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Go Deeper – Green AI: Navigating Asia’s Journey Towards Sustainable Artificial Intelligence

A comprehensive look at both the advancements and the challenges in integrating AI with environmental goals in the region.

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Green AI in Asia

TL/DR:

  • AI’s rapid growth in Asia brings environmental concerns due to its high energy consumption.
  • Green AI in Asia innovative solutions like energy-efficient hardware and renewable energy sources are being developed in the region.
  • Governments and communities play a crucial role in ensuring a sustainable, equitable, and ethical AI future.

Introduction

Artificial Intelligence (AI) is revolutionising industries across Asia, from smart cities to agriculture. However, its environmental footprint raises concerns about the region’s green aspirations. This article delves into the unique challenges and potential of AI’s environmental impact in Asia, while exploring innovative solutions and the role of governments and communities in shaping a sustainable AI future.

Asian Footprints, Western Precedents: The Data Revealed

The scale of AI’s energy consumption is staggering. Training a single large language model like Google’s PaLM, with its 540 billion parameters, can emit over 626,000 pounds of CO2, equaling five cars’ lifetime emissions.

Inference, the process of using these models for predictions, adds another layer, with daily estimates reaching 50 pounds of CO2 for an LLM, or a hefty 8.4 tons per year.

In Asia, Baidu’s Ernie-3.0 Titan language model, boasting 176 billion parameters, is no slouch in this energy race, highlighting the need for regional considerations (data is courtesy of arxiv.org)

Asian AI and the Carbon Conundrum

Asia’s rapid AI adoption intensifies the carbon issue. China, accounting for 27% of global AI investments, and India, with its projected $8 billion AI market by 2025, illustrate the region’s rapid embrace of this technology (statista.com, analyticsindiamag.com). From facial recognition systems in bustling metropolises to autonomous vehicles navigating crowded streets, these applications demand close examination of their energy footprint within the context of each nation’s energy mix and emission goals.

Beyond the Cloud: Asian Initiatives for Greener AI

Asia is not only facing the problem but also leading in finding solutions. Innovators across the region are developing cutting-edge technologies to reduce AI’s environmental impact.

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  1. Energy-Efficient Hardware: India’s Centre for Development of Advanced Computing (CDAC) is pioneering energy-efficient hardware solutions tailored for AI workloads. These innovations aim to decouple AI advancements from unsustainable energy practices (cdac.in).
  2. Green Data Centres: China’s Alibaba Cloud boasts its “Sustainable Computing Initiative,” utilising renewable energy sources and cutting-edge chip technologies to green its data centres (alibabacloud.com).
  3. Cooling Algorithms: Japan’s NEC Laboratories developed a machine learning algorithm that reduces data centre cooling energy consumption by up to 50%, a crucial innovation considering data centres in China alone consume 2.7% of the nation’s total energy (nec.com, China Academy of Information and Communications Technology).

Case Studies: Balancing Benefits and Challenges

1. Smart Agriculture: Balancing Efficiency with Energy Demand

Across Asia, the rise of smart agriculture promises both environmental benefits and challenges. AI-powered drones in Thailand, equipped with imaging technology, helped farmers reduce chemical pesticide use by 30% (World Resources Institute), a win for sustainability. However, these technologies necessitate energy for charging, data transmission, and cloud computing, potentially negating their ecological advantages. Finding ways to optimise energy consumption through AI itself, like NEC’s cooling innovation, is crucial for ensuring smart agriculture truly delivers on its green promise.

2. Facial Recognition: Security vs. Transparency and Sustainability

In China, vast networks of facial recognition cameras enhance public safety while raising concerns about energy consumption and data privacy. A single camera can consume up to 1,500 kWh per year, equivalent to a typical household fridge (South China Morning Post). Implementing facial recognition systems that leverage energy-efficient hardware and prioritise responsible data management, alongside exploring alternative security solutions, is crucial for mitigating the footprint and ensuring public trust.

3. Renewable Energy Integration: Powering AI with Clean Sources

The growing appetite of AI for energy necessitates a shift towards renewable resources. India’s National AI Strategy aims to power data centers with solar and wind energy, potentially reducing their carbon footprint by up to 80% (NITI Aayog). This not only reduces AI’s own emissions but also contributes to national clean energy goals. Japan’s NEC Laboratories have developed AI algorithms that optimise data center cooling, saving up to 50% in energy consumption (NEC). Such innovations pave the way for a more sustainable and efficient future for AI infrastructure.

Policy Catalysts: Steering AI Towards Sustainability

Governments across Asia are implementing initiatives to promote energy-efficient AI and address the environmental concerns associated with AI growth.

  1. Green Data Centres: Singapore’s Green Data Centre initiative incentivises energy-efficient data centre operations, promoting the adoption of best practices in design, build, and operation of data centres to reduce energy consumption and environmental impact.
  2. Ethical AI Guidelines: South Korea’s Ministry of Science and ICT has established ethical AI guidelines, emphasising the importance of transparency, accountability, and fairness in AI development and deployment, which can indirectly contribute to more sustainable AI practices.

Eco-friendly AI: Where Will the Green Path Lead For AI in Asia

Imagine a future where AI isn’t just a power-hungry consumer, but an environmental guardian. Imagine AI-powered drones planting trees at a rate exceeding deforestation, their movements optimised by algorithms trained on satellite imagery. Envision city-wide energy grids, seamlessly integrating renewable sources with the help of AI algorithms predicting demand and fluctuations (World Economic Forum). These scenarios, once science fiction, become increasingly plausible with rapid advancements in green AI research.

The Role of Green AI in Asian Startups and Innovation Hubs

Asia’s thriving startup ecosystem is playing a significant role in driving sustainable AI innovation. Entrepreneurs are developing creative solutions to address AI’s environmental impact, from AI-powered energy management systems to algorithms that optimise resource allocation. For example, Hong Kong-based startup, Green Earth Energy, uses AI to optimise solar panel performance, maximising clean energy generation.

Doing The Right Thing: Navigating Bias and Data Justice

The promise of a greener future through AI cannot be separated from ensuring ethical development and deployment. Biases embedded in training data can perpetuate environmental injustices, favoring urban centers with resource-intensive AI applications while neglecting rural communities grappling with climate change impacts. Studies show facial recognition algorithms struggle with darker skin tones, raising concerns about discriminatory surveillance practices in vulnerable communities (MIT Technology Review). Addressing these ethical issues through diverse data sets, transparent algorithms, and community inclusion is crucial for a truly green and equitable AI future.

The cost of greening AI technologies can be substantial, yet the long-term economic benefits, such as energy savings and increased efficiency, can offset initial investments. A study by the Asian Development Bank (ADB) highlights that sustainable AI practices could boost Asia’s economy by enhancing productivity while preserving environmental integrity.

Ethical dimension of AI deployment, encompassing issues like data privacy, equitable access, and social impact, is gaining prominence. Initiatives like India’s AI ethics guidelines underscore the need for a balanced approach that considers both human and environmental welfare.

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Embracing Cross-Cultural Perspectives to Achieve an AI Environmental Impact

Asia’s diverse landscape necessitates a nuanced approach to green AI. China’s centralised governance model contrasts with India’s decentralised ecosystem, requiring tailored policy frameworks and solutions. Culturally specific concerns, such as data privacy in Japan and resource extraction in Indonesia, need to be addressed within local contexts. Sharing best practices across borders and fostering regional collaboration can bridge these gaps and accelerate progress towards shared environmental goals.

Empowering Communities to Takes Center Stage for a Green AI in Asia

Green AI isn’t merely a top-down technological solution; it demands active participation from the communities it impacts. Open-source AI platforms like TensorFlow and PyTorch empower local communities to develop their own solutions and monitor environmental impacts using sensor networks and citizen science initiatives. Imagine farmers in rural Thailand utilising AI-powered soil analysis tools developed by their peers, optimising water usage and crop yields while minimising environmental footprint (FAO). Such grassroots innovations hold immense potential for a sustainable and inclusive AI future.

Data-Driven Insights, Visual Clarity:

To effectively communicate the complexities of AI’s environmental impact and potential, compelling data and visuals are critical. Charts illustrating the projected reduction in carbon footprint from China’s AI policy (NITI Aayog) or images showcasing AI-powered robots cleaning plastic from polluted rivers can make the abstract tangible and impactful. Engaging infographics and data visualisations can further enhance the article’s accessibility and inspire action.

By exploring these additional dimensions, we gain a holistic understanding of the challenges and opportunities shaping AI’s environmental future in Asia.

It’s clear this journey requires not just technological advancements, but also ethical considerations, cross-cultural collaboration, and the active participation of empowered communities.

Only then can we ensure that the path towards a greener future with AI is truly inclusive, sustainable, and bright.

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Will AI Take Your Job—or Supercharge Your Career?

AI-driven job disruption is already here. Discover practical steps for workers in Asia to stay employable, relevant, and ready for the future.

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AI job disruption

TL;DR – What You Need to Know in 30 Seconds

  • Generative AI is already reshaping careers, causing job losses in industries from finance to creative roles.
  • Workers must continually upskill, strategically plan career moves, and focus on roles AI complements rather than replaces.
  • Companies and governments must significantly increase retraining efforts to help workers adapt effectively.

Is AI About to Steal Your Job? Here’s How to Stay Ahead in Asia

For many, AI started as a helpful assistant for menial tasks, quick research, or even generating funny memes. But today, it’s taking a serious turn, reshaping industries, displacing jobs, and changing careers overnight.

Just ask Jacky Tan. After thriving for over 15 years as a freelance marketing consultant in Singapore, Jacky found his livelihood disrupted—not just by the pandemic—but by generative AI tools like ChatGPT, which empowered his clients to produce their own content. The result? Jacky, along with countless others, faced a stark choice: adapt quickly or risk becoming obsolete.

Jacky pivoted completely, leaving marketing to open a successful home-based food business, CheekyDon, specialising in Japanese rice bowls. But not everyone can—or will—reinvent themselves so easily. As AI continues to infiltrate the workforce, what can you do to ensure you’re prepared?

Job Disruption: More Real Than Ever

It’s no longer theoretical. Meta, ByteDance, DBS Bank, Grab, and Morgan Stanley have all announced layoffs or workforce reshuffling directly linked to AI-driven efficiencies. Analysts predict as many as 200,000 banking jobs globally could vanish within five years due to AI, highlighting sectors like finance, customer service, risk management, and tech as especially vulnerable.

The numbers don’t lie: The World Economic Forum anticipates 11 million new AI-related jobs globally by 2030—but 9 million existing roles will disappear. And the shift won’t just hit repetitive tasks. Highly skilled roles like writers, programmers, PR professionals, and even legal experts face substantial disruption.

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Why AI Displaces Jobs—and Creates New Ones

Here’s the paradox: while AI promises increased productivity, it often leads to job losses because current skills don’t match the needs of new AI-augmented roles. Retraining existing workers is crucial but challenging. In places like Singapore, where skilled workers are scarce, companies struggle to balance the speed of AI integration with retaining talent.

The good news? Jobs involving deep human interactions, emotional intelligence, strategic thinking, or managing AI tools themselves remain safer—for now.

How to Stay Relevant in an AI-Dominated Market

So, how can you protect your career from being displaced by AI? Here are actionable steps tailored for the rapidly shifting Asian job market:

1. Continuous Upskilling Is Non-Negotiable
The days of one-off training are over. Commit to lifelong learning by acquiring skills in AI-related fields, from data analytics to AI management tools. Invest in soft skills—like critical thinking, empathy, and strategic communication—which AI struggles to replicate effectively.

2. Proactively Plan Your Next Career Move
Ask yourself, as EY’s Samir Bedi suggests: “What am I upskilling for?” Plan two or three career steps ahead, not just for immediate skill gaps. Explore lateral career transitions that diversify your skillset, making you versatile across industries.

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3. Look for Roles Complemented by AI, Not Replaced by It
Jobs with tasks AI can augment rather than entirely replace—like managing automated systems, strategic marketing, or roles that require significant human touchpoints—are safer bets.

Employers Must Step Up, Too

The responsibility doesn’t rest solely on workers. Companies must actively retrain employees to handle AI disruptions effectively. Currently, only around half of Singaporean workers feel their employers provide sufficient training opportunities. Organisations that actively support their teams through retraining will reap long-term rewards, maintaining both institutional knowledge and market reputation.

Asia’s Workforce at the Crossroads

We’re facing nothing less than the Fourth Industrial Revolution, driven by generative AI. Unlike previous waves of automation, AI can replace tasks once thought too complex or creative for machines. But remember, while AI might take your current role, it also opens doors to entirely new career paths—provided you’re ready to step through them.

Are you prepared to let AI shape your future—or will you shape your own future with AI? Let us know in the comments below!

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The Three AI Markets Shaping Asia’s Future

Explore the three interconnected AI markets shaping Asia’s technological landscape—traditional AI, training infrastructure, and enterprise solutions—and discover how each drives innovation.

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AI markets

TL;DR – What You Need to Know in 30 Seconds

  • AI isn’t one monolithic market—it’s three interconnected segments:
  • 1. Pre-GenAI (traditional AI): Fundamental techniques that underpin data-driven solutions.
  • 2. AI Training Market: Resource-intensive frontier models driving the next AI breakthroughs.
  • 3. Enterprise AI Market: Real-world applications delivering measurable business outcomes.
  • Understanding their interplay is critical for Asian businesses aiming to maximise ROI from AI investments.

Are We Missing the Bigger Picture in the AI Race?

From smarter chatbots to insightful analytics, AI’s not one market—it’s three interconnected ones, each shaping how Asia leverages technology.

If you’ve spent any time recently skimming headlines about artificial intelligence, you’d be forgiven for thinking that generative AI is the only show in town. But AI isn’t just ChatGPT, Midjourney, or flashy avatars of celebrities endorsing your new favourite tech gadget. Behind the scenes, three distinct but intertwined markets are at play: the Pre-GenAI Market, the Training Market, and the Enterprise AI Market.

But what exactly are these three markets, and why should Asian businesses care?

Let’s unpack them one by one and understand how they converge to drive the future of innovation across Asia.

1. The Pre-GenAI Market: The Building Blocks of AI

Generative AI may be the current media darling, but the roots of AI go far deeper. We’re talking about traditional AI—technologies like machine learning (ML), reinforcement learning, and computer vision. These foundational techniques have been quietly evolving for decades, long before ChatGPT ever typed out its first response.

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Contrary to popular belief, traditional AI hasn’t lost its relevance—far from it. In fact, the rise of generative AI has amplified its importance. Why? Because generative AI feeds on data often produced by traditional AI methods. For instance, Dell Technologies frequently uses machine learning to streamline supply chains or improve factory efficiency. These methods don’t get less important just because GPT-5 is around the corner—they become essential.

In short, traditional AI is like rice in Asian cuisine—fundamental, reliable, and always necessary, no matter what fancy new dish appears on the menu.

2. The Training Market: Powering AI’s Frontier

Next up is the AI training market—think of it as AI’s heavy lifting division. This market is dominated by big names you’ll recognise (OpenAI, Google DeepMind, Nvidia, Meta) who are making gigantic investments in infrastructure to create foundational AI models. Picture rows and rows of servers, massive GPU clusters, and sprawling data centres, humming 24/7.

These frontier models—like GPT-4 or Gemini—require immense computational resources. This isn’t just about bragging rights; it’s about pushing the boundaries of what AI can do. The innovations here spill directly into practical tools businesses use every day, like AI-driven coding assistants or creative platforms for content creation.

In Asia, we’re seeing heavy investment in this market too. Take Singapore’s AI supercomputing initiatives or China’s Baidu and Alibaba building mega-AI clusters. These moves aren’t just technological vanity—they’re strategic investments in the future.

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3. The Enterprise AI Market: Real-World Results

And then there’s the enterprise AI market, arguably the most pragmatic of the three. Enterprises aren’t racing to build the next ChatGPT killer. Instead, they’re laser-focused on AI that solves real business problems—like optimising inventory management, enhancing customer support, or boosting marketing effectiveness.

Unlike the flashy training market, the enterprise market moves slower but deliberately. Enterprises demand reliability, compliance, and measurable outcomes—exactly the opposite of the ‘move fast and break things’ mentality we see in frontier AI research.

Across Asia, the enterprise AI market is thriving precisely because it offers clear returns. Banks in Indonesia deploy AI-driven chatbots to handle customer queries efficiently. E-commerce giants in Vietnam and Thailand integrate predictive analytics to forecast inventory and customer demand. It’s AI that’s practical, measurable, and directly linked to ROI.

How These AI Markets Interconnect

Here’s the real takeaway: These three markets aren’t isolated islands; they’re deeply interconnected ecosystems.

Traditional AI gathers and prepares the essential data. The training market produces foundational AI models and cutting-edge tech innovations. Enterprises then integrate both, using these tools and data to transform operations and customer experiences.

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Think about it this way: traditional AI builds the roads, the training market crafts powerful engines, and the enterprise market drives the cars, delivering real-world value. Without any one of these, the system falters.

For instance, enterprises use AI-powered data agents to analyse massive datasets prepared by traditional AI methods. They then leverage frontier AI models (like generative AI) trained in data centres to extract actionable insights. The whole system is interdependent—each component driving progress in the other.

Why Does This Matter to Asia?

Asia is a unique melting pot of digital maturity, economic growth, and competitive intensity. Understanding these three markets isn’t just academic—it’s crucial for businesses looking to harness AI’s full potential.

For instance, enterprises in Southeast Asia’s rapidly expanding digital economy (expected to hit $263 billion GMV by 2025 according to Google’s recent e-Conomy SEA 2024 report) need practical AI solutions that deliver immediate business value. On the other hand, countries like Singapore, South Korea, and Japan are leading investments into the training market, building the infrastructure needed to power Asia’s next generation of AI innovations.

Simply put, knowing how these three AI markets interact helps Asian businesses invest smarter, act faster, and innovate effectively.

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As we look ahead, Asia is uniquely positioned to benefit from understanding this AI ecosystem deeply. Whether you’re in manufacturing, finance, e-commerce, or healthcare, your business will inevitably interact with all three markets—whether you realise it or not.

Now, here’s something for you to ponder (and comment below!):

Which of these AI markets do you think will dominate Asia’s tech landscape by 2030? Will traditional methods endure, frontier models take over, or will enterprise solutions reign supreme?

We’d love to hear your thoughts.

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Embrace AI or Face Replacement—Grab CEO Anthony Tan’s Stark Warning

ChatGPT now generates previously banned images of public figures and symbols. Is this freedom overdue or dangerously permissive?

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Anthony Tan AI Grab

TL;DR – What You Need to Know in 30 Seconds

  • Grab CEO Anthony Tan believes workers and companies that don’t embrace AI risk being replaced by those who do.
  • Grab paused normal operations for a nine-week generative AI sprint, significantly boosting innovation.
  • AI tools developed by Grab, such as driver and merchant assistants, are empowering everyday entrepreneurs.
  • Globally, many companies are downsizing due to AI, but Tan insists AI enhances human capabilities rather than replacing them.

Is Your Refusal to Embrace AI Secretly Sealing Your Fate?

Anthony Tan, co-founder and CEO of Grab—the Southeast Asian super-app that transformed regional transport, food delivery, and financial services—has made a bold and slightly unsettling prediction: “Humans who don’t embrace AI will be replaced by humans who embrace AI.”

In other words, whether you’re a company or an individual, ignoring AI isn’t merely shortsighted—it’s career suicide.

But before we panic, what exactly does Tan mean?

Making Humans ‘Superhuman’

Speaking at Converge Live in Singapore, Tan explained to CNBC’s Christine Tan that AI isn’t just a fancy tech upgrade. Instead, it’s a crucial tool to “make you superhuman” by significantly boosting productivity and freeing up valuable time.

Tan himself isn’t just preaching—he’s practising. Despite not being a coder, he’s enthusiastically using AI coding assistants for personal and professional projects. He claims AI has radically changed his productivity, helping him accomplish things previously impossible.

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I can’t code myself, but I use AI to build my own projects, for research, for Grab,” Tan explained. “It totally changes how you spend your time.
Anthony Tan, Grab CEO
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Grab’s Radical AI Experiment

Grab didn’t stop at encouraging individual AI adoption. Instead, the company took it to a whole new level, implementing an ambitious, company-wide nine-week “generative AI sprint”.

This meant putting all regular business on pause to explore AI-driven solutions across the entire company. As Tan humorously admitted:

People thought I was crazy—maybe I am—but it really moved the needle.
Anthony Tan, Grab CEO
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During this sprint, Grab developed powerful AI tools, including:

  • Driver Co-pilot: An AI assistant reducing wait times and boosting job opportunities for drivers.
  • Merchant AI Assistant: Imagine a single mother in Jakarta now equipped with an AI-driven sous chef, packaging expert, and even a chief revenue officer—all in one assistant. This innovation isn’t just about efficiency; it’s empowerment, reshaping the livelihoods of Grab’s vast network of entrepreneurs.

The Wider Implications for Asia

This isn’t just a Grab-specific phenomenon. According to the World Economic Forum’s 2025 Future of Jobs Report, 40% of employers globally plan to downsize due to AI, and a whopping 86% anticipate AI reshaping their businesses by 2030.

Asia, in particular, with its digitally fluent workforce and vibrant entrepreneurial scene, stands uniquely poised to lead this transition. Grab’s aggressive AI strategy under Tan’s leadership could become a model for businesses across Southeast Asia, showcasing how AI can be harnessed responsibly and productively.

Human vs AI: Not a Zero-Sum Game

Tan stresses AI shouldn’t evoke fear—it should inspire excitement. AI adoption isn’t about machines replacing humans. It’s about humans becoming irreplaceable by effectively harnessing these tools.

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If you’re reluctant or sceptical, Anthony Tan’s message is clear: embrace AI now, or watch as those who do leave you behind.

Hot Take

Anthony Tan might sound dramatic—but he has a point. If you’re not actively exploring AI, you’re preparing yourself (and your company) to become obsolete. The clock is ticking: Will you adapt, or will you become the adaptation?

What do you think?

Are you inspired or intimidated by Anthony Tan’s AI-driven future? Drop your thoughts below!

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