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Unlocking transferable skills and embracing the energy expansion

The energy industry is transforming. With the global push towards renewable energy, there’s a growing need to address the concerns of workers in traditional sectors like oil and gas. One of the key topics in this conversation is the idea of “transferable skills.” The notion that skills acquired in one sector can be effectively utilized […]

The energy industry is transforming. With the global push towards renewable energy, there’s a growing need to address the concerns of workers in traditional sectors like oil and gas.

One of the key topics in this conversation is the idea of “transferable skills.” The notion that skills acquired in one sector can be effectively utilized in another is not just a possibility; it’s necessary for a smooth and just transition — or, as JAB Recruitment prefers to call it, an energy expansion.

Transition versus expansion: seizing opportunities with courage

The term “energy transition” often carries connotations of risk and uncertainty, particularly for those in the oil and gas sector. However, the team at JAB Recruitment looks at it differently. Rather than a transition that implies a shift from one sector to another, JAB believes that today’s industry offers opportunities for an across-the-board energy expansion. This includes traditional oil and gas, and renewable energy sources like offshore wind, carbon capture, and hydrogen.

The value of oil and gas skills in renewables

Robert Gordon University’s UK Offshore Energy Workforce Transferability Review found that over 90% of the UK’s oil and gas workforce have a medium to high skills transferability score and are well positioned to work in adjacent energy sectors. This statistic underscores the potential for cross-sector skills application. For instance, workers experienced in offshore oil and gas projects are often well placed for roles in offshore wind, carbon capture, and hydrogen projects—sectors that are seeing significant growth and investment.

The oil and gas workforce need not see the energy expansion as a career cliff edge. The expansion will happen over decades, allowing workers to adapt, upskill, and find their place in a growing energy landscape. It’s not about leaving behind the skills and expertise honed over the years; rather, it’s about applying them in a new context.

Looking at it from the renewable energy perspective, the message for employers is: don’t write off the oil and gas talent pool.

Jab Recruitment's Chris Black. © Supplied by Jab recruitment
Jab Recruitment’s Chris Black.

Overcoming barriers: training and perception

A major hurdle in the energy expansion is the perception of high retraining costs and the misunderstanding of the relationship between oil and gas and renewables. It’s crucial to dispel the myth that moving between these sectors requires prohibitively expensive training.

Yes, training and some new safety certifications will be required for new roles like it usually is for any new job. But, for oil and gas workers, many of the core skills required will likely be in place already. That means new training time might be counted in hours rather than days or weeks. Also, training opportunities today are more accessible than ever, often available in online, bite-sized formats that fit into busy working lives.

The issue isn’t just about the practicalities of retraining but also about the emotional and psychological impact of the word “transition.” For many in the industry, the word suggests the decline of industries like coal mining and steelwork, where workers were left behind as the world moved on. Therefore, the narrative needs to shift from one of risk to one of opportunity.

Mobilising a just expansion

JAB Recruitment recognises that oil and gas workers have valuable skills and may require funding routes for training and has a unique app powered by Moblyze to make the energy expansion as seamless as possible.

Using AI, the platform instantly matches you with job opportunities across the energy spectrum. The advanced algorithm analyses your skills and certifications, and then recommends jobs that fit your qualifications, including those in sectors you might not have considered before. It even identifies the training you might need for a role in a new sector, where you can get that training, and the grants available to you. This real-time matching is revolutionising how to find jobs. It’s no longer about searching for jobs; the jobs will find you.

The future: toward an all-energy workforce

The goal is the creation of an all-energy workforce that can perform across various energy sectors. This workforce flexibility is already happening in sectors like subsea where workers regularly cross over between oil and gas and offshore wind projects. The same applies in CCUS and Hydrogen.  JAB is recognised as the market leader in this space, partnering with clients to accelerate the evolution of energy talent.  This trend will grow, creating a fully adaptable workforce ready to tackle the energy challenges of tomorrow.

A major driver in creating an all-energy workforce will be the development of an offshore skills passport that will allow workers to move seamlessly between oil and gas and renewable projects. However, challenges remain, particularly in aligning certifications and training standards across different sectors and geographies. An offshore skills passport would require collaboration across countries and organisations. It’s a task that is complex but essential.

Answering the call

For the energy sector to truly expand it’s important to communicate the wide range of opportunities available to the existing workforce. In this, JAB Recruitment is leading the way – not only by diversifying services but also by supporting clients in this journey.

The energy expansion is an opportunity to innovate, grow, and create a sustainable future. The skills that have built the oil and gas industry are far from obsolete – they are the foundation upon which the new energy landscape will be built.


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Or download the JAB App via the Apple Store or Google Play.

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Oracle inks $30 billion cloud deal, continuing its strong push into AI infrastructure.

He pointed out that, in addition to its continued growth, OCI has a remaining performance obligation (RPO) — total future revenue expected from contracts not yet reported as revenue — of $138 billion, a 41% increase, year over year. The company is benefiting from the immense demand for cloud computing largely driven by AI models. While traditionally an enterprise resource planning (ERP) company, Oracle launched OCI in 2016 and has been strategically investing in AI and data center infrastructure that can support gigawatts of capacity. Notably, it is a partner in the $500 billion SoftBank-backed Stargate project, along with OpenAI, Arm, Microsoft, and Nvidia, that will build out data center infrastructure in the US. Along with that, the company is reportedly spending about $40 billion on Nvidia chips for a massive new data center in Abilene, Texas, that will serve as Stargate’s first location in the country. Further, the company has signaled its plans to significantly increase its investment in Abu Dhabi to grow out its cloud and AI offerings in the UAE; has partnered with IBM to advance agentic AI; has launched more than 50 genAI use cases with Cohere; and is a key provider for ByteDance, which has said it plans to invest $20 billion in global cloud infrastructure this year, notably in Johor, Malaysia. Ellison’s plan: dominate the cloud world CTO and co-founder Larry Ellison announced in a recent earnings call Oracle’s intent to become No. 1 in cloud databases, cloud applications, and the construction and operation of cloud data centers. He said Oracle is uniquely positioned because it has so much enterprise data stored in its databases. He also highlighted the company’s flexible multi-cloud strategy and said that the latest version of its database, Oracle 23ai, is specifically tailored to the needs of AI workloads. Oracle

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CISPE’s response to the European Commission’s report warns that the resulting regulatory uncertainty could hurt the region’s economy. “Imposing new, standalone water regulations could increase costs, create regulatory fragmentation, and deter investment. This risks shifting infrastructure outside the EU, undermining both sustainability and sovereignty goals,” CISPE said in its latest policy recommendation, Advancing water resilience through digital innovation and responsible stewardship. “Such regulatory uncertainty could also reduce Europe’s attractiveness for climate-neutral infrastructure investment at a time when other regions offer clear and stable frameworks for green data growth,” it added. CISPE’s recommendations are a mix of regulatory harmonization, increased investment, and technological improvement. Currently, water reuse regulation is directed towards agriculture. Updated regulation across the bloc would encourage more efficient use of water in industrial settings such as datacenters, the asosciation said. At the same time, countries struggling with limited public sector budgets are not investing enough in water infrastructure. This could only be addressed by tapping new investment by encouraging formal public-private partnerships (PPPs), it suggested: “Such a framework would enable the development of sustainable financing models that harness private sector innovation and capital, while ensuring robust public oversight and accountability.” Nevertheless, better water management would also require real-time data gathered through networks of IoT sensors coupled to AI analytics and prediction systems. To that end, cloud datacenters were less a drain on water resources than part of the answer: “A cloud-based approach would allow water utilities and industrial users to centralize data collection, automate operational processes, and leverage machine learning algorithms for improved decision-making,” argued CISPE.

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“AI workloads, especially training and archival, can absorb 10-20ms latency variance if offset by 30-40% cost savings and assured uptime,” said Gogia. “Des Moines and Richmond offer better interconnection diversity today than some saturated Tier-1 hubs.” Contract flexibility is also crucial. Rather than traditional long-term leases, enterprises are negotiating shorter agreements with renewal options and exploring revenue-sharing arrangements tied to business performance. Maximizing what you have With expansion becoming more costly, enterprises are getting serious about efficiency through aggressive server consolidation, sophisticated virtualization and AI-driven optimization tools that squeeze more performance from existing space. The companies performing best in this constrained market are focusing on optimization rather than expansion. Some embrace hybrid strategies blending existing on-premises infrastructure with strategic cloud partnerships, reducing dependence on traditional colocation while maintaining control over critical workloads. The long wait When might relief arrive? CBRE’s analysis shows primary markets had a record 6,350 MW under construction at year-end 2024, more than double 2023 levels. However, power capacity constraints are forcing aggressive pre-leasing and extending construction timelines to 2027 and beyond. The implications for enterprises are stark: with construction timelines extending years due to power constraints, companies are essentially locked into current infrastructure for at least the next few years. Those adapting their strategies now will be better positioned when capacity eventually returns.

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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

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

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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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