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Phantom data centers: What they are (or aren’t) and why they’re hampering the true promise of AI

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More In the age of AI, public utilities are now facing a new, unexpected problem: Phantom data centers. On the surface, it may seem absurd: Why (and how) would anyone fabricate something as complex as a data […]

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More


In the age of AI, public utilities are now facing a new, unexpected problem: Phantom data centers. On the surface, it may seem absurd: Why (and how) would anyone fabricate something as complex as a data center? But as AI demand skyrockets along with the need for more compute power, speculation around data center development is creating chaos, particularly in areas like Northern Virginia, the data center capital of the world. In this evolving landscape, utilities are being bombarded with power requests from real estate developers who may or may not actually build the infrastructure they claim.

Fake data centers represent an urgent bottleneck in scaling data infrastructure to keep up with compute demand. This emerging phenomenon is preventing capital from flowing where it actually needs to. Any enterprise that can help solve this problem — perhaps leveraging AI to solve a problem created by AI — will have a significant edge.

The mirage of gigawatt demands

Dominion Energy, Northern Virginia’s largest utility, has received aggregate requests for 50 gigawatts of power from data center projects. That’s more power than Iceland consumes in a year. 

But many of these requests are either speculative or outright false. Developers are eyeing potential sites and staking their claims to power capacity long before they have the capital or any strategy around how to break ground. In fact, estimates suggest that as much as 90% of these requests are entirely bogus.

In the early days of the data center boom, utilities never had to worry about fake demand. Companies like Amazon, Google and Microsoft — dubbed “hyperscalers” because they operate data centers with hundreds of thousands of servers — submitted straightforward power requests, and utilities simply delivered. But now, the frenzy to secure power capacity has led to an influx of requests from lesser-known developers or speculators with dubious track records. Utilities, which traditionally deal with only a handful of power-hungry customers, are suddenly swamped with orders for power capacity that would dwarf their entire grid.

Utilities struggle to sort fact from fiction

The challenge for utilities isn’t just technical — it’s existential. They’re tasked with determining what’s real and what’s not. And they’re not well-equipped to handle this. Historically, utilities have been slow-moving, risk-averse institutions. Now they’re being asked to vet speculators, many of whom are simply playing the real estate game, hoping to flip their power allotments once the market heats up.

Utilities have groups tasked with economic development, but these teams are not used to dealing with dozens of speculative requests at once. It’s akin to a land rush, where only a fraction of those claiming stakes actually plan to build something tangible. The result? Paralysis. Utilities hesitate to allocate power when they don’t know which projects will materialize, slowing down the entire development cycle.

A wall of capital

There’s no shortage of capital flowing into the data center space, but that abundance is part of the problem. When capital is easy to access, it leads to speculation. In a way, this is similar to the better mousetrap problem: Too many players chasing an oversupplied market. This influx of speculators creates indecision not just within utilities but also in local communities, which must decide whether to grant permits for land use and infrastructure development.

Adding to the complexity is that data centers aren’t just for AI. Sure, AI is driving a surge in demand, but there’s also a persistent need for cloud computing. Developers are building data centers to accommodate both, but differentiating between the two is increasingly difficult, especially when projects blend AI hype with traditional cloud infrastructure.

What’s real?

The legitimate players — the aforementioned Apples, Googles and Microsofts — are building genuine data centers, and many are adopting strategies like “behind-the-meter” deals with renewable energy providers or constructing microgrids to avoid the bottlenecks of grid interconnection. But as real projects proliferate, so too do the fake ones. Developers with little experience in the space are trying to cash in, leading to an increasingly chaotic environment for utilities.

The problem isn’t just financial risk — although the capital required to build a single gigawatt-scale campus can easily exceed several billion dollars — it’s the sheer complexity of developing infrastructure at this scale. A 6-gigawatt campus sounds impressive, but the financial and engineering realities make it almost impossible to build in a reasonable timeframe. Yet, speculators throw these massive numbers around, hoping to secure power capacity in the hopes of flipping the project later.

Why the grid can’t keep up with data center demands

As utilities struggle to sort fact from fiction, the grid itself becomes a bottleneck. McKinsey recently estimated that global data center demand could reach up to 152 gigawatts by 2030, adding 250 terawatt-hours of new electricity demand. In the U.S., data centers alone could account for 8% of total power demand by 2030, a staggering figure considering how little demand has grown in the last two decades.

Yet, the grid is not ready for this influx. Interconnection and transmission issues are rampant, with estimates suggesting the U.S. could run out of power capacity by 2027 to 2029 if alternative solutions aren’t found. Developers are increasingly turning to on-site generation like gas turbines or microgrids to avoid the interconnection bottleneck, but these stopgaps only serve to highlight the grid’s limitations.

Conclusion: Utilities as gatekeepers

The real bottleneck isn’t a lack of capital (trust me, there’s plenty of capital here) or even technology — it’s the ability of utilities to act as gatekeepers, determining who is real and who is just playing the speculation game. Without a robust process to vet developers, the grid risks being overwhelmed by projects that will never materialize. The age of fake data centers is here, and until utilities adapt, the entire industry may struggle to keep pace with the real demand.

In this chaotic environment, it’s not just about power allocation; it’s about utilities learning to navigate a new, speculative frontier so that enterprises (and AI) can thrive.

Sophie Bakalar is a partner at Collaborative Fund.

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The biggest AI flops of 2024

The past 12 months have been undeniably busy for those working in AI. There have been more successful product launches than we can count, and even Nobel Prizes. But it hasn’t always been smooth sailing. AI is an unpredictable technology, and the increasing availability of generative models has led people

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RRC Adopts Significant Overhaul of Oil, Gas Waste Management Rules

In a statement posted on its website recently, the Texas Railroad Commission (RRC) announced that it has “adopted a significant overhaul of rules regulating oil and gas waste management facilities in Texas”. The development comes “after extensive reviews of public comments and stakeholder input”, the RRC outlined in the statement,

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Oil Prices Slip on Glut Fears

Oil snapped a five-session rally as key technical markers show gains driven by tightness in global physical crude markets may have gone too far. West Texas Intermediate fell 0.5% to settle below $74 a barrel, reversing earlier gains after futures were unable to breach a psychological level of $75. WTI’s prompt spread dipped from a near three-month high to 65 cents in a sign of evaporating trader confidence that demand is outstripping supply. The relative strength index also showed prices at overbought levels, a reading that indicates crude was due for a pullback. Market optimism is being limited by expectations for a glut, the possible revival of idled OPEC+ production and lackluster demand from top importer China. “Fundamentals have improved enough for crude prices to find a floor, but not enough to sustain a durable rally,” said Jon Byrne, an analyst at Strategas Securities. At the moment, “$75 is the ceiling, with opportunities on the short side.” The commodity earlier grazed October highs after Saudi Arabia hiked oil prices to Asian customers, a vote of confidence for crude demand. That followed a jump in Oman and Dubai crude prices at the end of last year on scant supply from Iran and Russia. In broader markets, the US dollar plummeted after the Washington Post reported that US President-elect Donald Trump will limit his plans for tariffs. The dollar has since recovered some of the losses after Trump denied the report on social media. A weaker dollar makes commodities priced in the currency more attractive. Last week, crude broke out of its narrow trading range as US stockpiles fell for the sixth straight week while inventories at the vital storage hub of Cushing, Oklahoma, held at a 17-year seasonal low. Oil Prices: WTI for February delivery fell 0.5% to settle at $73.56

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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

Read More »

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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Three Aberdeen oil company headquarters sell for £45m

Three Aberdeen oil company headquarters have been sold in a deal worth £45 million. The CNOOC, Apache and Taqa buildings at the Prime Four business park in Kingswells have been acquired by EEH Ventures. The trio of buildings, totalling 275,000 sq ft, were previously owned by Canadian firm BMO. The financial services powerhouse first bought the buildings in 2014 but took the decision to sell the buildings as part of a “long-standing strategy to reduce their office exposure across the UK”. The deal was the largest to take place throughout Scotland during the last quarter of 2024. Trio of buildings snapped up London headquartered EEH Ventures was founded in 2013 and owns a number of residential, offices, shopping centres and hotels throughout the UK. All three Kingswells-based buildings were pre-let, designed and constructed by Aberdeen property developer Drum in 2012 on a 15-year lease. © Supplied by CBREThe Aberdeen headquarters of Taqa. Image: CBRE The North Sea headquarters of Middle-East oil firm Taqa has previously been described as “an amazing success story in the Granite City”. Taqa announced in 2023 that it intends to cease production from all of its UK North Sea platforms by the end of 2027. Meanwhile, Apache revealed at the end of last year it is planning to exit the North Sea by the end of 2029 blaming the windfall tax. The US firm first entered the North Sea in 2003 but will wrap up all of its UK operations by 2030. Aberdeen big deals The Prime Four acquisition wasn’t the biggest Granite City commercial property sale of 2024. American private equity firm Lone Star bought Union Square shopping centre from Hammerson for £111m. © ShutterstockAberdeen city centre. Hammerson, who also built the property, had originally been seeking £150m. BP’s North Sea headquarters in Stoneywood, Aberdeen, was also sold. Manchester-based

Read More »

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.

Read More »

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

Read More »

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

Read More »

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

Read More »

Three Aberdeen oil company headquarters sell for £45m

Three Aberdeen oil company headquarters have been sold in a deal worth £45 million. The CNOOC, Apache and Taqa buildings at the Prime Four business park in Kingswells have been acquired by EEH Ventures. The trio of buildings, totalling 275,000 sq ft, were previously owned by Canadian firm BMO. The financial services powerhouse first bought the buildings in 2014 but took the decision to sell the buildings as part of a “long-standing strategy to reduce their office exposure across the UK”. The deal was the largest to take place throughout Scotland during the last quarter of 2024. Trio of buildings snapped up London headquartered EEH Ventures was founded in 2013 and owns a number of residential, offices, shopping centres and hotels throughout the UK. All three Kingswells-based buildings were pre-let, designed and constructed by Aberdeen property developer Drum in 2012 on a 15-year lease. © Supplied by CBREThe Aberdeen headquarters of Taqa. Image: CBRE The North Sea headquarters of Middle-East oil firm Taqa has previously been described as “an amazing success story in the Granite City”. Taqa announced in 2023 that it intends to cease production from all of its UK North Sea platforms by the end of 2027. Meanwhile, Apache revealed at the end of last year it is planning to exit the North Sea by the end of 2029 blaming the windfall tax. The US firm first entered the North Sea in 2003 but will wrap up all of its UK operations by 2030. Aberdeen big deals The Prime Four acquisition wasn’t the biggest Granite City commercial property sale of 2024. American private equity firm Lone Star bought Union Square shopping centre from Hammerson for £111m. © ShutterstockAberdeen city centre. Hammerson, who also built the property, had originally been seeking £150m. BP’s North Sea headquarters in Stoneywood, Aberdeen, was also sold. Manchester-based

Read More »

2025 ransomware predictions, trends, and how to prepare

Zscaler ThreatLabz research team has revealed critical insights and predictions on ransomware trends for 2025. The latest Ransomware Report uncovered a surge in sophisticated tactics and extortion attacks. As ransomware remains a key concern for CISOs and CIOs, the report sheds light on actionable strategies to mitigate risks. Top Ransomware Predictions for 2025: ● AI-Powered Social Engineering: In 2025, GenAI will fuel voice phishing (vishing) attacks. With the proliferation of GenAI-based tooling, initial access broker groups will increasingly leverage AI-generated voices; which sound more and more realistic by adopting local accents and dialects to enhance credibility and success rates. ● The Trifecta of Social Engineering Attacks: Vishing, Ransomware and Data Exfiltration. Additionally, sophisticated ransomware groups, like the Dark Angels, will continue the trend of low-volume, high-impact attacks; preferring to focus on an individual company, stealing vast amounts of data without encrypting files, and evading media and law enforcement scrutiny. ● Targeted Industries Under Siege: Manufacturing, healthcare, education, energy will remain primary targets, with no slowdown in attacks expected. ● New SEC Regulations Drive Increased Transparency: 2025 will see an uptick in reported ransomware attacks and payouts due to new, tighter SEC requirements mandating that public companies report material incidents within four business days. ● Ransomware Payouts Are on the Rise: In 2025 ransom demands will most likely increase due to an evolving ecosystem of cybercrime groups, specializing in designated attack tactics, and collaboration by these groups that have entered a sophisticated profit sharing model using Ransomware-as-a-Service. To combat damaging ransomware attacks, Zscaler ThreatLabz recommends the following strategies. ● Fighting AI with AI: As threat actors use AI to identify vulnerabilities, organizations must counter with AI-powered zero trust security systems that detect and mitigate new threats. ● Advantages of adopting a Zero Trust architecture: A Zero Trust cloud security platform stops

Read More »

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.

Read More »

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

Read More »

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

Read More »

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

Read More »