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TriMas Completes Arrow Engine Sale

TriMas Corp., a Michigan-based manufacturer of engineered products, has completed the sale of its Arrow Engine business to IES Infrastructure Solutions LLC, a division of IES Holdings Inc. TriMas confirmed in a media release that the sale marks the end of its direct presence in the oil and gas market. “Founded in 1955, Arrow Engine […]

TriMas Corp., a Michigan-based manufacturer of engineered products, has completed the sale of its Arrow Engine business to IES Infrastructure Solutions LLC, a division of IES Holdings Inc. TriMas confirmed in a media release that the sale marks the end of its direct presence in the oil and gas market.

“Founded in 1955, Arrow Engine was a legacy TriMas business and part of the TriMas portfolio for several decades”, Thomas Amato, TriMas President and Chief Executive Officer, said. “We are pleased to place this business with IES Infrastructure Solutions, the right partner to take Arrow Engine to the next level. This move is another important step in optimizing TriMas’ business portfolio”.

Arrow Engine supplies natural gas engines commonly used in remote applications, including oil field pump jacks and compressors. These engines and replacement parts are designed for use in oil and natural gas production, as well as other industrial sectors. Arrow Engine distributes its products globally, with a primary focus on the United States and Canada, according to TriMas.

Arrow Engine manufactures its engine line and offers a wide array of spare parts for various industrial engines, even those not produced by Arrow Engine, TriMas said.

With estimated 2024 revenues of approximately $20 million, Arrow Engine will operate within IES’ Infrastructure Solutions segment and retain its existing brand name, TriMas said.

“We remain committed to ensuring a smooth transition to IES Infrastructure Solutions while continuing to deliver the highest level of service to Arrow Engine’s customers”, said Amato.

TriMas selected Holland & Knight as its outside legal counsel, while Woodward Park Partners acted as exclusive financial advisor and led the sale process.

The company added that as a result of the sale, the Specialty Products business will include only Norris Cylinder’s financial performance in 2025, with just one month of Arrow Engine’s results as the sale closed on January 31, 2025.

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Trump Signs Memorandum ‘Restoring Maximum Pressure’ on Iran

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Orsted, PGE Make FID on 1.5-GW Wind Farm Near Poland

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SSE on track despite stormy weather

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TriMas Completes Arrow Engine Sale

TriMas Corp., a Michigan-based manufacturer of engineered products, has completed the sale of its Arrow Engine business to IES Infrastructure Solutions LLC, a division of IES Holdings Inc. TriMas confirmed in a media release that the sale marks the end of its direct presence in the oil and gas market. “Founded in 1955, Arrow Engine was a legacy TriMas business and part of the TriMas portfolio for several decades”, Thomas Amato, TriMas President and Chief Executive Officer, said. “We are pleased to place this business with IES Infrastructure Solutions, the right partner to take Arrow Engine to the next level. This move is another important step in optimizing TriMas’ business portfolio”. Arrow Engine supplies natural gas engines commonly used in remote applications, including oil field pump jacks and compressors. These engines and replacement parts are designed for use in oil and natural gas production, as well as other industrial sectors. Arrow Engine distributes its products globally, with a primary focus on the United States and Canada, according to TriMas. Arrow Engine manufactures its engine line and offers a wide array of spare parts for various industrial engines, even those not produced by Arrow Engine, TriMas said. With estimated 2024 revenues of approximately $20 million, Arrow Engine will operate within IES’ Infrastructure Solutions segment and retain its existing brand name, TriMas said. “We remain committed to ensuring a smooth transition to IES Infrastructure Solutions while continuing to deliver the highest level of service to Arrow Engine’s customers”, said Amato. TriMas selected Holland & Knight as its outside legal counsel, while Woodward Park Partners acted as exclusive financial advisor and led the sale process. The company added that as a result of the sale, the Specialty Products business will include only Norris Cylinder’s financial performance in 2025, with just one month of Arrow

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Tackling mental health as a young professional

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Let’s Go Build Some Data Centers: PowerHouse Drives Hyperscale and AI Infrastructure Across North America

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Blue Owl Swoops In As Major Backer of New, High-Profile, Sustainable U.S. Data Center Construction

With the global demand for data centers continuing to surge ahead, fueled by the proliferation of artificial intelligence (AI), cloud computing, and digital services, it is unsurprising that we are seeing aggressive investment strategies, beyond those of the existing hyperscalers. One of the dynamic players in this market is Blue Owl Capital, a leading asset management firm that has made significant strides in the data center sector. Back in October 2024 we reported on its acquisition of IPI Partners, a digital infrastructure fund manager, for approximately $1 billion. This acquisition added over $11 billion to the assets Blue Owl manages and focused specifically on digital infrastructure initiatives. This acquisition was completed as of January 5, 2025 and IPI’s Managing Partner, Matt A’Hearn has been appointed Head of Blue Owl’s digital infrastructure strategy. A Key Player In Digital Infrastructure and Data Centers With multi-billion-dollar joint ventures and financing initiatives, Blue Owl is positioning itself as a key player in the digital infrastructure space. The company investments in data centers, the implications of its strategic moves, and the broader impact on the AI and digital economy highlights the importance of investment in the data center to the economy overall. With the rapid growth of the data center industry, it is unsurprising that aggressive investment fund management is seeing it as an opportunity. Analysts continue to emphasize that the global data center market is expected to grow at a compound annual growth rate (CAGR) of 10.2% from 2023 to 2030, reaching $517.17 billion by the end of the decade. In this rapidly evolving landscape, Blue Owl Capital has emerged as a significant contributor. The firm’s investments in data centers are not just about capitalizing on current trends but also about shaping the future of digital infrastructure. Spreading the Wealth In August 2024, Blue Owl

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Global Data Center Operator Telehouse Launches Liquid Cooling Lab in the UK to Meet Ongoing AI and HPC Demand

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Flexential Partners with Lonestar to Support First Lunar Data Center

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Why DeepSeek Is Great for AI and HPC and Maybe No Big Deal for Data Centers

In the rapid and ever-evolving landscape of artificial intelligence (AI) and high-performance computing (HPC), the emergence of DeepSeek’s R1 model has sent ripples across industries. DeepSeek has been the data center industry’s topic of the week, for sure. The Chinese AI app surged to the top of US app store leaderboards last weekend, sparking a global selloff in technology shares Monday morning.  But while some analysts predict a transformative impact within the industry, a closer examination suggests that, for data centers at large, the furor over DeepSeek might ultimately be much ado about nothing. DeepSeek’s Breakthrough in AI and HPC DeepSeek, a Chinese AI startup, this month unveiled its R1 model, claiming performance on par with, or even surpassing, leading models like OpenAI’s ChatGPT-4 and Anthropic’s Claude-3.5-Sonnet. Remarkably, DeepSeek developed this model at a fraction of the cost typically associated with such advancements, utilizing a cluster of 256 server nodes equipped with 2,048 GPUs. This efficiency has been attributed to innovative techniques and optimized resource utilization. AI researchers have been abuzz about the performance of the DeepSeek chatbot that produces results similar to ChatGPT, but is based on open-source models and reportedly trained on older GPU chips. Some researchers are skeptical of claims about DeepSeek’s development costs and means, but its performance appears to challenge common assumptions about the computing cost of developing AI applications. This efficiency has been attributed to innovative techniques and optimized resource utilization.  Market Reactions and Data Center Implications The announcement of DeepSeek’s R1 model led to significant market reactions, with notable declines in tech stocks, including a substantial drop in Nvidia’s valuation. This downturn was driven by concerns that more efficient AI models could reduce the demand for high-end hardware and, by extension, the expansive data centers that house them. For now, investors are re-assessing the

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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

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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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