From code to current: How to keep AI data centers in check for a sustainable grid

Stay Ahead, Stay ONMINE

From code to current: How to keep AI data centers in check for a sustainable grid

Manav Mittal is a senior project manager at Consumers Energy. As artificial intelligence continues to transform industries, from healthcare and finance to autonomous vehicles and smart cities, the demand for data processing is skyrocketing. AI-driven data centers, which power the algorithms behind these innovations, are the backbone of this revolution. However, with the expansion of […]

Manav Mittal is a senior project manager at Consumers Energy.

As artificial intelligence continues to transform industries, from healthcare and finance to autonomous vehicles and smart cities, the demand for data processing is skyrocketing. AI-driven data centers, which power the algorithms behind these innovations, are the backbone of this revolution. However, with the expansion of AI capabilities comes a growing concern: how will these energy-hungry facilities affect our already strained power grids?

Take Meta’s $10 billion AI-optimized data center in Louisiana, for example. This enormous facility, designed to handle the massive computational load required by AI, will demand a staggering amount of electricity. As AI becomes more integrated into our everyday lives, the strain on the power grid is only set to increase. But here’s the thing — AI doesn’t have to be a burden on the grid. With thoughtful strategies and a proactive approach, we can minimize the environmental and infrastructural costs of these data centers. The question isn’t whether AI will disrupt the grid, but how we can make it work for us without sacrificing sustainability.

Energy efficiency: The first line of defense

It’s easy to think of data centers as mere consumers of energy, but the truth is, they’re not all created equal. There’s plenty of room for improvement when it comes to energy efficiency. The first step in minimizing AI data center impacts on the grid is simply making these centers run more efficiently.

Cooling systems alone account for a huge chunk of energy consumption in data centers. Traditionally, large HVAC systems keep servers at optimal temperatures, but these systems are often inefficient. Thankfully, innovative cooling methods — like liquid cooling and even immersion cooling — are beginning to replace outdated systems. These newer technologies can significantly reduce energy usage, which is crucial when every watt counts.

And it’s not just cooling that needs to be rethought. Advances in hardware, such as more energy-efficient processors and GPUs, are improving the performance-to-energy ratio of data centers. These small innovations might not make the headlines, but their cumulative impact on energy consumption could be profound. Data centers should be incentivized to adopt these energy-saving technologies, not only to reduce their operating costs but to lessen their impact on the grid.

Renewable energy: A cleaner, greener future

Let’s be clear — data centers don’t have to rely on fossil fuels to power their operations. In fact, many major tech companies, including Meta, have made ambitious commitments to run their data centers on 100% renewable energy. This shift to clean energy is one of the most impactful ways to reduce the strain on the grid. If AI data centers can be powered by wind, solar and other renewable sources, we’re looking at a win-win situation: energy demand is met without contributing to greenhouse gas emissions.

However, making this transition requires more than just goodwill — it requires collaboration with renewable energy developers and utilities. Power purchase agreements are a vital tool here. These long-term contracts allow data centers to secure renewable energy directly from producers, ensuring that their electricity needs are met without disrupting the grid. The beauty of this approach is that it supports the broader goal of transitioning to a clean energy economy, all while minimizing the impact on local power infrastructure.

But let’s not stop there. Data centers should also consider on-site renewable energy generation. Installing solar panels or wind turbines at their facilities can reduce their reliance on the grid during peak demand periods. In fact, on-site energy production, combined with energy storage, could allow data centers to be largely self-sufficient, alleviating much of the pressure on local grids.

Modernizing the grid: Building for the future

While improving the energy efficiency of data centers and shifting to renewable energy are essential steps, we can’t ignore the infrastructure itself. The grid, as it exists today, was not built to handle the enormous, and sometimes unpredictable, energy demands of AI data centers. As data centers become larger and more prevalent, the grid needs to evolve to accommodate them.

Here’s where smart grids come into play. These modernized grids use sensors and real-time data to better manage energy distribution. With a smart grid, utilities can dynamically adjust power flow based on demand, ensuring that energy is directed where it’s needed most. By integrating AI into grid management, utilities can anticipate and respond to shifts in energy demand caused by data centers, ensuring a more stable grid overall.

In addition to smart grids, we need to consider energy storage. Renewable energy is intermittent by nature — solar panels don’t generate electricity at night, and wind turbines are silent on calm days. By incorporating energy storage systems, such as large-scale batteries, data centers can store excess energy generated during off-peak hours and use it when demand is high. This will help to smooth out the fluctuations in energy supply and ensure that data centers are less reliant on the grid during peak times.

Demand response: A shared responsibility

But why stop with data centers? AI-driven facilities have a responsibility to participate in demand response programs. These programs incentivize businesses and consumers to reduce their energy usage during periods of peak demand, which helps prevent grid overloads. Data centers are prime candidates for demand response because they can adjust their operations — such as shifting workloads to off-peak hours — without negatively impacting performance. By participating in these programs, AI data centers can significantly ease pressure on the grid, especially during high-demand periods, like hot summer afternoons when air conditioning use is at its peak.

The key here is that grid stability is a shared responsibility. While AI data centers are heavy consumers of electricity, they also have the tools to manage their consumption intelligently. Rather than adding to the grid’s burden, these facilities can be part of the solution. Through demand response, they can reduce their energy use when it’s most needed, helping to balance supply and demand and prevent power outages.

Collaboration: A holistic approach to grid sustainability

It’s clear that minimizing the impact of AI data centers on the power grid isn’t a task for data center operators alone. This challenge requires collaboration among technology companies, utilities, policymakers and local communities. Governments must provide the right incentives to encourage the adoption of clean energy and energy-efficient technologies. At the same time, utility companies must modernize the grid to accommodate the growing demands of AI data centers and other large energy consumers.

We also need to prioritize transparency and dialogue with communities. Local governments and residents should be included in conversations about how AI data centers impact energy infrastructure. Through collaboration, we can ensure that these facilities contribute positively to both the local economy and the environment.

Conclusion: A vision for a sustainable future

The rise of AI presents enormous opportunities for innovation, but it also poses significant challenges, particularly when it comes to energy consumption. AI data centers are indispensable to the future of technology, but they must be built in a way that minimizes their impact on the power grid and the environment.

By focusing on energy efficiency, incorporating renewable energy, modernizing grid infrastructure and participating in demand response programs, we can reduce the strain AI data centers place on the grid. Ultimately, it’s about balancing progress with sustainability. As we move toward a cleaner, smarter and more connected future, we must ensure that the rise of AI doesn’t come at the expense of our planet — or our power systems.

Stay Ahead

Explore More Insights

Stay ahead with more perspectives on cutting-edge power, infrastructure, energy, bitcoin and AI solutions. Explore these articles to uncover strategies and insights shaping the future of industries.

AWS launches ‘Capabilities by Region’ to simplify planning for cloud deployments

While Microsoft Azure’s Product Availability by Region portal lists services by geography, it lacks forward-looking timelines and the unified API comparability that AWS’s Capabilities by Region delivers, Jain noted. Region Picker, which is a similar offering from Google, too, falls short on granular, future-facing service or API roadmaps and focuses

Google Cloud aims for more cost-effective Arm computing with Axion N4A

It’s not alone: AWS introduced its own Arm-based chip, Graviton, in 2018 to reduce the cost of running internal cloud workloads such as Amazon retail IT, and now 50% of new AWS instances run on it. Microsoft, too, recently developed an Arm chip, Cobalt, to run Microsoft 365 and to

Perplexity’s open-source tool to run trillion-parameter models without costly upgrades

The obvious answer would be Nvidia’s new GB200 systems, essentially one giant 72-GPU server. But those cost millions, face extreme supply shortages, and aren’t available everywhere, the researchers noted. Meanwhile, H100 and H200 systems are plentiful and relatively cheap. The catch: running large models across multiple older systems has traditionally

Arista shares Q3 financials, touts ‘golden era in networking’

“I think a lot of these designs will materialize as the standards for Ethernet are getting stronger and stronger. We now have a UEC [Ultra Ethernet Consortium] spec. You heard me talk about the Scale-Up Ethernet spec for ESUN where we can bring different work streams onto the same Ethernet

Oil Rises but Logs Second Weekly Loss

Oil rose on Friday but still notched a second weekly loss as the market continued to weigh the threat to output from sanctions on Russia against a looming oversupply. West Texas Intermediate futures rose around 0.5% to settle below $60 a barrel, but were still down for the week. Adding to fears of a glut, oil prices have also been buffeted by swings in equity markets this week. Meanwhile, the White House’s move to clamp down on the buying of Russian crude led oil trading giant Gunvor Group to withdraw an offer for the international assets of Lukoil PJSC. The fate of the assets, which include stakes in oil fields, refineries and gas stations, remains unclear. One possible exception to that crackdown could emerge soon: President Donald Trump signaled an openness to exempting Hungary from sanctions on Russian energy purchases as he hosted Prime Minister Viktor Orban, briefly pushing futures to intraday lows. The development appeared to allay shortage fears, given that Budapest imports over 90% of its crude from Moscow. Senior industry figures have warned the latest US curbs on Russia’s two largest oil companies are beginning to have an impact on the market, particularly in diesel, where prices have been surging in recent days, with time spreads for the fuel signaling supply pressure. At the same time, the US measures have come against a backdrop of oversupply that has weighed on key crude oil metrics. The spread between the nearest West Texas Intermediate futures closed at the weakest level since February on Thursday. “If the market flips to contango, we may see more bearish funds enter the crude space,” said Dennis Kissler, senior vice president for trading at BOK Financial said of the potential that longer-dated contracts trade at a premium to nearer-term ones. “Most traders remain surprised

Ship With Russia Oil Makes Rare Move Offshore India

A tanker carrying crude from recently-sanctioned Rosneft PJSC has made a rare cargo transfer off Mumbai, as the Trump administration ramps up its scrutiny of India’s oil trade with Russia. But the unusual move has puzzled traders. The cargo was transferred from one blacklisted tanker to another sanctioned ship, meaning there’s been no attempt to hide its origin — typical of such a move — and the crude is still heading for an Indian port: Kochi in the south, rather than Mumbai on the west coast. India’s purchases of Russian oil have drawn the ire of President Donald Trump, and the US penalties on Rosneft along with Lukoil PJSC are expected to severely impact the trade. The market is keenly watching for disruptions to established flows before a grace period related to the sanctions ends later this month. “What we’re seeing now is this uncertainty in the market about what the sanctions risks are,” said Rachel Ziemba, an analyst at the Center for a New American Security in Washington. “The net result is more ship-to-ship transfers, more subterfuge, longer routes, more complicated transactions.” The Fortis took around 720,000 barrels of Russian Urals from Ailana on Tuesday near Mumbai, according to ship-tracking data compiled by Bloomberg, Kpler and Vortexa. The cargo was collected from the Baltic port of Ust-Luga before the US sanctioned Rosneft, and Ailana had idled in the area for nearly two weeks with no clear reason. Ailana is on its way back to Russia, while Fortis is expected to arrive at Kochi early next week with the cargo, ship-tracking data shows. Both vessels have been sanctioned by the European Union and the UK. Fortis’ owner and manager — Vietnam-based Pacific Logistic & Maritime and North Star Ship Management — didn’t respond to emailed requests for comment. There are no contact details on maritime database

Southwest Power Pool to develop 765-kV regional transmission ‘backbone’

Listen to the article 4 min This audio is auto-generated. Please let us know if you have feedback. Dive Brief: The Southwest Power Pool board of directors on Wednesday approved an $8.6 billion slate of 50 transmission projects across its 14-state footprint. The projects are intended to help the grid operator meet peak demand, which it expects will double, to reach 109 GW, in the next 10 years. Key to the 2025 Integrated Transmission Plan is development of a 765-kV regional transmission “backbone” that can carry four times the power SPP’s existing 345-kV lines do, and do so more efficiently. The grid operator’s transmission system “is at capacity and forecasted load growth will only exacerbate the existing strain,” it said. “Simply adding new generation will not resolve the challenges.” 765-kV transmission lines are the highest operating voltages in the U.S. but are new in both SPP and in the neighboring Electric Reliability Council of Texas market. Texas regulators approved the higher voltage lines for the first time in April. Dive Insight: Transmission developers in SPP and ERCOT are turning to 765-kV projects to mitigate line losses and move greater volumes of power into demand centers at a time when electricity demand is expected to rise significantly. “With the new load being integrated into the system, SPP could see an increase in the footprint’s annual energy consumption by as much as 136%,” the grid operator said in its ITP. “Investments in transmission are the key to keep costs low, maintain reliability, and power economic growth.” Even under conservative assumptions, SPP forecasts a 35% increase in demand, “making timely transmission investment essential,” the grid operator said. SPP selected Xcel Energy in February to construct the first 765-kV lines in its footprint. Those lines were identified in its 2024 plan. AEP Texas will build

The week in 5 numbers: Gas valuations soar but solar leads new capacity

The price gas power merger and acquisitions have reached in some markets, according to energy analytics firm Enverus. The artificial intelligence boom, along with expectations of increased manufacturing and electrification, is driving a surge in natural gas investment, but thermal generation remains risky, some analysts say, drawing parallels to the dot com bubble at the turn of the century.

Our laws must catch up to data centers’ rising power

Alexandra Klass is the James G. Degnan Professor of Law at Michigan Law, and Dave Owen is the Albert Abramson ’54 Distinguished Professor of Law at UC Law San Francisco. The United States faces massive growth in electricity demand. If utilities’ projections are right, data centers will drive much of that growth. And if utilities try to meet that demand in traditional ways, the results could be bad for consumers, the environment and the tech industry. Those traditional ways assume that utilities must meet the needs of electricity customers at all times. This requires utilities to build new power plants and transmission and distribution lines and (in most states) pass those costs, plus a profit margin, on to consumers. Utilities also will not allow major new users to connect to the grid until those users’ needs can be met. These principles are a poor fit for the present moment. Building new power plants and transmission lines has become increasingly difficult. If data centers must wait until that infrastructure is fully built, they may wait for years. Worse, utilities and government officials are citing the potential data-center boom as a reason to extend the life of old, expensive, and heavily polluting coal plants or to build new gas plants. If they do so, and if they pass those costs on to consumers, retail electricity prices and pollution will rise. And if current demand projections turn out to be overestimates — which has happened during past tech booms — consumers will pay for new power plants that never needed to be built. But this unfortunate scenario is not inevitable. We are scholars of energy, natural resources, and environmental law, and in a paper we explore a better way of meeting this moment. Our inspiration comes from legal systems for allocating water, particularly in

Oil Market Appears ‘Torn’

The oil market appears “torn”. That’s what Standard Chartered Bank Energy Research Head Emily Ashford outlined in a report sent to Rigzone by the Standard Chartered team this week, which included a Brent price forecast from Standard Chartered’s machine learning model for next Monday. “The market appears torn between the overwhelming media narrative of an impending supply glut just over the horizon on the one hand, and increasingly unpredictable U.S. policy with a focus on some of the largest producers, and the demand implications of the evolving tariff/trade war landscape on the other,” Ashford said in the statement. “Front-month Brent crude prices closed at $68.89 per barrel on 3 November, just $0.01 per barrel higher than our machine learning model SCORPIO’s forecast set last week,” Ashford added. “This week the model sees an increase of $1.67 per barrel to $66.56 per barrel settlement on Monday 10 November, mainly driven by data from the U.S. (pointing sideways but bullish altogether),” Ashford continued. In the report, Ashford cautioned that “the ongoing U.S. shutdown means that some key data releases remain on pause”. Ashford highlighted in the report that Standard Chartered’s “core view” is that crude oil sentiment “is currently overwhelmingly negative”. “We expect near-term weakness driven by perceived market oversupply and global demand indicators,” Ashford noted. “Low prices then start to quash U.S. shale output growth, and if OPEC+’s return of barrels is sustained, the market will highlight tightness and geographic concentration of spare capacity, which we expect to be supportive in the medium term,” Ashford added. Standard Chartered’s report projected that the ICE Brent nearby future crude oil price will average $68.50 per barrel overall in 2025 and $63.50 per barrel overall in 2026. The report forecast that the commodity will come in at $65 per barrel in the fourth quarter

Designing the AI Century: 7×24 Exchange Fall ’25 Charts the New Data Center Industrial Stack

SMRs and the AI Power Gap: Steve Fairfax Separates Promise from Physics If NVIDIA’s Sean Young made the case for AI factories, Steve Fairfax offered a sobering counterweight: even the smartest factories can’t run without power—and not just any power, but constant, high-availability, clean generation at a scale utilities are increasingly struggling to deliver. In his keynote “Small Modular Reactors for Data Centers,” Fairfax, president of Oresme and one of the data center industry’s most seasoned voices on reliability, walked through the long arc from nuclear fusion research to today’s resurgent interest in fission at modular scale. His presentation blended nuclear engineering history with pragmatic counsel for AI-era infrastructure leaders: SMRs are promising, but their road to reality is paved with physics, fuel, and policy—not PowerPoint. From Fusion Research to Data Center Reliability Fairfax began with his own story—a career that bridges nuclear reliability and data center engineering. As a young physicist and electrical engineer at MIT, he helped build the Alcator C-MOD fusion reactor, a 400-megawatt research facility that heated plasma to 100 million degrees with 3 million amps of current. The magnet system alone drew 265,000 amps at 1,400 volts, producing forces measured in millions of pounds. It was an extreme experiment in controlled power, and one that shaped his later philosophy: design for failure, test for truth, and assume nothing lasts forever. When the U.S. cooled on fusion power in the 1990s, Fairfax applied nuclear reliability methods to data center systems—quantifying uptime and redundancy with the same math used for reactor safety. By 1994, he was consulting for hyperscale pioneers still calling 10 MW “monstrous.” Today’s 400 MW campuses, he noted, are beginning to look a lot more like reactors in their energy intensity—and increasingly, in their regulatory scrutiny. Defining the Small Modular Reactor Fairfax defined SMRs

Top network and data center events 2025 & 2026

Denise Dubie is a senior editor at Network World with nearly 30 years of experience writing about the tech industry. Her coverage areas include AIOps, cybersecurity, networking careers, network management, observability, SASE, SD-WAN, and how AI transforms enterprise IT. A seasoned journalist and content creator, Denise writes breaking news and in-depth features, and she delivers practical advice for IT professionals while making complex technology accessible to all. Before returning to journalism, she held senior content marketing roles at CA Technologies, Berkshire Grey, and Cisco. Denise is a trusted voice in the world of enterprise IT and networking.

Google’s cheaper, faster TPUs are here, while users of other AI processors face a supply crunch

Opportunities for the AI industry LLM vendors such as OpenAI and Anthropic, which still have relatively young code bases and are continuously evolving them, also have much to gain from the arrival of Ironwood for training their models, said Forrester vice president and principal analyst Charlie Dai. In fact, Anthropic has already agreed to procure 1 million TPUs for training and its models and using them for inferencing. Other, smaller vendors using Google’s TPUs for training models include Lightricks and Essential AI. Google has seen a steady increase in demand for its TPUs (which it also uses to run interna services), and is expected to buy $9.8 billion worth of TPUs from Broadcom this year, compared to $6.2 billion and $2.04 billion in 2024 and 2023 respectively, according to Harrowell. “This makes them the second-biggest AI chip program for cloud and enterprise data centers, just tailing Nvidia, with approximately 5% of the market. Nvidia owns about 78% of the market,” Harrowell said. The legacy problem While some analysts were optimistic about the prospects for TPUs in the enterprise, IDC research director Brandon Hoff said enterprises will most likely to stay away from Ironwood or TPUs in general because of their existing code base written for other platforms. “For enterprise customers who are writing their own inferencing, they will be tied into Nvidia’s software platform,” Hoff said, referring to CUDA, the software platform that runs on Nvidia GPUs. CUDA was released to the public in 2007, while the first version of TensorFlow has only been around since 2015.

Cisco launches AI infrastructure, AI practitioner certifications

“This new certification focuses on artificial intelligence and machine learning workloads, helping technical professionals become AI-ready and successfully embed AI into their workflows,” said Pat Merat, vice president at Learn with Cisco, in a blog detailing the new AI Infrastructure Specialist certification. “The certification validates a candidate’s comprehensive knowledge in designing, implementing, operating, and troubleshooting AI solutions across Cisco infrastructure.” Separately, the AITECH certification is part of the Cisco AI Infrastructure track, which complements its existing networking, data center, and security certifications. Cisco says the AITECH cert training is intended for network engineers, system administrators, solution architects, and other IT professionals who want to learn how AI impacts enterprise infrastructure. The training curriculum covers topics such as: Utilizing AI for code generation, refactoring, and using modern AI-assisted coding workflows. Using generative AI for exploratory data analysis, data cleaning, transformation, and generating actionable insights. Designing and implementing multi-step AI-assisted workflows and understanding complex agentic systems for automation. Learning AI-powered requirements, evaluating customization approaches, considering deployment strategies, and designing robust AI workflows. Evaluating, fine-tuning, and deploying pre-trained AI models, and implementing Retrieval Augmented Generation (RAG) systems. Monitoring, maintaining, and optimizing AI-powered workflows, ensuring data integrity and security. AITECH certification candidates will learn how to use AI to enhance productivity, automate routine tasks, and support the development of new applications. The training program includes hands-on labs and simulations to demonstrate practical use cases for AI within Cisco and multi-vendor environments.

Chip-to-Grid Gets Bought: Eaton, Vertiv, and Daikin Deals Imply a New Thermal Capital Cycle

This week delivered three telling acquisitions that mark a turning point for the global data center supply chain; and more specifically, for the high-density liquid cooling mega-play now unfolding across the power-thermal continuum. Eaton is acquiring Boyd Thermal for $9.5 billion from Goldman Sachs Asset Management. Vertiv is buying PurgeRite for about $1 billion from Milton Street Capital. And Daikin Applied has moved to acquire Chilldyne, one of the most proven negative-pressure direct-to-chip pioneers. On paper, they’re three distinct transactions. In reality, they’re chapters in the same story: the acceleration of strategic vertical integration around thermal infrastructure for AI-class compute. The Equity Layer: Private Capital Builds, Strategics Buy From an equity standpoint, these are classic handoff moments between private-equity construction and corporate consolidation. Goldman Sachs built Boyd Thermal into a global platform spanning cold plates, CDUs, and high-density liquid loop design, now sold to Eaton at an enterprise multiple north of 5× 2026E revenue. Milton Street Capital took PurgeRite from a specialist contractor in fluid flushing and commissioning into a nationwide services platform. And Daikin, long synonymous with chillers and air-side thermal, is crossing the liquid Rubicon by buying its way into the D2C ecosystem. Each deal crystallizes a simple fact: liquid cooling is no longer an adjunct; it’s core infrastructure. Private equity did its job scaling the parts. Strategic players are now paying up for the system. Eaton’s Bid: The Chip-to-Grid Thesis For Eaton, Boyd Thermal is the final missing piece in its “chip-to-grid” thesis. The company already owns the electrical side of the data center: UPS, busway, switchgear, and monitoring. Boyd plugs the thermal gap, allowing Eaton to market full rack-to-substation solutions for AI loads in the 50–100 kW+ range. It’s a statement acquisition that places Eaton squarely against Schneider Electric, Vertiv and ABB in the race to

Space: The final frontier for data processing

There are, however, a couple of reasons why data centers in space are being considered. There are plenty of reports about how the increased amount of AI processing is affecting power consumption within data centers; the World Economic Forum has estimated that the power required to handle AI is increasing at a rate of between 26% and 36% annually. Therefore, it is not surprising that organizations are looking at other options. But an even more pressing reason for orbiting data centers is to handle the amount of data that is being produced by existing satellites, Judge said. “Essentially, satellites are gathering a lot more data than can be sent to earth, because downlinks are a bottleneck,” he noted. “With AI capacity in orbit, they could potentially analyze more of this data, extract more useful information, and send insights back to earth. My overall feeling is that any more data processing in space is going to be driven by space processing needs.” And China may already be ahead of the game. Last year, Guoxing Aerospace launched 12 satellites, forming a space-based computing network dubbed the Three-Body Computing Constellation. When completed, it will contain 2,800 satellites, all handling the orchestration and processing of data, taking edge computing to a new dimension.

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

Stay Ahead, Stay ONMINE