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UKAEA, Eni Partner to Advance Fusion Energy

The United Kingdom Atomic Energy Authority (UKAEA) has joined forces with Italian energy major Eni SpA to conduct research and development activities in the field of fusion energy. The pair aim to kick off the collaboration with the construction of the world’s largest and most advanced tritium fuel cycle facility, a vital fuel for future […]

The United Kingdom Atomic Energy Authority (UKAEA) has joined forces with Italian energy major Eni SpA to conduct research and development activities in the field of fusion energy.

The pair aim to kick off the collaboration with the construction of the world’s largest and most advanced tritium fuel cycle facility, a vital fuel for future fusion power stations. The “UKAEA-Eni H3AT (pronounced heat) Tritium Loop Facility”, to rise at Culham Campus, will be completed in 2028, the pair said in a media release. The facility will pave the way for the industry and academia to study how to process, store, and recycle tritium.

Tritium recovery and reuse will be essential for future fusion power plants, enhancing efficiency in fuel supply and generation, the joint statement said. Fusion energy, which replicates the Sun’s power by fusing hydrogen isotopes to produce helium, releases significant emissions-free energy through a safe and clean process. It has the potential to significantly improve energy security and support decarbonization efforts, the statement said.

This collaboration merges UKAEA’s fusion research expertise with Eni’s industrial plant engineering and operational skills.

“We believe that fusion energy can contribute to a net zero future, including going beyond the decarbonization of electricity”, Professor Ian Chapman, CEO of UKAEA, said. “The H3AT demonstration plant will set a new benchmark as the largest and most advanced tritium fuel cycle facility in the world, paving the way for innovative offerings in fusion fuel and demonstrating the UK’s leadership in this crucial area of research and development”.

“Fusion energy is meant to revolutionize the global energy transition path, accelerating the decarbonization of our economic and industrial systems, helping to spread access to energy, and reducing energy dependency ties within a more equitable transition framework”, Claudio Descalzi, Eni CEO, said. “Eni is strongly committed to various areas of research and development of this complex technology, in which it has always firmly believed. Today with our UK partners we are laying the foundations for further progress towards the goal of fusion which – if we consider its enormous scope of technological innovation – is increasingly concrete and not so far off in time. To continue this virtuous development, international system-level technological partnerships like this one are indispensable”.

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BP Reveals Another Discovery Offshore Egypt

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UKAEA, Eni Partner to Advance Fusion Energy

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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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