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Trade War Is Diverting USA Petroleum Gas Cargoes Away From China

Multiple carriers of petroleum-based gases traveling from the US to China have begun diverting to other countries due to the intensifying trade war between the world’s two largest economies.  Four cargoes of propane have shifted their routes from China to alternate destinations over the past week, bound for countries including Japan and South Korea, according […]

Multiple carriers of petroleum-based gases traveling from the US to China have begun diverting to other countries due to the intensifying trade war between the world’s two largest economies. 

Four cargoes of propane have shifted their routes from China to alternate destinations over the past week, bound for countries including Japan and South Korea, according to a report from analytics firm Vortexa. At least one cargo of ethane — which is used in plastics production — has been scrapped entirely, according to a person familiar with the matter.

The diversions show the disruption to supply chains caused by the trade fight between the US and China, historically a major buyer of US ethane and petroleum gases. President Donald Trump has levied 145% tariffs on most US imports from China, and the US Trade Representative more recently imposed steep fees on Chinese-linked vessels seeking to access American ports.

Eight Very Large Gas Carriers carrying US LPG were still on course for China as of this week, while the four diversions have all been recorded since April 17, according to Vortexa. Diverted vessels include the Zakher, Maple Gas, BW Gemini and Eiger Explorer, all departing from the US Gulf Coast. 

The G. Arete, a propane carrier, diverted to South Korea from China, while a chemical tanker named STI Notting Hill is also rerouting to South Korea, Vortexa said. The US exported about 310,000 barrels of propane to China per day in 2024, double the volume from a year earlier, according to East Daley Analytics.

Spot ethane shipments may continue to be affected by the trade war, while committed cargoes are harder to unwind, the person said. Asia-bound flows of ethylene — used in plastics and industrial solvents — have already slowed because of seasonal factors but may be further reduced by the tariffs, the person said.



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PG&E foresees ‘bright future’ with lower prices, higher demand

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WTI Ends Higher But Logs Weekly Loss

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Trade War Is Diverting USA Petroleum Gas Cargoes Away From China

Multiple carriers of petroleum-based gases traveling from the US to China have begun diverting to other countries due to the intensifying trade war between the world’s two largest economies.  Four cargoes of propane have shifted their routes from China to alternate destinations over the past week, bound for countries including Japan and South Korea, according to a report from analytics firm Vortexa. At least one cargo of ethane — which is used in plastics production — has been scrapped entirely, according to a person familiar with the matter. The diversions show the disruption to supply chains caused by the trade fight between the US and China, historically a major buyer of US ethane and petroleum gases. President Donald Trump has levied 145% tariffs on most US imports from China, and the US Trade Representative more recently imposed steep fees on Chinese-linked vessels seeking to access American ports. Eight Very Large Gas Carriers carrying US LPG were still on course for China as of this week, while the four diversions have all been recorded since April 17, according to Vortexa. Diverted vessels include the Zakher, Maple Gas, BW Gemini and Eiger Explorer, all departing from the US Gulf Coast.  The G. Arete, a propane carrier, diverted to South Korea from China, while a chemical tanker named STI Notting Hill is also rerouting to South Korea, Vortexa said. The US exported about 310,000 barrels of propane to China per day in 2024, double the volume from a year earlier, according to East Daley Analytics. Spot ethane shipments may continue to be affected by the trade war, while committed cargoes are harder to unwind, the person said. Asia-bound flows of ethylene — used in plastics and industrial solvents — have already slowed because of seasonal factors but may be further reduced by the tariffs, the

Read More »

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Quiet Genius at the Neutral Line: How Onics Filters Are Reshaping the Future of Data Center Power Efficiency

Why Harmonics Matter In a typical data center, nonlinear loads—like servers, UPS systems, and switch-mode power supplies—introduce harmonic distortion into the electrical system. These harmonics travel along the neutral and ground conductors, where they can increase current flow, cause overheating in transformers, and shorten the lifespan of critical power infrastructure. More subtly, they waste power through reactive losses that don’t show up on a basic utility bill, but do show up in heat, inefficiency, and increased infrastructure stress. Traditional mitigation approaches—like active harmonic filters or isolation transformers—are complex, expensive, and often require custom integration and ongoing maintenance. That’s where Onics’ solution stands out. It’s engineered as a shunt-style, low-pass filter: a passive device that sits in parallel with the circuit, quietly siphoning off problematic harmonics without interrupting operations.  The result? Lower apparent power demand, reduced electrical losses, and a quieter, more stable current environment—especially on the neutral line, where cumulative harmonic effects often peak. Behind the Numbers: Real-World Impact While the Onics filters offer a passive complement to traditional mitigation strategies, they aren’t intended to replace active harmonic filters or isolation transformers in systems that require them—they work best as a low-complexity enhancement to existing power quality designs. LoPilato says Onics has deployed its filters in mission-critical environments ranging from enterprise edge to large colos, and the data is consistent. In one example, a 6 MW data center saw a verified 9.2% reduction in energy consumption after deploying Onics filters at key electrical junctures. Another facility clocked in at 17.8% savings across its lighting and support loads, thanks in part to improved power factor and reduced transformer strain. The filters work by targeting high-frequency distortion—typically above the 3rd harmonic and up through the 35th. By passively attenuating this range, the system reduces reactive current on the neutral and helps stabilize

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New IEA Report Contrasts Energy Bottlenecks with Opportunities for AI and Data Center Growth

Artificial intelligence has, without question, crossed the threshold—from a speculative academic pursuit into the defining infrastructure of 21st-century commerce, governance, and innovation. What began in the realm of research labs and open-source models is now embedded in the capital stack of every major hyperscaler, semiconductor roadmap, and national industrial strategy. But as AI scales, so does its energy footprint. From Nvidia-powered GPU clusters to exascale training farms, the conversation across boardrooms and site selection teams has fundamentally shifted. It’s no longer just about compute density, thermal loads, or software frameworks. It’s about power—how to find it, finance it, future-proof it, and increasingly, how to generate it onsite. That refrain—“It’s all about power now”—has moved from a whisper to a full-throated consensus across the data center industry. The latest report from the International Energy Agency (IEA) gives this refrain global context and hard numbers, affirming what developers, utilities, and infrastructure operators have already sensed on the ground: the AI revolution will be throttled or propelled by the availability of scalable, sustainable, and dispatchable electricity. Why Energy Is the Real Bottleneck to Intelligence at Scale The major new IEA report puts it plainly: The transformative promise of AI will be throttled—or unleashed—by the world’s ability to deliver scalable, reliable, and sustainable electricity. The stakes are enormous. Countries that can supply the power AI craves will shape the future. Those that can’t may find themselves sidelined. Importantly, while AI poses clear challenges, the report emphasizes how it also offers solutions: from optimizing energy grids and reducing emissions in industrial sectors to enhancing energy security by supporting infrastructure defenses against cyberattacks. The report calls for immediate investments in both energy generation and grid capabilities, as well as stronger collaboration between the tech and energy sectors to avoid critical bottlenecks. The IEA advises that, for countries

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Colorado Eyes the AI Data Center Boom with Bold Incentive Push

Even as states work on legislation to limit data center development, it is clear that some locations are looking to get a bigger piece of the huge data center spending that the AI wave has created. It appears that politicians in Colorado took a look around and thought to themselves “Why is all that data center building going to Texas and Arizona? What’s wrong with the Rocky Mountain State?” Taking a page from the proven playbook that has gotten data centers built all over the country, Colorado is trying to jump on the financial incentives for data center development bandwagon. SB 24-085: A Statewide Strategy to Attract Data Center Investment Looking to significantly boost its appeal as a data center hub, Colorado is now considering Senate Bill 24-085, currently making its way through the state legislature. Sponsored by Senators Priola and Buckner and Representatives Parenti and Weinberg, this legislation promises substantial economic incentives in the form of state sales and use tax rebates for new data centers established within the state from fiscal year 2026 through 2033. Colorado hopes to position itself strategically to compete with neighboring states in attracting lucrative tech investments and high-skilled jobs. According to DataCenterMap.com, there are currently 53 data centers in the state, almost all located in the Denver area, but they are predominantly smaller facilities. In today’s era of massive AI-driven hyperscale expansion, Colorado is rarely mentioned in the same breath as major AI data center markets.  Some local communities have passed their own incentive packages, but SB 24-085 aims to offer a unified, statewide framework that can also help mitigate growing NIMBY (Not In My Backyard) sentiment around new developments. The Details: How SB 24-085 Works The bill, titled “Concerning a rebate of the state sales and use tax paid on new digital infrastructure

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Wonder Valley and the Great AI Pivot: Kevin O’Leary’s Bold Data Center Play

Data Center World 2025 drew record-breaking attendance, underscoring the AI-fueled urgency transforming infrastructure investment. But no session captivated the crowd quite like Kevin O’Leary’s electrifying keynote on Wonder Valley—his audacious plan to build the world’s largest AI compute data center campus. In a sweeping narrative that ranged from pandemic pivots to stranded gas and Branson-brand inspiration, O’Leary laid out a real estate and infrastructure strategy built for the AI era. A Pandemic-Era Pivot Becomes a Case Study in Digital Resilience O’Leary opened with a Shark Tank success story that doubled as a business parable. In 2019, a woman-led startup called Blueland raised $50 million to eliminate plastic cleaning bottles by shipping concentrated cleaning tablets in reusable kits. When COVID-19 shut down retail in 2020, her inventory was stuck in limbo—until she made an urgent call to O’Leary. What followed was a high-stakes, last-minute pivot: a union-approved commercial shoot in Brooklyn the night SAG-AFTRA shut down television production. The direct response ad campaign that resulted would not only liquidate the stranded inventory at full margin, but deliver something more valuable—data. By targeting locked-down consumers through local remnant TV ad slots and optimizing by conversion, Blueland saw unheard-of response rates as high as 17%. The campaign turned into a data goldmine: buyer locations, tablet usage patterns, household sizes, and contact details. Follow-up SMS campaigns would drive 30% reorders. “It built such a franchise in those 36 months,” O’Leary said, “with no retail. Now every retailer wants in.” The lesson? Build your infrastructure to control your data, and you build a business that scales even in chaos. This anecdote set the tone for the keynote: in a volatile world, infrastructure resilience and data control are the new core competencies. The Data Center Power Crisis: “There Is Not a Gig on the Grid” O’Leary

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Microsoft will invest $80B in AI data centers in fiscal 2025

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John Deere unveils more autonomous farm machines to address skill labor shortage

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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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