From cloud to edge

Arm thinks the AI market is about to enter a new phase, moving from cloud-based processing to edge computing. While much of the media’s attention has been focused to date on massive data centres, with models trained in and accessed from the cloud, Jesaitis said that most AI compute, especially inference tasks, is likely to be increasingly decentralised.

“The next ‘aha’ moment in AI is when local AI processing is being done on devices you couldn’t have imagined before,” Jesaitis said. These devices range from smartphones and earbuds to cars and industrial sensors. Arm’s IP is already embedded, literally, in these devices – it’s a company that only in the last year has been the IP behind over 30 billion chips, placed in devices of every conceivable description, all over the world.

The deployment of AI in edge environments has several benefits, with team at Arm citing three main ‘wins’. Firstly, the inherent efficiency of low-power Arm chips means that power bills for running compute and cooling are lower. That keeps the environmental footprint of the technology as small as possible.

Secondly, putting AI in local settings means latency is much lower (with latency determined by the distance between local operations and the site of the AI model). Arm points to uses like instant translation, dynamic scheduling of control systems, and features like the near-immediate triggering of safety functions – for instance in IIoT settings.

Thirdly, ‘keeping it local’ means there’s no potentially sensitive data sent off-premise. The benefits are obvious for any organisation in highly-regulated industries, but the increasing number of data breaches means even companies operating with relatively benign data sets are looking to reduce their attack surface.

Arm silicon, optimised for power-constrained devices, makes it well-suited for compute where it’s needed on the ground, the company says. The future may well be one where AI is found woven throughout environments, not centralised in a data centre run by one of the large providers.

Arm and global governments

Arm is actively engaged with global policymakers, considering this level of engagement an important part of its role. Governments continue to compete to attract semiconductor investment, the issues of supply chain and concentrated dependencies still fresh in many policymakers’ memories from the time of the COVID epidemic.

Arm lobbies for workforce development, working at present with policy-makers in the White House on an education coalition to build an ‘AI-ready workforce’. Domestic independence in technology relies as much on the abilities of workforce as it does on the availability of hardware.

Jesaitis noted a divergence between regulatory environments: the US prioritises what the government there terms acceleration and innovation, while the EU leads on safety, privacy, security and legally-enforced standards of practice. Arm aims to find the middle ground between these approaches, building products that meet stringent global compliance needs, yet furthering advances in the AI industry.

The enterprise case for edge AI

The case for integrating Arm’s edge-focused AI architecture into enterprise transformation strategies can be persuasive. The company stresses its ability to offer scale-able AI without the need to centralise to the cloud, and is also pushing its investment in hardware-level security. That means issues like memory exploits (outside of the control of users plugged into centralised AI models) can be avoided.

Of course, sectors already highly-regulated in terms of data practices are unlikely to experience relaxed governance in the future – the opposite is pretty much inevitable. All industries will be seeing more regulation and greater penalties for non-compliance in the years to come. However, to balance that, there are significant competitive advantages available to those that can demonstrate their systems’ inherent safety and security. It’s into this regulatory landscape that Arm sees itself and local, edge AI fitting.

Additionally, in Europe and Scandinavia, ESG goals are going to be increasingly important. Here, the power-sipping nature of Arm chips offers big advantages. That’s a trend that even the US hyperscalers are responding to: AWS’s latest SHALAR range of low-cost, low-power Arm-based platforms is there to satisfy that exact demand.

Arm’s collaboration with cloud hyperscalers such as AWS and Microsoft produces chips that combine efficiency with the necessary horsepower for AI applications, the company says.

What’s next from Arm and the industry

Jesaitis pointed out several trends that enterprises may be seeing in the next 12 to 18 months. Global AI exports, particularly from the US and Middle East, are ensuring that local demand for AI can be satisfied by the big providers. Arm is a company that can supply both big providers in these contexts (as part of their portfolios of offerings) and satisfy the rising demand for edge-based AI.

Jesaitis also sees edge AI as something of the hero of sustainability in an industry increasingly under fire for its ecological impact. Because Arm technology’s biggest market has been in low-power compute for mobile, it’s inherently ‘greener’. As enterprises hope to meet energy goals without sacrificing compute, Arm offers a way that combines performance with responsibility.

Redefining “smart”

Arm’s vision of AI at the edge means computers and the software running on them can be context-aware, cheap to run, secure by design, and – thanks to near-zero network latency – highly-responsive. Jesaitis said, “We used to call things ‘smart’ because they were online. Now, they’re going to be truly intelligent.”

(Image source: “Factory Floor” by danielfoster437 is licensed under CC BY-NC-SA 2.0.)

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In Chifeng, a city in northern China, a renewable-powered factory offers a clear example. The site produces hydrogen and ammonia using electricity generated entirely from nearby wind and solar farms. Unlike traditional plants connected to the wider grid, this facility runs on its own closed system. That setup brings a problem as well as a benefit: renewable power is clean, but it rises and falls with the weather.

To keep production stable, the factory relies on an AI-driven control system built by its owner, Envision. Rather than following fixed schedules, the software continuously adjusts output based on changes in wind and sunlight. As reported by Reuters, Zhang Jian, Envision’s chief engineer for hydrogen energy, compared the system to a conductor, coordinating electricity supply and industrial demand in real time.

When wind speeds increase, production ramps up automatically to take full advantage of the available power. When conditions weaken, electricity use is quickly reduced to avoid strain. Zhang said the system allows the plant to operate at high efficiency despite the volatility of renewable energy.

Projects like this are central to China’s plans for hydrogen and ammonia, fuels seen as important for cutting emissions in sectors such as steelmaking and shipping. They also point to a broader strategy: using AI to manage complexity as the country adds more renewable power to its grid.

Researchers argue that AI could play a significant role in meeting China’s climate goals. Zheng Saina, an associate professor at Southeast University in Nanjing who studies low-carbon transitions, said AI can support tasks ranging from emissions tracking to forecasting electricity supply and demand. At the same time, she cautioned that AI itself is driving rapid growth in power consumption, particularly through energy-hungry data centres.

China now installs more wind and solar capacity than any other country, but absorbing that power efficiently remains a challenge. According to Cory Combs, associate director at Beijing-based research firm Trivium China, AI is increasingly seen as a way to make the grid more flexible and responsive.

That thinking was formalised in September, when Beijing introduced an “AI+ energy” strategy. The plan calls for deeper links between AI systems and the energy sector, including the development of multiple large AI models focused on grid operations, power generation, and industrial use. By 2027, the government aims to roll out dozens of pilot projects and test AI across more than 100 use cases. Within another three years, officials want China to reach what they describe as a world-leading level of AI integration in energy.

Combs said the focus is on highly specialised tools designed for specific jobs, such as managing wind farms, nuclear plants, or grid balancing, rather than general-purpose AI. This approach contrasts with the United States, where much of the investment has gone into building advanced large-language models, according to Hu Guangzhou, a professor at the China Europe International Business School in Shanghai.

One area where AI could have immediate impact is demand forecasting. Fang Lurui, an assistant professor at Xi’an Jiaotong-Liverpool University, said power grids must match supply and demand at every moment to avoid outages. Accurate forecasts of renewable output and electricity use allow operators to plan ahead, storing energy in batteries when needed and reducing reliance on coal-fired backup plants.

Some cities are already experimenting. Shanghai has launched a citywide virtual power plant that links dozens of operators — including data centres, building systems, and electric vehicle chargers — into a single coordinated network. During a trial last August, the system reduced peak demand by more than 160 megawatts, roughly equivalent to the output of a small coal plant.

Combs said such systems matter because modern power generation is increasingly scattered and intermittent. “You need something very robust that is able to be predictive and account for new information very quickly,” he said.

Beyond the grid, China is also looking to apply AI to its national carbon market, which covers more than 3,000 companies in emissions-heavy industries such as power, steel, cement, and aluminium. These sectors together produce over 60% of the country’s carbon emissions. Chen Zhibin, a senior manager at Berlin-based think tank adelphi, said AI could help regulators verify emissions data, refine the allocation of free allowances, and give companies clearer insight into their production costs.

Still, the risks are growing alongside the opportunities. Studies suggest that by 2030, China’s AI data centres could consume more than 1,000 terawatt-hours of electricity each year — roughly the same as Japan’s current annual usage. Lifecycle emissions from the AI sector are projected to rise sharply and peak well after China’s 2030 emissions target.

Xiong Qiyang, a doctoral researcher at Renmin University of China who worked on one such study, said the results reflect the reality that coal still dominates China’s power mix. He warned that rapid AI expansion could complicate national climate goals if energy sources do not shift quickly enough.

In response, regulators have begun tightening rules. A 2024 action plan requires data centres to improve energy efficiency and increase their use of renewable power by 10% each year. Other initiatives encourage new facilities to be built in western regions, where wind and solar resources are more abundant.

Operators on the east coast are also testing new ideas. Near Shanghai, an underwater data centre is set to open, using seawater for cooling to cut energy and water use. The developer, Hailanyun, said the facility will draw most of its power from an offshore wind farm and could be replicated if the project proves viable.

Despite the growing energy demands of AI, Xiong argued that its overall impact on emissions could still be positive if applied carefully. Used to optimise heavy industry, power systems, and carbon markets, he said, AI may remain an essential part of China’s effort to cut emissions — even as it creates new pressures that policymakers must manage.

(Photo by Matthew Henry)

See also: Can China’s chip stacking strategy really challenge Nvidia’s AI dominance?

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For business leaders at BHP, the useful question was not “Where can we use AI?” but “Which decisions do we make repeatedly, and what information would improve them?”

Portfolio not showcase

BHP describes the end-to-end effects of AI on operations, or as it puts it, “from mineral extraction to customer delivery.” Leaders had decided to move beyond pilot rollouts, treating AI as an operational capability. It started with a small set of problems that affected the company’s performance; places where change could be measured in results.

The company found it could avoid unplanned downtime of machinery, plus it tightened its energy and water use. Each use case addressing a small but impactful problem was given an owner and an accompanying KPI. Results were reviewed with the same regularity used for other operational performance monitoring elsewhere in the company.

Where BHP uses AI daily

In addition to BHP focusing specifically on areas such as predictive maintenance and energy optimisation, it gave consideration to using AI in more adventurous yet important operations such as autonomous vehicles and real-time staff health monitoring. Such categories can translate well to other asset-heavy environments, across logistics, manufacturing, and heavy industry.

Predictive maintenance

Predictive maintenance is the process of planning repairs in scheduled downtime to reduce unexpected failures and costly, unplanned stoppages. Here, AI models analyse equipment data from on-board sensors and can anticipate maintenance needs. This cuts breakdown numbers and reduces equipment-related safety incidents. BHP runs predictive analytics across most of its load-and-haul fleets and its materials handling systems. A central maintenance centre provides real-time and longer-range indications of machine health and potential failure or degradation.

Prediction has become an integral part of its machinery-heavy operations, where previously, such information was presented as ‘just another’ report, one that could get lost in the bureaucracy of the company. It models and defines thresholds which trigger actions directly to teams planning maintenance.

Energy and water optimisation

Deploying predictive maintenance in this manner at its facilities in Escondida in Chile, the company reports savings of more than three giga-litres of water and 118 gigawatt hours of energy in two years, attributing the gains directly to AI. The technology gives operators real-time options and analytics that identify anomalies and automate corrective actions at multiple facilities, including concentrators and desalination plants.

The lesson it’s learned is placing AI where decisions happen: When operators and control teams can act on recommendations in real time, improvements compound. Conversely periodic reporting means decisions are only taken if staff both see the results of data, and then decide it’s necessary. The realtime nature of data analysis and the use of triggers-to-action mean the differences becomes quickly apparent.

Autonomy and remote operations

BHP is also using more advanced technologies like AI-supported autonomous vehicles and machinery. These are higher-risk areas, and the tech has been found to reduce worker exposure to risk, and cut the human error factor in incidents. At the company, complex operational data flows through regional centres from remote facilities. So, without the use of AI and analytics, staff would not be able to optimise every decision in the way that software achieves.

The use of AI-integrated wearables is increasing in many industries, including engineering, utilities, manufacturing, and mining. BHP leads the way in protecting its staff, who often work in very challenging conditions. Wearables can monitor personal conditions, reading heart rate and fatigue indicators, and provide real-time alerts to supervisors. One example might be ‘smart’ hard-hat sensor technology, used by BHP at Escondida, which measures truck driver fatigue by analysing drivers’ brain waves.

A plan leaders can run

Regardless of industry, decision-makers can draw learnings from BHP’s experiences in deploying AI at the (literal) coal-face. The following plan could help leaders in their own strategies to leverage AI in operational problem-areas:

1. Choose one reliability problem and one resource-efficiency problem that operations teams already track, then attach a KPI.
2. Map the workflow: who will see the output and what action they can take?
3. Put basic governance in place for data quality and model monitoring, then review performance alongside operational KPIs.
4. Start with decision support in higher-risk processes, and automate only after teams validate controls.

(Image source: “Shovel View at a Strip Mining Coal” by rbglasson is licensed under CC BY-NC-SA 2.0.)

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The latest research, entitled, ‘AI for Contractors,’ discovered automated proposal generation and progress tracking from site photos both reached a 92% effectiveness rating. Meanwhile, contract risk review achieved 85% effectiveness when compared to previous, more traditional methods.

The report highlights how AI is allowing project managers to focus on strategic decisions rather than time-consuming administrative tasks. Finance teams are also benefiting from AI technology, shifting from historical reporting to predictive insights, while operations leaders are able to apply data-driven intelligence for improved project delivery. Rather than AI fully replacing human expertise, the report found it actually enhances human input.

“For decades, construction firms have lacked the tools to transform the data they’ve collected into action. AI-enabled solutions are changing that,” says Gord Rawlins, president and CEO of CMiC. “This research highlights the high-impact results contractors are achieving today.”

AI changing contractor roles

Surveyed contractors see AI as a catalyst in reshaping everyday aspects of their operations, enabling predictive insights rather than reacting to problems once they have occurred. This introduces wider benefits, like tighter cost controls, improved scheduling, and higher quality project delivery. In other words, improved overall outcomes.

A substantial 85% of contractors foresee less time spent on repetitive tasks, while 75% have faith that AI can help mine historical data to learn from previous projects. Rather than relying fully on AI, 70% said the technology helps them make better, more informed decisions thanks to insights that may otherwise not be present.

AI implementation remains low, but companies are preparing for wider adoption

Currently, AI adoption in the construction industry is low, despite awareness levels of 32% to 34%. This seems to be due to several reasons, including a lack of clear understanding, internal approvals, and software access. However, Dodge’s research discovered more than half of companies surveyed are strategically preparing for AI with pilot programmes and staff training for AI-related positions.

According to the report, 40% of companies have a set budget for AI, 38% are developing teams for implementation, 19% are adapting old workflows, and 51% are assessing AI changes.

Early adopters lead the way

Overall awareness of AI use in the industry is quite low, with just 20% to 50% of contractors knowing that certain management tasks implement AI, and very few actively use these functions. Nevertheless, early adopters of AI provided positive feedback, as more than 70% revealed that AI tools are hugely effective compared to more traditional methods, suggesting a potential for quick growth in AI use throughout the industry.

Security and accuracy lead concerns

The main concerns of adopting AI revolve around security and accuracy. The report reveals that 57% are worried about the accuracy of AI output, while 54% have doubts over the security of company data.

Internal resistance to change (44%) and implementation costs (41%) are also cited as key concerns, but perhaps surprisingly, just 21% expressed concern over job losses. 31% believe current data quality is not yet adequate to support AI analysis.

According to the report, larger contractors are likely to rely more on AI than smaller firms, thus are more concerned about data quality and reliability. For instance, 69% of larger contractors cited lack of reliability or accuracy of AI outputs as a major concern, compared to 54% of smaller or mid-size contractors.

Research data confirms that contractors are generally open to adopting AI, but the accuracy of AI outputs tend to stand in the way, as well as the desire for better tools, more information, and greater internal support.

17% of contractors said they do not sufficiently trust AI results, an issue that becomes more pronounced in sensitive areas like payments. Distrust in AI operations rises to 35% and 31% not having faith in AI managing project budgets.

A major theme is the need for more understanding before using AI. On average, 21% of respondents said they want a better insight of how AI works before considering using it, climbing to 31% for more complex tasks like safety risk assessments.

Contractors also believe they are limited by their current software capabilities, with an average of 19% reporting their software does not offer the AI functions they require. The increases to 33% for managing resources.

Internal approval remains a notable obstacle, with 22% saying their company has not yet approved the use of AI, despite personal interest. Another barrier is a lack of time or resources that effectively evaluate AI tools. 13% stated this as a main reason why AI has not yet been adopted.

Although there are obvious challenges to mass AI use in the construction industry – and therefore significant market opportunity – only 5% believe AI would not be beneficial or improve current methods. That indicates a resistance that stems from various concerns rather than a lack of perceived value.

Steve Jones, Senior Director, Industry Insights Analytics at Dodge, spoke on the findings.

“We designed this study to look at the use of AI in the digital tools already deployed by contractors because that may offer the best solution to the challenge of data quality. But it is also heartening to see that many contractors are aware of the key challenges and the need for a rigorous approach to successfully implementing these tools at their organisations,” the Dodge research states.

Key interest in emerging AI functionalities

AI’s potential is clearly recognised, even if the industry’s readiness to adopt it isn’t quite matching the data. Certain areas are attracting the most attention when it comes to AI functions, like automated construction analysis, where 81% see potential benefits. 80% also show interest in intelligent permit submissions, while 79% believe in autonomous schedule and resource optimisation.

92% appreciate automated contract management and 76% recognise potential in AI-powered dynamic pricing. Although AI adoption remains limited, these strong numbers suggest the tide may soon be turning.

AI and the new age of the construction industry

The latest data suggests a strong openness, maybe even an eagerness, to AI adoption in the construction sector. However, better tools, clearer guidance, and more trustworthy outputs are just some of the areas that need to be addressed before interest becomes implementation.

“With high awareness, strong interest, and powerful validation from early adopters, contractors appear poised for significant expansion in their use of AI-enabled tools in meaningful ways,” said Steve Jones.

The industry is on a “tipping point for AI adoption” according to Jones. When companies start to provide clearer pathways for adoption, the move towards AI-powered construction workflows will undoubtedly accelerate rapidly, reshaping how projects are delivered forever.

(Image source: “Tianjin Construction Site.” by @yakobusan Jakob Montrasio is licensed under CC BY 2.0.)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.

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The platform uses NVIDIA Omniverse libraries, NVIDIA Isaac, and NVIDIA AI Enterprise software. EY says the setup gives organisations a clearer way to plan, test, and manage AI systems that operate in real environments, from factory robots to drones and edge devices.

Omniverse libraries support the creation of digital twins so firms can model and test systems before deployment. NVIDIA Isaac tools offer open models and simulation frameworks to design and validate AI-driven robots in detailed 3D settings. NVIDIA AI Enterprise provides the computing base needed to run heavier AI workloads.

EY describes the platform as built around three main areas:

• AI-ready data: Synthetic data to mirror a wide range of physical scenarios.
• Digital twins and robotics training: Tools that connect digital and physical systems, monitor performance in real time, and support operational continuity.
• Responsible physical AI: Governance and controls that address safety, ethics, and compliance.

The platform is meant to support everything from early planning to long-term maintenance in sectors like industrials, energy, consumer, and health.

Raj Sharma, EY Global Managing Partner – Growth & Innovation, says physical AI is already “transforming how businesses in sectors operate and help create value,” saying that it brings more automation and can help lower operating costs. He says the combination of EY’s industry experience and NVIDIA’s infrastructure is expected to speed up how companies move “from experimentation to enterprise-scale deployment.”

NVIDIA’s John Fanelli notes that more enterprises are bringing robots and automation into real settings to address workforce changes and improve safety. He says the EY.ai Lab, supported by NVIDIA AI infrastructure, helps organisations “simulate, optimise and safely deploy robotics applications at enterprise scale,” which he views as part of the next phase of industrial AI.

New leadership and a dedicated physical AI lab

EY has also appointed Dr. Youngjun Choi as its Global Physical AI Leader. He will oversee robotics and physical AI work and help shape EY’s role as an advisor in this area.

Choi, who has nearly 20 years’ experience in robotics and AI, previously led the UPS Robotics AI Lab, where he worked on digital twins, robotics projects, and AI tools to modernise its network. Before that, he served as research faculty in Aerospace Engineering at the Georgia Institute of Technology, contributing to aerial robotics and autonomous systems.

A key part of his role is directing the newly opened EY.ai Lab in Alpharetta, Georgia – the first EY site focused on physical AI. The Lab includes robotics systems, sensors, and simulation tools so organisations can test ideas and build prototypes before deploying them at scale.

Joe Depa, EY Global Chief Innovation Officer, says his clients want better ways to use technology for decision-making and performance. He adds that physical AI requires strong data foundations and trust from the start. With Choi leading the Lab, Depa says EY teams are beginning to “get beyond the surface of what is possible” and set up the base for scalable operations.

At the Lab, organisations can:

• Design and test physical AI systems in a virtual testbed,
• Build solutions for humanoids, quadrupeds, and other next-generation robots,
• Improve logistics, manufacturing, and maintenance with digital twins.

The new platform and Lab build on earlier collaboration between EY and NVIDIA, including an AI agent platform launched earlier this year. Both organisations plan to expand their physical AI work to areas like energy, health, and smart cities. They also aim to support automation projects that cut waste and help reduce environmental impact.

See also: Microsoft, NVIDIA, and Anthropic forge AI compute alliance

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

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According to the duo’s research, smart AI applications in just three industries could slash greenhouse gas emissions by 3.2-5.4 billion tonnes each year by 2035.

In contrast to much of what we’ve heard, these reductions would far outweigh the carbon that AI itself produces.

The study, ‘Green and intelligent: the role of AI in the climate transition,’ doesn’t just see AI as a tool for small improvements. Instead, it could help transform our entire economy into something sustainable and inclusive.

Net-zero as an opportunity, not a burden

The researchers suggest we should see the shift to a net-zero economy not as a burden but as “a great opportunity for innovation and sustainable, resilient, and inclusive economic growth.”

They focused on three of the major carbon culprits – power generation, meat and dairy production, and passenger vehicles – which together cause almost half of global emissions. The potential AI savings from just these sectors would more than cancel out the estimated 0.4 to 1.6 billion tonnes of annual emissions from running all those AI data centers.

As the authors put it, “the case for using AI for the climate transition is not only strong but imperative.”

Five big ways AI can help save our planet (and us)

1. Making complex systems smarter

Think about how our modern lives depend on intricate networks for energy, transport, and city living. AI can redesign these systems to work much more efficiently.

Remember those frustrating power outages when the wind stops blowing or clouds cover the sun? AI can help predict these fluctuations in renewable energy and balance them with real-time demand. DeepMind has already shown its AI can boost wind energy’s economic value by 20% by reducing the need for backup power sources.

2. Speeding up discovery and reducing waste

Almost half the emissions cuts needed to reach net-zero by 2050 will rely on technologies that are barely out of the lab today and AI is turbocharging these breakthroughs.

Take Google DeepMind’s GNOME tool, which has already identified over two million new crystal structures that could revolutionise renewable energy and battery storage. Or consider how Amazon’s AI packaging algorithms have saved over three million metric tons of material since 2015.

3. Helping us make better choices

Our daily decisions – from what we eat, to how we travel – could drive up to 70% of emissions reductions by 2050. But making the right choice isn’t always easy.

AI can be our personal environmental coach, breaking down information barriers and offering tailored recommendations. Already using Google Maps’ fuel-efficient routes? That’s AI helping you cut emissions while saving gas money. And those smart home systems like Nest use AI to optimise your heating and cooling, which could save millions of tonnes of CO2 if we all adopted them.

4. Predicting climate changes and policy effects

How do we plan for a changing climate? AI can process enormous datasets to forecast climate patterns with unprecedented accuracy.

Tools like IceNet (developed by the British Antarctic Survey and the Alan Turing Institute) are using AI to predict sea ice levels better than ever before, helping communities and businesses prepare. This capability also extends to helping governments design climate policies that actually work, by learning from countless case studies around the world.

5. Keeping us safe in extreme weather

As climate disasters intensify, early warning can save lives. AI-powered systems for floods and wildfires are becoming essential safety nets.

Google’s Flood Hub uses machine learning to provide flood forecasts up to five days in advance across more than 80 countries. That’s precious time for people to protect their homes and evacuate if necessary.

The numbers support AI cutting global carbon emissions

When researchers crunched the numbers, they found AI could:

• Cut power sector emissions by 1.8 billion tonnes yearly by 2035 just by optimising renewable energy
• Save between 0.9 and 3.0 billion tonnes annually by improving plant-based proteins to taste and feel more like meat
• Reduce vehicle emissions by up to 0.6 billion tonnes each year through shared mobility and better battery technology

Here’s the catch: we can’t just sit back and let market forces determine how AI develops. The researchers call for an “active state” to ensure that AI benefits everyone and the planet.

“Governments have a critical role in ensuring that AI is deployed effectively to accelerate the transition equitably and sustainably,” they conclude.

What this means in practice is creating incentives for green AI research, regulating to minimise environmental impact, and investing in infrastructure so communities worldwide can share in the benefits.

By guiding innovation and working together internationally, we can unlock AI’s full potential to reduce global carbon emissions and tackle the climate crisis—and build a future where both people and the planet can thrive.

(Photo by Abhishek Mishra)

See also: Power play: Can the grid cope with AI’s growing appetite?

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The massive data centres needed to train and run the latest AI are thirsty for electricity. Data centre power use in the UK is on track to multiply six times over by 2034, at which point it could be sucking up nearly a third of all our nation’s electricity. That’s a colossal strain to put on a system that was built for a completely different world, one with predictable, one-way power flows.

The AI Energy Council – a team-up of tech giants, energy firms, the Ofgem regulator, and the National Energy System Operator – has the critical job of trying to predict just how thirsty this AI beast will become. Their work is happening just as the government is pouring £2 billion into its AI Opportunities Action Plan, a grand vision for weaving AI into our hospitals, classrooms, and businesses.

UK Science and Technology Secretary Peter Kyle said: “Giving our researchers and innovators access to the processing power they need will not only maintain our standing as the world’s third-biggest AI power, but put British expertise at the heart of the AI breakthroughs which will improve our lives, modernise our public services, and spark the economic growth which is the cornerstone of our Plan for Change.

“We are clear-eyed though on the need to make sure we can power this golden era for British AI through responsible, sustainable energy sources. Today’s talks will help us drive forward that mission, delivering AI infrastructure which will benefit communities up and down the country for generations to come without ever compromising on our clean energy superpower ambitions.”

The sheer scale of the energy problem is hard to overstate. Globally, the electricity needed for data centres is expected to double in just five years, eventually demanding three times more power than the entire UK currently uses. And AI is the main culprit.

A single rack of AI servers can demand 120 kW of power, a massive leap from the 5-10 kW a normal rack needs. These aren’t steady sips of power, either. AI workloads can spike unpredictably, creating sudden, massive power surges that threaten the stability of the entire grid.

In response, the UK is planning a monumental overhaul. The centrepiece is the “Great Grid Upgrade,” a £58 billion investment designed to be a “once in a generation expansion” of the electricity network. This includes building a new high-capacity electrical superhighway running from north to south and expanding the offshore grid to bring in vast amounts of new wind power.

Ed Miliband, Secretary for Energy Security and Net Zero, commented: “We are making the UK a clean energy superpower, building the homegrown energy this country needs to get bills down for good and create new jobs as part of our Plan for Change.

“Bringing together the biggest players in AI and energy will help us discuss the role AI can play in building a new era of clean electricity for our country, and meeting the power demands of new technology as we build a clean power system for families and businesses.”

But there’s a huge roadblock. Even if we build the wind farms and solar panels, connecting them to the power grid to address surging AI demand right now is another story. The current process is slow, leaving more than 600 renewable energy projects – worth billions – stuck in a queue. Some have been told they could be waiting for 15 years.

Urgent reforms are being pushed through to try and clear this backlog, a vital step if our AI future is to be powered by green energy. The government is also trying to speed things up by declaring data centres “critical national infrastructure” and setting up “AI Growth Zones” where planning and power connections can be fast-tracked.

The data centre industry is shifting from being just part of the problem to becoming part of the solution. Instead of just being passive power hogs, they are becoming active partners in the energy grid. Many are chasing Net Zero targets, investing in their own on-site renewable power, and taking part in “demand-side response” programs. This means they can intelligently pause non-urgent AI tasks when the grid is under stress and fire them up again when green energy is plentiful, helping to balance the whole system.

AI itself could also help. The same complex algorithms that demand so much power can also be used to make our grid smarter, predicting energy spikes and optimising power flow in real-time.

The way forward is clear, but it won’t be easy. The UK has the right ideas and is putting serious money on the table to address the power grid demands of AI, but everything depends on speed and execution. The grid connection jam must be broken, and the Great Grid Upgrade needs to happen at pace.

(Photo by Andreas Jabusch)

See also: Anthropic tests AI running a real business with bizarre results

Want to learn more about AI and big data from industry leaders? Check out Data Centre Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including AI & Big Data Expo, Digital Transformation, Data Centre and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Behind every text message and video call lies a complex system that telecom companies spend billions maintaining. This partnership between Ericsson and AWS aims to make those networks not just smarter, but virtually self-sufficient.

Jean-Christophe Laneri, VP and Head of Cognitive Network Solutions at Ericsson, said: “This collaboration marks a pivotal milestone in network optimisation technology.

“AWS’ global infrastructure and AI, alongside Ericsson’s unique cross-domain telecom experience and insights, will assist communication service providers in adapting to changing business conditions with predictable costs and enhanced operational efficiency.”

When the internet stops working at home, the first port of call for most is the “off and on again” approach: replug connections and restart the router. If that fails, call customer service. Using agentic AI, this partnership aims to automate the identification of problems, test solutions, and fix issues before you even notice. However, rather than just a home connection, the aim is to use agentic AI to do this on the massive scale of telecom networks serving potentially millions of people.

Fabio Cerone, General Manager of the EMEA Telco Business Unit at AWS, explained: “By working together, AWS and Ericsson will help telecommunications providers automate complex operations, reduce costs, and deliver better experiences for their customers. We are delivering solutions that create business value today while building toward autonomous networks.”

The technology works through something called RAN automation applications, or “rApps” in industry speak. These are sophisticated tools that can learn to manage different aspects of a network. The breakthrough comes from how these tools can now work together using agentic AI to improve networks, similar to colleagues collaborating on a project.

While the technology is undeniably complex, the potential benefits for everyday mobile users are straightforward. Networks that can anticipate problems and heal themselves could mean fewer dropped calls, more consistent data speeds, and better coverage in challenging areas.

For instance, imagine you’re at a football match with 50,000 other fans all trying to use their phones. Today’s networks often buckle under such pressure. However, a smarter and more autonomous network might recognise the gathering crowd early, automatically redirect resources, and maintain service quality without requiring engineers to intervene.

While traditional networks follow precise programmed instructions, the new approach tells the network what outcome is desired – like “ensure video streaming works well in this area” – and the AI figures out how to make that happen, adjusting to changing conditions in real-time.

While terms like “intent-based networks” and “autonomous management systems” might sound like science fiction, they represent a fundamental shift in how essential services are delivered. As 5G networks continue expanding and 6G looms on the horizon, the sheer complexity of managing these systems has outgrown traditional approaches.

Mobile operators are under tremendous pressure to improve service while reducing costs; seemingly contradictory goals. Autonomous networks offer a potential solution by allowing companies to do more with less human intervention.

As our dependence on reliable connectivity grows – supporting everything from remote healthcare to education and emerging technologies like autonomous vehicles – the stakes for network performance continue to rise. The partnership between these tech giants to create self-healing mobile networks signals recognition that AI isn’t just a buzzword but a necessary evolution for critical infrastructure.

See also: NVIDIA helps Germany lead Europe’s AI manufacturing race

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Scheduled for 27-28 August 2025 at the JW Marriott Hotel in Riyadh, this event will bring together 300+ data and AI professionals, including CDOs, CIOs, data scientists, AI directors, C-suite executives and many others, to explore the latest advances, tackle challenges, and unlock opportunities in data and artificial intelligence.

With the Kingdom’s data analytics market projected to reach $8.8 billion by 2030, the summit comes at an important time, offering a platform for public and private sector leaders to collaborate, innovate, and approach the nation’s Vision 2030 goals.

A platform for innovation and collaboration

Building on the success of its debut last year, which was inaugurated by a leading official from the Saudi Data & AI Authority (SDAIA), the 2025 edition promises to be bigger and more impactful.

The summit will feature:

• 25+ cutting-edge solution providers showcasing the latest technologies in data and AI.

• 50+ industry experts sharing insights on emerging trends, challenges, and opportunities.

• 300+ attendees, including data engineers, architects, AI pioneers, and decision-makers from Saudi Arabia’s largest organisations.

• 1:1 meetings to foster collaboration and evaluate tailored solutions.

• CPD-accredited sessions to help professionals enhance their skills and advance their careers.

Unveiling the future of data and AI

The summit’s agenda will look at important topics shaping the future of data and AI, including:

• Navigating open data in Saudi Arabia

• AI fusion and machine learning innovations

• Data virtualisation and the power of data mesh

• Ethical data governance and cybersecurity analytics

• Unified data cloud architectures

Discussions will be led by thought leaders from the Kingdom’s top organisations, including Ministry of Hajj & Umrah, Insurance Authority, Council of Health Insurance, NEOM, AlNASSR Club Company | PIF, and Abdul Latif Jameel United Finance.

DAMA Saudi Arabia joins as supporting partner

The Data Management Association (DAMA Saudi Arabia), the Kingdom’s largest data management community, has joined the summit as a supporting partner. The partnership underscores DAMA’s commitment to fostering a robust data management ecosystem and aligns with the summit’s mission to elevate Saudi Arabia’s position as a global leader in data and AI.

Abdulaziz Almanea, Founder & Chairman of the Board, DAMA Saudi, spoke of the importance of the summit: “Artificial intelligence is only as good as the data behind it. Quality, governance, and ethics must come first to ensure trust, accuracy, and impact. As Saudi Arabia accelerates its data-driven transformation, industry events like the Smart Data & AI Summit serve as vital platforms for bringing experts together to shape the future of AI with responsible and innovative data practices.”

A legacy of excellence

The inaugural edition of the summit set a high benchmark, with attendees praising the quality of speakers, depth of discussions, and opportunities for networking and collaboration. Nayef Al-Otaibi, VP & Chief Digital Officer at Saudi Aramco, said, “The event was well-managed, the coordination was excellent, and the quality of the speakers was above expectations. It was a beautiful experience connecting with industry experts during the panel discussions and sharing our experiences. This could basically help us establish the platform and collaborate and work together in future.”

Driving Vision 2030 forward

The Smart Data & AI Summit is a strategic initiative to support Saudi Arabia’s Vision 2030 goals. By bringing together global expertise, cutting-edge technologies, and local insights, the summit aims to:

• Accelerate the Kingdom’s digital transformation.

• Foster innovation and collaboration across industries.

• Address regulatory challenges and ethical considerations in data and AI.

• Unlock new opportunities for investment and growth in the Kingdom’s data and AI sectors.

Sudhir Ranjan Jena, CEO & Co-founder of Tradepass, the organising body, spoke of the summit’s mission: “The data & AI sector is entering a transformative chapter, fuelled by technology disruptions, heightened expectations, and the unprecedented expansion of digital tools and platforms. In the upcoming edition, we will delve into Vision 2030 goals, unlock limitless opportunities, and explore emerging trends and solutions that will play an integral role in shaping the Kingdom’s post-oil economy.”

A high-impact speaker lineup

The summit will feature an impressive roster of speakers, including:

• Dr Ahmed Alzahrani – Director of Business Intelligence and Data Analytics Centre, Ministry of Hajj & Umrah

• Hajar Alolah – Data Governance and Management Office Director, Saudi Development Bank

• Abdullah AlBar – Chief Data Officer, Abdul Latif Jameel United Finance

• Usamah Algemili – Chief Data Executive, Insurance Authority

• Jawad Saleemi – Director – AI & Cloud, Telenor

• Abbasi Poonawala – Executive Director – Enterprise Architecture, Alinma Bank

• Nawaf Alghamdi – Director – Data Analytics & AI, Council of Health Insurance

These experts will share their insights on the latest trends, challenges, and opportunities in data and AI, offering attendees strategies to drive innovation and growth in their organisations.

For more information, visit: https://saudi.smartdataseries.com/

Media contact:

Shrinkhal Sharad
PR & Communication Lead
Tradepass
Email: shrinkhals@tradepassglobal.com
Phone: + (91) 80 6166 4401

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We’ve all got that know-it-all friend that can’t admit when they don’t know something, or resorts to giving dodgy advice based on something they’ve read online. Hallucinations by AI models are like that friend, but this one could be in charge of creating your cancer treatment plan.

That’s where Themis AI enters the picture. This MIT spinout has managed to achieve something that seems straightforward in theory but is actually quite complex, teaching AI systems to say, “I’m not sure about this.”

AI systems typically display overconfidence. Themis’ Capsa platform acts as a reality check for AI, helping models recognise when they’re venturing into guesswork rather than certainty.

Founded in 2021 by MIT Professor Daniela Rus, along with former research colleagues Alexander Amini and Elaheh Ahmadi, Themis AI has developed a platform that can integrate with virtually any AI system to flag moments of uncertainty before they lead to mistakes.

Capsa essentially trains AI to detect patterns in how it processes information that might indicate it’s confused, biased, or working with incomplete data that could lead to hallucinations.

Since launching, Themis claims it has helped telecoms companies avoid costly network planning errors, assisted oil and gas firms in making sense of complex seismic data, and published research on creating chatbots that don’t confidently make things up.

Most people remain unaware of how frequently AI systems are simply taking their best guess. As these systems handle increasingly critical tasks, those guesses could have serious consequences. Themis AI’s software adds a layer of self-awareness that’s been missing.

Themis’ journey towards tackling AI hallucinations

The journey to Themis AI began years ago in Professor Rus’s MIT lab, where the team was investigating a fundamental problem: how do you make a machine aware of its own limitations?

In 2018, Toyota funded their research into reliable AI for self-driving vehicles—a sector where mistakes could be fatal. The stakes are incredibly high when autonomous vehicles must accurately identify pedestrians and other road hazards.

Their breakthrough came when they developed an algorithm that could spot racial and gender bias in facial recognition systems. Rather than just identifying the problem, their system actually fixed it by rebalancing the training data—essentially teaching the AI to correct its own prejudices.

By 2021, they’d demonstrated how this approach could revolutionise drug discovery. AI systems could evaluate potential medications but – crucially – flag when their predictions were based on solid evidence versus educated guesswork or complete hallucinations. The pharmaceutical industry recognised the potential savings in money and time by focusing only on drug candidates the AI was confident about.

Another advantage of the technology is for devices with limited computing power. Edge devices use smaller models that cannot match the accuracy of huge models run on a server, but with Themis’ technology, these devices will be far more capable of handling most tasks locally and only request help from the big servers when they encounter something challenging.

AI holds tremendous potential to improve our lives, but that potential comes with real risks. As AI systems become more deeply integrated into critical infrastructure and decisionmaking, the ability to acknowledge uncertainty leading to hallucinations may prove to be their most human – and most valuable – quality. Themis AI is making sure they learn this crucial skill.

See also: Diabetes management: IBM and Roche use AI to forecast blood sugar levels

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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As AI models get ever more complex and weave themselves into yet more parts of our lives, a massive question mark hangs in the air: can we power this revolution without costing the Earth?

The numbers don’t lie: AI’s energy demand is escalating fast

The sheer computing power needed for the smartest AI out there is on an almost unbelievable upward curve – some say it’s doubling roughly every few months. This isn’t a gentle slope; it’s a vertical climb that’s threatening to leave even our most optimistic energy plans in the dust.

To give you a sense of scale, AI’s future energy needs could soon gulp down as much electricity as entire countries like Japan or the Netherlands, or even large US states like California. When you hear stats like that, you start to see the potential squeeze AI could put on the power grids we all rely on.

2024 saw a record 4.3% surge in global electricity demand, and AI’s expansion was a big reason why, alongside the boom in electric cars and factories working harder. 

Wind back to 2022, and data centres, AI, and even cryptocurrency mining were already accounting for nearly 2% of all the electricity used worldwide – that’s about 460 terawatt-hours (TWh).

Jump to 2024, and data centres on their own use around 415 TWh, which is roughly 1.5% of the global total, and growing at 12% a year. AI’s direct share of that slice is still relatively small – about 20 TWh, or 0.02% of global energy use – but hold onto your hats, because that number is set to rocket upwards.

The forecasts? Well, they’re pretty eye-opening. By the end of 2025, AI data centres around the world could demand an extra 10 gigawatts (GW) of power. That’s more than the entire power capacity of a place like Utah.

And, by 2027, the global power hunger of an AI data centre is tipped to reach 68 GW, which is almost what California had in total power capacity back in 2022. 

Towards the end of this decade, the figures get even more jaw-dropping. Global data centre electricity consumption is predicted to double to around 945 TWh by 2030, which is just shy of 3% of all the electricity used on the planet.

OPEC reckons data centre electricity use could even triple to 1,500 TWh by then. And Goldman Sachs? They’re saying global power demand from data centres could leap by as much as 165% compared to 2023, with those data centres specifically kitted out for AI seeing their demand shoot up by more than four times.

There are even suggestions that data centres could be responsible for up to 21% of all global energy demand by 2030 if you count the energy it takes to get AI services to us, the users.

When we talk about AI’s energy use, it mainly splits into two big chunks: training the AI, and then actually using it.

Training enormous models, like GPT-4, takes a colossal amount of energy. Just to train GPT-3, for example, it’s estimated they used 1,287 megawatt-hours (MWh) of electricity, and GPT-4 is thought to have needed a whopping 50 times more than that. 

While training is a power hog, it’s the day-to-day running of these trained models that can chew through over 80% of AI’s total energy. It’s reported that asking ChatGPT a single question uses about ten times more energy than a Google search (we’re talking roughly 2.9 Wh versus 0.3 Wh).

With everyone jumping on the generative AI bandwagon, the race is on to build ever more powerful – and therefore more energy-guzzling – data centres.

So, can we supply energy for AI – and for ourselves?

This is the million-dollar question, isn’t it? Can our planet’s energy systems cope with this new demand? We’re already juggling a mix of fossil fuels, nuclear power, and renewables. If we’re going to feed AI’s growing appetite sustainably, we need to ramp up and diversify how we generate energy, and fast.

Naturally, renewable energy – solar, wind, hydro, geothermal – is a huge piece of the puzzle. In the US, for instance, renewables are set to go from 23% of power generation in 2024 to 27% by 2026. 

The tech giants are making some big promises; Microsoft, for example, is planning to buy 10.5 GW of renewable energy between 2026 and 2030 just for its data centres. AI itself could actually help us use renewable energy more efficiently, perhaps cutting energy use by up to 60% in some areas by making energy storage smarter and managing power grids better.

But let’s not get carried away. Renewables have their own headaches. The sun doesn’t always shine, and the wind doesn’t always blow, which is a real problem for data centres that need power around the clock, every single day. The batteries we have now to smooth out these bumps are often expensive and take up a lot of room. Plus, plugging massive new renewable projects into our existing power grids can be a slow and complicated business.

This is where nuclear power is starting to look more appealing to some, especially as a steady, low-carbon way to power AI’s massive energy needs. It delivers that crucial 24/7 power, which is exactly what data centres crave. There’s a lot of buzz around Small Modular Reactors (SMRs) too, because they’re potentially more flexible and have beefed-up safety features. And it’s not just talk; big names like Microsoft, Amazon, and Google are seriously looking into nuclear options.

Matt Garman, who heads up AWS, recently put it plainly to the BBC, calling nuclear a “great solution” for data centres. He said it’s “an excellent source of zero carbon, 24/7 power.” He also stressed that planning for future energy is a massive part of what AWS does.

“It’s something we plan many years out,” Garman mentioned. “We invest ahead. I think the world is going to have to build new technologies. I believe nuclear is a big part of that, particularly as we look 10 years out.”

Still, nuclear power isn’t a magic wand. Building new reactors takes a notoriously long time, costs a fortune, and involves wading through complex red tape. And let’s be frank, public opinion on nuclear power is still a bit shaky, often because of past accidents, even though modern reactors are much safer.

The sheer speed at which AI is developing also creates a bit of a mismatch with how long it takes to get a new nuclear plant up and running. This could mean we end up leaning even more heavily on fossil fuels in the short term, which isn’t great for our green ambitions. Plus, the idea of sticking data centres right next to nuclear plants has got some people worried about what that might do to electricity prices and reliability for everyone else.

Not just kilowatts: Wider environmental shadow of AI looms

AI’s impact on the planet goes way beyond just the electricity it uses. Those data centres get hot, and cooling them down uses vast amounts of water. Your average data centre sips about 1.7 litres of water for every kilowatt-hour of energy it burns through.

Back in 2022, Google’s data centres reportedly drank their way through about 5 billion gallons of fresh water – that’s a 20% jump from the year before. Some estimates suggest that for every kWh a data centre uses, it might need up to two litres of water just for cooling. Put it another way, global AI infrastructure could soon be chugging six times more water than the entirety of Denmark.

And then there’s the ever-growing mountain of electronic waste, or e-waste. Because AI tech – especially specialised hardware like GPUs and TPUs – moves so fast, old kit gets thrown out more often. We could be looking at AI contributing to an e-waste pile-up from data centres hitting five million tons every year by 2030. 

Even making the AI chips and all the other bits for data centres takes a toll on our natural resources and the environment. It means mining for critical minerals like lithium and cobalt, often using methods that aren’t exactly kind to the planet.

Just to make one AI chip can take over 1,400 litres of water and 3,000 kWh of electricity. This hunger for new hardware is also pushing for more semiconductor factories, which, guess what, often leads to more gas-powered energy plants being built.

And, of course, we can’t forget the carbon emissions. When AI is powered by electricity generated from burning fossil fuels, it adds to the climate change problem we’re all facing. It’s estimated that training just one big AI model can pump out as much CO2 as hundreds of US homes do in a year.

If you look at the environmental reports from the big tech companies, you can see AI’s growing carbon footprint. Microsoft’s yearly emissions, for example, went up by about 40% between 2020 and 2023, mostly because they were building more data centres for AI. Google also reported that its total greenhouse gas emissions have shot up by nearly 50% over the last five years, with the power demands of its AI data centres being a major culprit.

Can we innovate our way out?

It might sound like all doom and gloom, but a combination of new ideas could help.

A big focus is on making AI algorithms themselves more energy-efficient. Researchers are coming up with clever tricks like “model pruning” (stripping out unnecessary bits of an AI model), “quantisation” (using less precise numbers, which saves energy), and “knowledge distillation” (where a smaller, thriftier AI model learns from a big, complex one). Designing smaller, more specialised AI models that do specific jobs with less power is also a priority.

Inside data centres, things like “power capping” (putting a lid on how much power hardware can draw) and “dynamic resource allocation” (shifting computing power around based on real-time needs and when renewable energy is plentiful) can make a real difference. Software that’s “AI-aware” can even shift less urgent AI jobs to times when energy is cleaner or demand on the grid is lower. AI can even be used to make the cooling systems in data centres more efficient.

On-device AI could also help to reduce power consumption. Instead of sending data off to massive, power-hungry cloud data centres, the AI processing happens right there on your phone or device. This could slash energy use, as the chips designed for this prioritise being efficient over raw power.

And we can’t forget about rules and regulations. Governments are starting to wake up to the need to make AI accountable for its energy use and wider environmental impact.

Having clear, standard ways to measure and report AI’s footprint is a crucial first step. We also need policies that encourage companies to make hardware that lasts longer and is easier to recycle, to help tackle that e-waste mountain. Things like energy credit trading systems could even give companies a financial reason to choose greener AI tech.

It’s worth noting that the United Arab Emirates and the United States shook hands this week on a deal to build the biggest AI campus outside the US in the Gulf. While this shows just how important AI is becoming globally, it also throws a spotlight on why all these energy and environmental concerns need to be front and centre for such huge projects.

Finding a sustainable future for AI

AI has the power to do some amazing things, but its ferocious appetite for energy is a serious hurdle. The predictions for its future power demands are genuinely startling, potentially matching what whole countries use.

If we’re going to meet this demand, we need a smart mix of energy sources. Renewables are fantastic for the long run, but they have their wobbles when it comes to consistent supply and scaling up quickly. Nuclear power – including those newer SMRs – offers a reliable, low-carbon option that’s definitely catching the eye of big tech companies. But we still need to get our heads around the safety, cost, and how long they take to build.

And remember, it’s not just about electricity. AI’s broader environmental impact – from the water it drinks to cool data centres, to the growing piles of e-waste from its hardware, and the resources it uses up during manufacturing – is huge. We need to look at the whole picture if we’re serious about lessening AI’s ecological footprint.

The good news? There are plenty of promising ideas and innovations bubbling up. 

Energy-saving AI algorithms, clever power management in data centres, AI-aware software that can manage workloads intelligently, and the shift towards on-device AI all offer ways to cut down on energy use. Plus, the fact that we’re even talking about AI’s environmental impact more means that discussions around policies and rules to push for sustainability are finally happening.

Dealing with AI’s energy and environmental challenges needs everyone – researchers, the tech industry, and policymakers – to roll up their sleeves and work together, and fast.

If we make energy efficiency a top priority in how AI is developed, invest properly in sustainable energy, manage hardware responsibly from cradle to grave, and put supportive policies in place, we can aim for a future where AI’s incredible potential is unlocked in a way that doesn’t break our planet.

The race to lead in AI has to be a race for sustainable AI too.

(Photo by Nejc Soklič)

See also: AI tool speeds up government feedback, experts urge caution

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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The company posted an all-time quarterly high revenue of KRW 79.14 trillion ($55.4 billion), marking a 10% increase year-over-year, according to its financial results released on Wednesday. Operating profit climbed to KRW 6.7 trillion ($4.68 billion), representing a modest 1.5% increase compared to the same period last year.

The results exceeded Samsung’s earlier forecast of KRW 79 trillion and analysts’ expectations.

Smartphone success counters chip challenges

The Mobile Experience (MX) Business emerged as the best performer, contributing KRW 37 trillion in consolidated revenue and KRW 4.3 trillion in operating profit – its highest level in four years. The success was driven primarily by strong sales of the flagship Galaxy S25 series, which features AI abilities via Galaxy AI.

“Enhanced cost competency and price declines for some components also contributed to solid double-digit profitability,” the company’s earnings report said.

In contrast, Samsung’s Device Solutions (DS) Division, which includes its semiconductor operations, posted KRW 25.1 trillion in revenue and KRW 1.1 trillion in operating profit – a 42% decline from the previous year. The performance reflects ongoing challenges in the semiconductor market, particularly in high-bandwidth memory (HBM) sales.

“Overall earnings were impacted by the erosion of average selling price (ASP), as well as a decrease in HBM sales due to export controls on AI chips and deferred demand in anticipation of upcoming enhanced HBM3E products,” Samsung said.

Trade tensions cloud future outlook

Despite the record revenue, Samsung has expressed caution about the second quarter, dropping its usual business outlook due to growing macroeconomic uncertainties stemming from global trade tensions and slowing economic growth.

“Due to the rapid changes in policies and geopolitical tensions among major countries, it’s difficult to accurately predict the business impact of tariffs and established countermeasures,” a Samsung executive stated during Wednesday’s earnings call.

Of particular concern are US President Donald Trump’s “reciprocal” tariffs, most of which have been suspended until July but threaten to impact dozens of countries including Vietnam and South Korea, where Samsung produces smartphones and displays.

While Samsung noted that its flagship products like semiconductors, smartphones, and tablets are currently exempt from these tariffs, the company revealed that Washington is conducting a product-specific tariff probe into these categories.

“There are a lot of uncertainties ahead of us […] we are communicating with related countries to minimise negative effects,” Samsung said during the call.

In response to its challenges, the company disclosed it is considering relocating production of TVs and home appliances.

AI investment and future strategy

Despite these headwinds, Samsung remains committed to its artificial intelligence strategy, allocating its highest-ever annual R&D expenditure for 2024. In the first quarter of 2025, the company increased R&D spending by 16% compared to the same period last year, amounting to KRW 9 trillion.

For the remainder of 2025, Samsung plans to expand its AI smartphone lineup through the introduction of “Awesome Intelligence” to the Galaxy A series and the launch of the Galaxy S25 Edge in Q2. Later in the year, the company will strengthen its foldable lineup with enhanced AI user experiences.

In the semiconductor space, Samsung aims to strengthen its position in the high-value-added market through its server-centric portfolio and the ramp-up of enhanced HBM3E 12H products to meet initial demand. The company expects AI-related demand to remain high in the second half of 2025, coinciding with the launch of new GPUs.

“In the mobile and PC markets, on-device AI is expected to proliferate, so the Memory Business will proactively respond to this shift in the business environment with its industry-leading 10.7Gbps LPDDR5x products,” Samsung stated.

The company’s foundry business remains focused on its 2nm Gate-All-Around (GAA) process development, which remains on schedule despite current challenges.

Market reaction and competitive landscape

Samsung shares were trading down approximately 0.6% following the announcement, reflecting investor concerns about the uncertain outlook.

The results highlight Samsung’s complex position in the AI market – succeeding in consumer-facing applications while working to catch up with competitors in AI-specific semiconductor components.

Local rival SK Hynix, which reported a 158% jump in operating profit last week to KRW 7.4 trillion, has overtaken Samsung in overall DRAM market revenue for the first time, capturing 36% global market share compared to Samsung’s 34%, according to Counterpoint Research.

SK Hynix’s success has been particularly pronounced in the high-bandwidth memory segment, which is crucial for AI server applications.

“Samsung has assumed that the uncertainties are diminished, it expects its performance to improve in the second half of the year,” the company noted, striking a cautiously optimistic tone despite the challenges ahead.

Samsung’s record revenue masks a pivotal crossroads for the tech giant: while its AI-enhanced smartphones flourish, its once-dominant semiconductor business risks falling behind in the AI revolution.

The coming quarters will reveal whether Samsung’s massive R&D investments can reclaim lost ground in HBM chips, or if we’re witnessing a fundamental power shift in Asian tech manufacturing that could alter the global AI supply chain for years to come.

For a company that rebuilt itself numerous times over its 56-year history, the AI semiconductor race may prove to be its most consequential transformation yet.

(Image credit: Anthropic)

See also: Baidu ERNIE X1 and 4.5 Turbo boast high performance at low cost

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Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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