Founder conflict has long been recognised as one of the top reasons startups fail. But in 2025, conflict is no longer the primary threat. Misalignment is.

Unlike conflict, which is loud, visible, and quickly addressed, misalignment is subtle. It emerges slowly: In priorities that no longer match, in decisions that create friction, in conversations that feel increasingly transactional. And the danger is that in today’s AI-accelerated operating environment, misalignment is easier than ever to overlook.

AI has automated the tasks, streamlined the workflows, and stabilised operations to the point where companies can appear perfectly functional, even when the founders are no longer building from the same place.

This dynamic is far more common than the ecosystem admits.

The drift: When founders grow, but not in the same direction

Having built multiple companies, I’ve seen how quickly alignment can shift even in partnerships that began with absolute clarity. The drift rarely comes from a single disagreement. It starts with micro-fractures:

• One founder is losing enthusiasm.
• Different interpretations of the future.
• Uneven emotional investment.
• Unspoken concerns about direction or focus.
• Misaligned expectations around roles or contribution.

Individually, none of these seems alarming. Collectively, they create a slow erosion of trust, motivation, and leadership coherence.

Most founders assume alignment is “set at the beginning.” But alignment is not static. It evolves, and without conscious recalibration, it deteriorates.

Also Read: The hustle’s toll: Why some of Southeast Asia’s brightest founders are stepping back

Why misalignment is more dangerous than conflict

Conflict forces a conversation. Misalignment avoids it.

When something feels off but not urgent, founders tend to deprioritise the check-in. Work continues. Deliverables move. The business looks healthy. And yet, momentum begins to decline in ways that are not easily measurable:

• Decisions take longer.
• Meetings feel heavier.
• Strategic clarity weakens.
• Founders retreat into their own domains.
• Culture becomes fragmented.

Profitability can even mask the issue. A company can be financially strong while internally stagnant.

That’s why misalignment is more insidious than conflict: It doesn’t feel like a problem until it becomes irreversible.

The AI paradox: Automation makes misalignment easy to ignore

One of the biggest shifts in founder dynamics today is the role of AI. AI has improved operational efficiency to the point where founders can run parallel workflows without real alignment.

This creates the AI Paradox: AI reduces friction, but also reduces communication.

Tasks get completed without discussion. Updates get automated. Politeness is maintained through templated responses. Execution continues without emotional engagement.

AI helps founders function, even when they’re fundamentally drifting apart.

This is operationally convenient, but strategically dangerous.

Left unchecked, AI can unintentionally amplify misalignment by keeping the company moving while the partnership weakens beneath the surface.

Founders need to recognise this as a new risk factor: AI accelerates execution, not alignment.

Purpose, motivation, vision, and partnership health remain fully human responsibilities.

The strategic cost of misalignment

When founders are misaligned, the company experiences a series of predictable consequences:

• Direction becomes inconsistent. Product, marketing, and sales teams receive mixed signals.
• Decision-making slows. Each choice requires more negotiation, more alignment, more explanation.
• Leadership energy declines. When founders are unaligned, their conviction dilutes across the team.
• Culture destabilises. Employees pick up on tension long before founders acknowledge it.
• Growth stagnates. Companies stuck in alignment drift stop scaling, even if metrics look stable.

From a venture perspective, misalignment is one of the most underestimated internal risks.

Also Read: Hiring for hypergrowth? Here’s what founders keep getting wrong

A framework for realigning founder partnerships in an AI-driven era

To make alignment practical — not philosophical — founders need a structured approach. Here’s a model that I strongly recommend:

• Quarterly alignment audit

Three essential questions:

• Are we still building toward the same vision?
• Do our priorities match the next phase of growth?
• Are both founders equally motivated and empowered?

If any answer is unclear, the partnership needs recalibration.

• Clarify motivational drivers

Founders differ. Some prioritise innovation, others stability, others ownership, others scaling. Motivations must be explicit, not assumed.

• Define Non-Negotiables

Both founders should articulate:

• What they need.
• What they expect.
• What they cannot compromise on.

• This creates alignment guardrails.

Ensure communication is not just “heard” but understood

Acknowledgement (“got it”) is not alignment. Understanding requires reflection, synthesis, and shared interpretation.

• Use AI as a diagnostic tool, not a replacement for difficult conversations

AI can highlight sentiment shifts, operational imbalance, or workload discrepancies. But founders must handle the alignment conversation themselves.

What founders should remember

Alignment is not a sentimental concept — it is strategic infrastructure. In a market where AI has made execution faster and founder bandwidth thinner, alignment is no longer a soft skill. It is a competitive advantage.

Companies don’t lose momentum because their models stop working. They lose momentum because their founders stop building in the same direction.

Misalignment does not announce itself. Founders need to make alignment a practice.

Because AI can automate the work, only aligned founders can build the future.

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Singapore ranks among the most heavily monitored workplaces in the world, with new data indicating a growing concern over artificial intelligence (AI) and increased surveillance across offices, hybrid setups, and digital work tools.

According to ADP’s People at Work 2025 report, 41 per cent of employees in the island nation say they feel constantly monitored, placing the country fifth globally among 34 markets surveyed. Only Egypt, Nigeria, Thailand, and India reported higher levels of workplace surveillance.

Also Read: The future is skills, not jobs

ADP’s findings suggest a growing connection between employee stress and the expansion of AI-driven or digital monitoring tools, which are increasingly integrated into HR systems and productivity platforms.

Workers feeling watched and worn down

Singapore’s figure is nine percentage points above the global average, underscoring how local employees are facing stronger pressure around productivity tracking and hybrid work accountability.

Globally, ADP found that workers who feel constantly monitored are:

• Four times more likely to be among the least productive
• Three times more likely to report stress

This mirrors concerns across several Southeast Asian markets, where rapid digitalisation and performance technologies are reshaping how employees perceive workplace fairness and privacy.

AI uncertainty rising, especially among younger workers

Beyond surveillance, the report highlights growing unease about the role of AI in shaping future jobs.

In Singapore:

• 19 per cent of workers say they are unsure how AI will affect their roles
• This is nine percentage points above the Asia-Pacific average

Knowledge workers — including programmers, academics, and creative professionals — reported the highest anxiety levels, approximately twice that of skilled task workers.

Younger adults aged 18 to 26 were the most unsettled age group, with 23 per cent expressing concern that AI may alter or threaten their future roles.

Trust becoming a key business factor

Jessica Zhang, Senior Vice President, ADP APAC, said organisations must rethink how they introduce new technologies. “Technology and talent are evolving in tandem and the rise of AI and hybrid work is redefining how employees experience trust, purpose, and productivity.”

Also Read: Surviving a recession: How to navigate layoffs and come out stronger

“To navigate this new landscape, organisations must deploy AI and other workplace tools responsibly – ensuring they support rather than strain the workforce. When businesses align digital transformation with clear communications and employee wellbeing, they build stronger trust, engagement, and sustainable performance,” she added.

Zhang’s comments reflect the growing calls across industries for clearer governance of employee data collection and the responsible use of monitoring technologies.

Why this matters to Southeast Asia

The report’s findings come as Southeast Asian companies rapidly integrate AI into workflows, from customer service automation to internal analytics. At the same time, hybrid work models have pushed employers to adopt tighter digital oversight for attendance, productivity, and compliance.

For Singapore, a regional hub for knowledge work and tech talent, the tension between innovation and worker wellbeing is becoming a central issue for employers navigating digital transformation.

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Malaysia-based cybersecurity firm ArmourZero has secured an undisclosed strategic angel investment from YTM Colonel (H) Tunku Syed Razman Ibni Tunku Syed Idrus Al Qadri.

Razman, who chairs the Malaysia-Saudi Arabia Business Council, is known for his experience in cross-border partnerships across Asia and the Middle East. The company stated that the investment will enable it to expand into markets in ASEAN, North Asia, and West Asia, where cybersecurity risks are escalating alongside the rapid adoption of AI.

Also Read: Embracing unity: A celebration of diversity and inclusion at ArmourZero

Razman’s involvement will support its plans to grow in markets prioritising digital resilience.

AI-generated code is speeding development and introducing new risks

AI is now embedded in mainstream software development. According to ArmourZero, 84 per cent of developers use AI-generated code, nicknamed “vibe coding”, to speed up development cycles. But this acceleration is also creating new security challenges.

A 2025 study by Schreiber & Tippe, which examined 7,703 AI-generated code files, identified more than 4,200 distinct vulnerabilities across 77 types of weaknesses. Python-based code showed the highest exposure, with vulnerability rates reaching 18 per cent.

These findings suggest that as AI tools generate more production code, the risk of exploitable flaws being introduced into software systems increases significantly.

AI also reduces breach costs — when used defensively

At the same time, AI offers meaningful defensive benefits. IBM’s 2025 Cost of a Data Breach Report found that organisations using AI and automation in their security operations reduced average breach costs by up to US$1.9 million compared to companies that did not.

This widening gap between AI-driven vulnerabilities and AI-enabled defence explains why the cybersecurity industry is under pressure to modernise its tooling.

Co-founded in 2022 by cybersecurity expert Tho Kit Hoong and tech innovator Chong Wai Lun, ArmourZero positions itself as an automated vulnerability management (AVM) platform.

The platform provides real-time vulnerability discovery across applications, web domains, and cloud infrastructures. It provides AI to accelerate remediation through intelligent suggestions and advanced false-positive detection. This enables the software developer teams, risk teams, and cybersecurity teams to collaborate closely, focus on genuine threats and resolve them more efficiently.

Also Read: Cybersecurity in the AI age: How startups can stay ahead

ArmourZero plans to launch an API security solution this month. The firm says the tool will automatically detect and remediate API-related vulnerabilities — a category increasingly viewed as one of the most common and costly sources of breach incidents.

In February this year, Gobi Partners announced an undisclosed strategic investment in ArmourZero, a cloud-based cybersecurity platform based in Malaysia.

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Over the past 24 hours, the crypto market shed 3.51 per cent, extending a punishing 13 per cent weekly decline driven by a confluence of macroeconomic headwinds, cascading derivatives liquidations, and a dramatic collapse in trader sentiment. This sell-off exemplifies how tightly interwoven crypto has become with traditional financial systems, particularly as correlations with equities have deepened to levels not seen in months.

Monday’s performance in US equities underscored this linkage, with the Dow Jones falling 1.18 per cent, the S&P 500 down 0.92 per cent, and the Nasdaq slipping 0.84 per cent, as technology stocks led the retreat. These losses emerged alongside diminishing expectations for a Federal Reserve rate cut in December, which had previously provided some support to risk assets. The recalibration of Fed expectations followed strong US economic data, which reinforced concerns about persistent inflation and delayed the anticipated pivot toward monetary easing.

The shifting macroeconomic landscape was further reflected in movements across fixed-income and foreign exchange markets. The 10-year US Treasury yield declined modestly by 1.0 basis point to settle at 4.139 per cent, while the two-year yield edged higher by 0.4 basis points to 3.610 per cent, signalling a slight flattening of the yield curve. Meanwhile, the US Dollar Index gained 0.29 per cent to close at 99.588, adding pressure on non-dollar assets.

Gold, often viewed as a safe haven, dropped 1.0 per cent to US$4044.96 per ounce, weighed down by both the stronger dollar and receding hopes for near-term rate cuts, which typically support precious metals by lowering opportunity costs. In energy markets, Brent crude settled slightly lower at US$64.20 per barrel, recovering marginally as loadings resumed at Russia’s Novorossiysk export terminal following a brief suspension caused by a Ukrainian drone strike. Across Asia, equities finished the session mixed but turned lower in early Tuesday trading, though US index futures pointed to a modest recovery at the open, suggesting some short-term stabilisation may be on the horizon.

Also Read: ArmourZero raises strategic capital to scale automated vulnerability management across Asia

The crypto downturn lies a powerful macro risk-off dynamic that has pulled digital assets into the same downdraft affecting equities. Over the past 24 hours, Bitcoin’s price correlation with the S&P 500 surged to 0.95, its highest since June 2025. This near-perfect synchronisation underscores how traders increasingly treat crypto not as an uncorrelated alternative asset but as a high-beta extension of the broader risk spectrum. The catalyst for this shift came from revised market pricing around Federal Reserve policy. Stronger-than-expected economic indicators have tempered expectations for a December rate cut, pushing the implied probability lower and driving the 10-year Treasury yield up by 14 basis points over recent sessions.

This tightening of financial conditions has hit speculative assets especially hard. Bitcoin’s breach below the psychologically critical US$91,500 level triggered a wave of algorithmic stop-loss orders, accelerating the decline and dragging down major altcoins such as Solana and Cardano, which posted weekly losses of 21.7 per cent and 22.4 per cent, respectively. The market now awaits pivotal upcoming events, the release of the November 20 Fed meeting minutes, and Nvidia’s earnings report on November 21, for further directional cues. Any sign of continued economic resilience or hawkish Fed rhetoric could prolong risk aversion.

Compounding the macro pressure, a violent unwind in crypto derivatives markets has magnified losses through forced liquidations. Trading volume in perpetual futures contracts spiked by 45.6 per cent to an astonishing US$423 trillion over 24 hours, reflecting frantic hedging and position adjustments. Simultaneously, total open interest in the derivatives market fell by 7.4 per cent, now standing at US$787 billion, down 8.4 per cent in a single day. This contraction signals a rapid deleveraging as overextended positions were forcibly closed. Options markets mirrored this bearish sentiment, with US$740 million in put options placed targeting a Bitcoin price of US$90,000 and Ethereum at US$2,800.

Funding rates for major altcoins also turned negative, with the average rate dipping to minus 0.0019775, which disincentivises holding long positions and encourages further shorting. This feedback loop of rising volatility, liquidations, and negative funding creates a self-reinforcing cycle that can deepen sell-offs beyond what fundamentals alone would justify. Market participants now watch open interest closely, as a continued decline could signal capitulation, potentially setting the stage for a relief rally once leverage is sufficiently purged.

Also Read: Two in five Singapore employees feel watched at work; AI fears intensify

Perhaps most telling is the collapse in market psychology, captured starkly by the Crypto Fear & Greed Index, which plunged to 15, entering “Extreme Fear” territory. This marks the lowest reading since March 2025, a period that ultimately coincided with a market bottom when Bitcoin found support near US$76,000. Retail investors, overwhelmed by the speed and severity of the decline, have fled to the perceived safety of stablecoins, pushing Tether’s dominance to 7.2 per cent, a 30-day high. Social sentiment has turned sharply negative, with average daily scores falling to 4.29 out of 10, and viral commentary reflecting deep pessimism toward even leading altcoins.

Phrases like “Solana’s fuel is running out” have gained traction, illustrating how quickly narrative momentum can reverse in stressed markets. Historically, sustained readings below 20 on the Fear & Greed Index have often preceded short-term bounces, as excessive fear creates oversold conditions ripe for contrarian positioning. However, such rebounds typically require a catalyst, and in the current environment, that catalyst remains uncertain.

Technically, Bitcoin’s daily RSI has plummeted to 9.05, a level that suggests extreme oversold conditions rarely seen outside major market dislocations. This raises the possibility of a reflexive bounce, particularly if macro conditions stabilise or if institutional buyers step in near key support levels. El Salvador recently deployed over US$100 million in purchases at the US$90,000 level, suggesting strong hands view this zone as a strategic entry point. Whether Bitcoin can hold this critical threshold in the face of ongoing liquidations and macro uncertainty will likely determine near-term market direction.

In summary, the current crypto sell-off is not an isolated event, but rather part of a broader reassessment of risk across global markets. It reflects the convergence of three powerful forces: a macro regime shift driven by sticky inflation and delayed monetary easing, a violent derivatives-driven deleveraging, and a collapse in market sentiment that has pushed fear to multi-month extremes.

While technical indicators hint at potential exhaustion, any sustainable recovery will depend on a stabilisation in equity markets, a reduction in liquidation pressure, and a recalibration of Fed expectations. Until then, the path of least resistance for crypto remains downward, with US$90,000 standing as the last line of defence before deeper levels come into play.

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The profile of the strategic acquirer has shifted dramatically, and Southeast Asian founders can no longer rely on the old playbook for liquidity. What once centred on corporate fit and market synergies has evolved into a far more complex landscape shaped by national security agendas, geopolitical realignments, and the growing influence of sovereign wealth funds (SWFs).

RETVRN Research’s State of Exits 2025 captures this change with startling clarity. SWFs—no longer content with passive LP roles—have become aggressive direct acquirers of critical technologies. Their deployment of US$47B into tech deals in 2024 underscores a structural change: exits are now influenced as much by state strategy as by commercial logic. Singapore and the Middle East, in particular, are emerging as dominant liquidity providers targeting semiconductors, AI, and data infrastructure.

For SEA startups, this creates both opportunity and pressure. Winning the attention of sovereign-linked acquirers requires building truly strategic capabilities—not nice-to-have features. AI, cybersecurity, climate tech, and data infrastructure now form the core of corporate and sovereign mandates.

But the biggest hurdle may be regulation. CFIUS reviews, GDPR penalties, China’s VIE limitations, and the UK’s National Security Act all create costly delays and valuation drag. In this new era, legal and IP readiness is not paperwork; it is survival.

REGIONAL

Two in five Singapore employees feel watched at work: AI fears intensify: Singapore ranks fifth globally for workplace monitoring, with younger and knowledge workers reporting the highest anxiety over AI’s impact on future roles. Only Egypt, Nigeria, Thailand, and India reported higher levels of workplace surveillance.

SGX to launch bitcoin, ether perpetual futures on November 24: The offering will be accessible to accredited and institutional investors through SGX’s derivatives platform. Perpetual futures let traders speculate on asset prices without expiry and are known for high leverage and continuous trading.

Malaysia’s FeedMe nets US$5M to expand restaurant software business: Investors include Integra Partners and Cento Ventures. FeedMe provides an all-in-one system for restaurant operations, including PoS, delivery integrations, e-invoicing, QR ordering, queue management, and payments.

Ex-Grab exec’s Tashi Network bags funding to kill AI’s centralisation problem: Investors include Blockchain Founders Fund and Exponential Science Capital. Tashi develops distributed consensus technology replacing centralised systems, supporting real-time machine coordination and preparing for a token launch on Solana.

ArmourZero raises strategic capital to scale automated vulnerability management across Asia: The investor is YTM Colonel (H) Tunku Syed Razman Ibni Tunku Syed Idrus Al Qadri. With AI creating thousands of code flaws, ArmourZero gains a new backer to strengthen regional defences and launch advanced security tools.

Grab, StraitsX partner on Web3 wallet, stablecoin settlements: The partnership will look into integrating Web3 wallets into the Grab app, and allowing stablecoin settlement for payments in participating Asian markets. If developed, this could let Grab users hold and transact with stablecoins such as XSGD and XUSD.

Aspire launches AI-driven platform to boost digital banking: The Singapore-based company said AspireOS offers modular tools for onboarding, payments, credit, and accounting, using AI to automate workflows without requiring a core system overhaul.

REPORTS, FEATURES & INTERVIEWS

Exits have changed forever; sovereign wealth funds are now in the driver’s seat: State-led capital now deploys US$47B into direct tech acquisitions, bypassing traditional VC roles and targeting semiconductors, AI, and strategic infrastructure.

Winners in a winter: AI and biotech defy exit downcycle: AI and biotech outperform the market, achieving premium valuations and faster exits as liquidity tightens and acquirers prioritise foundational technologies.

Beyond the buzz: 15 ground-level startups solving real problems in the Philippines (Part 1): A look at 15 Philippine startups building practical, problem-first solutions across fintech, health, climate, and digital commerce—signalling a maturing tech ecosystem.

INTERNATIONAL

Byju’s founder accused of using US$533M for personal gain: Byju’s Alpha, a US special purpose vehicle set up by Byju Raveendran and affiliates, allegedly moved the funds through UK-based OCI Limited, according to a filing in the Delaware Bankruptcy Court.

Leaked docs show OpenAI paid Microsoft US$866M in 2025 revenue share: Based on the reported 20% revenue-share, estimates suggest OpenAI’s revenue was at least US$2.5B in 2024 and US$4.3B in the first nine months of 2025, though some reports put the figures higher.

Hacker behind Obama X breach ordered to return US$5.4M in bitcoin: Joseph James O’Connor pleaded guilty in the US to charges including computer intrusion, wire fraud, and extortion, and was sentenced to five years in prison in 2023. The July 2020 attack compromised accounts belonging to Barack Obama, Joe Biden, Elon Musk, Bill Gates, and Warren Buffett.

Taiwan warns on DeepSeek, other Chinese AI apps over security, bias: The country reviewed Deepseek, Doubao, Yiyan, Tongyi, and Yuanbao for issues across categories including data collection, permissions, and biometric access. All five apps were found to violate multiple security indicators.

SEMICONDUCTOR

GlobalFoundries buys SG semiconductor firm AMF to expand AI portfolio: GlobalFoundries plans to leverage AMF’s 200mm manufacturing platform in Singapore and scale to 300mm production as demand grows. It will also set up a silicon photonics research centre in the island in partnership with the A*STAR.

SoftBank’s US$6.5B deal for US chip firm Ampere gets FTC nod: SoftBank, based in Japan, announced the all-cash purchase of Ampere, a US company that designs server processors used in data centres, in March. According to Bloomberg, the FTC had previously opened an in-depth investigation of the transaction.

GMI Cloud to build US$500M AI data centre with Nvidia chips: GMI Cloud is a US-based provider of GPU infrastructure and AI services. The new facility is designed to support large-scale AI model training and deployment for enterprises, and is expected to process close to 2M tokens per second.

AI

Indonesia reports US$465.6M in financial losses to AI scams: The most common methods involve criminals using AI to mimic victims’ voices and faces, often impersonating family or friends to trick people into sending money. As of August 2025, the market regulator OJK received 70K+ reports of AI-related scams.

Why AI is essential to understanding consumer behaviour for marketing success in 2025: Despite vast digital data, many brands still fail to understand consumer behaviour. AI-driven insights now separate successful marketers from those chasing vanity metrics without improving revenue or customer loyalty.

From uncertainty to action: Power of AI and digital shaping deal strategies in turbulent times: Despite global trade volatility, Asia-Pacific CEOs remain highly optimistic about AI-driven transformation, with rising M&A interest as companies pursue digital capabilities, restructure supply chains, and build resilience.

The hidden barrier to AI sustainability: Why clean data matters: As AI adoption accelerates across Asia Pacific, energy use is soaring. Sustainable AI now depends on data efficiency—cleaning, minimising, and optimising data to reduce emissions, improve performance, and support responsible large-scale deployment.

AI and cybersecurity in healthcare: Building resilience for better patient care: There is no disputing that technology’s ability to streamline operational efficiency would be a welcome boon to Singapore’s healthcare industry, which faces the need to grow its workforce to 82,000 by 2030. AI can help by increasing operational efficiency.

THOUGHT LEADERSHIP

The treachery of good advice: What I learned about leading and letting go: Great leaders don’t wait for advice; they establish quantitative feedback loops. You shouldn’t need a random person to tell you your product is slipping; your data should scream it.

Coded in your DNA: How Singapore can help avert a global data storage crisis: Our current methods of storing all this data are not sustainable, for several reasons. Most digital archives are now stored on magnetic and optical data storage systems, but we will run out of the materials used to produce these in less than a century if that.

Are social sellers missing an important piece of the data puzzle?: Having worked with several brands and influencers through the pandemic, the author observes that many enabling solutions for social commerce do not focus enough on data consolidation, which is crucial for automated marketing efforts.

From grid to code: Why good cybersecurity will help deliver net zero:
Energy infrastructure cyber disruptions will be a significant risk factor in the future, so we must be thinking about the solutions today. Energy infrastructure is the backbone of economies and societies, and regrettably, it is already the target of frequent cyberattacks.

How early-stage deeptech startups can attract and retain the right talent: While early-stage startups may not have the luxury of offering huge salaries, it is still important to ensure adequate and fair compensation aligned with industry standards. Founders can offer equity plans to help cushion the cash flow.

Europe’s tech Thoroughbreds: A collaborative future with Asia’s investors: Spotify, Klarna and Revolut have demonstrated that it is possible to grow unicorns, decacorns and even centicorns from Europe. Now, a new generation of European tech companies, which we call ‘Thoroughbreds’ and ‘Colts’, is ready to repeat that success.

The startups that will thrive are the ones that collaborate with purpose: Partnerships are becoming strategic architecture. Not just a way to expand reach, but a way to expand capability. The strongest ecosystems today are built not on speed, but on shared intelligence, shared conviction, and shared responsibility.

The 2026 horizon: What will define D2C in Asia-Pacific: In 2026, the most significant shift we’ll see is the evolution towards the ambient, AI-powered shopper — a consumer who expects brands to anticipate their needs before they even articulate them.

Why Singapore could be the global creative industry’s best-kept secret: Singapore’s creative sector has global potential, with bilingual talent and strong systems poised to transform local capabilities into worldwide impact.

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As data centres become the core of the AI economy, their greatest constraint is no longer compute, it’s power. For investors, founders, and operators in Southeast Asia, this convergence of artificial intelligence and energy presents both a bottleneck and a generational opportunity. This is our view from The Radical Fund: the next frontier of digital progress will come from climate-aligned infrastructure that fuses intelligence with power.

Over the following sections, we explore:

• The challenge: How exponential data and compute demand are colliding with the physical limits of energy systems
• The shift: Why efficiency alone is no longer enough, and how system-level innovation is reshaping the data centre model
• The regional lens: Why Southeast Asia, with billions in FDI and a fast-digitising economy, is uniquely positioned to lead this transition
• The opportunity: Where investors and founders can enable scalable solutions in cooling, compute, energy integration, and grid resilience

Digital abundance meets physical constraint

Artificial intelligence is redefining the boundaries of computation, data, and productivity. Yet behind every model, every query, and every algorithm sits a data centre consuming colossal amounts of power. Global data centre demand is projected to more than double by 2030, rivaling Japan’s total annual electricity use. The infrastructure built to enable the digital economy is colliding with the physical limits of energy systems that were designed for a different century.

Energy has become the new bottleneck of AI progress, the physical constraint in a digital race. Hyperscale facilities that power today’s cloud and AI workloads already account for roughly 1.5 per cent of global electricity consumption. This share is growing quickly as AI training and inference workloads multiply.

Nowhere is this constraint more visible than in Southeast Asia. The region is witnessing a surge of digital activity and foreign direct investment into hyperscale data centres. Malaysia, Indonesia, and the Philippines are positioning themselves as new digital gateways. Malaysia alone has announced more than MYR 99 billion, or US$23 billion, in planned data centre investments since 2023. Indonesia and the Philippines are following closely behind. Yet the regional grid remains fossil-heavy, underinvested, and unevenly modernised.

Also Read: Energy business, the engine of sustainable global transition

The world’s most advanced computation networks are running on infrastructure built for another era. Without rapid innovation at the intersection of energy and intelligence, the very systems driving the AI revolution could face their own energy ceiling.

The energy makeup of intelligence

A modern data centre is, in essence, a miniature energy ecosystem, with roughly 40 per cent of total energy use going to compute, and another 40 per cent to cooling. Both are rising sharply as high-performance GPUs replace traditional CPUs and as AI workloads scale.

Each hyperscale facility now draws as much power as a small city. Johor, Malaysia’s emerging AI hub, could account for nearly 30 per cent of national power consumption by 2030 if all planned capacity comes online. The concentration of demand is staggering.

Power Usage Effectiveness, or PUE, has long been the industry’s benchmark for efficiency. A perfect score of 1.0 means every watt powers computation alone. Yet even the most advanced facilities, with PUE ratios near 1.1, face a bigger challenge: total power demand is compounding at double-digit rates. Incremental improvements can no longer offset exponential growth. The conversation must shift from energy saving to system redesign.

This is not just a sustainability issue. It is an economic one. Energy costs account for between 30 per cent – 50 per cent of total data centre operating expenses. As power tariffs rise and emissions rules tighten, energy strategy becomes synonymous with business strategy.

The required system-level shift from efficiency to integration

The data centre industry has long approached sustainability as a collection of independent problems: efficiency on one side, compute on another, and grid supply somewhere outside the fence. That era is ending. The next generation of digital infrastructure will be designed as an integrated system, where power, heat, and compute flow dynamically across the same operational stack.

Southeast Asia offers fertile ground for this transformation. In Singapore and Malaysia, operators are testing liquid and immersion cooling systems capable of handling the extreme thermal densities of AI chips. These technologies replace traditional air-conditioning with precision systems that use water or non-conductive liquids to extract heat directly from processors. In a region where temperatures are high and land is scarce, the shift from air to liquid cooling can reduce cooling energy use by roughly a third, according to industry benchmarks, while freeing up space for more compute.

Integration extends beyond cooling. Graywater recycling and waste-heat recovery are becoming viable in data parks connected to urban industrial clusters. In Singapore, treated wastewater already accounts for over 40 per cent of the national supply, setting a foundation model for closed-loop cooling systems. In cooler regions such as Europe, wasted data centre heat is being reused in district heating systems. In time, Southeast Asia may find its own circular approaches suited to tropical climates and water scarcity.

The most significant leap will come from software addressing incompatible systems. Digital twins and real-time analytics platforms are emerging to orchestrate infrastructure dynamically, predicting load shifts, adjusting cooling, and optimising power flows without new hardware. This software-defined approach blurs the line between IT and energy operations, creating adaptive, self-optimising systems. Efficiency becomes not a fixed objective but a continuous function.

Energy independence as a strategy

Even as integration advances, the grid itself is becoming a constraint. Across Asia, grid connection delays now exceed data centre build times. In hotspots like Johor and Batam, connection queues stretch for years. Meanwhile, fossil price volatility, emission caps, and renewable intermittency have made energy planning both more complex and more strategic.

Forward-looking operators are responding with on-site generation and storage, together with hybrid power models that provide autonomy and resilience:

• Co-located solar and battery systems that offset daytime load and stabilise supply;
• Hydrogen-ready microgrids that future-proof against fossil fuel volatility; and
• Small modular reactors (SMRs) are being explored for stable, round-the-clock baseload power.

These models reduce exposure to fossil volatility and regulatory tightening, providing the ability to stay online when the grid cannot.

Energy independence is fast becoming a driver of valuation. Facilities that adhere to renewable integration standards, interconnection requirements, and carbon-reduction mandates face lower operational risk and, therefore, lower weighted average cost of capital. For investors, this translates into higher exit multiples. What began as environmental compliance is now a form of financial resilience.

The narrative is evolving from green compliance to energy resilience, from sustainability as an obligation to sustainability as a competitive advantage. The AI revolution will not be won in the cloud, but in the power grid that sustains it.

Also Read: The shifting geopolitics of sustainability, energy, and climate

The new asset class: AI-ready infrastructure

A new category of infrastructure is emerging, one that is intelligent, efficient, and sovereign. Energy-smart data centres will define the 2030s, and the convergence of compute, energy, and regulation will shape not only the digital economy but also national competitiveness.

Southeast Asia is already becoming a stage for this transformation. Singapore remains the premium ESG benchmark, but with limited land and water, it is guiding regional standards rather than expanding capacity. Malaysia has seized the opportunity, attracting a wave of investment from global hyperscalers. Indonesia is rising fast, driven by its massive population and government incentives. The Philippines and Vietnam are catching up as connectivity improves.

This FDI surge is more than a capital inflow. It signals a strategic repositioning. Nations are competing not just to host data but to control the digital-physical nexus of energy and computation. The outcome will determine who captures the value created by the AI economy.

Investors, policymakers, and builders are no longer operating in silos. They are co-designing an ecosystem where energy efficiency, grid intelligence, and data sovereignty intersect. For capital allocators, this presents a generational opportunity: to fund the foundations of an AI-ready, climate-aligned digital economy.

The next decade

The next decade will test whether the world can reconcile digital expansion with environmental limits. The AI era is not merely a software story; it is an energy story. Without reinvention at the infrastructure level, capacity, cost, and carbon will become binding constraints on innovation.

Southeast Asia stands at the forefront of this challenge. Its economies are growing rapidly, its populations are digital-first, and its geography places it at the crossroads of East and West. Yet its energy systems remain among the most carbon-intensive. Bridging that gap requires imagination and investment in equal measure.

This region can lead by designing the next generation of infrastructure from first principles, embedding energy intelligence into every layer of the digital stack. Governments can align data-centre policy with national energy transition plans, accelerating renewable integration and storage. Investors can support technologies that couple compute density with sustainability. Operators can adopt circular resource models for heat, water, and hardware.

Southeast Asia has the resources, capital, and talent to shape this future. The question is whether it will choose to lead or wait for others to define the standards.

At The Radical Fund, our view is clear. The AI revolution depends on reinventing energy infrastructure. The region that succeeds in aligning power with intelligence will not only fuel its digital growth, but it will also own the foundations of the next economy.

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In my decade and a half of marketing experience, I’ve witnessed how the explosion of digital tools and data has revolutionised marketing. Yet, despite all these advancements, many businesses—including those in retail, F&B, and technology—still struggle with a fundamental challenge: understanding what truly drives consumer decisions.

Halfway into 2025, most brands have yet to recognise the power of AI, beyond ChatGPT; AI is the essential driver for turning data into real marketing success.

From data overload to actionable insights

Every day, companies collect mountains of data—clicks, views, social mentions, and more. But too often, the story ends there. Many brands get stuck chasing vanity metrics, missing the deeper insights that reveal why customers act the way they do. In my experience, this is where most businesses falter: they have the data, but not the understanding.

Consider this: 84 per cent of companies in Singapore use digital channels to promote their products and services, but only 17 per cent can directly link their marketing strategies to increased revenue.

That’s far below Asia’s regional average of 41 per cent. For me, this highlights a critical gap. It’s not about having more data; it’s about making sense of it in a way that drives real, measurable results.

Why traditional marketing methods aren’t enough

Modern marketing isn’t about following the latest trends or relying on gut instinct. Success comes from making informed decisions based on a deep understanding of consumer behaviour. When businesses stick to outdated methods or surface-level analytics, they risk missing what truly drives conversions, loyalty, and growth.

How AI and data analytics are changing the game

AI and advanced analytics have become game-changers for brands. Take Coca-Cola, for example—they use AI to analyse customer preferences, buying habits, and social media trends to create hyper-targeted campaigns and new products. Amazon goes even further, applying AI for personalised recommendations, dynamic pricing, and real-time inventory forecasting. These strategies have boosted both customer satisfaction and sales.

Also Read: VC crunch hits Southeast Asia: US$129M raised in May 2025, down 70% MoM

Unlike traditional market research, which I’ve seen can be slow and expensive, AI lets us interpret vast amounts of complex data in real time. This means we can adapt marketing strategies based on what consumers are actually doing—not just what we think they might do.

What to expect from data-driven marketing solutions

As competition grows fiercer, I know that brands—my own included—expect more from their marketing investments. We need data platforms that analyse consumer behaviour across multiple touchpoints and deliver actionable insights. It’s no longer enough to track page views or clicks. I want to understand the “why” behind customer actions.

Understanding consumer psychology and predicting behaviour has become a personal focus for me as a marketer. With shifting preferences, fragmented channels, and rising expectations, I can’t afford to rely on surface-level metrics. I need to know what truly motivates my audience to engage, convert, and stay loyal.

Adopting a behaviourally informed approach is non-negotiable. We help brands go beyond performance metrics to tap into deeper insights—how customers think, what influences their decisions, and how to communicate with them more meaningfully.

Making data-driven marketing a strategic asset

For me, embracing AI-driven insights isn’t just an option—it’s a necessity. McKinsey and KPMG both highlight how data-driven marketing is about making smarter, more effective decisions that align with consumer needs. The brands I see succeeding are those leveraging these insights to improve retention, boost engagement, and use their marketing budgets more efficiently.

Future belongs to smart solutions

If you want to stay ahead of the competition and drive real, sustainable growth, investing in AI-powered, data-driven marketing is the way forward. With the right insights, you can transform your marketing strategies and build stronger, more lasting connections with your customers.

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Seeking a state of calm in the current trade storm is clearly challenging the minds of corporate strategists around the globe today, but one thing remaining constant is the focus and optimism over AI and digital capabilities.

Amid uncertainty, digital transformation remains a critical driver for business and deal strategies, with AI capabilities increasingly driving corporate acquisitions. At the same time, a shift to defensive consolidation helps companies build operational and competitive resilience.

The EY-Parthenon CEO Outlook Survey revealed unprecedented optimism in the transformative power of technology, particularly AI, from Asia-Pacific’s CEOs. In fact, 85 per cent predict that AI will be a decisive factor in establishing industry leadership in 2025. An even higher number, 87 per cent of leaders, believe that the accelerated adoption of AI and the associated up-skilling of their workforce will be crucial differentiators in the coming years.

The latest EY research on GenAI shows 90 per cent of firms surveyed have adopted AI into operations to some degree, but most remain in the early stages, and eight per cent have not integrated AI at all.  72 per cent of surveyed leaders plan to increase annual investment related to GenAI specifically, and the rush to AI challenges business leaders to seek both optimisation of adoption strategies and acquisition as a means to accelerate capabilities.

M&A outlook in flux but opportunities remain

The prevailing optimism around AI and technology coincided with an early surge in expectations in M&A deals at the start of the year. Nearly half of the CEOs surveyed (42 per cent) expressed strong confidence in investing in Capex and R&D, as they focus on leveraging technological advancements such as AI to secure competitive advantages.

Many CEOs identified M&A as a transformation accelerant in 2025, with confidence by Asia-Pacific CEOs (61 per cent) driving an even greater appetite for deals versus European and American peers.

Also Read: When the chain snaps: How tariffs are unraveling Southeast Asia’s SMEs

In response to significant trade and market disruption, a portfolio realignment is essential to counter geopolitical risk and shifting market growth potential. Trade policy and supply chain risks will now govern many investment decisions.

 CEOs need to navigate this landscape adeptly to fully harness the value of any planned transactions while integrating technology investments that reshape growth strategies.

Pockets of opportunity remain, such as in Southeast Asia. Despite risks from recent tariff announcements out of Washington, the region could see increased investment and partnerships as governments and businesses seek growth opportunities in the volatile landscape.

Businesses anticipate greater investments through joint ventures, partial ownership, and minority interests in Asian companies. This reflects their need to balance uncertainty in countries like China, Canada, Mexico, and the EU that face added complexity given implications of tariff announcements and growing trade challenges. Strategic partnerships will help businesses navigate risks and drive growth.

There are clear opportunities in markets and regions of growing domestic demand to “build” domestic supply chain ecosystems to avoid tariff risks. The major obstacle in this path is that this strategy hinges on more than procuring and manufacturing locally, but also whether the destination of finished goods can be locally contained.

Also Read: Asia’s trade turning point: How tariffs and geopolitics are redrawing supply chains

More mature markets, like Japan and Korea, face the complex challenge of consolidating and restructuring their companies’ supply chains, which have expanded globally both upstream (e.g., raw materials and production) and downstream (e.g., distribution and sales). In a tough economic climate, achieving cost efficiencies through these efforts becomes increasingly attractive.

Seeking a state of hyper-agility and resilience

Companies will view current critical trends and disruptions as either threats to their existence or opportunities. It is this difference that separates industry leaders from laggards. Today’s business landscape is shaped by disruptive forces: rapid technological advancements, including artificial intelligence (AI), climate change-driven sustainability agendas, and geopolitical tensions affecting supply chains and global operations.

Digital ecosystems expose new threats and opportunities, while remote work reshapes organisations. Rising cybersecurity risks, shifting consumer expectations, economic volatility and complex regulations demand agility. Meanwhile, emerging markets create new competitive dynamics and growth potential.

In terms of deal strategy, while uncertainty clouds the immediate future, M&A should still be leveraged as a transformation catalyst. CEOs should look to M&A, particularly in distressed times as an opportunity for accelerated transformation. Target deals will include those that align with long-term goals, such as adopting new technologies, entering new markets or strengthening competitive positioning through strategic consolidation.

CEOs clearly need to sharpen their focus on the interplay of macroeconomic, geopolitical, regulatory and technological forces. Proactively addressing these risks enables growth opportunities and helps mitigate disruption. The traditional business dashboard must factor in more diverse sources of insight and more outlier views than in the past. New indicators must be established to build a wider view of possible scenarios so that a path to operational resilience can be found.

The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organisation or its member firms.

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As AI adoption accelerates across Asia Pacific, the region is facing an urgent need to implement more sustainable AI practices. AI workloads – particularly those running on hyper-scale data centres – are energy-intensive. Given that Asia Pacific has accounted for the largest share of newly added data centre supply over the past decade, ensuring AI systems are sustainable is a growing priority.

AI’s environmental impact is a subject that is well-established, no longer a conversation limited to expert forums and panel discussions. Although AI technologies promise breakthroughs in healthcare, climate science, energy transition acceleration, and more, it’s crucial to address AI’s predicted environmental downsides.

At its core, AI models are trained and run on extremely powerful, energy-hungry computers that rely on electricity that largely comes from carbon-intensive sources. Left unchecked, this could lead to significant carbon emissions.

There are many ways that we can address AI sustainability, but one often overlooked lever we can pull to improve the efficiency of AI workloads is data optimisation, or “data efficiency”. AI models rely on vast pools of data to be effective, but indiscriminately dumping disorganised, irrelevant, or even duplicate data into AI models leads to systems having to do extra work processing excess information. We cannot afford to be wasteful when it comes to AI.

By optimising the data before we feed it to AI models, we can help better manage the environmental footprint of AI. This requires careful forethought and expert planning that looks for sustainability gains along the entire AI lifecycle and prioritises data efficiency when planning AI projects.

How to tackle data efficiency for AI workloads 

• Map out your data strategy upfront

Begin by clearly defining what data you need, where it will come from, how often it will be collected, and how it will be processed. Consider if data can be consolidated, stored using low-impact techniques, such as tape or other backup methods, or discarded if no longer necessary.  Offloading non-essential data to more energy-efficient storage methods can reduce power consumption. 

• Clean up before you start

Data efficiency in AI goes beyond just storing useful data. Data sets should be cleaned and optimised before training a model. Using raw, off-the-shelf data sets or repositories without minimising them results in unnecessary work and inefficiency. Cleaning data upfront ensures the model works more effectively and requires fewer resources.

• Get the training data set right

Data efficiency starts with an optimised data set for training, and using customer-specific data during model tuning helps further refine the model. By ensuring that data is as concise as possible from the start, you set a foundation for efficient processing throughout the entire AI lifecycle.

• Process data only once

Once data is processed for training/tuning, avoid redundant processing. Any additional training or fine-tuning should only occur on new data, minimising repeated energy-intensive operations.

Also Read: How a data-driven approach can optimise decarbonisation in the built environment

• Avoid data debt

Managing data is especially critical for AI workloads due to the massive volumes of data, including unstructured data. One of the key strategies for reducing the environmental burden of data is eliminating inaccurate, erroneous, out of date, or duplicated data. Like technical debt, data debt – where outdated or unnecessary data accumulates – can severely impact AI systems’ performance and sustainability.

• Location matters

Processing data as close to its source as possible minimises the energy required to move it. Optimising data movement reduces both the environmental and time-related costs, ensuring faster, greener AI operations.

As AI becomes integral to industries like manufacturing, logistics, and smart city initiatives, the need for more sustainable AI practices across Asia Pacific becomes more pressing. Singapore is already making strides in this area, with a focus on sustainable data centre innovation and initiatives like the . This approach is critical to ensuring AI systems can scale responsibly.

Asia Pacific’s growing dependence on AI-driven technologies presents a unique opportunity for the region to lead by example. Through initiatives that promote energy-efficient data management and more sustainable AI strategies, Singapore is positioning itself as a global leader in creating sustainable AI ecosystems.

To build a sustainable AI ecosystem in Asia Pacific, organisations must start with clean, lean data. As AI technologies become more embedded across industries, ensuring the data feeding into these models is optimised will not only help reduce energy consumption, but also foster innovation in a way that is more environmentally responsible.

For businesses in Singapore and the wider APAC region, prioritising data efficiency today will help ensure AI’s potential is fully realised without compromising the planet’s future.

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Tashi Network, a company operating as the coordination layer for intelligent systems, has closed an oversubscribed funding round, aiming to empower trustless coordination for robots and AI agents.

The round was co-led by Blockchain Founders Fund (BFF) and Exponential Science Capital (ESC). The roster of participants also included Taisu Ventures and MN Capital.

The deal also attracted high-profile industry angels, including Gabby Dizon, co-founder of Yield Guild Games, and Wei Zhou, CEO of Coins.ph and former CFO of Binance.

Also Read: Blockchain and AI copyright: A revolution in digital rights management

The capital raised will be used to support rapid network growth and the company’s upcoming token launch on the Solana ecosystem.

Based in Singapore and California, Tashi focuses on replacing centralised coordination systems with a verifiable, distributed framework that allows machines to synchronise, validate, and settle actions in real time, without relying on central servers.

The company’s technology essentially transforms coordination itself into a form of currency; something that is measurable, rewardable, and tradable across the intelligent economy.

The startup’s experienced team of serial entrepreneurs has had multiple previous successful exits, including one sale valued at US$34 million to a company listed on the New York Stock Exchange. Amar Bedi, CEO of Tashi, previously held roles at tech giants Grab, Uber, and KPMG.

“The next computing revolution will allow trustless coordination among edge devices,” stated Bedi. He emphasised that Tashi’s core consensus technology, known as Vertex, enables the offering of “trust-preserving, global-scale coordination without any servers for the first time.”

Solving the centralisation problem

Tashi was explicitly designed to address existing industry challenges, as evidenced by the recent major outages experienced by companies like AWS. Current systems, including centralised clouds and global chains, often fail to deliver the instant, local coordination required by AI, robotics, and autonomous systems.

The company addresses this foundational problem by bridging decentralised physical infrastructure network (DePIN) with a novel approach to peer-to-peer consensus.

To understand how Tashi operates, imagine a massive orchestra composed entirely of robot musicians and AI conductors. Normally, they rely on one powerful, centralised maestro (a server) to tell them when and how to play. If that maestro has a cough or disappears, the entire concert stops.

Also Read: How AI and blockchain collaborate for a transparent Web3 future

Tashi, however, gives every single musician a small, independent mechanism to verify and synchronise their actions with everyone else instantly and securely. This means they can perform a complex symphony perfectly without any single conductor being in charge, ensuring the music never stops.

The firm is already demonstrating significant traction; it has built products targeting specific industries and currently boasts over 100 ecosystem partners and has secured early paying clients. Its DePIN already supports more than 50,000 nodes, which are run by the community.

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