The rapid adoption of technology in India is driving a surge in demand for AI solutions across sectors like finance, education, healthcare, and agriculture. AI is revolutionising these industries by making services more efficient, personalised, and accessible. This growing dependence on AI has created a fertile ground for innovation, propelling India’s emergence as a global hub for AI startups. With over 6,200 AI startups operating in the country, India offers a dynamic and challenging landscape for entrepreneurs seeking to make a meaningful impact.
Fuelling AI Innovation: India’s Strategic Investment
Earlier this year, the government allocated USD 1.3 billion for the India AI Mission, solidifying its commitment to AI. This comprehensive program is designed to catalyse the AI innovation ecosystem within the country. At the heart of this ecosystem’s development lies the expansion of compute infrastructure, a critical resource for AI startups. By providing access to powerful computing resources, the India AI Mission is empowering startups to scale their solutions and compete on a global level.
Beyond infrastructure, the initiative focuses on fostering collaborations between academia, industry, and startups to drive R&D. By creating a supportive environment that promotes knowledge sharing and resource accessibility, the India AI Mission aims to position India as a leader in the AI landscape.
A Spotlight on Indian Startups
Driving Industry Innovation
Healthcare. India’s vibrant AI startup ecosystem is driving innovation in healthcare, with companies leveraging AI to address critical challenges and improve patient outcomes.
- Cancer-Focused AI Startups. Several startups are revolutionising cancer care with AI-driven innovations. Niramai, globally recognised for its innovation, uses AI and thermal imaging for early breast cancer detection, particularly effective in younger women and dense breast tissue. Onward Assist provides predictive analytics for oncology, helping oncologists manage patient data and improve the accuracy of cancer care decisions. Similarly, Atom360 focuses on oral cancer screening with an AI-powered app that offers quick, affordable access to critical information, enhancing oral healthcare in underserved areas.
- AI-Driven Diagnostic Solutions. AI is significantly advancing diagnostics, enhancing accuracy, and reducing misdiagnosis. SigTuple develops AI-driven diagnostic solutions for medical imaging and pathology, improving accuracy and efficiency in disease detection. Endimension Technology, incubated at IIT Bombay, develops algorithms for detecting abnormalities in medical scans, aiming to reduce misdiagnosis and radiologist workload. Tricog Health delivers AI solutions for rapid heart attack diagnosis, reducing diagnosis time and improving outcomes, especially in underserved regions.
Financial Services. Fintechs have been at the forefront of AI-led innovations, offering innovative solutions for insurance, lending, and microfinance. Artivatic uses AI to transform traditional insurance systems into digital, personalised offerings, making coverage more accessible and affordable for a broader range of consumers. ZestMoney leverages AI for digital lending, providing credit to individuals without a credit history through easy EMI plans, and enhancing financial access. Meanwhile, mPokket offers instant micro-loans to students and young professionals, addressing short-term financial needs with flexible loan options and minimal documentation.
Other Industries. Beyond healthcare and financial services, AI startups are driving innovation across various industries, tackling critical challenges. Entropik uses AI to analyse human emotions and behaviour, helping businesses gain deeper insights into consumer preferences for market research and optimising user experiences. In agriculture, Intello Labs applies AI and computer vision to assess the quality of fresh produce, reducing food waste and improving supply chain efficiency. Similarly, AgNext enhances food value chains by offering AI-driven, real-time quality assessments through its SaaS platform, promoting safety and transparency in agribusiness.
Transforming Businesses
Technology for Security & Fraud. AI startups are offering innovative solutions tailored to organisations’ needs. SpoofSense combats deepfakes and identity fraud with advanced facial liveness detection, ensuring secure user verification by distinguishing between real users and spoofed images. Eagle Eye Networks provides cloud-based video surveillance solutions, using AI to offer real-time monitoring and analytics. In the e-commerce space, ThirdWatch uses AI to detect and prevent fraud in real-time by analysing user behaviour and transaction patterns, reducing financial losses for online retailers.
Tech Development. AI startups are empowering organisations to accelerate innovation and enhance productivity. Haptik helps businesses build intelligent virtual assistants, powering chatbots and voice bots across industries to improve customer engagement. DhiWise automates the development process, enabling faster app creation by converting designs into code. Additionally, Fluid AI provides advanced AI solutions like predictive analytics and natural language processing for sectors like finance, retail, and healthcare. Mihup enhances contact centre performance with its conversation intelligence platform, while Yellow.ai enables enterprises to automate customer engagement through its GenAI-powered platform, creating seamless and scalable customer service experiences.
Empowering People
AI startups are empowering individuals by providing personalised services that enhance learning, creativity, and financial management. SuperKalam and ZuAI offer students tailored learning experiences, using AI to create interactive lessons and assessments that adapt to individual learning styles, improving student engagement and outcomes. For creative professionals, Mugafi combines AI with human mentoring to assist writers in generating ideas and developing scripts, enabling them to create intellectual property with greater efficiency. Wright Research empowers individuals to make informed financial decisions through AI-powered investment advice, while Vahan simplifies job searches for blue-collar workers by using AI to match candidates with suitable employment opportunities via WhatsApp.
Promoting ESG
AI startups are driving meaningful change by optimising processes and creating economic opportunities. Ossus Biorenewables enhances biofuel production through AI, reducing waste and increasing efficiency in renewable energy generation, while Ishitva Robotic Systems promotes sustainability by automating waste sorting and recycling, contributing to a more efficient and circular economy. Karya connects rural workers with digital tasks, offering fair wages and skills development by matching them to tasks suited to their abilities using machine learning. In agriculture, KissanAI helps farmers improve crop yields and manage resources effectively through personalised, data-driven recommendations. ElasticRun improves last-mile delivery logistics in rural areas, enabling businesses to reach underserved markets.
Conclusion
Nvidia CEO Jensen Huang noted India’s potential to become the “largest exporter of AI,” signalling vast global opportunities. India’s AI startups are at the forefront of innovation but face hurdles such as fierce competition for skilled talent, navigating complex regulations, and securing funding. With strategic focus on these challenges and the backing of initiatives like Digital India and Startup India, India’s AI ecosystem can seize emerging market opportunities, accelerate tech advancements, and make a substantial impact on the global AI landscape.
As AI evolves, the supporting infrastructure has become a crucial consideration for organisations and technology companies alike. AI demands massive processing power and efficient data handling, making high-performance computing clusters and advanced data management systems essential. Scalability, efficiency, security, and reliability are key to ensuring AI systems handle increasing demands and sensitive data responsibly.
Data centres must evolve to meet the increasing demands of AI and growing data requirements.
Equinix recently hosted technology analysts at their offices and data centre facilities in Singapore and Sydney to showcase how they are evolving to maintain their leadership in the colocation and interconnection space.
Equinix is expanding in Latin America, Africa, the Middle East, and Asia Pacific. In Asia Pacific, they recently opened data centres in Kuala Lumpur and Johor Bahru, with capacity additions in Mumbai, Sydney, Melbourne, Tokyo, and Seoul. Plans for the next 12 months include expanding in existing cities and entering new ones, such as Chennai and Jakarta.
Ecosystm analysts comment on Equinix’s growth potential and opportunities in Asia Pacific.
Small Details, Big Impact
TIM SHEEDY. The tour of the new Equinix data centre in Sydney revealed the complexity of modern facilities. For instance, the liquid cooling system, essential for new Nvidia chipsets, includes backup cold water tanks for redundancy. Every system and process is designed with built-in redundancy.
As power needs grow, so do operational and capital costs. The diesel generators at the data centre, comparable to a small power plant, are supported by multiple fuel suppliers from several regions in Sydney to ensure reliability during disasters.
Security is critical, with some areas surrounded by concrete walls extending from the ceiling to the floor, even restricting access to Equinix staff.
By focusing on these details, Equinix enables customers to quickly set up and manage their environments through a self-service portal, delivering a cloud-like experience for on-premises solutions.
Equinix’s Commitment to the Environment
ACHIM GRANZEN. Compute-intensive AI applications challenge data centres’ “100% green energy” pledges, prompting providers to seek additional green measures. Equinix addresses this through sustainable design and green energy investments, including liquid cooling and improved traditional cooling. In Singapore, one of Equinix’s top 3 hubs, the company partnered with the government and Sembcorp to procure solar power from panels on public buildings. This improves Equinix’s power mix and supports Singapore’s renewable energy sector.
TIM SHEEDY Building and operating data centres sustainably is challenging. While the basics – real estate, cooling, and communications – remain, adding proximity to clients, affordability, and 100% renewable energy complicates matters. In Australia, reliant on a mixed-energy grid, Equinix has secured 151 MW of renewable energy from Victoria’s Golden Plains Wind Farm, aiming for 100% renewable by 2029.
Equinix leads with AIA-rated data centres that operate in warmer conditions, reducing cooling needs and boosting energy efficiency. Focusing on efficient buildings, sustainable water management, and a circular economy, Equinix aims for climate neutrality by 2030, demonstrating strong environmental responsibility.
Equinix’s Private AI Value Proposition
ACHIM GRANZEN. Most AI efforts, especially GenAI, have occurred in the public cloud, but there’s rising demand for Private AI due to concerns about data availability, privacy, governance, cost, and location. Technology providers in a position to offer alternative AI stacks (usually built on top of a GPU-as-a-service model) to the hyperscalers find themselves in high interest. Equinix, in partnership with providers such as Nvidia, offers Private AI solutions on a global turnkey AI infrastructure. These solutions are ideal for industries with large-scale operations and connectivity challenges, such as Manufacturing, or those slow to adopt public cloud.
SASH MUKHERJEE. Equinix’s Private AI value proposition will appeal to many organisations, especially as discussions on AI cost efficiency and ROI evolve. AI unites IT and business teams, and Equinix understands the need for conversations at multiple levels. Infrastructure leaders focus on data strategy capacity planning; CISOs on networking and security; business lines on application performance, and the C-suite on revenue, risk, and cost considerations. Each has a stake in the AI strategy. For success, Equinix must reshape its go-to-market message to be industry-specific (that’s how AI conversations are shaping) and reskill its salesforce for broader conversations beyond infrastructure.
Equinix’s Growth Potential
ACHIM GRANZEN. In Southeast Asia, Malaysia and Indonesia provide growth opportunities for Equinix. Indonesia holds massive potential as a digital-savvy G20 country. In Malaysia, the company’s data centres can play a vital part in the ongoing Mydigital initiative, having a presence in the country before the hyperscalers. Also, the proximity of the Johor Bahru data centre to Singapore opens additional business opportunities.
TIM SHEEDY. Equinix is evolving beyond being just a data centre real estate provider. By developing their own platforms and services, along with partner-provided solutions, they enable customers to optimise application placement, manage smaller points of presence, enhance cloud interconnectivity, move data closer to hyperscalers for backup and performance, and provide multi-cloud networking. Composable services – such as cloud routers, load balancers, internet access, bare metal, virtual machines, and virtual routing and forwarding – allow seamless integration with partner solutions.
Equinix’s focus over the last 12 months on automating and simplifying the data centre management and interconnection services is certainly paying dividends, and revenue is expected to grow above tech market growth rates.
Quantum computing is emerging as a groundbreaking force with the potential to reshape industries and enhance national security with unparalleled speed and precision. Governments and technology providers worldwide are heavily investing in this transformative technology, which promises significant advancements in areas such as cryptography, drug development, AI, and finance. Countries like Australia, Singapore, Taiwan, Qatar, and the UK are investing heavily in quantum research, backed by tech giants like Nvidia, IBM, and Google.
Ecosystm research finds that nearly 30% of enterprises are expecting to use quantum computing in the next 5 years.
Beyond Bits: Exploring the Potential of Quantum Computers
We need quantum computers because they have the potential to solve certain problems that are impossible (or impractical) for classical computers. Last year, Google led a study revealing that its quantum processor can complete a task in 6.18 seconds that would take a traditional supercomputer 47 years.
Here are a few reasons why quantum computing is exciting:
Unleashing the power of qubits. While classical computers use bits that can be either 0 or 1, quantum computers use qubits, which can exist in both states simultaneously (a state called superposition). This allows them to explore multiple possibilities simultaneously, making them significantly faster for specific tasks.
Tackling complex problems. Problems like simulating molecules or breaking complex encryption codes involve massive calculations. Quantum computers, with their unique properties, can manage these complexities more efficiently.
Revolutionising specific fields. Quantum computing has the potential to transform areas such as materials science, drug discovery, AI, and financial modelling. By simulating complex systems and processes, they could lead to breakthroughs in various sectors.
Quantum computers will not replace traditional computers entirely, but rather function as powerful tools for specific tasks beyond the reach of classical machines. Let’s look at cybersecurity as an example.
Twenty years ago, hacking was a basic task. Imagine a time before social media, when a simple computer and basic technical know-how were enough to breach a network. The stakes were low, the landscape uncomplicated. But technology, like threats, has evolved. Today’s hackers use sophisticated techniques, employing strategies like “harvest now, decrypt later” – stealing data today to decrypt later using more powerful machines. This is where quantum computing enters the scene, posing a significant threat to current encryption methods. In response, tech giants like Google, Apple, and Zoom are implementing quantum-resistant encryption into their software, safeguarding user data from potential future decryption attempts. Governments and telecommunication providers worldwide are boosting quantum encryption to tackle the potential security crisis.
The thrill of quantum computing lies in its infancy. Unforeseen applications, beyond our current imagination, could be unlocked as the technology matures.
Current Quantum Projects Focused on Security
First Scalable Network Secures Maritime Trade. The Netherlands is improving the resiliency of transport infrastructure in their own major international maritime hub, using quantum. The Port of Rotterdam Authority joined a collection of quantum technology firms to create a comprehensive cybersecurity ecosystem – the first of its kind globally. The port uses quantum technology to safeguard sensitive information, improving safety for the seagoing vessels carrying 470 million tonnes of cargo annually.
UK Integrates Quantum Navigation for Secure Air Travel. The UK is improving its digital transport infrastructure by integrating the first ever un-jammable aviation navigation system, powered by quantum software. This initiative was prompted by an incident where a government plane carrying the UK defence secretary had its GPS signal jammed close to Russian territory. This technology ensures safe and reliable navigation for aircraft, particularly in hostile environments. The UK government is investing USD 56 million into their quantum sector, aiming to become a quantum-enabled economy by 2033.
Governments Putting Faith in Quantum
Taiwan’s First Domestic 5-Qubit Computer. Taiwan is aiming to be a leader in quantum computing by building its first domestic machine by 2027. This initiative is part of a larger USD 259 million, five-year plan for quantum technology, and has a multi-pronged approach including building the actual quantum computer hardware; developing solutions to secure data in a world with quantum computers (quantum cryptography); creating a strong supply chain for quantum computing components within Taiwan; and collaborating with international partners to develop testing platforms and expertise.
Singapore Explores Real-World Applications. The Singaporean government has pledged USD 518 million to their National Quantum Strategy (NQS). This investment will provide the necessary resources to explore real-time applications of quantum technology in healthcare and technology. Simultaneously, they launched the National Quantum Processor Initiative (NQPI) to develop quantum sensors that will aid in research. Singapore aims to lead the world in quantum tech for investment portfolios, cryptography, and drug discovery.
Australia Aiming for World’s First Utility-Scale Machine. Backed by a USD 620 million investment from the Australian and Queensland governments, PsiQuantum aims to build a fault-tolerant computer that can solve previously challenging problems in fields like renewable energy, healthcare, and transportation. PsiQuantum’s innovative “fusion-based architecture” tackles scaling challenges by using millions of light-based qubits, paving the way for a new era of computational power and potentially sparking the next industrial revolution. This project positions Australia as a global leader in harnessing the immense potential of quantum computing.
Tech Companies Making the Quantum Leap
IBM Enhances Quantum Performance. IBM’s latest quantum computing platform, Qiskit 1.0, has worked on features that enhance performance, stability, and reliability. The updated open-source SDK aims to facilitate accessible quantum programming and accelerate processing times. Qiskit 1.0 uses optimised circuits to create and manage the interplay between classical and quantum computing. IBM is even collaborating with Japan’s AIST to develop a 10,000-qubit quantum computer by 2029, which is 75 times more powerful than current systems.
Microsoft and Quantinuum Achieve Reliable Logical Qubits. This significant milestone is said to mark a new era of dependable quantum technology, dramatically reducing errors and enhancing the precision of quantum computations. They have demonstrated an 800x improvement in error rates, paving the way for hybrid supercomputing systems that combine AI, high-performance computing (HPCs), and quantum capabilities to tackle scientific problems, with new capabilities becoming available to Azure Quantum Elements customers in the coming months.
Quantum Cloud Services for Enterprise. Major tech players QMware, Nvidia, and Oracle are teaming up to create hybrid quantum computing service for businesses. Combining classical and quantum computing, the project aims to crunch complex problems in AI, machine learning, and optimisation – all in the cloud.
Building Towards a Quantum Future
In the short term, using HPCs with quantum algorithms can already provide noticeable speed improvements over traditional methods. Hybrid approaches, where HPCs and quantum computers work together, could lead to significant gains in speed and efficiency, potentially ranging from 10x to 100x improvement.
Three strategies: quantum-inspired, hybrid, and full-scale quantum computing each offer distinct advantages.
While quantum-inspired computing leverages quantum algorithms to run on classical systems, hybrid computing combines classical and quantum processors, optimising the strengths of both to take complex problems more efficiently. Intuitively, full-scale quantum computing represents the ultimate goal, where large, fault-tolerant quantum computers solve problems beyond the reach of current classical systems.
Looking further ahead, the development of large-scale quantum computers could revolutionise industries by solving problems far beyond the reach of classical computers, with potential speedups of 500x to 1000x.
As quantum technology progresses, different industries and applications will benefit from tailored approaches that best suit their unique needs.
Southeast Asia’s massive workforce – 3rd largest globally – faces a critical upskilling gap, especially with the rise of AI. While AI adoption promises a USD 1 trillion GDP boost by 2030, unlocking this potential requires a future-proof workforce equipped with AI expertise.
Governments and technology providers are joining forces to build strong AI ecosystems, accelerating R&D and nurturing homegrown talent. It’s a tight race, but with focused investments, Southeast Asia can bridge the digital gap and turn its AI aspirations into reality.
Read on to find out how countries like Singapore, Thailand, Vietnam, and The Philippines are implementing comprehensive strategies to build AI literacy and expertise among their populations.
Download ‘Upskilling for the Future: Building AI Capabilities in Southeast Asia’ as a PDF
Big Tech Invests in AI Workforce
Southeast Asia’s tech scene heats up as Big Tech giants scramble for dominance in emerging tech adoption.
Microsoft is partnering with governments, nonprofits, and corporations across Indonesia, Malaysia, the Philippines, Thailand, and Vietnam to equip 2.5M people with AI skills by 2025. Additionally, the organisation will also train 100,000 Filipino women in AI and cybersecurity.
Singapore sets ambitious goal to triple its AI workforce by 2028. To achieve this, AWS will train 5,000 individuals annually in AI skills over the next three years.
NVIDIA has partnered with FPT Software to build an AI factory, while also championing AI education through Vietnamese schools and universities. In Malaysia, they have launched an AI sandbox to nurture 100 AI companies targeting USD 209M by 2030.
Singapore Aims to be a Global AI Hub
Singapore is doubling down on upskilling, global leadership, and building an AI-ready nation.
Singapore has launched its second National AI Strategy (NAIS 2.0) to solidify its global AI leadership. The aim is to triple the AI talent pool to 15,000, establish AI Centres of Excellence, and accelerate public sector AI adoption. The strategy focuses on developing AI “peaks of excellence” and empowering people and businesses to use AI confidently.
In keeping with this vision, the country’s 2024 budget is set to train workers who are over 40 on in-demand skills to prepare the workforce for AI. The country will also invest USD 27M to build AI expertise, by offering 100 AI scholarships for students and attracting experts from all over the globe to collaborate with the country.
Thailand Aims for AI Independence
Thailand’s ‘Ignite Thailand’ 2030 vision focuses on boosting innovation, R&D, and the tech workforce.
Thailand is launching the second phase of its National AI Strategy, with a USD 42M budget to develop an AI workforce and create a Thai Large Language Model (ThaiLLM). The plan aims to train 30,000 workers in sectors like tourism and finance, reducing reliance on foreign AI.
The Thai government is partnering with Microsoft to build a new data centre in Thailand, offering AI training for over 100,000 individuals and supporting the growing developer community.
Building a Digital Vietnam
Vietnam focuses on AI education, policy, and empowering women in tech.
Vietnam’s National Digital Transformation Programme aims to create a digital society by 2030, focusing on integrating AI into education and workforce training. It supports AI research through universities and looks to address challenges like addressing skill gaps, building digital infrastructure, and establishing comprehensive policies.
The Vietnamese government and UNDP launched Empower Her Tech, a digital skills initiative for female entrepreneurs, offering 10 online sessions on GenAI and no-code website creation tools.
The Philippines Gears Up for AI
The country focuses on investment, public-private partnerships, and building a tech-ready workforce.
With its strong STEM education and programming skills, the Philippines is well-positioned for an AI-driven market, allocating USD 30M for AI research and development.
The Philippine government is partnering with entities like IBPAP, Google, AWS, and Microsoft to train thousands in AI skills by 2025, offering both training and hands-on experience with cutting-edge technologies.
The strategy also funds AI research projects and partners with universities to expand AI education. Companies like KMC Teams will help establish and manage offshore AI teams, providing infrastructure and support.
When OpenAI released ChatGPT, it became obvious – and very fast – that we were entering a new era of AI. Every tech company scrambled to release a comparable service or to infuse their products with some form of GenAI. Microsoft, piggybacking on its investment in OpenAI was the fastest to market with impressive text and image generation for the mainstream. Copilot is now embedded across its software, including Microsoft 365, Teams, GitHub, and Dynamics to supercharge the productivity of developers and knowledge workers. However, the race is on – AWS and Google are actively developing their own GenAI capabilities.
AWS Catches Up as Enterprise Gains Importance
Without a consumer-facing AI assistant, AWS was less visible during the early stages of the GenAI boom. They have since rectified this with a USD 4B investment into Anthropic, the makers of Claude. This partnership will benefit both Amazon and Anthropic, bringing the Claude 3 family of models to enterprise customers, hosted on AWS infrastructure.
As GenAI quickly emerges from shadow IT to an enterprise-grade tool, AWS is catching up by capitalising on their position as cloud leader. Many organisations view AWS as a strategic partner, already housing their data, powering critical applications, and providing an environment that developers are accustomed to. The ability to augment models with private data already residing in AWS data repositories will make it an attractive GenAI partner.
AWS has announced the general availability of Amazon Q, their suite of GenAI tools aimed at developers and businesses. Amazon Q Developer expands on what was launched as Code Whisperer last year. It helps developers accelerate the process of building, testing, and troubleshooting code, allowing them to focus on higher-value work. The tool, which can directly integrate with a developer’s chosen IDE, uses NLP to develop new functions, modernise legacy code, write security tests, and explain code.
Amazon Q Business is an AI assistant that can safely ingest an organisation’s internal data and connect with popular applications, such as Amazon S3, Salesforce, Microsoft Exchange, Slack, ServiceNow, and Jira. Access controls can be implemented to ensure data is only shared with authorised users. It leverages AWS’s visualisation tool, QuickSight, to summarise findings. It also integrates directly with applications like Slack, allowing users to query it directly.
Going a step further, Amazon Q Apps (in preview) allows employees to build their own lightweight GenAI apps using natural language. These employee-created apps can then be published to an enterprise’s app library for broader use. This no-code approach to development and deployment is part of a drive to use AI to increase productivity across lines of business.
AWS continues to expand on Bedrock, their managed service providing access to foundational models from companies like Mistral AI, Stability AI, Meta, and Anthropic. The service also allows customers to bring their own model in cases where they have already pre-trained their own LLM. Once a model is selected, organisations can extend its knowledge base using Retrieval-Augmented Generation (RAG) to privately access proprietary data. Models can also be refined over time to improve results and offer personalised experiences for users. Another feature, Agents for Amazon Bedrock, allows multi-step tasks to be performed by invoking APIs or searching knowledge bases.
To address AI safety concerns, Guardrails for Amazon Bedrock is now available to minimise harmful content generation and avoid negative outcomes for users and brands. Contentious topics can be filtered by varying thresholds, and Personally Identifiable Information (PII) can be masked. Enterprise-wide policies can be defined centrally and enforced across multiple Bedrock models.
Google Targeting Creators
Due to the potential impact on their core search business, Google took a measured approach to entering the GenAI field, compared to newer players like OpenAI and Perplexity. The useability of Google’s chatbot, Gemini, has improved significantly since its initial launch under the moniker Bard. Its image generator, however, was pulled earlier this year while it works out how to carefully tread the line between creativity and sensitivity. Based on recent demos though, it plans to target content creators with images (Imagen 3), video generation (Veo), and music (Lyria).
Like Microsoft, Google has seen that GenAI is a natural fit for collaboration and office productivity. Gemini can now assist the sidebar of Workspace apps, like Docs, Sheets, Slides, Drive, Gmail, and Meet. With Google Search already a critical productivity tool for most knowledge workers, it is determined to remain a leader in the GenAI era.
At their recent Cloud Next event, Google announced the Gemini Code Assist, a GenAI-powered development tool that is more robust than its previous offering. Using RAG, it can customise suggestions for developers by accessing an organisation’s private codebase. With a one-million-token large context window, it also has full codebase awareness making it possible to make extensive changes at once.
The Hardware Problem of AI
The demands that GenAI places on compute and memory have created a shortage of AI chips, causing the valuation of GPU giant, NVIDIA, to skyrocket into the trillions of dollars. Though the initial training is most hardware-intensive, its importance will only rise as organisations leverage proprietary data for custom model development. Inferencing is less compute-heavy for early use cases, such as text generation and coding, but will be dwarfed by the needs of image, video, and audio creation.
Realising compute and memory will be a bottleneck, the hyperscalers are looking to solve this constraint by innovating with new chip designs of their own. AWS has custom-built specialised chips – Trainium2 and Inferentia2 – to bring down costs compared to traditional compute instances. Similarly, Microsoft announced the Maia 100, which it developed in conjunction with OpenAI. Google also revealed its 6th-generation tensor processing unit (TPU), Trillium, with significant increase in power efficiency, high bandwidth memory capacity, and peak compute performance.
The Future of the GenAI Landscape
As enterprises gain experience with GenAI, they will look to partner with providers that they can trust. Challenges around data security, governance, lineage, model transparency, and hallucination management will all need to be resolved. Additionally, controlling compute costs will begin to matter as GenAI initiatives start to scale. Enterprises should explore a multi-provider approach and leverage specialised data management vendors to ensure a successful GenAI journey.
ASEAN, poised to become the world’s 4th largest economy by 2030, is experiencing a digital boom. With an estimated 125,000 new internet users joining daily, it is the fastest-growing digital market globally. These users are not just browsing, but are actively engaged in data-intensive activities like gaming, eCommerce, and mobile business. As a result, monthly data usage is projected to soar from 9.2 GB per user in 2020 to 28.9 GB per user by 2025, according to the World Economic Forum. Businesses and governments are further fuelling this transformation by embracing Cloud, AI, and digitisation.
Investments in data centre capacity across Southeast Asia are estimated to grow at a staggering pace to meet this growing demand for data. While large hyperscale facilities are currently handling much of the data needs, edge computing – a distributed model placing data centres closer to users – is fast becoming crucial in supporting tomorrow’s low-latency applications and services.
The Big & the Small: The Evolving Data Centre Landscape
As technology pushes boundaries with applications like augmented reality, telesurgery, and autonomous vehicles, the demand for ultra-low latency response times is skyrocketing. Consider driverless cars, which generate a staggering 5 TB of data per hour and rely heavily on real-time processing for split-second decisions. This is where edge data centres come in. Unlike hyperscale data centres, edge data centres are strategically positioned closer to users and devices, minimising data travel distances and enabling near-instantaneous responses; and are typically smaller with a capacity ranging from 500 KW to 2 MW. In comparison, large data centres have a capacity of more than 80MW.
While edge data centres are gaining traction, cloud-based hyperscalers such as AWS, Microsoft Azure, and Google Cloud remain a dominant force in the Southeast Asian data centre landscape. These facilities require substantial capital investment – for instance, it took almost USD 1 billion to build Meta’s 150 MW hyperscale facility in Singapore – but offer immense processing power and scalability. While hyperscalers have the resources to build their own data centres in edge locations or emerging markets, they often opt for colocation facilities to familiarise themselves with local markets, build out operations, and take a “wait and see” approach before committing significant investments in the new market.
The growth of data centres in Southeast Asia – whether edge, cloud, hyperscale, or colocation – can be attributed to a range of factors. The region’s rapidly expanding digital economy and increasing internet penetration are the prime reasons behind the demand for data storage and processing capabilities. Additionally, stringent data sovereignty regulations in many Southeast Asian countries require the presence of local data centres to ensure compliance with data protection laws. Indonesia’s Personal Data Protection Law, for instance, allows personal data to be transferred outside of the country only where certain stringent security measures are fulfilled. Finally, the rising adoption of cloud services is also fuelling the need for onshore data centres to support cloud infrastructure and services.
Notable Regional Data Centre Hubs
Singapore. Singapore imposed a moratorium on new data centre developments between 2019 to 2022 due to concerns over energy consumption and sustainability. However, the city-state has recently relaxed this ban and announced a pilot scheme allowing companies to bid for permission to develop new facilities.
In 2023, the Singapore Economic Development Board (EDB) and the Infocomm Media Development Authority (IMDA) provisionally awarded around 80 MW of new capacity to four data centre operators: Equinix, GDS, Microsoft, and a consortium of AirTrunk and ByteDance (TikTok’s parent company). Singapore boasts a formidable digital infrastructure with 100 data centres, 1,195 cloud service providers, and 22 network fabrics. Its robust network, supported by 24 submarine cables, has made it a global cloud connectivity leader, hosting major players like AWS, Azure, IBM Softlayer, and Google Cloud.
Aware of the high energy consumption of data centres, Singapore has taken a proactive stance towards green data centre practices. A collaborative effort between the IMDA, government agencies, and industries led to the development of a “Green Data Centre Standard“. This framework guides organisations in improving data centre energy efficiency, leveraging the established ISO 50001 standard with customisations for Singapore’s context. The standard defines key performance metrics for tracking progress and includes best practices for design and operation. By prioritising green data centres, Singapore strives to reconcile its digital ambitions with environmental responsibility, solidifying its position as a leading Asian data centre hub.
Malaysia. Initiatives like MyGovCloud and the Digital Economy Blueprint are driving Malaysia’s public sector towards cloud-based solutions, aiming for 80% use of cloud storage. Tenaga Nasional Berhad also established a “green lane” for data centres, solidifying Malaysia’s commitment to environmentally responsible solutions and streamlined operations. Some of the big companies already operating include NTT Data Centers, Bridge Data Centers and Equinix.
The district of Kulai in Johor has emerged as a hotspot for data centre activity, attracting major players like Nvidia and AirTrunk. Conditional approvals have been granted to industry giants like AWS, Microsoft, Google, and Telekom Malaysia to build hyperscale data centres, aimed at making the country a leading hub for cloud services in the region. AWS also announced a new AWS Region in the country that will meet the high demand for cloud services in Malaysia.
Indonesia. With over 200 million internet users, Indonesia boasts one of the world’s largest online populations. This expanding internet economy is leading to a spike in the demand for data centre services. The Indonesian government has also implemented policies, including tax incentives and a national data centre roadmap, to stimulate growth in this sector.
Microsoft, for instance, is set to open its first regional data centre in Thailand and has also announced plans to invest USD 1.7 billion in cloud and AI infrastructure in Indonesia. The government also plans to operate 40 MW of national data centres across West Java, Batam, East Kalimantan, and East Nusa Tenggara by 2026.
Thailand. Remote work and increasing online services have led to a data centre boom, with major industry players racing to meet Thailand’s soaring data demands.
In 2021, Singapore’s ST Telemedia Global Data Centres launched its first 20 MW hyperscale facility in Bangkok. Soon after, AWS announced a USD 5 billion investment plan to bolster its cloud capacity in Thailand and the region over the next 15 years. Heavyweights like TCC Technology Group, CAT Telecom, and True Internet Data Centre are also fortifying their data centre footprints to capitalise on this explosive growth. Microsoft is also set to open its first regional data centre in the country.
Conclusion
Southeast Asia’s booming data centre market presents a goldmine of opportunity for tech investment and innovation. However, navigating this lucrative landscape requires careful consideration of legal hurdles. Data protection regulations, cross-border data transfer restrictions, and local policies all pose challenges for investors. Beyond legal complexities, infrastructure development needs and investment considerations must also be addressed. Despite these challenges, the potential rewards for companies that can navigate them are substantial.
GenAI has taken the world by storm, with organisations big and small eager to pilot use cases for automation and productivity boosts. Tech giants like Google, AWS, and Microsoft are offering cloud-based GenAI tools, but the demand is straining current infrastructure capabilities needed for training and deploying large language models (LLMs) like ChatGPT and Bard.
Understanding the Demand for Chips
The microchip manufacturing process is intricate, involving hundreds of steps and spanning up to four months from design to mass production. The significant expense and lengthy manufacturing process for semiconductor plants have led to global demand surpassing supply. This imbalance affects technology companies, automakers, and other chip users, causing production slowdowns.
Supply chain disruptions, raw material shortages (such as rare earth metals), and geopolitical situations have also had a fair role to play in chip shortages. For example, restrictions by the US on China’s largest chip manufacturer, SMIC, made it harder for them to sell to several organisations with American ties. This triggered a ripple effect, prompting tech vendors to start hoarding hardware, and worsening supply challenges.
As AI advances and organisations start exploring GenAI, specialised AI chips are becoming the need of the hour to meet their immense computing demands. AI chips can include graphics processing units (GPUs), application-specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs). These specialised AI accelerators can be tens or even thousands of times faster and more efficient than CPUs when it comes to AI workloads.
The surge in GenAI adoption across industries has heightened the demand for improved chip packaging, as advanced AI algorithms require more powerful and specialised hardware. Effective packaging solutions must manage heat and power consumption for optimal performance. TSMC, one of the world’s largest chipmakers, announced a shortage in advanced chip packaging capacity at the end of 2023, that is expected to persist through 2024.
The scarcity of essential hardware, limited manufacturing capacity, and AI packaging shortages have impacted tech providers. Microsoft acknowledged the AI chip crunch as a potential risk factor in their 2023 annual report, emphasising the need to expand data centre locations and server capacity to meet customer demands, particularly for AI services. The chip squeeze has highlighted the dependency of tech giants on semiconductor suppliers. To address this, companies like Amazon and Apple are investing heavily in internal chip design and production, to reduce dependence on large players such as Nvidia – the current leader in AI chip sales.
How are Chipmakers Responding?
NVIDIA, one of the largest manufacturers of GPUs, has been forced to pivot its strategy in response to this shortage. The company has shifted focus towards developing chips specifically designed to handle complex AI workloads, such as the A100 and V100 GPUs. These AI accelerators feature specialised hardware like tensor cores optimised for AI computations, high memory bandwidth, and native support for AI software frameworks.
While this move positions NVIDIA at the forefront of the AI hardware race, experts say that it comes at a significant cost. By reallocating resources towards AI-specific GPUs, the company’s ability to meet the demand for consumer-grade GPUs has been severely impacted. This strategic shift has worsened the ongoing GPU shortage, further straining the market dynamics surrounding GPU availability and demand.
Others like Intel, a stalwart in traditional CPUs, are expanding into AI, edge computing, and autonomous systems. A significant competitor to Intel in high-performance computing, AMD acquired Xilinx to offer integrated solutions combining high-performance central processing units (CPUs) and programmable logic devices.
Global Resolve Key to Address Shortages
Governments worldwide are boosting chip capacity to tackle the semiconductor crisis and fortify supply chains. Initiatives like the CHIPS for America Act and the European Chips Act aim to bolster domestic semiconductor production through investments and incentives. Leading manufacturers like TSMC and Samsung are also expanding production capacities, reflecting a global consensus on self-reliance and supply chain diversification. Asian governments are similarly investing in semiconductor manufacturing to address shortages and enhance their global market presence.
Japan is providing generous government subsidies and incentives to attract major foreign chipmakers such as TSMC, Samsung, and Micron to invest and build advanced semiconductor plants in the country. Subsidies have helped to bring greenfield investments in Japan’s chip sector in recent years. TSMC alone is investing over USD 20 billion to build two cutting-edge plants in Kumamoto by 2027. The government has earmarked around USD 13 billion just in this fiscal year to support the semiconductor industry.
Moreover, Japan’s collaboration with the US and the establishment of Rapidus, a memory chip firm, backed by major corporations, further show its ambitions to revitalise its semiconductor industry. Japan is also looking into advancements in semiconductor materials like silicon carbide (SiC) and gallium nitride (GaN) – crucial for powering electric vehicles, renewable energy systems, and 5G technology.
South Korea. While Taiwan holds the lead in semiconductor manufacturing volume, South Korea dominates the memory chip sector, largely due to Samsung. The country is also spending USD 470 billion over the next 23 years to build the world’s largest semiconductor “mega cluster” covering 21,000 hectares in Gyeonggi Province near Seoul. The ambitious project, a partnership with Samsung and SK Hynix, will centralise and boost self-sufficiency in chip materials and components to 50% by 2030. The mega cluster is South Korea’s bold plan to cement its position as a global semiconductor leader and reduce dependence on the US amidst growing geopolitical tensions.
Vietnam. Vietnam is actively positioning itself to become a major player in the global semiconductor supply chain amid the push to diversify away from China. The Southeast Asian nation is offering tax incentives, investing in training tens of thousands of semiconductor engineers, and encouraging major chip firms like Samsung, Nvidia, and Amkor to set up production facilities and design centres. However, Vietnam faces challenges such as a limited pool of skilled labour, outdated energy infrastructure leading to power shortages in key manufacturing hubs, and competition from other regional players like Taiwan and Singapore that are also vying for semiconductor investments.
The Potential of SLMs in Addressing Infrastructure Challenges
Small language models (SLMs) offer reduced computational requirements compared to larger models, potentially alleviating strain on semiconductor supply chains by deploying on smaller, specialised hardware.
Innovative SLMs like Google’s Gemini Nano and Mistral AI’s Mixtral 8x7B enhance efficiency, running on modest hardware, unlike their larger counterparts. Gemini Nano is integrated into Bard and available on Pixel 8 smartphones, while Mixtral 8x7B supports multiple languages and suits tasks like classification and customer support.
The shift towards smaller AI models can be pivotal to the AI landscape, democratising AI and ensuring accessibility and sustainability. While they may not be able to handle complex tasks as well as LLMs yet, the ability of SLMs to balance model size, compute power, and ethical considerations will shape the future of AI development.
Organisations in Asia Pacific are no longer only focused on employing a cloud-first strategy – they want to host the infrastructure and workloads where it makes the most sense; and expect a seamless integration across multiple cloud environments.
While cloud can provide the agile infrastructure that underpins application modernisation, innovative leaders recognise that it is only the first step on the path towards developing AI-powered organisations. The true value of cloud is in the data layer, unifying data around the network, making it securely available wherever it is needed, and infusing AI throughout the organisation.
Cloud provides a dynamic and powerful platform on which organisations can build AI. Pre-trained foundational models, pay-as-you-go graphics superclusters, and automated ML tools for citizen data scientists are now all accessible from the cloud even to start-ups.
Organisations should assess the data and AI capabilities of their cloud providers rather than just considering it an infrastructure replacement. Cloud providers should use native services or integrations to manage the data lifecycle from labelling to model development, and deployment.
In this Ecosystm Byte, sponsored by Oracle, Ecosystm Principal Advisor, Darian Bird presents the top 5 trends for Cloud in 2023 and beyond. Read on to find out more.
Download ‘The Top 5 Cloud Trends for 2023 & Beyond’ as a PDF
We are seeing a rise in social and environmental consciousness – especially in the younger generation. Their awareness of human rights, the environment and inclusion is growing exponentially – they want to create impact. Organisations are being driven to develop and demonstrate an Environmental, Social and Governance (ESG) consciousness in their actions and investments.
In this Ecosystm Snapshot, we cover some of the recent examples of how governments and individual advocates are creating a difference; and how financial organisations and tech providers are embracing ESG.
Read how organisations such as Sun Cable, Equinix, Microsoft, NVIDIA, Prologis, Zensung, Ergo, Munich Re, Natwest, JPMorgan, Credit Suisse and others are working to make the world a better place.
