Oil Steadies as Tariff Delay Offsets Lower Risk to Russian Flows

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Oil Steadies as Tariff Delay Offsets Lower Risk to Russian Flows

Oil settled little changed, rebounding from the lowest since December, as a hazy timeline surrounding US President Donald Trump’s plans to impose reciprocal tariffs counteracted potentially easing risks to Russian supplies. West Texas Intermediate ended the session fractionally lower near $71 a barrel after Trump directed his administration to propose new levies on a country-by-country basis […]

West Texas Intermediate ended the session fractionally lower near $71 a barrel after Trump directed his administration to propose new levies on a country-by-country basis that could take weeks or months to complete. The delay reduced concerns that the levies will hamper demand and sparked speculation Trump may not follow through on the measures.

Crude had earlier dropped as low as $70.22 after Trump and Russian President Vladimir Putin agreed to talks on ending the war in Ukraine. A Kremlin spokesperson confirmed that Ukraine will participate in peace talks, raising the prospect that sanctions imposed by the Biden administration may be lifted and that Ukrainian drone attacks on Russia’s industry will abate, allowing Moscow’s crude to again flow freely.

“A potential resolution could significantly ease war-related costs, particularly in the energy sector,” City Index and Forex.com analyst Fawad Razaqzada wrote in a note. In regards to Trump’s tariff threats, “markets have so far taken them as merely a negotiating ploy.”

Trump’s various trade measures have also weighed on sentiment and prices over the past three weeks. The president’s policies risk stoking volatility in global markets and have the potential to create supply-demand imbalances that aren’t reflective of fundamentals, OPEC said in a monthly report on Wednesday. The release also showed that several members are better implementing supply curbs.

Prices:

WTI for March delivery fell 8 cents to settle at $71.29 a barrel in New York.
Brent for April settlement fell by 16 cents to settle at $75.02 a barrel.

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Generated by readers, the comments included herein do not reflect the views and opinions of Rigzone. All comments are subject to editorial review. Off-topic, inappropriate or insulting comments will be removed.

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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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