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Quebec Says It’s Open to LNG, Oil Projects After Trump Threats

Quebec has long resisted proposals to build new oil and gas pipelines through its territory. US President Donald Trump’s threats against Canada might change that. Quebec Environment Minister Benoit Charette said the government of the Canadian province is open to reconsidering two major energy infrastructure projects that it previously rejected.  TC Energy Corp.’s Energy East pipeline, which […]

Quebec has long resisted proposals to build new oil and gas pipelines through its territory. US President Donald Trump’s threats against Canada might change that.

Quebec Environment Minister Benoit Charette said the government of the Canadian province is open to reconsidering two major energy infrastructure projects that it previously rejected. 

TC Energy Corp.’s Energy East pipeline, which would have carried western Canadian crude to refineries in eastern Canada, was mothballed in 2017 over regulatory hurdles and intense political opposition in Quebec. A separate proposal known as GNL Quebec to build a liquefied natural gas pipeline and export terminal in the Saguenay region was rejected by both federal and provincial authorities on environmental grounds.

Both would still be turned down if presented under the same terms today, but if they were improved, it’s open for debate, Charette told reporters Wednesday. “If we address these concerns today, these are projects that could be accepted,” he said. 

The comments underscore how Trump’s threats to impose broad tariffs on US imports of Canadian goods have changed the political landscape in Canada. Trump has also made repeated statements that he believes Canada should become a US state — an idea that is rejected by a large majority of Canadians, according to polls. One survey by Ipsos, published shortly before Trump’s inauguration, found that 80% of respondents would “never” vote for Canada to become part of the US.   

Foreign Minister Melanie Joly, who’s from Quebec, told a Montreal business audience Tuesday that Trump’s threats might reshape how Quebeckers think about fossil fuel projects. 

“It’s a question people have to ask themselves here,” Joly said. “At the same time, we have environmental objectives. We have to reduce our C02 emissions. I’m very, very interested to know: Does what’s happened in the last few days change the game?”

South Bow Corp., the oil pipeline company spun out of former Energy East proponent TC Energy Corp., declined to comment on Energy East. TC Energy, which owns the gas pipeline that would have been converted to carry oil under this project, didn’t respond to a request for comment.



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Quebec Says It’s Open to LNG, Oil Projects After Trump Threats

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

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

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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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