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BKV Names New CFO

BKV Corp. has named a new chief financial officer (CFO) as John T. Jimenez is set to retire on May 15. David Tameron will succeed Jimenez, BKV said in a media release. “John’s vision and stewardship have helped transform BKV into the strong public company that we are today, building a team and culture which […]

BKV Corp. has named a new chief financial officer (CFO) as John T. Jimenez is set to retire on May 15. David Tameron will succeed Jimenez, BKV said in a media release.

“John’s vision and stewardship have helped transform BKV into the strong public company that we are today, building a team and culture which has positioned us for continued success. We are grateful for his leadership and wish him well in his retirement”, Chris Kalnin, BKV’s Chief Executive Officer, said.

“As we look to the future, David has consistently demonstrated exceptional leadership and a deep commitment to BKV and our continued growth. He is a natural fit to assume the role of CFO, as he possesses a profound financial acumen and brings historical knowledge and highly valuable experience from his previous three years on our financial team”, Kalnin added.

Tameron currently serves as BKV Vice President of Strategic Finance and Investor Relations.  Before joining BKV in August 2022, Tameron held several positions at Wells Fargo & Company, including Managing Director of Denver-based Corporate Banking from September 2017 to August 2022, and Managing Director of Institutional Equity Research from July 2006 to August 2017. 

Tameron holds an MBA from Duke University’s Fuqua School of Business and a BA in Finance from Arizona State University.

Tameron said, “The Company is at a pivotal stage in its evolution, and I look forward to working alongside our teams in optimizing corporate, financial, and operational achievements that will build on the solid foundation that has already been established”.

BKV said Tameron will assume his position on April 1, 2025. At that point, Jimenez will transition to a Senior Advisor role, assisting with the transition and serving in a retirement capacity until May 15, 2025.

Jimenez has been the CFO of BKV since April 2021, leading the finance team through a pivotal period for the company, BKV said. He optimized the finance function in preparation for becoming publicly traded, managed significant mergers and acquisitions, and, most notably, oversaw the successful completion of the company’s IPO in September 2024. Before joining BKV, Jimenez held various leadership roles at BP over more than 30 years, working in multiple countries, the company said.

“It has been an honor to serve as BKV’s CFO for the last four years, and I believe the company is well-positioned for growth. I am confident that BKV’s experienced leadership team, including David as CFO, along with its talented and dedicated employees, will take the company to even greater heights in the future”, Jimenez commented.

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BKV Names New CFO

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DNV’s UK ETO 2025 shows progress, trends to build on to achieve net zero

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

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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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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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