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Rumi raises $4.7M to change passive media into interactive AI experiences

How would you like to earn money while watching TV? Rumi, an AI media company, has raised $4.7 million in a pre-seed funding round to transform passive media with rewards. Rumi is launching its invite-only beta app today and it aims to allows users to engage with media content in real time. The round, co-led by A16z crypto CSX and EV3, will support Rumi’s mission to build the first decentralized AI infrastructure for media, combining cutting-edge artificial intelligence with user-powered indexing to transform streaming into an intelligent, interactive experience. Viewers can chat with characters or pro sports athletes about what they’re doing on the screen, identify actors and outfits on screen, or receive real-time fact checks during heated political debates — all powered by AI. “Rumi sits at the cutting edge of AI agents, consumer media and decentralized infrastructure,” said Salvador Gala, cofounder of EV3, in a statement. “Their approach to indexing audio and video content through a distributed network of AI-powered nodes empowers users to directly benefit from existing behaviors.” Streaming meets earning Rumi has raised $4.7 million. With users spending more than five hours a day watching content — often while multitasking — Rumi allows them to earn passive income by contributing spare compute to build the world’s richest knowledge base on video content that in turn allows AI agents to deeply understand and augment media in ways we can’t even imagine yet, the company said.  “AI can make storytelling truly immersive — but it first needs to understand the world’s content and culture to do so,” said Niko Cunningham, CEO of Rumi, in a statement. “Rumi is building that infrastructure and giving users a way to be part of it from day one.” This decentralized indexing system helps AI agents deliver smarter insights and personalized features such as contextual overlays, real-time cultural context and interactive recommendations, while protecting user privacy and respecting content IP. Business model and partnerships Rumi is licensing infrastructure to third party AI agents, developers, and creators, providing agents brand new functionality and monetization opportunities. Those opportunities include contextual ads which, from the viewer’s perspective, can be seamlessly integrated in the conversation with AI agents, and much more value-adding. Agents can organically surface recipe cards during cooking shows or fashion links tied to what characters are wearing on the screen.  Rumi is also building a “decentralized Nielsen,” offering real-time analytics and audience insights. Early partnerships include Virtuals.io, who will integrate Rumi’s APIs to provide agents using their platform media contextual-awareness and interactivity capabilities, in addition to TVision and Story Protocol who will rely on Rumi to provide unique viewership data and content analytics.  Rumi is now welcoming early users to its watch-to-earn beta, allowing participants to help build what it calls the first intelligent, user-powered media ecosystem. Sign up at https://www.rumilabs.io. How Rumi works Infrastructure allowing AI to understand human stories and culture locked in billions of hours of video. Imagine having an entire cast and crew, commentators, shopping assistants, fact-checkers, researchers, and writers in your living room — sitting on your couch with you as you watch your favorite movies, shows, sports, news. That’s media contextually-aware AI. To enable that future, the company built a decentralized network of indexers analyzing content as they watch, and getting compensated for their work and compute. Over time, the company plans to use the money to bring to market the world’s best AI architecture for distilling the story from video: relations between actions, characters, objects; story / character arcs — mapping, understanding and AI participating in the world’s stories — not just documents and facts like today’s current LLMs. The company is building infrastructure to support Live TV / sports (vs historical content that are covering now) and to build a brand new gateway to media, D2C app — first ever AI remote and media companion. How the “watch-to-earn” model works Rumi indexes videos and offers rewards to viewers. Users get paid for their compute and data contributions. They are providing value by analyzing video content and interacting with it on their machines. That data is valuable because it empowers the next generation of AI agents that can identify and deeply understand content, providing them the ability to augment and personalize content for you, and turn passive media into interactive experience. The viewership analytics data that we’re collecting on how people interact with content at the depth never reached before, is also used to improve content and advertising quality. The company said it will help individual creators and Hollywood studios improve quality of storytelling, as Rumi is collecting the richest and most granular trove of data on what resonates with people — stories, characters, objects. Rumi said it will boost advertisers’ return-on-investment by enabling contextual, agentic ads, that are more personalized and valuable to consumer, and feel seamless, organic — embedded in the natural conversation flow… ads that don’t feel like ads. Of course, people have to agree to the privacy policies of the company. As far as the target audience goes, Rumi said anyone with a computer and access to streaming platforms can join our network today, and get rewarded for their contributions to enabling the AI-powered Media future. The company is primarily focused on Web3 users, as they’re used to the model of sharing their spare resources, such as compute power or bandwidth, in exchange for points or tokens. Rival companies There are a few decentralized networks paying people for their compute resources to train AI models or run inference, such as Bittensor, Akash, or Render. However, Rumi said it is focused on video understanding, and have built its proprietary deep learning models and architectures that allow it to distill the essence of the story from the video, which Rumi believes will be critical for AI to truly understand the stories humans care about and how they connect to our culture, building up towards more human AI. Rumi makes money by providing media contextual awareness to AI Agents, allowing them to become way more than simple bots on social media. Rumi charges them for accessing its APIs, and serve their end users contextual Ads. It’s also selling viewership data and analytics to media companies, advertisers, AI Labs, and other parties interested in understanding how consumers interact with content (on which they spend perhaps five hours a day). Content licensing and rights management policies for AI Rumi said it is never storing, rebroadcasting nor exposing any actual IP-protected content. Its decentralized network is indexing it in similar fashion that Google indexed web pages. The company said it will never train any AI models on that IP-protected content. It is building infrastructure that benefits IP owners: Rumi gives them a brand new direct channel of communication with their audiences, via which they can deepen the consumer engagement in ways unimaginable before, and access new monetization opportunities. Courts have repeatedly upheld that publishing granular transcriptions or summaries of video content qualify as fair use—as seen in Google Books (Authors Guild v. Google), where digitizing and displaying searchable snippets of entire books was ruled transformative. And the same held in TVEyes v. Fox, where indexing and repurposing broadcast content for search and review was partially upheld as fair use. Rumi’s network of indexers transcribes content, but it doesn’t publish it to the public. Rumi’s protocol only exposes APIs to allow AI Agents to interact with that transcript and content fingerprint, prompt by prompt. Rumi said this opens endless opportunities for creators to make their content more personalized and interactive, so it ultimately benefits the IP owners, as they direct channels of communication with their audiences, which they can monetize. Why now? What macro trends are converging that make Rumi viable today? Consumers want more interactive, connected, personalized and immersive entertainment. It’s clear looking at the growth of gaming, social media, and the massive second screening trend: 80% of Gen Zs report constantly scrolling through their phones while watching TV. Consumer AI agents are about to boom, but they will not start with high stakes use cases like planning and paying for your vacation. They will start in your living room, making your entertainment more immersive, connected and educational. Tech is finally ready, and anyone can analyze content on their device, contribute to more immersive and personalized future of media, and earn while doing that, Rumi said.

How would you like to earn money while watching TV? Rumi, an AI media company, has raised $4.7 million in a pre-seed funding round to transform passive media with rewards.

Rumi is launching its invite-only beta app today and it aims to allows users to engage with media content in real time.

The round, co-led by A16z crypto CSX and EV3, will support Rumi’s mission to build the first decentralized AI infrastructure for media, combining cutting-edge artificial intelligence with user-powered indexing to transform streaming into an intelligent, interactive experience.

Viewers can chat with characters or pro sports athletes about what they’re doing on the screen, identify actors and outfits on screen, or receive real-time fact checks during heated political debates — all powered by AI.

“Rumi sits at the cutting edge of AI agents, consumer media and decentralized infrastructure,” said Salvador Gala, cofounder of EV3, in a statement. “Their approach to indexing audio and video content through a distributed network of AI-powered nodes empowers users to directly benefit from existing behaviors.”

Streaming meets earning

Rumi has raised $4.7 million.

With users spending more than five hours a day watching content — often while multitasking — Rumi allows them to earn passive income by contributing spare compute to build the world’s richest knowledge base on video content that in turn allows AI agents to deeply understand and augment media in ways we can’t even imagine yet, the company said. 

“AI can make storytelling truly immersive — but it first needs to understand the world’s content and culture to do so,” said Niko Cunningham, CEO of Rumi, in a statement. “Rumi is building that infrastructure and giving users a way to be part of it from day one.”

This decentralized indexing system helps AI agents deliver smarter insights and personalized features such as contextual overlays, real-time cultural context and interactive recommendations, while protecting user privacy and respecting content IP.

Business model and partnerships

Rumi is licensing infrastructure to third party AI agents, developers, and creators, providing agents brand new functionality and monetization opportunities. Those opportunities include contextual ads which, from the viewer’s perspective, can be seamlessly integrated in the conversation with AI agents, and much more value-adding. Agents can organically surface recipe cards during cooking shows or fashion links tied to what characters are wearing on the screen. 

Rumi is also building a “decentralized Nielsen,” offering real-time analytics and audience insights. Early partnerships include Virtuals.io, who will integrate Rumi’s APIs to provide agents using their platform media contextual-awareness and interactivity capabilities, in addition to TVision and Story Protocol who will rely on Rumi to provide unique viewership data and content analytics. 

Rumi is now welcoming early users to its watch-to-earn beta, allowing participants to help build what it calls the first intelligent, user-powered media ecosystem. Sign up at https://www.rumilabs.io.

How Rumi works

Infrastructure allowing AI to understand human stories and culture locked in billions of hours of video.

Imagine having an entire cast and crew, commentators, shopping assistants, fact-checkers, researchers, and writers in your living room — sitting on your couch with you as you watch your favorite movies, shows, sports, news. That’s media contextually-aware AI. To enable that future, the company built a decentralized network of indexers analyzing content as they watch, and getting compensated for their work and compute.

Over time, the company plans to use the money to bring to market the world’s best AI architecture for distilling the story from video: relations between actions, characters, objects; story / character arcs — mapping, understanding and AI participating in the world’s stories — not just documents and facts like today’s current LLMs.

The company is building infrastructure to support Live TV / sports (vs historical content that are covering now) and to build a brand new gateway to media, D2C app — first ever AI remote and media companion.

How the “watch-to-earn” model works

Rumi indexes videos and offers rewards to viewers.

Users get paid for their compute and data contributions. They are providing value by analyzing video content and interacting with it on their machines.

That data is valuable because it empowers the next generation of AI agents that can identify and deeply understand content, providing them the ability to augment and personalize content for you, and turn passive media into interactive experience.

The viewership analytics data that we’re collecting on how people interact with content at the depth never reached before, is also used to improve content and advertising quality.

The company said it will help individual creators and Hollywood studios improve quality of storytelling, as Rumi is collecting the richest and most granular trove of data on what resonates with people — stories, characters, objects.

Rumi said it will boost advertisers’ return-on-investment by enabling contextual, agentic ads, that are more personalized and valuable to consumer, and feel seamless, organic — embedded in the natural conversation flow… ads that don’t feel like ads. Of course, people have to agree to the privacy policies of the company.

As far as the target audience goes, Rumi said anyone with a computer and access to streaming platforms can join our network today, and get rewarded for their contributions to enabling the AI-powered Media future. The company is primarily focused on Web3 users, as they’re used to the model of sharing their spare resources, such as compute power or bandwidth, in exchange for points or tokens.

Rival companies

There are a few decentralized networks paying people for their compute resources to train AI models or run inference, such as Bittensor, Akash, or Render.

However, Rumi said it is focused on video understanding, and have built its proprietary deep learning models and architectures that allow it to distill the essence of the story from the video, which Rumi believes will be critical for AI to truly understand the stories humans care about and how they connect to our culture, building up towards more human AI.

Rumi makes money by providing media contextual awareness to AI Agents, allowing them to become way more than simple bots on social media. Rumi charges them for accessing its APIs, and serve their end users contextual Ads.

It’s also selling viewership data and analytics to media companies, advertisers, AI Labs, and other parties interested in understanding how consumers interact with content (on which they spend perhaps five hours a day).

Content licensing and rights management policies for AI

Rumi said it is never storing, rebroadcasting nor exposing any actual IP-protected content. Its decentralized network is indexing it in similar fashion that Google indexed web pages.

The company said it will never train any AI models on that IP-protected content. It is building infrastructure that benefits IP owners: Rumi gives them a brand new direct channel of communication with their audiences, via which they can deepen the consumer engagement in ways unimaginable before, and access new monetization opportunities.

Courts have repeatedly upheld that publishing granular transcriptions or summaries of video content qualify as fair use—as seen in Google Books (Authors Guild v. Google), where digitizing and displaying searchable snippets of entire books was ruled transformative. And the same held in TVEyes v. Fox, where indexing and repurposing broadcast content for search and review was partially upheld as fair use.

Rumi’s network of indexers transcribes content, but it doesn’t publish it to the public. Rumi’s protocol only exposes APIs to allow AI Agents to interact with that transcript and content fingerprint, prompt by prompt.

Rumi said this opens endless opportunities for creators to make their content more personalized and interactive, so it ultimately benefits the IP owners, as they direct channels of communication with their audiences, which they can monetize.

Consumers want more interactive, connected, personalized and immersive entertainment. It’s clear looking at the growth of gaming, social media, and the massive second screening trend: 80% of Gen Zs report constantly scrolling through their phones while watching TV.

Consumer AI agents are about to boom, but they will not start with high stakes use cases like planning and paying for your vacation. They will start in your living room, making your entertainment more immersive, connected and educational. Tech is finally ready, and anyone can analyze content on their device, contribute to more immersive and personalized future of media, and earn while doing that, Rumi said.

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Equinor lets EPC contract for Gullfaks field

@import url(‘https://fonts.googleapis.com/css2?family=Inter:[email protected]&display=swap’); a { color: var(–color-primary-main); } .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: #c19a06 !important; border-color: #c19a06 !important; } #onetrust-policy a, #onetrust-pc-sdk a, #ot-pc-content a { color: #c19a06 !important; } #onetrust-consent-sdk #onetrust-pc-sdk .ot-active-menu { border-color: #c19a06 !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: #c19a06 !important; border-color: #c19a06 !important; } #onetrust-consent-sdk .onetrust-pc-btn-handler { color: #c19a06 !important; border-color: #c19a06 !important; } Equinor Energy AS has let an engineering, procurement, and construction (EPC) contract to SLB to upgrade the subsea compression system for Gullfaks field in the Norwegian North Sea. Under the contract, SLB OneSubsea will deliver two next-generation compressor modules to replace the units originally supplied in 2015 as part of the world’s first multiphase subsea compression system. The upgraded modules will increase differential pressure and flow capacity, enhancing recovery and extending field life, SLB said, while installation within the existing subsea infrastructure will minimize downtime and reduce overall campaign costs, the company continued. Gullfaks field lies in block 34/10 in the northern part of the North Sea. Three large production platforms with concrete substructures make up the development solution for the main field.

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Oxy cutting oil-and-gas capex by $300 million, eyes 1% production growth

@import url(‘https://fonts.googleapis.com/css2?family=Inter:[email protected]&display=swap’); a { color: var(–color-primary-main); } .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: #c19a06 !important; border-color: #c19a06 !important; } #onetrust-policy a, #onetrust-pc-sdk a, #ot-pc-content a { color: #c19a06 !important; } #onetrust-consent-sdk #onetrust-pc-sdk .ot-active-menu { border-color: #c19a06 !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: #c19a06 !important; border-color: #c19a06 !important; } #onetrust-consent-sdk .onetrust-pc-btn-handler { color: #c19a06 !important; border-color: #c19a06 !important; } Occidental Petroleum Corp., Houston, will spend $5.5-5.9 billion on capital projects this year, an 8% drop from 2025 and $800 million less than executives’ early forecast late last year, as the company continues to emphasize efficiency gains. Spending on oil-and-gas operations will be $300 million less than last year. Sunil Mathew, chief financial officer, late last week told investors and analysts that Occidental’s capital spending budget for 2026 (adjusted for the recently completed divestiture of OxyChem) will focus on short-cycle projects and be roughly 70% devoted to US onshore assets. Still, onshore capex will drop by $400 million from last year in part because of a drop in Permian basin activities and efficiency improvements. Other elements of Occidental’s spending plan include: A reduction of about $100 million compared to last year for exploration work A $250 million drop in spending at the company’s Low Carbon Ventures group housing Stratos Mathew said capex, which will be weighted a little to the first half, sets up Occidental’s production to average 1.45 MMboe/d for the full year, a tick up from 2025’s average of 1.434 MMboe/d but down from the roughly 1.48

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Diamondback’s Van’t Hof growing ‘more confident about the macro’

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Ovintiv sets 2026 plan around Permian, Montney after declaring portfolio shift ‘complete’

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JLL: Hyperscale and AI Demand Push North American Data Centers Toward Industrial Scale

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7×24 Exchange’s Dennis Cronin on the Data Center Workforce Crisis: The Talent Cliff Is Already Here

The data center industry has spent the past two years obsessing over power constraints, AI density, and supply chain pressure. But according to longtime mission critical leader Dennis Cronin, the sector’s most consequential bottleneck may be far more human. In a recent episode of the Data Center Frontier Show Podcast, Cronin — a founding member of 7×24 Exchange International and board member of the Mission Critical Global Alliance (MCGA) — delivered a stark message: the workforce “talent cliff” the industry keeps discussing as a future risk is already impacting operations today. A Million-Job Gap Emerging Cronin’s assessment reframes the workforce conversation from a routine labor shortage to what he describes as a structural and demographic challenge. Based on recent analysis of open roles, he estimates the industry is currently short between 467,000 and 498,000 workers across core operational positions including facilities managers, operations engineers, electricians, generator technicians, and HVAC specialists. Layer in emerging roles tied to AI infrastructure, sustainability, and cyber-physical security, and the potential demand rises to roughly one million jobs. “The coming talent cliff is not coming,” Cronin said. “It’s here, here and now.” With data center capacity expanding at roughly 30% annually, the workforce pipeline is not keeping pace with physical buildout. The Five-Year Experience Trap One of the industry’s most persistent self-inflicted wounds, Cronin argues, is the widespread requirement for five years of experience in roles that are effectively entry level. The result is a closed-loop hiring dynamic: New workers can’t get hired without experience They can’t gain experience without being hired Operators end up poaching from each other Workers may benefit from the resulting 10–20% salary jumps, but the overall talent pool remains stagnant. “It’s not helping us grow the industry,” Cronin said. In a market defined by rapid expansion and increasing system complexity, that

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Aeroderivative Turbines Move to the Center of AI Data Center Power Strategy

From “Backup” to “Bridging” to Behind-the-Meter Power Plants The most important shift is conceptual: these systems are increasingly blurring the boundary between emergency backup and primary power supply. Traditionally, data center electrical architecture has been clearly tiered: UPS (seconds to minutes) to ride through utility disturbances and generator start. Diesel gensets (minutes to hours or days) for extended outages. Utility grid as the primary power source. What’s changing is the rise of bridging power:  generation deployed to energize a site before the permanent grid connection is ready, or before sufficient utility capacity becomes available. Providers such as APR Energy now explicitly market turbine-based solutions to data centers seeking behind-the-meter capacity while awaiting utility build-out. That framing matters because it fundamentally changes expected runtime. A generator that operates for a few hours per year is one regulatory category. A turbine that runs continuously for weeks or months while a campus ramps is something very different; and it is drawing increased scrutiny from regulators who are beginning to treat these installations as material generation assets rather than temporary backup systems. The near-term driver is straightforward. AI workloads are arriving faster than grid infrastructure can keep pace. Data Center Frontier and other industry observers have documented the growing scramble for onsite generation as interconnection queues lengthen and critical equipment lead times expand. Mainstream financial and business media have taken notice. The Financial Times has reported on data centers turning to aeroderivative turbines and diesel fleets to bypass multi-year power delays. Reuters has likewise covered large gas-turbine-centric strategies tied to hyperscale campuses, underscoring how quickly the co-located generation model is moving into the mainstream. At the same time, demand pressure is tightening turbine supply chains. Industry reporting points to extended waits for new units, one reason repurposed engine cores and mobile aeroderivative packages are gaining

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Cooling’s New Reality: It’s Not Air vs. Liquid Anymore. It’s Architecture.

By early 2026, the data center cooling conversation has started to sound less like a product catalog and more like a systems engineering summit. The old framing – air cooling versus liquid cooling – still matters, but it increasingly misses the point. AI-era facilities are being defined by thermal constraints that run from chip-level cold plates to facility heat rejection, with critical decisions now shaped by pumping power, fluid selection, reliability under ambient extremes, water availability, and manufacturing throughput. That full-stack shift is written all over a grab bag of recent cooling announcements. On one end of the spectrum we see a Department of Energy-funded breakthrough aimed directly at next-generation GPU heat flux. On the other, it’s OEM product launches built to withstand –20°F to 140°F operating conditions and recover full cooling capacity within minutes of a power interruption. In between we find a major acquisition move for advanced liquid cooling IP, a manufacturing expansion that more than doubles footprint, and the quiet rise of refrigerants and heat-transfer fluids as design-level considerations. What’s emerging is a new reality. Cooling is becoming one of the primary constraints on AI deployment technically, economically, and geographically. The winners will be the players that can integrate the whole stack and scale it. 1) The Chip-level Arms Race: Single-phase Fights for More Runway The most “pure engineering” signal in this news batch comes from HRL Laboratories, which on Feb. 24, 2026 unveiled details of a single-phase direct liquid cooling approach called Low-Chill™. HRL’s framing is pointed: the industry wants higher GPU and rack power densities, but many operators are wary of the cost and operational complexity of two-phase cooling. HRL says Low-Chill was developed under the U.S. Department of Energy’s ARPA-E COOLERCHIPS program, and claims a leap that goes straight at the bottleneck. It can increase

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Policy Shock: Big Tech Told to Power Its Own AI Buildout

The AI data center boom has been colliding with grid reality for more than two years. This week, the issue moved closer to the policy front lines. The White House is advancing a “ratepayer protection” framework that has gained visibility in recent days, aimed at ensuring large AI data center projects do not shift grid upgrade costs onto residential customers. It’s a signal widely interpreted by industry observers as encouraging hyperscalers to bring dedicated power solutions to the table. The Power Question Moves to Center Stage Washington now appears poised to push the industry toward a structural response to the data center power conundrum. The new federal impetus for major technology companies to shoulder the cost of their own power infrastructure is quickly emerging as one of the most consequential policy developments for the digital infrastructure sector in 2026. If formalized, the initiative would effectively codify a shift already underway which has found hyperscale and AI developers moving aggressively toward behind-the-meter generation and dedicated energy strategies. For an industry already grappling with interconnection delays, utility pushback, and mounting community scrutiny, the signal is unmistakable. The era of relying primarily on shared grid capacity for large AI campuses may be ending. From Market Trend to Policy Direction Large tech firms, including the biggest cloud and AI players, have been under increasing pressure from regulators and utilities concerned about ratepayer exposure and grid reliability. Policymakers are now signaling that future large-load approvals may hinge on whether developers can demonstrate energy self-sufficiency or dedicated supply. The logic is straightforward. AI campuses are arriving at hundreds of megawatts to gigawatt scale. Transmission upgrades are measured in multi-year timelines. Utilities face growing political pressure to protect residential customers. In that context, the emerging federal posture does not create a new trend so much as accelerate

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Enterprise Spotlight: Data Center Modernization

The demands for, and challenges of, deploying AI applications has ratcheted up the urgency to bring data centers into the AI age. It’s a strategic imperative and success requires partners across the infrastructure spectrum, from servers and storage to high-performance computing, networking, software, and security. IT leaders, intensely focused on data center modernization, need strategies, roadmaps, and products that will get them there. Download the March 2026 issue of the Enterprise Spotlight from the editors of CIO, Computerworld, CSO, InfoWorld, and Network World and learn how data center modernization is taking shape in 2026.

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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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