Bangkok has quickly become one of Southeast Asia’s largest data centre hubs, according to a report from DC Byte. With IT capacity now above 2.5GW, it sits just behind Johor in regional capacity ranking. Growth is powered by specific advantages: available land, steady power supply, and a location that connects East and West. The factors are drawing in major operators and global cloud companies.

Until recently, Thailand’s data centre market was made up of smaller, retail-style sites. Over the past two years, that has shifted toward full-scale campuses and large, multi-building projects. New development is also spreading outside central Bangkok into the Eastern Economic Corridor, especially in Chonburi, which is emerging as a strategic zone for hyperscale builds.

Global players and big investments

Amazon Web Services, Google, Microsoft, and Chinese providers including Huawei, Alibaba, and Tencent have committed to major projects in Thailand. Examples include Bytedance’s $8.8 billion investment, AWS’s $5 billion expansion, Google’s $1 billion site in Chonburi, and Microsoft’s first Thailand cloud region.

Operators like STT GDC, Equinix, DAMAC Digital, and Evolution Data Centres are also expanding. Between 2019 and 2024, Bangkok’s total IT capacity grew more than twenty-fold, with pipeline capacity rising at an average of about 40% each year.

At present, Bangkok has around 120MW of live capacity, with more expected before year-end as sites under construction come online. The next two years is expected to see another surge, with large projects from Google, DayOne, and Edgnex Data Centres by DAMAC among those set to launch.

Eastern Economic Corridor becomes Thailand’s data centre hub

The Eastern Economic Corridor (EEC) is fast becoming Thailand’s centre for large-scale deployments. Chonburi and Rayong are attractive for hyperscalers because of cheaper land, existing infrastructure, and easy access to ports and industrial zones.

Chonburi is now home to some of the country’s biggest upcoming builds. Projects like DayOne’s 120MW Tech Park and Bridge Data Centres’ planned 200MW campus will increase local capacity. Demand is so strong that both local and global operators are already locking in space before builds are complete.

One of the biggest announcements came in 2024, when Doma Infrastructure Group revealed plans for 1.5GW of green data centre campuses in the EEC. That accounted for most of the 1.7GW increase in early-stage capacity during the year, marking the shift from smaller facilities to large, multi-site campuses.

Cloud and AI power Thailand’s data centre demand

Cloud services remain the main source of growth, making up about 38% of Thailand’s total capacity in early 2025. AI is quickly catching up, rising from 20% of demand in 2024 to 28% just a year later. Growth is being fueled by AI training, large language models, and other data-heavy applications.

To meet the need, operators are working with partners like Siam AI Corporation, an NVIDIA cloud partner, to design facilities built for high-density AI workloads. The efforts could help Thailand develop into both a regional cloud hub and a centre for AI innovation.

Public and private efforts align

Government support is also shaping the market. Partnerships with NVIDIA aim to build sovereign AI capabilities, while major operators like True IDC, Edgnex Data Centres by DAMAC, and GSA Data Center Company are teaming up with Siam AI Corporation on AI-focused cloud services.

The mix of public and private investment is establishing the groundwork for next-generation infrastructure. With rising demand for AI and cloud services, Thailand is positioning itself to meet regional needs and strengthen its role in the wider Southeast Asian market.

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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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Malaysia has managed to secure a 19% tariff rate from the US, down from the initially threatened 25%, following negotiations that concluded with Prime Minister Anwar Ibrahim’s phone call with President Trump on July 31. The deal is said to have involved multiple components beyond traditional trade discussions, raising questions about whether the Malaysian approach represents a new model for regional engagement.

Multi-faceted negotiation strategy?

If Malaysia’s approach is proven true, it would differ from typical trade discussions by incorporating several non-traditional elements. According to The Straits Times‘ official sources, “the call was made earlier in the morning of July 31, after it was proposed by the Americans just a few hours back,” indicating last-minute urgency in the negotiations.

Potential factors in the negotiations may have included addressing US concerns about China’s market dominance. Sources suggest Malaysia could supply rare earth elements to the US, using the country’s deposits of more than 16 million tonnes worth an estimated RM1 trillion.

The would address Washington’s dependency concerns, given China’s current dominance in critical mineral production. Currently, rare earth ore mined in Malaysia is exported to China due to the country’s lack of domestic processing technology.

Malaysia may also recognise US halal certification for pharmaceuticals, representing a regulatory alignment opportunity. Malaysian trade officials told The Straits Times that “their certification is quite good” and the Malaysian Islamic Development Department has vetted the process, though no official arrangements have been confirmed.

Conflict resolution as a trade component

An unusual aspect of the tariff negotiations between Malaysia and the US involved regional conflict mediation. Anwar Ibrahim facilitated peace talks between Thailand and Cambodia on July 28, following Trump’s threat to halt tariff negotiations with both countries unless they resolved their two-month border conflict that resulted in multiple casualties.

Malaysia’s successful mediation resulted in a joint statement recognising the US as a “co-organiser” of the talks, with China as an “active participant” – a delicate diplomatic balance that satisfied all of the many stakeholders.

The innovative approach to linking regional stability with trade negotiations could become a template for other ASEAN nations seeking to enhance their value in US trade discussions.

Technology and policy boundaries

Malaysia’s approach appears to have balanced cooperation with maintaining policy independence. Since negotiations began, the country increased US gas purchases and tightened controls on “origin washing” and AI chip movement to address US concerns about Chinese sanctions evasion – measures that have been publicly confirmed.

However, Ibrahim maintained firm boundaries on domestic policies, specifically citing the Bumiputera policy as non-negotiable. The exemption of semiconductor exports from tariff coverage, 7.9% of Malaysia’s total exports, also protected a key technology sector.

Economic impact assessment

The US tariff outcome for Malaysia generated mixed reactions from analysts. UOB Kay Hian Wealth Advisors Sdn Bhd investment research head Mohd Sedek Jantan told Bernama (the Malaysian National News Agency)that “investor sentiment has also responded with relative composure” due to the lower-than-expected rate.

The tariff affects non-exempted goods, including furniture, rubber products, palm oil derivatives, and machines – sectors representing about 40% of Malaysia’s US$26 billion in US exports. Malaysia’s 19% rate compares to China’s 34% and Vietnam’s 20%.

Bank Muamalat Malaysia Bhd chief economist Dr Mohd Afzanizam Abdul Rashid also told Bernama that negative impacts may be “slightly mitigated” by policy frameworks outlined in Malaysia’s 13th Malaysia Plan, although trade tariffs typically show measurable GDP effects 12 to 15 months after implementation.

Regional context and questions

Trump’s confirmation of attendance at the October ASEAN Summit in Kuala Lumpur indicates continued US regional engagement, though the sustainability of this situation under in different administrations remains uncertain. Singapore Institute of International Affairs senior fellow Oh Ei Sun told The Straits Times that Trump recognises Southeast Asia as”one of the most dynamic regions of the world.”

The question for other Southeast Asian nations is whether Malaysia’s multi-component approach, if proven true – combining resource access, diplomatic mediation, and selective policy adjustments – represents a replicable model or reflects Malaysia’s unique circumstances and relationships.

The Ministry of Investment, Trade and Industry stated that the 19% rate was achieved through “a thorough and methodical negotiating process,” while Malaysia “stood firm on various ‘red line’ items,” specifics of which remain largely undisclosed.

Assessment and uncertainties

The tariff negotiations between Malaysia and the US illustrate evolving patterns in US-Southeast Asian trade relations, where traditional economic discussions increasingly incorporate broader strategic considerations, including resource security, technology cooperation, and regional stability.

However, several questions remain unanswered, particularly regarding what specific commitments were made beyond the tariff rate itself. The details of potential arrangements reported by The Straits Times – including rare earth supply agreements and halal certification recognition – have not been confirmed officially by either government.

It’s unclear whether this negotiation approach scales beyond Malaysia’s specific circumstances – including its non-aligned foreign policy positioning, strategic resource endowments, and Anwar’s diplomatic relationships. Other ASEAN nations may find different combinations of concessions and broader elements necessary, based on their particular economic profiles and political constraints.

The 19% tariff rate represents the confirmed near-term outcome, but the broader implications for technology transfer, supply chain resilience, and regional diplomatic dynamics will likely unfold over the coming years as both nations implement any commitments and navigate ongoing US-China competition in Southeast Asia.

(Image source: Photo by Minister of Investment, Trade & Industry Malaysia)

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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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The Trump administration is apparently preparing new AI chip export restrictions targeting Malaysia and Thailand over concerns about semiconductor smuggling to China, according to Bloomberg’s sources familiar with the matter. The proposed measures would add the two Southeast Asian nations to existing export control frameworks amid ongoing investigations into potential chip diversion schemes.

A draft rule from the Commerce Department seeks to prevent China — to which the US has effectively banned sales of Nvidia’s advanced AI processors — from obtaining those components through intermediaries in the two Southeast Asian nations, people familiar with the matter told Bloomberg. 

If proven true, the proposed AI chip export restrictions would mark the first formal step in Trump’s promised overhaul of his predecessor’s semiconductor trade policies. The timing of these potential restrictions comes as Malaysia has emerged as a critical hub for global technology investments. 

The timing of these potential restrictions comes as Malaysia has emerged as a critical hub for global technology investments. Oracle, Google, Microsoft, and Amazon have committed a combined investment of $16.9 billion through 2038 in the country’s digital infrastructure, with Oracle alone pledging $6.5 billion for its first public cloud region in the country.

Trade data also shows that chip shipments to Malaysia have surged in recent months, according to Trendforce’s report. This increase has drawn the attention of US officials who worry about the potential diversion of advanced semiconductors to China.

What ignited the concerns?

The proposed restrictions gain additional context from ongoing investigations in neighbouring Singapore. Singapore charged three men with fraud in a case domestic media have linked to the movement of Nvidia’s advanced chips from the city-state to Chinese artificial intelligence firm DeepSeek.

The servers involved in the case were supplied by Dell Technologies and Super Micro Computer to Singapore-based companies before they were sent to Malaysia, according to Singapore’s Law and Home Affairs Minister K Shanmugam. Malaysia said it will take “necessary action” against Malaysian companies if they are found to be involved in a fraud case linked to the alleged movement of Nvidia chips.

It is also worth noting that the draft measure represents a significant departure from the Biden administration’s approach. Officials plan to pair Malaysia and Thailand controls with a formal rescission of global curbs from the so-called AI diffusion rule, which had drawn objections from US allies and tech companies, including Nvidia.

Commerce Secretary Howard Lutnick has outlined the administration’s vision, stating that the US will “allow our allies to buy AI chips, provided they’re run by an approved American data centre operator, and the cloud that touches that data centre is an approved American operator”.

However, the draft measure is far from a comprehensive replacement and doesn’t answer questions about security conditions for the use of US chips in overseas data centres — a debate with particularly high stakes for the Middle East.

Mitigation Measures for Industry

Recognizing the potential disruption to legitimate business operations, the proposed AI chip export restrictions would include several measures to ease pressure on companies with significant operations in the region. 

One provision would allow firms headquartered in the US and a few dozen friendly nations to continue shipping AI chips to both countries, without seeking a licence, for a few months after the rule is published.

The licence requirements also would still include certain exemptions to prevent supply chain disruptions. Many semiconductor companies rely on Southeast Asian facilities for crucial manufacturing steps like packaging, and the process of encasing chips for use in devices.

Regional response and uncertainty

Government responses from the targeted nations have been measured. In response to earlier Bloomberg queries about curbs focused on smuggling risks, Thailand said it’s awaiting details, while Malaysia’s Ministry of Investment, trade and industry said clear and consistent policies are essential for the tech sector.

Nvidia, the dominant maker of AI chips, declined to comment, while spokespeople for the Thai and Malaysian governments didn’t respond to Bloomberg’s requests for comment. Nvidia chief executive officer Jensen Huang has previously said there’s “no evidence” of AI chip diversion, in general remarks that didn’t touch on any particular country.

Strategic implications for Southeast Asia

The proposed measures highlight the broader geopolitical tensions surrounding AI technology and export controls. Washington officials for years have debated which countries should be able to import American AI chips — and under what conditions. 

On one hand, the world wants Nvidia hardware, and US policymakers want the world to build AI systems using American technology — before China can offer a compelling alternative.

Singapore is Nvidia’s second-biggest market after the United States, accounting for 18% of its total revenue in its latest fiscal year.  Actual shipments to the Asian trading hub, however, contributed less than 2% of total revenue, as customers use it as a centre for invoicing sales to other countries.

What’s next?

The draft regulation remains subject to change, and it’s unclear whether Trump officials may ultimately regulate AI chip export restrictions to a wider swath of countries, beyond the Malaysia and Thailand additions. The Commerce Department did not respond to Bloomberg’s request for comment on the proposed measures.

The proposed restrictions underscore a fundamental tension in US technology policy: how to contain China’s AI capabilities without undermining America’s technological influence. By potentially restricting two countries that have become key destinations for US tech investment, Washington risks creating the very fragmentation it seeks to avoid – pushing regional partners toward alternative suppliers and technologies.

For Malaysia and Thailand, the challenge extends beyond compliance. These nations must now prove they can serve as trusted AI infrastructure hubs while managing the reality that their strategic location makes them attractive conduits for circumventing sanctions. 

The outcome will likely shape how other Southeast Asian countries approach their own AI development strategies.

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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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As US and Chinese companies intensify their battle for global robotaxi leadership, Baidu’s Apollo Go robotaxi expansion into Southeast Asia appears to be gaining momentum, with the Chinese tech giant reportedly eyeing regional deployment as early as late 2025.

According to a recent Wall Street Journal report, Baidu’s robotaxi unit is planning to expand into Southeast Asia as soon as the end of this year, with Singapore and Malaysia identified as primary target markets in the region. A person familiar with the matter told the publication that these markets align with the company’s broader international strategy.

The potential robotaxi expansion comes as Chinese autonomous driving companies adopt what industry observers describe as a “dual-pronged approach” to achieve profitability and navigate geopolitical tensions.

As noted by Nikkei Asia in a recent report examining how “Baidu, Waymo and others eye overseas markets as political tensions run high,” uncertainty surrounding US-China relations has made the Middle East, Southeast Asia, and Europe increasingly attractive alternative markets for Chinese firms.

The market leadership battle intensifies

Baidu’s Apollo Go recently announced its service has completed more than 11 million rides, surpassing Waymo’s reported 10 million, marking a milestone in the global robotaxi competition.

The Chinese company operates the largest fleet of the Chinese robotaxi companies, with more than 1,000 cars in operation globally, according to a WSJ report. Chinese robotaxi rides cost about 35 cents per mile compared with $2 in the US, according to a 2025 report [PDF] by US-based investment firm ARK.

The cost advantage stems from government subsidies, comprehensive supply chains, and lower labour costs, giving Chinese firms an edge in price-sensitive markets. While China remains Apollo Go’s main market, the company has already announced expansion plans in the Middle East.

Baidu outlined plans in March to deploy “dozens” of robotaxis in partnership with UAE-based Autogo in Abu Dhabi with a goal of starting commercial operations by 2026, according to Reuters.

Goldman Sachs sees massive growth potential

Investment bank Goldman Sachs forecasts significant growth in the global autonomous vehicle market, projecting that a global fleet of several million commercial autonomous vehicles used for ride-sharing could be operational by 2030. Currently, China’s robotaxi fleet – the world’s biggest – stands at about 1,700 vehicles.

The research firm estimates the Chinese robotaxi market could grow to $47 billion in value by 2035 from an estimated $54 million this year. The growth trajectory is expected to be driven by cheaper hardware and algorithm development, as well as declining operating costs for fleet owners.

Goldman Sachs analysts believe Apollo Go, Pony AI, and WeRide will likely remain among the major players due to high technological barriers to entry and their leading advantages in algorithms, data, and mapping capabilities.

Regional competition heats up

The Southeast Asian robotaxi expansion strategy reflects broader competitive dynamics in the autonomous driving space. WeRide said it has begun “public operation” of its robotaxi GXR minivan in several Chinese cities as well as Zurich and Abu Dhabi, while also partnering with Uber to expand into 15 cities over the next five years.

Meanwhile, US companies are focusing on domestic scaling. The Alphabet-owned Waymo, which launched the world’s first fully driverless service in Phoenix in 2020, remains the largest operator in the US. However, American firms have faced challenges, with General Motors halting investment in Cruise after spending $10 billion, citing high scaling costs.

Technology race continues

As the industry moves toward commercial viability, Chinese companies appear to have gained operational experience advantages. Baidu’s Zhang said the company was confident it could do well abroad, citing its claim that its vehicles had completed 10 million trips in China as of March without a serious traffic accident.

The company has been running its Apollo Go robotaxi services commercially in several Chinese cities since 2022, with vehicles operating at Level 4 autonomy – meaning they are driverless but restricted to certain approved areas.

However, challenges remain for all players in the space. Even at the current level of automation – where vehicles can operate in driverless modes only in certain government-approved areas – robotaxis would commercially scale only by 2030, according to a McKinsey report.

The post Baidu’s Apollo Go to launch robotaxi expansion in Southeast Asia appeared first on TechWire Asia.

Singapore is backing a national plan to help 300 local companies adopt AI in their operations. The programme, called AI Cloud Takeoff (AI CTO), is led by Google Cloud and Digital Industry Singapore (DISG). It builds on a smaller pilot with 30 companies that wrapped up in late 2024.

The goal of the programme is to accelerate AI and cloud adoption in Singapore. Under the initiative, AI Centers of Excellence (CoEs) will be set up in 300 digitally mature Singapore-based companies in the next year. The teams will guide how AI is used in departments, train staff, and ensure that tools are integrated safely and responsibly. Companies involved will work with Google Cloud-certified tech partners and can receive up to SG$500,000 in support as they reach key project milestones.

The AI CTO effort is part of a broader Enterprise Compute Initiative announced in Singapore’s 2025 Budget.

Getting businesses ready for enterprise AI

AI CTO starts with a two-week bootcamp run by Google Cloud. Companies will identify AI uses tied to their business goals and set up internal teams to manage AI projects. They’ll receive help designing and building a working AI solution inside six months.

Support includes Google Cloud credits for using services like BigQuery and Vertex AI, and employee training through Google Cloud Skills Boost. Technical consultants will help with development and integration so the AI tools work with a company’s current systems and follow data privacy rules.

Google Cloud has teamed with Accenture, AsiaPac, CloudMile, Deloitte, Kyndryl, NTT Data, and Searce to help companies get started.

Several companies from the original pilot shared what they’ve built so far.

Embed builds an in-venue AI experience tool

Embed, which provides cashless systems for family entertainment centres like Timezone and Dave & Buster’s, launched a solution called Sidequest AI as a result of their participation in the AI CTO pilot. The solution, integrated into Embed’s kiosks, uses BigQuery to process gameplay and payment data and Gemini on Vertex AI to recommend tasks or rewards for players. It’s already been adopted in North and South America.

Andy Welsh, CTO at Embed, said the programme helped them prioritise projects and understand how to apply AI to their product line.

Searce, the partner that worked with Embed, said the key was balancing cloud and local processing to keep things efficient. The solution is now running in live environments and ready to scale.

Seaco uses AI to streamline container checks

Seaco, a player in the shipping container leasing space, is using AI to speed up how it inspects and processes container returns. The company built a multilingual AI agent that analyses photos and voice notes taken by depot staff.

The tool detects container damage, pulls out key details from voice messages, and sends a summary to Seaco’s cloud-based system. The company estimates the system could automate around 220,000 inspections a year.

Damian Leach, CIO at Seaco, said they created an internal hackathon to collect AI ideas from staff, and the solution emerged as one of the most promising.

CloudMile, which worked on the tool, focused on making it easy for staff to use in different languages and in different locations. It was built to work smoothly with Seaco’s existing SAP setup.

YCH Group develops cargo screening AI for Vietnam SuperPort

YCH Group is working with Kyndryl to build two AI tools for Vietnam SuperPort, a new 83-hectare logistics hub that connects air, sea, road, and rail freight.

One AI agent pulls key information from scanned or handwritten cargo documents, and another analyses X-ray images to flag prohibited items. Both tools are integrated with YCH’s operations systems.

Dr. Yap Kwong Weng, CEO of Vietnam SuperPort, said the new tools are expected to raise worker productivity and cut cargo handling errors.

Kyndryl said the models were trained using YCH’s past cargo documentation and image samples, and the company has plans to scale its solutions in Southeast Asia.

What comes next

Applications are now open for the full AI CTO rollout. The Singapore government and Google Cloud hope to bring more companies on board, using the same structure tested in the pilot.

The broader goal is to help Singapore-based businesses move beyond experimentation and start building useful AI tools that solve real problems. The emphasis is on responsible, secure use – and making sure that companies have the skills and structure to support AI long term.

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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.

The mathematics of modern cybersecurity are brutally simple: if you have 25 security tools in your enterprise, you’re potentially exposed to 625 different attack paths. Meanwhile, cybercriminals only need to succeed once. This stark asymmetry is driving a fundamental shift towards cybersecurity platformisation, according to Sailesh Rao, President of Cortex at Palo Alto Networks.

Speaking exclusively to Tech Wire Asia during the recent Ignite on Tour Malaysia 2025, Rao delivered a sobering assessment of why the traditional “best-of-breed” approach to cybersecurity is not merely outdated—it’s dangerous.

“For ‘n’ nodes in the network, you can be compromised in ‘n’ squared ways,” Rao explained. “The bad guys have to just get through once. That’s the asymmetry that most people don’t understand.”

Put simply: each security tool can be attacked directly, but attackers can also exploit the connections between tools—and these connection points multiply rapidly as you add more solutions.

This mathematical reality demolishes the comfortable notion of “best-of-breed” security strategies. Rao argues that whilst multiple vendors feel safer, “the cost of putting all your eggs in multiple baskets and then having to watch all those baskets and keep them all in sync is way higher.”

It’s a counterintuitive argument that challenges decades of IT procurement wisdom, but one that becomes increasingly compelling when viewed through the lens of modern attack methodologies.

The fatal flaw in point solutions

The proliferation of cybersecurity point solutions over the past decade has created an unintended consequence: exponential vulnerability. Each additional security tool doesn’t just add one more potential failure point—it creates multiple integration gaps that sophisticated attackers can exploit.

“Just imagine if you have five tools in your enterprise. That’s at least 25 ways in which you can get compromised,” Rao noted. “If you have two tools, you can get compromised at each tool and on the path from one to the other.”

This mathematical reality becomes even more problematic when considering the speed of modern attacks. Recent data from Palo Alto Networks’ Unit 42 research team documented attacks that achieved full ransomware deployment within 40 minutes. “If you came 40 minutes later, you wouldn’t even have seen anything, and your IP is gone,” Rao observed.

The contrast with traditional breach timelines is stark. Whilst some attackers remain dormant in systems for years—Rao cited a Toyota case where attackers maintained access for a decade—others now operate with devastating efficiency, completing their objectives faster than most security teams can detect them.

The promise and peril of AI-driven security

No doubt, the emergence of AI as both a defensive and offensive weapon has fundamentally altered the cybersecurity landscape. Whilst organisations scramble to implement AI-powered security tools, cybercriminals are simultaneously leveraging the same technologies to enhance their attacks.

“We are now generating attacks in our lab, 100% generated by AI, that we then put on our products and have them fight,” Rao revealed. This arms race dynamic underscores why cybersecurity platformisation has become critical—fragmented security architectures simply cannot process and correlate threat intelligence quickly enough to counter AI-enhanced attacks. 

Success in this environment requires not just AI-powered tools, but also the data infrastructure to train them effectively. Palo Alto Networks has been investing in machine learning capabilities since 2013, well before the current AI boom.

“In 2013, when we were using machine learning, nobody was talking about it like we are today,” Rao noted. This early investment has yielded significant advantages, particularly in data collection and analysis capabilities.

The company now processes approximately one exabyte of security data—a scale that enables more sophisticated threat detection than competitors relying on synthetic data or smaller datasets. 

“We have more real data, cyber-specific, than anyone else,” Rao explained. “When you take a large dataset like we do and want to find the needle in the haystack, we don’t have to use synthetic data. We have real data.”

The open source security paradox

The proliferation of open-source AI models presents a particular challenge for cybersecurity platformisation strategies. Whilst open-source development offers innovation benefits, it also introduces significant security risks through its inherently distributed development model.

“Just like having 25 point solutions, imagine having 100 software engineers from all over the world updating your open-source tool,” Rao explained. “That variability is what hackers take advantage of.”

This variability manifests in inconsistent coding practices, varying security standards, and potential supply chain vulnerabilities that attackers can exploit. For organisations attempting to secure their infrastructure, managing these risks across multiple open-source components becomes exponentially more complex than securing a unified platform.

Reimagining human roles in autonomous security

Perhaps the most significant implication of cybersecurity platformisation is its impact on human security professionals. Current industry practices see most security teams analysing only 30-40% of security alerts—a statistic that Rao finds unacceptable.

“If you had 10 thieves outside your house and cops took four away, would you sleep comfortably?” he asked. “You wouldn’t take those odds anywhere else, but we do it in cybersecurity all the time.”

The solution lies in transitioning from human-centric to AI-centric security operations, with humans moving from detection roles to strategic oversight positions. “We want to make humans go from being detectors to defenders,” Rao explained. “We’re not good at looking for needles in a haystack. That’s not how our mind works.”

This evolution envisions security professionals as trainers of machine learning models rather than manual alert processors—a shift that requires significant retraining but promises more effective security outcomes.

Regulatory Compliance in a Platform World

As cybersecurity platformisation accelerates, regulatory compliance becomes both simpler and more complex. Palo Alto Networks maintains compliance with major standards including GDPR, FedRAMP, and IRAP, whilst working towards DORA compliance in Europe.

However, the rapid evolution of AI and cloud technologies often outpaces regulatory frameworks. “Some countries are still developing standards, so we work with regulatory bodies to help define some of them,” Rao noted.

The economics of consolidated security

The financial argument for cybersecurity platformisation extends beyond direct cost savings. Whilst organisations might initially view platform consolidation as putting “all eggs in one basket,” Rao argues this metaphor misses the point entirely.

“You put all your eggs in one basket already,” he observed, pointing to mobile phones as an example. “Your entire life is on this one platform. You don’t walk around with a different telephone, video recorder, tape recorder, or camera.”

The key difference lies in management philosophy: intensive monitoring of fewer platforms versus superficial oversight of many fragmented solutions.

Looking forward: The autonomous security future

As the cybersecurity industry grapples with an unprecedented skills shortage—Bill Gates recently identified software engineering as one of only three professions likely to survive AI displacement—organisations must reconsider how they deploy limited human resources.

“If you have a scarce resource, make sure you use it most effectively,” Rao advised. “You shouldn’t be using AI talent to integrate point solutions across various vendors. That’s not the best use.”

The vision of 80-85% autonomous cybersecurity operations may seem ambitious, but given the speed and sophistication of modern threats, it appears increasingly necessary rather than merely aspirational.

The choice facing organisations is stark: embrace cybersecurity platformisation now, or risk becoming another cautionary tale in an increasingly dangerous digital landscape. As Rao concluded, “We don’t want companies to learn all this after a breach—it’s a very expensive way to learn something.”

The post Palo Alto Network executive reveals why cybersecurity platformisation is a critical defence against AI attacks appeared first on TechWire Asia.

Grab has opened its first Artificial Intelligence Centre of Excellence (AI COE) at its Singapore headquarters, which is backed by Digital Industry Singapore (DISG). The centre will develop AI solutions to improve access to digital services, boost productivity, and support public projects across Southeast Asia.

Grab will use the new facility to train local talent, build tech tools for everyday use, and address regional social and business concerns. By 2025, the company expects to create at least 50 roles in engineering, product, data science, and analytics. These jobs are aimed at helping Singaporeans work on real AI problems with regional impact.

One major focus is making technology easier to use, especially for people who have trouble with standard apps or services. Grab is working with the Singapore Association of the Visually Handicapped (SAVH) to test a new voice assistant that helps visually-impaired users book rides using spoken commands. SAVH members helped test the tool and gave feedback to make sure it fits their needs.

Lyn Loh, who leads accessibility services at SAVH, said they were glad to support this effort. She said the voice assistant could help more people travel on their own and use digital services more easily.

Grab built the voice feature using models based on OpenAI’s technology, but with a local twist. The team trained the system on 80,000 voice samples that reflect Singaporean speech and building names. That helped the model improve its ability to recognise local accents and landmarks—from 46% to 89%. Starting in June, users in Singapore can donate voice samples through the app to further improve accuracy.

For now, the voice assistant is being tested in Singapore. Grab plans to expand its use in the future and is also exploring similar tools to support elderly users and others less familiar with smartphones.

The AI COE will also work on boosting productivity. That includes assisting drivers, delivery workers, and small businesses in using AI to make better decisions and improve how they work.

Grab is building its own AI model, trained on its own data, to better understand user behaviour and how partners use the app. This custom model is expected to improve the way Grab recommends rides, routes, and services to users and partners. It can help provide suggestions that are more relevant to each individual.

The company is also creating tools that let its employees test AI ideas more quickly. A new internal kit includes code and resources that make it easier to set up safe environments for AI testing. What used to take more than a week can now be done in half a day.

For Grab’s partners, the AI COE is rolling out tools like the Driver AI Companion and the Merchant AI Assistant. The driver tool offers tips on where to go for more ride requests, how to plan routes more efficiently, and what rewards are available based on driving patterns. The merchant tool assists businesses in understanding trends, getting suggestions on improving sales, and managing daily tasks more easily.

Another area of focus is public infrastructure and smart city tools. Grab collects a lot of data from its driver network, which it plans to use to support local government work. Its existing devices, such as KartaDongle and KartaDashcam, are being updated with new features to detect road hazards, monitor weather, and track traffic in real time.

One example is its collaboration with Singapore’s national water authority, PUB. By merging Grab’s data with PUB’s flood monitoring systems, the two teams can deliver real-time flood notifications to drivers and assist officials respond more quickly to storm-related situations. This reduces traffic interruptions and improves emergency response times.

Grab describes the AI COE as a way to use technology to improve daily life across Southeast Asia. The centre brings together data, tools, and people to build useful solutions for drivers, merchants, users, and cities. By focusing on real problems like accessibility, small business support, and urban planning, the company hopes to make AI more practical and inclusive across the region.

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