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Meet OpenAI’s Operator, an AI agent that uses the web to book you dinner reservations, order tickets, compile grocery lists and more

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has unveiled Operator, its first semi-autonomous AI agent, which is designed to “operate” a web browser much like a person would, on their behalf. The agent uses the cursor to point and click, types on […]

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OpenAI has unveiled Operator, its first semi-autonomous AI agent, which is designed to “operate” a web browser much like a person would, on their behalf. The agent uses the cursor to point and click, types on its own, browses the web and performs actions on various websites, such as booking restaurant reservations through OpenTable and assembling orders on Instacart and DoorDash. That’s instead of being confined to the ChatGPT interface or OpenAI’s application programming interface (API).

“This product is the beginning of our step into agents,” said CEO and cofounder Sam Altman in a demo livestreamed on the company’s YouTube Channel today at 1 pm ET.

OpenAI president and fellow cofounder Greg Brockman wrote on X: “2025 is the year of agents.”

The preview, now available to paying U.S. subscribers of OpenAI’s ChatGPT Pro ($200 per month) plan, aims to demonstrate the potential of agentic AI while gathering critical feedback to refine its capabilities.

Operator doesn’t take over your web browser, though. Instead, you visit a separate, new website — operator.chatgpt.com — and are confronted with a prompt input box similar to ChatGPT.

Typing a request into this box — “find me tickets for the LA Lakers game tonight” — will trigger Operator to open a separate, virtual browser running in the cloud on OpenAI servers. Then, the agent can execute tasks like filling out forms, managing online reservations, even booking tickets to sporting events and concerts, and navigating other common workflows. The user watches the cursor move on its own on the cloud-based browser in real time. If the agent encounters a problem, it will stop and message the user via a text output, similar to ChatGPT’s responses.

Also, below the virtual browser, the user will see suggestions of actions Operator can take on their behalf.

Yet, the user can take control at any time — similar to semi-autonomous driving systems in modern cars. Operator also asks the user to input their own payment credentials when it reaches a purchase screen on another website. Finally, users can save particular workflows that they wish to use going forward and start them again.

Operator is powered by what OpenAI calls computer-using agent (CUA) technology, a new variant of GPT-4o trained specifically to use computers.

Bridging AI and GUIs

Operator stands apart from other automation tools by mimicking human interaction with graphical user interfaces (GUIs).

Instead of relying on specialized APIs, the system leverages screenshots for visual input and uses virtual mouse and keyboard actions to complete tasks.

The underlying CUA model combines GPT-4o’s vision capabilities with reinforcement learning, enabling the agent to perceive, reason, and act on screen.

This approach allows Operator to handle diverse tasks, including ecommerce browsing, travel planning, and even repetitive tasks like creating playlists or managing shopping lists. Notable benchmarks illustrate its effectiveness:

87% success rate on WebVoyager, a test of live website navigation

58.1% success rate on WebArena, which simulates real-world ecommerce and content management scenarios

But there’s already tough competition: Just yesterday, Chinese tech firm ByteDance (TikTok’s parent company) launched its own AI agent for controlling web browsers and performing actions on a user’s. behalf. Called UI-TARS, it’s totally open-source and boasts similarly impressive benchmark performance (though does not appear to have been compared directly on the same benchmarks). That means OpenAI’s Operator will need to be significantly better or more reliable to justify the relatively high ($200/month) cost of accessing it through ChatGPT Pro subscriptions.

Already being tested in enterprise web navigation use cases

OpenAI is partnering with several businesses to ensure Operator meets real-world needs. Companies including Instacart, DoorDash and Etsy are already testing the technology for use cases ranging from grocery delivery to personalized shopping.

Brett Keller, CEO of Priceline, remarked on its utility for travel planning, calling it “a significant step in making travel more seamless and personalized.”

For public-sector applications, the City of Stockton is exploring ways to use Operator to simplify civic engagement. Jamil Niazi, the city’s director of information technology, highlighted AI’s potential to make enrolling in services easier for residents.

Yet there are limitations. Tech publication Every got an early preview, has been testing it for the past week, and found that:

“One of the peculiarities of Operator’s design is that it doesn’t use your browser. Instead, it uses a browser in one of OpenAI’s data centers that you can watch and interact with remotely. The upside of this design decision is that you can use Operator wherever and whenever — for example, on any mobile device.

“The downside is that many sites like Reddit already block AI agents from browsing so they can’t be accessed by Operator. In this research preview mode, Operator is also blocked by OpenAI from accessing certain resource-intensive sites like Figma or competitor-owned sites like YouTube for performance or legal reasons.”

Safety measures

Given its ability to act on users’ behalf, Operator has been developed with robust safety features:

User control: Operator requests confirmation for sensitive actions, such as making purchases or sending emails.

Watch mode: Ensures user supervision for critical tasks, particularly on sensitive sites like email or financial platforms.

Misuse prevention: The system is trained to refuse harmful requests and includes safeguards against adversarial attacks, such as malicious prompts embedded in websites.

OpenAI has also incorporated features to protect user privacy, including options to clear browsing data and opt out of data sharing for model improvements.

Enterprise edition coming

OpenAI envisions a broader role for Operator in both individual and enterprise settings. Over time, the company plans to expand access to Plus, Team, and Enterprise users, eventually integrating Operator into ChatGPT.

There are also plans to make the underlying CUA technology available via an API, enabling developers to create custom computer-using agents.

Despite its potential, Operator remains a work in progress. OpenAI has been transparent about its limitations, such as difficulties with complex interfaces or unfamiliar workflows. Early user feedback will play a pivotal role in improving the system’s accuracy, reliability and safety.

As OpenAI refines Operator through real-world use, it is seeking to transform AI from a passive tool into an active participant in the digital ecosystem. Whether it’s simplifying everyday tasks or innovating business workflows, OpenAI is positioning Operator as the next step in making AI accessible, practical, and secure.

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AI’s dark side shows in Gartner’s top predictions for IT orgs

Increasing legal claims against AI-induced safety problems related to autonomous vehicle or medical accidents are also a mounting concern, Plummer stated. By the end of 2026, “death by AI” legal claims will exceed 1,000 globally due to insufficient AI risk guardrails, Plummer stated. “As regulatory scrutiny intensifies, organizations will face pressure

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AI dominates Gartner’s top strategic technology trends for 2026

“AI supercomputing platforms integrate CPUs, GPUs, AI ASICs, neuromorphic and alternative computing paradigms, enabling organizations to orchestrate complex workloads while unlocking new levels of performance, efficiency and innovation. These systems combine powerful processors, massive memory, specialized hardware, and orchestration software to tackle data-intensive workloads in areas like machine learning, simulation,

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IBM signs up Groq for speedy AI inferencing option

The technology involved in the partnership will let customers use watsonx capabilities in a familiar way and allow them to use their preferred tools while accelerating inference with GroqCloud, IBM stated. “This integration will address key AI developer needs, including inference orchestration, load balancing, and hardware acceleration, ultimately streamlining the

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Oil Prices Stage a Rebound

In a market analysis sent to Rigzone on Wednesday, Konstantinos Chrysikos, Head of Customer Relationship Management at Kudotrade, highlighted that “oil prices staged a rebound today, recovering from their recent five-month lows”. “The market reacted positively to the news that the U.S. Department of Energy is seeking to purchase approximately one million barrels for the Strategic Petroleum Reserve (SPR),” Chrysikos said in the analysis.   “Additionally, the potential for a U.S.-India deal that may cut Russian flows and tighten the market lifted prices. Hopes regarding U.S.-China trade improvement could support demand expectations and help improve the outlook,” he added. Chrysikos went on to state in the analysis, however, that “the market could face a persistent global supply surplus, driven by record-high U.S. production and OPEC+’s commitment to planned output increases”. “These factors, combined with forecasts from major agencies pointing to continued inventory builds and slower global demand growth, could cap significant price gains,” Chrysikos warned. In a separate market analysis sent to Rigzone today, Rania Gule, a senior market analyst at XS.com-MENA, pointed out that, “historically, the trade relationship between the U.S. and China has had a direct impact on oil price dynamics, as the two economies together account for more than one-third of global energy consumption”. “Consequently, any sign of easing or escalation in their trade dispute immediately reverberates through markets,” Gule added. In a Skandinaviska Enskilda Banken AB (SEB) report sent to Rigzone by the SEB team on Wednesday, SEB Commodities Analyst Ole R. Hvalbye noted that, “so far this week, Brent crude has climbed by roughly $2.5 per barrel from Monday’s low of $60.07, the weakest print since early May”. “The slight recovery in Brent this week suggests that the recent downward momentum has eased, though the market remains highly sensitive to short-term headlines,” Hvalbye said in

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BP, JERA Halt US Offshore Wind Activities

JERA Co Inc and BP PLC’s offshore wind joint venture has indefinitely shelved the proposed Beacon Wind project in the United States. “JERA Nex bp has taken the difficult decision to reduce our activities in the U.S. to a minimal level and will close our operating activities in the market”, JERA Nex bp said in a statement on its website. “Unfortunately, as a result of this, all team members will leave the company in the coming months. “The U.S. is a market with significant long-term potential for offshore wind, which we still believe can play a key role in the country’s energy transition. Unfortunately, in the present environment we see no viable path to the development of our Beacon wind project and have concluded that we cannot continue our investment in the market. “For now, we will continue to maintain the Beacon lease and wait for a more favorable moment to resume project development”. Beacon Wind has a planned capacity of 2.5 gigawatts, enough to power about one million homes across the northeastern U.S., according to BP. The proposed project has secured 128,000 acres in federal waters between Cape Cod, Massachusetts, and Long Island, New York, according to BP. The U.S. government’s online Permitting Dashboard shows Beacon Wind’s permitting process has been paused since June. Upon taking office for his second non-consecutive term, President Donald Trump on January 20 ordered an indefinite pause of wind leasing in the Outer Continental Shelf to allow for a review of permitting considerations, saying environmental analyses had been inadequate. The halt means no new leases would be awarded. For existing leases, “the secretary of the interior, in consultation with the attorney general as needed, shall conduct a comprehensive review of the ecological, economic and environmental necessity of terminating or amending any existing wind energy leases”,

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U.S. Energy Secretary and Qatari Energy Minister Send Letter to EU Regarding Proposed Corporate Climate Regulations

WASHINGTON—U.S. Department of Energy (DOE) Secretary Chris Wright and Qatari Minister of State for Energy Affairs Saad Sherida Al-Kaabi sent a letter today to the Heads of State of European Union (EU) Member States, regarding the European Union’s proposed Corporate Sustainability Due Diligence Directive (CSDDD). Click here to read the letter or see the full text below. An Open Letter to the Heads of State of European Union (EU) Member States Dear Leaders of European Union Member States, We write to you today at a pivotal moment for the EU’s energy security and economic competitiveness. As two of its most trusted partners and the world’s leading LNG producers, we reaffirm our deep commitment to supporting the EU’s prosperity and stability. We write in this spirit, united in our views, to express our deep concern over the continued lack of action to address the universally acknowledged, serious, and legitimate concerns raised by the global business community regarding the Corporate Sustainability Due Diligence Directive (CSDDD). Particularly its unintended consequences for LNG export competitiveness and the availability of reliable, affordable energy for EU consumers. Over the past year, our two countries have engaged in constructive dialogue with representatives from numerous EU governments regarding the contents of the CSDDD, offering specific recommendations to avoid the unintended consequences we have previously raised. While we appreciate the efforts of those Member States that have welcomed dialogue, the broader lack of substantive engagement on these critical issues is deeply concerning, especially given the far-reaching implications of the legislation. We have consistently and transparently communicated how the CSDDD, as it is worded today, poses a significant risk to the affordability and reliability of critical energy supplies for households and businesses across Europe and an existential threat to the future growth, competitiveness, and resilience of the EU’s industrial economy.

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VoltaGrid, Halliburton Form Data Center Partnership

Halliburton Energy Services Inc and VoltaGrid LLC have announced a collaboration to deliver power solutions to data centers. “Under this agreement, VoltaGrid and Halliburton will combine their complementary strengths to develop, deploy and operate advanced, efficient and sustainable power generation systems utilizing turbines, reciprocating engines and VoltaGrid’s proprietary QPac platform”, the United States companies said in a joint statement. “The collaboration aims to meet the growing demand for reliable, lower-emission energy infrastructure in a rapidly expanding digital and industrial landscape. “Through the venture, Halliburton will leverage its global operational footprint, local infrastructure and regional regulatory expertise, while VoltaGrid will contribute its proprietary engineering design, technology innovation and procurement capabilities. “Together, the companies plan to offer turnkey distributed power generation solutions tailored to the needs of regional data centers based on a proven platform. “The collaboration will enable VoltaGrid’s existing large-scale data center customer base access to a global footprint via Halliburton’s operational excellence that is well suited for execution certainty, reliability and performance”. The partnership initially targets the Middle East, with an eye for key emerging markets. Earlier Houston, Texas-based gas power solutions provider VoltaGrid announced an agreement with Oracle Corp to deliver technology to enable the supply of natural gas electricity to the information technology major’s data centers. VoltaGrid will deploy 2.3 gigawatts (GW) of “cutting-edge, ultra-low-emissions infrastructure, supplied by Energy Transfer’s pipeline network, to support the energy demands of Oracle Cloud Infrastructure’s (OCI) next-generation artificial intelligence data centers”, it said in a press release October 15. “The VoltaGrid power infrastructure will be delivered through the proprietary VoltaGrid platform – a modular, high-transient-response system developed by VoltaGrid with key suppliers, including INNIO Jenbacher and ABB”. “This power plant deployment is being supplied with firm natural gas from Energy Transfer’s expansive pipeline and storage systems”, VoltaGrid added. OCI executive vice president

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Halliburton CEO ‘Pleased’ with Q3 Performance

In Halliburton’s third quarter results statement, which was posted on the company’s site on Tuesday, Halliburton Chairman, President, and CEO Jeff Miller said he is “pleased” with Halliburton’s third quarter performance. In the statement, the company announced a net income of $18 million, or $0.02 per diluted share, and adjusted net income, excluding “impairments and other charges” and other items, of $496 million, or $0.58 per diluted share, for the third quarter. Net income for the second quarter was $472 million, or $0.55 per diluted share, Halliburton highlighted in the statement. The company went on to note that Halliburton’s total revenue for the third quarter was $5.6 billion, compared to total revenue of $5.5 billion in the second quarter. Operating income was $356 million in the third quarter, compared to operating income of $727 million in the second quarter, Halliburton highlighted, adding that adjusted operating income in the third quarter, excluding “impairments and other charges”, was $748 million. Halliburton’s stock price opened at $24.43 on Tuesday and closed at $25.24. The stock opened at $22.30 on Monday and closed at $22.62. “We delivered total company revenue of $5.6 billion dollars and adjusted operating margin of 13 percent,” Miller said in the statement. “We also took steps that will deliver estimated savings of $100 million dollars per quarter, reset our 2026 capital budget and idled equipment that no longer meets our return expectations,” he added. In the statement, Halliburton pointed out that international revenue in the third quarter was $3.2 billion, which it described as “flat when compared to the second quarter”. Latin America revenue in the third quarter was $996 million, Halliburton revealed, adding that this was an increase of two percent sequentially. This increase was primarily driven by higher project management activity across the region and increased drilling services in

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Strategists Expect WoW USA Crude Inventory Drop

In a report sent to Rigzone by the Macquarie team late Monday, Macquarie strategists, including Walt Chancellor, revealed that they are forecasting that U.S. crude inventories will be down by 2.5 million barrels for the week ending October 17. “This follows a 3.5 million barrel build in the prior week, with the crude balance realizing modestly tighter than our expectations,” the strategists said in the report. “For this week’s balance, from refineries, we model an increase in crude runs (+0.3 million barrels per day) following a surprisingly weak print last week; turnaround timing represents a source of meaningful potential variability in this week’s stats,” they added. “Among net imports, we model a moderate reduction, with exports higher (+0.6 million barrels per day) and imports up slightly (+0.1 million barrels per day) on a nominal basis,” they continued. The strategists also warned in the report that the timing of cargoes remains a source of potential volatility in this week’s crude balance. “From implied domestic supply (prod.+adj.+transfers), we look for a slight decrease (-0.1 million barrels per day) on a nominal basis this week,” the strategists went on to note. “Rounding out the picture, we anticipate a slightly larger increase (+0.9 million barrels) in SPR [Strategic Petroleum Reserve] stocks this week,” they added. The strategists stated in the report that, “among products”, they “look for draws in gasoline (-4.0 million barrels) and distillate (-1.2 million barrels), with a build in jet (+0.5 million barrels)”. “We model implied demand for these three products at ~14.4 million barrels per day for the week ending October 17,” the strategists added. In its latest weekly petroleum status report at the time of writing, which was released on October 16 and included data for the week ending October 10, the U.S. Energy Information Administration (EIA) highlighted that

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AI gold rush sparks backlash against Core Scientific acquisition

Meanwhile, in a release issued last week, CoreWeave stated, “it has been unequivocal — to Core Scientific and publicly — that we will not modify our offer. Our offer is best and final.” Alvin Nguyen, senior analyst at Forrester Research, said what happens next with the overall data center market “depends on when AI demand slows down (when the AI bubble bursts).” He added, “if AI demand continues, prices continue to go up, and data centers change in terms of preferred locations (cooler climates, access to water, lots of space, more remote), use of microgrids/energy production, expect [major] players to continue to dominate.” However, said Nguyen, “if that slowdown is soon, then prices will drop, and the key players will need to either unload property or hold onto them until AI demand builds back up.” Generational shift occurring Asked what the overall effect of AI will be on CIOs in need of data center capacity, he said, “the new AI mega-factories alter data center placement: you don’t put them near existing communities because they demand too much power, water, land, you build them somewhere remote, and communities will pop up around them.” Smaller data centers, said Nguyen, “will still consume power and water in contention with their neighbors (industrial, commercial, and residential), potential limiting their access or causing costs to rise. CIOs and Network World readers should evaluate the trade offs/ROI of not just competing for data center services, but also for being located near a new data center.”

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Why cloud and AI projects take longer and how to fix the holdups

No. 2 problem: Unrealistic expectations lead to problematic requirements Early planning and business case validation show that the requirements set for the project can’t be met, which then requires a period of redefinition before real work can start. This situation – reported by 69% of enterprises – leads to an obvious question: Is it the requirements or the project that’s the problem? Enterprises who cite this issue say it’s the former, and that it’s how the requirements are set that’s usually the cause. In the case of the cloud, the problem is that senior management thinks that the cloud is always cheaper, that you can always cut costs by moving to the cloud. This is despite the recent stories on “repatriation,” or moving cloud applications back into the data center. In the case of cloud projects, most enterprise IT organizations now understand how to assess a cloud project for cost/benefit, so most of the cases where impossible cost savings are promised are caught in the planning phase. For AI, both senior management and line department management have high expectations with respect to the technology, and in the latter case may also have some experience with AI in the form of as-a-service generative AI models available online. About a quarter of these proposals quickly run afoul of governance policies because of problems with data security, and half of this group dies at this point. For the remaining proposals, there is a whole set of problems that emerge. Most enterprises admit that they really don’t understand what AI can do, which obviously makes it hard to frame a realistic AI project. The biggest gap identified is between an AI business goal and a specific path leading to it. One CIO calls the projects offered by user organizations as “invitations to AI fishing

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Riverbed tackles AI data bottleneck with new Oracle-based service

“Customers are looking for faster, more secure ways to move massive datasets so they can bring AI initiatives to life,” said Sachin Menon, Oracle’s vice president of cloud engineering, in a statement. “With Riverbed Data Express Service deployed on OCI, organizations will be able to accelerate time to value, reduce costs, and help ensure that their data remains protected.” Riverbed’s Aras explains that its Data Express Service uses post-quantum cryptography (PQC) to move petabyte-scale datasets through secure VPN tunnels to ensure that customer data remains protected during the transfer process. The technology is based on Riverbed’s SteelHead acceleration platform running RiOS 10 software. “Our cloud-optimized technology design delivers much higher data retrieval, data movement across the network, and data write rates, through highly performant data mover instances, instance parallelization and matched network fabric configurations. The design is tailored for each cloud, to ensure maximal performance can be achieved using cloud-specific product adjustments,” Aras says. “The time for preventing harvest-now, decrypt-later is now,” Aras says, referring to the security threat where encrypted data is intercepted and stored for decryption once quantum computers become powerful enough. The Riverbed service addresses use cases spanning AI model training, inference operations, and emerging agentic AI applications. Data Express is initially deployed on Oracle Cloud Infrastructure, but Riverbed said the service will orchestrate data movement across AWS, Azure, and Google Cloud Platform, as well as on-premises data centers. General availability is planned for Q4 2025.

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Roundup: Digital Realty Marks Major Milestones in AI, Quantum Computing, Data Center Development

Key features of the DRIL include: • High-Density AI and HPC Testing. The DRIL supports AI and high-performance computing (HPC) workloads with high-density colocation, accommodating workloads up to 150 kW per cabinet. • AI Infrastructure Optimization. The ePlus AI Experience Center lets businesses explore AI-specific power, cooling, and GPU resource requirements in an environment optimized for AI infrastructure. • Hybrid Cloud Validation. With direct cloud connectivity, users can refine hybrid strategies and onboard through cross connects. • AI Workload Orchestration. Customers can orchestrate AI workloads across Digital Realty’s Private AI Exchange (AIPx) for seamless integration and performance. • Latency Testing Across Locations. Enterprises can test latency scenarios for seamless performance across multiple locations and cloud destinations. The firm’s Northern Virginia campus is the primary DRIL location, but companies can also test latency scenarios between there and other remote locations. DRIL rollout to other global locations is already in progress, and London is scheduled to go live in early 2026. Digital Realty, Redeployable Launch Pathway for Veteran Technical Careers As new data centers are created, they need talented workers. To that end, Digital Realty has partnered with Redeployable, an AI-powered career platform for veterans, to expand access to technical careers in the United Kingdom and United States. The collaboration launched a Site Engineer Pathway, now live on the Redeployable platform. It helps veterans explore, prepare for, and transition into roles at Digital Realty. Nearly half of veterans leave their first civilian role within a year, often due to unclear expectations, poor skill translation, and limited support, according to Redeployable. The Site Engineer Pathway uses real-world relevance and replaces vague job descriptions with an experience-based view of technical careers. Veterans can engage in scenario-based “job drops” simulating real facility and system challenges so they can assess their fit for the role before applying. They

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BlackRock’s $40B data center deal opens a new infrastructure battle for CIOs

Everest Group partner Yugal Joshi said, “CIOs are under significant pressure to clearly define their data center strategy beyond traditional one-off leases. Given most of the capacity is built and delivered by fewer players, CIOs need to prepare for a higher-price market with limited negotiation power.” The numbers bear this out. Global data center costs rose to $217.30 per kilowatt per month in the first quarter of 2025, with major markets seeing increases of 17-18% year-over-year, according to CBRE. Those prices are at levels last seen in 2011-2012, and analysts expect them to remain elevated. Gogia said, “The combination of AI demand, energy scarcity, and environmental regulation has permanently rewritten the economics of running workloads. Prices that once looked extraordinary have now become baseline.” Hyperscalers get first dibs The consolidation problem is compounded by the way capacity is being allocated. North America’s data center vacancy rate fell to 1.6% in the first half of 2025, with Northern Virginia posting just 0.76%, according to CBRE Research. More troubling for enterprises: 74.3% of capacity currently under construction is already preleased, primarily to cloud and AI providers. “The global compute market is no longer governed by open supply and demand,” Gogia said. “It is increasingly shaped by pre-emptive control. Hyperscalers and AI majors are reserving capacity years in advance, often before the first trench for power is dug. This has quietly created a two-tier world: one in which large players guarantee their future and everyone else competes for what remains.” That dynamic forces enterprises into longer planning cycles. “CIOs must forecast their infrastructure requirements with the same precision they apply to financial budgets and talent pipelines,” Gogia said. “The planning horizon must stretch to three or even five years.”

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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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