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Google DeepMind makes AI history with gold medal win at world’s toughest math competition

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Google DeepMind announced Monday that an advanced version of its Gemini artificial intelligence model has officially achieved gold medal-level performance at the International Mathematical Olympiad, solving five of six […]

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Google DeepMind announced Monday that an advanced version of its Gemini artificial intelligence model has officially achieved gold medal-level performance at the International Mathematical Olympiad, solving five of six exceptionally difficult problems and earning recognition as the first AI system to receive official gold-level grading from competition organizers.

The victory advances the field of AI reasoning and puts Google ahead in the intensifying battle between tech giants building next-generation artificial intelligence. More importantly, it demonstrates that AI can now tackle complex mathematical problems using natural language understanding rather than requiring specialized programming languages.

“Official results are in — Gemini achieved gold-medal level in the International Mathematical Olympiad!” Demis Hassabis, CEO of Google DeepMind, wrote on social media platform X Monday morning. “An advanced version was able to solve 5 out of 6 problems. Incredible progress.”

The International Mathematical Olympiad, held annually since 1959, is widely considered the world’s most prestigious mathematics competition for pre-university students. Each participating country sends six elite young mathematicians to compete in solving six exceptionally challenging problems spanning algebra, combinatorics, geometry, and number theory. Only about 8% of human participants typically earn gold medals.


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How Google DeepMind’s Gemini Deep Think cracked math’s toughest problems

Google’s latest success far exceeds its 2024 performance, when the company’s combined AlphaProof and AlphaGeometry systems earned silver medal status by solving four of six problems. That earlier system required human experts to first translate natural language problems into domain-specific programming languages and then interpret the AI’s mathematical output.

This year’s breakthrough came through Gemini Deep Think, an enhanced reasoning system that employs what researchers call “parallel thinking.” Unlike traditional AI models that follow a single chain of reasoning, Deep Think simultaneously explores multiple possible solutions before arriving at a final answer.

“Our model operated end-to-end in natural language, producing rigorous mathematical proofs directly from the official problem descriptions,” Hassabis explained in a follow-up post on the social media site X, emphasizing that the system completed its work within the competition’s standard 4.5-hour time limit.

The model achieved 35 out of a possible 42 points, comfortably exceeding the gold medal threshold. According to IMO President Prof. Dr. Gregor Dolinar, the solutions were “astonishing in many respects” and found to be “clear, precise and most of them easy to follow” by competition graders.

OpenAI faces backlash for bypassing official competition rules

The announcement comes amid growing tension in the AI industry over competitive practices and transparency. Google DeepMind’s measured approach to releasing its results has drawn praise from the AI community, particularly in contrast to rival OpenAI’s handling of similar achievements.

“We didn’t announce on Friday because we respected the IMO Board’s original request that all AI labs share their results only after the official results had been verified by independent experts & the students had rightly received the acclamation they deserved,” Hassabis wrote, appearing to reference OpenAI’s earlier announcement of its own olympiad performance.

Social media users were quick to note the distinction. “You see? OpenAI ignored the IMO request. Shame. No class. Straight up disrespect,” wrote one user. “Google DeepMind acted with integrity, aligned with humanity.”

The criticism stems from OpenAI’s decision to announce its own mathematical olympiad results without participating in the official IMO evaluation process. Instead, OpenAI had a panel of former IMO participants grade its AI’s performance, a approach that some in the community view as lacking credibility.

“OpenAI is quite possibly the worst company on the planet right now,” wrote one critic, while others suggested the company needs to “take things seriously” and “be more credible.”

Inside the training methods that powered Gemini’s mathematical mastery

Google DeepMind’s success appears to stem from novel training techniques that go beyond traditional approaches. The team used advanced reinforcement learning methods designed to leverage multi-step reasoning, problem-solving, and theorem-proving data. The model was also provided access to a curated collection of high-quality mathematical solutions and received specific guidance on approaching IMO-style problems.

The technical achievement impressed AI researchers who noted its broader implications. “Not just solving math… but understanding language-described problems and applying abstract logic to novel cases,” wrote AI observer Elyss Wren. “This isn’t rote memory — this is emergent cognition in motion.”

Ethan Mollick, a professor at the Wharton School who studies AI, emphasized the significance of using a general-purpose model rather than specialized tools. “Increasing evidence of the ability of LLMs to generalize to novel problem solving,” he wrote, highlighting how this differs from previous approaches that required specialized mathematical software.

The model demonstrated particularly impressive reasoning in one problem where many human competitors applied graduate-level mathematical concepts. According to DeepMind researcher Junehyuk Jung, Gemini “made a brilliant observation and used only elementary number theory to create a self-contained proof,” finding a more elegant solution than many human participants.

What Google DeepMind’s victory means for the $200 billion AI race

The breakthrough comes at a critical moment in the AI industry, where companies are racing to demonstrate superior reasoning capabilities. The success has immediate practical implications: Google plans to make a version of this Deep Think model available to mathematicians for testing before rolling it out to Google AI Ultra subscribers, who pay $250 monthly for access to the company’s most advanced AI models.

The timing also highlights the intensifying competition between major AI laboratories. While Google celebrated its methodical, officially-verified approach, the controversy surrounding OpenAI’s announcement reflects broader tensions about transparency and credibility in AI development.

This competitive dynamic extends beyond just mathematical reasoning. Recent weeks have seen various AI companies announce breakthrough capabilities, though not all have been received positively. Elon Musk’s xAI recently launched Grok 4, which the company claimed was the “smartest AI in the world,” though leaderboard scores showed it trailing behind models from Google and OpenAI. Additionally, Grok has faced criticism for controversial features including sexualized AI companions and episodes of generating antisemitic content.

The dawn of AI that thinks like humans—with real-world consequences

The mathematical olympiad victory goes beyond competitive bragging rights. Gemini’s performance demonstrates that AI systems can now match human-level reasoning in complex tasks requiring creativity, abstract thinking, and the ability to synthesize insights across multiple domains.

“This is a significant advance over last year’s breakthrough result,” the DeepMind team noted in their technical announcement. The progression from requiring specialized formal languages to operating entirely in natural language suggests that AI systems are becoming more intuitive and accessible.

For businesses, this development signals that AI may soon tackle complex analytical problems across various industries without requiring specialized programming or domain expertise. The ability to reason through intricate challenges using everyday language could democratize sophisticated analytical capabilities across organizations.

However, questions persist about whether these reasoning capabilities will translate effectively to messier real-world challenges. The mathematical olympiad provides well-defined problems with clear success criteria — a far cry from the ambiguous, multifaceted decisions that define most business and scientific endeavors.

Google DeepMind plans to return to next year’s competition “in search of a perfect score.” The company believes AI systems combining natural language fluency with rigorous reasoning “will become invaluable tools for mathematicians, scientists, engineers, and researchers, helping us advance human knowledge on the path to AGI.”

But perhaps the most telling detail emerged from the competition itself: when faced with the contest’s most difficult problem, Gemini started from an incorrect hypothesis and never recovered. Only five human students solved that problem correctly. In the end, it seems, even gold medal-winning AI still has something to learn from teenage mathematicians.

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Blackstone to acquire majority stake in NetBrain Technologies

Global investment firm Blackstone announced it entered into an agreement to acquire a majority stake in network automation platform provider NetBrain Technologies. While financial details of the deal were not disclosed, Blackstone’s growth investment in NetBrain valued the technology provider at $750 million. “AI has the power to transform how

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CIOs recalibrate IT agendas to make room for rising AI spend

Moreover, they’re reporting that the executive drive for all things AI has them recalibrating their IT project agenda, prioritizing AI spending while bumping other items down or even off the to-do list. “Budgets are finite, and because AI investments are an imperative for CEOs, the boards, and CIOs to support

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IT leaders rethink talent strategies to cope with AI skills crunch

As a result, CIOs at most companies have a tougher time attracting machine learning engineers, prompt engineers, and other AI-specific talent, Goldberg says. That leaves many turning to AI consultants and training their existing data engineers, enterprise architects, and others so they can slide into those AI positions. “They’re assessing

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Oil Slips for Third Straight Session

Oil fell for a third session as talks between the US and its trading partners gain urgency ahead of next week’s deadline. West Texas Intermediate crude fell 1.5% to settle near $66 a barrel as risk-on sentiment faded, and oil traders rolled over positions ahead of the August contract’s expiration on Tuesday, adding to volatility. Investors also honed in on discussions between European Union and US negotiators as they seek to clinch a trade deal by Aug. 1, when President Donald Trump has threatened to hit most of the bloc’s exports with 30% tariffs. “The risks to the downside are the longer-term oversupply that we expect in the fourth quarter and the first quarter of 2026, with the tariff deadline on Aug. 1 as another potential catalyst to the downside,” said Joe DeLaura, global energy strategist at Rabobank. Crude has been drifting sideways since the end of the conflict between Iran and Israel toward the end of last month, dragging gauges of market volatility to the lowest since early April. While WTI and Brent prices have flatlined, many of the largest moves in the oil market have been in diesel prices, which are soaring because of tight supplies and refinery closures. Looking ahead, traders will be focused on US economic data on jobless claims and home sales due to be released later this week. Oil Prices WTI for August delivery, which expired Tuesday, declined 1.5% to settle at $66.21 a barrel. The more active September contract slipped 1% to $65.31 a barrel. Brent for September settlement shed 0.9% to $68.59 a barrel. What do you think? We’d love to hear from you, join the conversation on the Rigzone Energy Network. The Rigzone Energy Network is a new social experience created for you and all energy professionals to Speak Up about

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Spanish Outages Likely Cost Top Power User Repsol $200MM

Spain’s power outages probably cost oil producer Repsol SA about €170 million ($200 million) in losses at refineries and chemical plants last quarter, people familiar with the matter said.  A nationwide blackout in April led to an estimated loss of roughly €100 million at five refineries, and €40 million at three chemical plants, according to the people, who asked not to be named because the information isn’t public. Smaller power failures probably cost about €30 million, they added.  Repsol declined to comment. The unprecedented collapse of Spain’s power grid left more than 50 million people without electricity for several hours across the Iberian peninsula and small parts of France on April 28. The overall financial impact remains unknown, with most industrial, corporate and retail customers waiting for the energy market regulator’s review of the debacle before making insurance and damage claims.  Repsol’s oil refinery and the Escombreras industrial complex in Cartagena, Spain, on Jan. 16, 2025. The nationwide losses tally could reach between €2 billion and €4 billion, according to power industry executives with knowledge of the estimates, who also asked not to be named discussing non-public information.  The regulator, known as CNMC, hasn’t said when it will present its review. A government report last month laid blame at the grid operator and some unnamed power plants, without fully explaining why the network became so unstable in the first place. Repsol had additional electricity-related losses in the first quarter from a smaller outage that halted its Cartagena refinery six days earlier, as well as a technical problem at a substation operated by a local power company, which led to the Puertollano refinery halting on June 16, the people said.  The smaller April 22 outage was related to power network oscillations — the same type of problem that occurred on April 28.  In

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Booming solar and storage are propping up Puerto Rico’s grid this summer

Nearly every night for the past two weeks, Puerto Rico’s grid operator has called on tens of thousands of batteries scattered across the island to overcome energy shortfalls and help deliver power to approximately 3 million residents — and it’s working.  What started as a modest pilot program in 2023 has grown into the first operational behind-the-meter virtual power plant in Latin America and the Caribbean, and a crucial support for the territory’s dilapidated energy infrastructure, said Javier Rúa-Jovet, chief policy officer of the Solar and Energy Storage Association of Puerto Rico. “It’s grown … in the blink of an eye,” he said. “It’s something to be proud of.” In May, Puerto Rico’s Energy Bureau, which regulates utilities, approved an emergency expansion of the “customer battery energy sharing” program in anticipation of a projected generation shortfall during the warmer months.  Since then, LUMA Energy, the grid operator, celebrated what it called a “major energy milestone.” “[L]ast night we successfully dispatched approximately 70,000 batteries, contributing around 48 MW of energy to the grid,” the company said in a July 9 statement posted on X. “This collaboration helped address a generation shortfall of nearly 50 MW, assisting in preventing multiple load shedding events and keeping the lights on longer in many communities across the island,” it said. Before Hurricanes Irma and Maria devastated Puerto Rico in 2017, the island had a modest solar industry, driven largely by the desire to save money on power bills through net metering, and “storage was not a thing,” Rúa-Jovet said. What followed the deadly storms was the longest blackout in American history. “That kind of shifted, in people’s minds, the thinking on energy and the value of resiliency, and the importance of just basic energy,” Rúa-Jovet said. The Puerto Rico Energy Public Policy Act of 2019

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Turkey Eyes New Deal with Iraq on Idled Oil Pipeline

Turkey will seek a new deal with Iraq on an oil export pipeline between the two countries that’s been idled for more than two years in a payment dispute. Ankara is unhappy with the underutilization of the pipeline under the current deal and sees greater potential benefit for itself and Iraq from a new arrangement, according to a senior Turkish official with direct knowledge of the matter, who asked not to be identified because of the sensitivity of the matter. A decree in Turkey’s Official Gazette said a 1973 agreement governing the route would be “terminated” on July 27, 2026, together with all subsequent associated accords. The potential renegotiation is the latest step in the saga that started in March 2023, when Turkey closed the link after an arbitration court ordered the country to pay Iraq $1.5 billion over unauthorized exports. Numerous attempts to restart shipments have since failed – including due to disagreements between Iraq, its semi-autonomous Kurdistan region and companies operating in the area. But last week, Iraq’s federal and regional administrations agreed to an oil-transfer plan in a move toward resumption. Still, contracts with companies in the Kurdish region are yet to be signed. It wasn’t immediately clear if Turkey’s renegotiations would be another hurdle to a restart. Iraq’s oil ministry and the state oil-marketing company, SOMO, didn’t immediately respond to a request for comment on Turkey’s announcement about ending the agreement. In a letter to Iraq’s oil ministry, the Turkish energy ministry expressed its willingness to renew the pipeline deal, state-run Iraqi News Agency reported, citing an unidentified source at the Baghdad-based ministry.  Turkey sent its neighbor a draft for a new, more comprehensive energy cooperation agreement which spans oil, gas, petrochemical industries and electricity. The Iraqi oil ministry will negotiate with Turkish authorities over the proposed

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Wall Street Stubbornly Bullish on Energy Stocks

Wall Street analysts have high hopes for oil and gas stocks, encouraged by cheap valuations and President Donald Trump’s boosterism of the beleaguered energy sector.  Energy boasts the highest percentage of buy-rated equities among the 11 sectors in the S&P 500 Index: roughly three in four members have buy recommendations, compared with roughly half in the broader market, according to data compiled by Bloomberg. Plus, sell-side analysts see energy stocks rising about 16% over the next 12 months, second only to the health-care segment and roughly double the projected gain for the overall index. Energy stocks — one of three index segments in the red this year — appear to have room to run. They’re the cheapest S&P 500 sector based on price-to-earnings ratio, and Trump is a cheerleader for the industry, calling for companies to “drill, baby, drill.” “The thesis that some people have is that multiples and valuations are very, very low right now,” Leo Mariani, analyst at Roth Capital Partners, said in an interview. Looking further into the future, the sector is expected to post the highest earnings growth in 2026, according to Bloomberg Intelligence. Challenges ahead Of course, it’s understandable why investors may be skeptical about Wall Street’s bullish take. US crude prices have dropped about 7% this year, hurt by the fallout from Trump’s trade war and a drive by OPEC+ to restore curbed supplies. Also, the sector doesn’t have much momentum: Energy stocks have underperformed the market for four of the last five quarters. BMO Capital Markets expects US energy producers’ second-quarter earnings to fall 30% compared to the first three months of the year, and for cash flows to drop 15% in the same period because of weaker crude prices, according to analyst Phillip Jungwirth.  “Is there a catalyst to dramatically change that and reverse

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PJM leadership openings offer an opportunity to chart a new course

David Lapp is people’s counsel at the Maryland Office of People’s Counsel, an agency that advocates for Maryland’s residential utility consumers. The current openings for a new chief executive officer and two board seats present an opportunity for a much-needed paradigm shift at the PJM Interconnection. PJM is supposed to be independent under federal law, but in practice PJM has proven much more interested in advancing policies responsive to big corporate interests than the welfare of consumers. PJM’s ongoing public relations campaign illustrates the need for leadership change. Rather than responding with objective facts and reform to address root causes, PJM is resorting to blaming states for supply and infrastructure shortages, often defending the very policies that are driving up customer costs. PJM’s messaging in Maryland is illustrative. Consider four examples. First, with no qualification or quantification, PJM says it is “undeniable” that Maryland’s electric demand for power is growing and that it has a “supply” issue. According to PJM’s own forecasts, however, Maryland’s projected growth is quite modest. Twenty years from now, in 2045, PJM projects that the peak demand of Maryland’s largest utility, Baltimore Gas and Electric, will still be less than what its peak was in 2011, 14 years ago. Other areas of Maryland also show very modest increases in peak demand. The only real “supply” issue Maryland faces is really a PJM-wide issue: the massive growth and projected demand growth of data centers — about 30,000 MW by 2030, almost all of which is outside of Maryland. Second, PJM claims it is “undeniable” that power plants are shutting down without adequate replacements. But Maryland has few if any power plants likely to retire that have not already retired or announced plans to retire. PJM’s own data shows that despite some retirements, Maryland has more generation capacity

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Technology is coming so fast data centers are obsolete by the time they launch

 Tariffs aside, Enderle feels that AI technology and ancillary technology around it like battery backup is still in the early stages of development and there will be significant changes coming in the next few years. GPUs from AMD and Nvidia are the primary processors for AI, and they are derived from video game accelerators. They were never meant for use in AI processing, but they are being fine-tuned for the task.  It’s better to wait to get a more mature product than something that is still in a relatively early state. But Alan Howard, senior analyst for data center infrastructure at Omdia, disagrees and says not to wait. One reason is the rate at which people that are building data centers is all about seizing market opportunity.” You must have a certain amount of capacity to make sure that you can execute on strategies meant to capture more market share.” The same sentiment exists on the colocation side, where there is a considerable shortage of capacity as demand outstrips supply. “To say, well, let’s wait and see if maybe we’ll be able to build a better, more efficient data center by not building anything for a couple of years. That’s just straight up not going to happen,” said Howard. “By waiting, you’re going to miss market opportunities. And these companies are all in it to make money. And so, the almighty dollar rules,” he added. Howard acknowledges that by the time you design and build the data center, it’s obsolete. The question is, does that mean it can’t do anything? “I mean, if you start today on a data center that’s going to be full of [Nvidia] Blackwells, and let’s say you deploy in two years when they’ve already retired Blackwell, and they’re making something completely new. Is that data

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‘Significant’ outage at Alaska Airlines not a security incident, but a hardware breakdown

The airline told Network World that when the critical piece of what it described as “third-party multi-redundant hardware” failed unexpectedly, “it impacted several of our key systems that enable us to run various operations.” The company is currently working with its vendor to replace the faulty equipment at the data center. The airline has cancelled more than 150 flights since Sunday evening, including 64 on Monday. The company said additional flight disruptions are likely as it repositions aircraft and crews throughout its network. Alaska Airlines emphasized that the safety of its flights was never compromised, and that “the IT outage is not related to any other current events, and it’s not connected to the recent cybersecurity incident at Hawaiian Airlines.” The airline did not provide additional information to Network World about the specifics of the outage. “There are many redundant components that can fail,” said Roberts, noting that it could have been something as simple as a RAID array (which combines multiple physical data storage components into one or more logical units). Or, on the network side, it could have been the failure of a pair of load balancers. “It’s interesting that redundancy didn’t save them,” said Roberts. “Perhaps multiple pieces of hardware were impacted by the same issue, like a firmware update. Or, maybe they’re just really unlucky.”

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Cisco upgrades 400G optical receiver to boost AI infrastructure throughput

“In the data center, what’s really changed in the last year or so is that with AI buildouts, there’s much, much more optics that are part of 400G and 800G. It’s not so much using 10G and 25G optics, which we still sell a ton of, for campus applications. But for AI infrastructure, the 400G and 800G optics are really the dominant optics for that application,” Gartner said. Most of the AI infrastructure builds have been for training models, especially in hyperscaler environments, Gartner said. “I expect, towards the tail end of this year, we’ll start to see more enterprises deploying AI infrastructure for inference. And once they do that, because it has an Nvidia GPU attached to it, it’s going to be a 400G or 800G optic.” Core enterprise applications – such as real-time trading, high-frequency transactions, multi-cloud communications, cybersecurity analytics, network forensics, and industrial IoT – can also utilize the higher network throughput, Gartner said. 

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Supermicro bets big on 4-socket X14 servers to regain enterprise trust

In April, Dell announced its PowerEdge R470, R570, R670, and R770 servers with Intel Xeon 6 Processors with P-cores, but with single and double-socket servers. Similarly, Lenovo’s ThinkSystem V4 servers are also based on the Intel Xeon 6 processor but are limited to dual socket configurations. The launch of 4-socket servers by Supermicro reflects a growing enterprise need for localized compute that can support memory-bound AI and reduce the complexity of distributed architectures. “The modern 4-socket servers solve multiple pain points that have intensified with GenAI and memory-intensive analytics. Enterprises are increasingly challenged by latency, interconnect complexity, and power budgets in distributed environments. High-capacity, scale-up servers provide an architecture that is more aligned with low-latency, large-model processing, especially where data residency or compliance constraints limit cloud elasticity,” said Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research. “Launching a 4-socket Xeon 6 platform and packaging it within their modular ‘building block’ strategy shows Supermicro is focusing on staying ahead in enterprise and AI data center compute,” said Devroop Dhar, co-founder and MD at Primus Partner. A critical launch after major setbacks Experts peg this to be Supermicro’s most significant product launch since it became mired in governance and regulatory controversies. In 2024, the company lost Ernst & Young, its second auditor in two years, following allegations by Hindenburg Research involving accounting irregularities and the alleged export of sensitive chips to sanctioned entities. Compounding its troubles, Elon Musk’s AI startup xAI redirected its AI server orders to Dell, a move that reportedly cost Supermicro billions in potential revenue and damaged its standing in the hyperscaler ecosystem. Earlier this year, HPE signed a $1 billion contract to provide AI servers for X, a deal Supermicro was also bidding for. “The X14 launch marks a strategic reinforcement for Supermicro, showcasing its commitment

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Moving AI workloads off the cloud? A hefty data center retrofit awaits

“If you have a very specific use case, and you want to fold AI into some of your processes, and you need a GPU or two and a server to do that, then, that’s perfectly acceptable,” he says. “What we’re seeing, kind of universally, is that most of the enterprises want to migrate to these autonomous agents and agentic AI, where you do need a lot of compute capacity.” Racks of brand-new GPUs, even without new power and cooling infrastructure, can be costly, and Schneider Electric often advises cost-conscious clients to look at previous-generation GPUs to save money. GPU and other AI-related technology is advancing so rapidly, however, that it’s hard to know when to put down stakes. “We’re kind of in a situation where five years ago, we were talking about a data center lasting 30 years and going through three refreshes, maybe four,” Carlini says. “Now, because it is changing so much and requiring more and more power and cooling you can’t overbuild and then grow into it like you used to.”

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My take on the Gartner Magic Quadrant for LAN infrastructure? Highly inaccurate

Fortinet being in the leader quadrant may surprise some given they are best known as a security vendor, but the company has quietly built a broad and deep networking portfolio. I have no issue with them being considered a leader and believe for security conscious companies, Fortinet is a great option. Challenger Cisco is the only company listed as a challenger, and its movement out of the leader quadrant highlights just how inaccurate this document is. There is no vendor that sells more networking equipment in more places than Cisco, and it has led enterprise networking for decades. Several years ago, when it was a leader, I could argue the division of engineering between Meraki and Catalyst could have pushed them out, but it didn’t. So why now? At its June Cisco Live event, the company launched a salvo of innovation including AI Canvas, Cisco AI Assistant, and much more. It’s also continually improved the interoperability between Meraki and Catalyst and announced several new products. AI Canvas is a completely new take, was well received by customers at Cisco Live, and reinvents the concept of AIOps. As I stated above, because of the December cutoff time for information gathering, none of this was included, but that makes Cisco’s representation false. Also, I find this MQ very vague in its “Cautions” segment. As an example, it states: “Cisco’s product strategy isn’t well-aligned with key enterprise needs.” Some details here would be helpful. In my conversations with Cisco, which includes with Chief Product Officer and President Jeetu Patel, the company has reiterated that its strategy is to help customers be AI-ready with products that are easier to deploy and manage, more automated, and with a lower cost to run. That seems well-aligned with customer needs. If Gartner is hearing customers want networks

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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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