Training and deploying sophisticated AI models occur within vast, power-hungry data centres. A “typical AI-focused data centre consumes as much electricity as 100 000 households,” the IEA notes, with the largest facilities under construction projected to demand 20x times that amount.

Surging data centre investments

Global investment in data centres has nearly doubled since 2022, reaching half a trillion dollars in 2024, sparking concerns about escalating electricity needs.

While data centres accounted for approximately 1.5% of global electricity consumption in 2024 (around 415 terawatt-hours, TWh,) their local impact is far more significant. Consumption has grown annually by about 12% since 2017, vastly outpacing overall electricity demand growth.

The US leads this consumption (45%), followed by China (25%) and Europe (15%). Almost half of US data centre capacity is concentrated in just five regional clusters.

Looking ahead, the IEA projects global data centre electricity consumption to more than double by 2030 to reach approximately 945 TWh. To put that in context, that’s slightly more than Japan’s current total electricity consumption.

AI is pinpointed as the “most important driver of this growth”. The US is projected to see the largest increase, where data centres could account for nearly half of all electricity demand growth by 2030. By the decade’s end, US data centres are forecast to consume more electricity than the combined usage of its aluminium, steel, cement, chemical, and other energy-intensive manufacturing industries.

The IEA’s “Base Case” extends this trajectory, anticipating around 1,200 TWh of global data centre electricity consumption by 2035. However, significant uncertainties exist, with projections for 2035 ranging from 700 TWh (“Headwinds Case”) to 1,700 TWh (“Lift-Off Case”) depending on AI uptake, efficiency gains, and energy sector bottlenecks.

Fatih Birol, Executive Director of the IEA, said: “AI is one of the biggest stories in the energy world today – but until now, policymakers and markets lacked the tools to fully understand the wide-ranging impacts.

“In the United States, data centres are on course to account for almost half of the growth in electricity demand; in Japan, more than half; and in Malaysia, as much as one-fifth.”

Meeting the global AI energy demand

Powering this AI boom requires a diverse energy portfolio. The IEA suggests renewables and natural gas will take the lead, but emerging technologies like small modular nuclear reactors (SMRs) and advanced geothermal also have a role.

Renewables, supported by storage and grid infrastructure, are projected to meet half the growth in data centre demand globally up to 2035. Natural gas is also crucial, particularly in the US, expanding by 175 TWh to meet data centre needs by 2035 in the Base Case. Nuclear power contributes similarly, especially in China, Japan, and the US, with the first SMRs expected around 2030.

However, simply increasing generation isn’t sufficient. The IEA stresses the critical need for infrastructure upgrades, particularly grid investment. Existing grids are already strained, potentially delaying around 20% of planned data centre projects globally due to complex connection queues and long lead times for essential components like transformers.

The potential of AI to optimise energy systems

Beyond its energy demands, AI offers significant potential to revolutionise the energy sector itself.

The IEA details numerous applications:

• Energy supply: The oil and gas industry – an early adopter – uses AI to optimise exploration, production, maintenance, and safety, including reducing methane emissions. AI can also aid critical mineral exploration.
• Electricity sector: AI can improve forecasting for variable renewables, reducing curtailment. It enhances grid balancing, fault detection (reducing outage durations by 30-50%), and can unlock significant transmission capacity through smarter management—potentially 175 GW without building new lines.
• End uses: In industry, widespread AI adoption for process optimisation could yield energy savings equivalent to Mexico’s total energy consumption today. Transport applications like traffic management and route optimisation could save energy equivalent to 120 million cars, though rebound effects from autonomous vehicles need monitoring. Building optimisation potential is significant but hampered by slower digitalisation.
• Innovation: AI can dramatically accelerate the discovery and testing of new energy technologies, such as advanced battery chemistries, catalysts for synthetic fuels, and carbon capture materials. However, the energy sector currently underutilises AI for innovation compared to fields like biomedicine.

Collaboration is key to navigating challenges

Despite the potential, significant barriers hinder AI’s full integration into the energy sector. These include data access and quality issues, inadequate digital infrastructure and skills (AI talent concentration is lower in energy sectors,) regulatory hurdles, and security concerns.

Cybersecurity is a double-edged sword: while AI enhances defence capabilities, it also equips attackers with sophisticated tools. Cyberattacks on utilities have tripled in the last four years.

Supply chain security is another critical concern, particularly regarding critical minerals like gallium (used in advanced chips,) where supply is highly concentrated.

The IEA concludes that deeper dialogue and collaboration between the technology sector, the energy industry, and policymakers are paramount. Addressing grid integration challenges requires smarter data centre siting, exploring operational flexibility, and streamlining permitting.

While AI presents opportunities for substantial emissions reductions through optimisation, exceeding the emissions generated by data centres, these gains are not guaranteed and could be offset by rebound effects.

“AI is a tool, potentially an incredibly powerful one, but it is up to us – our societies, governments, and companies – how we use it,” said Dr Birol.

“The IEA will continue to provide the data, analysis, and forums for dialogue to help policymakers and other stakeholders navigate the path ahead as the energy sector shapes the future of AI, and AI shapes the future of energy.”

(Photo by Javier Miranda)

See also: UK forms AI Energy Council to align growth and sustainability goals

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.

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Andrew Stiefel, Senior Product Marketing Manager at Endor Labs, emphasised the importance of applying lessons learned from software security to AI systems.

“The US government’s 2021 Executive Order on Improving America’s Cybersecurity includes a provision requiring organisations to produce a software bill of materials (SBOM) for each product sold to federal government agencies.”

An SBOM is essentially an inventory detailing the open-source components within a product, helping detect vulnerabilities. Stiefel argued that “applying these same principles to AI systems is the logical next step.”  

“Providing better transparency for citizens and government employees not only improves security,” he explained, “but also gives visibility into a model’s datasets, training, weights, and other components.”

What does it mean for an AI model to be “open”?  

Julien Sobrier, Senior Product Manager at Endor Labs, added crucial context to the ongoing discussion about AI transparency and “openness.” Sobrier broke down the complexity inherent in categorising AI systems as truly open.

“An AI model is made of many components: the training set, the weights, and programs to train and test the model, etc. It is important to make the whole chain available as open source to call the model ‘open’. It is a broad definition for now.”  

Sobrier noted the lack of consistency across major players, which has led to confusion about the term.

“Among the main players, the concerns about the definition of ‘open’ started with OpenAI, and Meta is in the news now for their LLAMA model even though that’s ‘more open’. We need a common understanding of what an open model means. We want to watch out for any ‘open-washing,’ as we saw it with free vs open-source software.”  

One potential pitfall, Sobrier highlighted, is the increasingly common practice of “open-washing,” where organisations claim transparency while imposing restrictions.

“With cloud providers offering a paid version of open-source projects (such as databases) without contributing back, we’ve seen a shift in many open-source projects: The source code is still open, but they added many commercial restrictions.”  

“Meta and other ‘open’ LLM providers might go this route to keep their competitive advantage: more openness about the models, but preventing competitors from using them,” Sobrier warned.

DeepSeek aims to increase AI transparency

DeepSeek, one of the rising — albeit controversial — players in the AI industry, has taken steps to address some of these concerns by making portions of its models and code open-source. The move has been praised for advancing transparency while providing security insights.  

“DeepSeek has already released the models and their weights as open-source,” said Andrew Stiefel. “This next move will provide greater transparency into their hosted services, and will give visibility into how they fine-tune and run these models in production.”

Such transparency has significant benefits, noted Stiefel. “This will make it easier for the community to audit their systems for security risks and also for individuals and organisations to run their own versions of DeepSeek in production.”  

Beyond security, DeepSeek also offers a roadmap on how to manage AI infrastructure at scale.

“From a transparency side, we’ll see how DeepSeek is running their hosted services. This will help address security concerns that emerged after it was discovered they left some of their Clickhouse databases unsecured.”

Stiefel highlighted that DeepSeek’s practices with tools like Docker, Kubernetes (K8s), and other infrastructure-as-code (IaC) configurations could empower startups and hobbyists to build similar hosted instances.  

Open-source AI is hot right now

DeepSeek’s transparency initiatives align with the broader trend toward open-source AI. A report by IDC reveals that 60% of organisations are opting for open-source AI models over commercial alternatives for their generative AI (GenAI) projects.  

Endor Labs research further indicates that organisations use, on average, between seven and twenty-one open-source models per application. The reasoning is clear: leveraging the best model for specific tasks and controlling API costs.

“As of February 7th, Endor Labs found that more than 3,500 additional models have been trained or distilled from the original DeepSeek R1 model,” said Stiefel. “This shows both the energy in the open-source AI model community, and why security teams need to understand both a model’s lineage and its potential risks.”  

For Sobrier, the growing adoption of open-source AI models reinforces the need to evaluate their dependencies.

“We need to look at AI models as major dependencies that our software depends on. Companies need to ensure they are legally allowed to use these models but also that they are safe to use in terms of operational risks and supply chain risks, just like open-source libraries.”

He emphasised that any risks can extend to training data: “They need to be confident that the datasets used for training the LLM were not poisoned or had sensitive private information.”  

Building a systematic approach to AI model risk  

As open-source AI adoption accelerates, managing risk becomes ever more critical. Stiefel outlined a systematic approach centred around three key steps:  

1. Discovery: Detect the AI models your organisation currently uses.  
2. Evaluation: Review these models for potential risks, including security and operational concerns.  
3. Response: Set and enforce guardrails to ensure safe and secure model adoption.  

“The key is finding the right balance between enabling innovation and managing risk,” Stiefel said. “We need to give software engineering teams latitude to experiment but must do so with full visibility. The security team needs line-of-sight and the insight to act.”  

Sobrier further argued that the community must develop best practices for safely building and adopting AI models. A shared methodology is needed to evaluate AI models across parameters such as security, quality, operational risks, and openness.

Beyond transparency: Measures for a responsible AI future  

To ensure the responsible growth of AI, the industry must adopt controls that operate across several vectors:  

• SaaS models: Safeguarding employee use of hosted models.
• API integrations: Developers embedding third-party APIs like DeepSeek into applications, which, through tools like OpenAI integrations, can switch deployment with just two lines of code.
• Open-source models: Developers leveraging community-built models or creating their own models from existing foundations maintained by companies like DeepSeek.

Sobrier warned of complacency in the face of rapid AI progress. “The community needs to build best practices to develop safe and open AI models,” he advised, “and a methodology to rate them along security, quality, operational risks, and openness.”  

As Stiefel succinctly summarised: “Think about security across multiple vectors and implement the appropriate controls for each.”

See also: AI in 2025: Purpose-driven models, human integration, and more

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.

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DeepSeek, practically unknown just weeks ago, took the tech world by storm—gaining global acclaim for its cutting-edge performance while sparking debates reminiscent of the TikTok saga.

Its rise has been fuelled in part by its business model: unlike many of its American counterparts, including OpenAI and Google, DeepSeek offered its advanced powers for free.

However, concerns have been raised about DeepSeek’s extensive data collection practices and a probe has been launched by Microsoft and OpenAI over a breach of the latter’s system by a group allegedly linked to the Chinese AI startup.

A threat to US AI dominance

DeepSeek’s astonishing capabilities have, within a matter of weeks, positioned it as a major competitor to American AI stalwarts like OpenAI’s ChatGPT and Google Gemini. But, alongside the app’s prowess, concerns have emerged over alleged ties to the Chinese Communist Party (CCP).  

According to security researchers, hidden code within DeepSeek’s AI has been found transmitting user data to China Mobile—a state-owned telecoms company banned in the US. DeepSeek’s own privacy policy permits the collection of data such as IP addresses, device information, and, most alarmingly, even keystroke patterns.

Such findings have led to bipartisan efforts in the US Congress to curtail DeepSeek’s influence, with lawmakers scrambling to protect sensitive data from potential CCP oversight.

Reps. Darin LaHood (R-IL) and Josh Gottheimer (D-NJ) are spearheading efforts to introduce legislation that would prohibit DeepSeek from being installed on all government-issued devices. 

Several federal agencies, among them NASA and the US Navy, have already preemptively issued a ban on DeepSeek. Similarly, the state of Texas has also introduced restrictions.

Potential ban of DeepSeek a TikTok redux?

The controversy surrounding DeepSeek bears similarities to debates over TikTok, the social video app owned by Chinese company ByteDance. TikTok remains under fire over accusations that user data is accessible to the CCP, though definitive proof has yet to materialise.

In contrast, DeepSeek’s case involves clear evidence, as revealed by cybersecurity investigators who identified the app’s unauthorised data transmissions. While some might say DeepSeek echoes the TikTok controversy, security experts argue that it represents a much starker and documented threat.

Lawmakers around the world are taking note. In addition to the US proposals, DeepSeek has already faced bans from government systems in countries including Australia, South Korea, and Italy.  

AI becomes a geopolitical battleground

The concerns over DeepSeek exemplify how AI has now become a geopolitical flashpoint between global superpowers—especially between the US and China.

American AI firms like OpenAI have enjoyed a dominant position in recent years, but Chinese companies have poured resources into catching up and, in some cases, surpassing their US competitors.  

DeepSeek’s lightning-quick growth has unsettled that balance, not only because of its AI models but also due to its pricing strategy, which undercuts competitors by offering the app free of charge. That begs the question of whether it’s truly “free” or if the cost is paid in lost privacy and security.

China Mobile’s involvement raises further eyebrows, given the state-owned telecom company’s prior sanctions and prohibition from the US market. Critics worry that data collected through platforms like DeepSeek could fill gaps in Chinese surveillance activities or even potential economic manipulations.

A nationwide DeepSeek ban is on the cards

If the proposed US legislation is passed, it could represent the first step toward nationwide restrictions or an outright ban on DeepSeek. Geopolitical tension between China and the West continues to shape policies in advanced technologies, and AI appears to be the latest arena for this ongoing chess match.  

In the meantime, calls to regulate applications like DeepSeek are likely to grow louder. Conversations about data privacy, national security, and ethical boundaries in AI development are becoming ever more urgent as individuals and organisations across the globe navigate the promises and pitfalls of next-generation tools.  

DeepSeek’s rise may have, indeed, rattled the AI hierarchy, but whether it can maintain its momentum in the face of increasing global pushback remains to be seen.

(Photo by Solen Feyissa)

See also: AVAXAI brings DeepSeek to Web3 with decentralised AI agents

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.

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Operating virtually rather than from a single physical base, Cognitive Labs will explore AI technologies such as Graph Neural Networks (GNNs), Active Learning, and Large-Scale Language Models (LLMs). 

According to Ericsson, these innovations form the backbone of the company’s solutions for the next generation of mobile communications and signal the company’s commitment to extending AI’s transformative potential to other sectors, such as healthcare.

The initiative places a strong emphasis on open collaboration, engaging with the open-source community to further the development of AI technologies for wider societal benefit.

“Cognitive Labs represents our commitment to lead innovation in AI and make a significant contribution to the open source community,” said Jean-Christophe Laneri, Head of Cognitive Network Solutions at Ericsson.

“Our mission is to create world-class research that can benefit the academic community and enhances our products to better serve our customers.”

A triad of Ericsson AI labs

Central to the Cognitive Labs initiative are three distinct research arms, each focused on a specialised area of AI:  

• GAI Lab (Geometric Artificial Intelligence Lab): This lab explores Geometric AI, emphasising explainability in geometric learning, graph generation, and temporal GNNs. Ericsson anticipates this work will make significant contributions to areas like drug discovery and enable the creation of more efficient pharmaceuticals.  

• MLR Lab (Machine Learning and Reasoning Lab): Focusing on training model optimisation and reinforcement learning, this lab aims to advance energy-efficient training for AI models and support the creation of digital twins that simulate physical realities. The potential benefits include reductions in time and energy costs, key priorities in sustainable AI development.  

• FAI Lab (Fundamental Artificial Intelligence Lab): Dedicated to foundational AI models like Large-Scale Language Models, this lab seeks to shape the future of AI applications in telecoms. Its primary mission is to automate processes and improve technological efficiencies crucial to the sector.

By organising Cognitive Labs into these specialised units, Ericsson looks to tackle diverse challenges while maintaining a cohesive focus on leveraging AI for wide-ranging applications.  

Open-source collaboration

In line with its ties to the open-source community, Cognitive Labs prioritises the development of reusable AI libraries. This accessibility helps both researchers and industry professionals accelerate progress across multiple domains—from healthcare to digital communications.

Additionally, Ericsson hopes to attract leading global researchers and data scientists to its fold, enhancing its credentials as a leader in AI innovation.  

Spain is emerging as a cornerstone for Cognitive Labs, with Ericsson Spain playing a leading role due to its robust R&D activities in Madrid and Málaga. This focus has been bolstered by a new agreement with Universidad Pontificia Comillas, specifically with its School of Engineering (Comillas ICAI).  

The alliance between Ericsson and ICAI is set to accelerate AI research through joint publications and active participation in open-source initiatives. Teams of Ericsson’s top engineers and data scientists will closely collaborate with the university’s research teams to drive innovation at both national and European levels.

“The close collaboration between top data scientists and engineers from Ericsson and ICAI’s teams will enable Ericsson Spain to lead innovation at the national and European levels, strengthening its position as a benchmark in technology and development,” Ericsson stated.  

The partnership underscores Ericsson’s commitment not only to its Cognitive Labs initiative but also to developing Spain’s expertise in cutting-edge AI technologies. Through these R&D efforts, the company seeks to reinforce its position as a leading innovator in telecoms and beyond.  

With AI set to play a critical role in shaping the next generation of mobile communications, Ericsson’s Cognitive Labs marks a vital step forward—not only for the company but also for industries and communities that stand to benefit from its far-reaching, transformative potential.  

See also: World Economic Forum unveils blueprint for equitable AI 

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.

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Japanese technology experts are developing advanced models that grasp the unique nuances of the Japanese language and culture—essential for industries such as healthcare, finance, and manufacturing – where precision is key.

But this effort isn’t Japan’s alone. Consulting giants like Accenture, Deloitte, EY Japan, FPT, Kyndryl, and TCS Japan are partnering with NVIDIA to create AI innovation hubs across the country. The centres are using NVIDIA’s AI software and specialised Japanese language models to build tailored AI solutions, helping industries boost productivity in a digital workforce. The goal? To get Japanese companies fully on board with enterprise and physical AI.

One standout technology supporting the drive is NVIDIA’s Omniverse platform. With Omniverse, Japanese companies can create digital twins—virtual replicas of real-world assets—and test complex AI systems safely before implementing them. This is a game-changer for industries such as manufacturing and robotics, allowing businesses to fine-tune processes without the risk of real-world trial and error. This use of AI is more than just innovation; it represents Japan’s plan for addressing some major challenges ahead.

Japan faces a shrinking workforce presence as its population ages. With its strengths in robotics and automation, Japan is well-positioned to use AI solutions to bridge the gap. In fact, Japan’s government recently shared its vision of becoming “the world’s most AI-friendly country,” underscoring the perceived role AI will play in the nation’s future.

Supporting this commitment, Japan’s AI market hit $5.9 billion in value this year; a 31.2% growth rate according to IDC. New AI-focused consulting centres in Tokyo and Kansai give Japanese businesses hands-on access to NVIDIA’s latest technologies, equipping them to solve social challenges and aid economic growth.

Top cloud providers like SoftBank, GMO Internet Group, KDDI, Highreso, Rutilea, and SAKURA Internet are also involved, working with NVIDIA to build AI infrastructure. Backed by Japan’s Ministry of Economy, Trade and Industry, they’re establishing AI data centres across Japan to accelerate growth in robotics, automotive, healthcare, and telecoms.

NVIDIA and SoftBank have also formed a remarkable partnership to build Japan’s most powerful AI supercomputer using NVIDIA’s Blackwell platform. Additionally, SoftBank has tested the world’s first AI and 5G hybrid telecoms network with NVIDIA’s AI Aerial platform, allowing Japan to set a worldwide standard. With these developments, Japan is taking big strides toward establishing itself as a leader in the AI-powered industrial revolution.

(Photo by Andrey Matveev)

See also: NVIDIA’s share price nosedives as antitrust clouds gather

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.

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Perplexity’s AI-driven platform aims to revolutionise internet information searches by providing real-time, accurate, and contextual answers to queries using natural language processing. Unlike traditional search engines that return a list of links, Perplexity understands the intent behind questions and delivers clear, concise answers from a curated set of relevant sources.

The investment sees Wayra join an impressive roster of existing backers, including tech giants Nvidia and Databricks, as well as high-profile individuals such as Amazon founder Jeff Bezos and billionaire investor Stanley Druckenmiller. Venture capital firms Bessemer Venture Partners, NEA, and IVP have also placed their bets on Perplexity’s growth and expansion.

In addition to the investment, Telefónica has inked a global commercial agreement with Perplexity. This partnership will offer preferential benefits to Telefónica customers in Brazil, the UK, and Spain, with plans to explore expansion into other markets.

Telefónica has launched a Perplexity experience through television. The company has developed an exclusive living app within the Movistar Plus+ catalogue, allowing customers with compatible sets to interact with Perplexity directly through their TV. Users can ask questions on any topic using the Movistar Plus+ Voice Remote by pressing the Aura button, receiving AI-generated text answers in real-time, displayed on-screen and spoken aloud.

Chema Alonso, Chief Digital Data Officer at Telefónica, said: “The investment in Perplexity is part of one of the new keys of Wayra’s investment strategy in leading technology markets such as the US, in high-growth companies with global presence and reach, and leaders in technologies such as AI.

“With this commercial agreement, we are also reinforcing Telefónica’s commitment to integrating cutting-edge technologies into the company and offering transformative experiences like Perplexity’s answer engine to our customers”.

Perplexity’s AI-powered approach sets it apart from traditional search engines by offering a conversational interface and personalised results. The platform also provides a clear list of cited sources, enhancing the accuracy and relevance of its answers.

Aravind Srinivas, Co-Founder and CEO of Perplexity, commented: “This investment from Wayra not only validates our AI-powered approach but also opens up exciting opportunities for global expansion.”

By bringing Perplexity’s capabilities to television screens, Telefónica is delivering new ways for users to access information and interact with AI in their homes.

“We look forward to continuing to partner with Telefónica to push the boundaries of what’s possible in AI-driven information discovery and to create transformative experiences for users worldwide,” concludes Srinivas.

The investment from Telefónica comes as research suggests the telecoms industry is leading in generative AI adoption.

See also: King’s Business School: How AI is transforming problem-solving

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.

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This breakthrough represents a significant step in China’s ongoing efforts to become self-reliant in AI technology, especially in light of escalating US limitations on access to advanced semiconductors for its competitors.

According to the company’s Institute of AI, one of the models, TeleChat2-115B and another unnamed model were trained on tens of thousands of Chinese-made chips. This achievement is especially noteworthy given the tighter US export rules that have limited China’s ability to purchase high-end processors from Nvidia and other foreign companies. In a statement shared on WeChat, the AI institute claimed that this accomplishment demonstrated China’s capability to independently train LLMs and signals a new era of innovation and self-reliance in AI technology.

The scale of these models is remarkable. China Telecom stated that the unnamed LLM has one trillion parameters. In AI terminology, parameters are the variables that help the model in learning during training. The more parameters there are, the more complicated and powerful the AI becomes.

Chinese companies are striving to keep pace with global leaders in AI based outside the country. Washington’s export restrictions on Nvidia’s latest AI chips such as the A100 and H100, have compelled China to seek alternatives. As a result, Chinese companies have developed their own processors to reduce reliance on Western technologies. For instance, the TeleChat2-115B model has approximately 100 billion parameters, and therefore can perform as well as mainstream platforms.

China Telecom did not specify which company supplied the domestically-designed chips used to train its models. However, as previously discussed on these pages, Huawei’s Ascend chips play a key part in the country’s AI plans.

Huawei, which has faced US penalties in recent years, is also increasing its efforts in the artificial intelligence field. The company has recently started testing its latest AI processor, the Ascend 910C, with potential clients waiting in the domestic market. Large Chinese server companies, as well as internet giants that have previously used Nvidia chips, are apparently testing the new chip’s performance. Huawei’s Ascend processors, as one of the few viable alternatives to Nvidia hardware, are viewed as a key component of China’s strategy that will lessen its reliance on foreign technology.

In addition to Huawei, China Telecom is collaborating with other domestic chipmakers such as Cambricon, a Chinese start-up specialising in AI processors. The partnerships reflect a broader tendency in China’s tech industry to build a homegrown ecosystem of AI solutions, further shielding the country from the effects of US export controls.

By developing its own AI chips and technology, China is gradually reducing its dependence on foreign-made hardware, especially Nvidia’s highly sought-after and therefore expensive GPUs. While US sanctions make it difficult for Chinese companies to obtain the latest Nvidia hardware, a black market for foreign chips has emerged. Rather than risk operating in the grey market, many Chinese companies prefer to purchase lower-powered alternatives such as previous-gen models to maintain access to Nvidia’s official support and services.

China’s achievement reflects a broader shift in its approach to AI and semiconductor technology, emphasising self-sufficiency and resilience in an increasingly competitive global economy and in face of American protectionist trade policies.

(Photo by Mark Kuiper)

See also: Has Huawei outsmarted Apple in the AI 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.

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The collective voice of these industry leaders highlights a pressing issue: Europe’s bureaucratic approach to AI regulation may be stifling innovation and causing the region to lag behind its global counterparts.

“Europe has become less competitive and less innovative compared to other regions and it now risks falling further behind in the AI era due to inconsistent regulatory decision making,” the letter states, painting a stark picture of the continent’s current position in the AI race.

The signatories emphasise two key areas of concern. Firstly, they point to the development of ‘open’ models, which are freely available for use, modification, and further development. These models are lauded for their potential to “multiply the benefits and spread social and economic opportunity” while simultaneously bolstering sovereignty and control.

Secondly, the letter underscores the importance of ‘multimodal’ models, which integrate text, images, and speech capabilities. The signatories argue that the leap from text-only to multimodal models is akin to “the difference between having only one sense and having all five of them”. They assert that these advanced models could significantly boost productivity, drive scientific research, and inject hundreds of billions of euros into the European economy.

However, the crux of the matter lies in the regulatory landscape. The letter expresses frustration with the uncertainty surrounding data usage for AI model training, stemming from interventions by European Data Protection Authorities. This ambiguity, they argue, could result in Large Language Models (LLMs) lacking crucial Europe-specific training data.

To address these challenges, the signatories call for “harmonised, consistent, quick and clear decisions under EU data regulations that enable European data to be used in AI training for the benefit of Europeans”. They stress the need for “decisive action” to unlock Europe’s potential for creativity, ingenuity, and entrepreneurship, which they believe is essential for the region’s prosperity and technological leadership.

A copy of the letter can be found below:

While the letter acknowledges the importance of consumer protection, it also highlights the delicate balance regulators must strike to avoid hindering commercial progress. The European Commission’s approach to regulation has often been criticised for its perceived heavy-handedness, and this latest appeal from industry leaders adds weight to growing concerns about the region’s global competitiveness in the AI sector.

The pressure is rapidly mounting on European policymakers to create a regulatory environment that fosters innovation while maintaining appropriate safeguards. The coming months will likely see intensified dialogue between industry stakeholders and regulators as they grapple with these complex issues that will shape the future of AI development in Europe.

(Photo by Sara Kurfeß)

See also: SolarWinds: IT professionals want stronger AI regulation

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.

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This surge in demand comes as businesses across various sectors rapidly adopt AI-driven technologies.

Among these notable agreements is a deal with Microsoft, which revealed last month its plans to utilise Lumen’s network equipment to expand capacity for AI workloads. Lumen, known for providing secure digital connections for data centres, disclosed recently that it is engaged in active discussions with customers regarding additional sales opportunities valued at approximately $7 billion.

The widespread adoption of AI has prompted enterprises across multiple industries to invest heavily in infrastructure capable of supporting AI-powered applications. Lumen reports that major corporations are urgently seeking to secure high-capacity fibre, a resource becoming increasingly valuable, and potentially scarce due to growing AI requirements.

There is an optimistic prospect for further success, as Kate Johnson, the CEO of Lumen, expressed: “Our partners are turning to us because of our AI-ready infrastructure and expansive network. This is just the beginning of a significant opportunity for Lumen, one that will lead to one of the largest expansions of the internet ever.”

Another piece of evidence regarding the company’s strategic positioning in such a rapidly changing and highly unstable market is the creation of a new division, Custom Networks. This division will be responsible for managing the Lumen Private Connectivity Fabric solutions portfolio. At the same time, since the demand for networking is rising from various organisations seeking solutions designed to satisfy the specific needs of their target environments, it is rational to develop a new division for networks.

This highlights that telecommunications infrastructure plays a crucial role in the current AI revolution. As an increasing number of firms implement AI technologies in their operations, it is essential to have plenty of secure, expansive networks.

Lumen’s recent success in securing these substantial contracts underscores the company’s strong market position and its ability to meet the evolving needs of tech giants and cloud service providers. As the AI landscape continues to evolve, Lumen appears well-positioned to capitalise on the increasing demand for advanced networking solutions.

The telecommunications sector, and Lumen in particular, is likely to remain at the forefront of enabling AI advancements across industries. As this trend progresses, it will be interesting to observe how Lumen and its competitors adapt to meet the challenges and opportunities presented by this technological shift.

(Photo by Vladimir Solomianyi)

See also: UK backs smaller AI projects while scrapping major investments

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Samsung, which pioneered the foldable smartphone segment in 2019, faces increasing competition in this niche market. Data from Canalys shows that Samsung’s share of foldable phone shipments dropped from 81% in 2022 to 63% in 2023, highlighting the importance of this latest launch.

Responding to market pressures, Samsung has made significant improvements to its foldable lineup:

1. The Galaxy Z Fold 6, featuring a wide screen, is now the lightest and slimmest version in its series, aimed at attracting new customers to the form factor.
2. The clamshell Galaxy Z Flip 6 boasts longer battery life, a higher resolution camera, and a new vapour chamber for improved cooling. These enhancements address key issues identified through customer feedback.

Despite rising material costs and after maintaining stable prices for three years, Samsung has implemented a modest price increase. The Z Flip 6 is priced at $1,099.99, while the Z Fold 6 starts at $1,899.99, representing a $100 increase over last year’s models.

Samsung has introduced several new AI-powered features, including:

• A “listening mode” that provides simultaneous voice interpretation when paired with Galaxy Buds earphones.
• Collaboration with Google to develop new AI search functions, such as displaying step-by-step solutions to math problems when circled on the screen.

The company has also significantly enhanced its Galaxy Watch products:

• A new 3-nanometer chip triples application booting and processing efficiencies compared to last year’s model.
• The watch has received US FDA approval as a monitoring device for sleep apnea.
• New features include measurement of functional threshold power (FTP) for cycling enthusiasts and advanced glycation end-products (AGEs) related to diabetes.

Samsung’s commitment to health monitoring is further exemplified by the introduction of the Galaxy Ring. Priced at $399.99, this smart ring comes in gold, silver, and black, featuring a titanium frame with 10ATM water resistance and an IP68 rating. At 7mm wide and 2.6mm thick, it’s designed to be slim and lightweight, weighing between 2.3 and 3g depending on the size.

The Galaxy Ring primarily functions as a health tracker, equipped with an accelerometer, optical heart rate sensor, and skin temperature sensor. It can monitor sleep, heart rate, and activity, while introducing new Galaxy AI-powered metrics such as Energy Score and Wellness Tips. The ring offers 6-7 days of battery life and comes with a unique, transparent charging case that holds 1.5 times the charge.

Industry analyst Jack Leathem from Canalys emphasises the importance of AI-powered health and fitness features in wearables, noting that they are “core to attracting brand switchers in the premium segment” and crucial for differentiating Samsung from other smartwatch vendors.

The Galaxy Ring is only compatible with Android phones running the Samsung Health app, with some features exclusive to Galaxy phones. A standout feature for Galaxy Z Fold 6 and Z Flip 6 users (soon to be available on the S24) is the ability to control the phone’s camera or dismiss alarms using a double pinch gesture on the ring.

While the Galaxy Ring shows promise in hardware design and ecosystem integration, its success will ultimately depend on tracking accuracy and consistent battery performance. Samsung’s expansion into the smart ring market, coupled with its enhancements to foldable phones and smartwatches, demonstrates the company’s commitment to innovating across the wearable and smartphone sectors.

The new lineup of products – including the foldable phones, watches, and ring – will be available starting July 24 in South Korea, North America, and Europe, marking a significant step in Samsung’s strategy to innovate and compete in the high-end smartphone and wearable markets.

(Image Credit: Samsung)

See also: EU probes Microsoft-OpenAI and Google-Samsung AI deals

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The high-level meeting, dubbed the MS CEO Summit 2024, will be held on 14 May 2024 and feature Microsoft’s founder Bill Gates and Chairman and CEO Satya Nadella. They will engage in closed-door discussions with Kyung Kye-hyun of Samsung, Kwak Noh-jung of SK Hynix, Cho Joo-wan of LG Electronics, and Ryu Young-sang of SK Telecom.

Sources for The Korea Economic Daily suggest that Microsoft plans to explore joint ventures in AI technology across various sectors. Discussions with Samsung and SK Hynix will likely centre on the joint development and supply of AI chips.

Samsung and SK Hynix are recognised as being among the world’s leading memory chipmakers and can enhance Microsoft’s server capabilities with next-generation technologies such as High-Bandwidth Memory (HBM) AI chips and solid-state drives (SSDs).

Collaboration topics with LG Electronics will include integrating AI technologies into home appliances, a move that will boost Microsoft’s competitive edge against rivals like Google and Meta. With SK Telecom, Microsoft is expected to delve further into cloud and 5G services.

These meetings are timely, as the global tech landscape sees an increased focus on AI development. By potentially integrating Microsoft’s AI services into products like Samsung’s smartphones and LG’s home appliances, Microsoft could significantly elevate its market standing.

Kyung of Samsung’s Device Solutions indicated last month that their new AI accelerators, Mach-1 and Mach-2, will soon move into mass production. These accelerators are designed to optimise the synergy between GPUs and HBM chips, promising a revolution in processing speeds. Earlier this month, the company unveiled the industry’s first LPDDR5X DRAM which aims to boost on-device AI.

SK Telecom, under CEO Ryu, spearheads the Global Telco AI Alliance (GTAA). This consortium, including major global players like Deutsche Telekom and SingTel, aims to develop AI infrastructure and generative AI services across a customer base exceeding 1.3 billion globally.

Last year, SK Telecom invested $100 million in AI startup Anthropic to develop a large language model (LLM) specifically for telcos. The collaborative endeavour extends to the Telco AI Platform, an ongoing project initiated by the GTAA.

The MS CEO Summit 2024 presents an opportunity for enhanced AI cooperation and technological advancement, securing Microsoft’s position as a pivotal player in the industry.

(Photo by Natalie Pedigo)

See also: Meta raises the bar with open source Llama 3 LLM

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Introducing lightweight, industry-specific AI models for individual licence, SAS hopes to equip organisations with readily deployable AI technology to productionise real-world use cases with unparalleled efficiency.

Chandana Gopal, research director, Future of Intelligence, IDC, said: “SAS is evolving its portfolio to meet wider user needs and capture market share with innovative new offerings,

“An area that is ripe for SAS is productising models built on SAS’ core assets, talent and IP from its wealth of experience working with customers to solve industry problems.”

In today’s market, the consumption of models is primarily focused on large language models (LLMs) for generative AI. In reality, LLMs are a very small part of the modelling needs of real-world production deployments of AI and decision making for businesses. With the new offering, SAS is moving beyond LLMs and delivering industry-proven deterministic AI models for industries that span use cases such as fraud detection, supply chain optimization, entity management, document conversation and health care payment integrity and more.

Unlike traditional AI implementations that can be cumbersome and time-consuming, SAS’ industry-specific models are engineered for quick integration, enabling organisations to operationalise trustworthy AI technology and accelerate the realisation of tangible benefits and trusted results.

Expanding market footprint

Organisations are facing pressure to compete effectively and are looking to AI to gain an edge. At the same time, staffing data science teams has never been more challenging due to AI skills shortages. Consequently, businesses are demanding agility in using AI to solve problems and require flexible AI solutions to quickly drive business outcomes. SAS’ easy-to-use, yet powerful models tuned for the enterprise enable organisations to benefit from a half-century of SAS’ leadership across industries.

Delivering industry models as packaged offerings is one outcome of SAS’ commitment of $1 billion to AIpowered industry solutions. As outlined in the May 2023 announcement, the investment in AI builds on SAS’ decades-long focus on providing packaged solutions to address industry challenges in banking, government, health care and more.

Udo Sglavo, VP for AI and Analytics, SAS, said: “Models are the perfect complement to our existing solutions and SAS Viya platform offerings and cater to diverse business needs across various audiences, ensuring that innovation reaches every corner of our ecosystem. 

“By tailoring our approach to understanding specific industry needs, our frameworks empower businesses to flourish in their distinctive Environments.”

Bringing AI to the masses

SAS is democratising AI by offering out-of-the-box, lightweight AI models – making AI accessible regardless of skill set – starting with an AI assistant for warehouse space optimisation. Leveraging technology like large language models, these assistants cater to nontechnical users, translating interactions into optimised workflows seamlessly and aiding in faster planning decisions.

Sgvalo said: “SAS Models provide organisations with flexible, timely and accessible AI that aligns with industry challenges.

“Whether you’re embarking on your AI journey or seeking to accelerate the expansion of AI across your enterprise, SAS offers unparalleled depth and breadth in addressing your business’s unique needs.”

The first SAS Models are expected to be generally available later this year.

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 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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