What training for a marathon has taught me about wellbeing

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What training for a marathon has taught me about wellbeing

I ran in the London Marathon this year. That’s 26.2 miles with over 50,000 people on one of the most well-known marathon courses in the world. Taking part led to me celebrating another checkmark on my lifetime bucket list. They say persistence pays off, and after six times on the ballot, I found out on […]

I ran in the London Marathon this year.

That’s 26.2 miles with over 50,000 people on one of the most well-known marathon courses in the world.

Taking part led to me celebrating another checkmark on my lifetime bucket list.

They say persistence pays off, and after six times on the ballot, I found out on 3 February that my place was confirmed.

That left me just 12 weeks to train, and what I’ve learned in that time is how our bodies and minds are so much stronger than we think.

Marathon training has pushed me to limits I didn’t know I had and then showed me I could push beyond them.

I discovered the journey was not just about running, the most valuable lessons were found in the training and the quiet determination that will get me to the start line.

Sophie Taysom, Space Solutions.

Exercise really does make us happy

We know stress and anxiety levels are at record highs, and we also know that exercise is one of the best ways to manage our mental wellbeing.

Research shows that after just 15 minutes and nine seconds of exercise, people start to feel a positive uplift.

Now, a marathon is a bit longer than 15 minutes, but it’s a starting point.

Each run releases a cascade of neurochemicals—endorphin boost, increased serotonin, and cortisol reduction—all nature’s own stress relievers.

During the longer weekly runs, I felt my anxieties dissolve with each mile.

A simple escapism to get me away from the daily mental stimulations.

I was getting outside, away from home and work, and was given a feeling of freedom and control in a world which at times can feel overwhelming.

Between constant notifications, family responsibilities and work, finding time for myself initially felt selfish.

But I’ve learned that this time isn’t about prioritising myself over others, it’s about mapping time so everything else can fit properly.

Those hours of training weren’t stolen from my family, friends and life commitments; they were investments that made me more present, more patient, and more productive in all aspects of life.

Around 56,000 runners took part on the 45th London Marathon.

We just need to show up

Marathon training isn’t about speed; like anything in life, it’s about showing up.

On the darkest training days, jogging through that driving Scottish rain and questioning my sanity, I discovered that consistency transforms the impossible into inevitable.

The discipline of sticking to a plan, without excuses, builds a resilience that extends beyond running.

A marathon isn’t for everyone, but consistent commitment to your wellbeing is universal.

While countless books explore running for mental health, reading alone doesn’t create change. The hardest part is always taking that first step.

Today, I invite you to pause for five minutes and ask: what’s one thing can I start tomorrow that could still be part of my life in three months, a year, or three years?

Start slow, be consistent, and trust the process. Your own finish line awaits.

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In its new report, Deloitte said that its analysis of figures from the World Nuclear Association, the American Nuclear Society, the U.S. Department of Energy, and others showed that new nuclear power could potentially meet about 10% of the projected increase in data center demand over the next decade, assuming capacity is also significantly expanded by between 35GW and 62GW, and 30% of the expansion is earmarked for data centers. “Nuclear energy presents a potential solution for meeting some of the growing electricity demands of data centers, with its reliable and clean energy profile,” Deloitte’s report said, noting five key advantages of the technology: Reliable baseload power: Nuclear reactors operate 24/7, regardless of the weather, providing the reliable power so important to data centers. In addition, Deloitte said, “Their capacity factor, exceeding 92.5%, outperforms other sources like natural gas (56%) and renewables like wind (35%) and solar (25%).” High energy density: A small amount of fuel generates a lot of power, which minimizes the need for fuel storage and transportation. “This efficiency can translate to a smaller physical footprint and enhanced sustainability,” Deloitte said. Scalable power output: A full-sized reactor typically generates 800 megawatts (MW) or more of electricity, which accommodates the needs of large data centers. Low carbon emissions: Nuclear power plants produce virtually no greenhouse gas emissions during operation. Enhanced land use efficiency: Compared to other energy sources, nuclear power plants require relatively little land. Gartner’s Johnson echoed these advantages, and also predicted that nuclear energy, and small modular reactors (SMRs) in particular, will “provide a viable answer” to the question of what to do when electricity demand exceeds supply. They can, he said, “ensure independence from grid power fluctuations by providing dedicated on-site power for large data centers.” However, both Gartner and Deloitte also highlighted challenges in

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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

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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