These are challenging times for startups and small businesses, especially those committed to long-term sustainability goals. Geopolitical uncertainties — including fluctuating tariffs, ongoing conflicts, and disrupted global supply chains — continue to cast a shadow over already volatile markets.

While it’s encouraging that Singapore is emerging as a global leader in sustainability, building a green business remains a complex endeavour. Government initiatives like the Enterprise Sustainability Programme, which provides training and consultancy support, and the recently launched Carbon Development Grant offer crucial help.

However, these efforts often fall short of fully bridging the financial and operational gaps that sustainability-driven startups face.

Balancing profit and purpose

Sustainability is undeniably vital for the environment and future generations. But at the end of the day, businesses are primarily driven by the need to achieve profitability. The adoption of sustainable practices often involves significant upfront investment, and without clear, near-term returns, many companies view them as cost centres rather than value drivers.

That said, the landscape is shifting. More financial incentives are becoming available, such as green loans offering preferential interest rates, provided companies meet ESG reporting requirements. This creates a tangible business case for embedding ESG not just as a compliance checkbox, but as a tool for unlocking new capital and strategic growth opportunities.

In Singapore, all listed companies will be required to provide climate-related disclosures aligned with international standards starting from FY2025. However, most of these firms prefer to work with established providers — such as the Big Four accounting firms — for sustainability and reporting services. This makes it harder for newer, smaller sustainability consultancies to gain market share.

Meanwhile, SMEs are unlikely to face the same reporting requirements in the near future, due to concerns about added financial strain. Consequently, the market for sustainability services remains concentrated among larger enterprises. Startups that want to break in must offer specialised, enterprise-grade solutions — and that requires both capital and talent.

Also Read: Investing in a better future: Why sustainable investment matters

Funding challenges and emerging alternatives

Perhaps the biggest roadblock is funding. Traditional venture capital models emphasise high and fast returns, which often don’t align with the long timelines and capital-intensive nature of carbon and sustainability projects.

A promising but still-evolving alternative is tokenisation — a blockchain-based model that allows startups to raise capital from a broad investor base, somewhat akin to crowdfunding. Supported in part by Singapore’s Monetary Authority (MAS), this method offers greater access to funds but still inherits the same investor expectation for rapid ROI.

Reality, however, rarely matches these timelines. Take a reforestation project in Mongolia, for instance. Due to the harsh climate, tree saplings are first cultivated in greenhouses — a process that can take two to three years before planting even begins. Such projects require patient capital and mission-aligned investors.

Surviving and thriving through collaboration

In this environment, startups must be prudent and resourceful. One of the most effective ways to extend runway and accelerate progress is through strategic partnerships. By teaming up with like-minded businesses and leveraging shared services, startups can reduce costs while gaining access to complementary networks, technologies, and markets.

Collaboration can be a force multiplier. Whether through formal consortiums, incubators, or informal partnerships, collective action allows sustainability-minded businesses to scale impact faster and more efficiently.

But perhaps most importantly, green startups must cultivate endurance. Building a truly impactful, sustainable business isn’t a sprint — it’s a marathon. It demands flexibility, commitment, and a long-term mindset.

A long-term commitment to impact

Despite the many obstacles, the mission remains deeply worthwhile. Building a sustainable business is not just about regulatory compliance or generating carbon credits — it’s about creating lasting impact in the fight against climate change and improving the lives of communities around the world.

The journey is tough, especially in today’s financial climate. But by staying committed to our purpose, embracing innovation, and building strategic partnerships, we can not only survive the current challenges — we can emerge as resilient, future-ready leaders in the green economy.

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As someone who works closely with entrepreneurs and business leaders across Singapore and the ASEAN region, I’ve been watching with growing interest the convergence of two powerful forces: artificial intelligence and sustainability.

At first glance, these might seem like distinct domains—one rooted in algorithms and automation, the other in environmental and social responsibility. But at their intersection lies a wealth of opportunity, especially for small businesses willing to look beyond the obvious.

We’re living in a time when governments, corporations, and consumers are all rethinking what growth looks like. Singapore’s Green Plan 2030, ASEAN’s push toward decarbonisation, and rising investor focus on ESG metrics are reshaping how business is done. At the same time, AI tools are no longer locked behind corporate firewalls—open-source models, cloud-based platforms, and no-code tools have dramatically lowered the barriers to entry.

I see this convergence as the centre from which great untapped or little-tapped opportunities emerge. Let me share four such directions where I see strong potential for SMEs in our region to lead the way.

AI for sustainable agriculture

It’s easy to think of farming as old-world, but in Southeast Asia, agriculture remains vital—and ripe for transformation. In Singapore, I’ve seen how vertical farms are using AI and IoT to manage light, nutrients, and watering schedules, boosting yields while saving space and resources. These systems, while sophisticated, are increasingly affordable—basic setups are now built with open-source software and off-the-shelf sensors.

What’s exciting is how this same approach is reaching rural farms. In Vietnam, a company called MimosaTEK offers smart irrigation solutions that use AI to help farmers reduce water usage by up to 30 per cent. Imagine that impact at scale.

Entrepreneurs who understand data analytics and have even a modest grasp of agronomy can create platforms or consulting services to help traditional farmers modernise. Precision farming doesn’t require high-end robotics—it often begins with dashboards, SMS alerts, and remote monitoring linked to simple AI models.

Also Read: Unlocking agritech’s potential: Can Southeast Asia rise to the challenge?

Localised smart city solutions

The term “smart city” can sound like it belongs to governments and multinational tech firms, but there are practical ways SMEs are already playing a role. I’ve been following Vebits AI, a Singapore-based startup that built smart parking systems for private property owners—not the city government. That’s a great example of how small businesses can contribute to AI-driven urban improvements without trying to overhaul entire cities.

Opportunities lie in micro-mobility management, building-level sustainability dashboards, or last-mile delivery optimisation tools. Imagine working with university campuses, business parks, or condo developers to manage scooter-sharing, track utility use, or reduce delivery congestion.

In Manila, a local company partnered with a residential developer to use AI for predictive waste collection—cutting unnecessary trips and improving recycling rates. Projects like these don’t need deep capital reserves; they need a bit of data savvy, IoT integration skills, and strong B2B relationships with property owners or facility managers.

AI for renewable energy optimisation

Energy is a massive space—but there are smaller niches opening up where entrepreneurs can make a real difference. Sembcorp, for instance, uses AI to manage its renewable energy assets across Singapore. But what about all the smaller solar farms, community grids, or off-grid setups across ASEAN?

The International Renewable Energy Agency projects that Southeast Asia will double its solar capacity by 2030, yet much of it will be in smaller-scale installations. That’s where startups can step in—offering AI-powered forecasting, grid balancing, or battery usage optimisation.

A small team with knowledge of energy systems and predictive modelling could build cloud-based tools to help industrial parks in Johor or off-grid resorts in Bali manage fluctuating supply and demand. These tools don’t need to be complex—they need to be reliable, cost-effective, and region-aware. 

Also Read: How the upcycling movement can help build a true circular food economy

AI-enabled circular economy models

One of the most overlooked intersections between AI and sustainability is in the circular economy—rethinking how products are used, reused, and tracked across their lifecycle. Startups here in Singapore are already using AI to monitor waste streams and help manufacturers close the loop.

For instance, a local startup developed an AI-powered dashboard that alerts packaging producers when certain materials are underutilised or overstocked, helping them reduce waste by 15 per cent. That’s real impact—and real savings.

This space is wide open for SMEs with supply chain knowledge and a working grasp of operations or sustainability frameworks. You could build tools that track material flow, optimise end-of-life processes, or even help retailers match surplus with demand in real time. With regulatory pressure growing across ASEAN for extended producer responsibility, tools that support circular thinking will only become more relevant.

ASEAN market opportunities at the intersection of AI and sustainability

Entrepreneurs exploring the convergence of artificial intelligence and sustainability in ASEAN can tap into high-growth sectors backed by both policy momentum and investor interest. Here’s a quick snapshot of where the biggest opportunities lie:

Final reflections

What strikes me across all these areas is that you don’t need to invent new technologies—you need to apply what’s already out there in thoughtful, grounded ways. The convergence of AI and sustainability isn’t only about clean energy or climate models. It’s about building smarter farms, more liveable communities, resilient energy systems, and resource-efficient businesses—all of which are deeply relevant to ASEAN’s future.

So if you’re an entrepreneur wondering where the next wave of growth will come from, consider pointing your compass toward the spaces where technology meets stewardship. These aren’t just opportunities for profit—they’re opportunities for purpose. And in today’s world, that might just be the most enduring edge you can have.

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As Southeast Asia (SEA) rapidly rises as the world’s fourth-largest economy, the region faces a defining question: can its climate tech ecosystem mature quickly enough to meet net-zero goals by 2030? Optimism abounds with climate investment in the area, growing 15 per cent year-on-year from 2015 to 2023. Yet a staggering US$2.5 trillion investment gap remains.

At Echelon Singapore 2025, a panel of leading voices in climate innovation unpacked the opportunities and gaps that must be addressed to unlock a thriving climate tech ecosystem in SEA. It is widely known that the climate crisis is worsening, and SEA is highly vulnerable.

Rebecca Sharpe, Director of Better Earth Ventures, noted, “SDG 13, climate action, is actively regressing,” citing UN ESCAP’s 2023 findings. Yet she remains confident: “Innovation can and should play a critical role. We just need urgency and alignment.”

That urgency stems not just from deteriorating environmental metrics but also from Southeast Asia’s unique potential. With 34 per cent of the region’s population aged between 15 and 24, it is primed to lead in digital innovation, including climate tech. But potential alone is not enough.

Policy, regulation, and mindset in climate tech

A recurring theme among the panellists was the regulatory vacuum in the region. Sharpe pointed to Europe’s robust climate legislation, noting that such frameworks compel action.

“Without regulations, climate solutions are seen as ‘nice to have’, not must-haves,” she said. Singapore, often viewed as a regional leader, has a carbon tax but lacks enforceable climate mandates.

Also Read: Amasia introduces impact assessment framework for climate tech companies

Equally important is cultural context. Arka Irfani, CEO of Bell Living Lab, highlighted the irony of Asia’s historic sustainability practices giving way to growth-at-all-costs models. “The traditional mindset of being inclusive and mindful of future generations has been lost. We need to bring it back.”

Nicole Ngeow, Executive Director of the Prudence Foundation, offered a perspective from the philanthropic front lines. Her foundation supports community resilience in climate and health. But she stressed that innovation must be viable. “Philanthropy can fund early-stage pilots to derisk models, but there must be a pathway to sustainable business,” she explained.

This view aligns with emerging blended finance models, where philanthropic capital helps prove concepts, and commercial investors scale them. “It’s not an excuse to ignore market signals,” she added. “Startups must still demonstrate viable unit economics.”

Several speakers agreed that alignment across sectors—government, corporates, researchers, and startups—is key to scale. Irfani shared a powerful example: a three-month government-backed programme in Indonesia helped Bell Living Lab partner with over 100 farmers to convert coffee waste into sustainable materials.

“Alignment allowed us to scale from idea to impact,” he said. “But for long-term success, proximity to market demand is essential.”

Hyperlocalisation also emerged as a critical success factor. Sharpe noted that effective climate solutions often address specific local challenges—from mangrove restoration in Indonesia to nutrient-rich farming in India.

“Localisation doesn’t mean small scale. Often, these solutions are replicable across borders,” she said.

Developing transformative climate tech is one thing; communicating its value is another. Jatin Kumar, CTO of Xinterra, offered a masterclass in bridging the technical-to-practical divide. His AI-powered material innovation allows textiles to capture carbon dioxide, an idea that could sound esoteric.

Also Read: Why these startups focus on informal plastic waste workers in the fight against climate crisis

“Communication is everything,” Kumar said. “You must explain your technology in a way your audience understands—whether it’s a five-year-old or a textile manufacturer.” By translating emissions metrics into relatable impacts (“20 of these t-shirts equals the emissions offset of a tree”), Xinterra helps partners grasp both the science and the benefit.

Regarding funding, climate tech faces a structural challenge: its returns take time. “Investors don’t always get it,” Kumar said candidly. “Climate solutions aren’t instant wins. We need a shift from fast to slow money, like in biotech.”

Sharpe echoed this, noting that many generalist VCs exited the climate space post-pandemic due to longer timeframes and higher perceived risk. “We need new financial models that match the climate reality,” she said. Tools like the Asia Climate Lab, which maps active climate investors, are helping founders navigate this new terrain.

The panel concluded with a consensus: climate tech must move from fringe to front stage. “This isn’t just about branding,” Irfani noted. “For us, converting waste is the business model. For climate tech to thrive, authenticity matters.”

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A new whitepaper from Singapore-based Device-as-a-Service (DaaS) startup Cinch aimed to bring attention to a largely invisible but mounting problem: 2.9 million unused smartphones are sitting idle in Singaporean households, exacerbating the country’s e-waste challenges.

Titled Rethinking E-Waste: How Singapore’s Consumer Tech Ecosystem is Building a Blueprint for a Circular Economy in Southeast Asia, the report draws from a national survey and offers fresh data on consumer habits while proposing practical solutions rooted in collective action.

It reveals that Singaporeans replace their smartphones every 2.7 years, considerably faster than the global average of 3.5 years. However, rather than being recycled or resold, many older devices end up forgotten in drawers. Concerns around data privacy and a lack of convenient recycling or trade-in options were cited as key barriers to responsible disposal.

Despite these challenges, the appetite for sustainable solutions remains high: 90 per cent of surveyed consumers indicated they would be open to reusing, recycling, or returning devices if safer and easier processes were available.

Cinch’s whitepaper emphasises that the most effective long-term solution lies in adopting circular technology models, which extend the lifespan of devices through reuse, refurbishment, redeployment, and recycling.

This approach not only reduces e-waste but also lessens the environmental footprint associated with raw material extraction and carbon emissions.

Also Read: AI shopping adoption surges 39 per cent in APAC, fueling retail tech investments

The startup’s DaaS model exemplifies how circularity can be embedded into business operations. Through partnerships with organisations such as ALBA and CompAsia, Cinch aims to develop scalable systems that align with Singapore’s Green Plan 2030 and the National Environment Agency’s Producer Responsibility Scheme.

“No single company can solve e-waste alone. What’s needed is a national framework that rewards sustainable behaviour and embeds circularity into the tech ecosystem,” said Mahir Hamid, CEO of Cinch.

Emissions and cost benefits at scale

The environmental stakes are significant. According to the report, adopting circular models at scale could cut Singapore’s e-waste volume by 50 per cent and reduce tech-sector CO₂ emissions by 40 per cent. Each refurbished smartphone saves approximately 25 kilograms of CO₂ emissions, prevents 77 kilograms of raw material extraction, and avoids generating 56 grams of electronic waste.

Beyond environmental gains, consumers also benefit financially. Subscription-based DaaS models can lower upfront costs for devices by up to 96 per cent compared to outright purchases.

While Singapore’s government has implemented regulations and established collection infrastructure to address e-waste, Cinch’s report underscores the importance of multi-stakeholder collaboration. Businesses, policymakers, and consumers all play critical roles in driving circular economy adoption.

“Circularity isn’t an add-on to business. It is becoming the core of how tech consumption needs to evolve,” Hamid concluded.

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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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