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Reimagining cybersecurity in the era of AI and quantum

In partnership withCISCO AI and quantum technologies are dramatically reconfiguring how cybersecurity functions, redefining the speed and scale with which digital defenders and their adversaries can operate. The weaponization of AI tools for cyberattacks is already proving a worthy opponent to current defenses. From reconnaissance to ransomware, cybercriminals can automate attacks faster than ever before with AI. This includes using generative AI to create social engineering attacks at scale, churning out tens of thousands of tailored phishing emails in seconds, or accessing widely available voice cloning software capable of bypassing security defenses for as little as a few dollars. And now, agentic AI raises the stakes by introducing autonomous systems that can reason, act, and adapt like human adversaries. But AI isn’t the only force shaping the threat landscape. Quantum computing has the potential to seriously undermine current encryption standards if developed unchecked. Quantum algorithms can solve the mathematical problems underlying most modern cryptography, particularly public-key systems like RSA and Elliptic Curve, widely used for secure online communication, digital signatures, and cryptocurrency. “We know quantum is coming. Once it does, it will force a change in how we secure data across everything, including governments, telecoms, and financial systems,” says Peter Bailey, senior vice president and general manager of Cisco’s security business. “Most organizations are understandably focused on the immediacy of AI threats,” says Bailey. “Quantum might sound like science fiction, but those scenarios are coming faster than many realize. It’s critical to start investing now in defenses that can withstand both AI and quantum attacks.” Critical to this defense is a zero trust approach to cybersecurity, which assumes no user or device can be inherently trusted. By enforcing continuous verification, zero trust enables constant monitoring and ensures that any attempts to exploit vulnerabilities are quickly detected and addressed in real time. This approach is technology-agnostic and creates a resilient framework even in the face of an ever-changing threat landscape. Putting up AI defenses  AI is lowering the barrier to entry for cyberattacks, enabling hackers even with limited skills or resources to infiltrate, manipulate, and exploit the slightest digital vulnerability. Nearly three-quarters (74%) of cybersecurity professionals say AI-enabled threats are already having a significant impact on their organization, and 90% anticipate such threats in the next one to two years.  “AI-powered adversaries have advanced techniques and operate at machine speed,” says Bailey. “The only way to keep pace is to use AI to automate response and defend at machine speed.” To do this, Bailey says, organizations must modernize systems, platforms, and security operations to automate threat detection and response—processes that have previously relied on human rule-writing and reaction times. These systems must adapt dynamically as environments evolve and criminal tactics change. At the same time, companies must strengthen the security of their AI models and data to reduce exposure to manipulation from AI-enabled malware. Such risks could include, for instance, prompt injections, where a malicious user crafts a prompt to manipulate an AI model into performing unintended actions, bypassing its original instructions and safeguards. Agentic AI further ups the ante, with hackers able to use AI agents to automate attacks and make tactical decisions without constant human oversight. “Agentic AI has the potential to collapse the cost of the kill chain,” says Bailey. “That means everyday cybercriminals could start executing campaigns that today only well-funded espionage operations can afford.” Organizations, in turn, are exploring how AI agents can help them stay ahead. Nearly 40% of companies expect agentic AI to augment or assist teams over the next 12 months, especially in cybersecurity, according to Cisco’s 2025 AI Readiness Index. Use cases include AI agents trained on telemetry, which can identify anomalies or signals from machine data too disparate and unstructured to be deciphered by humans.  Calculating the quantum threat As many cybersecurity teams focus on the very real AI-driven threat, quantum is waiting on the sidelines. Almost three-quarters (73%) of US organizations surveyed by KPMG say they believe it is only a matter of time before cybercriminals are using quantum to decrypt and disrupt today’s cybersecurity protocols. And yet, the majority (81%) also admit they could do more to ensure that their data remains secure. Companies are right to be concerned. Threat actors are already carrying out harvest now, decrypt later attacks, stockpiling sensitive encrypted data to crack once quantum technology matures. Examples include state-sponsored actors intercepting government communications and cybercriminal networks storing encrypted internet traffic or financial records.  Large technology companies are among the first to roll out quantum defenses. For example, Apple is using cryptography protocol PQ3 to defend against harvest now, decrypt later attacks on its iMessage platform. Google is testing post-quantum cryptography (PQC)—which is resistant to attacks from both quantum and classical computers—in its Chrome browser. And Cisco “has made significant investments in quantum-proofing our software and infrastructure,” says Bailey. “You’ll see more enterprises and governments taking similar steps over the next 18 to 24 months,” he adds.  As regulations like the US Quantum Computing Cybersecurity Preparedness Act lay out requirements for mitigating against quantum threats, including standardized PQC algorithms by the National Institute of Standards and Technology, a wider range of organizations will start preparing their own quantum defenses.  For organizations beginning that journey, Bailey outlines two key actions. First, establish visibility. “Understand what data you have and where it lives,” he says. “Take inventory, assess sensitivity, and review your encryption keys, rotating out any that are weak or outdated.” Second, plan for migration. “Next, assess what it will take to support post-quantum algorithms across your infrastructure. That means addressing not just the technology, but also the process and people implications,” Bailey says. Adopting proactive defense  Ultimately, the foundation for building resilience against both AI and quantum is a zero trust approach, says Bailey. By embedding zero trust access controls across users, devices, business applications, networks, and clouds, this approach grants only the minimum access required to complete a task and enables continuous monitoring. It can also minimize the attack surface by confining a potential threat to an isolated zone, preventing it from accessing other critical systems. Into this zero trust architecture, organizations can integrate specific measures to defend against AI and quantum risks. For instance, quantum-immune cryptography and AI-powered analytics and security tools can be used to identify complex attack patterns and automate real-time responses.  “Zero trust slows down attacks and builds resilience,” Bailey says. “It ensures that even if a breach occurs, the crown jewels stay protected and operations can recover quickly.” Ultimately, companies should not wait for threats to emerge and evolve. They must get ahead now. “This isn’t a what-if scenario; it’s a when,” says Bailey. “Organizations that invest early will be the ones setting the pace, not scrambling to catch up.” This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

In partnership withCISCO

AI and quantum technologies are dramatically reconfiguring how cybersecurity functions, redefining the speed and scale with which digital defenders and their adversaries can operate.

The weaponization of AI tools for cyberattacks is already proving a worthy opponent to current defenses. From reconnaissance to ransomware, cybercriminals can automate attacks faster than ever before with AI. This includes using generative AI to create social engineering attacks at scale, churning out tens of thousands of tailored phishing emails in seconds, or accessing widely available voice cloning software capable of bypassing security defenses for as little as a few dollars. And now, agentic AI raises the stakes by introducing autonomous systems that can reason, act, and adapt like human adversaries.

But AI isn’t the only force shaping the threat landscape. Quantum computing has the potential to seriously undermine current encryption standards if developed unchecked. Quantum algorithms can solve the mathematical problems underlying most modern cryptography, particularly public-key systems like RSA and Elliptic Curve, widely used for secure online communication, digital signatures, and cryptocurrency.

“We know quantum is coming. Once it does, it will force a change in how we secure data across everything, including governments, telecoms, and financial systems,” says Peter Bailey, senior vice president and general manager of Cisco’s security business.

“Most organizations are understandably focused on the immediacy of AI threats,” says Bailey. “Quantum might sound like science fiction, but those scenarios are coming faster than many realize. It’s critical to start investing now in defenses that can withstand both AI and quantum attacks.”

Critical to this defense is a zero trust approach to cybersecurity, which assumes no user or device can be inherently trusted. By enforcing continuous verification, zero trust enables constant monitoring and ensures that any attempts to exploit vulnerabilities are quickly detected and addressed in real time. This approach is technology-agnostic and creates a resilient framework even in the face of an ever-changing threat landscape.

Putting up AI defenses 

AI is lowering the barrier to entry for cyberattacks, enabling hackers even with limited skills or resources to infiltrate, manipulate, and exploit the slightest digital vulnerability.

Nearly three-quarters (74%) of cybersecurity professionals say AI-enabled threats are already having a significant impact on their organization, and 90% anticipate such threats in the next one to two years. 

“AI-powered adversaries have advanced techniques and operate at machine speed,” says Bailey. “The only way to keep pace is to use AI to automate response and defend at machine speed.”

To do this, Bailey says, organizations must modernize systems, platforms, and security operations to automate threat detection and response—processes that have previously relied on human rule-writing and reaction times. These systems must adapt dynamically as environments evolve and criminal tactics change.

At the same time, companies must strengthen the security of their AI models and data to reduce exposure to manipulation from AI-enabled malware. Such risks could include, for instance, prompt injections, where a malicious user crafts a prompt to manipulate an AI model into performing unintended actions, bypassing its original instructions and safeguards.

Agentic AI further ups the ante, with hackers able to use AI agents to automate attacks and make tactical decisions without constant human oversight. “Agentic AI has the potential to collapse the cost of the kill chain,” says Bailey. “That means everyday cybercriminals could start executing campaigns that today only well-funded espionage operations can afford.”

Organizations, in turn, are exploring how AI agents can help them stay ahead. Nearly 40% of companies expect agentic AI to augment or assist teams over the next 12 months, especially in cybersecurity, according to Cisco’s 2025 AI Readiness Index. Use cases include AI agents trained on telemetry, which can identify anomalies or signals from machine data too disparate and unstructured to be deciphered by humans. 

Calculating the quantum threat

As many cybersecurity teams focus on the very real AI-driven threat, quantum is waiting on the sidelines. Almost three-quarters (73%) of US organizations surveyed by KPMG say they believe it is only a matter of time before cybercriminals are using quantum to decrypt and disrupt today’s cybersecurity protocols. And yet, the majority (81%) also admit they could do more to ensure that their data remains secure.

Companies are right to be concerned. Threat actors are already carrying out harvest now, decrypt later attacks, stockpiling sensitive encrypted data to crack once quantum technology matures. Examples include state-sponsored actors intercepting government communications and cybercriminal networks storing encrypted internet traffic or financial records. 

Large technology companies are among the first to roll out quantum defenses. For example, Apple is using cryptography protocol PQ3 to defend against harvest now, decrypt later attacks on its iMessage platform. Google is testing post-quantum cryptography (PQC)—which is resistant to attacks from both quantum and classical computers—in its Chrome browser. And Cisco “has made significant investments in quantum-proofing our software and infrastructure,” says Bailey. “You’ll see more enterprises and governments taking similar steps over the next 18 to 24 months,” he adds. 

As regulations like the US Quantum Computing Cybersecurity Preparedness Act lay out requirements for mitigating against quantum threats, including standardized PQC algorithms by the National Institute of Standards and Technology, a wider range of organizations will start preparing their own quantum defenses. 

For organizations beginning that journey, Bailey outlines two key actions. First, establish visibility. “Understand what data you have and where it lives,” he says. “Take inventory, assess sensitivity, and review your encryption keys, rotating out any that are weak or outdated.”

Second, plan for migration. “Next, assess what it will take to support post-quantum algorithms across your infrastructure. That means addressing not just the technology, but also the process and people implications,” Bailey says.

Adopting proactive defense 

Ultimately, the foundation for building resilience against both AI and quantum is a zero trust approach, says Bailey. By embedding zero trust access controls across users, devices, business applications, networks, and clouds, this approach grants only the minimum access required to complete a task and enables continuous monitoring. It can also minimize the attack surface by confining a potential threat to an isolated zone, preventing it from accessing other critical systems.

Into this zero trust architecture, organizations can integrate specific measures to defend against AI and quantum risks. For instance, quantum-immune cryptography and AI-powered analytics and security tools can be used to identify complex attack patterns and automate real-time responses. 

“Zero trust slows down attacks and builds resilience,” Bailey says. “It ensures that even if a breach occurs, the crown jewels stay protected and operations can recover quickly.”

Ultimately, companies should not wait for threats to emerge and evolve. They must get ahead now. “This isn’t a what-if scenario; it’s a when,” says Bailey. “Organizations that invest early will be the ones setting the pace, not scrambling to catch up.”

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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How network diversity protects utility operations in an evolving landscape

From redundancy to resilience: The next level of network diversity Despite billions invested in grid modernization and widespread adoption of dual-carrier approaches, many utilities still find their communications infrastructure vulnerable to disruption. It’s a paradox: utilities have engineered redundancy across power generation and distribution, yet communications—the digital backbone of the modern grid—often lacks the comprehensive diversity needed to truly eliminate single points of failure.  True grid resilience now depends on comprehensive network diversity that enables consistent quality of service, operational flexibility and long-term adaptability. The cost of connectivity blind spots As utilities expand advanced metering infrastructure (AMI), SCADA systems, distributed energy resource (DER) management and connected field operations, the strain on communications networks continues to grow. Relying on a single technology exposes utilities to significant risk—from natural disasters and carrier outages to rural coverage gaps and network congestion. The result: delayed outage restoration, compromised grid visibility and operational inefficiencies that undermine reliability and customer trust. The versatility imperative: Why utilities can’t just “upgrade” Utility communications can’t follow the consumer tech cycle. Equipment often remains in service for 20 to 30 years, even as wireless standards evolve from private radio networks to LTE, 4G, 5G and emerging spectrum such as Anterix Band 106. Each shift brings new performance expectations—but utilities can’t afford to rip and replace infrastructure every few years. Instead, they need adaptable, field-proven solutions that bridge technologies and spectrum generations, ensuring consistent, secure connectivity today while accommodating tomorrow’s innovations without interrupting critical operations. Three pillars of network diversity While many utilities have already adopted dual-carrier strategies, the expanding complexity of modern grid operations demands a renewed look at comprehensive network diversity. True resilience comes from a multi-path approach—one that enables seamless communication under any condition. Three key strategies define this new standard of reliability. A. Public-to-public redundancy Dual-radio routers

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Why conductor strength matters for grid reliability

As utilities work to strengthen and modernize America’s electric grid, they face growing mechanical and environmental challenges below and between the lines. Buried grounding networks, pole grounds and substation grids must all withstand decades of stress from soil movement, moisture, corrosion and fault current events. Each of these physical forces can compromise a system’s electrical integrity — making mechanical strength as vital as electrical performance in ensuring long-term reliability. In earlier decades, utility conductors were relatively short, stationary and installed in stable soil. Today’s infrastructure is different. Expansion into remote terrain, widespread undergrounding and the integration of renewable and distributed resources have multiplied the number of grounding paths and exposed more cable to movement, vibration and stress. These systems must remain reliable through decades of shifting soils, thermal cycling and fault events — all while supporting uninterrupted power delivery. When a grounding conductor fails, the results can be costly. Broken bonds or weakened terminations can increase ground resistance, trigger equipment faults or leave assets unprotected from lightning and surge events. Repairs often require excavation, downtime and new material — expenses that compound across large service territories. In short, mechanical failure doesn’t just compromise safety; it undermines reliability, budgets and public confidence. That’s why conductor strength has become a defining factor in grid resilience. Copper-Clad Steel (CCS) conductors are engineered to meet this demand. By metallurgically bonding a copper layer to a high-tensile-strength steel core, CCS combines the conductivity of copper with the durability of steel. The result is a grounding conductor that resists stretching, breakage and deformation while maintaining long-term electrical integrity. Unlike soft copper, which can elongate or fracture under mechanical strain, CCS retains its shape and strength even after repeated mechanical or thermal stress. That strength translates into reliability you can measure. Stronger conductors stay tight at terminations, maintain

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Russian Crude Output Rose Last Month

Russia’s crude oil production edged up in October, but remained below its OPEC+ quota as international pressure mounted on the country’s energy sector. Russia pumped an average 9.411 million barrels a day last month, people with knowledge of the data said, asking not to be identified discussing confidential information. While that’s 43,000 barrels a day higher than in September, it’s 70,000 a day below a quota that includes compensation cuts for previous overproduction, Bloomberg calculations show. Oil watchers are closely following Russian production data to assess the impact of sanctions — and Ukrainian drone strikes — against the country’s energy industry. The latest US penalties on the sector, which hit oil giants Rosneft PJSC and Lukoil PJSC, have already eroded crude exports as some refiners in India, China and Turkey prove less willing to take sanctioned barrels. Meanwhile, Ukrainian attacks have intensified, putting pressure on Russia’s crude-processing sector even as refinery owners rush to repair infrastructure.  If Moscow eventually finds itself unable to find buyers for oil from its sanctioned producers, and struggles to restore refining, it’ll be forced to halt output at some fields, risking damage to wells. The Energy Ministry didn’t immediately respond to a request for comment on the production data. Deputy Prime Minister Alexander Novak said last month that the nation has capacity to raise oil production further, but will do it gradually, according to Tass news service. Compensation Cuts Russia, historically one of the biggest laggards in complying with OPEC+ output agreements, has agreed to make additional cuts to compensate for previous overproduction. The monthly schedule for those curbs has been regularly revised, with the latest plan published earlier this month.  It shows that October was the last month when Russia had to make such cuts. Moscow’s pledge to reduce daily output by 10,000 barrels below a quota of 9.491

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Oil Rises but Logs Second Weekly Loss

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

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Gunvor Scraps Lukoil Deal

Commodity trader Gunvor Group has withdrawn its offer for the international assets of sanctioned Russian oil producer Lukoil PJSC after the US Treasury Department called it “the Kremlin’s puppet” and said the oil and gas trader would never get a license. Gunvor pushed back on the Treasury comment on social media, calling it “fundamentally misinformed and false.” The Geneva-based company said it would seek to correct a “clear misunderstanding” but that it would withdraw its bid for now. President Trump has been clear that the war must end immediately. As long as Putin continues the senseless killings, the Kremlin’s puppet, Gunvor, will never get a license to operate and profit. — Treasury Department (@USTreasury) November 6, 2025 The comment is a remarkable volte-face after a week in which Gunvor has been in talks with the US Office of Foreign Assets Control, part of the Treasury Department, and other bodies in charge of sanctions to help press its case for a deal that would have transformed it into an integrated oil producing and processing colossus. Gunvor swooped on the assets at the end of last month following the US blacklisting of Lukoil and fellow Russian oil giant Rosneft PJSC, and its exit may leave the door open to other suitors. Gunvor on Thursday also announced it had raised $2.81 billion in a credit facility financed by US arms of global banks. Like other major commodity traders, the firm funds the bulk of its trades of oil, gas and metals around the world with bank financing. For the trader, the comments are likely to revive questions about its connections in Moscow at a time when many oil industry participants are wary of any links to Russia.  The trader’s co-founder, Gennady Timchenko, is a friend of Russian President Vladimir Putin, and when the US imposed sanctions

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Ship With Russia Oil Makes Rare Move Offshore India

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

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

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

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Top network and data center events 2025 & 2026

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

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

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

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Cisco launches AI infrastructure, AI practitioner certifications

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

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

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

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Space: The final frontier for data processing

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

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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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