IMF Lowers Growth Outlook for Gulf Oil Producers after Price Slump

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IMF Lowers Growth Outlook for Gulf Oil Producers after Price Slump

The International Monetary Fund downgraded its growth forecasts for oil exporting countries in the Middle East and North Africa, including Saudi Arabia and Iraq, citing escalating global trade tensions and lower energy prices. The IMF cut the 2025 outlook for oil exporters in the region to 2.3 percent, 1.7 percentage points lower than the fund’s […]

The IMF cut the 2025 outlook for oil exporters in the region to 2.3 percent, 1.7 percentage points lower than the fund’s forecast from October. The projections were announced by the Washington based lender on Thursday in its latest regional economic outlook.

The IMF expects oil prices to decline to an average of $66.9 per barrel this year, nearly $6 below the October projection. It cited strong supply growth from non-OPEC+ countries and subdued demand because of a slowing global economy.

Prices for Brent crude have slumped around 15 percent this year to roughly $63 a barrel. That’s been down to the US-led trade wars and OPEC+ announcing a faster-than-expected increase in oil output.

Iraq got one of the largest downgrades. The IMF now expects its gross domestic product to contract 1.5 percent this year, rather than rise 4.1 percent, as was the presumption in October. Saudi Arabia’s outlook was lowered to 3 percent from 4.6 percent.

While non-oil growth is expected to be bolstered by infrastructure projects and other diversification efforts in the Gulf, the IMF says some government spending may be lowered in line with crude prices.

There’s been a “recalibration of investment spending plans resulting from softer oil prices, further amplified by the decline in oil prices from the recent escalation of trade tensions,” said the IMF.

All the same, the direct impact of changes in tariffs is “generally limited” on the GCC because of the tariff exemption on energy exports and the limited non-oil exports to the US, the lender said.

The whole MENA region is expected to grow 2.6 percent this year, 1.4 percentage points lower than October’s estimate.

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In 1849, the discovery of gold in California ignited a frenzy, drawing prospectors from around the world in pursuit of quick fortune. While few struck it rich digging and sifting dirt, a different class of entrepreneurs quietly prospered: those who supplied the miners with the tools of the trade. From picks and shovels to tents and provisions, these providers became indispensable to the gold rush, profiting handsomely regardless of who found gold. Today, a new gold rush is underway, in pursuit of artificial intelligence. And just like the days of yore, the real fortunes may lie not in the gold itself, but in the infrastructure and equipment that enable its extraction. This is where neocloud players and chipmakers are positioned, representing themselves as the fundamental enablers of the AI revolution. Neoclouds: The Essential Tools and Implements of AI Innovation The AI boom has sparked a frenzy of innovation, investment, and competition. From generative AI applications like ChatGPT to autonomous systems and personalized recommendations, AI is rapidly transforming industries. Yet, behind every groundbreaking AI model lies an unsung hero: the infrastructure powering it. Enter neocloud providers—the specialized cloud platforms delivering the GPU horsepower that fuels AI’s meteoric rise. Let’s examine how neoclouds represent the “picks and shovels” of the AI gold rush, used for extracting the essential backbone of AI innovation. Neoclouds are emerging as indispensable players in the AI ecosystem, offering tailored solutions for compute-intensive workloads such as training large language models (LLMs) and performing high-speed inference. Unlike traditional hyperscalers (e.g., AWS, Azure, Google Cloud), which cater to a broad range of use cases, neoclouds focus exclusively on optimizing infrastructure for AI and machine learning applications. This specialization allows them to deliver superior performance at a lower cost, making them the go-to choice for startups, enterprises, and research institutions alike.

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

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

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

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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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