Can we repair the internet?

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Can we repair the internet?

From addictive algorithms to exploitative apps, data mining to misinformation, the internet today can be a hazardous place. Books by three influential figures—the intellect behind “net neutrality,” a former Meta executive, and the web’s own inventor—propose radical approaches to fixing it. But are these luminaries the right people for the job? Though each shows conviction, and even sometimes inventiveness, the solutions they present reveal blind spots. The Age of Extraction: How Tech Platforms Conquered the Economy and Threaten Our Future ProsperityTim WuKNOPF, 2025 In The Age of Extraction: How Tech Platforms Conquered the Economy and Threaten Our Future Prosperity, Tim Wu argues that a few platform companies have too much concentrated power and must be dismantled. Wu, a prominent Columbia professor who popularized the principle that a free internet requires all online traffic to be treated equally, believes that existing legal mechanisms, especially anti-monopoly laws, offer the best way to achieve this goal. Pairing economic theory with recent digital history, Wu shows how platforms have shifted from giving to users to extracting from them. He argues that our failure to understand their power has only encouraged them to grow, displacing competitors along the way. And he contends that convenience is what platforms most often exploit to keep users entrapped. “The human desire to avoid unnecessary pain and inconvenience,” he writes, may be “the strongest force out there.” He cites Google’s and Apple’s “ecosystems” as examples, showing how users can become dependent on such services as a result of their all-encompassing seamlessness. To Wu, this isn’t a bad thing in itself. The ease of using Amazon to stream entertainment, make online purchases, or help organize day-to-day life delivers obvious gains. But when powerhouse companies like Amazon, Apple, and Alphabet win the battle of convenience with so many users—and never let competitors get a foothold—the result is “industry dominance” that must now be reexamined. The measures Wu advocates—and that appear the most practical, as they draw on existing legal frameworks and economic policies—are federal anti-monopoly laws, utility caps that limit how much companies can charge consumers for service, and “line of business” restrictions that prohibit companies from operating in certain industries. Columbia University’s Tim Wu shows how platforms have shifted from giving to users to extracting from them. He argues that our failure to understand their power has only encouraged them to grow. Anti-monopoly provisions and antitrust laws are effective weapons in our armory, Wu contends, pointing out that they have been successfully used against technology companies in the past. He cites two well-known cases. The first is the 1960s antitrust case brought by the US government against IBM, which helped create competition in the computer software market that enabled companies like Apple and Microsoft to emerge. The 1982 AT&T case that broke the telephone conglomerate up into several smaller companies is another instance. In each, the public benefited from the decoupling of hardware, software, and other services, leading to more competition and choice in a technology market. But will past performance predict future results? It’s not yet clear whether these laws can be successful in the platform age. The 2025 antitrust case against Google—in which a judge ruled that the company did not have to divest itself of its Chrome browser as the US Justice Department had proposed—reveals the limits of pursuing tech breakups through the law. The 2001 antitrust case brought against Microsoft likewise failed to separate the company from its web browser and mostly kept the conglomerate intact. Wu noticeably doesn’t discuss the Microsoft case when arguing for antitrust action today. Nick Clegg, until recently Meta’s president of global affairs and a former deputy prime minister of the UK, takes a position very different from Wu’s: that trying to break up the biggest tech companies is misguided and would degrade the experience of internet users. In How to Save the Internet: The Threat to Global Connection in the Age of AI and Political Conflict, Clegg acknowledges Big Tech’s monopoly over the web. But he believes punitive legal measures like antitrust laws are unproductive and can be avoided by means of regulation, such as rules for what content social media can and can’t publish. (It’s worth noting that Meta is facing its own antitrust case, involving whether it should have been allowed to acquire Instagram and WhatsApp.) How to Save the Internet: The Threat to Global Connection in the Age of AI and Political ConflictNick CleggBODLEY HEAD, 2025 Clegg also believes Silicon Valley should take the initiative to reform itself. He argues that encouraging social media networks to “open up the books” and share their decision-making power with users is more likely to restore some equilibrium than contemplating legal action as a first resort. But some may be skeptical of a former Meta exec and politician who worked closely with Mark Zuckerberg and still wasn’t able to usher in such changes to social media sites while working for one. What will only compound this skepticism is the selective history found in Clegg’s book, which briefly acknowledges some scandals (like the one surrounding Cambridge Analytica’s data harvesting from Facebook users in 2016) but refuses to discuss other pertinent ones. For example, Clegg laments the “fractured” nature of the global internet today but fails to acknowledge Facebook’s own role in this splintering. Breaking up Big Tech through antitrust laws would hinder innovation, says Clegg, arguing that the idea “completely ignores the benefits users gain from large network effects.” Users stick with these outsize channels because they can find “most of what they’re looking for,” he writes, like friends and content on social media and cheap consumer goods on Amazon and eBay. Wu might concede this point, but he would disagree with Clegg’s claims that maintaining the status quo is beneficial to users. “The traditional logic of antitrust law doesn’t work,” Clegg insists. Instead, he believes less sweeping regulation can help make Big Tech less dangerous while ensuring a better user experience. Clegg has seen both sides of the regulatory coin: He worked in David Cameron’s government passing national laws for technology companies to follow and then moved to Meta to help the company navigate those types of nation-specific obligations. He bemoans the hassle and complexity Silicon Valley faces in trying to comply with differing rules across the globe, some set by “American federal agencies” and others by “Indian nationalists.” But with the resources such companies command, surely they are more than equipped to cope? Given that Meta itself has previously meddled in access to the internet (such as in India, whose telecommunications regulator ultimately blocked its Free Basics internet service for violating net neutrality rules), this complaint seems suspect coming from Clegg. What should be the real priority, he argues, is not any new nation-specific laws but a global “treaty that protects the free flow of data between signatory countries.” What the former Meta executive Nick Clegg advocates—unsurprisingly—is not a breakup of Big Tech but a push for it to become “radically transparent.” Clegg believes that these nation-specific technology obligations—a recent one is Australia’s ban on social media for people under 16—usually reflect fallacies about the technology’s human impact, a subject that can be fraught with anxiety. Such laws have proved ineffective and tend to taint the public’s understanding of social networks, he says. There is some truth to his argument here, but reading a book in which a former Facebook executive dismisses techno-determinism—that is, the argument that technology makes people do or think certain things—may be cold comfort to those who have seen the harm technology can do. In any case, Clegg’s defensiveness about social networks may not gain much favor from users themselves. He stresses the need for more personal responsibility, arguing that Meta doesn’t ever intend for users to stay on Facebook or Instagram endlessly: “How long you spend on the app in a single session is not nearly as important as getting you to come back over and over again.” Social media companies want to serve you content that is “meaningful to you,” he claims, not “simply to give you a momentary dopamine spike.” All this feels disingenuous at best. What Clegg advocates—unsurprisingly—is not a breakup of Big Tech but a push for it to become “radically transparent,” whether on its own or, if necessary, with the help of federal legislators. He also wants platforms to bring users more into their governance processes (by using Facebook’s model of community forums to help improve their apps and products, for example). Finally, Clegg also wants Big Tech to give users more meaningful control of their data and how companies such as Meta can use it. Here Clegg shares common ground with the inventor of the web, Tim Berners-Lee, whose own proposal for reform advances a technically specific vision for doing just that. In his memoir/manifesto This Is for Everyone: The Unfinished Story of the World Wide Web, Berners-Lee acknowledges that his initial vision—of a technology he hoped would remain open-source, collaborative, and completely decentralized—is a far cry from the web that we know today. This Is for Everyone: The Unfinished Story of the World Wide WebTim Berners-LeeFARRAR, STRAUS & GIROUX, 2025 If there’s any surviving manifestation of his original project, he says, it’s Wikipedia, which remains “probably the best single example of what I wanted the web to be.” His best idea for moving power from Silicon Valley platforms into the hands of users is to give them more data control. He pushes for a universal data “pod” he helped develop, known as “Solid” (an abbreviation of “social linked data”). The system—which was originally developed at MIT—would offer a central site where people could manage data ranging from credit card information to health records to social media comment history. “Rather than have all this stuff siloed off with different providers across the web, you’d be able to store your entire digital information trail in a single private repository,” Berners-Lee writes. The Solid product may look like a kind of silver bullet in an age when data harvesting is familiar and data breaches are rampant. Placing greater control with users and enabling them to see “what data [i]s being generated about them” does sound like a tantalizing prospect. But some people may have concerns about, for example, merging their confidential health records with data from personal devices (like heart rate info from a smart watch). No matter how much user control and decentralization Berners-Lee may promise, recent data scandals (such as cases in which period-tracking apps misused clients’ data) may be on people’s minds. Berners-Lee believes that centralizing user data in a product like Solid could save people time and improve daily life on the internet. “An alien coming to Earth would think it was very strange that I had to tell my phone the same things again and again,” he complains about the experience of using different airline apps today. With Solid, everything from vaccination records to credit card transactions could be kept within the digital vault and plugged into different apps. Berners-Lee believes that AI could also help people make more use of this data—for example, by linking meal plans to grocery bills. Still, if he’s optimistic on how AI and Solid could coordinate to improve users’ lives, he is vague on how to make sure that chatbots manage such personal data sensitively and safely. Berners-Lee generally opposes regulation of the web (except in the case of teenagers and social media algorithms, where he sees a genuine need). He believes in internet users’ individual right to control their own data; he is confident that a product like Solid could “course-correct” the web from its current “exploitative” and extractive direction. Of the three writers’ approaches to reform, it is Wu’s that has shown some effectiveness of late. Companies like Google have been forced to give competitors some advantage through data sharing, and they have now seen limits on how their systems can be used in new products and technologies. But in the current US political climate, will antitrust laws continue to be enforced against Big Tech? Clegg may get his way on one issue: limiting new nation-specific laws. President Donald Trump has confirmed that he will use tariffs to penalize countries that ratify their own national laws targeting US tech companies. And given the posture of the Trump administration, it doesn’t seem likely that Big Tech will see more regulation in the US. Indeed, social networks have seemed emboldened (Meta, for example, removed fact-checkers and relaxed content moderation rules after Trump’s election win). In any case, the US hasn’t passed a major piece of federal internet legislation since 1996. If using anti-monopoly laws through the courts isn’t possible, Clegg’s push for a US-led omnibus deal—setting consensual rules for data and acceptable standards of human rights—may be the only way to make some more immediate improvements. In the end, there is not likely to be any single fix for what ails the internet today. But the ideas the three writers agree on—greater user control, more data privacy, and increased accountability from Silicon Valley—are surely the outcomes we should all fight for. Nathan Smith is a writer whose work has appeared in the Washington Post, the Economist, and the Los Angeles Times.

book cover — **The Age of Extraction: How Tech Platforms Conquered the Economy and Threaten Our Future Prosperity**
Tim Wu

In The Age of Extraction: How Tech Platforms Conquered the Economy and Threaten Our Future Prosperity, Tim Wu argues that a few platform companies have too much concentrated power and must be dismantled. Wu, a prominent Columbia professor who popularized the principle that a free internet requires all online traffic to be treated equally, believes that existing legal mechanisms, especially anti-monopoly laws, offer the best way to achieve this goal.

Pairing economic theory with recent digital history, Wu shows how platforms have shifted from giving to users to extracting from them. He argues that our failure to understand their power has only encouraged them to grow, displacing competitors along the way. And he contends that convenience is what platforms most often exploit to keep users entrapped. “The human desire to avoid unnecessary pain and inconvenience,” he writes, may be “the strongest force out there.”

He cites Google’s and Apple’s “ecosystems” as examples, showing how users can become dependent on such services as a result of their all-encompassing seamlessness. To Wu, this isn’t a bad thing in itself. The ease of using Amazon to stream entertainment, make online purchases, or help organize day-to-day life delivers obvious gains. But when powerhouse companies like Amazon, Apple, and Alphabet win the battle of convenience with so many users—and never let competitors get a foothold—the result is “industry dominance” that must now be reexamined.

The measures Wu advocates—and that appear the most practical, as they draw on existing legal frameworks and economic policies—are federal anti-monopoly laws, utility caps that limit how much companies can charge consumers for service, and “line of business” restrictions that prohibit companies from operating in certain industries.

Columbia University’s Tim Wu shows how platforms have shifted from giving to users to extracting from them. He argues that our failure to understand their power has only encouraged them to grow.

Anti-monopoly provisions and antitrust laws are effective weapons in our armory, Wu contends, pointing out that they have been successfully used against technology companies in the past. He cites two well-known cases. The first is the 1960s antitrust case brought by the US government against IBM, which helped create competition in the computer software market that enabled companies like Apple and Microsoft to emerge. The 1982 AT&T case that broke the telephone conglomerate up into several smaller companies is another instance. In each, the public benefited from the decoupling of hardware, software, and other services, leading to more competition and choice in a technology market.

But will past performance predict future results? It’s not yet clear whether these laws can be successful in the platform age. The 2025 antitrust case against Google—in which a judge ruled that the company did not have to divest itself of its Chrome browser as the US Justice Department had proposed—reveals the limits of pursuing tech breakups through the law. The 2001 antitrust case brought against Microsoft likewise failed to separate the company from its web browser and mostly kept the conglomerate intact. Wu noticeably doesn’t discuss the Microsoft case when arguing for antitrust action today.

Nick Clegg, until recently Meta’s president of global affairs and a former deputy prime minister of the UK, takes a position very different from Wu’s: that trying to break up the biggest tech companies is misguided and would degrade the experience of internet users. In How to Save the Internet: The Threat to Global Connection in the Age of AI and Political Conflict, Clegg acknowledges Big Tech’s monopoly over the web. But he believes punitive legal measures like antitrust laws are unproductive and can be avoided by means of regulation, such as rules for what content social media can and can’t publish. (It’s worth noting that Meta is facing its own antitrust case, involving whether it should have been allowed to acquire Instagram and WhatsApp.)

Clegg also believes Silicon Valley should take the initiative to reform itself. He argues that encouraging social media networks to “open up the books” and share their decision-making power with users is more likely to restore some equilibrium than contemplating legal action as a first resort.

But some may be skeptical of a former Meta exec and politician who worked closely with Mark Zuckerberg and still wasn’t able to usher in such changes to social media sites while working for one. What will only compound this skepticism is the selective history found in Clegg’s book, which briefly acknowledges some scandals (like the one surrounding Cambridge Analytica’s data harvesting from Facebook users in 2016) but refuses to discuss other pertinent ones. For example, Clegg laments the “fractured” nature of the global internet today but fails to acknowledge Facebook’s own role in this splintering.

Breaking up Big Tech through antitrust laws would hinder innovation, says Clegg, arguing that the idea “completely ignores the benefits users gain from large network effects.” Users stick with these outsize channels because they can find “most of what they’re looking for,” he writes, like friends and content on social media and cheap consumer goods on Amazon and eBay.

Wu might concede this point, but he would disagree with Clegg’s claims that maintaining the status quo is beneficial to users. “The traditional logic of antitrust law doesn’t work,” Clegg insists. Instead, he believes less sweeping regulation can help make Big Tech less dangerous while ensuring a better user experience.

Clegg has seen both sides of the regulatory coin: He worked in David Cameron’s government passing national laws for technology companies to follow and then moved to Meta to help the company navigate those types of nation-specific obligations. He bemoans the hassle and complexity Silicon Valley faces in trying to comply with differing rules across the globe, some set by “American federal agencies” and others by “Indian nationalists.”

But with the resources such companies command, surely they are more than equipped to cope? Given that Meta itself has previously meddled in access to the internet (such as in India, whose telecommunications regulator ultimately blocked its Free Basics internet service for violating net neutrality rules), this complaint seems suspect coming from Clegg. What should be the real priority, he argues, is not any new nation-specific laws but a global “treaty that protects the free flow of data between signatory countries.”

What the former Meta executive Nick Clegg advocates—unsurprisingly—is not a breakup of Big Tech but a push for it to become “radically transparent.”

Clegg believes that these nation-specific technology obligations—a recent one is Australia’s ban on social media for people under 16—usually reflect fallacies about the technology’s human impact, a subject that can be fraught with anxiety. Such laws have proved ineffective and tend to taint the public’s understanding of social networks, he says. There is some truth to his argument here, but reading a book in which a former Facebook executive dismisses techno-determinism—that is, the argument that technology makes people do or think certain things—may be cold comfort to those who have seen the harm technology can do.

In any case, Clegg’s defensiveness about social networks may not gain much favor from users themselves. He stresses the need for more personal responsibility, arguing that Meta doesn’t ever intend for users to stay on Facebook or Instagram endlessly: “How long you spend on the app in a single session is not nearly as important as getting you to come back over and over again.” Social media companies want to serve you content that is “meaningful to you,” he claims, not “simply to give you a momentary dopamine spike.” All this feels disingenuous at best.

What Clegg advocates—unsurprisingly—is not a breakup of Big Tech but a push for it to become “radically transparent,” whether on its own or, if necessary, with the help of federal legislators. He also wants platforms to bring users more into their governance processes (by using Facebook’s model of community forums to help improve their apps and products, for example). Finally, Clegg also wants Big Tech to give users more meaningful control of their data and how companies such as Meta can use it.

Here Clegg shares common ground with the inventor of the web, Tim Berners-Lee, whose own proposal for reform advances a technically specific vision for doing just that. In his memoir/manifesto This Is for Everyone: The Unfinished Story of the World Wide Web, Berners-Lee acknowledges that his initial vision—of a technology he hoped would remain open-source, collaborative, and completely decentralized—is a far cry from the web that we know today.

If there’s any surviving manifestation of his original project, he says, it’s Wikipedia, which remains “probably the best single example of what I wanted the web to be.” His best idea for moving power from Silicon Valley platforms into the hands of users is to give them more data control. He pushes for a universal data “pod” he helped develop, known as “Solid” (an abbreviation of “social linked data”).

The system—which was originally developed at MIT—would offer a central site where people could manage data ranging from credit card information to health records to social media comment history. “Rather than have all this stuff siloed off with different providers across the web, you’d be able to store your entire digital information trail in a single private repository,” Berners-Lee writes.

The Solid product may look like a kind of silver bullet in an age when data harvesting is familiar and data breaches are rampant. Placing greater control with users and enabling them to see “what data [i]s being generated about them” does sound like a tantalizing prospect.

But some people may have concerns about, for example, merging their confidential health records with data from personal devices (like heart rate info from a smart watch). No matter how much user control and decentralization Berners-Lee may promise, recent data scandals (such as cases in which period-tracking apps misused clients’ data) may be on people’s minds.

Berners-Lee believes that centralizing user data in a product like Solid could save people time and improve daily life on the internet. “An alien coming to Earth would think it was very strange that I had to tell my phone the same things again and again,” he complains about the experience of using different airline apps today.

With Solid, everything from vaccination records to credit card transactions could be kept within the digital vault and plugged into different apps. Berners-Lee believes that AI could also help people make more use of this data—for example, by linking meal plans to grocery bills. Still, if he’s optimistic on how AI and Solid could coordinate to improve users’ lives, he is vague on how to make sure that chatbots manage such personal data sensitively and safely.

Berners-Lee generally opposes regulation of the web (except in the case of teenagers and social media algorithms, where he sees a genuine need). He believes in internet users’ individual right to control their own data; he is confident that a product like Solid could “course-correct” the web from its current “exploitative” and extractive direction.

Of the three writers’ approaches to reform, it is Wu’s that has shown some effectiveness of late. Companies like Google have been forced to give competitors some advantage through data sharing, and they have now seen limits on how their systems can be used in new products and technologies. But in the current US political climate, will antitrust laws continue to be enforced against Big Tech?

Clegg may get his way on one issue: limiting new nation-specific laws. President Donald Trump has confirmed that he will use tariffs to penalize countries that ratify their own national laws targeting US tech companies. And given the posture of the Trump administration, it doesn’t seem likely that Big Tech will see more regulation in the US. Indeed, social networks have seemed emboldened (Meta, for example, removed fact-checkers and relaxed content moderation rules after Trump’s election win). In any case, the US hasn’t passed a major piece of federal internet legislation since 1996.

If using anti-monopoly laws through the courts isn’t possible, Clegg’s push for a US-led omnibus deal—setting consensual rules for data and acceptable standards of human rights—may be the only way to make some more immediate improvements.

In the end, there is not likely to be any single fix for what ails the internet today. But the ideas the three writers agree on—greater user control, more data privacy, and increased accountability from Silicon Valley—are surely the outcomes we should all fight for.

Nathan Smith is a writer whose work has appeared in the Washington Post, the Economist, and the Los Angeles Times.

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Chevron Corp’s Australian unit and its joint venture partners have reached a final investment decision to further develop the massive Gorgon natural gas project in Western Australia, it said in a statement on Friday. Chevron Australia and its partners — including Exxon Mobil Corp. and Shell Plc — will spend A$3 billion ($2 billion) connecting two offshore natural gas fields to existing infrastructure and processing facilities on Barrow Island as part of the Gorgon Stage 3 development, it said in the statement. Six wells will also be drilled. Gorgon, on the remote Barrow Island in northwestern Australia, is the largest resource development in Australia’s history, and produces about 15.6 million tons of liquefied natural gas a year. 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.

At the Crossroads of AI and the Edge: Inside 1623 Farnam’s Rising Role as a Midwest Interconnection Powerhouse

That was the thread that carried through our recent conversation for the DCF Show podcast, where Severn walked through the role Farnam now plays in AI-driven networking, multi-cloud connectivity, and the resurgence of regional interconnection as a core part of U.S. digital infrastructure. Aggregation, Not Proximity: The Practical Edge Severn is clear-eyed about what makes the edge work and what doesn’t. The idea that real content delivery could aggregate at the base of cell towers, he noted, has never been realistic. The traffic simply isn’t there. Content goes where the network already concentrates, and the network concentrates where carriers, broadband providers, cloud onramps, and CDNs have amassed critical mass. In Farnam’s case, that density has grown steadily since the building changed hands in 2018. At the time an “underappreciated asset,” the facility has since become a meeting point for more than 40 broadband providers and over 60 carriers, with major content operators and hyperscale platforms routing traffic directly through its MMRs. That aggregation effect feeds on itself; as more carrier and content traffic converges, more participants anchor themselves to the hub, increasing its gravitational pull. Geography only reinforces that position. Located on the 41st parallel, the building sits at the historical shortest-distance path for early transcontinental fiber routes. It also lies at the crossroads of major east–west and north–south paths that have made Omaha a natural meeting point for backhaul routes and hyperscale expansions across the Midwest. AI and the New Interconnection Economy Perhaps the clearest sign of Farnam’s changing role is the sheer volume of fiber entering the building. More than 5,000 new strands are being brought into the property, with another 5,000 strands being added internally within the Meet-Me Rooms in 2025 alone. These are not incremental upgrades—they are hyperscale-grade expansions driven by the demands of AI traffic,

Schneider Electric’s $2.3 Billion in AI Power and Cooling Deals Sends Message to Data Center Sector

When Schneider Electric emerged from its 2025 North American Innovation Summit in Las Vegas last week with nearly $2.3 billion in fresh U.S. data center commitments, it didn’t just notch a big sales win. It arguably put a stake in the ground about who controls the AI power-and-cooling stack over the rest of this decade. Within a single news cycle, Schneider announced: Together, the deals total about $2.27 billion in U.S. data center infrastructure, a number Schneider confirmed in background with multiple outlets and which Reuters highlighted as a bellwether for AI-driven demand. For the AI data center ecosystem, these contracts function like early-stage fuel supply deals for the power and cooling systems that underpin the “AI factory.” Supply Capacity Agreements: Locking in the AI Supply Chain Significantly, both deals are structured as supply capacity agreements, not traditional one-off equipment purchase orders. Under the SCA model, Schneider is committing dedicated manufacturing lines and inventory to these customers, guaranteeing output of power and cooling systems over a multi-year horizon. In return, Switch and Digital Realty are providing Schneider with forecastable volume and visibility at the scale of gigawatt-class campus build-outs. A Schneider spokesperson told Reuters that the two contracts are phased across 2025 and 2026, underscoring that this arrangement is about pipeline, as opposed to a one-time backlog spike. That structure does three important things for the market: Signals confidence that AI demand is durable.You don’t ring-fence billions of dollars of factory output for two customers unless you’re highly confident the AI load curve runs beyond the current GPU cycle. Pre-allocates power & cooling the way the industry pre-allocated GPUs.Hyperscalers and neoclouds have already spent two years locking up Nvidia and AMD capacity. These SCAs suggest power trains and thermal systems are joining chips on the list of constrained strategic resources.

The Data Center Power Squeeze: Mapping the Real Limits of AI-Scale Growth

As we all know, the data center industry is at a crossroads. As artificial intelligence reshapes the already insatiable digital landscape, the demand for computing power is surging at a pace that outstrips the growth of the US electric grid. As engines of the AI economy, an estimated 1,000 new data centers1 are needed to process, store, and analyze the vast datasets that run everything from generative models to autonomous systems. But this transformation comes with a steep price and the new defining criteria for real estate: power. Our appetite for electricity is now the single greatest constraint on our expansion, threatening to stall the very innovation we enable. In 2024, US data centers consumed roughly 4% of the nation’s total electricity, a figure that is projected to triple by 2030, reaching 12% or more.2 For AI-driven hyperscale facilities, the numbers are even more staggering. With the largest planned data centers requiring gigawatts of power, enough to supply entire cities, the cumulative demand from all data centers is expected to reach 134 gigawatts by 2030, nearly three times the current load.3 This presents a systemic challenge. The U.S. power grid, built for a different era, is struggling to keep pace. Utilities are reporting record interconnection requests, with some regions seeing demand projections that exceed their total system capacity by fivefold.4 In Virginia and Texas, the epicenters of data center expansion, grid operators are warning of tight supply-demand balances and the risk of blackouts during peak periods.5 The problem is not just the sheer volume of power needed, but the speed at which it must be delivered. Data center operators are racing to secure power for projects that could be online in as little as 18 months, but grid upgrades and new generation can take years, if not decades. The result

The Future of Hyperscale: Neoverse Joins NVLink Fusion as SC25 Accelerates Rack-Scale AI Architectures

Neoverse’s Expanding Footprint and the Power-Efficiency Imperative With Neoverse deployments now approaching roughly 50% of all compute shipped into top hyperscalers in 2025 (representing more than a billion Arm cores) and with nation-scale AI campuses such as the Stargate project already anchored on Arm compute, the addition of NVLink Fusion becomes a pivotal extension of the Neoverse roadmap. Partners can now connect custom Arm CPUs to their preferred NVIDIA accelerators across a coherent, high-bandwidth, rack-scale fabric. Arm characterized the shift as a generational inflection point in data-center architecture, noting that “power—not FLOPs—is the bottleneck,” and that future design priorities hinge on maximizing “intelligence per watt.” Ian Buck, vice president and general manager of accelerated computing at NVIDIA, underscored the practical impact: “Folks building their own Arm CPU, or using an Arm IP, can actually have access to NVLink Fusion—be able to connect that Arm CPU to an NVIDIA GPU or to the rest of the NVLink ecosystem—and that’s happening at the racks and scale-up infrastructure.” Despite the expanded design flexibility, this is not being positioned as an open interconnect ecosystem. NVIDIA continues to control the NVLink Fusion fabric, and all connections ultimately run through NVIDIA’s architecture. For data-center planners, the SC25 announcement translates into several concrete implications: 1. NVIDIA “Grace-style” Racks Without Buying Grace With NVLink Fusion now baked into Neoverse, hyperscalers and sovereign operators can design their own Arm-based control-plane or pre-processing CPUs that attach coherently to NVIDIA GPU domains—such as NVL72 racks or HGX B200/B300 systems—without relying on Grace CPUs. A rack-level architecture might now resemble: Custom Neoverse SoC for ingest, orchestration, agent logic, and pre/post-processing NVLink Fusion fabric Blackwell GPU islands and/or NVLink-attached custom accelerators (Marvell, MediaTek, others) This decouples CPU choice from NVIDIA’s GPU roadmap while retaining the full NVLink fabric. In practice, it also opens

Flex’s Integrated Data Center Bet: How a Manufacturing Giant Plans to Reshape AI-Scale Infrastructure

At this year’s OCP Global Summit, Flex made a declaration that resonated across the industry: the era of slow, bespoke data center construction is over. AI isn’t just stressing the grid or forcing new cooling techniques—it’s overwhelming the entire design-build process. To meet this moment, Flex introduced a globally manufactured, fully integrated data center platform aimed directly at multi-gigawatt AI campuses. The company claims it can cut deployment timelines by as much as 30 percent by shifting integration upstream into the factory and unifying power, cooling, compute, and lifecycle services into pre-engineered modules. This is not a repositioning on the margins. Flex is effectively asserting that the future hyperscale data center will be manufactured like a complex industrial system, not built like a construction project. On the latest episode of The Data Center Frontier Show, we spoke with Rob Campbell, President of Flex Communications, Enterprise & Cloud, and Chris Butler, President of Flex Power, about why Flex believes this new approach is not only viable but necessary in the age of AI. The discussion revealed a company leaning heavily on its global manufacturing footprint, its cross-industry experience, and its expanding cooling and power technology stack to redefine what deployment speed and integration can look like at scale. AI Has Broken the Old Data Center Model From the outset, Campbell and Butler made clear that Flex’s strategy is a response to a structural shift. AI workloads no longer allow power, cooling, and compute to evolve independently. Densities have jumped so quickly—and thermals have risen so sharply—that the white space, gray space, and power yard are now interdependent engineering challenges. Higher chip TDPs, liquid-cooled racks approaching one to two megawatts, and the need to assemble entire campuses in record time have revealed deep fragility in traditional workflows. As Butler put it, AI

Data Center Jobs: Engineering, Construction, Commissioning, Sales, Field Service and Facility Tech Jobs Available in Major Data Center Hotspots

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist Data Center Facility Technician (All Shifts Available) Impact, TX This position is also available in: Ashburn, VA; Abilene, TX; Needham, MA and New York, NY. Navy Nuke / Military Vets leaving service accepted! This opportunity is working with a leading mission-critical data center provider. This firm provides data center solutions custom-fit to the requirements of their client’s mission-critical operational facilities. They provide reliability of mission-critical facilities for many of the world’s largest organizations facilities supporting enterprise clients, colo providers and hyperscale companies. This opportunity provides a career-growth minded role with exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer Montvale, NJ This traveling position is also available in: New York, NY; White Plains, NY; Richmond, VA; Ashburn, VA; Charlotte, NC; Atlanta, GA; Hampton, GA; Fayetteville, GA; New Albany, OH; Cedar Rapids, IA; Phoenix, AZ; Salt Lake City, UT; Dallas, TX or Chicago, IL. *** ALSO looking for a LEAD EE and ME CxA Agents and CxA PMs. *** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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