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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Google Cloud used its Security Summit 2025 to highlight a wave of new tools aimed at protecting artificial intelligence projects while also using AI to strengthen cybersecurity operations.

The company pointed to the dual challenge facing organisations: securing their AI systems and applying AI to defend against threats. The announcements covered updates in Google’s security portfolio, including Security Command Center, Google Security Operations, Chrome Enterprise, and Trusted Cloud.

Safeguarding AI agents

As AI agents become more common in enterprise environments, Google Cloud introduced new capabilities in its Security Command Centre to monitor and protect them. A preview feature will give teams automated discovery of AI agents and Model Context Protocol servers, flagging vulnerabilities and risky interactions.

Google is also expanding its Model Armor tool to cover prompts and responses in Agentspace, offering real-time defences against threats like prompt injection or jailbreaking. New posture controls for Agentspace and Agent Builder are also on the way, designed to enforce organisational security policies.

To help with ongoing protection, new detections have been added to identify suspicious behaviour in AI agents, drawing on intelligence from both Mandiant and Google.

AI in security operations

The company also shared its vision for an “agentic” security operations centre, where AI-driven agents can coordinate tasks to support human analysts. An Alert Investigation agent, now in preview, can automatically enrich events, analyse command-line inputs, and build process trees, with recommendations for next steps. Google said this could ease manual workloads and reduce response times.

Mandiant Consulting, part of Google Cloud, is expanding its services to address security concerns raised by the rapid use of generative and agentic AI. The firm is offering support in AI governance, environment hardening, and threat modelling. It has also emphasised the role of adversarial testing to identify risks before deployment.

Updates in Google Unified Security

The company’s broader platform, Google Unified Security, also received several upgrades. A new SecOps Labs feature in Google Security Operations will give customers early access to AI-powered experiments for detection and response. New dashboards, now generally available, integrate SOAR data for improved visibility.

Chrome Enterprise added new mobile protections, including URL filtering on iOS and enhanced reporting to track browser activity. The features are intended to help organisations manage employee browsing risks, including use of unapproved generative AI tools.

Cloud security enhancements

On the cloud side, Google announced previews of Compliance Manager and Data Security Posture Management in Security Command Center. The tools aim to simplify compliance monitoring and sensitive data governance, including AI-specific workloads. Risk Reports, also in preview, will summarise potential weaknesses based on simulated attacker techniques.

Identity and Access Management is adding “agentic IAM” later this year, which will provision agent identities in environments. Other updates include a role picker that uses Gemini to suggest least-privilege roles, and re-authentication requirements for sensitive actions.

Data security updates include expanded scanning of AI-related assets and image data, while network security improvements extend Cloud Armour and Cloud NGFW features for broader protection.

Google security focus on responsible AI adoption

The company framed these updates as part of its effort to make AI adoption more secure while using AI itself to enhance defence. By embedding controls into data protection, compliance, and identity systems, Google Cloud said it aims to give organisations a stronger foundation for managing both risks and opportunities in AI.

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Microsoft is again pointing to a cheaper future for Xbox Cloud Gaming, according to The Verge. The idea of a more affordable payment tier has surfaced before – first through FTC documents that revealed a “dedicated” version was in the works, and again when Xbox CFO Tim Stuart suggested a free ad-supported plan could be possible. Now the topic has returned, this time on Microsoft’s own Xbox podcast.

Jason Ronald, Microsoft’s VP of Next Generation, spoke about Xbox Cloud Gaming, Xbox Play Anywhere, and the company’s ongoing chip partnership with AMD. At the moment, the only way to stream Xbox games through the cloud is by paying for Game Pass Ultimate at $19.99 a month.

“One of the things we see is there’s a lot of players who use Game Pass Ultimate to access the cloud, whether that’s the primary way they play, or an additional way to play on the go,” Ronald said. “I think for us, it really opens up the opportunity to make it much more affordable, and make it more accessible to players. Whether that’s going into new regions, or new ways to actually access the [Xbox] cloud.”

His comments stop short of confirming a new tier, but the possibility has been in Microsoft’s plans for some time. Last year, the company expanded Xbox Cloud Gaming beyond just the Game Pass library, letting Ultimate subscribers stream certain titles they already owned. A lower-priced option, especially one aimed at mobile play, could build on those changes. Microsoft had even considered selling games directly through its Android app, with instant streaming available, but a legal dispute put that idea on hold.

Nvidia sets the stage at Gamescom

Microsoft’s renewed hints about affordability land just as Nvidia is preparing major GeForce Now news. Nvidia’s service has long had an edge over Xbox Cloud Gaming, offering PC-level performance, higher resolutions, stronger bitrates, and lower latency. Microsoft itself has integrated GeForce Now into Xbox game pages, acknowledging its technical lead.

As reported by Tom’s Hardware, Nvidia is kicking off Gamescom 2025 with a livestream that promises GeForce Now announcements, new GeForce features, and reveals of “highly anticipated” PC titles. The company has made it clear, however, that no new GPU hardware will debut at the event. Its RTX 50 series cards launched only eight months ago, and Nvidia typically waits a year or more before releasing refreshed models. Industry watchers expect news of an RTX 50 “Super” refresh in 2026 instead.

That leaves GeForce Now itself as the focus. The “ultimate” plan still runs on older Ada Lovelace GPUs and caps out at an RTX 4080-class experience. DLSS is also limited to version 3, even though Ada Lovelace supports features of DLSS 4. Many expect Nvidia to announce an upgrade of its data centres to newer Blackwell server GPUs, which could redefine what its top-tier cloud service can deliver.

Looking ahead to Microsoft’s next Xbox

While Nvidia sharpens GeForce Now, Microsoft is also preparing for its next generation of hardware. Ronald confirmed that Xbox is working with AMD on dedicated silicon to power its next console and cloud ambitions.

“Together with AMD, we’re designing dedicated silicon and hardware to enable the next generation of gaming experiences,” he said. “We’re investing deeply in the next generation of rendering technologies, like neural rendering, which will bring a new level of quality to the games that you’re having.”

Microsoft’s roadmap points toward deeper integration between Xbox and Windows, more flexibility in digital stores, and greater use of AI inside games. Ronald added that the company is also building silicon to support AI features, saying these will be “transformative in how you actually experience your gameplay.” Some touted features will appear first on the upcoming Xbox Ally X device, which includes an NPU chip similar to Microsoft’s Copilot Plus PCs.

The combination of cheaper cloud access, stronger hardware, and AI-driven features suggests Microsoft is positioning Xbox for a new phase where streaming, PC, and console gaming overlap more.

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The Huawei Cloud AI Ecosystem Summit APAC 2025 brought together government leaders, industry experts, and technology partners to discuss how artificial intelligence is already changing the way people work, learn, and live in Malaysia and ASEAN. The gathering highlighted not only new technologies but also the partnerships and governance needed to make AI effective and trustworthy.

The summit is part of the Huawei Cloud APAC AI Ecosystem Initiative, a programme aimed at building an inclusive AI community by developing local skills, encouraging cooperation between sectors, and ensuring AI benefits are shared widely.

Government support for AI development

At the ASEAN AI Summit’s opening day, Huawei Technologies (Malaysia) CEO Simon Sun announced new AI initiatives. Malaysia’s Prime Minister, YAB Dato’ Seri Anwar Ibrahim, was present to witness the launch, underscoring the government’s view that AI is central to the country’s economic growth. The commitment is reflected in strategies that link public and private sectors and aim to prepare the country for a future where AI shapes every major industry.

Huawei Cloud’s three core capabilities

Huawei Cloud has built its AI approach around three capabilities. First, a global network of 34 regions and 101 availability zones (AZs) – including five regions and 17 AZs in ASEAN – provides the infrastructure for low-latency access. Second, an AI cloud service that supports more than 160 open-source models, allowing flexibility for development in different industries. Third, the Pangu multimodal models form the backbone of the company’s “AI for Industries” strategy; tailored solutions for manufacturing, healthcare, transport, among others.

On day two, the AI Ecosystem Summit drew about 300 delegates from the region. Li Yin, CTO of Huawei Cloud Enterprise Intelligence, led a session titled Leap to Cloud, Heading to AI, in which she shared examples of how Huawei Cloud has worked with customers in more than 30 industries and applied AI to over 500 scenarios worldwide.

See also: Huawei to unveil tech to cut China’s reliance on foreign AI memory chips

Li explained that with the Pangu foundational large model, ModelArts AI toolchain, and proven engineering methods, organisations can use own data to develop and refine models quickly. She pointed to three areas where Huawei Cloud will continue to invest: strengthening secure AI computing infrastructure, building industry-focused solutions like enterprise AI assistants and AI video applications, and expanding the partner ecosystem to speed adoption.

Malaysia’s focus on ethical and sustainable AI

Minister of Digital, YB Gobind Singh Deo, used his keynote to make clear that Malaysia’s AI journey is about more than just technology. Ethical use, sustainability, and shared benefit are all priorities.

“Our National AI Office (NAIO) has been speeding up the completion of the AI Technology Action Plan 2026 – 2030 and relevant regulatory frameworks to ensure the adoption of AI technology in key sectors in the country are ethical, sustainable and of high value,” he said.

He linked the goals to the Malaysia Digital Economy Blueprint and the Malaysia Digital (MD) initiative, saying both are strengthened through close cooperation with technology partners. Every step we take is action-driven, grounded in strong public-private collaborations, to shape Malaysia’s digital economy,” he said.

Building Malaysia’s AI talent pipeline

Simon Sun highlighted Huawei’s investment in local expertise through the Huawei Malaysia AI Talent Programme.

“We have set the goal of nurturing 30,000 Malaysian AI talents, comprising students, government officials, industry leaders, think tanks, associations, and others under this initiative in the coming three years,” he said.

He said AI is already making an impact in areas like fraud detection in banking, predictive maintenance in factories, supply chain management, and personalised learning in schools. Huawei’s localised partnerships, he said, ensure global expertise is applied in ways that suit ASEAN’s needs.

Real-world applications from Huawei partners

The summit also gave the stage to Huawei customers, who shared how they use AI in their own sectors.

William Zhou, Vice President of IFLYTEK Open Platform, said that while computing power and platforms form the base of AI systems, the real value comes from the application layer – where solutions are integrated into daily work. He said that Knowledge Q&A systems are among the most requested features from customers in government, telecom, and finance, but said successful deployment depends on close collaboration.

“The key is not the technology alone, but working closely with the customer to fine-tune the model and increase efficiency,” Zhou said, pointing to a Middle Eastern project that improved performance significantly in just two months.

He also described how subtitling and translation tools are vital in multilingual regions, with IFLYTEK solutions optimised for English, Malay, and Cantonese, which enable fast turnaround for media companies in Southeast Asia. In sectors where data must stay on-site, he said the ‘Spark’ all-in-one on-premise AI solution allows organisations to train and run models securely.

Dato Fadzli Shah, Co-Founder of Zetrix, discussed the link between AI, blockchain, and self-sovereign identity. He said these technologies could allow data from separate systems to be referenced securely without forcing organisations to adopt a single standard. Blockchain-backed digital identities, he added, could be used in education, finance, and trade to help ensure credentials remain verifiable.

He said Malaysia should develop specialist AI models trained on local data to ensure accurate interpretation of laws, policies, and cultural contexts. “We believe no single AI will dominate globally; instead, there will be natural product-market fit for specific stacks serving specific solutions.”

Henry Li Nan, Managing Director of TrustDecision Malaysia, shared how AI-powered decision intelligence is helping the finance industry tackle fraud. His company processes more than 130 million interception events a year, protects over seven billion devices, and prevents an estimated USD$10 billion in potential losses annually.

Working with Huawei, TrustDecision uses cloud-native infrastructure to deliver real-time detection, compliance, and risk management services.

“The result is faster detection, smarter prevention, and greater confidence for financial institutions to stay ahead of threats,” Li said.

National AI Office: Matching the speed of change

Shamsul Izhan Abdul Majid, Head of the NAIO, warned that the speed of AI development is unlike anything seen before, with new versions emerging every few weeks. This, he said, means that plans and standards must be developed quickly and in cooperation with industry.

He called data “the most important asset” and said that in sensitive fields like healthcare or defence, Malaysia’s approach is to bring AI to the data rather than move the data to the AI.

See also: Huawei tries to push AI chips abroad as US pressure grows

Since its formation in December last year, the NAIO has worked with six sectors and identified 55 AI potential use cases, with more expected as engagement expands to state and local levels. The office is also promoting the creation of locally-trained models with strong cybersecurity safeguards and a focus on making AI understandable for everyone, not just technical experts.

“Doing AI for everyone requires collaboration,” he said. “The AI Office brings together experts and companies to plan Malaysia’s AI journey for the next five years… We must stay ready, responsible, and innovative.”

Closing call to action

In closing, Simon Sun encouraged all participants to take the ideas shared at the summit and turn them into practical projects. He described the event as “the starting point for more actions and ideas to shape a smarter and stronger ASEAN, powered by AI and driving digital economies.”

The summit’s discussions made one thing clear: AI’s future in Malaysia and ASEAN will depend not only on powerful technology, but on how well it is adapted to real-world needs, governed responsibly, and supported by a skilled and informed community.

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Huawei is expected to unveil a technology that could lessen China’s dependence on high-bandwidth memory (HBM) chips for running artificial intelligence reasoning models, according to the state-run Securities Times.

As reported by the South China Morning Post, the announcement will be made at the 2025 Financial AI Reasoning Application Landing and Development Forum in Shanghai today. The event focuses on AI in the financial sector.

Huawei did not respond to a request for comment on Monday. If confirmed, the development would mark another step by the US-sanctioned company in strengthening China’s AI hardware capabilities and reducing reliance on foreign technology.

HBM chips are a key component in advanced AI systems, particularly for running reasoning models. The models take an already-trained AI system and apply it to real-world data, making decisions based on patterns the AI has learned. HBM is important for these workloads because it can move large amounts of data quickly between the processor and memory.

The current market for HBM is dominated by US companies Micron and AMD, as well as South Korean firms Samsung Electronics and SK Hynix. The chips are often integrated directly into AI processors used in data centres.

China’s two main memory chip producers, Yangtze Memory Technologies and Changxin Memory Technologies, have expanded their capabilities, but analysts say they are still behind their US and Korean competitors in technical performance. That gap has left China dependent on imports for the most advanced HBM products, an issue made more pressing by US export controls on advanced chipmaking tools and technologies.

While China works to strengthen its domestic supply chain, demand for HBM worldwide is rising sharply. Orders have surged as major tech companies build more AI data centres.

Micron, one of the top HBM producers, raised its forecast for fourth-quarter revenue and profit on Monday, citing strong demand for AI infrastructure. The company now predicts $11.2 billion revenue, plus or minus $100 million, up on its earlier estimate of $10.7 billion. Adjusted earnings per share are forecast at $2.85, plus or minus 7 cents, up from a prior estimate of $2.50.

Micron also increased its adjusted gross margin outlook to 44.5%, from 42%, plus or minus 1%, pointing to stronger pricing notably in DRAM product lines.

“We look at all of our different end markets around the world, the pricing trends have been robust, and we have had great success in being able to push that pricing up,” said Sumit Sadana, Micron’s chief business officer, during an industry event on Monday.

Analysts say the combination of limited HBM supply and surging AI demand has allowed producers to raise prices – a reversal from past years when memory chipmakers faced shrinking margins.

SK Hynix, another leading HBM supplier, expects the market for AI-focused memory chips to grow by about 30% per year until 2030.

Trade measures could still affect the sector. The US recently imposed 100% tariffs on certain imported chips, although the duties will not apply to companies that manufacture in the US or have committed to doing so.

In June, Micron said it would increase its planned US investment by $30 billion, bringing its total commitment to $200 billion in the country.

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Chinese AI start-up Z.ai, formerly known as Zhipu AI, has made its GLM models compatible with Huawei Technologies’ processors, adding momentum to China’s efforts to build up its own technology supply chain, according to the South China Morning Post.

The Beijing-based company said its models now work with Huawei’s Ascend chips, used in AI servers, and Kirin processors, which power smartphones and laptops.

“The tie-up marks a major breakthrough in cloud-device collaboration between home-grown large [language] models and computational architecture, highlighting the deeper integration of a domestic AI ecosystem,” Z.ai said.

The move comes shortly after Huawei announced it would open-source its Compute Architecture for Neural Networks (CANN) – the software toolkit for its Ascend processors. Opening the code lets developers build, adapt, and scale applications for domestic chips without relying on foreign platforms.

CANN competes with Nvidia’s proprietary CUDA toolkit, long used by Chinese AI developers who depend on the US company’s GPUs in many data centres. Working with Z.ai helps Huawei push wider use of its own processors in AI projects.

Last month, Huawei’s Ascend division became a founding member of the Model-Chips Ecosystem Innovation Alliance, alongside Chinese AI companies like StepFun, Infinegence AI, SiliconFlow, MetaX, Biren Technology, Enflame, Iluvatar Corex, Cambricon Technologies, and Moore Threads.

Z.ai said it will use CANN to fine-tune its GLM models on Huawei’s Ascend-powered cloud, showing the open-source toolkit in action.

In June, OpenAI described Zhipu – before its rebranding – as making “notable progress” in delivering AI infrastructure to governments and state-owned firms in non-Western markets. Backed by more than 10 billion yuan (US$1.4 billion) in funding, Z.ai filed pre-IPO documents in April, with plans to go public as early as 2026.

China’s response to GPT-5

OpenAI’s latest flagship model, GPT-5, debuted last week with claims of being “smarter, faster, and more useful,” offering improved abilities in coding, maths, writing, health, and visual perception. It also includes a “thinking” function that switches between standard and deep reasoning modes depending on the task.

“It’s like a PhD-level expert in anything, any area,” said OpenAI CEO Sam Altman.

In China, where OpenAI services like ChatGPT are unavailable, experts were unconcerned about falling behind.

“GPT-5 is not significantly ahead of Chinese models, so it won’t put substantial pressure on Chinese researchers and developers,” said Zhang Linfeng, assistant professor at Shanghai Jiao Tong University. He added the model “doesn’t come with revolutionary breakthroughs; it lacks memorable characteristics.”

Zhang noted that the “thinking” feature is already in some Chinese systems, including Alibaba’s AI products. Still, he credited OpenAI with reducing hallucinations – incorrect AI outputs – and improving coding and general intelligence.

Despite the muted reaction, interest was high. A GPT-5 discussion on Zhihu drew more than 3.2 million views, with some users praising the upgrades. GPT-5 is now the default model on ChatGPT for both free and paid users, and Microsoft is adding it to products like GitHub Copilot and Visual Studio Code.

AI talent in high demand

Competition for AI experts in China is also heating up where Alibaba’s Tongyi Lab, creator of the Qwen open-source models, has lost senior staff to rivals.

Yan Zhijie, who joined Alibaba in 2015 and led Tongyi’s speech lab, left in February. He joined JD.com’s Explore Academy but later moved to Tencent, leaving soon after an internal restructuring. His role was filled by Li Xiangang, co-founder of 01.AI.

Bo Liefeng, former head of Tongyi’s applied vision division, has also moved to Tencent’s Hunyuan AI team. Tencent and Alibaba did not comment.

Alibaba says its Qwen models have been downloaded over 400 million times worldwide, leading to the creation of 140,000 derivative models.

The flow of talent mirrors trends in the US, where firms like Meta have hired away AI experts from Google, OpenAI, and Apple. In China, ByteDance, Alibaba, and Tencent have all launched fresh recruitment drives, many aimed at AI research roles.

ByteDance’s “Top Seed Talent Programme” lists 65 AI-related openings, while Alibaba’s “Star Top Talent” campaign is targeting researchers in foundational models, infrastructure, and AI applications. Tencent recently opened internal applications for roles tied to its Yuanbao chatbot, Hunyuan model, and WeChat e-commerce.

As China pushes to build its AI ecosystem with home-grown chips and models, the fight for top talent is becoming as important as the technology itself.

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Data centres are a core part of how countries manage and protect information today. Without local infrastructure, governments and businesses must rely on services from abroad – raising concerns around data privacy, performance, and national control. For countries like Malaysia, building out a strong data centre industry supports domestic needs and opens the way to becoming a regional hub for digital exports, including AI model training and cloud services.

A new report [PDF] from the Asia Pacific Data Centre Association (APDCA), prepared by KPMG, lays out how important the sector has become for Malaysia. The study found that in 2024 alone, data centres contributed around USD 1.4 billion to the national economy and supported more than 4,400 jobs. By 2030, those numbers are expected to grow significantly – reaching roughly USD 34 billion in output and supporting over 30,000 jobs.

A key piece of digital infrastructure

Data centres are essential for running everything from AI and fintech to e-commerce and public services. They help businesses run smoothly, make government operations more efficient, and give Malaysia a competitive edge in the global digital economy. Reliable infrastructure is no longer just a nice-to-have – it’s central to long-term growth and sovereignty.

Malaysia’s push to lead in digital services depends heavily on data centres. Beyond the construction and maintenance work involved, they unlock broader productivity gains in multiple industries – from cloud computing to artificial intelligence. The makes them a crucial part of Malaysia’s strategy to grow its knowledge economy and reduce brain drain.

The sector creates high-skilled jobs in areas like cloud infrastructure, network security, and data engineering. The are roles that help retain local talent and attract tech-focused foreign investment. According to LinkedIn data, over 357,000 professionals in Malaysia already work in digital-related roles – a sign that the workforce is well-positioned to support continued growth in this space.

Johor takes the lead

Much of Malaysia’s data centre capacity is concentrated in Johor, which in 2024 accounted for nearly 80% of the country’s total. The region has become a top destination for hyperscale and enterprise data centres, with a very low vacancy rate and strong interest from global tech players.

Johor’s location in the Johor-Singapore Special Economic Zone (JS-SEZ) gives it a strategic edge. Combined with solid infrastructure and available land, it’s become a natural choice for developers. As demand grows, new investments are also flowing into other parts of the country, like Selangor and the Klang Valley, helping to distribute benefits more widely.

Malaysia’s capacity is expected to rise from around 505MW in 2024 to roughly 3,600MW by 2030. That growth is being driven by demand for colocation, edge computing, and large-scale cloud operations.

Supporting broader economic goals

The impact of data centres goes well beyond the facilities themselves. They support jobs in construction, electrical work, HVAC systems, and more. They also create demand for digital services – everything from software engineering to customer support – that extend in industries.

The data centres serve as the foundation for high-growth sectors like AI, advanced manufacturing, and smart logistics. They enable better public services, rural internet access, and digital tools in education and healthcare. All of this contributes to better living standards and a more connected society.

The government sees this potential and has backed the sector through major infrastructure plans, including the Malaysia Digital Economy Blueprint and the Twelfth Malaysia Plan. A number of regulatory and incentive programs have also helped draw in investment, including tax breaks and energy supply improvements.

A fast-growing, competitive market

Malaysia is one of the fastest-growing data centre markets in Asia Pacific. Between 2025 and 2030, total data centre capacity is projected to double from 1.26GW to 2.53GW. At the same time, colocation revenue is expected to jump from $710 million to $1.87 billion. That growth is being driven by demand from hyperscale cloud providers and enterprise customers looking for reliable, cost-effective infrastructure in the region.

Compared to mature but capacity-limited markets like Singapore, Malaysia offers available land, lower costs, and growing government support. These factors make it a strong option for global providers looking to expand in Southeast Asia.

Supporting AI growth

The rise of AI has pushed global demand for data centre services even higher. Since the launch of tools like ChatGPT, companies have been racing to build infrastructure capable of training and deploying large models.

Malaysia is seen as well-positioned to meet this demand. The government has established the National AI Office (NAIO) to guide AI development and has set targets for inclusive AI growth and talent development. AI is expected to contribute around $115 billion to Malaysia’s economy between 2025 and 2030.

Local infrastructure is a big part of that. Owning and operating AI-ready data centres gives Malaysia more control over how data is handled, offers national security benefits, and strengthens its position in areas like renewable energy and sustainable facility design.

A broader supply chain opportunity

The growth of data centres also creates export potential for related industries. Malaysia could become a provider of AI and cloud services to nearby countries in ASEAN. It could also export technologies like cooling systems, develop expertise in managing electronic waste, and grow local capabilities in building GPU infrastructure.

This kind of industrial diversification supports long-term job creation and helps Malaysia build a more complex, tech-focused economy.

The policy environment matters

The government has already approved over $24 billion in data centre projects, most of it coming from foreign investors. To keep that momentum going, clear and consistent policy will be important.

Initiatives like the Green Lane Pathway – developed by Tenaga Nasional Berhad (TNB) – have helped speed up infrastructure setup. It now takes around 12 months to get power access, compared to the usual 36 – 48 months.

Other frameworks, like the Corporate Renewable Energy Support Scheme (CRESS), help providers tap into clean energy. At the same time, the new Planning Guidelines for Data Centres and upcoming rules around energy and water use show that Malaysia is also trying to manage sustainability more seriously.

Still, some areas need improvement. Unclear tax and energy tariff changes could reduce Malaysia’s appeal compared to other countries. Engaging with data centre operators and offering better regulatory certainty could help address this.

Building the workforce

Malaysia has made strong progress in growing its digital talent pool, but there’s still a gap when compared to other mature markets like Singapore, South Korea, and Japan. Lower attrition rates in Thailand and Vietnam also point to the need for better talent retention strategies.

Closer partnerships between government, industry, and education providers could help bridge these gaps and ensure that Malaysia remains competitive.

A strong outlook

Malaysia’s data centre market is set to expand quickly and play a key role in supporting national digital goals. The combination of strong government support, demand from global providers, and available resources gives the country a clear opportunity to become a major player in Southeast Asia’s digital infrastructure story.

As Jeremy Deutsch, Chair of the APDCA, put it: “Data centres are the foundational infrastructure that powers and enables AI, cloud adoption, and digital transformation in sectors. The report provides compelling evidence of the sector’s tangible benefits and broad-based impact on the Malaysian economy, rakyat, and nation.”

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Alibaba has rolled out an updated version of its Qwen3 large language model (LLM), showing improvements in math, coding, reasoning, and multilingual tasks. As reported by the South China Morning Post, one variant of the model, the Qwen3-235B-A22B-2507-Instruct, has been released on Hugging Face and Alibaba’s ModelScope platform, along with a more efficient FP8 version that’s easier to run on limited hardware.

The company claims that the new version performs better in a range of tasks like logic, tool use, and long-context understanding. Based on benchmark tests, it also edges ahead of some competitors.

One key update is the FP8 model – short for 8-bit floating point – which cuts down on the memory and computing power needed to run the model. This makes it more useful for smaller teams or companies without access to large-scale infrastructure. Users can run it on basic hardware or deploy it more efficiently in the cloud, which helps lower energy costs and speeds up response times. Although Alibaba didn’t share exact numbers, similar FP8 setups typically reduce costs and resource use by a wide margin.

In short, the FP8 format allows organisations to do more with less. Instead of relying on massive clusters, users can run the model on a single GPU or even a personal machine, making it more practical for private use or local development. It also gives teams a chance to fine-tune models without the usual infrastructure headaches.

The new version also posted strong scores on several public tests. In the 2025 American Invitational Mathematics Examination, Qwen3 scored 70.3. That’s well above DeepSeek’s 46.6 and OpenAI’s GPT-4o, which scored 26.7. In coding tests, it pulled in 87.9 points on the MultiPL-E benchmark – just ahead of DeepSeek and OpenAI but slightly behind Claude Opus 4 Non-thinking from Anthropic, which scored 88.5.

As the name suggests, the latest Qwen model only supports a non-thinking mode – meaning it gives direct answers without showing steps or reasoning chains. But it’s able to process longer input, with a token limit now stretched to 256,000, making it better at handling large documents or long conversations in a single run.

The shift is tied to another change: Alibaba is dropping its earlier “hybrid” reasoning model approach, VentureBeat wrote. The idea behind hybrid mode was to let users switch between thinking and non-thinking behaviour, depending on the task. Users could toggle it manually – for example, adding a “/think” command before a prompt – to have the model work through a chain of logic before giving an answer.

That flexibility gave users more control, but also introduced design issues. Sometimes the model behaved unpredictably depending on the prompt, and users had to decide when to turn the reasoning on or off. After reviewing feedback, the Qwen team said it will now train separate models for instruction and reasoning tasks instead of blending both modes in one model.

A company post on social media read, “After talking with the community and thinking it through, we decided to stop using hybrid thinking mode. Instead, we’ll train Instruct and Thinking models separately so we can get the best quality possible.”

That means the new 2507 release focuses solely on following instructions and generating direct responses. For now, the reasoning model will be a separate track.

Alibaba is also starting to put its Qwen models into real-world products. A 3-billion-parameter version will power Xiaowei Hui, a smart assistant from HP that runs on the company’s PCs in China. The assistant is expected to help with tasks like writing and meeting summaries.

The Qwen3 series, launched in April, spans from smaller 600 million parameter models up to the larger 235 billion ones. One variant – the Qwen3-235B-A22B-no-thinking – ranks among the top open-source models globally, coming in just behind Chinese models from Moonshot AI and DeepSeek, based on a recent LMArena report.

Hugging Face’s own rankings from last month also placed several Qwen models in the top ten among Chinese LLMs, further boosting the model family’s reputation in the open-source AI space.

Nvidia CEO Jensen Huang also weighed in during his trip to China last week, where he acknowledged the progress China has made with its open-source AI work. He described Alibaba’s Qwen, along with DeepSeek and Moonshot’s Kimi, as “very advanced” and “the best open reasoning models in the world today.”

The Qwen team’s decision to split instruction and reasoning into separate models marks a shift in strategy – one that favours predictability and performance over flexibility. While this means users won’t be able to toggle reasoning on and off in a single model anymore, it could lead to better results in each task area.

For teams looking to run powerful models on lower-cost infrastructure, the new FP8 version adds another reason to consider Qwen3. And for those tracking benchmarks, Alibaba’s latest model is now firmly in the race with some of the best-known names in AI.

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Oracle is in talks to sign a large software deal with Skydance Media, a move contingent on Skydance completing its planned purchase of Paramount Global.

According to Bloomberg, sources familiar with the matter say the deal would be worth about US$100 million a year. If it goes through, Paramount and its group of media companies would begin using Oracle’s cloud software to run parts of their business. The discussions are private, and the companies haven’t confirmed any details.

Skydance already uses Oracle’s products with the media company spending US$2.2 million on Oracle’s cloud services in the year ending May 2024, according to public filings. Skydance was founded by David Ellison, whose father, Larry Ellison, is Oracle’s chairman and a major shareholder. Larry Ellison is also backing Skydance’s US$8 billion bid to take over Paramount, providing cash and investment support.

None of Oracle, Skydance or Paramount responded to requests for comment.

If the merger is approved and the Oracle deal is finalised, the combined Skydance-Paramount group would become one of Oracle’s larger cloud clients. It would also deepen the existing relationship between the two companies. A person familiar with the discussions said moving Paramount’s operations to Oracle’s cloud could lead to major cost savings – possibly in the hundreds of millions – as outdated systems are replaced with newer tools.

Paramount owns a number of high-profile media brands, including CBS, MTV, and Nickelodeon. The networks generate huge amounts of video and audio data needing a lot of storage and computing power. By shifting to Oracle’s cloud, the company could centralise much of that work, making it easier to manage and potentially offering cost savings.

David Ellison has said he wants to run Paramount more efficiently if the deal goes through. A big part of that plan involves updating its technology systems. While talks with Oracle are still ongoing, the deal isn’t finalised and could still change, according to sources.

The merger between Paramount and Skydance was first announced last year but still needs approval from US regulators. The final major hurdle is the Federal Communications Commission (FCC). Last week, Ellison met with FCC chair Brendan Carr and other officials to discuss the proposal and try to move things forward.

Meanwhile, Paramount has been facing scrutiny. Earlier in July, the company reached a legal settlement in a case brought by US President Donald Trump, who accused CBS News of media bias. On Monday (Jul 21), a group of US senators sent a letter to Ellison asking whether the timing of that settlement was tied to efforts to close the merger.

Oracle has been expanding its cloud business beyond its traditional database software. The company has been picking up customers that need large-scale computing and storage, particularly those working with artificial intelligence. Oracle already counts TikTok, Uber, and Zoom among its major clients.

The cloud deal with Skydance-Paramount comes shortly after Oracle announced a new round of spending in Europe. The company said it plans to invest US$3 billion over the next five years to expand its data centre capacity in Germany and the Netherlands. Oracle has earmarked US$2 billion for Germany and US$1 billion for Dutch operations.

According to Oracle, this investment will help local companies, governments, and organisations shift their workloads to the cloud and make use of newer AI tools. The announcement didn’t tie the spending directly to the Skydance talks, but it highlights the growing demand for AI computing infrastructure.

Big names in tech – including Microsoft, Amazon, Google, Meta, OpenAI, and xAI – have also committed large amounts of money to data centres that can power AI models. Oracle is part of the US-based group, Stargate, that includes OpenAI and others. The project plans to pour hundreds of billions into new infrastructure to support AI.

Oracle’s stock rose about 1% in early trading on the day the Skydance talks were reported. Shares are up more than 37% this year and are just below their highest point to date.

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Ingram Micro is working to bring systems back online after a ransomware attack took down parts of its network, disrupting ordering systems and customer service channels across multiple regions.

The incident started on July 3. Customers first noticed problems when they could no longer place orders or reach the company through regular phone lines. Resellers and managed service providers — a major part of Ingram Micro’s client base — were among the first to report issues. Local websites began displaying maintenance pages with basic contact details for sales and support teams.

At first, the cause was unclear. The company described the issue as “technical difficulties.” Attempts to reach executives or press representatives went unanswered. It wasn’t until July 6 that Ingram Micro confirmed it had been hit by ransomware.

“Ingram Micro recently identified ransomware on certain of its internal systems,” the company said in a statement. It explained that after discovering the issue, systems were shut down as a precaution while cybersecurity specialists were brought in. Law enforcement was also notified.

The company added that it’s working to restore affected systems so it can resume processing and shipping orders. It apologised for the disruption caused to customers, vendors, and other business partners.

As of now, the outage continues to affect both physical product orders and license management systems, including Microsoft 365 and Dropbox. A source familiar with the situation said staff at Ingram Micro’s Bulgaria-based service centre were told to stay home on July 4 and avoid connecting their work laptops to the company network. Internal systems were still down at the time.

The disruption is significant given the scale of Ingram Micro’s operations. The company brought in $48 billion in revenue last year and posted $262.2 million in profit. It sells a wide range of products and services, from hardware and software to cloud licenses, IT asset disposal, returns management, logistics, and product remarketing. In Q1 this year, which ended on March 29, the company recorded $12.28 billion in revenue and $69.2 million in net income.

A group calling itself SafePay has claimed responsibility. Cybersecurity site Bleeping Computer obtained a copy of the ransom note, which accuses Ingram Micro of poor network security. The group claims it was able to access the company’s systems and stay inside undetected for some time.

“It was the misconfiguration of your network that allowed our experts to attack you,” the note says. “Treat this situation as simply as a paid training session for your system administrators.”

SafePay says it gained access to sensitive documents, including financial records, intellectual property, customer and employee data, bank account details, transaction history, and legal complaints. The group claims it encrypted all critical files and moved copies to a remote server. It says those files could be published online if no deal is reached.

“WE ARE THE ONES WHO CAN CORRECTLY DECRYPT YOUR DATA AND RESTORE YOUR INFRASTRUCTURE IN A SHORT TIME,” the note reads in all caps.

The attackers said the motive is purely financial. Ingram Micro has been given seven days to start negotiations.

At this point, the full extent of the breach remains unclear. Claims made by ransomware gangs often include exaggerations and should be verified independently. It’s also not confirmed how the attackers got in. One possible entry point, according to sources speaking to Bleeping Computer, is Ingram’s GlobalProtect VPN platform. That theory has not been confirmed.

SafePay was the most active ransomware group in May, according to threat intelligence company Fortra. The firm linked 70 separate incidents to the gang or its affiliates during that month alone. One of the group’s more prominent victims was fleet management software firm Microlise, which was attacked in October last year.

For now, Ingram Micro continues to operate with limited system access while recovery efforts are underway.

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Thousands of small and medium-sized businesses (SMBs) encountered cyberattacks in 2025 involving fake versions of popular productivity tools, according to new data from Kaspersky. Nearly 8,500 users were affected by malicious or unwanted software posing as legitimate apps — most often Zoom and Microsoft Office. Attackers also began using AI tools like ChatGPT and DeepSeek to trick users into downloading harmful files.

Kaspersky looked at how often threats were disguised as common online tools. Across 12 apps examined, researchers found over 4,000 unique malicious or suspicious files in 2025. A noticeable rise came from files pretending to be AI tools. ChatGPT-related threats jumped 115% in the first four months of the year compared to the same period in 2024. Kaspersky identified 177 files pretending to be ChatGPT and 83 mimicking DeepSeek, a large language model released in 2025.

Kaspersky’s Vasily Kolesnikov said attackers tend to go after tools that are widely talked about. “The more publicity and conversation there is around a tool, the more likely a user will come across a fake package on the internet,” he said. Kolesnikov advised SMB employees and everyday users to double-check URLs and avoid suspicious email links or software offers that seem too generous.

Aside from AI tools, collaboration platforms remain a common disguise for malware. Fake Zoom files rose nearly 13% to 1,652 this year. Threats mimicking Microsoft Teams and Google Drive also climbed — by 100% and 12%, respectively — with 206 and 132 files flagged. These tools have become essential for distributed teams, making them easy targets for impersonation.

Among the apps reviewed, Zoom stood out as the most copied, accounting for 41% of all detected threats. Microsoft Office apps were also high on the list: Outlook and PowerPoint each made up 16%, Excel nearly 12%, while Word and Teams followed at 9% and 5%.

The most common types of threats aimed at SMBs in 2025 were downloaders, trojans, and adware.

Phishing and spam tactics also on the rise

Kaspersky also reported a steady stream of phishing scams and spam aimed at SMBs. Many scams attempt to grab login credentials for services like banking platforms or delivery apps. One example involved a fake Google login page offering to promote a business on X — a scheme built to steal user credentials.

Spam continues to flood inboxes as well. Some messages now claim to offer AI-powered business automation. Others promote email marketing tools, business loans, or services like lead generation and reputation management — all crafted to appeal to small business owners.

According to Kaspersky, attackers are tailoring these phishing and spam campaigns to match what SMBs typically search for online, making them harder to spot at a glance.

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Alibaba Cloud is expanding its presence in Southeast Asia with new data centres in Malaysia and the Philippines, part of a broader push to meet rising demand for AI services in the region.

The company recently launched its third data centre in Malaysia and plans to open a second in the Philippines by October. These follow similar investments in Thailand, Mexico, and South Korea earlier this year.

Alibaba has committed to spending US$53 billion on AI infrastructure over the next three years. It’s also positioning Southeast Asia as a key market, even after shutting down data centres in Sydney and Mumbai last year, as reported by South China Morning Post.

AI hub and talent development in Singapore

As part of its 10-year anniversary in Singapore, Alibaba Cloud announced a new AI Global Competency Center (AIGCC) in the country. The centre is intended to support over 5,000 businesses and 100,000 developers, offering tools for building and deploying AI systems.

It also includes an AI Innovation Lab that will provide curated datasets, usage credits, and support services. The AIGCC is expected to work with more than 1,000 companies and startups, with plans to introduce over 10 AI agents for industries like healthcare, logistics, and finance.

To build a larger talent pool, Alibaba Cloud says it will partner with over 120 institutions to train 100,000 AI professionals annually.

New AI tools and system upgrades

Alibaba Cloud also rolled out upgrades to its AI infrastructure and software tools.

Its real-time data service, Data Transmission Service (DTS), now supports an “One Channel for AI” feature that can convert various types of data—text, images, audio, video—into formats that can be used for AI training and Retrieval-Augmented Generation (RAG) applications. The goal is to reduce technical complexity and speed up deployment.

On the AI inference side, Alibaba’s Platform for AI (PAI) introduced updates to support large models and complex architectures like Mixture of Experts. A new Model Weights Service is also available to cut cold start times and improve scaling, with test results showing up to 90% faster performance in some cases.

The company’s ninth-generation Intel-based Elastic Compute Service (ECS) instance is also expanding to more markets, including Japan, the UAE, and the UK. Since its April launch, nearly 10,000 businesses have adopted the new instance, which offers better computing efficiency and faster networking for AI, HPC, and database workloads.

Green AI research and challenges

A new study released during Alibaba Cloud’s global summit looked at how businesses are approaching “green AI”—AI systems designed to reduce environmental impact. Conducted by Forrester and commissioned by Alibaba, the study surveyed more than 460 IT and business leaders globally.

While most respondents agreed that green AI is important, many said they’re still in early stages. Some of the top challenges include sourcing sustainable hardware (80%) and improving data centre energy efficiency (73%).

Capability gaps were also common. Around three-quarters of respondents said their organisations lack the knowledge or skills to build and operate green AI systems. The study recommended several steps to close the gap, including using renewable energy in data centres, building smaller models, and improving collaboration on standards and open-source tools.

AI in practice: Customer examples

Alibaba Cloud’s AI offerings are being used by a growing number of customers across Asia and beyond.

Indonesia’s GoTo Group migrated its core business intelligence data platform to Alibaba Cloud’s MaxCompute. The company says the migration, which moved tens of petabytes of data over six months, helped improve cost efficiency and system performance with no downtime. GoTo Financial has also moved its lending systems to Alibaba Cloud, using PolarDB and Tair to support over 500 microservices with low latency.

VisionTech, a generative AI startup in Singapore, uses Alibaba Cloud infrastructure to scale its multilingual AI bots across Southeast Asia. The company says it cut infrastructure costs by more than 25% and now uses Alibaba’s Qwen model to manage real-time translation across English, Chinese, Malay, and Japanese.

Japanese tech provider FLUX is also working with Alibaba Cloud to bring the Qwen model to local businesses. FLUX plans to build its own LLM product using Alibaba’s tools and apply it to core operations for clients across industries.

In the Middle East, Alibaba Cloud signed an agreement with Al-Futtaim, a diversified business group based in Dubai, to support AI development across its business units. The deal includes access to Alibaba’s cloud and AI infrastructure, as well as open-source frameworks and training support.

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