NTT launches physics of AI group and AI inference chip design for 4K video

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NTT launches physics of AI group and AI inference chip design for 4K video

NTT Research announced at its annual Upgrade event that it has started a new AI basic research group, dubbed the Physics of Artificial Intelligence Group. Physical AI has become a big deal in 2025, with Nvidia leading the charge to create synthetic data to pretest self-driving cars and humanoid robotics so they can get to market faster. NTT Research is launching its Physic of Artificial Intelligence (PAI) Group to get on board. NTT Research’s new independent group is spinning off of its Physic of Intelligence (PHI) Lab to advanced our understanding of the “black box” of AI for better trust and safety outcomes. NTT Research, which has an annual $3.6 billion R&D budget, is a division of NTT, Japan’s big telecommunications company. Last year, NTT created its “Physics of Intelligence” vision initially formed in collaboration with the Harvard University Center for Brain Science, key contributions made over the past five years, and ongoing collaboration with academic partners. PAI Group banner The new group will be led by Hidenori Tanaka, NTT Research Scientist and expert in physics, neuroscience, and machine learning, in broader pursuit of human/AI collaboration. The new group will continue to advance an interdisciplinary approach to understanding AI pioneered by the team over the past five years. Early on, the PHI Lab recognized the importance of understanding the “black box” nature of AI and machine learning to develop novel systems with drastically improved energy efficiency for computation. With AI now advancing at an astonishing rate, issues of trustworthiness and safety have also become critical to industry applications and governance of AI adoption. In collaboration with leading academic researchers, the Physics of Artificial Intelligence Group aims to address similarities between biological and artificial intelligences, further unravel the complexities of AI mechanisms and build trust that leads to more harmonious fusion of human and AI collaboration. The goal is to obtain a better understanding of how AI works in terms of being trained, accumulating knowledge, and making decisions so that we can design cohesive, safe, and trustworthy AI in the future. This approach echoes what physicists have done over many centuries: people had understood objects move when forces are applied, but it was physics that revealed the precise details of the relationship, which allowed humans to design machines we know today. For example, the development of the steam engine informed our understanding of thermodynamics, which in turn enabled the creation of advanced semiconductors. Similarly, the work of this group will shape the future of AI technology. The new group will continue to collaborate with the Harvard University Center for Brain Science (CBS), led by Harvard Professor Venkatesh Murthy, and with Princeton University Assistant Professor (and former NTT Research Scientist) Gautam Reddy. It also plans to collaborate with Stanford University Associate Professor Surya Ganguli, with whom Tanaka has co-authored several papers. The group’s core team includes Tanaka, NTT Research Scientist Maya Okawa and NTT Research Post-doctoral Fellow Ekdeep Singh Lubana. Previous contributions to date include: • A widely cited neural network pruning algorithm (over 750 citations in just 4 years)• A bias-removal algorithm for large language models (LLMs), recognized by the U.S. National Institute of Standards and Technology (NIST) for its scientific and practical insights; and• New insights into the dynamics of how AI learns concepts Going forward, the Physics of Artificial Intelligence Group has a three-pronged mission. 1) It intends to deepen our understanding of the mechanisms of AI, all the better to integrate ethics from within, rather than through a patchwork of fine-tuning (i.e. enforced learning). 2) Borrowing from experimental physics, it will continue creating systematically controllable spaces of AI and observe the learning and prediction behaviors of AI step-by-step. 3) It aspires to heal the breach of trust between AI and human operators through improved operations and data control. “Today marks a new step towards society’s understanding of AI through the establishment of NTT Research’s Physics of Artificial Intelligence Group,” NTT Research president and CEO Kazu Gomi said in a statement. “The emergence and rapid adoption of AI solutions across all areas of everyday life has had a profound impact on our relationship with technology. As AI’s role continues to grow, it is imperative we explore how AI makes people feel and how this can shape the advancement of new solutions. The new group aims to demystify concerns and bias around AI solutions to create a harmonious path forward for the coexistence of AI and humanity.” The Physics of Artificial Intelligence Group embraces an interdisciplinary approach to AI, with physics, neuroscience and psychology coming together. This approach looks beyond conventional benchmarks, recognizing the need to support goals such as fairness and safety which lead to sustainable AI adoption. In terms of energy efficiency, other groups in the PHI Lab are already engaged in efforts to reduce the energy consumption of AI computing platforms through optical computing and a path-breaking, thin-film lithium niobate (TFLN) technology. On top of that, inspired by the vast differential between watts consumed by LLMs and the human or animal brain, the new group will also explore ways to leverage similarities between biological brains and artificial neural networks. “The key for AI to exist harmoniously alongside humanity lies in its trustworthiness and how we approach the design and implementation of AI solutions,” Tanaka said, in a statement. “With the emergence of this group, we have a path forward to understanding the computational mechanisms of the brain and how it relates to deep learning models. Looking ahead, our research hopes to bring about more natural intelligent algorithms and hardware through our understanding of physics, neuroscience, and machine learning.” Since 2019, the PHI Lab has spearheaded research for new ways of computing systems by leveraging photonics-based technologies. TFLN-based devices are explored through this effort, while the Coherent Ising Machine provides new perspectives on complex optimization problems historically very difficult to solve on classical computers. In addition to a joint research agreement (JRA) with Harvard, the PHI Lab has worked over the years with the California Institute of Technology (Caltech), Cornell University, Harvard University, Massachusetts Institute of Technology (MIT), Notre Dame University, Stanford University, Swinburne University of Technology, the University of Michigan and the NASA Ames Research Center. Altogether, the PHI Lab has delivered over 150 papers, five appearing in Nature, one in Science and twenty in Nature sister journals. NTT announces AI inference chip for real-time 4K video processing NTT’s AI inference chip. NTT Corp. also announced a new, large-scale integration (LSI) for the real-time AI inference processing of ultra-high-definition video up to 4K-resolution and 30 frames per second (fps). This low-power technology is designed for edge and power-constrained terminal deployments in which conventional AI inferencing requires the compression of ultra-high-definition video for real-time processing. For example, when this LSI is installed on a drone, the drone can detect individuals or objects from up to 150 meters (492 feet) above the ground, the legal maximum altitude of drone flight in Japan, whereas conventional real-time AI video inferencing technology would limit that drone’s operations to about 30 meters (98 feet). One use case includes advancing drone-based infrastructure inspection for operations beyond an operator’s visual line of sight, reducing labor and costs. “The combination of low-power AI inferencing with ultra-high-definition video holds an enormousamount of potential, from infrastructure inspection to public safety to live sporting events,” said Gomi, in a statement. “NTT’s LSI, which we believe to be the first of its kind to achieve such results, represents an important step forward in enabling AI inference at the edge and for power-constrained terminals.” NTT Research president and CEO Kazu Gomi talks about the AI inference chip. In edge and power-constrained terminals, AI devices are limited to power consumption an order of magnitude lower than that of GPUs used in AI servers; tens of watts by the former compared to hundreds of watts by the latter. The LSI overcomes these restraints by implementing an NTT-created AI inference engine. This engine reduces computational complexity while ensuring detection accuracy, improving computing efficiency using interframe correlation and dynamic bit-precision control. Executing the object detection algorithm You Only Look Once (YOLOv3) using this LSI is possible with a power consumption of less than 20 watts. NTT plans to commercialize this LSI within fiscal year 2025 through its operating company NTT Innovative Devices Corporation. NTT announced and demonstrated this LSI at Upgrade, the company’s annual research and innovation summit. Upgrade 2025 is being held in San Francisco April 9-10, 2025. Looking ahead, researchers are studying the application of this LSI to the data-centric infrastructure (DCI) of the Innovative Optical and Wireless Network (IOWN) Initiative led by NTT and the IOWN Global Forum. DCI leverages the high-speed and low-latency capabilities of the IOWN All-Photonics Network to address the challenges of modern networking infrastructure including obstacles to scalability, limitations in performance and high energy consumption. Additionally, NTT researchers are collaborating with NTT DATA, Inc. on the advancement of this LSI in relation to its proprietary Attribute-Based Encryption (ABE) technologies. ABE enables fine-grained access control and flexible policy setting at the data layer, with shared-secret encryption technologies allowing for secure data sharing that can be integrated into existing applications and data stores. The Identity of IOWN A new book from NTT. And yesterday, NTT announced that Akira Shimada, president and CEO of NTT, and Katsuhiko Kawazoe, senior executive vice president and CTO of NTT, have published a book, The Identity of IOWN, in which they discuss the IOWN (Innovative Optical and Wireless Network) initiative spearheaded by NTT, a globaltechnology leader. The newly translated book explores NTT’s vision of IOWN and how it will enable a more sustainable society in an increasingly data-driven world. “The Identity of IOWN” is now available on Amazon following publication during NTT’s annual research and innovation summit, Upgrade. Upgrade 2025 is being held in San Francisco April 9-10, 2025.

NTT Research announced at its annual Upgrade event that it has started a new AI basic research group, dubbed the Physics of Artificial Intelligence Group.

Physical AI has become a big deal in 2025, with Nvidia leading the charge to create synthetic data to pretest self-driving cars and humanoid robotics so they can get to market faster. NTT Research is launching its Physic of Artificial Intelligence (PAI) Group to get on board.

NTT Research’s new independent group is spinning off of its Physic of Intelligence (PHI) Lab to advanced our understanding of the “black box” of AI for better trust and safety outcomes. NTT Research, which has an annual $3.6 billion R&D budget, is a division of NTT, Japan’s big telecommunications company.

Last year, NTT created its “Physics of Intelligence” vision initially formed in collaboration with the Harvard University Center for Brain Science, key contributions made over the past five years, and ongoing collaboration with academic partners.

The new group will be led by Hidenori Tanaka, NTT Research Scientist and expert in physics, neuroscience, and machine learning, in broader pursuit of human/AI collaboration.

The new group will continue to advance an interdisciplinary approach to understanding AI pioneered by the team over the past five years.

Early on, the PHI Lab recognized the importance of understanding the “black box” nature of AI and machine learning to develop novel systems with drastically improved energy efficiency for computation. With AI now advancing at an astonishing rate, issues of trustworthiness and safety have also become critical to industry applications and governance of AI adoption.

In collaboration with leading academic researchers, the Physics of Artificial Intelligence Group aims to address similarities between biological and artificial intelligences, further unravel the complexities of AI mechanisms and build trust that leads to more harmonious fusion of human and AI collaboration. The goal is to obtain a better understanding of how AI works in terms of being trained, accumulating knowledge, and making decisions so that we can design cohesive, safe, and trustworthy AI in the future.

This approach echoes what physicists have done over many centuries: people had understood objects move when forces are applied, but it was physics that revealed the precise details of the relationship, which allowed humans to design machines we know today. For example, the development of the steam engine informed our understanding of thermodynamics, which in turn enabled the creation of advanced semiconductors. Similarly, the work of this group will shape the future of AI technology.

The new group will continue to collaborate with the Harvard University Center for Brain Science (CBS), led by Harvard Professor Venkatesh Murthy, and with Princeton University Assistant Professor (and former NTT Research Scientist) Gautam Reddy. It also plans to collaborate with Stanford University Associate Professor Surya Ganguli, with whom Tanaka has co-authored several papers. The group’s core team includes Tanaka, NTT Research Scientist Maya Okawa and NTT Research Post-doctoral Fellow Ekdeep Singh Lubana.

Previous contributions to date include:

• A widely cited neural network pruning algorithm (over 750 citations in just 4 years)
• A bias-removal algorithm for large language models (LLMs), recognized by the U.S. National Institute of Standards and Technology (NIST) for its scientific and practical insights; and
• New insights into the dynamics of how AI learns concepts

Going forward, the Physics of Artificial Intelligence Group has a three-pronged mission. 1) It intends to deepen our understanding of the mechanisms of AI, all the better to integrate ethics from within, rather than through a patchwork of fine-tuning (i.e. enforced learning). 2) Borrowing from experimental physics, it will continue creating systematically controllable spaces of AI and observe the learning and prediction behaviors of AI step-by-step. 3) It aspires to heal the breach of trust between AI and human operators through improved operations and data control.

“Today marks a new step towards society’s understanding of AI through the establishment of NTT Research’s Physics of Artificial Intelligence Group,” NTT Research president and CEO Kazu Gomi said in a statement. “The emergence and rapid adoption of AI solutions across all areas of everyday life has had a profound impact on our relationship with technology. As AI’s role continues to grow, it is imperative we explore how AI makes people feel and how this can shape the advancement of new solutions. The new group aims to demystify concerns and bias around AI solutions to create a harmonious path forward for the coexistence of AI and humanity.”

The Physics of Artificial Intelligence Group embraces an interdisciplinary approach to AI, with physics, neuroscience and psychology coming together. This approach looks beyond conventional benchmarks, recognizing the need to support goals such as fairness and safety which lead to sustainable AI adoption. In terms of energy efficiency, other groups in the PHI Lab are already engaged in efforts to reduce the energy consumption of AI computing platforms through optical computing and a path-breaking, thin-film lithium niobate (TFLN) technology. On top of that, inspired by the vast differential between watts consumed by LLMs and the human or animal brain, the new group will also explore ways to leverage similarities between biological brains and artificial neural networks.

“The key for AI to exist harmoniously alongside humanity lies in its trustworthiness and how we approach the design and implementation of AI solutions,” Tanaka said, in a statement. “With the emergence of this group, we have a path forward to understanding the computational mechanisms of the brain and how it relates to deep learning models. Looking ahead, our research hopes to bring about more natural intelligent algorithms and hardware through our understanding of physics, neuroscience, and machine learning.”

Since 2019, the PHI Lab has spearheaded research for new ways of computing systems by leveraging photonics-based technologies. TFLN-based devices are explored through this effort, while the Coherent Ising Machine provides new perspectives on complex optimization problems historically very difficult to solve on classical computers.

In addition to a joint research agreement (JRA) with Harvard, the PHI Lab has worked over the years with the California Institute of Technology (Caltech), Cornell University, Harvard University, Massachusetts Institute of Technology (MIT), Notre Dame University, Stanford University, Swinburne University of Technology, the University of Michigan and the NASA Ames Research Center. Altogether, the PHI Lab has delivered over 150 papers, five appearing in Nature, one in Science and twenty in Nature sister journals.

NTT announces AI inference chip for real-time 4K video processing

NTT Corp. also announced a new, large-scale integration (LSI) for the real-time AI inference processing of ultra-high-definition video up to 4K-resolution and 30 frames per second (fps). This low-power technology is designed for edge and power-constrained terminal deployments in which conventional AI inferencing requires the compression of ultra-high-definition video for real-time processing.

For example, when this LSI is installed on a drone, the drone can detect individuals or objects from up to 150 meters (492 feet) above the ground, the legal maximum altitude of drone flight in Japan, whereas conventional real-time AI video inferencing technology would limit that drone’s operations to about 30 meters (98 feet). One use case includes advancing drone-based infrastructure inspection for operations beyond an operator’s visual line of sight, reducing labor and costs.

“The combination of low-power AI inferencing with ultra-high-definition video holds an enormous
amount of potential, from infrastructure inspection to public safety to live sporting events,” said Gomi, in a statement. “NTT’s LSI, which we believe to be the first of its kind to achieve such results, represents an important step forward in enabling AI inference at the edge and for power-constrained terminals.”

NTT Research president and CEO Kazu Gomi talks about the AI inference chip.

In edge and power-constrained terminals, AI devices are limited to power consumption an order of magnitude lower than that of GPUs used in AI servers; tens of watts by the former compared to hundreds of watts by the latter. The LSI overcomes these restraints by implementing an NTT-created AI inference engine. This engine reduces computational complexity while ensuring detection accuracy, improving computing efficiency using interframe correlation and dynamic bit-precision control. Executing the object detection algorithm You Only Look Once (YOLOv3) using this LSI is possible with a power consumption of less than 20 watts.

NTT plans to commercialize this LSI within fiscal year 2025 through its operating company NTT Innovative Devices Corporation. NTT announced and demonstrated this LSI at Upgrade, the company’s annual research and innovation summit. Upgrade 2025 is being held in San Francisco April 9-10, 2025.

Looking ahead, researchers are studying the application of this LSI to the data-centric infrastructure (DCI) of the Innovative Optical and Wireless Network (IOWN) Initiative led by NTT and the IOWN Global Forum. DCI leverages the high-speed and low-latency capabilities of the IOWN All-Photonics Network to address the challenges of modern networking infrastructure including obstacles to scalability, limitations in performance and high energy consumption.

Additionally, NTT researchers are collaborating with NTT DATA, Inc. on the advancement of this LSI in relation to its proprietary Attribute-Based Encryption (ABE) technologies. ABE enables fine-grained access control and flexible policy setting at the data layer, with shared-secret encryption technologies allowing for secure data sharing that can be integrated into existing applications and data stores.

The Identity of IOWN

And yesterday, NTT announced that Akira Shimada, president and CEO of NTT, and Katsuhiko Kawazoe, senior executive vice president and CTO of NTT, have published a book, The Identity of IOWN, in which they discuss the IOWN (Innovative Optical and Wireless Network) initiative spearheaded by NTT, a global
technology leader.

The newly translated book explores NTT’s vision of IOWN and how it will enable a more sustainable society in an increasingly data-driven world.

“The Identity of IOWN” is now available on Amazon following publication during NTT’s annual research and innovation summit, Upgrade. Upgrade 2025 is being held in San Francisco April 9-10, 2025.

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Still, IT and networking issues increased in 2024, according to Uptime Institute. The analysis attributed the rise in outages due to increased IT and network complexity, specifically, change management and misconfigurations. “Particularly with distributed services, cloud services, we find that cascading failures often occur when networking equipment is replicated across an entire network,” Lawrence explained. “Sometimes the failure of one forces traffic to move in one direction, overloading capacity at another data center.” The most common causes of major network-related outages were cited as: Configuration/change management failure: 50% Third-party network provider failure: 34% Hardware failure: 31% Firmware/software error: 26% Line breakages: 17% Malicious cyberattack: 17% Network overload/congestion failure: 13% Corrupted firewall/routing tables issues: 8% Weather-related incident: 7% Configuration/change management issues also attributed for 62% of the most common causes of major IT system-/software-related outages. Change-related disruptions consistently are responsible for software-related outages. Human error continues to be one of the “most persistent challenges in data center operations,” according to Uptime’s analysis. The report found that the biggest cause of these failures is data center staff failing to follow established procedures, which has increased by about 10 percentage points compared to 2023. “These are things that were 100% under our control. I mean, we can’t control when the UPS module fails because it was either poorly manufactured, it had a flaw, or something else. This is 100% under our control,” Brown said. The most common causes of major human error-related outages were reported as:

Liquid cooling technologies: reducing data center environmental impact

“Highly optimized cold-plate or one-phase immersion cooling technologies can perform on par with two-phase immersion, making all three liquid-cooling technologies desirable options,” the researchers wrote. Factors to consider There are numerous factors to consider when adopting liquid cooling technologies, according to Microsoft’s researchers. First, they advise performing a full environmental, health, and safety analysis, and end-to-end life cycle impact analysis. “Analyzing the full data center ecosystem to include systems interactions across software, chip, server, rack, tank, and cooling fluids allows decision makers to understand where savings in environmental impacts can be made,” they wrote. It is also important to engage with fluid vendors and regulators early, to understand chemical composition, disposal methods, and compliance risks. And associated socioeconomic, community, and business impacts are equally critical to assess. More specific environmental considerations include ozone depletion and global warming potential; the researchers emphasized that operators should only use fluids with low to zero ozone depletion potential (ODP) values, and not hydrofluorocarbons or carbon dioxide. It is also critical to analyze a fluid’s viscosity (thickness or stickiness), flammability, and overall volatility. And operators should only use fluids with minimal bioaccumulation (the buildup of chemicals in lifeforms, typically in fish) and terrestrial and aquatic toxicity. Finally, once up and running, data center operators should monitor server lifespan and failure rates, tracking performance uptime and adjusting IT refresh rates accordingly.

Cisco unveils prototype quantum networking chip

Clock synchronization allows for coordinated time-dependent communications between end points that might be cloud databases or in large global databases that could be sitting across the country or across the world, he said. “We saw recently when we were visiting Lawrence Berkeley Labs where they have all of these data sources such as radio telescopes, optical telescopes, satellites, the James Webb platform. All of these end points are taking snapshots of a piece of space, and they need to synchronize those snapshots to the picosecond level, because you want to detect things like meteorites, something that is moving faster than the rotational speed of planet Earth. So the only way you can detect that quickly is if you synchronize these snapshots at the picosecond level,” Pandey said. For security use cases, the chip can ensure that if an eavesdropper tries to intercept the quantum signals carrying the key, they will likely disturb the state of the qubits, and this disturbance can be detected by the legitimate communicating parties and the link will be dropped, protecting the sender’s data. This feature is typically implemented in a Quantum Key Distribution system. Location information can serve as a critical credential for systems to authenticate control access, Pandey said. The prototype quantum entanglement chip is just part of the research Cisco is doing to accelerate practical quantum computing and the development of future quantum data centers. The quantum data center that Cisco envisions would have the capability to execute numerous quantum circuits, feature dynamic network interconnection, and utilize various entanglement generation protocols. The idea is to build a network connecting a large number of smaller processors in a controlled environment, the data center warehouse, and provide them as a service to a larger user base, according to Cisco. The challenges for quantum data center network fabric

Zyxel launches 100GbE switch for enterprise networks

Port specifications include: 48 SFP28 ports supporting dual-rate 10GbE/25GbE connectivity 8 QSFP28 ports supporting 100GbE connections Console port for direct management access Layer 3 routing capabilities include static routing with support for access control lists (ACLs) and VLAN segmentation. The switch implements IEEE 802.1Q VLAN tagging, port isolation, and port mirroring for traffic analysis. For link aggregation, the switch supports IEEE 802.3ad for increased throughput and redundancy between switches or servers. Target applications and use cases The CX4800-56F targets multiple deployment scenarios where high-capacity backbone connectivity and flexible port configurations are required. “This will be for service providers initially or large deployments where they need a high capacity backbone to deliver a primarily 10G access layer to the end point,” explains Nguyen. “Now with Wi-Fi 7, more 10G/25G capable POE switches are being powered up and need interconnectivity without the bottleneck. We see this for data centers, campus, MDU (Multi-Dwelling Unit) buildings or community deployments.” Management is handled through Zyxel’s NebulaFlex Pro technology, which supports both standalone configuration and cloud management via the Nebula Control Center (NCC). The switch includes a one-year professional pack license providing IGMP technology and network analytics features. The SFP28 ports maintain backward compatibility between 10G and 25G standards, enabling phased migration paths for organizations transitioning between these speeds.

Engineers rush to master new skills for AI-driven data centers

According to the Uptime Institute survey, 57% of data centers are increasing salary spending. Data center job roles that saw the highest increases were in operations management – 49% of data center operators said they saw highest increases in this category – followed by junior and mid-level operations staff at 45%, and senior management and strategy at 35%. Other job categories that saw salary growth were electrical, at 32% and mechanical, at 23%. Organizations are also paying premiums on top of salaries for particular skills and certifications. Foote Partners tracks pay premiums for more than 1,300 certified and non-certified skills for IT jobs in general. The company doesn’t segment the data based on whether the jobs themselves are data center jobs, but it does track 60 skills and certifications related to data center management, including skills such as storage area networking, LAN, and AIOps, and 24 data center-related certificates from Cisco, Juniper, VMware and other organizations. “Five of the eight data center-related skills recording market value gains in cash pay premiums in the last twelve months are all AI-related skills,” says David Foote, chief analyst at Foote Partners. “In fact, they are all among the highest-paying skills for all 723 non-certified skills we report.” These skills bring in 16% to 22% of base salary, he says. AIOps, for example, saw an 11% increase in market value over the past year, now bringing in a premium of 20% over base salary, according to Foote data. MLOps now brings in a 22% premium. “Again, these AI skills have many uses of which the data center is only one,” Foote adds. The percentage increase in the specific subset of these skills in data centers jobs may vary. The Uptime Institute survey suggests that the higher pay is motivating workers to stay in the

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.

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

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

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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