA) system for delivering network insights and AIOps.  AVA is aimed at providing an intelligent assistant that’s not there to replace

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Most significant networking acquisitions of 2025

Cisco makes two AI deals: EzDubs and NeuralFabric Last month Cisco completed its acquisition of EzDubs, a privately held AI software company with speech-to-speech translation technology. EzDubs translates conversations across 31 languages and will accelerate Cisco’s delivery of next-generation features, such as live voice translation that preserves the characteristics of speech, the vendor stated. Cisco plans to incorporate EzDubs’ technology in its Cisco Collaboration portfolio. Also in November, Cisco bought AI platform company NeuralFabric, which offers a generative AI platform that lets organizations develop domain-specific small language models using their own proprietary data. Coreweave buys Core Scientific Nvidia-backed AI cloud provider CoreWeave acquired crypto miner Core Scientific for about $9 billion, giving it access to 1.3 gigawatts of contracted power to support growing demand for AI and high-performance computing workloads. CoreWeave said the deal augments its vertical integration by expanding its owned and operated data center footprint, allowing it to scale GPU-powered services for enterprise and research customers. F5 picks up three: CalypsoAI, Fletch and MantisNet F5 acquired Dublin, Ireland-based CalypsoAI for $180 million. CalypsoAI’s platform creates what the company calls an Inference Perimeter that protects across models, vendors, and environments. F5 says it will integrate CalypsoAI’s adaptive AI security capabilities into its F5 Application Delivery and Security Platform (ADSP). F5’s ADSP also stands to gain from F5’s acquisition of agentic AI and threat management startup Fletch. Fletch’s technology turns external threat intelligence and internal logs into real-time, prioritized insights; its agentic AI capabilities will be integrated into ADSP, according to F5. Lastly, F5 grabbed startup MantisNet to enhance cloud-native observability in F5’s ADSP. MantisNet leverages extended Berkeley Packet Filer (eBPF)-powered, kernel-level telemetry to provide real-time insights into encrypted protocol activity and allow organizations “to gain visibility into even the most elusive traffic, all without performance overhead,” according to an F5 blog

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Aviz Networks launches enterprise-grade community SONiC distribution

First, the company enabled FRR (Free Range Routing) features that exist in the community code but aren’t consistently implemented across different ASICs. VRRP (Virtual Router Redudancy Protocol) provides router redundancy for high availability. Spanning tree variants prevent network loops in layer 2 topologies. MLAG allows two switches to act as a single logical device for link aggregation. EVPN enhancements support layer 2 and layer 3 VPN services over VXLAN overlays. These protocols work differently depending on the underlying silicon, so Aviz normalized their implementation across Broadcom, Nvidia, Cisco and Marvell chips. Second, Aviz fixed bugs discovered in production deployments. One customer deployed community SONiC with OpenStack and started migrating virtual machines between hosts. The network fabric couldn’t handle the workload and broke. Aviz identified the failure modes and patched them.  Third, Aviz built a software component that normalizes monitoring data across vendors. Broadcom’s Tomahawk ASIC generates different telemetry formats than Nvidia’s Spectrum or Cisco’s Silicon One. Network operators need consistent data for troubleshooting and capacity planning. The software collects ASIC-specific logs and network operating system telemetry, then translates them into a standardized format that works the same way regardless of which silicon vendor’s chips are running in the switches. Validated for enterprise deployment scenarios The distribution supports common enterprise network architectures.  IP CLOS provides the leaf-spine topology used in modern data centers for predictable latency and scalability. EVPN/VXLAN creates layer 2 and layer 3 overlay networks that span physical network boundaries. MLAG configurations provide link redundancy without spanning tree limitations. Aviz provides validated runbooks for these deployments across data center, edge and AI fabric use cases. 

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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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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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