Everyone is talking AI. Everyone is building AI.

The fact is, we are now living in an AI-ready and AI-fuelled world. Some businesses are just starting to dip their toes into AI, while others are already using it to drive innovation and shape strategy.

But here’s a shocking truth: Over 85 per cent of AI projects fail.

And as business leaders, what could you do to avoid the 85 per cent?

AI projects crash and burn

AI isn’t the magic bullet it’s sometimes made out to be. A report from Gartner highlights that a staggering 85 per cent of AI projects fail to meet expectations, leaving companies with little to show for their efforts – and sometimes, a lot less in the bank.

New research from data management platform Qlik reveals that 11 per cent of UK businesses have more than 50 AI projects stuck in the planning stage. Meanwhile, 20 per cent have had up to 50 projects progress beyond the planning phase, only to be halted or cancelled due to various setbacks.

“AI has the potential to impact nearly every industry and department, but it’s not universally applicable,” says James Fisher, Chief Strategy Officer at Qlik.

The key factors? 

Poor data quality, inadequate data availability, and a lack of understanding about AI’s requirements are at the core of many failures. AI models, without access to clean, relevant, and accurate data, simply cannot perform. According to a 2023 McKinsey report, 70 per cent of AI projects fail because of issues with data quality and integration.

Data, however, is not the only case.

Define your mistakes and own up to it

The data kryptonite

The recent public debacle involving law firm Levidow, Levidow & Oberman serves as a cautionary tale. The firm used ChatGPT to generate legal opinions, which contained fake quotes and citations. The results were disastrous: the firm faced legal fines and a PR nightmare. The firm and its lawyers “abandoned their responsibilities when they submitted non-existent judicial opinions, then continued to stand by the fake opinions after judicial orders called their existence into question,” a judge said in a June ruling, which also levied a US$5,000 fine.

If there’s one thing I learned from my time integrating generative AI chatbots like ChatGPT, Gemini, or Claude, even the free and the upgraded, premium, newest version, one problem always stuck with me – their data is never up to date. AI models can only work with the information they are trained on, and if that data isn’t accurate or current, the results will be unreliable.

“AI applications are only as good as the data they are trained on,” says Troy Demmer, co-founder of Gecko Robotics. “Trustworthy AI requires trustworthy data inputs.”

Also Read: How AI helped me build a seven-figure side hustle while healing

Worldwide data creation is projected to grow to more than 180 zettabytes by the end of 2025. With so much data available, possessing quality data starts with having a complete picture of the information generated by your organisation. These issues also highlight the need for meticulous data management and strategic planning, including the integration of cloud-based models and large language models (LLMs).

Rising cost, insufficient funding

Generative AI tools might appear cheap and accessible at first. But when companies move from pilot projects to full-scale deployments, the costs quickly spiral out of control. Gartner estimates that a retrieval-augmented generation (RAG) AI document search project can cost up to US$1 million to deploy, with recurring costs of up to US$11,000 per user annually. In more specialised fields like medical or financial AI models, costs can exceed US$20 million.

Not to mention, 42 per cent of companies face setbacks due to inadequate funding or resource allocation.

AI isn’t cheap, and pilot projects that produce no value can be money pits. You want to integrate feature A in your AI bots, while still maintaining feature B, and oh, let’s add in feature C. And that will be, say, another US$30,000 to US$300,000 more.

And we don’t even know if it will work that well.

Chasing shiny and unrealistic expectations

Thanks to the crazy hype, every business leaders are now seeing AI as a magic bullet. If it’s not “AI-powered” or ‘using AI”, they don’t want it.

Expectations often exceed what AI can deliver, leading to frustration when the technology fails to meet the hype. As Ajgaonkar, CTO of product innovation at Insight, points out, some leaders expect AI to magically predict things like pricing without considering the complex data preparation and training required.

The key to machine learning success is constant tuning. “In AI engineering, teams often expect too much too soon,” explains Shreya Shankar, a machine learning engineer at Viaduct. “They don’t build the infrastructure needed to continually test and improve the system.”

Business leaders often expect AI to effortlessly analyse historical data, pull relevant insights, and apply them to new customer requests, such as predicting purchasing behaviour based on past transactions. Instead of doing the necessary groundwork – cleaning data, testing, and retraining models to ensure accurate results – they’re eager to jump straight to the end goal, bypassing the critical steps that drive success.

This, in turn, leads to unrealistic expectations.

The real key to machine learning success is something that is mostly missing from generative AI: the constant tuning of the model. It’s all the work that happens before and after the prompt, in other words, that delivers success.

Siloed teams, failed collaboration: The blind leading the blind

No one really noticed this,

But the common cause of AI failure isn’t really about the technology, sometimes, it’s about the people.

This starts with the people at the top – and their view on AI.

Also Read: How AI helped me build a seven-figure side hustle while healing

Business leaders frequently misinterpret the problems AI is supposed to solve. Many executives also have inflated expectations, fuelled by the hype around AI from sales pitches and flashy demos. They underestimate the time, resources, and careful planning needed for AI to succeed. Often, models are delivered at only 50 per cent of their potential due to shifting priorities and unrealistic timelines.”

Deloitte found that 40 per cent of companies struggle because their technical and business teams aren’t aligned. Even if the AI model works technically, if these teams don’t work together, the project often fails to deliver tangible value to the business. Additionally, many engineers and data scientists are drawn to the latest technological trends, even when simpler solutions would suffice.

Teams may spend time on cutting-edge technologies that don’t necessarily address the core issue.

Check the boxes: The five phases of AI readiness

No matter the size of your business, don’t panic.

If you’re feeling uncertain about your AI product (still), there’s a simple way to check your progress.

Just run through the five phases of AI readiness. If you’ve ticked all the boxes, you’re on the right path.

Awareness: The knowledge bases

At this stage, your goal is to build awareness of AI and how it can be applied to your industry. Educate leadership through workshops and seminars, research AI use cases for your organisation, and identify where AI can solve real business problems. Research shows that 60 per cent of organisations are still in this phase, with no formal AI initiatives in place.

• A manufacturing company exploring AI might find that predictive maintenance could reduce downtime by 20-30 per cent, saving millions annually. But first, they need to understand the basics of how AI works.

Exploration: Start small

In this phase, businesses experiment with small-scale, low-risk AI projects to demonstrate its potential. These pilot projects are often low-cost and involve small teams (e.g., one data scientist and one engineer). Gartner reports that 25 per cent of companies in this phase see measurable returns within six months of starting AI pilots.

A focused, straightforward pilot helps secure stakeholder buy-in, provides early insights to refine your strategy, and sets the stage for more complex AI applications in the future.

Operationalisation: Building scalable infrastructure

Once you’ve moved beyond pilots, it’s time to build the infrastructure needed for scalable AI adoption. This includes setting up governance structures, ensuring data privacy, and deploying AI in real-world use cases.

Establish an AI Center of Excellence (CoE), create scalable data platforms like data lakes, and develop policies for compliance. McKinsey reports that companies in this phase see a 20 per cent improvement in operational efficiency.

•  Use AI-powered routing to escalate critical cases, such as VIP churn risks or sensitive issues, while allowing AI to handle routine queries. By setting clear business rules, AI can make accurate distinctions between scenarios and smoothly hand off more complex cases to human agents, ensuring the right support at the right time. Liberty London uses AI to direct customer service inquiries based on agent skillset and customer intent, streamlining the process. This approach resulted in a 73 per cent reduction in first reply time and a nine per cent boost in customer satisfaction.

Also Read: 5 reasons why impact investing is becoming mainstream investing

Proficient: Making AI a part of everything

AI becomes part of everyday operations. Businesses establish systems to monitor the performance and fairness of AI models while training employees to use AI tools effectively. AI solutions are scaled across departments, and employees are trained to integrate AI into their daily roles.

The crucial element of AI readiness here is human involvement. By analysing both AI-resolved and human-assisted issues, you can gain a comprehensive view of performance. Track key metrics, such as automated resolution rates, human escalation frequency, and customer satisfaction, to refine and improve the process.

Leader: An “AI-first” culture

The final phase is where businesses fully integrate AI into their core strategy, operations, and innovation. Companies at this level use cutting-edge techniques like generative AI and autonomous systems to drive competitive advantage. They foster an AI-first culture through continuous employee up-skilling.

Only 10 per cent of organisations are at this stage, but they account for 70 per cent of all economic gains from AI.

Be part of the 15 per cent, not the 85 per cent

This isn’t a one-size-fits-all solution for every business leader, nor is it the ultimate guide to creating a perfect AI model or product for your company.

But there is one thing you, as a business leader, can learn from.

Success doesn’t hinge on avoiding failure—it’s about learning from it and adapting.

If your business is struggling with AI, the problem may not lie with the technology itself, but with how it’s integrated into your organisation. Take a step back, and check the boxes. The key to AI success starts with a solid foundation: ensuring alignment between your teams, setting realistic expectations, and creating the right infrastructure to support growth.

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Small and mid-sized enterprises (SMEs) in Singapore and across the globe are quickly adopting artificial intelligence (AI) into their operations, yet a significant capability gap persists, with 95 per cent of SME decision-makers admitting they need more training to effectively leverage the technology.

This paradox comes despite 72 per cent of these leaders considering themselves AI experts.

Also Read: SMEs struggle to turn data into decisions, says OpenMinds CEO Jan Wong

The findings stem from a global TeamViewer survey, which polled 1,400 business leaders, including 427 from SMEs. The research, conducted in October 2024, highlights a critical challenge for the rapidly evolving digital economies, particularly in Southeast Asia, where SME agility is a key growth driver.

The AI paradox: High adoption, low maturity

While AI is firmly established on the SME agenda, its integration often lacks depth. A notable 86 per cent of SME leaders are comfortable with employees outside of IT using AI tools.

However, this widespread comfort does not always translate into frequent usage. Only every third SME respondent uses AI daily, and just 16 per cent report weekly use.

Despite less frequent use, SMEs surprisingly perceive themselves as more AI-mature than larger enterprises. Thirty-five per cent of SME decision-makers describe their AI usage as “very mature,” compared to only 22 per cent of larger organisations. This disparity between perception and actual proficiency underscores the urgent need for targeted training and support.

The stakes: Automation gaps and business optimism

The consequences of inaction weigh heavily on SMEs. For 28 per cent of SME decision-makers, the biggest fear is increased operational costs due to missed opportunities for automation. This concern diverges from the broader business community, where falling behind competitors (cited by 26 per cent) was the primary worry.

Despite these challenges, optimism about AI’s potential remains high. Seventy-two per cent of SME leaders expect AI to drive the most significant productivity surge of the century, with 76 per cent seeing it as essential for overall business performance. Furthermore, 70 per cent believe AI can help expand job opportunities for parents and caregivers.

Overcoming hurdles: Skills, security, and infrastructure

The report identifies several persistent barriers slowing down AI maturity for SMEs:

Insufficient AI training: More than a third of leaders (38 per cent) cite this as the main obstacle.

Security concerns: A significant 74 per cent are worried about data management risks. Moreover, 65 per cent state they only use AI tools within tightly controlled security frameworks.

Lack of confidence in risk management: A telling 77 per cent admit they would not bet a week’s salary on their organisation’s ability to effectively manage risks like unauthorised AI tool usage.

Infrastructure readiness: Nearly half of SME decision-makers (47 per cent) report not having the necessary systems in place to scale AI as quickly as they would like.

Southeast Asia’s unique challenge and investment outlook

For Southeast Asian SMEs, the diverse digital maturity across countries and sectors presents a unique challenge, necessitating tailored AI strategies. The region’s economic growth is heavily propelled by the agility and innovation of its SME sector, making timely and effective AI adoption crucial for national digital economies.

Also Read: A new insights attitude for SMEs in the era of the ‘insights engine’

Despite the current hurdles, momentum for AI investment is building. Three in four (75 per cent) SME leaders plan to increase their AI investment in the next 12 months, with the same proportion expecting this rise within the next six to twelve months. This signals a clear intent to move from experimental adoption towards more advanced implementation.

Bridging the gap: TeamViewer’s solution

Companies like TeamViewer are stepping in to help SMEs bridge this capability gap. TeamViewer CoPilot, a digital assistant integrated into remote support sessions, helps IT agents stay focused, move faster, and make better decisions. This practical solution aims to improve IT efficiency, reduce downtime, and raise service quality without adding complexity or requiring additional resources.

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If there’s one thing 2025 reminded me of, it’s that automation isn’t about replacing people — it’s about reclaiming presence.

For almost five months, I ran the Speakers Society Accelerator manually. That might sound strange coming from someone who built her entire ecosystem around AI and automation. But this programme was different.

Unlike my previous ventures, which focused on marketing, business systems, and technology, this one revolved around speaking. And speaking means people. Human emotion, interaction, connection. I couldn’t rely on data alone; I needed to feel how the audience moved, thought, and responded.

So I did something very un-automated. I launched with a minimal viable system — enough structure to run smoothly, but intentionally manual, so I could observe the process in real-time.

The turning point

After months of repeating onboarding flows, follow-ups, and communication loops, I started noticing patterns. The same actions. The same touchpoints. The same responses.

And that’s when it clicked: If I’m doing the same thing more than twice, it’s time to automate it. That decision reshaped the second half of my year.

Building the funnel that runs itself

I revisited my roots in systems design — mapping how information, workflows, and human decisions flow together. That’s when I realised the real power lies in creating a centralised ecosystem where every interaction informs the next move. For me, that ecosystem came to life through Unify, the marketing automation platform I built under People’s Inc. 360, which now acts as the quiet engine behind it all.

AI didn’t just make things faster. It made things clearer.

It allowed me to see which actions actually mattered and where human touch truly made a difference.

Also Read: AI isn’t just automation – it’s a mirror of how we should learn

Surprisingly, one of my biggest learnings was this: Not every message needs to come from me.

There’s a difference between communication and connection. Some updates only need to be delivered — others need to resonate. Automation handles the first; I handle the second.

Here’s how the system evolved:

• Entry point: Every lead starts from a single opt-in page — whether through our automated webinar, challenge signup, or social funnel. The moment someone signs up, unify tags and segments them by intent and engagement.
• Warm-up sequence: A five-email automation sequence kicks in. It blends storytelling (to build trust) with clear CTAs — either to join a masterclass, book a clarity call, or download a speaker guide.
• Conversion layer: If they engage (open/click), Unify automatically triggers the next phase — pushing them toward a personalised CTA. For non-responders, they enter a “soft nurture” path that re-engages after seven days through reminders or social proof snippets.
• Post-conversion flow: Once they sign up for the Speakers Society Accelerator, automation takes over: onboarding emails, WhatsApp follow-ups, and a Telegram welcome message. Each step mirrors a real conversation but runs entirely on autopilot.
• Community retargeting: Every 30 days, inactive leads receive a “reconnect” flow — highlighting new events, case studies, or free trainings to bring them back into the ecosystem.

The outcome? By the time someone books a call or joins a programme, they’ve already gone through 10+ meaningful touchpoints — without me typing a single message. That’s how the funnel doesn’t just sell — it builds trust at scale.

Reclaiming creativity and connection

As the systems began running smoothly, I found myself with something I hadn’t had in a while — time.

Time to focus on creativity, storytelling, and community. Time to think deeply about the future of engagement, not just the mechanics of it. With less administrative drag, I could finally pour energy into what matters most: Building genuine relationships and creating transformative experiences.

That clarity has now influenced how I approach my other ventures. With the right insights, we’re designing engagement loops that encourage programme completion, because most people don’t fail due to bad curriculum; they simply stop before finishing. Automation helps ensure they don’t fall through the cracks.

Also Read: How AI and automation are shaping the future of work

The bigger picture

This shift goes beyond one business or funnel. Across the ecosystem, we’re seeing founders embrace AI not just for efficiency, but for humanity. The irony is beautiful — the more we automate, the more room we create to connect.

In my world, AI and automation are no longer about doing more — they’re about doing better. They help me spend time where it truly counts: With people, not processes.

My takeaway for 2025

If you make all the money in the world but have no time to enjoy it, you’ve built a trap — not a business. Automate so you can live.

Because freedom of time, wealth, and happiness isn’t about how much you build, but how well you systemise the things that don’t need your soul.

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H3 Zoom, a Singapore-based deeptech startup specialising in AI-powered inspection and asset management for critical infrastructure, has announced a US$1.8 million first close in its Series A funding round.

The investment was led by JRE Ventures, the corporate VC arm of JR East, with participation from SGInnovate and M7 Holdings.

The capital is earmarked to significantly expand H3 Zoom’s Asian footprint, with an immediate focus on Japan and Hong Kong, while simultaneously deepening current operations in Singapore.

Also Read: Transforming asset inspections: How WaveScan’s smart sensors and AI are shaping predictive maintenance

The funding will also accelerate product R&D, as well as strengthen engineering and go-to-market teams.

Shaun Koo, CEO and founder of H3 Zoom, commented: “This funding allows us to turn building data into actionable maintenance decisions at scale — shortening inspection cycles, reducing rectification costs, and improving public safety.”

Founded in 2016, H3 Zoom combines proprietary vision language models and robotics-enabled data capture to help asset owners and operators transition from manual, labour-intensive inspections to scalable, data-driven solutions.

Through the use of drone capture and automated defect analytics, the platform delivers tangible efficiency gains. This includes lowering operational and access costs compared to traditional inspection methods, improving safety by reducing the need for prolonged work-at-height activities, and standardising reporting for the Building and Construction Authority’s Periodic Façade Inspection (BCA PFI) submissions with greater traceability.

Its technology has been deployed across multiple high-rise buildings, transport assets, and critical infrastructure within Singapore.

For the JR East Group, ensuring the safe and sustainable operation of diverse assets—including railways, stations, commercial facilities, and hotels—is a shared priority. JRE Ventures intends to accelerate H3 Zoom’s business expansion and proof-of-concept activities in the Japanese market, while actively exploring broader collaboration opportunities across Southeast Asia.

Also Read: Operva AI elevates facade inspections with autonomous drone, collaborative cloud platform

Looking ahead, the startup is fast-tracking its AI roadmap, which includes launching the AI Engineering Co-Pilot, deepening multimodal workflows (360° imagery with voice notes), and strengthening enterprise-grade APIs. The company is also piloting robotics-assisted inspections to surface latent defects more safely.

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NTT DOCOMO Ventures and NTT Finance Corporation today announced at the NTT Startup Challenge Pitch Day event in Jakarta that they will jointly establish a new investment vehicle, Synexia Ventures.

The investment vehicle will be established on December 15 and aims to strengthen collaboration with promising startups in Southeast Asia (SEA). For Japanese companies, the region is increasingly transforming from a manufacturing base into a hub for innovation creation.

Synexia Ventures will target startups in countries such as Singapore, Indonesia, Malaysia, and the Philippines, focusing on fields that are NTT Group’s strategic priorities: AI, IoT, smart cities, robotics, and drones.

Based in Singapore, it will be jointly managed by Kuan Hsu (General Partner of KK Fund), along with NDV and NTT Finance. Kuan Hsu will lead on-the-ground operations in SEA, leveraging his deep VC experience in the region while working closely with NDV and NTT Finance’s investment teams.

Also Read: H3 Zoom lands US$1.8M to accelerate AI-powered building inspections in Japan, HK

Since 2017, NTT Group has promoted collaboration with SEA startups through the NTT Com Startup Challenge, organised by NTT DOCOMO BUSINESS. In response to the strong momentum for new business creation among NTT Group companies in Southeast Asia, the programme was expanded in 2024 to encompass the entire NTT Group and evolved into the NTT Startup Challenge.

On November 11, at the NTT Startup Challenge Pitch Day in Jakarta, approximately 1,200 startups from across Asia interested in collaborating with the NTT Group have applied. Ten startups selected from among the applicants will present their businesses and collaboration proposals with NTT Group.

NDV and NTT Finance will promote the NTT Group’s business development in SEA and the creation of new value through open innovation by leveraging this new investment vehicle.

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The long-rumoured merger between Southeast Asia’s tech titans, Grab and GoTo, has once again captured headlines; this time with the highest level of political validation yet.

Indonesia’s Minister of State Secretariat, Prasetyo Hadi, confirmed that talks between the two companies are indeed underway, even hinting that one could soon absorb the other. His remarks add fresh weight to speculation that the country’s sovereign wealth fund, Danantara, may play a role in managing assets tied to the merger, aligning with broader regulatory plans to formalise the status of online motorcycle taxi drivers.

Yet, GoTo continues to push back. In an official statement, the company denied any merger agreement or ongoing corporate action with Grab, reaffirming its commitment to good governance and stakeholder protection. Its upcoming extraordinary shareholders’ meeting in December, it insisted, is unrelated to any consolidation plans.

This tension—between government confirmation and corporate denial—reflects both the sensitivity and potential magnitude of a Grab-GoTo deal. If realised, the merger could reshape Indonesia’s digital economy, streamline competition, and influence regional dynamics in ride-hailing and fintech.

For now, the market waits, watching closely as whispers of consolidation grow louder in Southeast Asia’s increasingly competitive super app arena.

REGIONAL

Indonesia’s minister confirms Grab-GoTo merger is on the table: Minister Prasetyo Hadi confirms Grab and GoTo are moving toward a merger, with state agency Danantara possibly managing assets and oversight.

GoTo says no merger decision made with Grab: The company said there has been no decision or agreement regarding any merger or corporate action with Grab. The company said its follows regulations for public companies and will prioritise the interests of shareholders, drivers, small businesses, and consumers.

SoftBank, other shareholders seek to remove GoTo CEO: They want Patrick Walujo to be replaced after the company’s market value dropped by more than 40% during his tenure. Shareholders have requested an extraordinary general meeting to propose a vote on leadership changes.

Kaya Founders raises US$25M fund to back early-stage startups: The new fund comprises two vehicles targeting founders from pre-seed through series A stages. Kaya Founders has backed more than 40 startups in sectors such as e-commerce, fintech, education, and healthcare.

Philippines lending fintech firm OneLot raises US$3.3M to expand used car loans: Investors include Accion Ventures, 468 Capital, and Seedstars. The platform provides inventory loans and digital solutions to help used car dealers manage and finance vehicle purchases.

MAS, Indonesia’s OJK renew fintech partnership to boost innovation: MAS and OJK plan to share knowledge, promote industry cooperation, refer fintech firms to each other’s regulatory sandboxes, and enable cross-border information sharing within legal boundaries.

SEA digital economy to surpass US$300B by 2025: report: E-commerce GMV is projected to hit US$185B, with video commerce contributing about 25%. Food delivery GMV is expected to reach US$23B, while revenue nears US$2.4B, with most platforms now profitable or close to it.

Philippine startup funding drops 32%: Gobi Partners: The deal volume declined 54% year-on-year. The report cites the slowdown to tighter global liquidity, governance issues, and corruption scandals affecting investor confidence.

Temasek shifts focus to growth, late-stage startups: It will reduce direct investments in early-stage startups. The Singapore state investor aims to prioritise businesses with established models and clearer unit economics.

Asia rises in the AI chip race: China to outgrow US by 30% by 2030: AI chip revenues will hit US$330B by 2030, with China leading global growth at 283%—outpacing the US and reshaping Asia’s role in semiconductor innovation.

Transcelestial secures additional funding to strengthen Japan’s disaster-resilient networks: Backed by MPower and SEEDS, Transcelestial will deploy laser links across Japan’s defence, disaster response, and satellite networks.

H3 Zoom lands US$1.8M to accelerate AI-powered building inspections in Japan, HK: Investors include JRE Ventures, SGInnovate and M7 Holdings. H3 Zoom helps asset owners and operators transition from manual, labour-intensive inspections to scalable, data-driven solutions.

Singapore’s Bioactivx nets US$1.4M seed led by Cocoon Capital: The company develops synthetic regenerative implants, including its main product, Bioactiv Matrix, a bio-inspired skin substitute for wound care. Bioactivx said the funding will support clinical trials and regulatory approval for Bioactiv Matrix.

Singapore’s AI startup PowerCred raises US$1.1M to accelerate expansion: Lead investors are TNB Aura and Tenity. PowerCred focuses on transforming unstructured documents for enterprises. It said it processes over 1M documents quarterly for clients in SEA.

REPORTS, FEATURES & INTERVIEWS

High adoption, low understanding: The Philippines’s blockchain knowledge gap: Philippine blockchain adoption is driven by speculation, not comprehension — education, regulation, and transparency are key to long-term progress.

Inside Philippines’s blockchain paradox: Strong adoption, weak foundations: Blockchain innovation in the country lags due to poor connectivity, scarce venture funding, and narrowly focused crypto regulations.

INTERNATIONAL

Subsidiaries shine, parent falters: The real story of Kakao’s Q3: The most striking detail hidden within the strong headline figures is the divergence between the consolidated net profit and the amount attributed to Kakao’s core shareholders.

SoftBank profit jumps on soaring AI valuations: The Tokyo-based conglomerate reported a net income of US$16.2B in Q2, beating analyst expectations by a wide margin. It saw its profit rise mainly due to higher valuations of its holdings in AI and chip companies such as Nvidia and Intel.

Microsoft’s Copilot taps influencers to rival ChatGPT, Gemini: The company’s Copilot assistant has 150M monthly active users, trailing behind OpenAI’s ChatGPT with 800M weekly users and Google’s Gemini at 650M monthly users.

South Korea’s WeMakePrice declared bankrupt by Seoul court: A Seoul court has ordered the liquidation of the e-commerce platform operator, after ending its rehabilitation process in September. WeMakePrice and TMON entered court-led rehabilitation in July 2024 due to missed payments to vendors amid liquidity problems.

Japanese investors back US$38.1B into European tech startups: This marks a sharp rise from the US$6.1B invested in the five years before the EU-Japan Economic Partnership Agreement took effect. Mitsubishi, Sanden, Yamato Holdings, and Marunouchi have been active backers.

Intel CTO exit to join OpenAI, CEO Tan takes over AI oversight: Sachin Katti, who became Intel’s CTO and chief AI officer in April, will help design and build OpenAI’s compute infrastructure for artificial general intelligence research.

ECHELON

From legacy to lift-off: How AI is driving scalable growth in complex industries: By prioritising practical adoption over radical change, AI innovators can drive sustainable impact across traditionally intricate industries.

From prospects to progress: Unlocking a thriving climate tech ecosystem in SEA: Referencing an ESCAP report that warns the region has just five years left to meet its decarbonisation goals, speakers underscored that urgency often drives innovation.

Hiring for AI startups: Building high-impact teams from day one: Despite automation reshaping roles, demand remains strong for data scientists, UX/UI designers, and other AI-related professionals who can bridge technical and human-centred aspects of innovation.

SEMICONDUCTOR

SoftBank sells its entire stake in Nvidia for US$5.8B: The firm said in its earnings statement that it sold 32.1M Nvidia shares in October. SoftBank’s investments in OpenAI and PayPay helped it post a US$19B gain on its Vision Fund in its fiscal second quarter.

TSMC posts record October sales on AI demand: The Taiwan-based chipmaker generated US$11.9B. Sales rose 16.9% year-on-year and 11% from September, surpassing its previous monthly high of US$10.8B set in April. For Q4, TSMC projected revenue between US$32.2B and US$33.4B.

Nvidia CEO sees strong demand for Blackwell chips: Jensen Huang noted that Nvidia relies on TSMC to produce the wafers needed for these chips but did not disclose specific order numbers. He added that Nvidia’s success depends on TSMC’s support, marking his fourth trip to Taiwan in 2025.

AI

South Korea to invest US$960M in developing AI talent: The plan, called “AI Talent Development Plan for All,” aims to support AI education from elementary school to postgraduate research, and seeks to address a shortage of skilled AI professionals in the country.

The use of GenAI is turning innocent employees into insider threats: Here’s how to fix it: GenAI use in workplaces exposes new insider threats, making hardware-level zero trust vital to protect data beyond traditional security.

THOUGHT LEADERSHIP

Turning turmoil into opportunity: Singapore’s playbook: Amid global trade wars and slowing growth, Singaporean leaders urge diversification, digital innovation, and ASEAN cooperation to safeguard competitiveness and ensure long-term business resilience.

The hustle’s toll: Why some of Southeast Asia’s brightest founders are stepping back: Startup founders in the region are quietly redefining resilience as burnout and mental health awareness reshape the region’s startup culture.

Open source: The secret to boosting Singapore’s startup ecosystem: Singapore’s startups face talent shortages and rising competition. Embracing open source offers faster innovation, global collaboration, and secure scaling to stay competitive.

On-chain data and Web3 security: Insights from industry experts: Experts from SlowMist, BugRap, Bybit, and SMU explored how on-chain data analytics enhances Web3 security through real-time monitoring, fraud detection, and predictive risk intelligence.

Why Bitcoin decoupled from Nasdaq and what it means for the US$112K breakout: Markets rallied as the US shutdown nears resolution, with crypto buoyed by CFTC regulation hopes, Uniswap’s token overhaul, and Bitcoin strength.

The essential authority: Redefining influence in an age of endless noise: Amid the noise of performative leadership, real influence demands self-mastery, clarity, and courage to shape the unspoken conversations defining tomorrow’s world.

Resource Multiplier Effect: How smart teams create more with less: The Resource Multiplier Effect shows how founders achieve outsized growth by combining time, talent, capital, and networks—turning limited resources into compounding advantage.

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How food is produced, how food is consumed and how food is distributed is ripe for substantial changes.

Addressing the challenges posed by our ever-changing climate requires an agile approach with policies, education and technology taking centre stage. From diminishing water resources to extreme weather events, the impacts cascade through every link of the food supply chains. 

Food waste has exposed vast inefficiencies in our food supply chains. It is estimated that between 691 and 783 million people in the world faced hunger in 2022. The number of people facing hunger in Africa has increased by 11 million people since 2021 and by more than 57 million people since the outbreak of the pandemic.

Milder winters and warmer springs may seem like a win for some of us who enjoy the sunshine-filled days. However, it can cause major disruption to our food supply chains. In many regions now dealing with extreme heat, such as the Mediterranean, farmers are facing challenges with the optimal production of their fruit and vegetables. Both require seasonality to achieve the right levels of production. 

Furthermore, climate vulnerable countries are exporting more than ever when they may themselves be short in supply, given the increased demand and stagnant production. 

Today, many of us take our nicely packed supermarket shelves for granted. However, the future may present a very different picture as scarcity creeps in, and we continue to feel the effects of climate on our food security.

Record numbers of people visiting food banks 

Across the United States, according to the US Department of Agriculture, food insecurity occurs when households are unable to acquire adequate food because they have insufficient money and other resources. The cost of living crisis is having a widespread impact on the food security of the general population, with many food banks struggling to meet the record demands. 

Also Read: The climate change and gender equality connection: How to support underfunded women-owned business

Just last year, the Guardian newspaper reported a record number of households in the UK depending on food banks. This isn’t a remote problem in the UK. It is also being reported on in the US and Canada with supply issues and out-of-control costs affecting the working class. 

This is a great concern as most working families are not eligible for government assistance because of their working status. If the average family working a full-time job cannot put enough food on the table, then what hope do the poorer classes have?

The complexities involved demand robust, reliable technology as well as expert insights. Projects like Morpheus.Network leverage blockchain technology to streamline complex processes. Using a mixture of advanced technologies, including Blockchain, Automation and AI, they can address the food waste inefficiencies identified earlier. 

By providing supply chain managers with unparalleled shipment and item visibility, Morpheus. The network empowers automation and optimization, ensuring the safe and secure movement of food while simultaneously saving valuable time and resources. 

Tackling the issue at source

Food producers are a critical and often silent component in the high risks to our food supplies brought about by climate change. EthicHub is one of many projects focused on incentivizing farmers to produce by connecting them with lenders globally and facilitating direct sales to buyers. 

Financial rewards for farmers are crucial for sustaining agricultural livelihoods. EthicHub not only provides farmers with access to much-needed capital but also enhances the economic viability of their production. 

By leveraging coffee as collateral for loans and improving unit economics, EthicHub tackles the root causes of food supply problems, ensuring that farmers receive the financial rewards they deserve while contributing to the future of sustainable food systems.

Some of the findings in the recent European Climate Risk Assessment Report should scream alarm bells for policy makers and consumers. One section notes that Risks from heat and drought to crop production are already at a critical level in southern Europe. Blockchain is one of the most promising technologies that will enable us to address the ongoing issues in food production, consumption and distribution.

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From AI and semiconductors to mobility and finance — behind every wave of innovation lies power and energy infrastructure. It is the invisible engine that sustains modern industries, shaping not only technological progress but also the flow of global capital and industrial order.

Energy transition today is no longer just an environmental cause; it represents a structural reallocation of markets and influence. In the AI era, true competitiveness depends not on designing innovation, but on managing the energy that drives it.

Energy as capital, not just conservation

The global energy industry is not merely a sustainability topic—it is the gateway of capital and the backbone of the global economy. Every major industrial revolution has been powered—literally—by an energy shift: coal in the 19th century, oil in the 20th, and now renewables in the 21st.

AI, semiconductors, mobility, and finance all rely on a stable and scalable energy foundation. Yet many people fail to grasp its true scale and strategic value. Having long enjoyed relatively cheap electricity, we’ve forgotten the volatility of the oil shocks and the geopolitical weight of resources.

Korea’s major conglomerates are prime examples. Their industrial empires were built on energy-intensive foundations—and today, they’re evolving those same roots by merging energy transition with finance, renewables, and emerging tech. Beneath all visible innovation lies a deep layer of infrastructure and capital flow.

The world’s largest industry is the energy business—because without energy, even AI and finance are abstractions

How fossil fuel titans are reinventing themselves

Consider ExxonMobil, once the emblem of fossil dominance. It’s now investing billions into CCUS (Carbon Capture, Utilisation, and Storage) and DAC (Direct Air Capture) technologies—tools designed to bridge the old energy world with the new.

These companies are adopting what might be called “dual-transition logic”: continuing traditional oil operations while channeling profits into carbon reduction and renewable technologies. This balancing act carries the risk of self-cannibalisation, yet they accept it as the price of long-term survival.

What’s truly remarkable is how universal this shift has become. Nearly every global energy player—from Shell and BP to Saudi Aramco—is now part of the same race toward transition. The question is no longer if or when the shift will happen; it already has.

Also Read: On the precipice of energy transition

Why tech giants are leading the charge

Global tech leaders such as Microsoft, Google, and Amazon are not joining the energy transition for image or compliance. They are doing so because their business survival depends on it.

AI data centres now consume staggering amounts of electricity. Power supply and efficiency have become the defining variables for corporate scalability and resilience. As AI continues to shape every sector, these companies understand one truth:

Whoever controls energy, controls computation—and therefore, the future.

Their investments in renewable projects, direct power purchase agreements (PPAs), and green hydrogen aren’t just eco-initiatives; they’re strategic moves to secure their operational lifeline. In essence, these tech giants are saying:

“If this transition is inevitable, we’ll shoulder the cost and lead the change.”

In doing so, they’re transforming sustainability into a competitive advantage—positioning themselves not only as technology leaders but as architects of the next industrial era.

Mobile power plants: How EVs are opening a new electricity market

Global automakers are no longer competing solely on design or performance—they are entering the power market. As the industry accelerates toward electrification, the EV battery has evolved into a mobile energy asset. While vehicles consume electricity on the road, when parked, they can act as Energy Storage Systems (ESS), capable of storing—and even trading—power.

If electricity trading becomes fully liberalised, automakers could unlock a new layer of business: power monetisation. Vehicles would not only drive but also buy, sell, and stabilise electricity flows, effectively functioning as mobile energy platforms.

This convergence of energy and mobility creates a new ecosystem where automakers operate as both manufacturers and distributed energy providers. As in-car entertainment, charging networks, and connected lifestyle services expand, these platforms will deepen user engagement and brand stickiness, turning vehicles into personalised hubs of both energy and experience.

In essence, the car is no longer just a mode of transport—it is becoming a node of the power grid and a core interface of the energy economy.

Also Read: Will hybrid schooling break walls for the next generation?

The age of energy hegemony

China builds control

China’s dominance in renewables is no coincidence—it is the result of a deliberate, whole-of-nation strategy to own the next general-purpose infrastructure: energy. Under the national vision of “Energy Rise (能源崛起)”, Beijing has systematically built an ecosystem spanning the entire value chain—from raw materials and components to manufacturing, construction, and operation.

This full-stack control gives China unmatched cost efficiency, production speed, and geopolitical leverage; a single supplier’s pause can disrupt global wind or solar projects. Beyond scale, the country’s “Carbon Neutrality by 2060” goal functions as a roadmap toward energy sovereignty, reducing vulnerability to external shocks while consolidating influence over global standards. In essence, China’s energy rise fuses technological sovereignty, economic power, and industrial hegemony into a long-term strategy of control.

Canada builds trust

Canada combines resource abundance with a high level of energy security and self-sufficiency, providing it with substantial influence in global trade and diplomacy. Its independent and stable energy base strengthens its negotiating position—even in periods of US trade tension—and underpins competitiveness across oil, LNG, and electricity.

Today, Canada’s energy sector is shifting from extraction to innovation, integrating LNG with renewables and investing in low-carbon infrastructure. The country approaches this transition in a systematic, partnership-oriented manner, viewing energy not only as an industry but as part of its national identity. Through this lens, Canada positions itself as a trusted partner in global sustainability, demonstrating how a resource-based economy can evolve toward innovation-led growth.

Korea builds integration

Korea’s energy transition is characterised by pragmatic sequencing, using hydrogen, fuel cells, and advanced batteries as bridge technologies to balance decarbonisation with economic stability. Large industrial groups (e.g., Samsung, LG, Hyundai, SK) are active across the hydrogen value chain and next-generation battery systems. A principal differentiator is systems integration: combining power electronics, semiconductors, materials, and precision manufacturing into cohesive solutions.

This integration capability is relevant to complex domains such as fusion and SMRs (Small Modular Reactors), where engineering depth and cross-sector coordination matter. Demonstrations and concept models presented at international forums have emphasised modularity, safety, and deployability for export-oriented markets. While commercialisation timelines remain uncertain, the integration-led approach positions Korea as a potential system integrator in the broader clean-energy ecosystem rather than a single-technology player.

With AI, data, power, and capital intertwined, true competitiveness today lies not in “designing innovation,” but in managing the energy that powers it. This is the essence of the energy business—the engine of capital and infrastructure that drives the transition.

Acknowledgement: Special thanks to Ms. Calli Seunghee Moon, Business Development Expert of the Global Manufacturing Division at SK AX, and author of Energy Business (2025) and The Age of Climate Technology (2023), for her insights and contributions to this article.

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Equity markets hover at critical technical junctures while macroeconomic headwinds, particularly the spectre of a prolonged US government shutdown, have only just begun to recede. Cryptocurrency markets, deeply intertwined with broader risk sentiment, have rebounded modestly, buoyed by improved macro conditions and renewed institutional interest in Layer 1 infrastructure. Beneath the surface, divergences in both traditional and digital asset markets suggest that the current calm may be temporary and highly contingent on incoming data, policy developments, and capital flows that remain in flux.

Equity markets continue to tread carefully around key technical support levels. The S&P 500, a bellwether for global investor sentiment, finds itself sandwiched between its 50-day and 100-day moving averages, zones that often act as fulcrums between continuation and reversal. Although recent price action has been subdued, the possibility of a year-end rally persists, especially given the surprisingly strong third-quarter earnings results that delivered a 15 per cent year-over-year profit growth across the index. This strength is increasingly concentrated and increasingly fragile.

The so-called Magnificent 7, once a monolithic engine of market returns, now exhibit stark performance divergence. Tesla, emblematic of this fragmentation, encapsulates the broader uncertainty. Analyst forecasts span from bullish projections of a 6x price surge to bearish scenarios anticipating steep corrections. Such volatility in outlook underscores a market increasingly sceptical of uniform growth assumptions and more attuned to company-specific fundamentals, execution risk, and macro dependencies.

This skepticism is well-founded. While optimism around artificial intelligence remains intact, particularly in the context of long-term structural transformation, the near-term outlook for capital expenditure shows signs of potential deceleration. The year 2026 may witness a slowdown in AI-related capex, especially in downstream sectors where valuations appear stretched relative to near-term revenue visibility.

Compounding this risk is the fact that many of the Magnificent 7 remain deeply tethered to consumer behavior, whether through digital advertising, cloud services, or hardware sales. Should broader economic conditions falter, driven by persistent inflation, tighter credit conditions, or geopolitical shocks, their vaunted cash flow strength could erode faster than anticipated. Investors would be wise to adopt a selective approach, distinguishing between companies with resilient business models and those riding speculative momentum.

Also Read: Why Answer Engine Optimisation is the next frontier for modern marketers

Currency markets add another layer of complexity. The US Dollar Index (DXY), which had been testing the psychologically significant 100 level, pulled back slightly to 99.60 following news of a Senate resolution to end the 40-day government shutdown. The dollar remains strong, and positioning appears crowded. Such crowding increases the risk of sharp reversals should upcoming macro data or, more likely, signals from the Federal Reserve shift market expectations. A stronger dollar typically acts as a headwind for US multinational earnings and emerging market assets alike, and its influence on capital flows cannot be overstated. In the context of crypto, where dollar strength often inversely correlates with asset prices, this dynamic remains a critical variable.

Global themes further complicate the narrative. China’s strategic push into humanoid robotics, exemplified by XPENG’s IRON project, signals a broader ambition to dominate next-generation industrial and consumer technologies. Simultaneously, Chinese companies are accelerating overseas expansion, challenging incumbents in markets from Southeast Asia to Latin America. India, by contrast, has underperformed relative to both China and Japan, raising questions about its near-term growth inflexion and policy responsiveness. In such an environment, a barbell strategy, combining exposure to large-cap growth leaders with defensively positioned, dividend-paying equities, offers a prudent approach to navigating regional and sectoral divergences.

The macro backdrop improved meaningfully over the weekend with the Senate’s bipartisan agreement to end the government shutdown, the longest in US history. This resolution directly addresses a significant source of liquidity drain. Since October 10, approximately US$700 billion in economic activity has been disrupted or delayed, constraining both consumer and institutional risk appetite. With the shutdown concluded, capital can begin to reallocate toward risk assets, a dynamic already reflected in the 4.83 per cent 24-hour gain in crypto markets following a 3.94 per cent weekly loss. Bitcoin’s 0.70 seven-day correlation with the S&P 500 underscores how tightly crypto remains linked to traditional market sentiment. Relief in one arena quickly transmits to the other.

Layer 1 ecosystems have emerged as a focal point of this renewed optimism. Solana’s 4.42 per cent sector gain was catalysed by Western Union’s announcement that it will launch a US dollar stablecoin exclusively on Solana in the first quarter of 2026. This is not a speculative foray but a strategic institutional endorsement of Solana’s scalability and throughput.

Similarly, Ethereum received a significant vote of confidence through EigenCloud’s US$200 million deployment of ETH-based infrastructure to support AI systems. These developments indicate that blockchain is no longer merely a speculative playground but an operational backbone for real-world financial and technological infrastructure. Institutional adoption of this magnitude validates the long-term utility of high-performance Layer 1 networks and draws capital toward ecosystems demonstrating clear use cases and execution capability.

Also Read: Bull-proof, bear-proof: How smart startups win in every market cycle

Technically, the crypto market rebounded from oversold territory, with the 14-day RSI at 37.4 signalling exhaustion among sellers. Bitcoin retested its 50-week moving average near the US$103,000 level, a zone that often acts as a magnet for price action. Spot trading volume rose 14 per cent to US$159 billion, while derivatives open interest climbed 5.76 per cent, suggesting that traders are cautiously re-engaging.

This optimism remains tempered. Ethereum ETFs recorded US$466 million in outflows on November 7 alone, highlighting persistent institutional scepticism toward ETH despite its technological advancements. Moreover, the market must sustain a close above the seven-day simple moving average at US$3.46 trillion in total market cap to confirm bullish momentum. Failure to do so could trigger a retest of the US$3.37 trillion Fibonacci support level.

Gold’s rise to US$4,007 per ounce amid dollar softening and shutdown-related uncertainty further illustrates the fragile nature of current sentiment. Safe-haven demand remains elevated, even as risk assets rally. This duality, bullish price action coexisting with defensive positioning, is a hallmark of late-cycle or transitional market regimes.

Whether Bitcoin can hold above US$105,000 in this environment depends not only on technicals but on broader macro confirmation. Sustained liquidity normalisation, stable dollar conditions, and continued institutional validation of blockchain infrastructure must all align. Until those pillars solidify, the relief rally, while welcome, should be approached with disciplined risk management and selective exposure.

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South Korean technology giant Kakao reported a seemingly stellar third quarter (Q3 2025) consolidated performance, achieving a total revenue of US$1.44 billion and an operating profit of US$143.6 million. This represents an 8.6 per cent increase in revenue year-on-year (YoY) and a dramatic 59.4 per cent increase in operating profit YoY. The operating profit margin expanded to 10 per cent, a rise of 3.2 percentage points YoY.

However, a forensic examination of the financial statements reveals critical vulnerabilities and a significant shift in who benefits from this overall consolidated success, suggesting the company may be attempting to downplay a sequential contraction in its core profit streams and a substantial dilution of earnings flowing back to controlling interests.

The deceptive profit split: Controlling interests see earnings plummet

The most striking detail hidden within the strong headline figures is the divergence between the consolidated net profit and the amount attributed to Kakao’s core shareholders.

Also Read: Kakao Pay, Artem Ventures back Paywatch’s US$20M Series A

While consolidated net profit from continued operations was US$133 million, representing a quarter-on-quarter (QoQ) increase of 12.3 per cent, the net profit attributable to controlling interests actually dropped sharply by 22.5 per cent sequentially.

Net profit (controlling interests) stood at US$86 million in Q3 2025, down significantly from US$111 million in Q2 2025. Conversely, non-controlling interests (the share of profit belonging to external stakeholders in subsidiaries) skyrocketed to US$46.9 million. This figure is up 542.9 per cent QoQ, compared to US$7.3 million in Q2 2025.

This massive surge in profitability attributed to non-controlling interests indicates that the gains driving the impressive consolidated performance are being generated disproportionately by subsidiaries — such as Kakao Pay (46.1 per cent stake), Kakao Games (40.7 per cent stake), or Kakao Mobility (57.2 per cent stake) — rather than the parent company itself, severely reducing the ultimate benefit to Kakao’s own equity holders.

Core business slowdown and rising costs

Beneath the consolidated growth, key segments showed signs of sequential strain, coupled with escalating infrastructural expenditure.

The core platform segment grew modestly by 0.4 per cent QoQ. Crucially, the dominant revenue stream, Talk Biz, saw its revenue fall QoQ by 1.4 per cent, reporting US$368.7 million. The decline suggests a slowdown in the commercialisation of its main messaging platform, KakaoTalk.

Simultaneously, the traditional Portal Biz continued its steady decline, shrinking by 7.1 per cent QoQ and 4.8 per cent YoY, reporting US$50 million.

While growth in the Platform segment was salvaged by Platform-Others (up 4.1 per cent QoQ and 23.7 per cent YoY), reaching US$312 million, the company also faced burgeoning costs necessary to sustain its technological infrastructure and push for growth:

• Outsourcing/infrastructure costs were US$179.9 million in Q3 2025, marking a substantial increase of 34.3 per cent YoY and 11.7 per cent QoQ. This sharp rise in foundational spending is a considerable headwind against margin expansion.
• Marketing expenses also saw aggressive QoQ scaling, jumping by 15.7 per cent to US$70.3 million, indicating the need for higher promotional spend to generate marginal revenue growth.

Content segment contractions

The content division, crucial for regional expansion and IP monetisation, presented a mixed, yet concerning picture:

• The game segment generated US$106 million. While this was an 8.1 per cent QoQ increase, it represents a brutal 34 per cent contraction YoY. The high volatility and substantial year-on-year revenue reduction demonstrate ongoing challenges in maintaining stable revenue streams from gaming.
• The story segment, which includes webtoons and web novels often highlighted for global growth, also saw revenues contract, falling by 3.3 per cent QoQ and 3.3 per cent YoY, reporting US$145.9 million. This sequential decline suggests stagnation in a division perceived as a long-term growth driver.
• The only bright spot in content was music, which posted robust growth of 20 per cent YoY, reaching US$389.9 million.

The core parent company is losing ground

Further deepening the concerns regarding sustainable performance is the health of Kakao as a separate entity (excluding consolidated subsidiaries).

Also Read: Indonesia’s minister confirms Grab-GoTo merger is on the table

The separate statements of income reveal that the parent company’s total revenue was US$440.5 million, which was down 2.5 per cent QoQ. More troubling, the separate entity’s operating profit declined by 6.8 per cent QoQ and 1.1 per cent YoY, standing at US$69.4 million. The separate operating profit margin contracted sequentially by 0.7 percentage points to 15.7 per cent.

This data confirms that the robust consolidated growth reported by Kakao is almost entirely a result of successful, yet expensive, operations and high profitability within its subsidiaries, while the core business of the parent entity itself is weakening both in revenue and profitability.

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