Most conversations about AI focus on tools. What model to use? What agent to deploy? What workflow to automate?

But after spending the past few months building AI-first systems inside real communities, I’ve realised something far more important than tooling choices: AI rarely breaks first. Trust does.

And once trust erodes, scale doesn’t save you. It accelerates the damage.

From products to communities

I didn’t set out to build “another AI product”.

What we’ve been building instead are AI-first, custom systems designed for existing communities — founders, creators, speakers, operators. These are not anonymous users on a landing page. These are people with shared history, shared context, and ongoing relationships.

That distinction matters.

When AI is embedded inside a community, it stops being neutral software. It becomes part of how people:

• Ask questions
• Make decisions
• Interpret authority
• Relate to each other

This is why I keep returning to a simple framing: Communities are the currency. AI is the engine. Human relationships are the result.

Founders who design AI without understanding the relationship triangle tend to break things they didn’t realise they were touching.

Vibe coding changed the speed — not the responsibility

AI-assisted development has radically compressed time.

What once took months can now take days. What used to be a “test landing page” is now a working MVP.

We are no longer validating ideas with email opt-ins. We are validating them with real products, in public, with real people.

This is powerful, and it’s also where misuse begins.

Because when building becomes easy, clarity becomes the true bottleneck.

Founders often rush to ship without answering:

• What is the outcome this AI is optimised for?
• What decisions are allowed to influence?
• Where must a human always intervene?
• What does “done” actually mean?

When those questions are unanswered, AI doesn’t fail loudly. It fails quietly — through misalignment.

Also Read: The great stabilisation: Why 2026 will be the year AI “grows up”

What actually breaks when AI scales

The assumption is that AI will fail technically. In reality, what breaks first is almost always human.

Trust breaks before tech does.

AI sounds confident by default. Communities assume intent by default.

When founders test AI systems inside communities without transparency — without clearly saying this is early, this is experimental, this is evolving — people don’t feel included. They feel misled.

In practice, I’ve seen two very different outcomes:

• In communities where experimentation was explicit, members gave better feedback, tolerated rough edges, and stayed engaged.
• In communities where AI changes appeared suddenly and opaquely, engagement dropped — not dramatically, but quietly.

And quiet disengagement is the hardest to recover from.

User experience breaks when expectations aren’t designed

Speed creates a dangerous illusion.

Fast answers feel like accurate answers. A confident tone feels like authority.

Without clear boundaries, AI begins to:

• Answer beyond its scope
• Sounds definitive when it should be conditional
• Close loops that should remain open

One principle has consistently prevented damage: Analyse, guide, recommend — but do not instruct.

In systems where this boundary was respected, users treated AI as support. Where it wasn’t, users outsourced judgment too quickly and blamed the system when things went wrong.

The difference wasn’t the model. It was the design decision.

Founders automate responsibility away — unintentionally

This is the most subtle failure mode.

As AI handles more replies, routes more conversations, and “keeps things moving”, founders begin to disengage — not out of laziness, but out of misplaced trust in the system.

Silence gets filled by automation. Judgment gets deferred.

In one case, a system functioned perfectly from a technical standpoint, but users grew confused about who was actually accountable. The AI had become the voice of the product.

That confusion didn’t create errors. It created hesitation.

The issue wasn’t hallucination. It was abdication.

Also Read: How are the companies you invest in leveraging AI? 

The hidden variable: Founder operating style

Working closely with multiple founders across different AI-first builds surfaced a pattern I didn’t expect to be so stark:

AI doesn’t neutralise founder behaviour. It amplifies it.

Three archetypes consistently emerge.

• The co-founder of the builder

This founder treats AI as a collaborator, not a replacement.

Communication is two-way. Roles and responsibilities are explicit. Good questions are asked early. Cashflow and constraints are respected.

In these environments, AI performs exceptionally well — not because it’s more advanced, but because decision ownership remains human.

Observable outcomes:

• Faster iteration with less resistance.
• Higher-quality feedback from the community.
• Fewer rollbacks, fewer trust repairs.
• Users feel invited into the build, not managed by it.

Here, AI scales clarity — not chaos.

• The builder-by-habit founder

This founder is capable, competent, and often technically strong, but less collaborative in exploration.

They build because they can. They optimise execution more than alignment.

In these cases, AI reveals something uncomfortable: The founder might be better served by configuring an existing system instead of inventing a new one.

Observable outcomes:

• More features, less coherence
• Slower momentum despite higher build velocity
• Eventual consolidation back into off-the-shelf tools

AI doesn’t fail here. It exposes opportunity cost.

• The reactive founder

This is the most fragile archetype.

The founder responds only when asked. Avoids proactive decision-making. Delegates judgment without context.

AI fills the gaps, and the system drifts.

Observable outcomes:

• Accountability becomes unclear.
• The AI becomes the de facto authority.
• Community confidence erodes.
• Founder ends up firefighting instead of leading.

AI doesn’t fix leadership gaps. It scales them.

The real misuse of AI

Most founders believe they are scaling:

• Speed
• Efficiency
• Support

What they are actually scaling is:

• Unclear intent
• Weak boundaries
• Unfinished thinking

AI does not create these problems. It accelerates whatever already exists. That’s why copying AI stacks without copying operating discipline fails so often.

What this looks like in practice

Founders who scale AI responsibly tend to decide a few things early — not as rules, but as design principles:

• What decisions AI can support, but never make.
• Where human override is mandatory.
• How experimentation is communicated to users.
• When not to build, even if they can.

They understand constraints:

• Not everything integrates.
• Not all data is extractable.
• Not all workflows should be automated.

They build MVPs first — not because they’re careless, but because no system is complete at launch. What matters is whether it evolves with its community.

The real takeaway

AI-first isn’t about replacing humans.

It’s about revealing how founders think, decide, and lead — faster than ever before.

When AI is embedded inside communities, those truths surface immediately.

Communities are the currency. AI is the engine. Founder behaviour determines whether trust compounds or collapses.

—

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Indonesia’s Davos push was less about headline-grabbing meetings and more about signalling intent in a world where semiconductors have become geopolitical currency. By courting Nvidia, AWS, and leading US cybersecurity firms, Jakarta is making it clear that it no longer sees chips as a peripheral manufacturing play, but as foundational infrastructure for its digital and economic ambitions.

What stands out is timing. As AI accelerators, data centre GPUs, and advanced packaging emerge as global bottlenecks, Indonesia is positioning itself precisely where supply chains are under strain. Its recent progress — from the Batam assembly facility to advanced packaging investments in East Java — gives the pitch credibility. This is no longer a greenfield dream; it is an ecosystem under construction.

Yet ambition alone will not secure a place in the upper tiers of the semiconductor value chain. Assembly and testing are important entry points, but they are also crowded and margin-thin. The harder work lies in talent depth, sustained capital flows, and policy consistency over decades, not election cycles. Fast permits and generous incentives buy attention, not loyalty.

For Nvidia, AWS, and others, Indonesia offers optionality: cost advantages, geopolitical neutrality, and scale. Whether that optionality turns into long-term commitment will depend on execution. Davos opens doors; factories, engineers, and stable rules decide who walks through them.

REGIONAL

Indonesia courts Nvidia and AWS as it eyes a bigger role in global chip supply chains: Over the past three years, Indonesia has moved aggressively to establish a foothold in the semiconductor industry, transitioning from a near-absent player to a credible assembly and testing destination.

Singapore places a US$786M bet on AI sovereignty: A large public fund creates room to expand national compute capacity, subsidise cloud access, and build shared research infrastructure that universities, labs, and startups can tap.

Juspay raises US$50M, makes secondaries mainstream in Indian fintech: WestBridge-backed round underscores how employee liquidity, cleaner cap tables, and price discovery are reshaping Asia’s private funding playbook. The company claims its annualised TPV now exceeds US$1T and that it processes 300M+ transactions daily.

Singapore’s AI startup Level3AI raises US$13M: Investors include Lightspeed, Beenext, and 500 Global. Level3AI builds customer support and sales agents for enterprises. It deploys its engineers to co-design the AI agents with the client. The agents are then integrated into the client’s text and voice support channels.

Singapore’s data centre firm DayOne eyes IPO at US$20B valuation: The firm considers hiring banks for a share sale that could occur as early as this year, and is also evaluating a dual listing in the US and Singapore. Earlier this month, DayOne completed a US$2B+ Series C round, which was expected to value the company at around US$10B.

Malaysia reopens Grok AI access after temporary ban: This follows the social media platform’s introduction of additional safety measures. The country temporarily blocked Grok earlier this month following concerns over a feature that allowed users to generate and share sexualised images.

FEATURES & INTERVIEWS

How SPUN uses agentic AI to untangle Southeast Asia’s visa mess: SPUN is a plug-and-play platform blending AI document checks with human savvy, boasting a 99% approval rate across thousands of applications. No blind automation here. AI flags dodgy docs or regulatory quirks, escalating to specialists.

How SMEs can vet and choose AI partners that truly deliver: AI is becoming a great equaliser for SMEs, lowering barriers through generative tools that boost agility, automate testing, improve software quality, and help smaller firms compete with enterprise giants globally.

INTERNATIONAL

UAE’s K2, WeRide to launch autonomous bus service in Abu Dhabi: K2, a local mobility solutions provider, will leverage its experience in fleet management and real-world mobility deployments, while WeRide will contribute its autonomous vehicle technology and deployment expertise.

South Korea launches US$186M AI manufacturing fund: This marks the largest allocation to date and an 11.5% increase from last year. The funding aims to support AI-powered manufacturing. The programme will fund shared equipment and facilities for testing, evaluation, and pilot projects.

BYD targets 1.3M overseas EV deliveries in 2026: The Chinese company plans to double its European showrooms to 2,000, and establish a local supply chain for European production. BYD also operates factories in Thailand and Brazil to support rising demand.

South Korea’s robotics industry faces supply chain risk: Korea ranked fourth globally in installed robotic equipment in 2024, with a high robot density of 1,012 robots per 10,000 employees. Despite this, nearly 89% of Korea’s imports of permanent magnets and 60% of raw materials like rare earth elements depend on China.

TikTok US uninstalls jump 150% after joint venture announcement: The deal was announced last January 22. Some users reacted skeptically after being asked to accept an updated privacy policy that mentions collecting sensitive data, though similar language already appeared in a version from August 2024.

Zoom’s Anthropic stake may be worth up to US$4B, analysts say: In May 2023, Anthropic announced a partnership with Zoom and said Zoom Ventures had invested, though the amount was not disclosed. Zoom reported US$51M in strategic investments that quarter, with analysts estimating most of it went to Anthropic.

EU tightens rules on WhatsApp to tackle harmful content: The move follows the platform’s reported 51.7M average MAUs in the EU during the HI 2025, surpassing the 45M-user threshold set by the DSA. The designation aims to strengthen oversight and accountability for platforms with significant user bases within the EU.

Antler Japan invests US$1.5M in 10 early-stage startups: The firm announced a new six-week Inception Residency for 2026, increasing initial funding to US$150,000 per startup. Selected companies operate in fields such as robotics, AI, logistics, legal tech, and biotech.

SEMICONDUCTOR

Taiwan electronics output hits record high on AI chip demand: The overall industrial production index rose to 112.2, with the manufacturing subindex climbing 17.9 percent to 113.1. The electronics component industry saw a 24.7% rise. In December, the industrial production index increased by 21.6% to 131.8.

Nvidia launches AI weather forecasting tools: The new models are designed to improve forecast accuracy and speed across different timescales. These open-source tools aim to make weather AI accessible for scientists, startups, and government agencies globally, reducing reliance on traditional supercomputers.

Microsoft, Tsinghua use Nvidia chip to train AI without real data: Using only synthetic data, the team trained a 7B-parameter coding model that outperformed larger 14B-parameter models on benchmarks. The experiment used 128 Nvidia H20 chips for 220 hrs during supervised fine-tuning and 32 H200 chips for 7 days during reinforcement learning.

Microsoft unveils new AI chip to cut reliance on Nvidia: The new Maia 200, an AI inference accelerator chip fabricated on TSMC’s 3nm process, is designed to improve the efficiency of large-scale AI workloads. The chip features native FP8/FP4 tensor cores, a redesigned memory system with 216GB HBM3e memory at 7TB/s.

AI

AI to add about US$607B to India’s economy by 2035: PwC: AI may add up to 15% to global GDP by that year, driven by productivity gains in sectors such as manufacturing, healthcare, agriculture, energy, and education. In agri, the sector’s gross value addition is projected to increase from US$637B in FY25 to US$2.4T in FY47.

Human-centric skills in the age of AI: How to never lose touch with humanity in the workplace: AI lacks the nuanced understanding and ethical reasoning that define human interactions. This is why human-centric skills remain relevant.

AI data centres vs climate: How can business leaders find a workable balance? AI adoption in Southeast Asia strains water and power resources, forcing businesses to confront ESG impacts and use AI more responsibly.

The US$1M per person revolution: How AI is reshaping Southeast Asia’s startup landscape: Southeast Asian startups are adopting AI to drive US$1 million revenue per employee through smart automation across research, content, and sales.

THOUGHT LEADERSHIP

Building the ASEAN AI archipelago: How SEA can secure its place in the global AI value chain: ASEAN’s AI future depends on regional interoperability, deep localisation for SMEs, and an integrated semiconductor backbone—moving Southeast Asia from fragmented adoption to a resilient, collaborative force in the global AI value chain.

The surprising economics of orbital data centres — and the real solution: Falling launch costs make space-based solar viable for AI energy, but orbital data centres fail economically; the numbers favour beaming clean, firm power from space to Earth-based compute in future.

Hiring for human skills in a tech-heavy world: Southeast Asia must shift from tech-first thinking to human-centric problem-solving, using AI as a tool guided by empathy, critical thinking, and purpose-driven skills to ensure technology serves societies and governments.

Commercialisation ≠ sales: Understanding the difference early matters more than it seems: Early startups confuse sales with commercialisation, mistaking deals for validation. Without a clear commercialisation system, early revenue creates false traction, premature scaling, and fragile growth instead of repeatable businesses models.

The strategy trap: Why your best plan is failing to launch: Most SME strategies fail not from poor vision but weak execution, misaligned metrics and incentives, vague priorities, and unchanged behaviours, where leaders avoid decisions needed to turn intent into action.

Gold hits US$5K and crypto bleeds: What comes next? Markets opened amid geopolitical tensions, gold surged past US$5,000, equities diverged, Asia hedged dollar risk, while crypto slid on hacks and liquidations, exposing trust across traditional and digital financial systems.

The resume is dead: Why 80% of companies fail to hire based on real skills: Resumes dominate hiring despite being unreliable, leaving skills untested. As companies gamble on credentials, AI-driven, skills-based hiring is emerging to prioritise real ability, potential, and proof over polish.

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This was a day of stark contrasts and palpable anticipation, as traditional equities climbed higher, gold achieved a historic milestone, the US dollar retreated significantly, and the crypto sphere staged a notable comeback.

The narrative is complex, with investors juggling the immediate bullish sentiment fueled by technical rebounds and institutional plays against a backdrop of looming macroeconomic risks, including US tariff threats, an upcoming Federal Reserve decision, and large tech earnings reports. My view is one of cautious observation: while the short-term bounces in both equities and digital assets offer a glimmer of optimism, the underlying instability suggests a market holding its breath, keenly aware that a single headline could trigger a rapid reversal.

The US stock market delivered solid gains on Monday, pushing major indices closer to record territory. The S&P 500, a key benchmark, advanced a respectable 0.50 per cent to close at 6,950.23 points, placing it within a mere 0.4 per cent of establishing a new all-time high. This performance was mirrored by the Dow Jones Industrial Average, which saw a healthy 0.64 per cent increase, adding over 300 points to finish the session at 49,412.40 points. The tech-heavy Nasdaq Composite also participated in the rally, rising 0.43 per cent to reach 23,601.36 points. These moves suggest a market largely driven by optimism and positioning ahead of crucial economic events scheduled for the week.

The safe haven asset, gold, provided one of the day’s most dramatic headlines, soaring past the US$5,000 per ounce threshold for the first time in history. The precious metal was trading near a record high of US$5,100 per ounce early Tuesday morning. This incredible surge is a direct consequence of strong safe-haven demand, with investors flocking to stability amidst heightened global uncertainty.

Also Read: Gold hits US$5K and crypto bleeds: What comes next?

Paradoxically, the US dollar, another traditional safe haven, moved in the opposite direction. It weakened to its lowest level since 2022, with the euro exchange rate sitting near EUR0.84125 per US$1 on Tuesday morning. This divergence highlights the specific nature of current investor fears, which seem more attuned to geopolitical tremors than domestic US economic factors.

Simultaneously, the crude oil market saw modest fluctuations. Brent crude futures, the international benchmark, slipped slightly by 0.4 per cent to settle at US$65.59 a barrel on Monday. The market action here seems a delicate balance between potential supply disruptions caused by a US winter storm and the possibility of progress in ongoing peace talks, dampening fears of an immediate crisis impact on oil flows.

A significant driver of this volatility, and the corresponding boost for gold, was US President Donald Trump’s announcement. He signalled a potential tariff hike on South Korean goods, including autos and pharmaceuticals, to a flat 25 per cent. This sort of protectionist rhetoric inevitably fuels global market jitters, pushing capital toward perceived safety and away from riskier assets.

In Asia, markets displayed a modest recovery. The MSCI Asia Pacific Index initially showed weakness but found some footing, while the South Korean Kospi index, despite the potential tariff threat looming over its economy, reversed early losses to climb by 0.8 per cent. This resilience indicates that while investors are concerned, they remain reactive to immediate market dynamics and technical trading patterns.

The cryptocurrency market, often marching to its own drum but increasingly correlated with mainstream finance, experienced its own compelling rebound. The total crypto market cap rose 1.34 per cent over the last 24 hours, shaking off deeply oversold conditions. This recovery was not accidental; it was a response to specific market catalysts. A primary factor was a technical rebound, with the RSI14 hitting 26.98, a classic indicator of oversold territory signalling exhaustion in selling pressure. Bitcoin, the market leader, reclaimed the US$88K support level after briefly testing US$86K, offering a measure of relief to anxious holders.

Also Read: Crypto in the danger zone: Technical weakness, low volume, and a critical support test

Institutional conviction also played a crucial role in the crypto resurgence. News that BitMine had acquired 40,302 ETH, valued at an impressive US$120 million, and had staked over 2 million ETH in total, provided a significant boost to market confidence. This followed on the heels of BlackRock’s Bitcoin Premium Income ETF filing, indicating that major players see long-term value despite short-term headwinds.

Even as gold touched an all-time high of US$5,069, social media chatter indicated a palpable shift of focus towards higher beta assets like Bitcoin and Ethereum. This rotation is evident in the rising crypto-Nasdaq correlation, which climbed to 0.52, amplifying equity-linked moves within the digital asset space.

Ultimately, today’s market dynamics, spanning traditional stocks, commodities, and the volatile crypto realm, reflect a complex interplay of technical factors, institutional moves, and overarching macro concerns. My perspective suggests the gains seen across the board represent a temporary reprieve, a technical healing process if you will, rather than a definitive shift in market direction.

Major risks such as potential US government shutdown fears and persistent ETF outflows in the crypto sector remain significant headwinds. The market is positioned at a crucial juncture, watching key levels like Bitcoin’s US$88K support and Ethereum’s US$2,960 level, waiting to see if institutional accumulation can truly counter the prevailing retail caution in the days ahead.

The true test for global markets will arrive later this week, as the world awaits the Federal Reserve’s pronouncements and the highly anticipated wave of technology company earnings reports, events that will undoubtedly shape the near-term financial landscape.

—

The lead image in this article was generated by AI.

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Cybersecurity has moved far beyond the server room. According to Penta’s report, Cyber risk is stakeholder risk: What leaders need to know now, cyber incidents are now as much about trust, reputation and leadership as they are about firewalls and patches. Drawing on data from January 2024 to August 2025, the report makes one thing clear: how an organisation responds to a cyber incident now defines its standing with stakeholders more than the breach itself.

For business leaders, this marks a decisive shift. Cybersecurity is no longer a purely technical challenge managed by chief information security officers behind the scenes. It has become a board-level and executive issue, with implications for brand equity, regulatory scrutiny and investor confidence.

One of the most striking cybersecurity trends highlighted in the report is the collapse of stakeholder trust following cyber incidents. Sentiment around data security and incident response remains strongly negative, particularly among regulators and investors. In an era of heightened transparency, silence or slow reactions are interpreted as incompetence or indifference.

This erosion of trust cuts across geographies and sectors. Cyber incidents now generate negative sentiment globally, regardless of where the breach occurs. However, industries with direct consumer interfaces suffer the sharpest declines, reflecting public sensitivity around personal data and service disruption.

Cybersecurity meets geopolitics

The report also underscores how cybersecurity has become entangled with geopolitical risk. State-linked attacks, such as those attributed to the group known as “Salt Typhoon,” have reframed cyber threats as matters of national security and diplomacy. Attacks on major telecom providers have raised concerns not only about corporate resilience but also about critical infrastructure and espionage.

Also Read: The great rotation: Why investors are balancing record gold with high risk crypto

For leaders, this adds another layer of complexity. Cybersecurity decisions are no longer judged solely on commercial outcomes but also on their broader societal and political implications. But perhaps the most important insight from the report is the emergence of what it calls the “response” narrative. In today’s environment, the defining story is not how a breach occurred but how it was handled.

Penta points to the CrowdStrike outage as a telling example. While the incident itself drew intense scrutiny, visible executive accountability and clear communication helped stabilise sentiment within 30 days. The lesson for leaders is stark: rapid transparency and visible leadership can materially shape stakeholder perception, even in the wake of significant disruption.

Technical containment remains essential, but it is no longer sufficient on its own. Cybersecurity resilience is now measured by an organisation’s ability to communicate, reassure and demonstrate control in real time.

The report’s sector analysis shows how cybersecurity risk manifests differently across industries. Technology companies face the highest visibility, accounting for 74 per cent of mentions, and are increasingly seen as sources of systemic risk. High-profile incidents involving cloud and security providers amplify concerns across the wider digital ecosystem.

Retail emerges as the most sentiment-sensitive sector, with negative sentiment reaching minus 77. Legal action and breaches at well-known brands highlight how exposed consumer-facing businesses are when customer data is compromised.

Telecommunications sits at the intersection of cybersecurity and national security, while financial services continues to battle scepticism around digital asset security and crypto resilience. Healthcare sentiment has collapsed amid fears over patient safety and systemic disruption, and the automotive sector is under growing strain as connected vehicles expand the attack surface, with cyber attacks rising 50 per cent year-on-year.

The report’s overarching message is that cybersecurity is now inseparable from stakeholder management. Leaders must treat cyber risk as a reputational and governance issue, not just a technical one. This means preparing not only incident response plans but also communication strategies, leadership visibility and regulatory engagement.

In a world where trust can evaporate overnight, cybersecurity has become a defining test of leadership. Those who recognise this shift — and act accordingly — will be better positioned to recover when, not if, a breach occurs.

—

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Antler is scaling up its bet on Japan’s early-stage startup ecosystem, investing ¥240 million (US$1.55 million) across 10 Japanese startups in 2025 and committing to significantly larger pre-seed cheques for founders entering its Japan programme in 2026.

The move signals growing conviction that Japan — long viewed as a challenging environment for venture-backed startups — is becoming a viable launchpad for globally competitive technology companies, particularly in deeptech, AI, robotics, and applied enterprise software.

Also Read: OpenAI calls for ‘AI infrastructure revolution’ to reboot Japan’s growth

Under its updated model, Antler Japan will run a streamlined six-week Inception Residency in 2026, offering US$150,000 net upfront per company, nearly doubling its previous initial investment. Founders can also access up to US$250,000 in matching follow-on capital within six to nine months, bringing total capital at inception to as much as US$400,000.

“We are deliberately doubling down on Japan as a source of globally competitive companies,” said Jussi Salovaara, co-founder and managing partner at Antler. “The local talent pool is highly skilled, technically rigorous, and already comfortable building at the frontier of modern technology.”

A highly selective funnel

Antler’s Japan operation remains sharply selective. Since entering the market in 2022, the firm has funded 20 companies out of more than 2,100 applications — an acceptance rate of roughly 0.5 per cent. The 2025 cohort reflects a clear strategic tilt: founders building for international markets from day one, often combining Japanese technical depth with global operating experience.

Each of the 10 startups received ¥24 million in pre-seed funding through Antler’s Inception Residency model, which supports founders from company formation through early validation and investor readiness.

The portfolio spans sectors where Japan’s strengths in engineering, manufacturing, and compliance-heavy industries create defensible advantages. These include Refined Robotics, which is developing wheel-legged delivery robots claimed to be up to 25 times more energy-efficient; Logistical, which uses AI to reduce empty freight runs in transport networks; and Rubi Labs, whose “Lapis” platform targets AML and counter-terrorism financing risks by detecting anomalies across fragmented KYC and transaction systems.

Also Read: Forget China and the US–Japan is the true powerhouse of mobile game spending

Other companies include KanjuTech, working on brain-inspired AI for autonomous systems that requires significantly less data and energy; Smart Tissues, which is developing biomaterials for chronic wound healing; and Snappy Compliance, an AI-driven regulatory automation platform for robotics firms.

Why Japan, and why now

Japan’s startup ecosystem has historically lagged peers in Southeast Asia and the US due to risk-averse corporate culture, limited early-stage capital, and founders content with domestic scale. That calculus is beginning to change.

Venture capital deployment in Japan reached roughly US$7 billion in 2025, up 20 per cent year-on-year, driven largely by AI, robotics, biotech, and enterprise technology. Labour shortages, supply chain reconfiguration, and demographic pressures are accelerating enterprise demand for automation and applied AI.

Government intervention has also played a role. Startup visas, large public funds, and renewed industrial policy have lowered barriers for both domestic and foreign founders. Notably, every Japanese company Antler added to its portfolio in 2025 included at least one international founder.

“The Japan market offers access to world-leading customers across multiple verticals, strong and resilient supply chains, a highly skilled and affordable talent pool, and increasingly positive signals around startup support and capital availability,” said Florian Geier, senior director at Antler Japan.

A regional strategy, not a local one

Antler’s Japan strategy is closely tied to its broader Asia footprint, including Singapore and Southeast Asia. Japanese startups in its portfolio are testing expansion, partnerships, and customer acquisition across ASEAN markets earlier in their lifecycle, while founders from the region increasingly view Japan as a base for R&D and enterprise access.

The firm sees Japan as a long-term core hub within its 27-location global platform, rather than a standalone market. Investor Demo Day for the 2025 cohort is scheduled for February 13, targeting downstream investors across Asia.

The next Inception Residency will begin on May 11, 2026, aimed at technically strong founders with global ambitions.

Also Read: “SEA + Japan is a long game”: MUIP’s Gerrard Lai on cross-border startup collaboration

For Antler, the strategy is clear: deploy more capital earlier, raise the bar on founder quality, and lean into Japan’s underutilised strengths. In an early-stage market often dismissed as slow-moving, the firm is betting that disciplined aggression — not incrementalism — will produce the next generation of globally relevant companies.

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Most people think only big corporations can be “learning organisations.” They imagine expensive HR departments, internal academies, and endless workshops.

But the truth is the opposite: SMEs actually have the best conditions for continuous learning.  No bureaucracy. No silos. No political layers. Just the need to move fast, solve problems, and adapt.

The tragedy is that most small businesses never convert their hard-earned experience into reusable knowledge because they just lack the structure.

Every time someone resigns… Every time a project ends… Every time a mistake repeats…

They lose time, money, and momentum. Not because they lack talent, but because they lack retention.

This is the hidden threat every SME faces: Knowledge leakage.

Big corporations talk about “knowledge management.” Small businesses live or die by it.

Where SMEs bleed knowledge

Unlike corporates, SMEs rely on:

• A handful of key operators
• Undocumented processes
• Tribal knowledge held in people’s heads
• Informal “verbal SOPs” that change daily

So when one employee leaves, an entire workflow evaporates. When one client asks a recurring question, someone rebuilds the answer from scratch. And when training is needed, everyone is too busy “doing” to teach.

This isn’t incompetence. It’s the reality of survival-mode environments where execution outruns organisation.

But in 2025 and beyond, SMEs that fail to build learning systems will fall behind:

Not because they can’t work hard, but because they can’t scale wisdom.

Also Read: Gaming in SEA: Understanding the growing opportunity for SMEs and payment providers

Why learning organisations beat big corporations

A large company needs endless meetings, frameworks, and departments just to update a process.

An SME can change course in a single afternoon if it has internal clarity.

Learning organisations inside SMEs move faster because they:

• Shorten onboarding
• Reduce repeated mistakes
• Scale consistent quality
• Unlock multi-role capability
• Build internal leadership early
• Adapt to market signals quickly

SMEs don’t need bureaucracy to grow. They need structure without stagnation.

The truth is: Small businesses that systematise learning, not just hire talent, will outgrow bigger, slower firms.

How SMEs can become learning organisations (without red tape)

Document as you go — not “at the end”

Most SMEs fail because documentation is treated like homework. Instead, bake it into the workflow:

• After finishing a task, take five minutes to record how it was done.
• Use Loom, Notion, Google Docs, or even WhatsApp voice notes.
• Store every win, mistake, and shortcut in a central, searchable place.

Every successful project hides a repeatable blueprint. Capture it before it disappears.

Build a micro-learning culture

Forget two-hour seminars or quarterly training sessions — SMEs don’t have that luxury.

Instead:

• Run 10-minute weekly learning huddles.
• Share a quick win, a mistake, or a tip from a real client project.
• Let junior staff present what they learned that week.

Small, frequent learning beats big, infrequent training.

Make mentorship operational, not theoretical

Mentorship programs fail because they’re abstract. But SMEs can embed mentorship inside the work itself:

• Pair juniors with seniors on live tasks.
• Let juniors own 10–20 per cent of a project with supervision.
• Rotate roles weekly so everyone touches different functions.

This builds multi-skilled talent faster than any classroom.

Also Read: AI adoption is an area of maturity for SMEs, but they have advantage over big corporations: Aicadium’s Robert Young

Use AI as the “second brain” of the company

AI transforms SMEs into learning organisations by:

• Recording institutional knowledge
• Generating SOPs from your past projects
• Turning conversations into playbooks
• Storing templates, workflows, and client responses
• Assisting new hires with context and memory

The SME that uses AI as a knowledge repository becomes more resilient than one reliant on individual memory.

Reward knowledge contribution

Most SMEs reward “output.” Learning organisations reward “transfer.”

Incentivise staff for:

• Writing SOPs
• Recording tutorials
• Teaching juniors
• Improving old processes
• Sharing insights after client projects

When people are rewarded for teaching, the company stops losing knowledge.

The future belongs to SMEs that learn faster than they grow

Being a learning organisation isn’t about:

• Hiring more managers
• Creating more slides
• Building more departments

It’s about building systems of retention so that experience compounds instead of evaporates.

Big companies talk about “knowledge management.” Small companies don’t need the jargon; they need to build the habit and process.

Because the real competitive advantage isn’t the talent you hire — it’s the knowledge you keep.

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In its 2025 Annual Retail Report, global fintech platform Adyen reveals a sharp rise in AI adoption among Asia Pacific (APAC) consumers and retailers, highlighting a significant shift in shopping behaviors and business strategies across the region.

The report, based on a survey of 41,000 consumers across 28 markets including Singapore, Australia, Hong Kong, India, Japan, and Malaysia, underscores how the tech is reshaping the retail experience and signaling broader trends for the region’s digital economy.

According to Adyen, over a third (38 per cent) of APAC consumers now use AI to assist with shopping—a 39 per cent increase from 2024. Notably, more than one in ten APAC consumers (11 per cent) tried AI-powered shopping for the first time in the past year.

The appeal of AI lies largely in its ability to offer fresh inspiration and personalised recommendations. Nearly two-thirds (63 per cent) of AI-using consumers said it helps them discover new choices for everything from outfits to meals faster than any human assistant could.

Additionally, 62 per cent expressed interest in using AI to find unique brands and shopping experiences, opening doors for retailers to drive sales through partnerships and cross-selling strategies.

Cross-generational adoption

While younger generations remain at the forefront of AI shopping, older cohorts are quickly catching up. In Malaysia and Hong Kong, Gen Z adoption stands at 74 per cent and 64 per cent, respectively.

Also Read: Ecosystem Roundup: AI’s capital frenzy, bolttech’s US$147M funding, and Southeast Asia’s VC crunch

Meanwhile, Singapore’s Generation X and Millennials have shown substantial growth, with AI shopping adoption increasing by 45 per cent and 28 per cent respectively over the past year. Even among consumers aged 60 and above, nearly a third (30 per cent) reported using AI to assist with purchases.

“The introduction of AI in shopping has created new shopper journeys that are more exciting than ever,” said Warren Hayashi, President of Asia Pacific at Adyen. “For retailers, embracing AI isn’t just about staying current; it’s about meeting evolving consumer expectations and staying competitive in a fast-changing retail landscape”.

On the business side, more than a third (34 per cent) of APAC retailers plan to increase their AI investments in the coming year to enhance sales, marketing, product innovation, and security. Payments data — a largely untapped resource — presents significant potential for AI-driven optimisation.

While AI garners attention, the report also points to gaps in omnichannel capabilities. Less than half (46 per cent) of APAC retailers currently support seamless cross-channel shopping, though another 19 per cent plan to enable this within the next year.

Consumer expectations are evolving quickly: 46 per cent want businesses to offer integrated experiences across online platforms, social media, and physical stores. Despite the rise of digital commerce, 42 per cent of consumers still value in-store shopping equally alongside online channels.

As the region’s retail landscape continues to digitise, AI is emerging not only as a tool for personalisation and convenience but also as a strategic differentiator for retailers navigating an increasingly competitive market.

Also Read: The art of artificial intelligence: How Hagia Labs is reimagining creativity

Balancing innovation with security concerns

Despite enthusiasm for AI, concerns around fraud persist. About 26 per cent of consumers expressed heightened worries about scams, and 20 per cent avoid storing payment details on devices for security reasons. Currently, 40 per cent of APAC retailers are leveraging AI to combat fraud by detecting anomalies and predicting fraudulent activities using their transaction data.

“Besides optimising revenue, AI could aid in the fraud-fighting efforts of retailers,” Hayashi noted. “It can spot anomalies, identify patterns, and predict fraud attempts – ultimately ensuring consumer trust and protecting retailers’ hard-earned revenue”.

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Image Credit: Mike Petrucci on Unsplash

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In 2024, venture capital across Asia-Pacific sank to its lowest level since the 2021 peak. In Southeast Asia, startup funding dropped by 42 per cent, with investors either pulling back entirely or doubling down only on high-traction, near-profitable, or already profitable startups. At Spacely AI, we had no choice but to rethink everything.

Early on, we made a decision: build a product people would pay for, and structure our growth around revenue—not runway. We raised a modest pre-seed round, stretched every dollar, and aimed for profitability from day one. We didn’t call it seed-strapping at the time—but that’s exactly what it was.

We haven’t reached profitability yet. But this approach extended our runway far beyond projections. It allowed us to keep our team small—under 10 full-time employees. We avoided layoffs. And unexpectedly, it gave us something most founders struggle to find during turbulent times: leverage, clarity, and freedom.

The VC model is breaking in Southeast Asia

The region’s venture landscape is facing serious headwinds. Billion-dollar exits are few and far between. And without reliable exits, LPs are more cautious, which makes it harder for VC funds to raise capital. Many are quietly failing to raise their third or fourth fund. It’s not because they can’t find good startups, but because the math no longer adds up.

The silver lining? There’s still capital out there—but it’s more selective than ever. It’s reserved for companies showing real traction and a clear path to profitability. The bar has shifted. The days when “potential” alone could raise millions are over. That’s the new reality: many VCs simply can’t invest, not because they don’t believe in you—but because they’re trying to survive, too.

Also Read: Building future sustainable business: The role of rural commerce platforms

The rise of seed-strapping

This is why the smartest founders I know are shifting to the “seed-strapping” model. Seed-strapping is quickly becoming the new startup playbook—raise just enough capital to reach cash-flow positive, then let revenue take you the rest of the way. You don’t need a massive seed round. You need just enough to reach profitability.

We stayed lean with fewer than 10 FTEs, automated as much as possible with AI, and focused entirely on finding product-market fit. We didn’t grow through expensive paid campaigns. Our customer base and revenue were built through organic acquisition. That forced us to stay disciplined. No distractions. Just sell, build, test, repeat.

Let me be clear: this path is not easy. At one point, we reduced salaries across the entire company by 50 per cent. It was painful, but necessary. It wasn’t about bravado. It was about survival. And in the midst of this, we found focus. That constraint gave us perspective. And it opened our eyes to the real power of AI—not just as a product, but as a company-building force.

AI is the deflationary force changing everything

One of the biggest tailwinds behind seed-strapping is AI. Not just because we’re an AI company—but because it changed how we work, scale, and think about cost.

Every founder faces the same three levers: raise money, cut costs, or grow revenue. And AI can supercharge all three. We’ve trained ourselves to ask: “Can we AI this before hiring for it?” For example, at Spacely AI, we run all our growth channels (social media, blog, and SEO) with one growth analyst. That analyst is empowered with the right AI tools, templates, and workflows to do the job of an entire team. The result? Lower cost, more output, and better quality.

AI didn’t just help us survive. It helped us operate better. Founders who understand this dynamic—who treat AI as a margin engine, not just a product feature—are going to win.

Revenue is the best funding you can get

Cutting costs and increasing productivity only get you so far. The other side of survival is revenue. That’s where real leverage lives.

VC money is useful. But customer money is better. Revenue is non-dilutive. It’s fast. It’s proof that you’re solving a real problem. And every US$10,000 in MRR buys you more than just another month of runway—it gives you proof.

Most startup advice focuses on perfecting your pitch. But what if you pitched less and sold more? What if you built your business around the customers you’re trying to serve—not the investors you’re trying to impress?

There’s a quote I read recently that feels especially true in this climate: “Profits solve all problems.” Reflecting on our journey, I couldn’t agree more.

Also Read: Turning intimidation into innovation: Embracing sustainability’s new opportunities

The new playbook: PMF, margin, and discipline

If you’re building a startup in Southeast Asia right now, I’d challenge you to adopt this lens. The old “growth at all costs” mentality doesn’t fit the current market. The new playbook looks like this:

• PMF first: Lock in one clear use case. Prove it. Then scale.
• Profitable unit economics: 70 per cent+ gross margin, 12-month payback period or better.
• Lean teams, AI-enabled: Ten high-performers with AI > 50 without.

We didn’t invent this strategy. We adopted it out of necessity. But it’s made us sharper and more resilient.

Profitability is the ultimate leverage

There’s a saying: “The best time to raise money is when you don’t need it.” Every founder loves that phrase—and for good reason. Once you approach or hit profitability, the entire game changes.

You get clarity—on what to build, who to build for, and what it takes to scale. You gain options. Not just the option to raise or not raise, but the power to choose who to raise from—and who to walk away from.

Let’s be honest: fundraising takes time. Some say six months. Lately, I’ve heard twelve. Profitability—or even a credible path to it—gives you the endurance to survive those cycles. More importantly, it keeps you in control.

The founder’s checklist for surviving the funding winter

If you’re navigating this market, here’s what I recommend:

• Solve a painful problem customers will pay for
• Use AI to increase productivity and stay lean
• Focus on leading indicators of PMF—activation, engagement, referrals (for SaaS)
• Track your burn rate, CAC, and LTV
• Rally your team around cash-flow positive as a company-wide goal
• Treat VC funding as optional fuel, not oxygen

Final thoughts

We’re in a funding winter. But winters don’t last forever—and they often produce the most resilient companies. If you can build around customers, automate smartly, and seed-strap your way forward, you’ll emerge stronger, faster, and freer.

At Spacely AI, we chose to seed-strap because we didn’t want to depend on a volatile capital market. AI helped us stay lean. Our focus on customer value gave us breathing room. And our users—real people paying real money—turned out to be the best VCs we could ever ask for.

If you’re building in 2025, don’t wait for a term sheet to start acting like a real company. The new playbook is clear: sell first, build something people want, and spend your customers’ money wisely.

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At a recent AI vs Human keynote showdown, someone in the audience threw me a question many founders quietly ask: “But AI hallucinates. Isn’t that dangerous?”

My reply was simple, but it caught a few off guard: “Yes. But humans hallucinate, too. And often, it’s far more dangerous.”

The debate isn’t whether AI makes mistakes — we know it does. The real problem is who we choose to trust when confidence meets uncertainty. As founders, that’s where the true risk lives.

What is hallucination, really?

Let’s start by demystifying the term.

AI hallucination happens when large language models (LLMs) like GPT generate responses that are factually incorrect but sound completely plausible. They aren’t lying. They’re simply predicting text based on probability patterns.

Public examples prove this risk. Sky News’ Sam Coates confronted ChatGPT live for generating false podcast transcripts. OpenAI’s own testing data shows significant hallucination rates:

• 33 per cent false information rate for its o3 model.
• 48 per cent for its o4-mini model.

AI can sound extremely confident even while being wrong, and that’s precisely what triggers automation bias, when humans trust machine outputs simply because they “sound right.”

But here’s the uncomfortable truth: Humans hallucinate too, and we rarely catch ourselves doing it.

The human hallucination problem: Narratives we build

AI hallucinates through prediction. Humans hallucinate through narrative.

We build impressions. Those impressions become judgments. Judgments turn into stories. And those stories drive our business decisions.

• We overestimate market size based on a handful of customer interviews.
• We assume product-market fit based on early interest.
• We hire poorly because of a great interview.
• We raise funding on projections fuelled more by hope than data.

These aren’t rare. They are startup norms.

In many cases, founders hallucinate entire business models with full conviction. The difference? There’s rarely a system that alerts us when we’re slipping into narrative-driven delusion.

Also Read: AI adoption in SEA e-commerce: The clock is ticking for sellers

The confidence trap: Why founders trust the wrong things

Both AI and humans share one dangerous similarity: They deliver outputs with confidence, whether right or wrong.

That confidence triggers trust. And trust, unchecked, leads to bad decisions.

• AI: “The answer is definitely X.”
• Founder brain: “We’ll definitely 10x next year.”

The issue isn’t hallucination itself, it’s how quickly we surrender our skepticism when something sounds certain.

The true founder risk isn’t just AI hallucination. It’s our reflex to accept confidence as truth.

My operator view: How I design around hallucination

Across my ventures, I’ve built AI into daily workflows. But I never outsource my thinking.

Here’s my personal system design:

• Separate generation from verification: AI helps structure thoughts, draft options, and synthesise. But facts get independently verified.
• Build multi-step logic chains: I don’t ask for one-shot answers. I design prompts that generate reasoning, assumptions, counterpoints, and validations.
• Cross-check everything: Whether it’s market data, analysis, or competitor signals, I verify across multiple sources.
• Use AI as augmentation, not authority: Seraphina AI, my personal assistant, mirrors my thought process because it was trained to follow how I already operate. It amplifies my logic but doesn’t replace it.

The meta-moment: While writing this article

Even while drafting this article with AI assistance, I actively ask: “Is the AI hallucinating here?”

The answer? No, because I’m not asking it to invent facts. I’m using it to structure my thinking, arrange arguments, and explore narrative flows. The core reasoning remains mine, AI simply amplifies and organises.

AI is my logic partner, not my fact source. That distinction is where most founders struggle: they surrender too much authority too quickly.

The founder’s three guardrails against hallucination

Here’s the framework I live by and recommend to every founder:

• Separate generation from verification: Never let AI verify its own outputs. Always layer external data and checks.
• Build multi-step prompts: Don’t chase immediate answers. Build prompt chains that explore reasoning, objections, and edge cases.
• Treat AI like a team member: You wouldn’t trust a junior hire’s first draft without review. Apply the same discipline to your AI assistant.

Also Read: Startups, is your email strategy driving growth, or just gathering dust?

The harder truth: Human hallucination is more dangerous

The brutal reality? We can engineer systems to reduce AI hallucinations. But human hallucination, especially founder hallucination, is far more difficult to catch.

• Ego pushes us to double down on flawed assumptions.
• Investor pressure accelerates premature scaling.
• Team echo chambers reinforce dangerous narratives.
• Emotional attachment clouds product decisions.

Human hallucination isn’t probabilistic — it’s emotional. And emotions rarely fit into predictable guardrails. That’s why many startups fail — not from AI errors, but from founders’ unchecked certainty.

AI hallucination is mechanical. Human hallucination is narrative.

The founder advantage today isn’t about trusting AI more or less. It’s about developing the cognitive discipline to manage both AI and human fallibility simultaneously.

The hybrid founder edge

The founders who thrive in this AI-powered era won’t be those who fear hallucination.

They’ll be the ones who:

• Build operating systems that minimise blind spots.
• Maintain cognitive sovereignty over both algorithms and their own internal narratives.
• Use AI to amplify clear thinking, not replace it.

AI doesn’t replace thinking. It exposes who never learned how to think systematically in the first place. And in this new landscape, that, not hallucination itself, will define who scales and who fails.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

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As we reach the midpoint of 2024, it’s an ideal time to reflect on emerging trends that have shaped our perspectives. For me, it’s Multimodal Large Language Models (MLLMs).

2023 was a game-changer for AI, no thanks to ChatGPT. We saw a surge in large language models (LLMs) and generative AI, which made everything from chatting with bots to getting content way faster and better.

I won’t lie — I wasn’t very fond of the AI hype. Seeing everyone generate low-quality stock images for their posts and slides and being wowed by trivial advancements was honestly quite frustrating.

While the generative AI hype still prevails, I do have to admit it is maturing. Slowly.

These advancements and the growing consumer adoption of AI technology have paved the way for what we’re seeing in 2024: the emergence of multimodal AI models (MLLMs).

‘Multimodality’ is a somewhat new term for an old concept, i.e. the way humans have always learned about things. People have always gathered information through various senses like sight, sound, and touch. Then, our brains merge these different types of ‘data’ to create our understanding of reality.

So basically, multimodal language models are advanced AI systems that can process and understand multiple types of data, like text, images, audio, and video, all at once. I know, shocking, right?

Ironically, many people have interacted with aspects of multimodal AI without even realising it.

I didn’t even realise I was building my startup, LFG, around this technology and its concept. Learning about multimodal AI has completely shifted my views on its implications and potential.

The rise of visual commerce in retail

This is an emerging trend that has stood out for me this year

So far, the exciting trend I’ve seen in 2024 is the rise of visual commerce, especially in the fashion and beauty sector. Multimodal AI is making waves here by enabling consumers to use natural language, images, and videos to search and buy, transforming how we shop for clothes, accessories, and beauty products.

Also Read: From mining engineer to travel tech visionary: Darryl Han transforms trip discovery

In the US, startups focusing on multimodal search have received significant funding and support, like Daydream (US$50M seed funding) and Lumona (YCW24), underscoring the growing importance of this technology.

With ViSenze (a Singapore tech company at the forefront of multi-search), for example, you can upload a photo of a dress you love (even from a social media post), and their AI-powered search will find similar styles available for purchase. This makes shopping more engaging and personalised, and it’s clear that visual content is becoming a major player in retail decisions.

The shift towards personalised travel experiences

Insights gained about the direction of the travel industry

While this technology is being experimented with and refined in the fashion and beauty sectors, I believe its potential impact on the travel industry is even more profound. Travel encompasses a whole range of services and experiences, from flights and hotels to tours and local attractions. You can already see how big this sector is on its own.

There is a shift in consumer mindsets that personalisation is no longer optional — it’s essential. Multimodal AI can simplify and personalise these offerings by analysing a combination of text, images, and videos, making it easier for travellers to discover exactly what they’re looking for and enhancing their overall experience.

For instance, a traveller searching for “hidden, speakeasy late bars in Kuala Lumpur” can benefit from an AI that not only processes the textual description but also analyses images and videos to find the perfect match. This leads to more precise and personalised recommendations, enhancing user satisfaction.

Also Read: Into the metaverse: How to extract real business value from the hype?

Some further implications of multimodal AI for travel that I’m eager to build and see developed include the following:

• Dynamic pricing: Adjusting prices and offers in real-time based on market trends and user behaviour, maximising revenue and satisfaction.
• Streamlined bookings: Understanding natural language queries and providing instant booking assistance and results, improving user experience.
• Smart assistants: Offering real-time support with voice commands, travel document and location analysis, and instant translations, making travel easier and more enjoyable.

Embracing multimodal AI for future growth

Lesson learned that will shape my approach for the rest of 2024

As multimodal AI continues to advance, it will undoubtedly shape the future of any form of commerce, driving growth and enhancing the overall travel and shopping experience for consumers worldwide.

For travel startups like ours, we’ll definitely be exploring and leveraging these multimodal applications to redefine how travellers search, discover and experience the world — bringing a more intuitive and enjoyable journey before the trip begins.

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