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CNOOC Aims to Exceed 2 MMboed in Production This Year

CNOOC Ltd. has set a target of raising its net production to over two million barrels of oil equivalent a day (MMboed) in 2025 while keeping capital expenditure at last year’s level. Output in 2024 is expected to have totaled 720 MMboe, marking a sixth consecutive year of record highs, the Chinese state-backed company said. […]

CNOOC Ltd. has set a target of raising its net production to over two million barrels of oil equivalent a day (MMboed) in 2025 while keeping capital expenditure at last year’s level.

Output in 2024 is expected to have totaled 720 MMboe, marking a sixth consecutive year of record highs, the Chinese state-backed company said. CNOOC Ltd., majority-owned by China National Offshore Oil Corp. (CNOOC)., put the total volume goal for 2025 between 760 MMboe and 780 MMboe, of which 69 percent is to come from China.

For 2026, it will aim for 780–800 MMboe. For 2027, the target is 810–830 MMboe, according to a plan it announced Wednesday.

This year, production has been allotted about 20 percent of CNOOC Ltd.’s planned capex of CNY 125–135 billion ($17.15–18.52 billion). Development activities have been earmarked around 61 percent, while exploration would get approximately 16 percent.

Capex last year, when CNOOC Ltd. announced over a dozen production startups mostly at home, is expected to be CNY 132 billion ($18.11 billion).

“The company endeavors to search for large and medium-sized oil and gas fields, to strengthen the resource base for reserves and production growth”, it said. “In 2025, the capital expenditure for exploration in China will mainly be directed to sustain crude oil reserves while expand [sic] natural gas reserves, led by the construction of the three trillion-cubic-meters-level gas regions [in the South China Sea].

“For overseas exploration, the Company will continue to focus on the Atlantic Ocean rim and the ‘Belt and Road’ countries. Drilling will continue in Guyana and rolling exploration is planned in Nigeria. Seismic survey will be conducted in Mozambique and Iraq. At the same time, the company will continue to seek for high-quality acreage, especially operating assets.

“The company will promote exploration and development integration, as well as engineering standardization, to accelerate the conversion of reserves into production”.

CNOOC Ltd. also said it would continue pursuing new oil and gas technology. “Relying on the ‘Hi-Energy’ artificial intelligence model, the Company will facilitate the in-depth integration of digital intelligence technology with the oil and gas business to promote lean management”, it said.

“The company will drive the integrated development of hydrocarbon sector and new energy sectors”.

In 2025, projects expected to come onstream include the Bozhong 26-6 Oilfield Development Project (Phase I) and the Kenli 10-2 Oilfields Development Project (Phase I) in China, as well as the Buzios7 Project in Brazil and the Yellowtail Project in Guyana, CNOOC Ltd. said.

It has already announced two startups this year — the Panyu 11-12/10-1/10-2 Oilfield Adjustment Joint Development Project and the Dongfang 29-1 gas field, both in the South China Sea.

CNOOC Ltd. also said it would expedite offshore wind and onshore solar projects. “Green power substitution will be expedited”, it said. “In 2025, the green electricity consumption is expected to exceed 1 billion kWh, with an increase of 30 percent year-on-year.

“The Company has incorporated carbon price into investment evaluation process, and has been advancing the regional CCS/CCUS [carbon capture, utilization and storage] pilot projects”.

On shareholder returns, CNOOC Ltd. said it plans at least 45 percent in annual dividend ratio for 2025–27 subject to shareholder approval.

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Indian Oil, Vitol to Launch Trading JV in 2026

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API ‘Strengthens Offshore Safety Standards’

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Wholesale food giant completes largest solar rooftop project to date

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Cisco, Nvidia strengthen AI ties with new data center switch, reference architectures

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IT shortcuts curb AI returns

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Qualcomm goes all-in on inferencing with purpose-built cards and racks

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AI data center building boom risks fueling future debt bust, bank warns

However, that’s only one part of the problem. Meeting the power demands of AI data centers will require the energy sector to make large investments. Then there’s data center demand for microprocessors, rare earth elements, and other valuable metals such as copper, which could, in a bust, make data centers the most expensively-assembled unwanted assets in history. “Financial stability consequences of an AI-related asset price fall could arise through multiple channels. If forecasted debt-financed AI infrastructure growth materializes, the potential financial stability consequences of such an event are likely to grow,” warned the BoE blog post. “For companies who depend on the continued demand for massive computational capacity to train and run inference on AI models, an algorithmic breakthrough or other event which challenges that paradigm could cause a significant re-evaluation of asset prices,” it continued. According to Matt Hasan, CEO of AI consultancy aiRESULTS, the underlying problem is the speed with which AI has emerged. “What we’re witnessing isn’t just an incremental expansion, it’s a rush to construct power-hungry, mega-scale data centers,” he told Network World. The dot.com reversal might be the wrong comparison; it dented the NASDAQ and hurt tech investment, but the damage to organizations investing in e-commerce was relatively limited. AI, by contrast, might have wider effects for large enterprises because so many have pinned their business prospects on its potential. “Your reliance on these large providers means you are indirectly exposed to the stability of their debt. If a correction occurs, the fallout can impact the services you rely on,” said Hasan.

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Intel sees supply shortage, will prioritize data center technology

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How to set up an AI data center in 90 days

“Personally, I think that a brownfield is very creative way to deal with what I think is the biggest problem that we’ve got right now, which is time and speed to market,” he said. “On a brownfield, I can go into a building that’s already got power coming into the building. Sometimes they’ve already got chiller plants, like what we’ve got with the building I’m in right now.” Patmos certainly made the most of the liquid facilities in the old printing press building. The facility is built to handle anywhere from 50 to over 140 kilowatts per cabinet, a leap far beyond the 1–2 kW densities typical of legacy data centers. The chips used in the servers are Nvidia’s Grace Blackwell processors, which run extraordinarily hot. To manage this heat load, Patmos employs a multi-loop liquid cooling system. The design separates water sources into distinct, closed loops, each serving a specific function and ensuring that municipal water never directly contacts sensitive IT equipment. “We have five different, completely separated water loops in this building,” said Morgan. “The cooling tower uses city water for evaporation, but that water never mixes with the closed loops serving the data hall. Everything is designed to maximize efficiency and protect the hardware.” The building taps into Kansas City’s district chilled water supply, which is sourced from a nearby utility plant. This provides the primary cooling resource for the facility. Inside the data center, a dedicated loop circulates a specialized glycol-based fluid, filtered to extremely low micron levels and formulated to be electronically safe. Heat exchangers transfer heat from the data hall fluid to the district chilled water, keeping the two fluids separate and preventing corrosion or contamination. Liquid-to-chip and rear-door heat exchangers are used for immediate heat removal.

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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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