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US investigators are using AI to detect child abuse images made by AI

Generative AI has enabled the production of child sexual abuse images to skyrocket. Now the leading investigator of child exploitation in the US is experimenting with using AI to distinguish AI-generated images from material depicting real victims, according to a new government filing. The Department of Homeland Security’s Cyber Crimes Center, which investigates child exploitation across international borders, has awarded a $150,000 contract to San Francisco–based Hive AI for its software, which can identify whether a piece of content was AI-generated. The filing, posted on September 19, is heavily redacted and Hive cofounder and CEO Kevin Guo told MIT Technology Review that he could not discuss the details of the contract, but confirmed it involves use of the company’s AI detection algorithms for child sexual abuse material (CSAM). The filing quotes data from the National Center for Missing and Exploited Children that reported a 1,325% increase in incidents involving generative AI in 2024. “The sheer volume of digital content circulating online necessitates the use of automated tools to process and analyze data efficiently,” the filing reads. The first priority of child exploitation investigators is to find and stop any abuse currently happening, but the flood of AI-generated CSAM has made it difficult for investigators to know whether images depict a real victim currently at risk. A tool that could successfully flag real victims would be a massive help when they try to prioritize cases. Identifying AI-generated images “ensures that investigative resources are focused on cases involving real victims, maximizing the program’s impact and safeguarding vulnerable individuals,” the filing reads. Hive AI offers AI tools that create videos and images, as well as a range of content moderation tools that can flag violence, spam, and sexual material and even identify celebrities. In December, MIT Technology Review reported that the company was selling its deepfake-detection technology to the US military.  For detecting CSAM, Hive offers a tool created with Thorn, a child safety nonprofit, which companies can integrate into their platforms. This tool uses a “hashing” system, which assigns unique IDs to content known by investigators to be CSAM, and blocks that material from being uploaded. This tool, and others like it, have become a standard line of defense for tech companies.  But these tools simply identify a piece of content as CSAM; they don’t detect whether it was generated by AI. Hive has created a separate tool that determines whether images in general were AI-generated. Though it is not trained specifically to work on CSAM, according to Guo, it doesn’t need to be. “There’s some underlying combination of pixels in this image that we can identify” as AI-generated, he says. “It can be generalizable.”  This tool, Guo says, is what the Cyber Crimes Center will be using to evaluate CSAM. He adds that Hive benchmarks its detection tools for each specific use case its customers have in mind. The National Center for Missing and Exploited Children, which participates in efforts to stop the spread of CSAM, did not respond to requests for comment on the effectiveness of such detection models in time for publication.  In its filing, the government justifies awarding the contract to Hive without a competitive bidding process. Though parts of this justification are redacted, it primarily references two points also found in a Hive presentation slide deck. One involves a 2024 study from the University of Chicago, which found that Hive’s AI detection tool outranked four other detectors in identifying AI-generated art. The other is its contract with the Pentagon for identifying deepfakes. The trial will last three months. 

Generative AI has enabled the production of child sexual abuse images to skyrocket. Now the leading investigator of child exploitation in the US is experimenting with using AI to distinguish AI-generated images from material depicting real victims, according to a new government filing.

The Department of Homeland Security’s Cyber Crimes Center, which investigates child exploitation across international borders, has awarded a $150,000 contract to San Francisco–based Hive AI for its software, which can identify whether a piece of content was AI-generated.

The filing, posted on September 19, is heavily redacted and Hive cofounder and CEO Kevin Guo told MIT Technology Review that he could not discuss the details of the contract, but confirmed it involves use of the company’s AI detection algorithms for child sexual abuse material (CSAM).

The filing quotes data from the National Center for Missing and Exploited Children that reported a 1,325% increase in incidents involving generative AI in 2024. “The sheer volume of digital content circulating online necessitates the use of automated tools to process and analyze data efficiently,” the filing reads.

The first priority of child exploitation investigators is to find and stop any abuse currently happening, but the flood of AI-generated CSAM has made it difficult for investigators to know whether images depict a real victim currently at risk. A tool that could successfully flag real victims would be a massive help when they try to prioritize cases.

Identifying AI-generated images “ensures that investigative resources are focused on cases involving real victims, maximizing the program’s impact and safeguarding vulnerable individuals,” the filing reads.

Hive AI offers AI tools that create videos and images, as well as a range of content moderation tools that can flag violence, spam, and sexual material and even identify celebrities. In December, MIT Technology Review reported that the company was selling its deepfake-detection technology to the US military. 

For detecting CSAM, Hive offers a tool created with Thorn, a child safety nonprofit, which companies can integrate into their platforms. This tool uses a “hashing” system, which assigns unique IDs to content known by investigators to be CSAM, and blocks that material from being uploaded. This tool, and others like it, have become a standard line of defense for tech companies. 

But these tools simply identify a piece of content as CSAM; they don’t detect whether it was generated by AI. Hive has created a separate tool that determines whether images in general were AI-generated. Though it is not trained specifically to work on CSAM, according to Guo, it doesn’t need to be.

“There’s some underlying combination of pixels in this image that we can identify” as AI-generated, he says. “It can be generalizable.” 

This tool, Guo says, is what the Cyber Crimes Center will be using to evaluate CSAM. He adds that Hive benchmarks its detection tools for each specific use case its customers have in mind.

The National Center for Missing and Exploited Children, which participates in efforts to stop the spread of CSAM, did not respond to requests for comment on the effectiveness of such detection models in time for publication. 

In its filing, the government justifies awarding the contract to Hive without a competitive bidding process. Though parts of this justification are redacted, it primarily references two points also found in a Hive presentation slide deck. One involves a 2024 study from the University of Chicago, which found that Hive’s AI detection tool outranked four other detectors in identifying AI-generated art. The other is its contract with the Pentagon for identifying deepfakes. The trial will last three months. 

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This week in 5 numbers: Rising demand, rising rates

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Cisco admins urged to patch IOS, IOS XE devices

“It requires an authenticated user, so at least it’s not an unauthenticated RCE (remote code execution),” said Shipley. The vulnerability has a high CVSS score of 7.7, “but [it’s] not the worst we’ve seen of late.”    Ed Dubrovsky, chief operating officer of US-based incident response firm Cypfer, also noted that a

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U.S. Department of Energy Launches Mine of the Future Initiatives to Bolster the U.S. Mining Industry

WASHINGTON — The U.S. Department of Energy’s (DOE) Office of Fossil Energy (FE) today announced two new programs under its Mine of the Future initiative, which aims to transform U.S. mining practices and speed the development of a secure and resilient domestic critical mineral and material supply chain by employing cutting-edge mining technologies and processes. First, FE has announced its intent to provide up to $80 million in federal funding for mining technology proving grounds that will support and accelerate innovative technology development and commercialization. FE also announced up to $15 million in federal funding for DOE’s National Laboratories to conduct critical research, development, and demonstration activities that will advance DOE strategies to strengthen our domestic critical mineral supply chain. Through both programs, the Mine of the Future initiative will help deliver on President Trump’s commitment to expand the nation’s critical materials portfolio, reduce our reliance on foreign sources, and re-establish the United States as a global leader in the mining sector.  “DOE is committed to fostering a new generation of skilled miners and equipping them with the tools to lead the world in mineral production and processing technology,” said Assistant Secretary of Fossil Energy, Kyle Haustveit. “Through these new Mine of the Future programs, DOE is making a significant investment to accelerate the development and deployment of cutting-edge solutions for the U.S. mining sector.” Notice of Intent: Mining Technology Proving Grounds  FE announced its intent to issue an $80 million funding opportunity to establish Mining Technology Proving Grounds and accelerate the development of innovative technologies for the U.S. mining sector. If issued, the funding opportunity will help establish field sites for real-world testing, optimization, and deployment of next-generation mining technologies. These sites will also serve as vital training grounds for a new generation of skilled American miners. The Proving Ground initiative will help to de-risk the adoption of new mining technologies for commercialization and widespread industry

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Oil Posts Biggest Weekly Gain Since July

Oil notched its biggest weekly gain in more than three months as mounting pressure on Russia to end its war in Ukraine muddies the outlook for exports from the OPEC+ member, with algorithmic traders adding momentum to the rally. Global benchmark Brent advanced to settle above $70 a barrel for the first time since late July, and was up roughly 5.2% this week, while West Texas Intermediate closed near $66. The commodity followed broader markets higher on Friday amid stronger-than-expected US economic data, which allayed fears of near-term demand deterioration. The dollar weakened, making commodities priced in the currency more attractive. The equities move compounded earlier gains on a confluence of bullish geopolitical developments. Trump pressed Turkey to stop buying oil from Russia, said he would ask Hungary to do the same and earlier in the week rebuked NATO members for buying fuel from the OPEC+ producer. The mounting pressure comes as Ukraine has stepped up drone strikes against Russian energy infrastructure. Meanwhile, European diplomats warned the Kremlin this week that the North Atlantic Treaty Organization is ready to respond to further violations of its airspace with full force, including by shooting down Russian planes, according to officials familiar with the exchange. The United Nations, meanwhile, will reimpose broad sanctions on Iran after days of diplomacy in New York failed to ease a standoff over Tehran’s nuclear program. “It’s a risky market in which to be short at the moment, made ever more risky by the still elevated speculative shorts that have been accumulated across the crude complex,” said Rory Johnston, oil market researcher and founder of Commodity Context. In another sign of heightened bullishness, commodity trading advisers, which tend to amplify price swings, flipped to net-long on Friday for the first time since early August, according to data from

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It’s all one system: Integrate transmission and interconnection planning to support load growth

Judy W. Chang is a commissioner of the Federal Energy Regulatory Commission. Steven Wellner and Kathleen Ratcliff serve as her advisors. The views expressed in this article are those of the authors and do not reflect the views of other FERC commissioners or any stance of the agency. Generational change is occurring in the nation’s energy sector. While FERC is considering urgent short-term fixes, it also must evaluate longer-term solutions that shape the sector. One solution that meets this era of change is the integration of transmission planning and interconnection processes. The current challenges facing the electric industry are multifaceted, with compounding effects. Rapid and accelerating load growth, backlogged interconnection queues, limited regional/interregional transmission buildout, ongoing supply chain issues and increasingly frequent extreme weather events are exacerbating reliability concerns and raising electricity bills for consumers. States, utilities, grid operators, customers, and market participants grappling with these challenges are exploring short-term fixes, some of which call into question the open-access principles and market constructs this country has used to plan and build the grid’s infrastructure to date. Open access and competitive wholesale electricity markets have been our bedrock principles because they have delivered real value to customers: They have kept energy costs affordable, met customers’ needs efficiently and ensured those who create the need for new infrastructure bear their fair share of the associated costs. The question, then, is what reforms can meet our current demands while preserving the benefits of open access and competition? The answer is clear: Modernizing, harmonizing, and integrating our transmission planning and interconnection processes can meet those challenges. Dramatic load growth is on the horizon The biggest issue facing the industry is the expectation of rapid, dramatic load growth in the near term. While previous circumstances that have driven load growth were largely distributed throughout the country,

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Á la carte energy market will give Western states choice, flexibility and reliability

Brian Turner is the Western regulatory director for national trade association Advanced Energy United and serves on the West-Wide Governance Pathways Initiative Launch Committee.  They said it couldn’t be done. They said California would never relinquish control over the markets run by the California Independent System Operator.  That is why I helped launch the West-Wide Governance Pathways Initiative, a collaborative effort to create a fully independent organization that can manage Western electricity markets free from any one state’s control. The Pathways Initiative was designed to secure input and buy-in from a broad range of stakeholders on what an independently governed market could look like so we could build the trust needed to move forward.  And now, it’s happening. The California legislature, nearly unanimously, just approved Assembly Bill 825, a bill allowing the state’s utilities to participate in electricity markets governed by a new, independent West-wide organization. The new Regional Organization for Western Energy, or ROWE, will be incorporated in early 2026. Much like a popular new restaurant with a small plates menu, the ROWE will offer affordable, reliable energy services à la carte: Each state in the West can take what it needs to satisfy its energy appetite. The table is set for each state to sit on an equal basis. No special service or extra helpings. Every state will have its consumer and policy interests respected and integrated through multiple mechanisms woven through the governance structure. In fact, the ROWE will have the most state- and public-interest-oriented governance of any wholesale electricity market in the country. The structure includes:  Board members selected not just for their electricity sector expertise, but for public interest experience.  A public policy committee of the board charged with maintaining communication with state officials and public power entities around the West and consulting with them on pending

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India Told to Drop Russian Oil for US Trade Deal

India said it held “constructive meetings” with the US this week on a trade deal, although Washington’s demand that New Delhi stop buying Russian oil is weighing on the negotiations. US trade negotiators made it clear to their Indian counterparts that resolving the Russia issue was crucial to reducing India’s tariff rate and sealing a trade deal, a person familiar with the matter said, asking not to be identified because the discussions are private. While the negotiations this week were positive, they failed to yield any significant breakthroughs, the person said. An Indian team led by Commerce Minister Piyush Goyal offered concessions to the US officials, including easing some restrictions on the import of genetically modified corn, and offering to buy more American defense and energy goods, the person said. Goyal said Wednesday that India was willing to buy more energy goods from the US “in the years to come.” President Donald Trump doubled tariffs on Indian goods to 50 percent last month, accusing New Delhi of helping Russian leader Vladimir Putin finance his war in Ukraine. India’s government has struck a defiant tone, saying it won’t halt Russian purchases and calling the US’s actions “unfair, unjustified and unreasonable.” The trade negotiations in Washington from Sept. 22-24 were constructive, with both sides exchanging views on the “possible contours of the deal,” India’s government said in a statement. Goyal met with US Trade Representative Jamieson Greer and Sergio Gor, Trump’s nominee for ambassador to India. The minister also met with Indian businesses and investors during the trip. A spokesperson for the US embassy in New Delhi said it doesn’t comment on private diplomatic conversations, while reiterating the Trump administration’s position that India’s actions were undermining US efforts to counter Russia. The 25 percent penalty on India “aims to deter countries from supporting the Russian economy

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FERC scraps plan to update gas infrastructure policy after DOE directive

At the Trump administration’s request, the US Federal Energy Regulatory Commission (FERC), an independent agency, abandoned its plan Sept. 12 to update the policy it uses to evaluate natural gas pipeline and LNG projects. The decision to scrap the long-running proceeding to revise the 1999 certificate policy for new natural gas facilities could indicate that the Department of Energy (DOE) intends to take a larger role in FERC policy, law firm Akin Gump Strauss Hauer & Feld LLP said in a recent blog post. While FERC’s adoption of the request to end the proceedings will not result in any immediate changes to FERC policy, Akins said “it does signal that the DOE intends to be more involved in FERC policy making going forward and may be the first in a string of DOE efforts to reshape energy policy within FERC’s traditional purview.” FERC had until the end of September to act on a directive from Energy Secretary Chris Wright to terminate the proceeding that the commission has worked on since 2018. Wright said the move was necessary to remove industry uncertainty. “We believe that the 1999 Certificate Policy Statement, as subsequently applied by the Commission, continues to provide the appropriate framework for reviewing proposed natural gas projects in a legally durable manner, pursuant to the Natural Gas Act and consistent with judicial precedent, as it has for over 25 years. Therefore, we are now withdrawing the draft Updated Certificate Policy Statement and closing the proceeding,” FERC wrote in the Sep. 12 order. The commission added that the topics addressed in the proceeding were “better considered on a case-by-case basis.” By terminating the proceeding, FERC would now have to initiate a new docket and develop a new record if it wants to seek a policy change in the future, Akins said.

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Q3 Executive Roundtable Recap

AI-scale workloads are reshaping the fundamentals of data center design. For Data Center Frontier’s Q3 2025 Executive Roundtable, three industry leaders tackled the most urgent challenges: managing thermal and water risk at scale, balancing CapEx vs. OpEx in the race to build, and breaking down silos as cooling, water, and power systems converge. <!–> Sept. 26, 2025 3 min read –>

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‘Nomads at the Summit’ Podcasts – Recorded Live at DCF Trends Summit 2025

@import url(‘https://fonts.googleapis.com/css2?family=Inter:[email protected]&display=swap’); a { color: #1796c1; } .ebm-page__main h1, .ebm-page__main h2, .ebm-page__main h3, .ebm-page__main h4, .ebm-page__main h5, .ebm-page__main h6 { font-family: Inter; } body { line-height: 150%; letter-spacing: 0.025em; font-family: Inter; } button, .ebm-button-wrapper { font-family: Inter; } .label-style { text-transform: uppercase; color: var(–color-grey); font-weight: 600; font-size: 0.75rem; } .caption-style { font-size: 0.75rem; opacity: .6; } #onetrust-pc-sdk [id*=btn-handler], #onetrust-pc-sdk [class*=btn-handler] { background-color: #1796c1 !important; border-color: #1796c1 !important; } #onetrust-policy a, #onetrust-pc-sdk a, #ot-pc-content a { color: #1796c1 !important; } #onetrust-consent-sdk #onetrust-pc-sdk .ot-active-menu { border-color: #1796c1 !important; } #onetrust-consent-sdk #onetrust-accept-btn-handler, #onetrust-banner-sdk #onetrust-reject-all-handler, #onetrust-consent-sdk #onetrust-pc-btn-handler.cookie-setting-link { background-color: #1796c1 !important; border-color: #1796c1 !important; } #onetrust-consent-sdk .onetrust-pc-btn-handler { color: #1796c1 !important; border-color: #1796c1 !important; background-color: undefined !important; } Welcome to Nomads at the Summit, a new podcast series from Data Center Frontier in partnership with the Nomad Futurist Foundation. Recorded live at the 2025 Data Center Frontier Trends Summit (Aug. 26-28), here we sit down with industry leaders, innovators, and change-makers shaping the future of digital infrastructure. Join hosts Nabeel Mahmood and Phillip Koblence of Nomad Futurist, alongside DCF editorial leadership including Editor at Large Melissa Farney and Senior Editor David Chernicoff, for these candid conversations that highlight the ideas, talent, and technologies driving the next chapter of the data center industry. Whether you attended the DCF Trends Summit in person or are just now tuning in from afar, Nomads at the Summit gives you a behind-the-scenes look at the people and innovations defining what’s next in digital infrastructure. <!–> EPISODE LIST ]–> Waste Heat to Water – The Path Towards Water Positive Data Centers In this DCF Trends-Nomads at the Summit Podcast episode, Matt Grandbois, Vice President at AirJoule, introduces a game-changing approach to one of the data center industry’s most pressing challenges: water sustainability. As power-hungry, high-density environments collide with growing water scarcity

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Equinix unveils distributed AI infrastructure targeting inferencing, cloud connectivity

Data center provider Equinix has launched its Distributed AI infrastructure, which includes a new AI-ready backbone to support high performance distributed AI deployments spanning multiple data center facilities, a global AI Proving Ground to test new solutions, and Fabric Intelligence to better support next generation enterprise workloads. Equinix designed Distributed AI from the ground up to support the scale, speed, and complexity of modern intelligent systems, such as autonomous, agentic AI capable of reasoning, acting, and learning independently. AI is inherently distributed, drawing on multiple data sources in different locations. To effectively train a model, data must be drawn from multiple locations and processed where it lies, not moved around. This requires a new kind of infrastructure that is globally distributed, deeply interconnected, and fully programmable. Distributed AI links more than 270 data centers in over 60 markets, effectively including almost all Equinix’s facilities, according to the vendor.

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Cisco expands its quantum networking portfolio with new software prototypes

The software stack supports three other prototype applications to help enable quantum networking and the data center. The first is what Pandey describes as a network-aware distributed quantum compiler that lets quantum algorithms run across multiple networked processors. “The compiler is the piece of technology you need to enable practical, pragmatic, distributed quantum computing. It takes a quantum workload, a quantum circuit, and it partitions it so that it runs in a distributed environment, in a connected set of qubits or quantum compute nodes,” Pandey said. Significantly, it’s multivendor; the quantum compute nodes can be from the same vendor or from other vendors, such as IBM: “It could be as messy a brownfield, heterogeneous environment as you want. It doesn’t matter to the compiler, which will take an algorithm, partition it across any heterogeneous, brownfield environment,” Pandey said.  “What makes it unique, and an industry-first, is that it accounts for quantum interconnect requirements between processors and supports distributed quantum error correction. Existing compilers target circuits for only single computers,” Pandey stated. “Ours compiles circuits for network-connected computers potentially made of heterogeneous quantum compute technologies and can distribute that partitioned circuit across an entire data center of processors, all connected through a quantum network.” The distributed quantum error correction is a key feature of the software. Error correction ensures the accuracy and reliability of quantum computations and is a challenge for any distributed or standalone network.  The Cisco software in this case understands the error correction intricacies of each of the quantum computing modalities in the network, and “we can ensure that those are carried over from node to node, giving us a distributed or a holistic view of the entire distributed environment and result,” Pandey said.  In addition, “we are developing our own algorithms [to determine] the best way, using our network, to do a

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NVIDIA and OpenAI Forge $100B Alliance to Power the Next AI Revolution

The new strategic partnership between OpenAI and NVIDIA, formalized via a letter of intent in September 2025, is designed to both power and finance the next generation of OpenAI’s compute infrastructure, with initial deployments expected in the second half of 2026. According to the joint press release, both parties position this as “the biggest AI infrastructure deployment in history,” explicitly aimed at training and running OpenAI’s next-generation models.  At a high level: The target scale is 10 gigawatts (GW) or more of deployed compute capacity, realized via NVIDIA systems (comprising millions of GPUs).  The first phase (1 GW) is slated for the second half of 2026, built on the forthcoming Vera Rubin platform.  NVIDIA will progressively invest up to $100 billion into OpenAI, contingent on deployment of capacity in stages.  An initial $10 billion investment from NVIDIA is tied to the execution of a definitive purchase agreement for the first gigawatt of systems.  The equity stake NVIDIA will acquire is described as non-voting / non-controlling, meaning it gives financial skin in the game without governance control.  From a strategic standpoint, tying investment to capacity deployment helps OpenAI lock in capital and hardware over a long horizon, mitigating supply-chain and financing risk. With compute frequently cited as a binding constraint on advancing models, this kind of staged, anchored commitment gives OpenAI a more predictable growth path (at least in theory; that said, the precise economic terms and risk-sharing remain to be fully disclosed.) Press statements emphasize that millions of GPUs will ultimately be involved, and that co-optimization of NVIDIA’s hardware with OpenAI’s software/stack will be a key feature of the collaboration.  Importantly, this deal also fits into OpenAI’s broader strategy of diversifying infrastructure partnerships beyond any single cloud provider. Microsoft remains a central backer and collaborator, but this NVIDIA tie-up further

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Balancing AI’s opportunities and challenges to serve enterprises

AI has taken the technology industry by storm, with enterprises deploying emerging applications to create business value. Amid this shift, operators are leveraging network automation, optical innovation and more to support enterprise AI use cases. Still, the technology ecosystem must balance AI’s opportunities with its challenges. While AI can improve operations, it can also leave companies more vulnerable to cyberattacks. As organizations deploy more AI tools and employees increasingly use them, the overall attack surface expands and opens more security gaps. This article explores how internet carriers are building their networks to support enterprises, while also discussing how operators are establishing trust with customers. Table stakes: reliability, diversity and reach AI’s requirements are similar to content distribution, cloud networking and previous industry shifts, but place even greater pressure on carrier-delivered enterprise network services.  In these services, network diversity is integral, allowing carriers to eliminate single points of failure in the event of an outage, then quickly reroute traffic through the next best available path. This improved reliability is vital for enabling real-time enterprise AI operations amid increased instances of network disruption due to geopolitical sabotage or accidental damage. As more hyperscalers build sprawling AI data center campuses, network reach will also prove even more crucial. By continuously expanding their network footprints, carriers can help enterprises access these sites no matter where they’re located, with operators’ high-capacity connectivity infrastructure facilitating the transfer of massive data volumes between these campuses. Similar to how content distribution networks rely on a robust network underlay, backbone connectivity provides the high-capacity, long-haul transport underpinning the delivery of AI inferencing responses. While the backbone itself does not cache or deliver these responses, its densely interconnected networks ensure that this AI traffic reaches regional and access networks, which then distribute responses to end users. Lightspeed: optical innovation With

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

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

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

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

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2025 playbook for enterprise AI success, from agents to evals

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

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

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

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