In Southeast Asia’s startup ecosystem, founders are taught to focus on what can be measured: product velocity, fundraising, customer acquisition, growth metrics and operational scale.

These matter. But something deeper is quietly changing underneath them.

AI is collapsing the cost of competence. Products that once took years to build can now be replicated in months, sometimes weeks. Interfaces increasingly resemble one another. Messaging converges around the same language. Entire categories begin sounding interchangeable.

The result is not merely technological commoditisation. It is perceptual commoditisation. Even when companies are genuinely different, markets increasingly experience them as the same.

This is the real competitive crisis emerging in the AI economy. Most startups still believe they are competing at the layer of product. In reality, the battle has already shifted upstream, toward perception, interpretation and cognitive positioning.

Because in saturated markets, people do not evaluate deeply anymore. They filter aggressively.

Recognition replaces analysis. Familiarity replaces investigation. Cognitive shortcuts become survival mechanisms.

This is why traditional branding advice increasingly feels outdated. Brand is not a logo. It is not a visual identity. It is not social media aesthetics or clever taglines.

Those are surface artifacts. The real function of brand is environmental.

Brand shapes the interpretive conditions through which people decide what feels credible, relevant, trustworthy or important before conscious evaluation even begins. Before investors analyse metrics, before customers compare features, before talent evaluates offers, something has already shaped perception.

That perception influences whether people lean in or move on. Behavioural science has repeatedly shown that human decision-making is far less rational than most businesses assume. Daniel Kahneman’s work on cognitive shortcuts and heuristics demonstrated that people rely heavily on mental simplification when navigating uncertainty.

Also Read: Asian startups have an investor problem nobody is naming

AI amplifies this tendency because markets are now flooded with infinite information, infinite content and infinite comparison. The more options people encounter, the more they depend on interpretive shortcuts: trust, familiarity, clarity, narrative coherence, social proof, and perceived inevitability.

In other words, the future advantage is not merely visibility. It is interpretive control.

This is already visible in venture capital behaviour. Early-stage startups are routinely valued far beyond present-day financial performance because investors are not simply buying current capability. They are buying belief in future dominance.

That belief is shaped not only by technology or traction, but by whether a company feels culturally relevant, strategically inevitable and psychologically credible. This aligns with broader market data. Research from Ocean Tomo shows intangible assets now account for roughly 90 per cent of the market value of S&P 500 companies.

What markets increasingly value is not just operational capability. They value perceived defensibility.

Grab is a regional example of this dynamic. Much of its enterprise value comes not only from infrastructure or platform functionality, but from years of accumulated familiarity, behavioural trust and embedded relevance across Southeast Asia. That is not merely marketing. It is cognitive positioning at scale.

The same dynamic shapes pricing power. Companies with stronger perception resilience consistently command premiums even in highly competitive markets. Singapore Airlines continues to sustain premium positioning not solely because of operational performance, but because customers already associate the airline with reliability, confidence and quality before comparisons begin.

This is where most startup conversations about branding fail. They focus on expression instead of environment.

But in the AI economy, the companies that win will increasingly function less like products and more like worlds. The strongest companies build interpretive ecosystems that shape how people perceive reality around them.

Apple does not merely sell devices. It constructs a world around simplicity, taste and creative identity.

Nike does not merely sell shoes. It builds psychological associations around ambition, struggle and self-transformation.

The most powerful startups of the next decade will do something similar: they will shape meaning before evaluation starts.

This is where worldbuilding becomes commercially strategic rather than creatively abstract. Worldbuilding is the deliberate construction of signals, narratives, symbols, experiences and emotional triggers that create a coherent psychological environment around a company.

Also Read: Why so many startups are cutting down on the number of tools they use

Every interaction becomes part of the interpretive system: the founder’s language, the product behaviour, the onboarding experience, the interface, the hiring narrative, the investor story, the media presence, the customer community.

Together, these signals shape what people believe the company represents long before direct comparison takes place. In high-noise AI markets, this matters enormously. Because attention alone is becoming fragile.

AI-generated content has created an economy of infinite visibility but declining memorability. The startups that survive will not necessarily be the loudest. They will be the ones that reduce uncertainty fastest.

The ones that create cognitive ease. The ones that feel coherent under pressure. The ones that people instinctively understand and remember.

This is why emotional triggers matter more than many founders realise. Fear of irrelevance. Desire for belonging. Status signalling. Identity reinforcement. Risk reduction. Future aspiration.

The strongest companies understand that markets do not merely buy functionality. They buy emotional resolution.

Economist Robert Shiller described this dynamic as “narrative economics,” the idea that stories themselves shape economic behaviour and market outcomes. In the AI era, narrative becomes even more powerful because AI accelerates production faster than humans can process meaning. As sameness increases, interpretation becomes the new competitive frontier.

So what should startups actually do? The solution is not “better branding” in the traditional sense. It is strategic worldbuilding.

Founders need to stop asking: “How do we market our startup?” The more important question is: “What environment shapes how people perceive us before conscious evaluation begins?”

This changes how startups should think about growth entirely. Instead of treating brand as a late-stage marketing layer, startups should build interpretive infrastructure from the beginning: clarity of worldview, consistency of signals, narrative coherence, emotional resonance, behavioural trust, and psychological memorability.

Because in AI-saturated markets, the greatest threat is no longer invisibility. It is becoming cognitively interchangeable.

Over the next decade, many startups will fail not because their technology was weak, but because markets stopped perceiving meaningful differences between them. The companies that endure will not simply compete for attention. They will shape the environments through which attention becomes belief in the first place.

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In Southeast Asia’s startup market, the era of “raise fast, spend faster” has given way to a more disciplined question: how can companies keep growing without giving away too much of themselves?

That shift is creating room for financing products that sit between bank loans and venture capital. OSK Ventures International and Affin Hwang Investment Bank are now moving into that gap with the launch of Pothos Fund I, a dedicated venture debt fund aimed at high-growth companies across Southeast Asia.

Also Read: Venture debt in SEA: The non-dilutive capital that comes with hidden legal strings

The fund, managed through Pothos GP, has a three-year investment tenure and will provide debt-equity hybrid financing to companies that have moved beyond the earliest stage of startup life. Rather than backing ideas that are still being tested, Pothos Fund I will target revenue-generating businesses with proven models, stronger management teams and more predictable cash flows.

For founders, the appeal is straightforward. Venture debt can extend a company’s runway or fund expansion without forcing management teams to raise another equity round at an unfavourable valuation. For investors, the product offers exposure to private technology companies through a structure that includes contractual income, downside protection and selective equity participation.

The launch also gives sophisticated investors in Malaysia access to an asset class that has historically been more common among large institutional investors.

Why venture debt is becoming more relevant

Venture debt is not new, but it has become more visible as startups and investors reassess the cost of capital. In simple terms, it is a loan designed for venture-backed or high-growth companies that may not yet fit the credit models used by traditional banks. It is often paired with warrants or other equity-linked features, giving lenders some upside if the borrower performs well.

In Southeast Asia, the model has become more relevant for several reasons. The region’s digital economy has matured, producing more companies with recurring revenue, payment histories and expansion plans across multiple markets. At the same time, equity funding has become more selective after the global correction in tech valuations.

That combination has put pressure on founders to become more capital-efficient. Raising equity remains essential for many startups, especially those in capital-intensive sectors such as fintech, logistics, climatetech and artificial intelligence infrastructure. But for companies with clearer revenue visibility, debt can be a useful tool to finance working capital, product development, market expansion or acquisitions.

The timing is important. Southeast Asia’s startup ecosystem is no longer defined only by early-stage venture rounds. More companies now sit in the middle: too mature to be treated like seed-stage bets, but not yet large or profitable enough to borrow easily from commercial banks. It is this middle layer that venture debt funds are trying to serve.

What OSKVI and Affin Hwang bring to the table

The partnership combines OSKVI’s venture investing background with Affin Hwang’s capital markets and private markets structuring experience.

OSKVI, listed on Bursa Malaysia, has invested in, supported and exited more than 50 technology and enterprise companies across Southeast Asia over the past two decades. That history matters in venture debt, where lenders need to assess not only cash flow but also investor backing, founder quality, sector dynamics and the likelihood that a company can raise future capital if needed.

Also Read: Venture debt: How it stacks up against loans and equity

Affin Hwang brings a different set of capabilities, including fundraising, distribution, private markets structuring and access to institutional and sophisticated investors. Those strengths are useful at a time when wealth managers, family offices and other sophisticated investors in the region are looking for alternatives to public equities and traditional fixed income.

Pothos GP, the fund manager of Pothos Fund I, is a subsidiary of OSKVI, with strategic equity participation from Affin Hwang Investment Bank.

Amelia Ong, CEO of OSK Ventures International, framed the fund as part of a broader shift in how startups are financed.

“Having worked with entrepreneurs across Southeast Asia for many years, we have seen firsthand how access to the right capital at the right time can make all the difference,” she said. “As companies mature, their financing needs evolve, and venture debt provides a valuable option alongside traditional equity funding.”

A more crowded alternative capital market

Pothos Fund I enters a regional market where venture debt is still underdeveloped compared with the US or India, but no longer empty. In Southeast Asia, players such as InnoVen Capital, Genesis Alternative Ventures and AFG Partners have helped familiarise founders and investors with non-dilutive or less-dilutive growth capital.

Globally, the space includes specialist lenders such as Hercules Capital, as well as bank-linked providers such as HSBC Innovation Banking, which absorbed parts of Silicon Valley Bank’s operations outside the US after SVB’s collapse.

India offers a useful comparison for Southeast Asia. Over the past decade, venture debt firms such as Trifecta Capital and Stride Ventures have built sizeable businesses by lending to startups that had institutional equity backing and clearer paths to revenue. Southeast Asia has similar ingredients, but its market remains more fragmented, with startups operating across different regulations, currencies and customer behaviours.

That fragmentation can make lending harder. A startup expanding from Malaysia to Indonesia, Vietnam, or the Philippines faces different legal systems, payment rails and market risks. For venture debt funds, this means underwriting must go beyond a company’s balance sheet. It requires a view on the founders’ execution record, existing investor support, customer concentration, repayment capacity and the durability of demand.

The founder’s trade-off

Venture debt is often described as less dilutive, but it is not free money. Borrowers need to make repayments, and lenders typically include covenants or protections. If a company misses growth targets or burns cash faster than expected, debt can become a burden.

That is why funds such as Pothos Fund I are more likely to suit startups that already have revenue and a credible plan for cash generation, rather than early-stage companies still searching for product-market fit. Used well, venture debt can help a company avoid raising equity during a weak funding market. Used poorly, it can add pressure at precisely the moment a startup needs flexibility.

For Southeast Asian founders, the significance of Pothos Fund I lies less in the launch of a single fund and more in what it signals about the market’s direction. The region’s financing stack is becoming more layered. Equity capital remains important, but founders increasingly have more choices: revenue-based financing, venture debt, private credit, bank partnerships and strategic capital.

Also Read: Lighthouse Canton to offer access to venture debt to investors on Alta platform

That evolution is healthy. A mature startup ecosystem needs more than one type of money. It needs risk capital for bold ideas, growth capital for scaling businesses and credit products for companies that have earned the right to borrow.

Pothos Fund I is arriving at a moment when investors want more discipline and founders want more control. If it can find the right borrowers, it could help fill one of Southeast Asia’s persistent funding gaps: capital for companies that are growing up, but not yet ready to behave like traditional corporates.

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Ask a warehouse operator in Singapore what keeps them up at night, and forklifts come up before fires, floods, or fraud.

They should. Between 2022 and 2023, vehicular incidents were the leading cause of fatal workplace injuries in Singapore, and one in four of those deaths involved a forklift, as per the Ministry of Manpower (MOM).

MOM didn’t wait for the data to accumulate further. In November 2024, it introduced enhanced forklift refresher training requirements, on top of a 2015 circular on the safe use of storage racks, following fatal rack-collapse cases.

That’s the backdrop on warehouse safety in Singapore.

But here’s the forecast – within five years, every warehouse operating in Singapore will run on some form of AI-based safety monitoring. Not because it’s trendy. Because three things are converging at once, and none of them is slowing down.

The rules are no longer satisfied by good intentions

Singapore’s Workplace Safety and Health Act asks employers to take “reasonably practicable” steps to protect workers. For years, that meant a folder of risk assessments, a monthly walk-through, and a rack inspection schedule with daily visual checks, weekly compiled reports, and annual professional audits.

On paper, it works. In practice, a blocked emergency exit gets cleared for an audit and drifts back within days. PPE compliance holds in the morning shift and slips by the afternoon. A bent upright from a forklift impact goes unnoticed until the next scheduled inspection, weeks later.

Non-compliance isn’t a soft cost anymore. Fines under WSH regulations can run up to SG$500,000 for corporate entities, and severe violations trigger a Stop Work Order — a warehouse shutdown overnight, mid-fulfilment cycle. The Workplace Safety and Health Council has already named warehousing an accident hotspot, specifically around forklift use and loading operations. Regulators are asking for continuous, demonstrable compliance now, not a clean paper trail collected once a quarter.

That’s a standard periodic inspection that was never built to meet — and it’s precisely the standard AI-based monitoring is built to meet instead, because it doesn’t inspect on a schedule. It watches continuously, which is the only way “reasonably practicable” starts to mean something real rather than something documented after the fact.

Also Read: Why your data warehouse is just a very expensive attic

The floor is shrinking while the volume grows

Singapore’s freight and logistics market is worth roughly US$26 billion this year and is on track to hit over US$35 billion by 2031, growing at more than 6 per cent annually, with warehousing itself among the fastest-growing segments, propelled by e-commerce and just-in-time stocking demand.

That growth is landing on a footprint that isn’t expanding at the same rate. Land is scarce and expensive. Warehouses are going vertical, consolidating operations, and running leaner headcounts than the volume suggests they need. A supervisor who once covered one aisle now effectively covers three.

More product moving through less space, watched by fewer people, is a formula periodic manual checks were never designed to handle, and it’s exactly the gap AI is being built to close. A camera system that already exists on-site doesn’t need more headcount to watch more aisles; it just needs to be given the job.

The technology stopped being the limitation

The biggest change, if we consider the last five years in warehouse safety technology, is not that the cameras have become better. It is that the purpose of the camera has evolved.

For most warehouses, CCTV has historically been a forensic tool. Footage becomes valuable after something has happened, like an injury, a collision, damaged stock or a disputed near miss. Someone identifies the approximate time, retrieves the recording and reconstructs the event.

Computer vision-based monitoring changes that sequence.

Instead of waiting for a supervisor to review footage, AI models can analyse visual conditions as operations unfold and identify predefined risk patterns. That distinction matters because many warehouse incidents develop over seconds rather than hours, leaving very little time for conventional supervision to intervene.

The technical barriers to doing this at operational scale have also fallen. The AI warehouse monitoring systems can increasingly work with existing surveillance infrastructure like CCTVs on site, while edge computing allows safety-critical processing to happen close to where footage is generated rather than depending entirely on cloud connectivity.

But detection itself may prove to be only the first stage.

Warehouses generate thousands of visual observations across shifts, aisles and loading areas. Analysed over time, those observations can reveal something more valuable than individual violations, for example, the patterns of exposure.

Also Read: Boardrooms to warehouses: How SEA leaders can build cyber resiliency from top-down

This is where newer technological developments like vision-language models (VLMs) and agentic AI systems could push warehouse safety further. Rather than simply classifying an event, these systems are beginning to interpret sequences of activity, retrieve relevant evidence and help safety teams identify recurring conditions across larger volumes of operational data.

That changes the role of collected footage on the floor again. It translates from evidence of what happened to detection of what is happening, and eventually to intelligence about what is likely to keep happening unless the underlying condition changes.

Warehouses generate an enormous volume of operational data every second, but most of it has traditionally gone unused because it couldn’t be analysed in real time. AI changes that by transforming visual information into measurable safety intelligence, allowing organisations to intervene before isolated events develop into systemic risks.

Five years is the generous estimate

None of this replaces a supervisor’s judgment or a good toolbox talk. What it removes is the lag between a hazard forming and someone catching it — where most warehouse incidents live.

Put the three forces together — regulators demanding continuous proof, a market outgrowing its floor space and headcount, and AI infrastructure finally cheap and local enough to run on cameras a warehouse already owns — and five years starts to look conservative, not ambitious.

The operators moving now aren’t betting on a trend. They’re the ones who read the regulatory notices, looked at the growth numbers, and did the math first.

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Strengths. Weaknesses. Opportunities. Threats.

Someone fills four boxes with familiar phrases, takes a photo, and never looks at it again.

That is not a strategy framework. It is office wallpaper.

Used at the right time, however, frameworks such as 5W1H, SWOT, and PESTLE can help a person avoid one of the most expensive mistakes in innovation: building the wrong thing with great enthusiasm.

This matters more in the age of AI.

AI can produce 50 product ideas before lunch. It can turn a rough thought into a neat business plan, a product description, and a list of potential customers. It can make an early idea look much more finished than it really is.

The problem is not a shortage of possibilities. The problem is deciding which possibility deserves your time.

That is what frameworks are for.

Start with the problem, not the solution

The first framework is also the simplest: 5W1H.

Who has the problem? What exactly happens? When does it happen? Where does it happen? Why is it costly or frustrating? How do people cope with it now?

These questions sound obvious. They are not.

Many weak ideas begin with a solution looking for a problem. Someone wants to use AI, build an app, add a sensor, or invent a feature. Then they go searching for a reason to justify it.

5W1H flips the order. It forces the inventor or business owner to describe the real situation first.

Consider a restaurant owner who says, “I need an AI tool for stock management.” That is a solution. The better question is: what is actually going wrong? Is food being wasted because demand changes? Is staff input unreliable? Are suppliers late? Is the problem fresh ingredients, storage, purchasing, or forecasting?

Also Read: Can AI really improve collaboration and productivity

The answer changes what should be built.

A good problem statement does not make an idea less creative. It gives creativity a target.

Use SWOT before the money is spent

SWOT is most useful after you have a possible solution but before you have committed too much time or money.

A strength is not just something you are proud of. It is an advantage you can use. A weakness is not an admission of failure. It is a constraint that may shape the first version. An opportunity is not a vague trend. It is a change you can act on. A threat is not a reason to give up. It is a risk you need to design around.

Imagine an SME that has developed a better way to inspect a component before it leaves the factory.

Its strength may be deep knowledge of the production line. Its weakness may be limited software skills. Its opportunity may be rising demand for traceability. The threat may be that a large global supplier can quickly copy a visible feature.

That last point is important. A SWOT analysis can lead directly to an IP question. If the innovation is easy to see and valuable, should the company explore patent protection? If the value sits inside a hard-to-observe process, should it be kept confidential as a trade secret?

The framework does not answer the question. It makes sure you ask it while there is still time to act.

PESTLE helps you see the weather

PESTLE looks outside the business: political, economic, social, technological, legal, and environmental forces.

It is easy to dismiss as another consultant’s acronym. That would be a mistake.

A good idea can fail because it arrives at the wrong time, in the wrong market, or under the wrong rules. A PESTLE scan helps you notice the weather before you set sail.

A product may be timely because regulations are changing. A new solution may struggle because customers are cutting costs. Climate pressure may create demand for less waste. A shift in trade rules may make local alternatives more valuable. An aging population may create a need for a different kind of service.

These are not background details. They shape whether an invention has a market.

For Southeast Asian businesses, this matters because the region contains many different markets. A solution that works in Singapore may need a different price, partner, or compliance path in Indonesia, Vietnam, Thailand, or the Philippines.

A framework is a set of better questions

The point is not to complete three templates and declare yourself innovative.

A framework is useful when it turns a fuzzy idea into a sharper question.

5W1H asks whether you understand the problem. SWOT analysis asks whether your solution aligns with your real strengths and risks. PESTLE asks whether the external environment is helping or hindering your timing.

Also Read: From copilots to colleagues: How agentic AI is redefining enterprise productivity

Together, they can turn an exciting thought into a practical experiment.

What do we need to test first? Who should we talk to? What would prove we are wrong? What part of the idea creates the value? Could a competitor easily copy it? What should stay secret? What might be worth protecting?

This is where frameworks become more than management language. They become a bridge between a bright idea and a decision.

AI needs a good brief too

AI is most useful when it is given good context. A vague prompt produces a vague answer, even when it sounds confident.

If you use 5W1H to define the problem, SWOT to understand the business, and PESTLE to see the market conditions, you can give AI a much better brief. Then it can help generate options, compare approaches, identify questions, and organise research.

It can make the thinking faster.

It cannot make the thinking optional.

The world does not need more beautifully presented ideas that fail the moment they meet reality. It needs more people who can turn a real problem into a clear, tested, and defensible solution.

That is not boring.

That is how innovation gets built.

If you have an idea that needs sharper questions before it needs a big budget, start exploring it for free at IPGuru.ai.

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The post SWOT is not boring; you are just using it too late appeared first on e27.

The environmental debate around AI is often placed in the sustainability section of the company, where it becomes a reporting matter, a disclosure matter, or a reputational matter. By the time it gets there, most of the important decisions have already been made.

The environmental impact of AI is not shaped mainly by the annual report. It is shaped by product choices made much earlier. Which model was selected. How often it is called. Whether the system defaults to generation when retrieval would do. Whether latency targets force expensive compute. Whether every user action triggers inference or only the ones that matter. Whether the team built a feature that solves a real problem or simply adds a layer of fashionable intelligence to something that was already working.

Every model choice is also a resource choice

A strange habit has developed in many AI teams. Model choice is discussed as though it were mainly a question of quality, capability, or technical ambition. Bigger model or smaller model. Faster model or smarter model. In practice, that decision also carries implications for cost, latency, infrastructure strain, and carbon impact.

If a team chooses a heavier model for a use case that only needs a narrower and cheaper one, that is not only an architecture decision. It is a product judgement. The team has decided that the extra compute is justified by the user value. In many cases, nobody says it that directly, which is exactly why the ethics stay fuzzy.

Latency pressure can become an ethical problem, not just a product one

There is another layer that companies do not examine closely enough. The modern product instinct is to push for lower latency at almost any cost. Faster feels better. Faster looks more advanced. Faster improves adoption and makes the feature feel magical.

But in AI systems, faster can also mean more expensive infrastructure choices, more aggressive provisioning, less efficient batching, and more resource-hungry serving patterns. A company may think it is making a user experience decision when it insists on near-instant generation everywhere. In reality, it may be making a hidden decision about energy intensity and carbon burden for a very marginal gain in perceived user delight.

Also Read: How to get beyond the chatbot and boost your AI productivity

A serious team should be able to ask a harder question. Does this use case truly require this speed, or are we burning more compute to remove a few seconds of waiting that users would have accepted quite happily? That is not anti-innovation. It is disciplined judgement.

Carbon is often the result of weak product discipline upstream

Many firms speak about AI emissions as though they are the unavoidable byproduct of progress. That framing lets product teams off too easily. A large share of the environmental cost in AI is not simply the price of doing business. It is the price of design choices that were never challenged properly.

Consider how much waste enters the system through product habits that are treated as normal. Features that call a model too often. Workflows that trigger repeated generation because the first output is not grounded well enough. Interfaces that encourage users to regenerate endlessly because nobody designed for confidence or finality. Architectures that use large models for routine classification or extraction tasks. Orchestration layers that look sophisticated but create multiple expensive calls where one would have been enough.

None of this is abstract. It is the operational reality of many AI products.

When viewed that way, environmental ethics starts looking less like a sustainability speech and more like a test of product seriousness. Teams that cannot control unnecessary inference, retries, and overbuilt flows are not only weak on cost discipline. They are weak on environmental discipline too.

The most responsible AI products will not always be the most technically flamboyant

There is still too much status attached to using the most powerful model available. In some companies, restraint is interpreted as compromise. Smaller models look less ambitious. Simpler architectures look less impressive. Retrieval-first systems can sound less glamorous than generative ones. But the product leader with mature judgement will increasingly ask a more grounded question.

What level of intelligence is actually required for this task?

That question matters because many business problems do not need the full weight of frontier capability on every interaction. Some tasks need reasoning depth. Some need consistency. Some need structured extraction. Some need speed. Some need a safe and bounded answer. Treating all of them as invitations for maximum model power is not thoughtful design. It is often a failure to match compute intensity to user value.

Cost, carbon and user value should be discussed together, not separately

One reason this issue remains weakly governed is that organisations split the conversation into silos. Product talks about user benefits. Engineering talks about performance. Finance talks about cost. Sustainability talks about carbon. By the time those views meet, the feature is usually already live, and the room is arguing over trade-offs that were baked in earlier.

This is the wrong sequence.

Also Read: Product management as method acting: Becoming your user

A stronger company would ask these questions together from the beginning. What value is this feature creating? What is the latency expectation that truly matters? What is the marginal gain from using a more expensive model? What does that do to operating cost at scale? What does it imply for resource consumption? Is there a lighter path to the same user outcome?

Environmental ethics should shape the product brief, not the corporate statement

The most distinctive shift companies need to make is procedural. Environmental responsibility in AI should be built into the product brief itself.

A team should be able to explain why this model class is appropriate for this job. Why this latency level is worth the infrastructure burden. Why is this frequency of inference necessary? Why this workflow cannot be narrowed? Why does this user need to justify this operational intensity? If the team cannot answer those questions clearly, then the sustainability language that appears later is unlikely to mean very much.

This is what makes the issue strategic rather than symbolic. Product leaders decide what gets built, how much complexity gets added, what kind of performance is pursued, and where efficiency is allowed to shape the experience. Those decisions are environmental decisions whether they are written that way or not.

A company that leaves this entirely to sustainability reporting is effectively saying it wants to measure the consequence without governing the cause.

The next generation of strong AI products will look more selective

There is a common assumption that the future belongs to products that apply AI more broadly and more aggressively. In practice, the stronger products may be the ones that apply it more selectively and more intelligently.

They will know where generation is truly useful and where deterministic systems are better. They will know where latency matters and where patience is acceptable. They will know when to reserve heavy models for exceptional cases rather than routine flow. They will treat inference as something to allocate deliberately, not something to spray across the interface because it feels innovative.

That kind of selectivity will produce better economics, better operational control, and a cleaner environmental posture. More importantly, it will reflect a better philosophy of product building.

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Ask most people what is holding back AI adoption in Southeast Asia, and the answer usually circles back to language. Bahasa Indonesia, Vietnamese, Thai and Malay are still treated as the great unsolved frontier for global models, the assumption being that once AI speaks the region fluently, enterprises will follow.

Kai Yong Kang, Partner at GenAI Fund, thinks that framing is out of date.
Global models, he argues, can already hold a conversation, translate, summarise and handle basic customer service across the region’s major languages. The real gap has moved elsewhere — into whether an enterprise can trust an AI system to run inside its business, securely and at scale.

Also Read: The AI revolution in emerging markets: Local models, global impact

“The gap is no longer simply whether AI can speak a Southeast Asian language,” he says. “It is whether an enterprise can trust it to execute a business process accurately, securely and at scale.”

That distinction sits at the heart of a conversation with Kang, whose firm has spent the past year running some of the region’s most active AI builder programmes, from a 3,000-strong buildathon with KFC Vietnam and Tasco, to a venture-building sprint with a toll-collection operator, to advisory work inside Vietnam’s National Assembly.

Where localisation quietly breaks down

Kang’s central argument is that most companies stop localising too early. They translate the interface and assume the job is done. Genuine localisation, he says, runs across three layers: language, culture, and operations, a layer which most companies skip. An AI system might understand exactly what a customer wants and still fail if it cannot pull the right information, apply company policy, or complete the action itself.

GenAI Fund got a close look at this problem through Agentic AI Build Week, a five-day buildathon it created that drew more than 3,000 registered AI builders producing over 400 solutions for enterprises including KFC Vietnam, Tasco and Guardian. A companion report the firm authored, The State of AI Builders in Southeast Asia 2026, drew on 2,719 approved builder registrations across 55 countries. Agentic systems made up roughly 18 per cent of project themes, followed by automation and workflows at 15 per cent, conversational AI at 10 per cent, and retrieval-augmented generation at 7 per cent.

The team that won KFC Vietnam’s F&B track, Twohearts, is Kang’s favourite illustration of the point. Long before the buildathon, the group had quietly been running a chunk of The Joi Factory’s delivery orders through Messenger and Zalo since 2020, giving them an intimate feel for how Vietnamese customers actually order and where a human needs to step in. They turned that muscle memory into an agentic ordering system that pulls live menu data, applies vouchers and loyalty points, confirms orders, pushes them to the point-of-sale system, and hands off anything complicated to a person.

“Teams with direct experience of a workflow often localise more effectively than teams approaching the problem as a purely technical or translation exercise,” Kang notes.

Who should be paying for language data, and who actually is

Southeast Asia’s language-data gap is often framed as a funding problem waiting for a government cheque. Kang’s view is messier: responsibility should be shared across governments, universities, technology companies and enterprises, because data alone was never going to be enough. What’s missing is less about corpora, more about repeatable mechanisms connecting datasets to real institutional problems.

Also Read: Featherless.ai wants to make AI model switching as easy as streaming Netflix

GenAI Fund’s engagement with Vietnam’s National Assembly is the case study he returns to. It began modestly, with an AI and Digital Parliament workshop in March 2025 where local AI startups demonstrated their tools to lawmakers. Within six months, that had turned into an actual deployment, with portfolio company Arcanic AI supplying the technology and telco Viettel backing the wider digital transformation.

By July 2026, a delegation led by Secretary General Lê Quang Mạnh was in Hong Kong studying AI governance and digital government at a programme GenAI Fund helped advise.

On the private-sector side, Kang points to Wash3000, a venture-building sprint GenAI Fund ran with VETC, part of Tasco Group and one of Vietnam’s largest electronic toll operators. Builders got access to live car-wash sites, mapping infrastructure from GoongIO, and VETC’s user base of more than four million people, proprietary context no public dataset could replicate.

The “wrapper” question, and why it misses the point

Southeast Asian founders building on foundation models are routinely dismissed as “just a wrapper.” Kang rejects the premise. Almost every modern software company sits on infrastructure someone else built — cloud, payments, maps — and using someone else’s model doesn’t make a product commercially thin by default. The GenAI Fund report backs this up: 81.7 per cent of participating builders use more than one AI platform, which tells Kang that model access itself has stopped being a moat. The value has migrated to the application layer above it.

Revve AI, a portfolio company building an AI customer-operations platform for contact centres across voice, email, Zalo and Facebook Messenger, is his working example of a defensible application layer. Its edge comes from omnichannel coverage of locally important platforms, deep integration with enterprise systems, a shared AI-and-human workspace for handoffs, and a no-code workflow builder with audit trails and version control.

Crucially, contact-centre managers can rewrite scripts and escalation rules themselves — the enterprise stays in the driver’s seat rather than depending on the vendor for every change. Vietnamese banks including VIB, VPBank and Sacombank are already running it.

“Enterprise customers do not pay for architectural purity; they pay for outcomes,” Kang says. “The real question is not whether a product is a wrapper, but what valuable layer it owns, and whether that value will remain as the underlying models continue to improve.”

If models stopped being the problem tomorrow

Kang doesn’t think foundation-model capability is where the fight will be won or lost much longer. Open-weight releases such as Moonshot AI’s Kimi K3 (a multimodal model with a one-million-token context window) are pushing capability once locked inside a handful of labs out into the open. Southeast Asian builders are already behaving accordingly: the GenAI Fund report found 27.5 per cent use at least one Chinese AI model, rising to roughly 39 per cent among those with three to five years of experience. They are picking models by task, not brand loyalty.

The real bottleneck, in his telling, is adoption — getting an enterprise to trust a system, integrate it properly, and rebuild a workflow around it. The report identified 661 builders already working inside large enterprises, even as 74.7 per cent of the broader community has fewer than two years of AI/ML experience. The talent is there; what’s missing is structured exposure to real problems.

Also Read: AI’s biggest bottleneck isn’t intelligence but fragmentation: i10X co-founder

GenAI Fund has built its business around plugging that gap in three ways: a matchmaking platform pairing over 3,000 AI startups with more than 150 enterprise use cases from companies including Coca-Cola and Shinhan Bank, which the firm says has helped drive over 500 proofs of concept; venture-building sprints like Wash3000 that hand builders real operating environments; and buildathons like Agentic AI Build Week that compress the distance between an enterprise problem and a working prototype into days.

For Kang, that’s the real competitive terrain going forward. “The next competitive advantage will not come from having access to the best model,” he says. “It will come from building the fastest and most reliable path from model capability to enterprise adoption.” In a region still being pitched language fluency as the finish line, that’s a considerably harder and more useful target to aim for.

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Earlier this year I reviewed an AI-driven microfinance product being launched in Indonesia by a regional fintech. The model was elegant. It took transaction data from a payments app, layered in mobile usage patterns and a few social signals, and produced a credit score for each individual applicant within seconds. Approval rates were higher than the regional bank’s microfinance arm had ever managed. The cost per origination was lower than a single weekly group meeting. The default rate, in the first two cohorts, was respectable.

The product was being described internally as “microfinance scaled by AI.” It was, in any meaningful sense, not microfinance at all.

The subject of my master’s research, more than a decade ago, was risk management inside Grameen-style group lending in Indonesia. The thing I learned then, and that the AI microfinance conversation in 2026 keeps re-confirming, is this: the social mechanism inside group lending was not a delivery channel for credit. It was the credit. The current generation of AI-driven products is quietly removing that mechanism while keeping the label, what I have started thinking of as the solidarity break.

The model that worked

For roughly five decades, the microfinance model that scaled across Bangladesh, India, Indonesia, and the Philippines worked on a particular set of substitutions. There was no individual credit history, so the lender substituted group solidarity. There was no individual collateral worth seizing, so the lender substituted social pressure inside a peer group of five or six borrowers. There was little enforcement infrastructure, so the lender substituted weekly meetings, public repayment, and the implicit threat of group default.

These substitutions were not poetic. They produced repayment rates above 95 per cent in some of the lowest-income populations in the world, sustained for decades, in markets where conventional credit risk modelling would have rejected almost every applicant.

The discipline that made it work was cross-subsidisation inside the group. The strongest two or three members carried the weakest. The borrowers who could afford to repay early did, partly to maintain the group’s standing, partly because their access to the next loan depended on it.

Also Read: How business lending culture lost its way

What AI changed

Three things have happened in the past five years.

Individual data became dense enough. The behavioural data AI models now have access to, mobile usage, payments, geolocation, alternative income signals, is dense enough that lenders can underwrite individual borrowers in populations where individual credit data was previously sparse. The substitution group lending was designed for is no longer necessary in the same way.

Origination cost collapsed. The weekly meeting, the loan officer’s field visit, the group formation process, all expensive at scale. AI-driven origination is not. The unit economics improve dramatically. So does the temptation to abandon the slower model.

Pricing became personal. AI models price each borrower individually based on their risk profile. Inside a group lending model, every borrower paid the same rate. The strongest members effectively subsidised the weakest. AI-priced lending charges the weakest more, because their individual risk profile justifies it.

Where the math breaks down

The weakest members, exactly the customers microfinance was designed to serve, are now priced individually, at rates that reflect their individual risk without any cross-subsidy. The mathematics of risk-based pricing says they should pay more. The mathematics of social inclusion says they will not be able to. In the gap between those two, what used to be a microfinance product becomes a high-rate consumer loan to a marginal borrower, which is a different financial instrument with a different social function.

The discipline mechanism is also gone. Group lending’s repayment rates were never about underwriting. They were about the social architecture around the loan. An app-based individual loan has none of that architecture. Default behaviour, when it arrives, is not detected by a co-borrower noticing their groupmate is in trouble. It is detected by a model after the missed payment.

What is starting to work

A few institutions are quietly attempting hybrid models.

Group-formed, individually-scored. Some lenders preserve the group formation process, for credit education, mutual support, informal accountability, while still pricing individual members on their own risk profile. The group provides the social architecture. The individual scoring provides the precision.

Pricing floors and ceilings. A small number of institutions, including some sharia-aligned microfinance providers, deliberately compress the pricing range, refusing to price the weakest members above a threshold even when the model would justify it. The cost is absorbed into the institution’s margin.

Community-rated lending. A few cooperative-style platforms are experimenting with community-level credit risk pooling, where members of a defined community vouch for one another at scale, and the platform underwrites against the community signal rather than the individual.

Also Read: Bridging the financial gap: How digital lending is powering financial inclusion in Southeast Asia

What needs to be preserved

Three principles are worth defending.

Cross-subsidisation as design choice. If a product aims to serve the poor, pricing should be designed to subsidise across the borrower base, not to extract from the weakest. AI makes the extraction technically possible. It does not make it appropriate.

Social architecture around the loan. The mechanisms that produced 95 per cent repayment in some of the poorest markets were social, not statistical. Abandoning them in favour of pure algorithmic underwriting trades one risk model for another.

Honest naming. A product priced individually, with no group accountability, no cross-subsidy, and no inclusion floor is not microfinance. It may be a useful product. It is a different product. Calling it by the same name confuses the policy conversation and the regulatory framework.

The macro stakes

Microfinance in Indonesia, the Philippines, Vietnam, and across South and Southeast Asia has been a quiet success of the last four decades. It pulled tens of millions of households into formal credit, built a generation of community-based financial institutions, and produced one of the most replicable models in development finance.

What is replacing it now is not necessarily worse. But it is different. The institutions, regulators, and investors looking at the microfinance landscape in 2026 should be honest about what they are actually building. The label has not changed. The product underneath it largely has.

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Imagine you are the founder.

You enter the Philippines with no inherited customer base, no branches and no familiar name. You start with a platform and a promise. Then you spend years doing the unglamorous work required to make both credible: securing the right approvals, building local partnerships, educating customers, answering difficult questions and delivering what you said you would deliver.

Slowly, the name begins to mean something. Customers recognise it. Partners take your call. Journalists understand where you fit. Your brand becomes a shortcut for trust in a market where people are understandably cautious about who handles their money.

Then an unrelated platform from the other side of the world begins advertising to the same audience under a similar consumer-facing identity.

Unfortunately, this scenario played out for a business we work with.

To the founder, it feels opportunistic. Someone else can apparently enter the market’s digital attention space without carrying the cost of the credibility you built. A few social-media ads can place two different entities in the same feeds, searches and app-store results.

Whether that confusion is intentional almost does not matter. The commercial and reputational risk exists either way.

The hard part comes after building the app

New financial institutions often describe their advantage in terms of technology: better architecture, faster onboarding, fewer legacy systems. Those things matter, but they are not what makes a customer place money with an unfamiliar institution.

Trust comes from accumulated proof. The institution turns up consistently. Its executives answer questions. Its partnerships work. Customers receive the product they were promised. Problems are handled visibly and responsibly. Over time, the market learns what the brand represents and why it belongs there.

That work is especially difficult in the Philippines because consumers do not experience financial services as neat regulatory categories. The Bangko Sentral ng Pilipinas’ Consumer Finance and Inclusion Survey found that half of Filipino adults owned a formal financial account. Bank-account ownership was 23 per cent, while e-money-account ownership was 36 per cent.

A customer can easily move between a bank, wallet, lending app, employer platform and e-commerce checkout without necessarily knowing which regulated entity sits behind each service. Incumbent banks have the advantage of familiarity. Wallets enjoy frequency of use and word of mouth referral. A new digital bank often begins with neither advantage.

The internet removes geography from brand competition

For decades, two companies with similar names could operate on opposite sides of the world without difficulty. Their customers, distribution channels and media environments rarely overlapped.

Today, with artificial intelligence propelling search, the internet has collapsed that protection. A company does not need local branches, or even meaningful local awareness, to buy access to an audience. Social platforms, search engines and app stores allow an overseas business to appear beside a locally established one almost immediately.

Also Read: Japan is moving into Southeast Asia faster than the West, and most brands haven’t noticed yet

Local incorporation, intellectual property and regulatory approval remain essential. But they do not determine what appears in a customer’s feed. The consumer sees a brand advertisement, not a corporate registry. They type a name, not a licence number.

This can make identity confusion an operating risk, not merely a branding irritation. A customer downloading an app, verifying an account or responding to a service message is making a security decision. When two unrelated services appear under similar identities, the burden of distinguishing them falls on the person with the least information at the most sensitive moment.

What can communications actually do?

Brand ownership belongs with legal and regulatory specialists. The job of communications is to reduce the space in which confusion can occur.

Make legitimacy visible

Do not assume customers understand the difference between a licensed bank, a wallet, a lender and a technology platform. Use the institution’s full regulated name consistently. Make its legal entity, official website, verified accounts, app publisher and regulatory and deposit-protection status easy to find and easy to repeat.

Today, more than ever, these details should not be buried in a footer. They are part of the brand and product’s trust architecture.

Own the verification journey

Communications teams should build content around the questions a cautious customer will ask and an AI might answer: Which app is official? Who operates it? Where can I verify that? How do I know it is a genuine message from the institution I trust? Publish clear answers on the website, help centre, emails to customers, partner channels and app-store pages.

Structure that information so search engines and AI assistants can retrieve it accurately. If machines increasingly mediate discovery, machine-readable identity is now part of reputation management.

Also Read: Your founder brand could add or subtract US$500K to US$1M before you walk into a room: Here’s how

Brief the ecosystem before the confusion spreads

Customers are not your only audience. Partners, customer-service teams, fraud specialists, journalists and creators should also know how to describe your company and how to direct people to official channels. A single identity sheet and an agreed response can prevent five departments from giving five different explanations.

Educate without advertising the other party

The founder’s instinct may be to name the other company and warn the market. Sometimes direct clarification becomes necessary. But a public fight can give an unfamiliar entrant attention, search relevance and an implied association with the established brand.

Start with neutral consumer guidance: how to identify the official institution and where to verify it. Monitor wrong-app complaints, search results, advertisements, customer questions and suspected misdirection. Decide in advance what evidence or level of harm would trigger a named public response.

If that threshold is crossed, lead with verifiable facts rather than conclusions about motive.

Do not let silence create the opening

A brand is not defended only during a collision. It is defended through continued presence. Founders often treat communications as something to switch on around funding rounds, launches or crises. But long quiet periods weaken the connection between the name and the meaning the company worked to establish.

The uncomfortable lesson is that a registered name and a credible platform are not enough. You must remain recognisable, verifiable and present.

The internet democratised access to markets. It also democratised access to other companies’ audiences. Communications cannot make that system fair. It can make it much harder for customers to take the wrong turn.

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The post You built the brand, the internet gave someone else access to it appeared first on e27.

Southeast Asia’s agritech problem is not a lack of ideas. Across Indonesia, Vietnam, the Philippines and Thailand, founders have spent the past decade building tools for farm finance, market access, input distribution, traceability, climate resilience and supply-chain efficiency. Many have proved that technology can work in pockets of rural Asia. Far fewer have shown that these models can produce the kind of exits venture capital needs.

That gap is becoming harder to ignore. According to insights from the “AgTech Investment in Emerging Markets 2025” report by AgBase, Briter, and Mercy Corps, emerging-market agritech is facing a liquidity challenge: capital has flowed into pilots and early-stage rounds, but meaningful exits remain scarce.

Also Read: Agritech investors are learning that infrastructure matters

For Southeast Asia, the implication is stark. If public listings are unlikely to be the main route to investor returns, founders and funders may need to treat mergers and acquisitions (M&As) as the default endgame.

This is not a retreat from ambition. It may be the more realistic way to build durable agritech companies in a region where agriculture is fragmented, infrastructure is uneven, and large conglomerates still control much of the physical value chain.

The venture model meets rural reality

The global funding reset after 2023 exposed a mismatch that had been building for years. During the boom, many agritech startups were encouraged to behave like software companies: grow fast, acquire users cheaply, expand across markets, and worry about profitability later. That approach may work for some consumer internet or enterprise software businesses. It sits less comfortably with agriculture.

In Southeast Asia, customer acquisition often does not happen through online ads or self-serve software sign-ups. It happens through field agents, cooperatives, village leaders, demo plots, credit officers, warehouse operators and traders. Trust is earned over planting seasons, not sales funnels. A farmer may adopt a new input, financing product or digital marketplace only after seeing proof that it improves yield, reduces risk, or raises income.

That makes agritech operationally heavy. Startups frequently need to build or coordinate logistics, storage, quality control, procurement, financing and advisory services before their digital layer can create value. The result is slower scaling, higher upfront costs and less predictable margins than many generalist venture investors are used to.

Indonesia shows what happens when this tension is ignored. The country attracted strong agritech interest before the funding correction, backed by its large farming population, fragmented supply chains and rising demand for food security. But as capital became more selective, companies built on subsidised growth and weak controls came under pressure. Some had to restructure; others struggled to prove that user growth translated into sustainable economics.

The lesson is not that Indonesian agritech is broken. It is that scale without discipline can destroy value. In agriculture, a million registered users with high churn is less compelling than a smaller, stickier network that improves farmer income, controls supply quality, and monetises through processing, trading, finance or retail margins.

Why IPOs are the wrong benchmark

In mature startup ecosystems, an initial public offering (IPO) can provide liquidity, brand recognition and a way for early investors to exit. But Southeast Asian agritech does not yet have the depth of public-market demand, profitability profile or repeatable exit history to make IPOs a dependable path.

Also Read: Agritech’s next business model may not charge the farmer

The report contrasts this with markets such as India, where exits are more multi-modal, supported by deeper domestic capital markets, secondaries and strategic acquisitions. Brazil, meanwhile, has developed a more sophisticated mix of corporate venture capital, rural debt and strategic M&A linked to its powerful agribusiness sector. Africa remains earlier, with more grant-heavy funding and consolidation often taking place between startups.

Southeast Asia sits in a different place. Strategic corporate buyers, such as food processors, plantation groups, input companies, retailers, commodity traders and conglomerates, are likely to be the most credible acquirers. That makes the exit runway narrower, but not necessarily weaker. It simply demands that startups build with those buyers in mind.

For founders, this changes the definition of success. A company does not need to become a standalone public-market giant to be valuable. It needs to solve a problem that a larger player cannot easily fix internally.

Building for the buyer

The most acquirable agritech companies in Southeast Asia are likely to be those that fit into existing commercial rails. Rather than trying to replace incumbents, they become the innovation layer incumbents need.

One obvious area is biological inputs, including biofertilisers, biostimulants and other alternatives that can improve soil health or reduce chemical dependency. These products require research, trials, farmer education and regulatory work. For a large agribusiness group facing pressure from export buyers to lower residues and improve sustainability, acquiring a proven biologicals startup may be faster than building the capability from scratch.

Another is farm management and traceability software. Standalone software-as-a-service, subscription software sold directly to farmers, has often struggled because farmers are reluctant to pay for tools that do not clearly raise income or reduce risk. But software that helps a processor or exporter track produce from farm to buyer can be strategically valuable. As global markets demand better proof of sustainability, food safety and supply-chain resilience, granular farm-level data becomes a licence to operate.

This is especially relevant for Southeast Asia, where smallholders remain central to crops such as rice, coffee, palm oil, fruit and aquaculture. Large buyers need visibility into these fragmented networks. Startups that already have farmer relationships, data systems and field operations can become attractive acquisition targets.

Capital must change too

If M&A is the more likely exit route, the funding model also needs adjustment. Pure equity financing pushes startups towards large valuation jumps and eventual liquidity events. That can distort behaviour in a sector where growth depends on crop cycles, physical infrastructure and farmer trust.

Also Read: Why Indonesia’s agritech winners will be phygital, not purely digital

A more mature capital stack would combine equity with debt, mezzanine financing, concessional capital and strategic investment. Development finance institutions and donors can help de-risk infrastructure or early models in harder markets. Specialised funds and corporate venture arms can then support growth where commercial demand is clearer. Traditional VCs should enter when the path to cash flow or acquisition is visible, not merely when the addressable market looks large on paper.

This sequencing matters because agriculture often requires “phygital” infrastructure: digital tools tied to physical networks. Cold chains, warehouses, collection centres and field teams are expensive, but they can also become defensible moats. A startup that controls quality, trust and last-mile relationships may be far more valuable to a corporate buyer than a digital-only platform with shallow engagement.

The report also points to cash-flow sustainability as an overlooked return pathway. If an agritech company can improve farmer income by 20 to 30 per cent, reduce churn and achieve repayment rates above 95 per cent in agri-finance, it may create room for dividends, structured buybacks or partial exits. These are less glamorous than unicorn stories, but they may be better suited to the sector.

A more realistic playbook

For Southeast Asian agritech, building for M&A means focusing less on vanity metrics and more on strategic usefulness. Startups should prove unit economics early, especially by capturing margins in processing, trading, finance or retail rather than relying only on farmer fees. They should bundle services — inputs, credit, advice and market access — because farmers rarely experience their problems in isolation.

They should also understand which corporate balance sheets might eventually value their capabilities. A traceability startup should know the compliance pressures facing exporters. A biologicals company should understand the procurement needs of plantations and food producers. A financing platform should know where banks, cooperatives or state-linked enterprises lack rural underwriting data.

The broader point is that Southeast Asian agritech cannot simply import the venture playbook used in software markets. Agriculture is slower, messier and more physical. But that does not make it less investable. It means the path to liquidity must match the structure of the industry.

The region’s food systems face real pressure from climate change, volatile prices and rising demand. Technology will have a role in making them more resilient. But for that innovation to survive, investors need exits and founders need capital that does not force them into unnatural growth.

Also Read: From Lagos to Jakarta: Why SEA agritech needs Africa’s “boots on the ground” playbook

The public markets may not open widely for Southeast Asian agritech anytime soon. The strategic buyers, however, are already there — in the mills, warehouses, plantations, ports and retail networks that move food through the region. The next generation of agritech winners may be those that build not for a speculative IPO, but for the moment those incumbents decide they cannot afford to operate without them.

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