Business leaders are being told AI will replace their development teams. Make everyone 10x more productive. Eliminate the need for senior engineers.

Some of this is true. Most of it is dangerously misleading.

Here’s what you actually need to know — and what it means for how you structure your teams.

The demo isn’t the product

You’ve seen the demos. “I built this app in one prompt.” Impressive. Misleading.

One-shot treats AI like a magic genie. Describe what you want perfectly, and get exactly what you need. But AI can generate code that works without grasping the What and Why behind a complex business problem.

That understanding isn’t discovered in prompts. It’s discovered in iteration — by building something, watching it fail, and building again. One-shot assumes perfect knowledge upfront. It’s waterfall wearing a hoodie.

Real AI Augmented Development is something different. It’s AI as a teammate — working alongside experienced practitioners through the full development cycle. Tools like Claude Code let developers stay in flow, iterating rapidly without context switches that break concentration. Not a tool you reach for occasionally, but a collaborator integrated into how you think, plan, and build.

What AI actually replaces

To understand what’s changing, you need to understand how software engineers contribute.

Most knowledge work — including software development — follows a progression that goes back to the guild system:

• Directed contribution. You’re given a specific, well-defined task in an unambiguous context. “Implement this spec.” “Build this API endpoint exactly as described.” You don’t need to understand the What and Why. You execute the How, under someone’s guidance.
• Independent contribution. You’re trusted to tackle problems independently — first in well-defined situations, then in ambiguous ones. You figure out both what to do and how to do it. You understand enough about the business to make judgment calls when the spec is incomplete or wrong.
• Working through others. You set vision and direction. You guide others. You’re accountable for outcomes, not just outputs.

Here’s what AI Coding Assistants like Claude Code do well: Directed contribution. Give them a specific, well-defined task in an unambiguous context, and they execute. Often better than a human, because they don’t get tired, don’t make typos, and don’t need coffee breaks.

This is precisely the work that large offshore development teams were built to do. And not just offshore — too many developers everywhere, even with years and even decades of work experience, including Singapore, operate in directed contribution mode.

Also Read: AI fluency or disaster: Decide before it decides for you

The model that’s dying

For decades, the dominant model looked like this: Product managers sit near the business. They write detailed PRDs and specs. Those specs get shipped over the wall to a large development team — often offshore in the Philippines, Vietnam, Indonesia, and India. Each developer gets a well-defined slice. They implement exactly what’s described. Ship it back.

The economics seemed compelling. Senior engineers in the US cost US$150,000 or more. Offshore developers cost a fraction of that. Scale up the team, ship the specs, get the code back.

But this model carried a hidden cost: the collaboration tax.

Communication gaps. Lost context. Misalignment with business needs. Revision cycles. Specs that are outdated before they’re implemented. The PM who wrote the requirements isn’t sitting with the developers who have questions.

Research on proximity and collaboration is unambiguous. A University of Michigan study found that researchers on the same floor are 57 per cent more likely to collaborate than those in different buildings. For every 100 feet of shared walking path, collaborations increased by 20 per cent. MIT research on the “Allen Curve” — named after MIT professor Thomas Allen — shows that even basic conversations become much less likely when workers are more than 10 meters apart.

Look at startups when they’re hitting things solidly and delivering customer value. They’re sitting in one another’s laps. The communication bandwidth is massive. Questions get answered in seconds, not days.

The collaboration tax was always there. Companies accepted it because the labour cost arbitrage seemed worth it.

AI augmented development changes the equation.

The math has changed

When AI handles directed contribution, you don’t need a 20-person team executing specs. You need a small team of experienced practitioners who can work with AI to iterate rapidly on complex problems.

Consider the economics:

• Traditional model: 1 PM + 15-20 developers. Lower cost per person, but high headcount, high collaboration tax, slower cycles, and lower alignment with business needs.
• Emerging model: 1 technically-fluent AI PM + 3-4 senior co-located engineers, all working with AI tools. Higher cost per person, but dramatically fewer people. Lower total cost. Faster cycles. Higher quality. Better business alignment.

The smaller team isn’t just cheaper. It’s better.

AI augmented development compresses the cycle from weeks to hours. A working prototype can replace a 50-page PRD. Instead of describing what you want the software to do, you can show it — then iterate based on reality rather than imagination.

At Apple, we had a saying: Demo beats deck. A working demonstration trumps a polished presentation every time. AI augmented development is that principle writ large. When you can produce a working prototype in hours, why spend days writing a document describing what it should do?

But this only works with high-bandwidth collaboration. Tight feedback loops. The ability to walk through a prototype together, ask questions, and make changes on the spot. You can’t do that across communication gaps — whether those gaps are time zones, organisational silos, or simply being on different floors.

Also Read: Building with intention: The ethical dilemma of AI innovation and responsible creation

The what/why/how blur

Historically, product management owned the What and Why. Developers owned the How.

Those lines are blurring.

When AI can generate working prototypes from descriptions, the distance between “what we want” and “how it works” collapses. Product managers get closer to the How. Developers get pulled into the What and Why.

This isn’t a threat. It’s an evolution.

The technically-fluent AI PM isn’t someone who writes PRDs and waits for engineering. They’re producing prototypes that aren’t always throwaway demos — they’re starting points that engineering extends. A technically-fluent PM can prototype a feature in an afternoon, walk engineering through it, and iterate together — rather than writing a 20-page spec and waiting two sprints to see if engineering understood it. They understand the How well enough to make informed tradeoffs.

And developers now need to understand the What and Why deeply enough to make judgment calls when iterating. “This requirement doesn’t make sense given what I understand about the user” — because they do understand the user.

Everyone needs more business context. Everyone needs more technical fluency. The boundaries are dissolving.

Seniority isn’t what you think

Here’s where business leaders get confused.

“Our team has senior developers. They have 10 years of experience.”

But years of experience aren’t the same as how someone contributes.

Someone with 10 years of experience doing directed contribution work isn’t a senior developer. They have one year’s experience, executed under someone’s guidance, ten times over.

This isn’t just an offshore problem. Too many developers in Singapore, in London, in San Francisco, have spent careers in directed contribution mode — not because they were incapable of more, but because the organisations they worked for didn’t ask more of them. The PRD comes over the wall. They implement their slice. Ship it back. Repeat. And everyone prays that it all comes together and works.

Ten years of this doesn’t develop the skills the new model demands.

What AI augmented development requires is Independent Contribution in Ambiguous Settings. People who understand the business problem — the What and Why, not just the How. People who can make judgment calls when the spec is incomplete or wrong. People who can collaborate at high bandwidth and low latency because they share context with the business.

What this means for Southeast Asia

This isn’t about reshoring jobs to the US. It’s about the death of the human wave model that much of Southeast Asia’s software outsourcing industry was built on.

Vietnam produces 50,000 IT graduates annually. Over 45 per cent of its developer workforce is at the junior level — trained to do directed contribution work. The Philippines has built a massive tech services industry on similar foundations.

The question for the region isn’t whether AI will disrupt the traditional outsourcing model. It already is. The question is whether Southeast Asia can compete on value, not low-cost volume.

Can the region produce engineers who operate in Independent Contribution mode? Engineers who understand the What and Why, not just the How? Engineers who can be part of elite co-located teams — whether those teams sit in Singapore, Jakarta, Ho Chi Minh City, or alongside clients in Tokyo, Sydney, or San Francisco?

The opportunity isn’t to fight the transformation. It’s to ride it.

Also Read: AI’s reality check: Why 95 per cent of pilots fail and how to measure what actually matters

What business leaders should do

Audit your teams — not for years of experience, but for mode of contribution. How many of your people are doing directed contribution work that AI can now handle? How many can operate independently in ambiguous situations? How many understand the What and Why of your business, not just the How of their technical domain?

Rethink your team structure. Smaller. Co-located. Located where the business sits. The two-pizza team that Amazon pioneered is finally becoming real — but it only works when the team has the proximity and bandwidth to collaborate intensively. Higher cost per person, lower total cost, better outcomes. And maybe you go for a one-pizza team!

Invest in AI fluency — not just tool access. Throwing AI tools at people without helping them understand what AI can and cannot do is setting them up to fail. The failures we’ve seen — like Deloitte’s fabricated citations in government reports — come from people who knew enough to be cautious but not enough to be fluent.

And think hard about your talent pipeline. Entry-level tech hiring has collapsed — junior developer roles are down 60 per cent since 2022. The apprenticeship ladder that produced your current senior engineers is disappearing. Where do your future senior engineers come from if you’ve eliminated the model that trained them?

The bottom line

AI augmented development favours smaller teams of independent contributors who understand the business — co-located where the business sits. Higher cost per person, dramatically fewer people, better outcomes.

The human wave is dying. The question is whether you’re building the team that replaces it or the team that gets replaced.

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The series commenced with an introduction detailing how ETA enables ambitious professionals to transition from corporate careers to business ownership while mitigating typical “startup” risks.

Part 1 introduced the model’s US origins in the 1980s and its recent arrival in SEA. Part 2 examined why SEA’s Small and Medium Enterprise (SME) sector is primed for ETA, focusing on the impending inter-generational transfer of family assets. Part 3 mapped the key players: searchers, operators, investors, and ecosystem players.

In this concluding instalment, we analyse the existing gaps within the ETA ecosystem that hinder its broader adoption as an asset class and outline the specific initiatives we are undertaking to cultivate an ETA ecosystem uniquely tailored to the SEA context.

Overcoming headwinds: Addressing key obstacles to ETA adoption

The micro-to-small cap sector’s M&A process in SEA faces friction points uncommon in mature markets, ranging from fundraising and due diligence to securing debt capital and legal documentation. 

• Lack of familiarity and education: SME owners are largely unaware of the operator-led acquisition model, defaulting to family handover or sale to a strategic player or a private equity firm. Over-explaining ETA frameworks doesn’t help either, as a lack of local, successful case studies breeds skepticism in low-trust environments such as across the emerging markets in the region. 
• Thin and conservative investor ecosystem: The local investor base remains unfamiliar with the search fund model’s unique risk-return profile. Investors favour “sexy” industries such as AI or fintech despite their much, much higher risk profile, while overlooking profitable but “boring” businesses that search funds target.
• Cultural preferences and trust deficit: SEA business landscape is highly relational. Founders’ strong emotional attachment to their legacy and prefer family succession. Building trust then requires a multi-year effort, running counter to the transactional nature of M&A. A typical two-year search period in a traditional search fund is often insufficient to build that profound level of trust with business owners. 
• Operational and diligence complexity: Due diligence is made complicated by non-standardised financial reporting, reliance on cash transactions (although this is rapidly changing with the use of e-wallets and e-invoicing), the use of multiple financial books, mixing personal and business expenses and business practices reliant on informal local relationships or regulatory “shortcuts.”
• Valuation gaps and structural challenges: Sellers’ emotionally-driven valuation expectations often exceed usual market multiples in the micro-to-small cap segment. Selling at lower-than-expected values is seen as “losing face”, and owners would rather shut down their businesses than be seen as a laughing stock among their peers for selling cheaply. Typical M&A structures like earn-outs and seller financing are less accepted as compared to Western markets.
• Lack of acquisition financing: Banks, while familiar with mainstream products like term loans, are hesitant with more complex acquisition financing. Further, banks’ typical requirement of a personal guarantee is incompatible with the ETA’s professionalised ownership and management structure.

Also Read: The new succession: Charting the rise of Entrepreneurship Through Acquisition (ETA) in SEA – Part 1

The growth blueprint: Essential pillars for ecosystem development

ETA represents an emergent and untested asset class within the region. This raises a critical question: is it merely a Silicon Valley import, echoing the regional replication of venture capital a decade or more ago, or does it offer genuine innovation capable of resolving the SME succession crisis in SEA? The distinction hinges on our ability to overcome the identified challenges, which demands a coordinated and multi-faceted strategy. Achieving a mature ETA market in this region necessitates the development of four fundamental pillars.

• Education and evangelism: The foremost priority is to educate all interested parties. This necessitates a proactive effort to promote the ETA model to SME owners, illustrating its value as a practical and attractive succession option that protects their company’s heritage. It also involves informing highly entrepreneurial mid-career professionals about ETA as an alternative to launching a startup. Finally, it requires building the right narratives with potential investors and private credit providers about the workings and historical risk-adjusted returns of this investment category. Regional government agencies such as Enterprise Singapore are examining how ETA can solve the succession challenges amongst SMEs they support.
• Building institutional knowledge through success stories: Nothing fosters ecosystem confidence more effectively than demonstrable success. Every successful search, acquisition, and eventual sale in the region will act as a strong proof point, generating momentum for the entire ecosystem. Local case studies are vital for validating the model in the eyes of cautious sellers and for attracting more capital and operational talents into the ecosystem. Therefore, publishing these success stories, ideally with the support of higher education institutions like INSEAD and SMU, is a crucial task for early participants to establish the foundation for the industry’s eventual success.
• Developing professional intermediaries and standardised processes: A mature M&A market depends on a network of experienced intermediaries, including sell-side advisors, lawyers, and accountants, who grasp the unique requirements of search fund transactions. The service quality of these intermediaries can vary widely, ultimately affecting transaction success rates. Furthermore, creating industry-wide approved standard templates and best practices for critical components such as confidential information memorandums formats, data rooms, and deal structures can significantly lower transaction hurdles, speed up search timelines, and reduce costs for everyone involved.
• Cultivating a dedicated and patient investor base: The ecosystem requires a committed pool of capital that understands and accepts the long-term, patient nature of SME investment. This involves nurturing a community of local and international investors willing to provide not only financial support but also essential mentorship and governance. This is a considerable challenge, as the region’s capital markets have been hindered by a lack of successful exits stemming from a period of capital misallocation in the venture capital industry.

The critical factor for unlocking the growth in ETA in SEA is ultimately not capital, but trust. The primary obstacles in the region, seller hesitation, investor conservatism, lack of high-quality operators, and deeply ingrained cultural preferences, are fundamentally rooted in issues of trust, rather than finance. Therefore, the most effective strategies for growth are essentially mechanisms for cultivating this necessary trust.

Also Read: The new succession: Charting the rise of Entrepreneurship Through Acquisition (ETA) in SEA – Part 2

In contrast to Western markets, where M&A is often a highly transactional process, a strong, pre-existing relationship frequently serves as the foundation for a deal in a trust-deficient SEA. This suggests that the most successful acquirers and builders must prioritise being trusted educators and advisors, with dealmaking being a parallel function. While this relationship-intensive approach may result in a more gradual pace of market expansion, it will establish a more resilient, culturally relevant, and sustainable foundation for long-term success.

GenCap’s approach to ETA in Southeast Asia

Within Southeast Asia’s still-nascent ETA landscape, Gen Capital Partners’ (GenCap) model is designed around the region’s specific operating realities. As co-founder alongside Eric Koh, I established GenCap to pair a Searcher, responsible for M&A, finance, sourcing, and diligence, with an Operator focused on operations, growth, and organisation from the outset. This structure creates a clear division of labour that spans the full ETA process.

Early collaboration allows investors to evaluate a management team rather than a single individual. Post-acquisition, the Searcher typically oversees finance and corporate development, while the Operator assumes the CEO role. The model is intended to shorten parts of the search process by combining the Searcher’s relationship-building across the deal ecosystem with the Operator’s domain and operating experience.

In a relationship-driven Southeast Asian context, this approach seeks to address common challenges faced by solo searchers, including key-person risk and credibility gaps. Pairing financial and operational capabilities upfront is designed to establish trust with both investors and selling founders, particularly in founder-led SME transitions.

Beyond executing acquisitions, GenCap is also involved in broader ETA ecosystem development, including participation in educational initiatives and efforts toward more standardised approaches to ETA in the region. Through collaborations with regional platforms and engagement with investor, operator, and searcher networks, the firm’s activities contribute to greater familiarity with the ETA model and its application within Southeast Asia.

Also Read: The new succession: Charting the rise of Entrepreneurship Through Acquisition (ETA) in SEA – Part 3

Conclusion

The ETA model is a transformative, de-risked alternative to traditional entrepreneurship, now imperative for SEA. The region faces a critical juncture: SMEs dominate the economy but are simultaneously threatened by an accelerating business succession crisis as founders retire. This creates a vast market of healthy businesses needing new leadership.

This capital gap, the ‘missing middle’ of underserved SMEs, requires sophisticated management and structured capital that traditional financing can’t provide. While nascent, the ETA ecosystem is building momentum in hubs like Singapore, Thailand and Malaysia, backed by dedicated investors. Growth requires concerted efforts in education, celebrating local successes, building professional infrastructure, and cultivating trust.

For high-potential entrepreneurs, this means a direct path to CEO roles. For investors, access to a high-potential asset class. Most importantly, it provides SEA with a sustainable, market-driven solution: ensuring the legacies of retiring founders become platforms for the next generation’s growth. The era of ETA in SEA has begun.

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As we approach the end of the year, US stock futures are holding steady overnight ahead of critical, delayed economic data. Investors brace for a flurry of releases, including the long-awaited third-quarter GDP figures, which promise to fill significant gaps in Wall Street’s understanding of the economy’s current health. Yet market participants largely dismiss the likelihood that these reports will dramatically alter the prevailing narrative around future interest rate cuts.

S&P 500 futures, Nasdaq 100 futures, and Dow Jones Industrial Average futures all traded near the flatline, extending a pattern of stability that has characterised the session. This cautious stance follows three consecutive days of gains for major US indices at the start of the week, a streak that has rekindled optimism about a potential year-end rally.

The S&P 500, in particular, hovers just 0.3 per cent below its all-time high reached earlier this month, a level it had retreated from after several sessions in which investors rotated away from artificial intelligence and technology stocks. The benchmark index’s recent rebound has been fuelled by unexpectedly favourable data from the prior week, including a surprising drop in inflation metrics and a labour market report that showed signs of cooling without signalling distress.

These developments have solidified expectations that the Federal Reserve will begin reducing interest rates in 2026, keeping bets on monetary easing largely intact despite the upcoming data deluge. Traders now view Tuesday’s economic releases as a final opportunity for fresh insights before the Christmas holiday pause, with the delayed Q3 GDP report standing out as a crucial indicator of underlying economic momentum following the federal government shutdown that disrupted regular reporting schedules.

Parallel to the equity market’s measured progress, precious metals continue their remarkable ascent, adding further momentum to an already stunning rally. Gold and silver futures both advanced, building on gains that position these traditional safe-haven assets for their strongest annual performance in over forty years. This sustained strength in bullion markets reflects deep-seated investor concerns about long-term economic stability and the erosive impact of persistent inflation, even as stock indices flirt with record territory.

The divergence between equities and metals underscores a nuanced market psychology where participants simultaneously chase growth-oriented assets while maintaining hedges against potential volatility. Gold’s resilience, in particular, suggests that despite optimism around eventual rate cuts, many institutional and retail investors remain wary of structural economic vulnerabilities.

This precious metals surge comes amid declining real yields and heightened geopolitical tensions, factors that historically bolster demand for non-yielding assets perceived as stores of value during periods of uncertainty. The market’s ability to sustain a prolonged rally in gold and silver, even as stocks recover, highlights a bifurcated investment landscape in which capital flows to both risk assets and traditional havens, depending on shifting risk perceptions across time horizons.

Also Read: Why Asian markets are rising while crypto quietly crosses a US$3 trillion threshold

While traditional markets exhibit cautious optimism, the cryptocurrency sector experienced notable turbulence, recording a 0.56 per cent decline over the past twenty-four hours. This pullback represents a risk-off shift following recent gains, interrupting otherwise positive momentum reflected in seven-day and thirty-day trends of plus 1.51 per cent and plus 3.5 per cent, respectively. The immediate dip stems from a confluence of technical and fundamental pressures, beginning with a significant leveraged long squeeze across derivatives markets. Perpetual swap open interest surged 13.31 per cent within a single day to reach US$815.6 billion, creating a fragile foundation of overextended bullish positions.

This vulnerability materialised when Bitcoin failed to breach the psychologically important US$90,500 resistance level, triggering a cascade of forced liquidations. Bitcoin-specific liquidations alone spiked 80.45 per cent to US$83.75 million, overwhelming market liquidity and accelerating the downward momentum. Technical indicators reinforced this fragility, with Bitcoin’s fourteen-day Relative Strength Index plunging to 32.77, signalling oversold conditions yet revealing weak recovery momentum. Funding rates turned negative for many altcoins relative to Bitcoin, registering at negative 0.000948 per cent, a clear indication of overheated long positioning that required correction. Market observers now watch closely whether Bitcoin can defend the US$88,000 support level, as a decisive break below this threshold could unleash another wave of algorithmic selling.

Compounding these technical pressures, institutional activity introduced substantial bearish momentum through large-scale profit-taking. BlackRock executed a significant sell-off, offloading 2,019 Bitcoin valued at approximately US$180 million alongside 29,928 Ethereum tokens worth roughly US$91 million.

These transactions occurred near local price peaks, suggesting strategic institutional exits after recent rallies. This move by the world’s largest asset manager amplified existing selling pressure across crypto markets, particularly impacting Ethereum, which faced the added headwind of substantial exchange-traded fund outflows. Ethereum ETFs witnessed US$555 million in net outflows during the current week, marking the largest weekly withdrawal since October.

Consequently, Ethereum’s market dominance relative to other cryptocurrencies eroded, falling to 12.17 per cent, a decline of 0.4 percentage points week-over-week, as capital rotated toward Bitcoin, perceived as a comparatively safer asset within the digital ecosystem. BlackRock’s actions underscore a recurring pattern where institutional players systematically take profits after strong rallies, introducing volatility that retail investors often absorb. This dynamic highlights the growing influence of traditional finance giants on crypto price action, where large block trades can overwhelm order books optimised for smaller, retail-sized transactions.

Regulatory ambiguity further clouded the crypto market’s outlook, contributing to the recent pullback through delayed policy frameworks and persistent compliance concerns. Specific delays in advancing the US Clarity Act, legislation designed to provide regulatory certainty for digital assets, triggered US$952 million in outflows from crypto-focused investment funds. This capital flight reflects investor frustration with the prolonged uncertainty surrounding legal frameworks, particularly for alternative cryptocurrencies beyond Bitcoin.

Also Read: The great crypto disconnect: US inflation drops, but BTC keeps falling

Market sentiment metrics captured this anxiety, with the Fear and Greed Index remaining entrenched at 29, a reading categorised as Fear, for the second consecutive trading session. This sustained caution occurs despite Bitcoin’s dominance rising to 58.99 per cent, a trend suggesting that within the crypto ecosystem, Bitcoin increasingly functions as a regulatory safe haven.

Investors appear to favour Bitcoin’s first-mover status and clearer regulatory treatment relative to smaller tokens facing uncertain compliance pathways. The regulatory environment creates a two-tiered market dynamic in which policy delays disproportionately affect altcoins while reinforcing Bitcoin’s position as the primary store of value in digital asset portfolios. This divergence complicates recovery prospects for the broader crypto market, as altcoin performance often depends on regulatory catalysts that remain absent.

The interplay between these three forces, leveraged unwinding, institutional profit-taking, and regulatory stagnation, created a perfect storm for the crypto market’s short-term decline. Yet this dip occurs within a broader context of resilience, evidenced by the positive seven-day and thirty-day trends that suggest underlying demand remains intact.

The derivatives market shows early signs of capitulation, with extreme liquidation levels that could pave the way for stabilisation if Bitcoin holds critical support at US$88,000. Market structure improvements since previous downturns, including reduced exchange leverage caps and more sophisticated institutional custody solutions, may limit the depth of any correction compared to historical precedents.

The key question revolves around whether altcoins can decouple from Bitcoin’s dominance trajectory, which has climbed steadily toward 59.5 per cent. A peak in Bitcoin dominance often precedes broad-based altcoin rallies, but such a shift requires either regulatory breakthroughs or renewed risk appetite that current sentiment metrics do not yet support.

Traders monitor Ethereum ETF flow reversals as a leading indicator of changing institutional sentiment, alongside USDT dominance trends, which reflect stablecoin positioning ahead of anticipated volatility. These metrics provide key insights into whether the current pullback represents a tactical reset or the start of a deeper consolidation phase.

As traditional and digital markets approach the holiday season, their trajectories reveal both contrasts and underlying connections. The stock market’s proximity to record highs coexists with gold’s four-decade rally, reflecting investor strategies that balance growth exposure with inflation hedges.

Meanwhile, crypto markets demonstrate their evolving maturity through institutional participation patterns and sensitivity to macro factors such as regulatory shifts, even as they experience volatility distinct from that of traditional assets. The delayed Q3 GDP data will test the resilience of equity optimism, potentially reinforcing or challenging the narrative of a soft landing that underpins expectations for rate cuts. For precious metals, sustained strength depends on whether inflation proves persistently sticky despite recent encouraging prints.

In crypto, the path forward hinges on technical stabilisation above key support levels and catalysts that could reignite institutional inflows, particularly for Ethereum following its recent outflows. Market participants must navigate these crosscurrents with heightened awareness that holiday-thinned liquidity could amplify reactions to unexpected data or news.

The confluence of year-end positioning, delayed economic updates, and regulatory limbo creates a volatile environment in which risk management takes precedence over aggressive positioning. As the calendar turns, the interplay between monetary policy expectations, regulatory evolution, and technical market structures will determine whether the current cautious optimism across asset classes solidifies into a sustainable foundation for the new year or gives way to renewed uncertainty in a rapidly changing financial landscape.

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Back in January, if you had suggested that AI agents would be autonomously settling cross-border payments on blockchain networks by the end of the year, most insiders would have dismissed the idea. They would have said it was a problem for 2030.

And yet, here we are. 2025 did not just accelerate the convergence of AI and blockchain. It highlighted something unexpected. The most significant progress is not coming from Silicon Valley or Singapore’s corporate districts. It is emerging from the remittance corridors between Manila and Dubai, from the mobile money systems that connect Nairobi, and from the early tokenisation pilots within Jakarta’s fintech ecosystem.

The year emerging markets got impatient

The standout feature of 2025 was not the technology itself, but who adopted it and moved quickly.

Across Southeast Asia, where digital economies are projected to reach US$1 trillion by 2030 (subject to the usual caveats), we saw a notable shift. Governments and startups stopped treating AI and blockchain as exploratory technologies and began deploying them. Indonesia and Vietnam emerged as early leaders, applying AI-enabled blockchains to supply chain verification and remittance optimisation. These are practical, essential use cases. The urgency is clear. Roughly US$700 billion moves through the region each year in transfers, with an estimated US$42 to US$49 billion lost in fees. Companies that managed to reduce even a fraction of those costs drew investor interest.

Africa followed a similar trajectory. Kenya introduced its National AI Strategy 2025 to 2030, positioning AI and blockchain integration as part of governance infrastructure rather than experimental technology. The Africa Blockchain Festival in Kigali reflected this shift. It felt less like a typical conference and more like a marketplace of working projects. Teams were tackling areas such as land titling and subsidy distribution, the practical problems with real users. Sub-Saharan Africa’s crypto adoption remained steady despite wider economic uncertainty, with more than eight per cent of transfers under US$10,000 routed through blockchain networks. This indicates usage rather than speculation.

Also Read: Southeast Asia is ready for AI, but not on Silicon Valley’s terms

The digital convergence belt: A macro worth watching

Perhaps the most interesting development this year was conceptual. Some analysts described a growing “digital convergence belt”, an innovation corridor stretching from Southeast Asia through the Middle East to Africa. The label may sound like a buzzword, but the underlying trend is observable.

These regions share similar constraints. They have fragmented financial infrastructure, large unbanked populations and governments open to experimentation. They also share something less tangible: a pragmatic attitude that differs from Western regulatory caution. When existing systems are already limited, new approaches feel less risky.

For founders, this creates strategic opportunities. The convergence belt rewards those who can operate across varied regulatory environments, design for mobile-first populations and think in terms of regional corridors instead of national markets.

AI agents enter the picture

A technical shift that is likely to define 2025 in hindsight is the rise of autonomous AI agents operating on blockchain infrastructure. These are not chatbots with wallets attached. They function as economic actors. They execute transactions, manage compliance across jurisdictions in real time and operate without direct human involvement.

At Venom Foundation, where I lead work across Southeast Asia and African markets, our focus has included building toward this through our x402 protocol integration, which is scheduled for a full launch in Q1 2026. We are one contributor within a broader movement. Multiple independent teams reached a similar conclusion this year. Blockchain provides the trust layer, AI provides the intelligence layer, and together they enable forms of automation that neither can achieve alone.

Also Read: From hustle to high performance: The 3 shifts that will shape 2026

What 2026 will demand

Three lessons from this year are likely to shape which founders succeed in 2026.

• Infrastructure is narrative. The projects gaining the most traction were not always the most technically advanced. They were the ones who clearly communicated why their solutions mattered to a remittance sender in Surabaya or a smallholder farmer in Rwanda. Technical capability without a human story does not travel.
• Regulatory arbitrage has limits. The convergence belt encourages experimentation, but durable businesses need regulatory clarity. The most forward-thinking founders are treating compliance as an integral part of the product.
• We are approaching a point where AI agents become the primary users of blockchain networks. My current estimate is that by 2027, agent-to-agent transactions may outnumber those initiated by humans on certain chains. This shift will require rethinking gas economics, governance models and network architecture for systems where most participants are not human.

The autonomy horizon

If 2025 was the year AI and blockchain learned how to collaborate, 2026 will be the year they begin operating independently for routine tasks. Imagine supply chains that adjust themselves, micro-finance systems that autonomously assess creditworthiness and distribute capital, and identity networks that evolve through usage rather than administrative revisions.

This is not speculative fiction. The component technologies exist. What remains is effective implementation, and much of that work is being carried out in markets that have the strongest need for these solutions.

The convergence belt is not attempting to catch up with Western markets. In several important ways, it has already moved ahead.

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The majority of Singapore businesses are still using invoicing or accounting systems with simple inventory management instead of comprehensive AI capabilities. In particular, over 90% are still without AI agent involvement.

Singapore’s business landscape has long been recognized for its efficiency and adaptability. Yet, when it comes to enterprise resource planning (ERP) and artificial intelligence (AI), the majority of companies remain anchored to traditional systems. Most small and medium-sized enterprises (SMEs) continue to rely on basic invoicing and accounting software, often paired with simple inventory management modules. These tools, while functional, lack the sophistication of AI-driven ERP platforms. Recent market observations suggest that over 90% of Singapore businesses have yet to integrate AI agents into their operations, leaving a significant gap in digital transformation.

What is an AI agent?

An AI agent is a software entity designed to autonomously perform tasks, make decisions, and interact with systems or users based on contextual understanding. Unlike static automation scripts, AI agents are dynamic, learning from data and adapting to changing environments. They can analyze large volumes of information, predict outcomes, and execute actions without constant human intervention. In ERP systems, AI agents can streamline workflows, optimize resource allocation, and provide real-time insights that empower decision-makers.

What are the differences between an AI agent and AI chatbot (ChatGPT, Copilot etc.)?

While AI chatbots such as ChatGPT or Copilot are primarily conversational tools designed to interact with users through natural language, AI agents go beyond dialogue. Chatbots excel at answering questions, drafting content, or assisting with customer service. AI agents, however, are built for autonomous execution. They can monitor ERP systems, detect anomalies, trigger corrective actions, and even negotiate supply chain adjustments. In essence, chatbots are reactive, responding to prompts, whereas AI agents are proactive, anticipating needs and acting independently.

Also read: How the top 10 best HR systems in Singapore reveal the new standards for HR technology

How will agentic AI benefit Singapore businesses?

The adoption of agentic AI in ERP systems could be transformative for Singapore businesses. Benefits include:

• Enhanced efficiency: AI agents automate repetitive tasks, freeing employees to focus on strategic initiatives.
• Real-time analytics: Businesses gain immediate insights into financial health, inventory levels, and customer trends.
• Predictive capabilities: AI agents forecast demand, optimize procurement, and reduce waste.
• Competitive advantage: Early adopters can differentiate themselves in crowded markets by offering faster, smarter services.
• Scalability: AI agents enable SMEs to expand operations without proportionally increasing headcount.

Risk of late deployment of AI agent

“Delaying the deployment of AI agents in ERP systems poses significant risks,” said Sam Wong, a veteran ERP expert from Synchro. “Companies that hesitate may find themselves struggling with inefficiencies, rising operational costs, and missed opportunities. Competitors who embrace agentic AI early will be able to deliver superior customer experiences, optimize supply chains, and respond faster to market changes. In the long run, late adopters risk being marginalized, as AI-driven ERP becomes the industry standard.”

Also read: Why Singapore manufacturers must embrace MES for the future

Why we write this article

By leveraging the team’s own technical expertise, PRbyAI aims to empower B2B customers with knowledge that helps non-technical audiences make informed decisions. In a rapidly evolving digital economy, understanding the role of AI agents in ERP systems is crucial for businesses seeking sustainable growth.

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About PRbyAI

PRbyAI is a tech-driven Martech startup specializing in AISEO, a cutting-edge approach to search engine optimization powered by artificial intelligence. The company helps clients generate leads, tap into new markets, and strengthen their digital presence. By combining marketing expertise with advanced AI tools, PRbyAI positions itself as a trusted partner for businesses navigating digital transformation.

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