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AI and microgrids: Moving towards seamless, resilient energy solutions

Brad Meissner is director of product management at Generac. Global energy demand is on the rise and will continue to experience significant growth — particularly electricity use, which is expected to grow at an annual rate of around 4%, according to the International Energy Agency.  As this trend persists, driven by factors like the electrification […]

Brad Meissner is director of product management at Generac.

Global energy demand is on the rise and will continue to experience significant growth — particularly electricity use, which is expected to grow at an annual rate of around 4%, according to the International Energy Agency

As this trend persists, driven by factors like the electrification of transportation and the rising demand for data centers, microgrids are emerging as essential solutions to address the challenges of modern energy systems. These decentralized systems self-generate, store and distribute power intelligently, alleviating strain on traditional grids. However, while the potential of microgrids is vast, they are still largely fragmented and custom-designed, limiting scalability. The integration of artificial intelligence is transforming this space, enabling more efficient, resilient and secure energy management solutions.

The future of energy isn’t just about generation — it’s about intelligent, adaptive networks that maximize reliability and sustainability. Is your business ready to embrace this shift? 

Let’s unpack how AI-powered microgrids are overhauling energy systems — optimizing distribution, enhancing resilience and reducing reliance on traditional grids.

Current state of microgrid projects and the need for clear standards

Today, microgrid projects are mostly tailored to specific needs, making them bespoke solutions that vary widely in design and operation. While this customization meets particular requirements, it creates a fragmented market where the integration of various energy resources (e.g., solar, battery storage and backup generators) is often cobbled together. This lack of standardization leads to inefficiencies and higher costs, limiting scalability and interoperability.

The lack of a cohesive framework makes it difficult to achieve the kind of seamless integration needed for large-scale deployment. For example, integrating renewable energy sources into microgrids, while essential for sustainability, adds complexity when each project uses different hardware, software and operational models. To address these challenges, the industry needs clear standards to ensure that systems are secure, resilient and interoperable. Such standards could enable more streamlined, plug-and-play solutions, helping to lower costs, reduce integration time and ensure that cybersecurity and compliance requirements are met.

The impact of AI on microgrid controls

Current microgrid systems are already impressive, offering effective, rules-based decision-making processes. However, they can be even better. AI introduces a new level of adaptability and intelligence, unlocking great capability. This allows for dynamic and responsive energy management, optimizing performance in real time. As a result, microgrids can become even more efficient and reliable, meeting energy demands with greater precision and flexibility.

Next-generation Energy Management Systems powered by AI will bring greater intelligence to microgrid operations. These AI-driven systems will be capable of incorporating variables such as weather patterns, demand tariffs and energy usage forecasts. By using machine learning, the system can learn from past experiences and optimize energy distribution more efficiently. For example, AI can forecast energy generation from renewable sources and balance that with stored energy or backup generators to ensure consistent power supply.

The predictive capabilities of AI will also play a key role in enhancing resilience. By continuously analyzing data, AI systems can anticipate demand spikes, predict potential outages and adjust energy flows accordingly. These capabilities will enable microgrids to function more efficiently, maintain grid stability and improve overall reliability, even during unforeseen disruptions.

How AI is transforming microgrid optimization

Microgrids provide a way to decentralize energy production, improving grid resilience by reducing strain on the main grid. However, for microgrids to reach their full potential, they must be optimized for efficiency, energy storage and renewable energy integration. AI will play a critical and multi-faceted role in achieving these goals.

It can help manage the balance between different energy sources, such as solar, battery storage and generators. By continuously analyzing current and projected energy production and demand, AI can optimize energy flows to ensure that power is distributed efficiently and at the lowest possible cost. This optimization process reduces waste and ensures that energy is available when needed most.

AI can also enhance the sustainability of microgrids. By optimizing the use of renewable energy sources like solar or wind, AI helps reduce reliance on fossil fuels and lowers the carbon footprint of microgrids. Moreover, AI-driven systems can evaluate trade-offs between cost, efficiency and emissions, ensuring that microgrids deliver clean, reliable energy while minimizing environmental impact.

Enhancing real-time energy distribution and security

AI’s role in real-time energy distribution and security is one of its most impactful applications in both microgrids and larger utility systems. AI-powered systems can continuously monitor energy demand and supply, making real-time adjustments to ensure that power is distributed efficiently. This dynamic approach to energy management improves the reliability and stability of both microgrids and larger grids.

In regions that are prone to power outages or natural disasters, AI-powered microgrids can be invaluable. These systems can anticipate disruptions, adjust energy flows and ensure that critical infrastructure — such as hospitals, data centers and emergency services — maintain power during grid outages. This ability to provide backup power in real time enhances energy security and resilience, especially in vulnerable areas.

AI’s ability to predict and respond to shifts in demand and supply is also crucial for enhancing grid security. It can identify potential threats, such as weather events or equipment malfunctions, and adjust system parameters to minimize damage. This proactive approach to grid management helps ensure that energy systems remain secure, even in the face of emerging risks.

AI is playing a pivotal role in reshaping the future of microgrids and the broader energy sector. By enabling smarter, more dynamic energy management, it is helping to optimize microgrid performance, improve grid resilience and enhance sustainability. As microgrid projects move toward standardized, out-of-the-box solutions, AI will be crucial in ensuring these systems are secure, compliant and capable of providing long-term, reliable support.

In the years ahead, AI will continue to drive the evolution of energy systems. The future of energy is intelligent, and AI is leading the way. Now is the time to evolve your energy journey with advanced microgrid solutions.

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Secretary Wright Announces Termination of 24 Projects, Generating Over $3 Billion in Taxpayer Savings

WASHINGTON— U.S. Secretary of Energy Chris Wright today announced the termination of 24 awards issued by the Office of Clean Energy Demonstrations (OCED) totaling over $3.7 billion in taxpayer-funded financial assistance. After a thorough and individualized financial review of each award, DOE found that these projects failed to advance the energy needs of the American people, were not economically viable and would not generate a positive return on investment of taxpayer dollars.  Of the 24 awards cancelled, nearly 70% (16 of the 24 projects) were signed between Election Day and January 20th. The projects primarily include funding for carbon capture and sequestration (CCS) and decarbonization initiatives. By terminating these awards, DOE is generating an immediate $3.6 billion in savings for the American people. “While the previous administration failed to conduct a thorough financial review before signing away billions of taxpayer dollars, the Trump administration is doing our due diligence to ensure we are utilizing taxpayer dollars to strengthen our national security, bolster affordable, reliable energy sources and advance projects that generate the highest possible return on investment,” said Secretary Wright. “Today, we are acting in the best interest of the American people by cancelling these 24 awards.” Earlier this month, DOE issued a Secretarial Memorandum entitled, “Ensuring Responsibility for Financial Assistance,” which outlined DOE’s policy for evaluating financial assistance on a case-by-case basis to identity waste of taxpayer dollars, protect America’s national security and advance President Trump’s commitment to unleash affordable, reliable and secure energy for the American people. DOE utilized this review process to evaluate each of these 24 awards and determined that they did not meet the economic, national security or energy security standards necessary to sustain DOE’s investment. DOE’s Secretarial Policy on Ensuring Responsibility for Financial Assistance is available here.                  

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Taiwan Will Send Delegation to Alaska LNG Talks Next Week

Taiwan will send a delegation to a summit in Alaska to discuss procuring liquefied natural gas from a long-delayed project championed by US President Donald Trump. “We already got the invitation” from the US, Taiwan’s Deputy Foreign Minister Chen Ming-chi said in an interview with Bloomberg News on Thursday, referring the Alaska Sustainable Energy Conference from June 3 to 5. A high-ranking official will lead the delegation, he said. Taiwan is interested in purchasing LNG from the US project, as well as investing in the necessary pipeline and related infrastructure for the facility, Chen said. He declined to disclose the officials who will lead the delegation. Proponents of the $44 billion Alaska LNG export project are trying to use the gathering as a way to rally support and financing for the facility, which has became a focus for the White House. The plant has been proposed in various forms for decades, but has struggled to secure binding long-term contracts and investment. Chen said Taiwan’s investment amount for the project needs to be discussed further between the two sides, and additional negotiation will be required. Taiwan’s CPC Corp. signed a non-binding letter of intent in March to invest in Alaska LNG’s pipeline and purchase fuel from the project. 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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Oil Dips After US-China Trade Tensions Flare

Oil edged down after a choppy session as traders parsed mixed messaging on the status of trade talks between the US and China. West Texas Intermediate futures swung in a roughly $2 range before settling down fractionally near $61 a barrel. Futures had sunk after US President Donald Trump said China had violated its trade agreement and threatened to broaden restrictions on its tech sector, reviving concerns that a tariff war between the world’s two largest economies would hurt oil demand. Crude later pared losses when Trump signaled openness to speaking with Chinese President Xi Jinping. Meanwhile, OPEC+ was said to consider an output increase of more than 411,000 barrels a day in July in a push for market share. The revival of idled output by OPEC and its allies at a faster-than-expected pace has bolstered expectations that a glut will form this year. “Global oil market fundamentals remain somewhat loose now and should loosen up much more later this year, with growing non-OPEC supply and relatively mild, but persistent stock builds,” Citigroup analysts including Francesco Martoccia said in a note. Geopolitical risks from Russia to Iran continue to provide price support against an otherwise softening physical backdrop, they added. Meanwhile, commodity trading advisers, which tend to exacerbate price swings, increased short positions to sit at 91% short in Brent on Friday, compared with roughly 70% short on May 29, according to data from Bridgeton Research Group. Still, some metrics are pointing to near-term strength in the oil market. WTI’s front-month futures were trading about 93 cents more per barrel than the contract for the next month, the biggest premium since early January. Libya’s eastern government threatened to curb oil production and exports in protest after a militia stormed the state oil company’s headquarters. A shutdown could result in a

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Congress votes to rescind California vehicle emissions waiver

Dive Brief: The U.S. Senate passed three joint resolutions May 22 nullifying California’s ability to set emissions standards for passenger cars, light duty vehicles and trucks that are stricter than national standards set by the U.S. Environmental Protection Agency. Auto and petroleum industry lobbyists targeted California’s Advanced Clean Car II regulations, adopted in 2022, which require all new passenger cars, trucks and SUVs sold in the state to be zero-emission vehicles by the 2035 model year. Federal law set in 1990 allows 17 additional states and the District of Columbia to follow California’s regulations. California Gov. Gavin Newsom, a Democrat, announced the state’s intention to file a lawsuit blocking the congressional resolutions, which await the signature of President Donald Trump to become law. Dive Insight: California’s ability to set its own vehicle emissions standards stem from the 1967 Air Quality Act, passed at a time when smog and poor air quality often permeated the Los Angeles basin. While air quality in California has improved over the years, experts fear a setback from the Senate’s action. “Public health could potentially suffer as a consequence,” said Michael Kleeman, a professor at the University of California, Davis, Department of Civil and Environmental Engineering. “This is, plain and simple, a vote against clean air to breathe,” said Aaron Kressig, transportation electrification manager at Western Resource Advocates, in an emailed statement. He warned of potential lost days at school or work and premature deaths.    “Over 150 million people in the United States are already exposed to unhealthy levels of air pollution,” Steven Higashide, director of the Clean Transportation Program at the Union of Concerned Scientists, said in an emailed statement. “The standards are based on the best available science, and were finalized with extensive public input.”  Along with public health concerns, the debate around California’s

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DOE cancels $3.7B in carbon capture, decarbonization awards

The U.S. Department of Energy on Friday canceled $3.7 billion in awards from its Office of Clean Energy Demonstrations, including $940 million in grants for two carbon capture projects planned by Calpine. The canceled awards were mainly for carbon capture and sequestration and other decarbonization projects, according to DOE. Affected companies include PPL Corp., Ørsted and Exxon Mobil Corp. The Calpine projects are for CCS projects at its 550-MW gas-fired Sutter power plant in Yuba City, California, and its 810-MW Baytown power plant in Baytown, Texas. “After a thorough and individualized financial review of each award, the DOE found that these projects failed to advance the energy needs of the American people, were not economically viable and would not generate a positive return on investment of taxpayer dollars,” DOE said. Sixteen of the 24 terminated awards were signed between President Donald Trump’s election in November and Jan. 20, according to DOE. The DOE assessed the canceled awards under a review process outlined earlier this month. The department said it is reviewing 179 awards that total over $15 billion in financial assistance. “DOE is prioritizing large-scale commercial projects that require more detailed information from the awardees for the initial phase of this review, but this process may extend to other DOE program offices as the reviews progress,” the department said. DOE created the Office of Clean Energy Demonstrations in late 2021 to manage about $27 billion in funding appropriated by the Infrastructure Investment and Jobs Act and the Inflation Reduction Act, according to a mid-November report from the U.S. Government Accountability Office. Below is a list from DOE of the canceled awards announced on Friday. Optional Caption Permission granted by US Department of Energy DOE’s decision to terminate the awards was “shortsighted,” according to Steven Nadel, executive director of the American

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California’s solar, wind curtailment jumps 29% in 2024: EIA

Solar and wind energy output in California was curtailed by 29% more in 2024 than the year before, with solar accounting for 93% of curtailed energy that year, the Energy Information Administration said in a Wednesday report. “In 2024, [the California Independent System Operator] curtailed 3.4 million megawatthours (MWh) of utility-scale wind and solar output, a 29% increase from the amount of electricity curtailed in 2023,” EIA said.  EIA said that CAISO curtailed the most solar in the spring “when solar output was relatively high and electricity demand was relatively low, because moderate spring temperatures meant less demand for space heating or air conditioning.” Optional Caption Retrieved from Energy Information Administration. Wind and solar capacity in California increased from 9.7 GW in 2014 to 28.2 GW by the end of 2024, EIA said. California curtails solar and wind generation to keep the grid stable and to leave room for natural gas generation, in order to comply with North American Electric Reliability Corp. requirements and “have generation online in time to ramp up in the evening hours,” according to the report. CAISO is responding to increased curtailments by “trading with neighboring balancing authorities to try to sell excess solar and wind power, incorporating battery storage into ancillary services, energy, and capacity markets, and including curtailment reduction in transmission planning,” according to EIA. Later this year, companies in the state are also planning to start using excess renewable energy to “make hydrogen, some of which will be stored and mixed with natural gas for summer generation at the Intermountain Power Project’s new facility scheduled to come online in July,” the report said. One of those companies, SoHyCal, said that once it begins using solar energy for this purpose, it “[expects] to produce a total of three tons per day of green hydrogen powered

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Cisco bolsters DNS security package

The software can block domains associated with phishing, malware, botnets, and other high-risk categories such as cryptomining or new domains that haven’t been reported previously. It can also create custom block and allow lists and offers the ability to pinpoint compromised systems using real-time security activity reports, Brunetto wrote. According to Cisco, many organizations leave DNS resolution to their ISP. “But the growth of direct enterprise internet connections and remote work make DNS optimization for threat defense, privacy, compliance, and performance ever more important,” Cisco stated. “Along with core security hygiene, like a patching program, strong DNS-layer security is the leading cost-effective way to improve security posture. It blocks threats before they even reach your firewall, dramatically reducing the alert pressure your security team manages.” “Unlike other Secure Service Edge (SSE) solutions that have added basic DNS security in a ‘checkbox’ attempt to meet market demand, Cisco Secure Access – DNS Defense embeds strong security into its global network of 50+ DNS data centers,” Brunetto wrote. “Among all SSE solutions, only Cisco’s features a recursive DNS architecture that ensures low-latency, fast DNS resolution, and seamless failover.”

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HPE Aruba unveils raft of new switches for data center, campus modernization

And in large-scale enterprise environments embracing collapsed-core designs, the switch acts as a high-performance aggregation layer. It consolidates services, simplifies network architecture, and enforces security policies natively, reducing complexity and operational cost, Gray said. In addition, the switch offers the agility and security required at colocation facilities and edge sites. Its integrated Layer 4 stateful security and automation-ready platform enable rapid deployment while maintaining robust control and visibility over distributed infrastructure, Gray said. The CX 10040 significantly expands the capacity it can provide and the roles it can serve for enterprise customers, according to one industry analyst. “From the enterprise side, this expands on the feature set and capabilities of the original 10000, giving customers the ability to run additional services directly in the network,” said Alan Weckel, co-founder and analyst with The 650 Group. “It helps drive a lower TCO and provide a more secure network.”  Aimed as a VMware alternative Gray noted that HPE Aruba is combining its recently announced Morpheus VM Essentials plug-in package, which offers a hypervisor-based package aimed at hybrid cloud virtualization environments, with the CX 10040 to deliver a meaningful alternative to Broadcom’s VMware package. “If customers want to get out of the business of having to buy VM cloud or Cloud Foundation stuff and all of that, they can replace the distributed firewall, microsegmentation and lots of the capabilities found in the old VMware NSX [networking software] and the CX 10k, and Morpheus can easily replace that functionality [such as VM orchestration, automation and policy management],” Gray said. The 650 Group’s Weckel weighed in on the idea of the CX 10040 as a VMware alternative:

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Indian startup Refroid launches India’s first data center CDUs

They use heat exchangers and pumps to regulate the flow and temperature of fluid delivered to equipment for cooling, while isolating the technology cooling system loop from facility systems. The technology addresses limitations of traditional air cooling, which industry experts say cannot adequately handle the heat generated by modern AI processors and high-density computing applications. Strategic significance for India Industry analysts view the development as a critical milestone for India’s data center ecosystem. “India generates 20% of global data, yet contributes only 3% to global data center capacity. This imbalance is not merely spatial — it’s systemic,” said Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research. “The emergence of indigenously developed CDUs signals a strategic pivot. Domestic CDU innovation is a defining moment in India’s transition from data centre host to technology co-creator.” Neil Shah, VP for research and partner at Counterpoint Research, noted that major international players like Schneider, Vertiv, Asetek, Liquidstack, and Zutacore have been driving most CDU deployments in Indian enterprises and data centers. “Having a local indigenous CDU tech and supplier designed with Indian weather, infrastructure and costs in mind expands options for domestic data center demand,” he said. AI driving data center cooling revolution India’s data center capacity reached approximately 1,255 MW between January and September 2024 and was projected to expand to around 1,600 MW by the end of 2024, according to CBRE India’s 2024 Data Center Market Update. Multiple market research firms have projected the India data center market to grow from about $5.7 billion in 2024 to $12 billion by 2030. Bhavaraju cited aggressive projections for the sector’s expansion, with AI workloads expected to account for 30% of total workloads by 2030. “All of them need liquid cooling,” he said, noting that “today’s latest GPU servers – GB200 from Nvidia

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Platform approach gains steam among network teams

Revisting the platform vs. point solutions debate The dilemma of whether to deploy an assortment of best-of-breed products from multiple vendors or go with a unified platform of “good enough” tools from a single vendor has vexed IT execs forever. Today, the pendulum is swinging toward the platform approach for three key reasons. First, complexity, driven by the increasingly distributed nature of enterprise networks, has emerged as a top challenge facing IT execs. Second, the lines between networking and security are blurring, particularly as organizations deploy zero trust network access (ZTNA). And third, to reap the benefits of AIOps, generative AI and agentic AI, organizations need a unified data store. “The era of enterprise connectivity platforms is upon us,” says IDC analyst Brandon Butler. “Organizations are increasingly adopting platform-based approaches to their enterprise connectivity infrastructure to overcome complexity and unlock new business value. When enhanced by AI, enterprise platforms can increase productivity, enrich end-user experiences, enhance security, and ultimately drive new opportunities for innovation.” In IDC’s Worldwide AI in Networking Special Report, 78% of survey respondents agreed or strongly agreed with the statement: “I am moving to an AI-powered platform approach for networking.” Gartner predicts that 70% of enterprises will select a broad platform for new multi-cloud networking software deployments by 2027, an increase from 10% in early 2024. The breakdown of silos between network and security operations will be driven by organizations implementing zero-trust principles as well as the adoption of AI and AIOps. “In the future, enterprise networks will be increasingly automated, AI-assisted and more tightly integrated with security across LAN, data center and WAN domains,” according to Gartner’s 2025 Strategic Roadmap for Enterprise Networking. While all of the major networking vendors have announced cloud-based platforms, it’s still relatively early days. For example, Cisco announced a general framework for Cisco

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Oracle to spend $40B on Nvidia chips for OpenAI data center in Texas

OpenAI has also expanded Stargate internationally, with plans for a UAE data center announced during Trump’s recent Gulf tour. The Abu Dhabi facility is planned as a 10-square-mile campus with 5 gigawatts of power. Gogia said OpenAI’s selection of Oracle “is not just about raw compute, but about access to geographically distributed, enterprise-grade infrastructure that complements its ambition to serve diverse regulatory environments and availability zones.” Power demands create infrastructure dilemma The facility’s power requirements raise serious questions about AI’s sustainability. Gogia noted that the 1.2-gigawatt demand — “on par with a nuclear facility” — highlights “the energy unsustainability of today’s hyperscale AI ambitions.” Shah warned that the power envelope keeps expanding. “As AI scales up and so does the necessary compute infrastructure needs exponentially, the power envelope is also consistently rising,” he said. “The key question is how much is enough? Today it’s 1.2GW, tomorrow it would need even more.” This escalating demand could burden Texas’s infrastructure, potentially requiring billions in new power grid investments that “will eventually put burden on the tax-paying residents,” Shah noted. Alternatively, projects like Stargate may need to “build their own separate scalable power plant.” What this means for enterprises The scale of these facilities explains why many organizations are shifting toward leased AI computing rather than building their own capabilities. The capital requirements and operational complexity are beyond what most enterprises can handle independently.

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New Intel Xeon 6 CPUs unveiled; one powers rival Nvidia’s DGX B300

He added that his read is that “Intel recognizes that Nvidia is far and away the leader in the market for AI GPUs and is seeking to hitch itself to that wagon.” Roberts said, “basically, Intel, which has struggled tremendously and has turned over its CEO amidst a stock slide, needs to refocus to where it thinks it can win. That’s not competing directly with Nvidia but trying to use this partnership to re-secure its foothold in the data center and squeeze out rivals like AMD for the data center x86 market. In other words, I see this announcement as confirmation that Intel is looking to regroup, and pick fights it thinks it can win. “ He also predicted, “we can expect competition to heat up in this space as Intel takes on AMD’s Epyc lineup in a push to simplify and get back to basics.” Matt Kimball, vice president and principal analyst, who focuses on datacenter compute and storage at Moor Insights & Strategy, had a much different view about the announcement. The selection of the Intel sixth generation Xeon CPU, the 6776P, to support Nvidia’s DGX B300 is, he said, “important, as it validates Intel as a strong choice for the AI market. In the big picture, this isn’t about volumes or revenue, rather it’s about validating a strategy Intel has had for the last couple of generations — delivering accelerated performance across critical workloads.”  Kimball said that, In particular, there are a “couple things that I would think helped make Xeon the chosen CPU.”

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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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Fueling seamless AI at scale

In partnership withArm From large language models (LLMs) to reasoning agents, today’s AI tools bring unprecedented computational demands. Trillion-parameter models, workloads running on-device, and swarms

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