Europe’s Diesel Market Flashes Warning of Supply Pressures

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Europe’s Diesel Market Flashes Warning of Supply Pressures

Benchmark diesel futures are showing signs of market tightness in Europe, as fuel traders continue to face supply pressures. The most-immediate contracts on Tuesday hit the steepest backwardation — where barrels for prompt delivery are more expensive than those further down the curve — since March. The structure is often interpreted as a sign of […]

Benchmark diesel futures are showing signs of market tightness in Europe, as fuel traders continue to face supply pressures.

The most-immediate contracts on Tuesday hit the steepest backwardation — where barrels for prompt delivery are more expensive than those further down the curve — since March. The structure is often interpreted as a sign of limited supply relative to demand, with traders willing to pay a premium for fuel that’s available sooner.

Europe’s diesel supplies have been pressured by refinery outages — both planned and unplanned — in recent weeks. January also saw a sharp drop-off in shipments into the European Union and UK, according to Vortexa Ltd. data compiled by Bloomberg.

There are also signs of supply limitations in the US market, which regularly supplies Europe with cargoes. The country’s stockpiles are at their lowest for the time of year since 2014 and futures there are also strongly backwardated.

“Cargoes diverting to the US, reverse arbitrage movements from Amsterdam-Rotterdam-Antwerp to PADD 1 are indicative of an undoubtedly tight US market,” said George Shaw, an oil analyst at Kpler. For Europe, that’s “having some reciprocal effect on gasoil spreads, as it needs to price higher in order to attract cargo flow now that demand is seasonally recovering.”

Recent cold weather is also supportive for Europe’s market: lower temperatures can stoke demand for heating oil, a type of diesel. A recent jump in natural gas prices is another potential tailwind for prices.

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2025 playbook for enterprise AI success, from agents to evals

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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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