The United Arab Emirates has announced plans to introduce AI education in curriculum in all government schools, making AI a mandatory subject from kindergarten through to grade 12, starting next academic year.

The initiative, announced by Sheikh Mohammed bin Rashid, Vice President and Ruler of Dubai, aims to provide children as young as four with understanding of AI technologies, principles, and ethical considerations.

“Our goal is to teach our children a deep understanding of AI from a technical perspective, while also fostering their awareness of the ethics of this new technology, enhancing their understanding of its data, algorithms, applications, risks, and its connection to society and life,” Sheikh Mohammed stated on May 4.

Global context: A competitive landscape

The UAE’s announcement comes amid a global rush by multiple nations to integrate AI education into their curricula, with each country adopting distinctive approaches based on their educational priorities and technological aspirations.

In Beijing, China, primary and secondary school pupils will receive a minimum of eight hours of AI-focused lessons each academic year beginning this autumn. Children as young as six will learn how to engage with AI-powered tools, gain a foundational understanding of the technology, and explore ethical considerations surrounding its use.

The Beijing Municipal Education Commission recently announced that schools may integrate these lessons into existing subjects like information technology and science or offer them as standalone courses.

Its plan includes developing a multi-year AI curriculum, establishing a structured education and training system, introducing support initiatives, and promoting awareness of the program. China’s approach is particularly notable as it has already taken concrete steps toward implementation.

Last December, China’s Ministry of Education selected 184 schools to trial AI-focused curricula, forming the basis for future expansion. According to Minister Huai Jinpeng, AI represents a crucial component of China’s educational strategy.

Estonia, with its already strong digital education foundation, is taking a different path. Estonia’s government recently partnered with OpenAI to introduce AI-driven education tools to secondary school pupils and teachers. From September, students in Years 10 and 11 will gain access to customised AI learning platforms, with additional support for educators in lesson planning and administrative tasks.

Comparing approaches

While the UAE curriculum appears comprehensive on paper, with 7 key areas spanning from foundational concepts to community engagement, it’s important to note that there has been no announcement yet on whether private schools, which are regulated separately, will be instructed to roll out AI classes.

This contrasts with China’s approach which appears to be moving toward broader implementation beyond pilot programmes. The UAE’s plan is ambitious in its age range, starting with four-year-olds, which is younger than many other programmes globally.

The curriculum is broken into three cycles, with tailored units for each age group. Four-year-olds will engage in visual and interactive activities to discover AI through play, while older students progress to comparing machines to humans, designing their own AI systems, andtually learning command engineering with real-world scenarios.

South Korea and Canada have taken different approaches, incorporating AI into existing school curricula, offering AI-powered learning materials and classroom tools for teachers rather than creating entirely new subject areas. The integration model may prove more practical for educational systems that face challenges in adding new subjects to already crowded curricula.

Critical assessment

What distinguishes the various approaches is not necessarily which country is “leading,” but rather how each nation’s AI education strategy aligns with its broader technological and economic goals.

For the UAE, the emphasis appears to be on creating a framework that starts from the earliest years of education. Sarah Al Amiri, Minister of Education, described the integration of AI in education as a “national imperative” that “supports economic growth, fosters sustainable development and significantly enhances individual capabilities.”

However, experts might question whether starting AI education at age four is developmentally appropriate, or if the UAE’s education system has the necessary infrastructure and teacher training to deliver such an ambitious curriculum effectively. The practical considerations will determine the program’s success beyond the ambitious announcement.

China’s approach benefits from the country’s established technological infrastructure and its significant investments in AI research and development, potentially giving it advantages in implementation. The selection of 184 schools for trial programmes demonstrates a methodical approach focused on gathering data before broader implementation.

In the UK, the approach has been more fragmented with at least one private school launching an experimental learning space where students use virtual reality headsets and AI platforms instead of traditional teaching methods. This reflects a more market-driven approach compared to the centralised government initiatives seen in the UAE, China, and Estonia.

Balancing technology and pedagogy

All these initiatives face similar challenges regarding the balance between technological innovation and sound pedagogical approaches. While AI can transform education by making learning more accessible and personalised, education authorities worldwide remain cautious.

The United Nations has highlighted the importance of responsible AI implementation, recommending clear guidelines, inclusivity, and a focus on human-centred learning.

The rush to implement AI education also raises questions about equity and access. Will these programmes exacerbate existing digital divides between well-resourced and under-resourced schools? Will all teachers receive adequate training to deliver these curricula effectively?

Looking forward

Rather than crowning any nation as the definitive leader in AI education, it’s more accurate to observe that there is something of a global recognition of AI litreacy as a component of future education. Each country’s approach reflects its unique educational philosophy, technological capabilities, and strategic priorities.

The UAE’s ambitious programme, China’s methodical implementation, Estonia’s partnership model, and other nations’ varying approaches will provide data on the results of AI education strategies. The true test will be in implementation, teacher training, curriculum quality, and ultimately, student outcomes.

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Microsoft has introduced its first in-house AI models, a move that could reshape its position in the AI race. The company rolled out MAI-Voice-1, a speech model, and MAI-1-preview, a text-based model it calls a glimpse of what’s coming next inside Copilot.

The MAI-Voice-1 model is built for speed. According to Microsoft, it can generate a full minute of audio in less than a second using just a single GPU. The model is already in use inside some of the company’s tools. For example, Copilot Daily uses it to deliver short news summaries through an AI voice host. It also helps produce podcast-style conversations that break down complex topics into easier explanations.

The second release, MAI-1-preview, is designed for text tasks. Microsoft trained the model on roughly 15,000 Nvidia H100 GPUs, giving it the scale to handle instruction-following and natural Q&A. Users can already try it on Copilot Labs, where they can test its ability to respond to everyday queries. Microsoft says the model will soon support text-based use cases inside its Copilot assistant.

Competition with OpenAI

These launches come while Microsoft is still heavily tied to OpenAI, the maker of ChatGPT. Microsoft has invested more than $13 billion into the startup, which now has a valuation of about $500 billion. OpenAI continues to rely on Microsoft’s cloud infrastructure to run its own models, while Microsoft uses OpenAI’s systems inside Bing, Windows, and other products.

At the same time, the two companies are drifting into competition. Last year, Microsoft added OpenAI to the list of rivals it names in its annual report, alongside Amazon, Apple, Google, and Meta. OpenAI has also been spreading its infrastructure needs across other providers such as CoreWeave, Google, and Oracle, as demand for ChatGPT climbs. The chatbot now draws about 700 million weekly users.

Early results and rankings

Performance comparisons show Microsoft’s work still trails some of its peers. On Thursday, the new MAI-1-preview ranked 13th for text workloads on LMArena, behind models from Anthropic, DeepSeek, Google, Mistral, OpenAI, and Elon Musk’s xAI. While not at the top, Microsoft has positioned MAI-1-preview as its first foundation model built entirely in-house.

“MAI-1-preview represents our first foundation model trained end to end in house,” Mustafa Suleyman, Microsoft AI chief, wrote on X.

A consumer focus

Suleyman has been clear about the group’s direction. In an interview last year, he explained that Microsoft’s AI models are aimed at consumer use rather than the enterprise market. “My logic is that we have to create something that works extremely well for the consumer and really optimise for our use case,” he said. He pointed to Microsoft’s access to large amounts of consumer data—such as ad performance and telemetry—as a strength in training models for everyday companions.

The company has also said that it does not plan to rely on one general-purpose model. Instead, it sees potential in offering multiple specialised models designed for different types of requests. “We believe that orchestrating a range of specialised models serving different user intents and use cases will unlock immense value,” Microsoft AI wrote in a blog post.

Building an AI division

MAI-1-preview builds on earlier small-scale models released under the Phi name. But this marks the first time Microsoft has trained a foundation model of this size from start to finish. The effort reflects how the company has been building out its AI group since hiring Suleyman and many of his former colleagues from the startup Inflection.

Suleyman previously co-founded DeepMind, the research lab Google bought in 2014. In the past year, Microsoft has brought on about two dozen former DeepMind researchers to expand its internal team. The hires show how the company is drawing on talent with long experience in AI development to accelerate its own projects.

For now, Microsoft is positioning its new models as additions to its Copilot ecosystem while it continues to rely on OpenAI for many core features. But the release of MAI-Voice-1 and MAI-1-preview signals a step toward more independence in model development. Analysts say it could also set up a new phase of competition between Microsoft and the company it helped make into an AI giant.

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Nvidia shares slipped on Wednesday as uncertainty grew around its business in China, caught in the middle of the trade fight between Washington and Beijing.

CEO Jensen Huang said he expects approval to restart sales of Nvidia chips in China after striking a deal with US President Donald Trump to pay commissions to the government. But with no formal rules yet, and doubts about whether Chinese regulators might discourage purchases, Nvidia left potential China sales out of its forecast for the current quarter.

That decision led to an outlook that looked steady but less than what investors have come to expect. Nvidia projected revenue of about US$54 billion for the third quarter, just above Wall Street estimates of US$53.14 billion, according to LSEG data. The forecast was enough to beat analyst targets but fell short of the “blowout” growth the market has grown used to, pushing the stock down 3.2 per cent in after-hours trading. That drop cut about US$110 billion from Nvidia’s US$4.4 trillion valuation.

As reported by Reuters, Huang downplayed concerns that the AI spending surge could be cooling, telling investors the opportunity could expand into a multi-trillion-dollar market over the next five years. “A new industrial revolution has started. The AI race is on,” he said, adding that Nvidia sees $3 trillion to $4 trillion in AI infrastructure spending by the end of the decade.

“Nvidia’s biggest bottleneck isn’t silicon, it’s diplomacy,” said Michael Ashley Schulman, chief investment officer at Running Point Capital. He added the company’s growth is “still impressive, but not as exponential.”

Second-quarter revenue reached US$46.74 billion, above the US$46.06 billion analysts expected. But the data centre segment, a key driver of Nvidia’s growth, missed some estimates. Analysts suggested that big cloud providers may be spending more carefully. Nvidia said around half of its US$41 billion in data centre revenue came from major cloud companies, slightly below Visible Alpha’s estimates of US$41.42 billion.

The company’s forecast also assumed no shipments of its H20 chips to China, even though some licenses to sell them have already been granted. Nvidia said that if geopolitical hurdles ease and orders come in, H20 sales to China could add between US$2 billion and US$5 billion in the third quarter.

“That is a big question mark to watch,” said Ben Bajarin, CEO of consulting firm Creative Strategies.

Analysts also pointed out that Nvidia’s share price, which has risen by about one-third this year, may have created lofty expectations that are hard to meet. “The mega caps are the ones propelling a lot of the capex that Nvidia is benefiting from. But obviously Nvidia still is growing, is able to sell,” said Matt Orton of Raymond James Investment Management, who argued the durability of the AI trade remains intact.

Even so, demand for Nvidia’s chips remains strong. Businesses racing to build generative AI systems continue to buy the company’s processors, which are designed to handle huge amounts of data quickly. CFO Colette Kress said Nvidia’s “sovereign AI” push — aimed at selling AI hardware and software to governments, including outside China — is on track to bring in US$20 billion this year. She added that cloud and enterprise customers could spend as much as US$600 billion on AI in 2025 alone, with total infrastructure spending tied to AI reaching US$3 trillion to US$4 trillion by the end of the decade.

Huang said much of this growth will come from hyperscalers like Microsoft and Amazon, which are expected to spend about US$600 billion on data centres this year. He added that for a US$60 billion data centre, Nvidia can capture roughly US$35 billion in revenue.

Big Tech firms including Meta and Microsoft are spending heavily on AI, much of it flowing toward Nvidia chips. For the current quarter, Nvidia forecast adjusted gross margins of 73.5 per cent, a touch above analyst estimates of 73.3 per cent.

“The data centre results, while massive, showed hints that hyperscaler spending could tighten at the margins if near-term returns from AI applications remain difficult to quantify,” said Jacob Bourne, an analyst at eMarketer.

Shares of rival Advanced Micro Devices, which is developing competing AI servers, also fell 1.4 per cent after Nvidia’s results.

AI enthusiasm, with Nvidia at the centre, has been one of the main drivers of the S&P 500’s rally over the past two years. But the company’s latest report drew a more muted response.

“This is the smallest reaction to an earnings report in Nvidia’s AI incarnation,” said Jake Behan, head of capital markets at Direxion in New York. “While it may not have been a blowout, it’s not a miss.”

Outside China, Nvidia is still seeing strong demand for its H20 chips. Kress said one customer alone bought US$650 million worth during the second quarter.

Huang also said the company’s high-end Blackwell chips are already largely booked through 2026, while its older Hopper processors remain in demand. “The buzz is: everything sold out,” Huang told analysts, describing the pace of orders.

The company also said its board had approved an additional US$60 billion in share buybacks.

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Eight-year-old Sidra Al Bordeeni hadn’t ridden a bike in over a year. A missile strike in Gaza took her arm while she and her family were sheltering at a school-turned-refuge, according to Reuters. Now living in a Jordanian refugee camp, she’s back on two wheels with the help of a new prosthetic limb.

Her mother, Sabreen, said the injury happened during an airstrike on Nuseirat School. The family couldn’t leave Gaza, and healthcare services had broken down, leaving no way to save her daughter’s hand. “She’s outside playing again,” Sabreen said. “All the other kids are curious about her new arm. I’m just thankful she’s happy again.”

The prosthetic was made in Karachi by Bioniks, a company in Pakistan that builds custom artificial limbs using 3D modelling. The process starts with a smartphone app that takes images from multiple angles to create a design tailored to each user.

Since 2021, Bioniks has made over a thousand prosthetic arms in Pakistan. Most were for patients inside the country, paid for by individuals, companies, and donations. This is the company’s first project involving people affected by conflict abroad.

Sidra and another young girl, three-year-old Habebat Allah, received their prosthetics after going through online consultations and fittings. Habebat lost both arms and a leg in Gaza. Bioniks CEO Anas Niaz later flew to Amman to meet them and deliver the devices in person.

This isn’t Bioniks’ first experience building prosthetics for children. In 2016, the company produced Pakistan’s first 3D-printed prosthetic arm for a five-year-old named Mir Bayyan.

Mir’s father had contacted the team to ask if they could help his son, and Bioniks agreed to design the arm using an Xplorer 3D printer and an open-source model from UK-based group Team UnLimbited. The design was customised with an Iron Man theme—Mir’s favourite superhero—and even included a small light beam in the palm to resemble the character’s energy blast.

Sidra’s arm was sponsored by Mafaz Clinic in Amman, while Habebat’s was paid for by donations from Pakistan. Mafaz CEO Entesar Asaker said they worked with Bioniks because the company’s tools are affordable, remote-friendly, and allow for virtual troubleshooting.

Each Bioniks arm costs around $2,500. That’s lower than the $10,000 to $20,000 price tag of U.S.-made prosthetics. While less complex than high-end models, Bioniks’ arms still offer key functions for children, and the remote process makes them more accessible compared to prosthetics made in countries like Turkey and South Korea.

“We’re planning to expand to other conflict zones like Ukraine,” Niaz said. “The goal is to become a global provider.”

Children in war zones often don’t receive prosthetics because most designs are made for adults. Kids also need replacements every year or so as they grow, which adds to the cost. Niaz said Bioniks is looking into funding options for future replacements for Sidra and Habebat. Most of the parts, he added, can be reused to help another child.

To make the devices more relatable for kids, the company sometimes includes design elements from cartoons or movies. Past examples have included characters like Iron Man or Elsa. Niaz said it can make the adjustment easier and encourages regular use.

Growing need, limited access

The humanitarian crisis in Gaza has left thousands of children with amputations. The U.N. humanitarian agency OCHA estimates there are now around 4,500 new amputees in Gaza, on top of roughly 2,000 existing cases before the current conflict. A large share are children.

An April report from the Palestinian Bureau of Statistics estimated that more than 7,000 children have been injured since Israel’s military campaign in Gaza began in October 2023. Local authorities say the death toll has passed 50,000, with children making up about a third of the total.

Gaza’s healthcare system has been pushed to the brink. According to the World Health Organization, ongoing border closures and supply shortages have left hospitals unable to treat patients properly, especially those with complex injuries.

“Remote services help when medical travel isn’t possible,” said Asadullah Khan, who manages ProActive Prosthetic in the UK. “People can still get assessments and fittings without needing to visit a clinic.”

While Bioniks is working to bring this kind of model to more places, funding remains a challenge, and the company is still in the early stages of building long-term partnerships.

Sidra is still getting used to the prosthetic. She’s added a bracelet to the wrist and has started doing things she couldn’t before—like forming a heart shape with her hands. She used to ask others to complete it for her. Now, she does it herself, snapping a photo to send to her father, who remains in Gaza.

“What I’m waiting for,” she said, “is the day I can use both arms to hug my dad.”

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Meta has regrouped its artificial intelligence operations under a new unit called Meta Superintelligence Labs (MSL), according to a source familiar with the matter. The decision comes after the company’s latest open-source model, Llama 4, received poor feedback, and several senior researchers left. With competitors like OpenAI, Google, and China’s DeepSeek gaining ground, Meta is looking to reset its AI strategy and rebuild momentum.

The new group will be led by Alexandr Wang, the former CEO of Scale AI, who joins Meta as its new chief AI officer. Wang made his name building Scale into a key data provider for many large AI models. He won’t be working alone—Nat Friedman, ex-CEO of GitHub and a known investor in AI startups, will co-lead MSL and focus on product development and applied research. According to the source, Friedman has been advising Meta over the past year and is already familiar with its roadmap.

Meta CEO Mark Zuckerberg is personally backing the shake-up. Over the past month, he’s taken a direct role in recruiting, sending messages to candidates via WhatsApp and courting several startup founders. One of those was Safe Superintelligence (SSI), co-founded by OpenAI’s Ilya Sutskever. Although a deal with SSI didn’t go through, its co-founder and CEO, Daniel Gross, is said to have joined MSL.

In early June, Meta also invested $14.3 billion into Scale AI, further tying the company to its new leadership. Zuckerberg hopes the reshaped team will accelerate Meta’s push toward artificial general intelligence (AGI)—a still-hypothetical form of AI that can outperform humans in most tasks—and help create new revenue streams from tools like Meta AI, image-to-video ad generators, and smart wearables.

MSL is already pulling in talent from top AI labs. The source said Meta has made at least 11 new hires in recent weeks, including names from OpenAI, Google DeepMind, Anthropic, and DeepSeek. Some of the individuals joining include:

• Jack Rae and Pei Sun, both previously with DeepMind
• Joel Pobar, returning to Meta after time at Anthropic
• Jiahui Yu, Shuchao Bi, Shengjia Zhao, and Hongyu Ren from OpenAI
• Huiwen Chang, who worked on image generation at Google Research
• Trapit Bansal, known for work on reasoning in AI models
• Ji Lin and Johan Schalkwyk, both with backgrounds in model architecture and voice systems

These hires come after OpenAI CEO Sam Altman said Meta had offered some of his staff bonuses of up to $100 million to switch companies.

Despite the influx of talent, some analysts are sceptical about how soon Meta will see results. Its last big technology bet, Reality Labs, has cost the company more than $60 billion since 2020, with only a few products—such as Ray-Ban smart glasses and Quest headsets—making it into users’ hands.

AGI remains a long-term goal for the tech industry. Microsoft recently spent $650 million to hire most of Inflection AI’s staff, including co-founder Mustafa Suleyman, while Amazon has been hiring AI researchers from startups like Adept. But the path to AGI is unclear. Meta’s own chief AI scientist, Yann LeCun, has said the current tools and approaches won’t be enough. Meanwhile, SoftBank’s Masayoshi Son believes a breakthrough could happen within a decade.

In an internal memo on Monday, Zuckerberg said Meta is now focused on building “personal superintelligence for everyone.” The memo confirmed that MSL will combine Meta’s foundation model research, FAIR team, and AI product development under one roof. A new lab within MSL will also start work on a next-generation model aimed at pushing the current technical limits.

Zuckerberg wrote that Meta is “uniquely positioned” to take this step due to its large user base, experience running global apps, and capacity to invest in large-scale computing. He also pointed to Meta’s early work on AI-powered wearables as another potential edge.

The memo ended with a call for more staff to join the new lab, and suggested more hires will be announced in the coming weeks.

Whether this new approach helps Meta catch up with its competitors—or delivers anything close to AGI—remains to be seen.

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Malaysia now has another initiative focused on artificial intelligence. Launched last Friday, AI Malaysia (AIM) is described as a platform to help businesses adopt AI and connect with others in the region. The initiative comes from ASEAN-BAC Malaysia and is part of a broader effort to encourage innovation and responsible tech use.

But Malaysia already has several ongoing efforts in AI, including the National AI Office (NAIO), which was set up in late 2024. The arrival of AIM raises a question: What gap is it meant to fill?

A new player in Malaysia’s AI plans

AI Malaysia, or AIM, was launched as a way to help companies better understand and use AI. According to its backers, AIM wants to create links between government, businesses, and researchers. It aims to encourage responsible development while helping firms stay competitive.

AIM also places importance on Malaysia’s place in ASEAN. Part of its mission is to support cross-border collaboration and to take part in regional discussions about AI safety, fairness, and trust.

This is not the country’s first attempt at organising its AI efforts. The Malaysian government created NAIO with a clear public role: to build national policy, oversee long-term strategy, and guide public sector adoption. AIM, by contrast, appears to focus more on business users and cross-industry collaboration.

What AIM wants to do

Early details imply that AIM will act as a connector rather than a regulator. The initiative is expected to help companies understand how artificial intelligence can enhance operations, reduce costs, and open new market opportunities. It also plans to create events, partnerships, and working groups to support these goals.

Still, there’s limited public information about how AIM’s work will be structured or measured. Its launch comes at a time when many in Malaysia are already trying to make sense of the country’s broader digital strategies. This includes the MyDIGITAL initiative, the National AI Roadmap, and separate industry-based programs.

Overlap with existing national plans

NAIO is the country’s main agency for AI policy and planning. Since its launch, it has announced plans for an AI Technology Action Plan (2026–2030), a national AI Code of Ethics, and a regulatory framework to support responsible adoption. It also plays a role in driving public sector AI projects and shaping Malaysia’s position in global AI forums.

With AIM now added to the mix, it’s unclear how the two efforts will work together. Both aim to support adoption, encourage responsible use, and strengthen Malaysia’s role in ASEAN’s digital economy. Without clear boundaries, there is a chance of overlap—or confusion—between their roles.

AIM’s timing and regional context

Across Southeast Asia, countries are setting up national AI centres and publishing ethical AI guidelines. Indonesia, Singapore, and Vietnam have each released AI roadmaps and are investing in cloud and data infrastructure.

Malaysia is already part of ASEAN’s push for regional AI governance. In this context, AIM may serve as the country’s private-sector link to regional conversations. Its focus on cross-border collaboration could help Malaysian businesses find AI use cases that work across sectors and borders.

Bridging the public-private gap

One possible benefit of AIM is its closer link to business needs. While NAIO sets policy, AIM could help companies test ideas, run pilots, and find technical partners. This could include universities, AI startups, or cloud service providers.

This approach might help Malaysia address some of the known challenges in Malaysia’s AI plans. Companies have previously reported trouble accessing skilled talent, finding funding for AI research, and getting regulatory support for pilot programs. AIM may be able to help fill those gaps if its structure supports collaboration rather than competition.

The talent gap remains a shared concern

Malaysia still faces a shortage of AI talent, both in terms of technical skill and practical experience. To address this, the government has rolled out the AI Talent Roadmap (2024-2030) and school-level programs like Cikgu Juara Digital, which teaches kids and teachers about AI, coding, and robotics.

AIM’s role in talent development is not yet clear. While its launch announcement mentioned a focus on innovation and education, it did not outline any new programs. If AIM plans to support training or help match workers with AI-related jobs, it could complement the existing government strategies.

International support builds momentum

Malaysia’s push into AI has drawn interest from international tech companies. Microsoft recently committed $2.2 billion to support cloud and AI infrastructure in the country. This investment includes the planned National AI Innovation Center, training programs, and partnerships with local organisations.

This support could benefit both AIM and NAIO, as both seek to encourage responsible adoption and stronger digital foundations. At the same time, it points out the need for coordination. If too many initiatives are created without clear roles, they risk competing for the same partners, resources, and attention.

Avoiding fragmentation

When governments and industry bodies launch multiple programs with similar goals, the risk is confusion rather than progress. Malaysia has made clear that AI is a national priority. To keep on track, it will need to ensure that AIM, NAIO, and other programs support one another rather than overlap.

So far, AIM appears to position itself as a complement to NAIO, not a replacement. Whether that balance holds will depend on how clearly each group defines its tasks and how well they coordinate over time.

AI Malaysia is still in its early days. There are few details available about its structure, funding, or programs. Its website and public statements suggest it wants to be a meeting point for AI innovation and policy, but success will depend on how well it works alongside existing efforts.

For AIM to matter, it will need to show how it adds value—not just duplicate work already being done by the National AI Office or other agencies. That might mean helping businesses run pilots, linking developers with users, or giving feedback to policymakers about on-the-ground challenges.

If AIM can stay focused and fill those gaps, it could help speed up AI use in Malaysia. If not, it may risk becoming one more name on a long list of well-meaning plans.

The post Malaysia adds another AI initiative—How AIM fits into the bigger picture appeared first on TechWire Asia.

Broadcom is now shipping its Tomahawk 6 switch chip, offering 102.4 terabits per second of bandwidth on a single chip. That’s double the capacity of any current Ethernet switch and is aimed squarely at powering larger, more complex AI networks.

Built to handle both scale-up and scale-out network designs, Tomahawk 6 supports 100G and 200G SerDes and co-packaged optics, giving cloud providers and hyperscalers flexible options when connecting clusters of over a million processing units. It also introduces new routing features that help networks respond to congestion and failure in real time—critical for AI training and inference tasks.

Broadcom says Tomahawk 6 was designed for networks handling tens of thousands to over a million AI accelerators. With that kind of scale, the network can become a bottleneck. This chip aims to clear that hurdle by boosting throughput and reducing latency, while still relying on open, standards-based Ethernet.

Power efficiency and connectivity choices

Tomahawk 6 uses Broadcom’s high-performance SerDes to support longer copper cable runs without signal boosters, helping lower both power use and system cost. Customers also have the option to choose a version of the chip with 1,024 100G SerDes lanes on a single die—useful for dense clusters that rely heavily on direct copper connections.

For those that need optical links, Tomahawk 6 includes a version with co-packaged optics. This setup is designed to cut power use and latency further while improving long-term connection reliability—something data centre operators running hyperscale AI workloads increasingly demand.

Built for large AI workloads

The chip comes with updated routing software, dubbed Cognitive Routing 2.0, which includes telemetry and adaptive congestion control. These tools are designed to manage the unique traffic patterns of AI workloads like fine-tuning, reinforcement learning, and large model reasoning.

Training large language models requires splitting tasks across many graphics processors, with each handling a portion of the overall work. This parallel setup helps speed things up, but it also depends on the chips staying in sync by sending data back and forth. That constant communication uses a lot of bandwidth. Inference can be just as demanding, since it often involves pulling data from remote storage over the network. As these workloads grow, the pressure on the network increases.

Tomahawk 6 can support up to 512 XPUs in a scale-up setup and more than 100,000 XPUs in a two-tier scale-out network using 200 Gbps links. It delivers 102.4 terabits per second of Ethernet switching on a single chip. The chip includes 100G and 200G PAM4 SerDes that support long-range passive copper connections and optional co-packaged optics for systems that require optical links. It works with any Ethernet-based network interface card or processor and supports a range of topologies, including Clos, rail-only, rail-optimised, torus, and traditional scale-up architectures. These capabilities make it suitable for large-scale AI training and inference networks that demand both high bandwidth and flexibility.

Using Ethernet as the foundation for both connection types means organisations can stick with familiar tools and processes, even as they adjust cluster layouts to handle different workloads. That flexibility helps keep operations simpler and more efficient.

Broadcom says multiple large-scale deployments are already planned, some involving over 100,000 AI accelerators using Tomahawk 6 to manage traffic within and between racks.

Open standards and interoperability

As part of the rollout, Broadcom introduced the Scale Up Ethernet (SUE) Framework, which outlines how chipmakers and system vendors can build open, scalable connections between processors and network interface cards. The framework, announced at the Open Compute Project event in Dublin this April, is publicly available and will be shared with industry standards groups.

Tomahawk 6 also complies with the Ultra Ethernet Consortium’s specifications, supporting newer transport protocols and signalling features for large-scale training systems. It works across a wide range of network designs, including scale-up clusters, Clos topologies, rail-based systems, and torus-style layouts.

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Red Hat and AMD have strengthened their partnership to better support AI workloads and modernise virtual machines (VMs) in hybrid cloud setups. This joint effort brings together AMD’s hardware, including Instinct GPUs and EPYC CPUs, with Red Hat’s open source platforms.

The goal is to make it easier for businesses to manage resource-heavy AI tasks and existing virtual infrastructure without needing major upgrades. Both companies are working to give IT teams the tools to scale up AI deployments while reducing system costs. This is becoming more important as organisations seek to use large language models, data pipelines, and inference engines within environments that may not have been designed with AI in mind.

Model deployment tests with AMD Instinct MI300X

Red Hat and AMD tested the performance of AI models using AMD Instinct MI300X GPUs on Microsoft Azure’s ND MI300X v5 virtual machines. These tests showed that both small and large language models could run across numerous GPUs within a single VM. This avoids the need to split workloads across many virtual machines, which often increases cost and complexity.

This setup could make AI inference more efficient. It’s meant to support teams that are training or deploying models at scale without needing to redesign their infrastructure. The fact that a single VM can support multi-GPU operations simplifies deployment and can help organisations avoid scaling challenges. With fewer systems to manage, teams can spend less time on operational tasks and more on development.

Red Hat AI Inference server gains AMD GPU support

To build on this, AMD Instinct GPUs will now support the Red Hat AI Inference Server. This tool helps users run open source AI models in enterprise environments. It gives them a tested path to deploy models on AMD hardware without extra tuning or setup. The combination is aimed at reducing the friction many teams face when moving AI projects from test environments into production.

The AI Inference Server is based on the vLLM open source project. Red Hat and AMD are both contributing to it. Their work includes performance tuning for dense and quantised models, kernel-level updates, and better support for collective GPU operations. These efforts allow organisations to use existing AI models more effectively and improve overall performance.

Community work around vLLM

Support for multi-GPU workloads has become more important as AI use grows. Red Hat and AMD are working to help users get the most out of their current GPUs. Their changes to vLLM aim to reduce the delays associated with managing distributed GPU workloads.

They are also working with IBM and others to grow the vLLM ecosystem. The shared goal is to make it easier to run large models on open source tools, without forcing users into one cloud or vendor setup. Open contribution models like this one are helping shape a future where AI development is not tied to any single provider.

The work on vLLM also helps smaller organisations that may lack the scale of large tech firms. By providing tested tools and code optimisations, Red Hat and AMD are helping reduce the barrier to entry for teams that want to work with LLMs but don’t have extensive infrastructure.

Red Hat launches llm-d to improve model Inference at scale

Red Hat recently introduced a new open source project called llm-d. This tool is designed for teams running generative AI models across large, distributed systems. It is built on Kubernetes and works with the vLLM runtime.

AMD, NVIDIA, IBM Research, and Google Cloud have all funded the project. It aims to help developers and researchers in building large-scale model inference workflows. According to Red Hat, llm-d is designed to work with different model types and can be adapted to run in public, private, or hybrid environments.

Red Hat’s decision to offer llm-d as an open source resource demonstrates the company’s commitment to providing accessible and transparent tools. With several contributors, the project may benefit from faster iteration and broader compatibility.

AMD EPYC CPUs help modernise virtual machines

While much of the focus has been on AI, the Red Hat and AMD partnership also includes support for more traditional IT systems. Red Hat OpenShift Virtualization runs on AMD EPYC processors. It empowers teams to manage both VMs and containers from a single platform.

This can help reduce the number of systems needed to run both new and legacy applications. It also supports better hardware use, which can lower power and licensing costs. Managing VMs and containers together lets organisations extend the life of existing workloads while building for future needs.

Red Hat says OpenShift Virtualization works well with AMD EPYC CPUs on popular servers from Dell, HPE, and Lenovo. This gives businesses a clearer path to simplify their data centres while keeping workloads in place. As more enterprises mix containerised applications with traditional systems, this support becomes more useful.

RHEL 10 gains support from broader partner network

Red Hat Enterprise Linux 10 is the latest version of its operating system. It has gained support from AMD and other hardware and cloud vendors. The update includes tools for AI work, stronger security features, and better performance in hybrid cloud setups.

Red Hat says the wider ecosystem support means users can expect more stable setups when combining RHEL 10 with their choice of hardware. This version of RHEL aims to be ready for both cloud-native deployments and long-standing workloads that still run on physical servers.

Red Hat and SiFive explore RISC-V support

In a separate partnership, Red Hat is working with SiFive to test RHEL 10 on the RISC-V chip architecture. The developer preview gives early access to a version of the OS built for open hardware.

This effort supports a long-term goal of giving users more choice in hardware. It also shows Red Hat’s continued focus on keeping its tools open and portable. The RISC-V effort reflects a growing interest in alternative processor architectures and has the potential to provide more options for edge computing and research use cases.

The work between Red Hat and AMD reflects a push to support both AI development and ongoing IT operations. While newer tools like AI inference servers and llm-d get most of the attention, support for virtual machines and open hardware shows the effort to balance future needs with present systems.

This approach may help businesses take on AI projects without having to replace their existing infrastructure. It also gives developers more control over how they run their workloads—whether that’s in the cloud, in the data centre, or somewhere in between.

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The traditional warehouse is becoming more irrelevant with time. As e-commerce volumes surge and customer expectations for rapid delivery intensify AI-powered warehouse modernisation has transformed from a competitive advantage to a fundamental business necessity.

“Without investing in technology to improve warehouse operations, organisations will fail to meet business objectives,” warns Vivien Tay, APAC Vertical Lead for Warehousing, Transport & Logistics at Zebra Technologies, in an exclusive interview with Tech Wire Asia.

The stark assessment is backed by compelling data: 88% of APAC warehouse associates share this concern, according to Zebra’s 2025 Warehousing Vision Study. The study reveals a warehouse sector under immense pressure from multiple directions: e-commerce growth, consumer expectations for faster deliveries, and persistent labour shortages.

The challenges have created an environment where technology adoption is no longer optional but necessary for survival. Speaking to Tech Wire Asia about the report’s deeper implications, Tay explained, “Today’s warehouses face a markedly different landscape compared to five years ago. The surge in e-commerce, rising consumer expectations for faster deliveries, and ongoing labour shortages are placing immense pressures on traditional systems and manual processes, which are increasingly unable to keep up.”

Safety concerns driving AI-powered warehouse modernisation

The pressures of e-commerce growth have significantly transformed warehouse environments, creating new safety challenges that demand technological solutions. According to the study, 79% of APAC warehouse associates report heightened safety concerns as operations become more fast-paced, with 72% specifically expressing fears of injury.

“The factors have also intensified safety risks on the warehouse floor,” Tay told Tech Wire Asia. “To stay ahead, businesses are turning to AI, automation, and real-time analytics to improve operational visibility, enhance safety, and enable better productivity.” The study reveals that AI applications are expected to have the greatest impact on worker safety, with 82% of APAC leaders seeing positive impact potential in this area. Technology solutions that can detect potential hazards and issue preventative alerts are becoming increasingly valuable as warehouse operations accelerate.

The technology investment roadmap

APAC warehouse leaders are planning substantial technology investments to address these challenges, with 63% intending to implement AI software and 65% planning to deploy augmented reality (AR) in the next five years. And, 64% of APAC leaders plan to accelerate their modernisation timelines by 2029, recognising the urgency of digital transformation.

“By enhancing asset visibility, they can track and manage resources more effectively, ensuring that every asset is optimally used,” Tay explained. “By embracing these innovations, warehouses can improve workflow optimisation, enhance safety, and reduce physical strain on workers.” The study highlights specific AI-powered warehouse modernisation priorities:

• Predictive analytics (implemented by 65% of decision-makers)
• Real-time task optimisation
• Workflow automation
• Intelligent assistance for connected frontline workers

“Warehouse leaders are increasingly looking for AI solutions that go beyond back-end data crunching – they want intuitive, responsive software that directly empowers their frontline teams,” Tay noted in the interview.

Technology as a worker ally, not a replacement

The study highlights a significant paradigm shift in how warehouse workers view technology – as an ally rather than a threat. The positive perception is reflected in the finding that 90% of APAC associates feel more valued when equipped with the right technological tools.

“By demonstrating how technology can directly improve both their day-to-day tasks and long-term career development, associates are more likely to view these advancements as beneficial,” Tay told Tech Wire Asia. “When associates understand that technologies are introduced to reduce physical strain and enhance productivity, rather than replace jobs, they are more likely to embrace these changes and engage in higher-value, more strategic activities.” The sentiment is reinforced by the finding that 77% of APAC associates feel they spend too much time on tasks that could be automated, indicating a workforce ready to embrace technological assistance.

Implementation challenges in AI-powered warehouse modernisation

Despite the clear benefits, implementing new technologies comes with significant challenges. Warehouse leaders report difficulties in determining ROI, securing adequate technical support, and integrating new systems with legacy infrastructure. “Although warehousing leaders surveyed plan to implement the latest technologies in AI and real-time analytics, the adoption rate is not fast enough,” Tay explained. “Challenges like missing orders in service level agreements and ensuring order accuracy can significantly impact profit & output, leading to negative business outcomes.”

The study found that 45% of APAC warehouse leaders admit they find it challenging to maintain fill rates and 51% struggle to prepare orders outlined in their service level agreements. Order accuracy (43%) and outbound processes (40%) were cited as the top two operational challenges. “Moreover, the ongoing shortage of qualified workers remains a significant concern for APAC warehouse operators, with 73% of decision-makers reporting difficulty in attracting skilled labour,” Tay added.

Real-world success through AI-powered warehouse modernisation

A compelling example of successful warehouse transformation comes from PT Dunia Express Transindo (Dunex), an Indonesian logistics provider. The company deployed Zebra’s mobile computers to allow workers to scan boxes stackedup to 5 metres high, eliminating the need for dangerous climbing.

“Dunex faced a challenge in their warehouse where the cargo was stored on a racking system with boxes stacked vertically. The setup made barcode scanning difficult, as barcodes were often out of reach or could only be scanned using short-range scanners. Workers had to climb or bring boxes down to scan them, which was time-consuming and inefficient,” Tay explained.

The implementation nearly doubled Dunex’s daily outbound delivery capacity from 5 tons to 10 tons, demonstrating the significant productivity gains possible through thoughtful technology deployment. “Zebra’s solutions helped streamline the warehouse operations, enabling Dunex to be more responsive to the demands of the on-demand economy and better support its customers as their businesses grow,” Tay said.

Prioritising automation in warehouse operations

When asked how companies should prioritise which processes to automate first, Tay provided clear guidance: “Companies should prioritise automating high-volume and repetitive processes. Tasks like data entry, inventory tracking, and standard administrative work often consume significant time and resources, making them ideal candidates for automation.”

She emphasised that this strategic approach delivers multiple benefits: “By automating these routine tasks, companies can free up employees’ time to focus on higher-value work, improving overall productivity and job satisfaction. And, processes that are error-prone or require precision, like data transcription or calculations, should be automated early to reduce mistakes and enhance efficiency.”

Safety-focused automation should also be prioritised: “Equally important is automating tasks that directly impact safety, like monitoring equipment conditions or managing hazard identification systems. Intelligent automation in these areas can help ensure consistent adherence to safety standards and reduce human error, preventing accidents and injuries.”

Sustainability through AI-powered warehouse modernisation

Sustainability emerges as another important factor, with 60% of warehouse leaders planning to increase investments in sustainable technologies over the next five years. Zebra facilitates this through initiatives like its Take Back Program, which reduces e-waste while providing operational and cost advantages.

“Sustainability is emerging as a key driver of business success, prompting warehouses to balance environmental priorities with operational efficiency,” Tay explained. “With 78% of decision-makers prioritising the reduction of emissions and waste, and 60% planning to increase investments in sustainable technologies over the next five years, the industry is at an important turning point.” She highlighted specific sustainability solutions:

“Zebra enables customers to meet environmental goals while improving efficiency through a robust Take Back Programme and a suite of sustainably designed products. The initiatives are aimed at reducing e-waste, extending product life cycles, and providing operational and cost advantages to businesses.”

The future of AI-powered warehouse modernisation

As warehouse square footage is projected to increase by 27% globally by 2030 and labour spending continues to rise at a 7% compound annual growth rate, the pressure to modernise operations will only intensify. “The study anticipates a significant shift toward intelligent automation to enhance operational efficiency and worker safety. The includes the adoption of AI, machine learning, and augmented reality to optimise workflows and improve safety,” Tay explained when discussing the future outlook. Specifically, Zebra is investing in emerging technologies beyond AI and AR:

“We are expanding IoT connectivity to enable real-time decision-making and predictive maintenance, supporting more agile resource management. Zebra is also assessing opportunities to apply blockchain technologies to enhance supply chain transparency, traceability, and security.”

The company’s ambitious roadmap also includes collaborative robotics: “Meanwhile, our investments in robotics and autonomous systems, including collaborative robots, aim to improve productivity and workplace safety.” For warehouse operators looking to thrive in this rapidly evolving landscape, the formula appears straightforward: invest in AI-powered warehouse modernisation that enhances visibility, improves safety, and empowers workers or risk becoming obsolete in an increasingly demanding market environment.

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Doing business in Asia means being able to address the specific geographic, linguistic, and cultural complexities of the region. But more than that, it means getting technology investment aligned with local businesses’ goals, which are to achieve smart, experience-focused results for customers at home and abroad. According to some, it’s human-centred thinking, combined with strategy and the best in technology that can achieve Asia’s business goals.

Part of the impediment to getting the investment levels and targets right can be a lack of credible expertise available in-house. There’s growing demand for outside IT services (up by 6.5% in 2024, according to Forrester) in the region, yet a slower growth in available technology consultancies with whom to partner. Several large companies work in the region in an effort to correct the shortage of skilled hands in-house, with one multinational, Concentrix, part of the recent growth in the region’s IT consultancy offerings.

Anywhere in the world, companies like Concentrix focus on ensuring measurable ROI in digitisation. Its broad experience working in APAC dictates that aligning technology investments with customer and employee experience is the foundation of success.

In Asia, transformation needs to provide first-rate interactions with a brand in ways that are locally-relevant to Asian customers and the organisation’s Asian employees. Concentrix’ approach in particular is experience-driven, therefore, aiming for the improvements in processes that local consumers and businesses demand.

Building on infrastructure

Part of many digitisation projects is investment in infrastructure – ensuring the business has a technological foundation on which to build. Yet according to investment company SSC, “Infrastructure investments in the APAC region are becoming progressively intricate due to changing regulations.” That’s the first warning shot across the bows of digital transformation initiatives that aren’t steered by the needs of and required expertise in the region.

For example, Southeast Asian businesses are finding that IT investments they make can lead to them becoming the subject of fraud and financial crime – something they perhaps never envisioned as a result of digitisation. Preempting potential problems takes specific insight and existing experience of issues such as promo abuse, where consumers use bots and multiple email accounts to ‘game’ digitising retailers. While many domestic businesses know their markets intimately, sometimes partnerships can complement in-house abilities with a deep knowledge of the vagaries and pitfalls awaiting the unwary, out there in the complexities of the digital landscape.

While there’s risk in all sectors, one report on financial crime in the APAC states that much of it arises “from the state of hyperconnectivity and data ‘overwhelm’ in today’s Industry 4.0.” In a region that’s the definition of digital interconnectivity, where the use of advanced technology is an everyday part of business, having seasoned pros from organisations like Concentrix actually in the same room as decision-makers can make a significant difference in achieving business success.

Distinguishing the latest from the greatest technologies

The arrival of new technology platforms, as of itself, does not guarantee financial success. Misalignment of technology’s abilities with overall business goal of experiential improvements for staff and customers can mean a brand seriously misfiring publicly or losing its operational efficiency internally.

The region is very much prone to swift customer reaction in the event of a mis-step. Over half (56%) of APAC consumers say they would stop doing business with brands they do not trust (according to Twilio [PDF]). Twilio’s report also found that even choosing the wrong digital channel with which to communicate with customers can make 20% of customers look to another brand for their needs.

Refining the nature of need

One very interesting statistic, however, tells why some digitisation projects, despite often huge investment, don’t necessarily yield the results desired. McCann Worldgroup’s global study, published at the time of the COVID epidemic, found that “brands should address [APAC customers’] concerns, rather than selling them dreams.”

Are the region’s companies and organisations being sold digitisation dreams by faceless vendors and resellers, rather than solutions that address their desire to provide more to customers and employees? And isn’t there a better way than relying solely on marketing narratives from ‘big tech’ companies? As a consultancy and partner, Concentrix thinks there is.

Tech for its own sake

Sometimes, the problem is technology deployment for its own sake. It’s simply not practical to throw money at new technology and hope for success in business. As we’ve discussed above, it’s easy to go very wrong and still be significantly out of pocket, with little hope of seeing an ROI from digitisation efforts. With that comes a worse service for hard-won customers at home and abroad, and staff who should have been happier and more productive.

In experience earned from its work with many APAC businesses, Concentrix has found that concentrating on two initial outcomes is one of the possible ways to choose a digitisation journey’s next steps: For customers, every interaction needs to be intentional and personalised; with employees, the goals are to improve wellness and engagement, and keep staff retention figures healthily high.

“Our approach to digital transformation starts with people: those within the organization and those it serves. By combining deep human insights with advanced technology and domain expertise, we reimagine experiences end to end. As we drive AI-led transformation, this human-centric approach consistently amplifies the value we deliver, especially through elevated customer interactions at scale.”

Ashish Pandey, GVP, Concentrix Southeast Asia

Which technology achieves those objectives is less important, perhaps, as long as it provides the means to succeed.

Clearly, the needs of every organisation are different, and so there are no quick answers to be presented in the space of one, short article. In the coming weeks, we’ll discuss in greater detail just how Asian businesses can ensure their digitisation investments align with customers’ and workers’ needs. We’ll look at approaches to and advice on choosing a technology partner. And we’ll explore how organisations can mitigate emerging risks. Most importantly, we’ll look at how companies can achieve real return on investment – all without falling into the common traps, like procuring technology for its own sake, cybercrime, and forgetting about the people working at the heart of any business.

Check back on these pages over the course of the next few weeks to get answers, pointers, advice, and a lot of incisive discussion. If nothing else, decision makers will get significant food for thought as they consider their next steps in digital business evolution.

The post Digitisation for better experiences: Satisfying demand, not selling dreams appeared first on TechWire Asia.

The continuing partnership between Payments Network Malaysia (PayNet) and Ant International (owner of Alipay+) has helped Malaysian SMEs during the first year of its existence, according to statements by the two companies.

In October 2023, the nascent partnership between PayNet and Ant International began to let Malaysian businesses accept Alipay+ transactions via DuitNow. DuitNow is a Malaysian QR code-based payment system that allows digital transfer of funds between participants using their bank details, eWallet or mobile. It’s a payment system that’s particularly suited to small transactions made at SMBs and has proved increasingly popular among a range of retailers from tiny street food vendors upwards.

In the last 17 months, the number of businesses and financial organisations that accept DuitNow has more than doubled. Of those, payments via Alipay+ comprised more than 80 percent of international inbound QR payments made from funds that originate outside Malaysia.

There was a 600% growth in revenue moved over the PayNet infrastructure in December 2024, compared to figures from the previous year.

Alipay+, owned by Ant International, went live on DuitNow QR in October 2023, which enabled payment via QR code by international travellers to Malaysian businesses. The number of Alipay+ payment partners that support DuitNow QR has more than doubled, with 15 international digital wallet apps currently supported, offering more options to customers wishing to spend in Malaysia but without the inconvenience of using new payment apps or network.

The partnership between Ant International and PayNet means users from countries where Alipay+ is popular are more likely to spend in Malaysia, which encourages visitors especially from China, Thailand, Singapore, Mongolia, the Philippines, and South Korea. While in the country, tourists simply use their digital wallet app as usual, getting the experience they’ve grown accustomed to in their home countries.

Currently, there are over 2.5 million DuitNow QR touchpoints in Malaysia, with the number set to increase as the payment network expands. The DuitNow facility is rapidly gaining popularity for foreign visitors, with the number of Alipay+ transactions made via DuitNow QR growing by an average of 50% every quarter in the last two years. Alipay+ is at present the largest contributor to cross-border QR payments made in Malaysia by overseas visitors.

Gary Yeoh, Chief Marketing Officer of PayNet, highlighted Alipay+ as a major factor in the expansion of Malaysian businesses accepting cross-border payments. “Our partnership […] has significantly enhanced cross-border payment acceptance for local merchants, helping them tap into a growing wave of global travellers. With Alipay+, […] DuitNow QR is empowering SMEs to compete on an international scale, reinforcing Malaysia’s position as a premier travel and shopping destination.”

Tourism is an important element of the Malaysian economy, with Kuala Lumpur, Malacca, Penang the most visited cities last year. The ‘Visit Malaysia 2026 campaign’ hopes to attract 35.6 million visitors to the country next year, up from over 25 million in 2024 – nearly a 50% growth in two years.

“We’re just getting started, and in the years ahead, we can make an even greater impact together, positioning Malaysia as a global tourism hub and generating more growth for Malaysian businesses,” said Edward Yue, General Manager for Southeast Asia, Australia and New Zealand, Ant International.

PayNet operates Malaysia’s national retail payments infrastructure, including services such as DuitNow, JomPAY, FPX, MyDebit, MEPS, and IBG. DuitNow QR is part of a regional cross-border payment initiative and is interoperable with QR payment networks in Singapore, Thailand, and Indonesia.

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Malaysia’s 5G Advanced rollout has moved forward, as Digital Nasional Berhad (DNB) and Ericsson announce a new partnership.

The collaboration was announced during Mobile World Congress (MWC) 2025, and aims to implement 5G Advanced technologies across industrial zones and introduce what the companies describe as the “world’s first 5G-powered mobile workspace solution.” The partnership centres on two initiatives: a 5G Advanced deployment to enhance industrial connectivity across Malaysia, and a 5G-powered mobile workspace solution that is designed to replace traditional wi-fi in enterprise environments.

Positioning Malaysia as a 5G global frontrunner

Datuk Azman Ismail, CEO of DNB, highlighted the significance of this collaboration. “By combining DNB’s expertise in 5G deployment with Ericsson’s global leadership in connectivity, we are strengthening Malaysia’s position as a digital economy leader, powering innovation across key sectors like manufacturing, healthcare, and agriculture,” he said.

The partnership’s core focus areas include:

1. Accelerating enterprise digitalisation: Expanding 5G connectivity in strategic industrial zones and collaborating with mobile network operators to deliver connectivity services.
2. Driving IoT and wearables innovation: Using Reduced Capability (RedCap) technologies to enable connectivity for industrial automation and smart devices.
3. Co-creating future-ready solutions: Using DNB’s 5G Advanced network as an platform to develop applications with solution providers, developers, and academic institutions.
4. Advancing sustainability: Integrating AI-powered energy optimisation tools to maximise efficiency and reduce environmental impact, in support of Malaysia’s journey net-zero emission goals.
5. Strengthening network security: Implementing security measures to help safeguard Malaysia’s digital infrastructure against cyber threats.
6. Expanding global API ecosystem: Integrating with a worldwide Application Programming Interface network.

DNB implements a 5G-based office network solution

In a separate announcement during MWC 2025, DNB said it has begun deploying Ericsson’s Enterprise Virtual Cellular Network (EVCN) at its Kuala Lumpur headquarters. The companies claim this is the first instance of a complete “5G-first” office environment, replacing existing wi-fi with cellular technology. Instead of using standard wi-fi infrastructure, DNB’s headquarters now connects devices through 5G cellular networks. The system integrates with Microsoft Intune and Entra ID to manage the 5G-enabled client hardware throughout the organisation.

The change brings several advantages over traditional enterprise networking solutions:

• Security and control: 5G infrastructure eliminates extant and future wi-fi vulnerabilities, and gives administrators greater control over devices’ connections.
• Operational efficiency and cost savings: Simplified network management and large-scale device setup reduce the need for legacy infrastructure, cutting costs.
• Mobility and user experience: Employees get consistent experiences on their 5G-enabled devices, in the office or working remotely.

“By integrating Ericsson’s Enterprise Virtual Cellular Network with DNB’s nationwide 5G infrastructure, we are empowering organisations to move beyond traditional IT models and embrace a new era of cloud-native, secure, and scalable solutions,” David Hagerbro, Head of Ericsson Malaysia, Sri Lanka and Bangladesh said.

Implications of Malaysia’s 5G Advanced rollout

Malaysia’s implementation of 5G-A systems represents an early test case worth monitoring. While DNB and Ericsson have outlined ambitious plans, the accurate measure of success will be in practical adoption rates, measurable efficiency improvements, and the results of cost-benefit analysis by early adopters.

Key questions remain about how widely these solutions will be adopted beyond initial deployments:

• Will the promised security benefits outweigh the costs of transitioning from established WiFi infrastructure?
• Can the system scale effectively in different enterprises with varying technical requirements?
• Will the everyday experience of workers and businesses show meaningful improvements over current connectivity solutions?

The coming months will likely reveal whether Malaysia’s approach to 5G Advanced implementation offers a viable model for other countries or whether adjustments will be needed as real-world applications expose unforeseen challenges. Technology observers across Southeast Asia will be watching to see if the technology delivers on its potential.

The post Malaysia’s 5G Advanced rollout: From industry to office appeared first on TechWire Asia.