In less than two years, generative AI has gone from novelty to necessity. It writes our emails, designs our slides, drafts our articles, generates our images, scripts our videos, and even suggests what we should think next. For many organisations, the question is no longer whether to adopt generative AI, but how fast they can integrate it into every workflow.

Yet quietly, beneath the enthusiasm, a new sentiment is emerging across creative, professional, and knowledge‑based industries: fatigue.

Not burnout from overwork—but a subtler exhaustion. A sense that creativity is becoming automated, flattened, and strangely hollow.

This is generative AI fatigue. And it forces us to ask an uncomfortable question: are we over‑automating creativity itself?

The promise: Efficiency, scale, and democratisation

Let’s be clear: generative AI works.

It lowers barriers to entry. A solo founder can produce what once required an agency. A junior employee can draft with confidence. A non‑designer can create visuals. A non‑writer can publish.

From a business perspective, this is revolutionary. Generative AI compresses time, reduces cost, and scales output. In an economy obsessed with speed and efficiency, this feels like progress.

It also democratises access. For many people who previously lacked language fluency, technical skill, or formal training, AI tools provide a starting point—a scaffold.

But scale and speed come with trade‑offs. And those trade‑offs are now becoming visible.

The symptom: Everything starts to sound the same

Scroll LinkedIn. Read Medium. Browse Substack. Watch short‑form videos.

You’ll notice a pattern.

Polished. Structured. Clean.

And eerily interchangeable.

Thought leadership posts follow identical rhythms. Articles echo the same metaphors. Marketing copy repeats familiar frameworks. Even “personal” stories feel optimised rather than lived.

Also Read: Creativity at the heart of business growth

This is not because people have suddenly lost originality. It’s because generative AI systems are trained on what already exists—and rewarded for producing what statistically resembles success.

AI doesn’t invent culture. It averages it.

When creativity becomes prompt‑based and output‑driven, uniqueness is no longer the goal. Predictability is.

The result? Content abundance—and meaning scarcity.

The deeper problem: Creativity without friction

Creativity has always been inefficient.

It requires boredom, false starts, uncertainty, and discomfort. It often involves writing badly before writing well. Thinking slowly. Sitting with ideas that don’t immediately resolve.

Generative AI removes much of this friction.

At first, this feels liberating. But over time, it creates a subtle dependency: we stop wrestling with ideas and start selecting from options.

When AI does the first draft, the hard part disappears. And with it, something else quietly vanishes—the depth that comes from struggle.

This matters because creativity is not just output. It is a process.

Without process, creativity becomes aesthetic production rather than thinking.

The workplace impact: Faster, but shallower

In corporate environments, generative AI is often positioned as a productivity multiplier. Employees are encouraged—sometimes pressured—to use it to work faster, respond quicker, and produce more.

But speed has consequences.

When everyone uses similar tools trained on similar data, differentiation erodes. Strategy documents converge. Campaign ideas blur. Internal thinking becomes less exploratory and more formulaic.

Ironically, the very tool meant to enhance creativity may be making organisations more risk‑averse. AI optimises for what has worked before, not what might work next.

Innovation, however, lives in deviation—not repetition.

The psychological toll: Creative disengagement

There is also a human cost.

Many creatives report a loss of ownership over their work. When ideas are co‑generated, authorship becomes ambiguous. Pride diminishes. Motivation fades.

Others feel a constant pressure to “keep up”—not with other people, but with machines. If AI can produce ten variations in seconds, why should your one carefully considered idea matter?

This leads to a quiet disengagement. People stop investing emotionally in their output. Work becomes transactional. Creativity becomes mechanical.

Fatigue sets in—not from effort, but from meaninglessness.

Also Read: After failure, rekindling our creativity and finding balance

Are we confusing productivity with value?

At the heart of generative AI fatigue is a fundamental misalignment: we are measuring the wrong thing.

We celebrate output volume, not insight. Speed, not originality. Optimisation, not depth.

But creativity has never been about efficiency. The most influential ideas in art, technology, and culture did not emerge because they were fast or scalable. They emerged because someone saw the world differently—and took the time to articulate that difference.

When everything is optimised, nothing feels essential.

A reframe: AI as assistant, not author

The solution is not rejection. Generative AI is not going away, nor should it.

But we need a cultural reset.

Also Read: Can generative AI usher us into the gilded age of ad creativity?

AI should support creativity, not replace the thinking behind it. It should help with execution, not identity. Drafting, not deciding. Formatting, not forming opinions.

The most valuable creative work going forward will not be the most polished—it will be the most human.

Messy ideas. Strong points of view. Lived experience. Moral judgment. Context.

These are things AI cannot automate.

The future: Scarcity of thought, not tools

In a world flooded with generative content, originality will become rarer—and therefore more valuable.

The competitive advantage will not be who uses AI best, but who knows when not to use it.

Those who can still think slowly, write imperfectly, and sit with uncertainty will stand out.

Generative AI fatigue is not a rejection of technology. It is a signal.

A reminder that creativity was never meant to be frictionless—and that meaning cannot be automated.

The question is no longer whether AI can create.

It’s whether we still remember why we do.

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

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Featured image generated using AI.

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e27 has been nurturing a supportive ecosystem for entrepreneurs since its inception. Our Contributor Programme offers a platform for sharing unique insights. As part of our ‘Contributor Spotlight’ series, we shine a spotlight on an outstanding contributor and dive into the vastness of their knowledge and expertise.

In the sections below, she reflects on her journey, the lessons she’s learned, and what keeps her going.

How I found my place

I see myself, and hope people see me, as a connector of talent, be it in my primary expertise (technical recruitment), or making connections between investors, educators, speakers and builders. For many, many years, I’ve had the pleasure of partnering with visionary deep tech and high-growth founders, investors, and companies to find the best minds who will bring their technological dreams to life. It’s a heartfelt process of weaving together strategy, empathy, and a relentless search for the right people to create the engineering and leadership foundations for Asia’s next wave of innovation.

A perspective that evolved over time

I once believed that scaling technology unicorns was the most meaningful work. I am now more drawn to the raw energy of early-stage deep tech. It’s really quite exciting being there from the very beginning, knowing that every person you bring on board will be writing the chapter of the story for the future.

Also Read: The art and science of feedback: A guide for first-time founders and new managers

The problem I’m focused on solving

In a nutshell, I connect brilliant ideas with brilliant people. Founders have these innovative, world-changing visions, but they can’t build them alone. Investors keep working passionately on making Singapore a viable place to safeguard and grow industries. My work is to dive into that vision and passion and then go out to find the amazing engineers, leaders, and creators who can turn those dreams into a reality.

The startup conversation we’re still not having

The startup world is a rollercoaster right now, and it’s not just about hiring fast during the good times. The real opportunity is in creating strong, connected teams from the start that can stick together and innovate through anything. It’s about building a culture where people feel secure and motivated to do their best work, no matter what the market looks like.

Why I write

I’ve been really lucky to have had a front-row seat to some of Asia’s biggest tech growth stories, and I’ve seen what works and what doesn’t. I’m currently embracing my granny-goddess era by sharing those hard-won lessons by writing here, to help other founders succeed on their own journeys.

My advice for aspiring thought leaders

The most articulate people I know are often great listeners as well. Be sincerely curious about what others think. If you communicate from a place of empathy and a desire to connect, your message naturally becomes clearer and more powerful. It’s less about sounding smart and more about being understood.

Also Read: A founder’s field guide to managing performance and giving feedback that lands

Influences that shaped my thinking

I’m on a mission to read everything Bill Bryson has written. I’ve been a fan of his books for a few years now. Past influences include Haruki Murakami, Milan Kundera — when I find an author I love, I often go on a hunt to track down and procure their “hidden gems”. Years ago, someone who was in love with me said that Eva Luna (by Isabel Allende) was his favourite book — it still remains the book that I aim to reread, every single year.

What drives my curiosity

Being a design-led person and a brand owner, I’m fascinated by how great design can spark positive social change. To see a well-designed product create deep conversations, or even a simple well-designed process, make life better and more equitable for people, this really floats my boat. It’s a wonderful reminder about how creativity matters and can even be a powerful force for good in the world.

Take a look at Lee’s articles here for more insights and perspectives on her expertise.

—

Are you ready to join a vibrant community of entrepreneurs and industry experts? Do you have insights, experiences, and knowledge to share?

Join the e27 Contributor Programme and become a valuable voice in our ecosystem.

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Investors processed unexpectedly soft retail sales data that simultaneously lifted hopes for Federal Reserve easing while exposing fragility across multiple asset classes. The Dow Jones Industrial Average managed a modest 0.1 per cent gain to establish a new record closing high. This narrow advance masked broader weakness as the S&P 500 declined 0.34 per cent to 6,941.33 and the Nasdaq Composite fell 0.6 per cent to 23,099.18. This divergence reflected a rotation away from technology and growth-oriented assets toward more defensive industrial names.

The fundamental catalyst, December retail sales, suggested a concerning loss of consumer momentum. Core sales dipped 0.1 per cent, contrary to expectations of expansion. This signalled that household spending power may have peaked by the end of 2025, with potential implications for fourth-quarter GDP growth calculations.

The bond market reacted decisively to the economic softening, with Treasury yields dropping sharply. The 10-year yield fell to approximately 4.14 per cent, its lowest level in a month. This move underscored how quickly market participants recalibrated their expectations for monetary policy. Money markets now price in elevated probabilities for three interest rate cuts during 2026. Federal Reserve officials, including Cleveland President Beth Hammack, emphasised that there is no immediate urgency for policy adjustments. This tension between market pricing and central bank communication created an undercurrent of uncertainty that permeated risk assets throughout the session.

Gold capitalised on the lower-yield environment, surging to consolidate above the psychologically significant US$5,000 per ounce threshold. Its non-yielding appeal has strengthened relative to fixed-income alternatives. WTI crude oil held steady near US$64.20 per barrel. Diplomatic developments in US-Iran negotiations supported prices by tempering fears of supply disruptions.

Also Read: The US$71000 Bitcoin bounce lacks foundation but Japan’s rally has real teeth

A noteworthy disruption emerged in the financial services sector, with shares of Charles Schwab and LPL Financial plummeting by at least seven per cent. Altruist Corp launched an AI-driven tax strategy tool, triggering broader anxiety about technological displacement across wealth management. This industry had long been considered relatively insulated from automation.

The severity of the reaction suggested investors recognised this as more than a niche competitive threat. It represented a potential inflection point for an entire professional services category. Global markets displayed their own complexities with Asian equities reaching an all-time high earlier in the trading day. South Korean strength led these gains, though Treasury trading remained subdued due to a Japanese market holiday. This limited cross-market feedback loops during a pivotal session.

The cryptocurrency market reflected these macro crosscurrents, declining 2.03 per cent to a total valuation of $2.35 trillion over the preceding 24 hours. This move exhibited a moderate 50 per cent correlation with the S&P 500. Digital assets increasingly moved in tandem with traditional risk sentiment rather than operating as an independent store of value. Beneath this surface correlation lay crypto-specific stressors of alarming magnitude. Bitcoin mining difficulty experienced its largest downward adjustment since 2021.

This signalled widespread miner capitulation as operational unprofitability forced network participants to shut down equipment. The exodus created direct selling pressure while simultaneously undermining confidence in the ecosystem’s foundational security layer. When those responsible for transaction validation and network integrity face existential financial pressure, the implications extend far beyond immediate price action.

Compounding this structural weakness, institutional capital continued its retreat from regulated Bitcoin exposure. Spot ETF assets under management contracted by US$13.6 billion within a single week, falling from US$110.92 billion to US$97.31 billion. This outflow represented a reversal of one of the primary drivers behind the previous bull market cycle. Derivatives markets experienced a violent deleveraging event, with open interest dropping 9.76 per cent in 24 hours.

Funding rates turned negative, triggering forced liquidations of overextended long positions. The convergence of miner distress, institutional withdrawal, and speculative unwinding created a self-reinforcing negative feedback loop. Each element amplified the others, producing cascading selling pressure across the digital asset landscape.

Also Read: From US$70K to freefall: Can Bitcoin hold the US$60K lifeline after US$1B liquidation event?

Technical indicators suggested the market was approaching an inflection point, with Bitcoin’s relative strength index plunging to 24.33. This indicated an oversold condition that historically precedes short-term bounces. The critical threshold rested at US$68,000, where a successful defence could catalyse a relief rally toward US$70,500.

A breakdown below this support level threatened to extend the downtrend significantly. The path forward depended on two key variables. ETF flows needed to reverse before additional miner selling emerged. The outcome of White House stablecoin legislation talks also mattered, with a policy deadline approaching at the end of February 2026. Regulatory clarity around stablecoin yields might provide the catalyst needed to restore institutional confidence, though timing remained uncertain.

The day ultimately revealed markets operating at an inflection point, with traditional and digital asset classes moving in concert yet retaining distinct vulnerability profiles. Traditional markets grappled with the contradiction between softening economic data and still hawkish central bank rhetoric. Crypto markets faced acute structural pressures at their operational core. The miner capitulation represented more than a price catalyst. It signalled stress at the very foundation of blockchain security models.

This moment of fragility also contained the seeds of potential renewal. Network difficulty adjustments have historically preceded major cycle bottoms by forcing inefficient participants out of the ecosystem. The coming weeks would test whether coordinated policy responses and technological adaptation could stabilise these interconnected markets.

Deeper recalibration might remain necessary before sustainable growth could resume. Investors now faced the challenge of distinguishing between temporary volatility and fundamental regime shifts across both traditional finance and its emerging digital counterpart.

The interplay among macroeconomic data points, technological disruption, and network-level stressors created a multifaceted environment that demands nuanced analysis rather than simplistic narratives. Market participants who recognised these layered dynamics stood better positioned to navigate the uncertain terrain ahead.

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In startup conversations, few phrases raise a quieter red flag than this one: “I want to sell to everyone.”

It’s usually said with optimism. Sometimes with ambition. Often with confidence. But almost always, it signals a deeper issue, not of scale, but of clarity.

Founders don’t struggle because they think too big. They struggle because they think too vaguely.

The most expensive confusion in the go-to-market

A brand cannot speak to a first-time founder the same way it speaks to a seasoned operator. A product cannot sell to a solo creator the same way it sells to an enterprise team. And yet, many early-stage startups attempt to do exactly that, flattening their message in the hope of maximising reach.

The result is predictable. Messaging becomes generic. Value propositions blur. Sales conversations stretch. And the product slowly morphs into something that tries to please everyone, and resonates deeply with no one.

When a founder says “everyone”, what they are really saying is: “I haven’t made the hard decision yet.”

Luck, budget, and brute force are not a strategy

Of course, there are exceptions.

With enough capital, distribution power, or sheer luck, a broadly positioned product may still find traction. But luck is not a repeatable system, and brute force is not a defensible moat.

In the early stages of a company, clarity consistently outperforms scale. The startups that move fastest are not the ones shouting the loudest; they are the ones that know exactly who they are speaking to and why.

Also Read: Revisiting “Something Ventured”: What the birth of venture capital still teaches Founders today

What strategic clarity looks like in practice

Founders who build multiple businesses quickly learn this lesson the hard way. Different products require different audiences.
Different audiences require different languages. And different problems demand different promises.

An AI platform designed to act as a founder’s digital twin, Seraphina AI, for example, is not competing with generic productivity tools. It is built for people who already have opinions, frameworks, and a voice, and want leverage, not replacement.

A female founders community, Royal Visionary Society, focused on freedom, sustainability, and long-term well-being, is not optimised for founders chasing growth at any cost.

A speaking ecosystem, Speakers Society, designed around placement, positioning, and monetisation, is not for hobbyists looking to overcome stage fright.

A marketing automation platform, People’s Inc. 360, built for operational scale, is not meant for teams that equate growth with hiring more people.

Each of these businesses succeeds not by expanding its audience indiscriminately, but by narrowing its focus deliberately.

Different doors. Different conversations. Same strategic discipline.

The quiet advantage of being clear

The strongest brands in the ecosystem share a common trait: restraint.

They know who they are for. They know who they are not for. And they design their product, messaging, pricing, and systems around that decision.

This clarity shows up everywhere, from onboarding flows to sales conversations, from roadmap decisions to customer support.

Trying to appeal to everyone does not make a company more inclusive. It makes it forgettable.

Also Read: Bridging innovation and market success: The role of a commercial co-founder in biotech startups

The hidden cost of over-inclusivity

When founders avoid choosing a clear audience, the costs compound quietly:

• Product roadmaps bloat with edge cases.
• Marketing messages lose sharpness.
• Sales teams struggle to qualify leads.
• Customers feel vaguely interested, but never fully committed.

Most churn is not caused by poor execution. It is caused by unclear positioning.

People do not leave because a product is too specific. They leave because they never felt seen.

A simple clarity test for founders

Before worrying about traffic, funding, or scale, founders should be able to answer three questions clearly:

• Who should immediately feel understood when they encounter this product? Not impressed. Understood.
• Who is this deliberately not built for? Every strong brand repels by design.
• If this product disappeared tomorrow, who would genuinely feel the loss? If the answer is “anyone”, it is probably no one.

If these answers are unclear, the problem is not distribution. It is positioning.

Conviction is the real growth lever

The companies that scale well are not the ones that hedge their message. They are the ones who commit.

They choose a lane. They build with intention. They speak directly, even when it means being misunderstood by those outside their audience.

Because in a crowded ecosystem, clarity is not a limitation. It is leverage.

And real growth does not come from dilution. It comes from conviction.

—

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

Enjoyed this read? Don’t miss out on the next insight. Join our WhatsApp channel for real-time drops.

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Startup ecosystems are often portrayed as bottom-up systems driven by founders, venture capital, and technological breakthroughs. That view is incomplete. In practice, startup ecosystems are also downstream expressions of state power, shaped by policy decisions, institutional participation, and geopolitical alignment.

This US withdrawal from international climate and energy institutions alters the conditions under which startups are built, financed, and scaled, where climate and energy governance are strategic infrastructure for global markets.

The climate-energy stack the US stepped away from

The US withdrawal spans a broad range of climate, energy, and environmental institutions. Together, these bodies form the global climate–energy operating system. They do not build grids, finance startups, or operate markets directly. Their influence is structural rather than transactional.

Climate science bodies establish baselines that flow into regulation, finance, and insurance. Energy agencies coordinate definitions of “renewable,” “transition,” and “clean” that underpin procurement and investment decisions. Nature and forestry platforms shape land-use rules, carbon markets, and supply-chain traceability. UN coordination mechanisms align agencies, donors, and reporting frameworks across borders.

These institutions sit upstream of markets. They determine what is measured, how it is measured, and which activities are recognised as legitimate or investable. Startups rarely engage with them directly, but their outputs shape the environment in which startups operate.

By withdrawing, the United States is not exiting climate or energy markets. It is exiting the multilateral rule-shaping layer that influences how those markets evolve globally.

Survival without the US does not mean neutrality

From a financial perspective, most affected institutions are likely to survive. European governments, Japan, Nordic states, and philanthropic actors can backfill near-term funding gaps. Many of these bodies already operate with diversified funding sources and experience donor volatility.

Institutional survival, however, should not be confused with institutional neutrality or effectiveness.

Also Read: Code, power, and chaos: The geopolitics of cybersecurity

As US participation recedes, three structural shifts are likely. First, agenda-setting power (and hence influence) concentrates among the remaining major funders. Second, standards and methodologies evolve according to the regulatory philosophies of those still at the table, gradually redefining what becomes “normal” or “default” in global markets. Third, even modest funding disruptions can slow research cycles, narrow mandates, and reduce technical ambition.

For startups and investors, the critical point is not collapse but tilt. The global climate–energy regime becomes less US-centric and more shaped by European regulatory logic, Asian industrial priorities, and Global South adaptation needs.

That tilt matters because it reshapes the assumptions embedded in products, platforms, and business models.

The fiscal reality: Small savings, large signals

From the US federal budget perspective, the direct savings from withdrawal are modest. The combined reduction in assessed dues and typical voluntary contributions amounts to tens of millions of dollars per year.

Measured against a federal budget and annual deficits exceeding a trillion dollars, and rapidly rising interest costs, these savings are economically immaterial. They do not alter the debt trajectory or meaningfully expand fiscal space.

Markets, however, respond less to absolute numbers than to signals of power and intent. A decision to step away from rule-writing institutions sends a strong signal about priorities, alignment, and future engagement. That signal reshapes expectations about where standards will be set, where capital will flow, and which jurisdictions will define the next generation of market rules.

The financial impact is small. The geopolitical signal is large, and the market price signals.

What this means for corporates: The end of a single global rulebook

For large enterprises, the immediate impact is not loss of market access but loss of predictability.

As climate and energy governance fragments, companies face growing divergence between US, European, and Asia-Pacific standards. The assumption that a single global compliance strategy will suffice becomes increasingly untenable. Firms operating across regions must navigate multiple definitions, reporting regimes, and certification systems.

The strategic response is operational rather than ideological. Climate and energy policy must be treated as trade policy, supply-chain policy, and security policy. Scenario planning must assume fragmentation, not convergence.

The era in which global companies could rely on a single, slowly evolving rulebook is ending.

What this means for startups: Geopolitics enters the product roadmap

Startups experience these shifts earlier and more acutely than incumbents. The most exposed ones are climate tech, energy software, grid and storage systems, ESG and climate data platforms, supply-chain SaaS, carbon markets, advanced materials, and industrial automation.

The core challenge is that global scalability becomes more complex. Different blocs increasingly favour distinct standards, data requirements, and compliance pathways. A product designed around US regulatory assumptions may encounter friction in Europe or Asia—not because it lacks technical merit, but because it no longer aligns with how legitimacy is defined.

For founders, the implications are practical. Go-to-market strategies must account for regulatory geography alongside customer geography. Early product decisions may need to anticipate multiple standards regimes. Policy and regulatory expertise may need to be integrated earlier than in previous startup cycles.

There is an opportunity embedded in this complexity. Startups that can bridge standards, abstract compliance, or translate between regimes gain value as fragmentation increases. In a splintered system, interoperability becomes a competitive moat.

Also Read: How cybersecurity companies can build trust through digital PR

What this means for investors: Repricing policy risk

For investors, the withdrawal changes how climate and energy risk should be underwritten. Policy convergence can no longer be assumed. This increases jurisdictional risk, complicates exit pathways, and heightens sensitivity to political change.

Capital will increasingly favour companies with geographic optionality, diversified revenue exposure, and resilience to policy shifts. Business models that depend heavily on continued US federal leadership or multilateral climate mechanisms will be discounted.

The investor question shifts from “Is this aligned with climate policy?” to a more strategic inquiry: “Which political system does this company scale under?”

Geopolitical literacy becomes a core investment competency rather than a peripheral concern.

Supply chains: Where geopolitics becomes physical

Beyond software and data, the effects propagate into physical value chains. Critical minerals, energy hardware, batteries, grid equipment, and industrial manufacturing face higher coordination costs, greater reliance on bilateral agreements, and increased exposure to sanctions and political risk. Governments must now work a lot harder to find bilateral partners, as multilateralism now breaks up.

For startups embedded in these chains, technical excellence alone is no longer sufficient. Understanding geopolitical context—who controls resources, who sets standards, and who provides security—becomes central to long-term viability.

Conclusion: Geopolitics as a startup variable

This is not a story about climate virtue or environmental ambition. It is a story about how state power reshapes markets and innovation ecosystems.

The US withdrawal from international climate and energy institutions saves little money, but it changes who writes the rules that govern future markets. That shift increases complexity, raises the premium on geopolitical awareness, and alters competitive dynamics across the startup stack.

For founders, executives, and investors, the implication is clear:

Geopolitics is no longer background noise. It is a core variable in startup strategy, capital allocation, and scale.

Those who understand this will adapt early. Those who do not will experience it as friction they cannot fully explain—until it becomes a constraint they cannot escape.

—

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

Enjoyed this read? Don’t miss out on the next insight. Join our WhatsApp channel for real-time drops.

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In the high-growth markets of Southeast Asia, a recurring frustration exists among fund allocators and regional strategists: The funding gap. You identify a venture with a brilliant solution, provide non-dilutive funding and grants, and the project delivers great short-term results. But the moment the funding cycle ends, the venture struggles to secure independent funding, and the momentum evaporates.

This is not just an operational problem; it is a structural failure. When an otherwise strong venture is rejected for a follow-on institutional fund, it is rarely because its idea failed. It is because they lack structural alignment with the allocator’s logic.

The rigour gap: From pilot to audit

Institutional fund allocators, from foundations and development banks to multilateral agencies, do not invest in upside in the same way a private seed investor might. They invest in the removal of systemic risk.

For a venture to be ready, it must withstand a level of audit rigour that most early-stage ventures are not built for. Rejection often stems from the fact that while a venture is operationally fast, it lacks the institutional legacy required to track and justify funds to a fiduciary standard. If the internal operations are not built for transparency, the venture is an institutional mismatch, regardless of how viable the solution appears to be.

Avoiding the funding cliff

The biggest pain point for fund allocators is the project cycle cliff. Allocators want to know that their fund is a catalyst, not a life support system.

They reject ventures that appear to have a fund-seeking model rather than a fund-ready model. A fund-seeking model relies on the next check for survival; a fund-ready venture uses non-dilutive funding and grants to build financial sovereignty. If a venture cannot demonstrate how its operations survive long after the funding cycle closes, it represents a failed evaluation metric for the allocator’s portfolio.

Also Read: The cold logic of the angel: Stop funding dreams, start funding plumbing

The logic gap: Why market traction is not a proxy for institutional readiness

This is where the distinction becomes critical for growth operators. In the private sector, specifically with Venture Capital, validation is often proven by revenue and rapid market capture. VCs buy your future and your speed to market.

However, an institutional fund allocator funds your proof. They require technical validation benchmarks for data privacy, clinical safety, or financial inclusion that the private market often overlooks in the early stages. A venture can have massive market traction but zero technical de-risking. To an institutional allocator, that traction is unproven because it has not passed the technical hurdles of the sector’s rigour.

Real-world examples of structural alignment

Consider the case of Zipline, the logistics venture. While their core funding came from venture capital, their early deployments in the region were enabled through formal government and institutional partnerships. These relationships required strict operational, safety, and regulatory compliance. These institutional engagements served as de-risking mechanisms that helped demonstrate to private investors that the venture could operate under real regulatory constraints. By meeting these institutional standards early, Zipline provided the operational validation that supported later equity investment.

On the other side, consider an impact-driven social venture (registered as a non-profit) like One Acre Fund. While they prioritise social outcomes, they operate with the operational discipline of a scaled retail system. Grants and philanthropic funds are not treated as subsidies, but as a risk fund used to design, test, and refine agricultural interventions.

What distinguishes them is operational rigour. Performance is measured with audit-level precision, unit economics are tracked closely, and program effectiveness is evaluated continuously. For institutional funders, this shifts the posture from funding activities to a delivery system capable of converting funds into measurable funding outcomes.

Professionalising the funding answer key

To bridge the gap between private sector speed and development sector rigour, a venture must move from being the Hero who survives by grit to the architect who builds by system.

This requires what I call the allocator’s logic, which means building a venture structure that mirrors the answer key reviewers use when evaluating multi-million dollar funds:

• Systemic transparency: Financial and operational reporting must be built for an institutional audit, not just a pitch deck.
• Funding longevity metrics: Defining clear indicators for how the venture generates independent funding or survives once the institutional cycle ends.
• Outcome sovereignty: Showing that the venture is building a proprietary methodology that can be replicated across Southeast Asia without the founder’s constant intervention.

Also Read: In Southeast Asia, cybersecurity is booming, but funding is not

The strategic value of a non-dilutive fund

Securing non-dilutive funds and grants is not just about the money; it is about the signalling effect. When a venture passes the rigour of an institutional allocator, it tells the rest of the market that the venture is de-risked. This makes future equity rounds or strategic exits much cleaner, as the institutional legacy has already been established.

For the growth operator, this fund protects ownership when valuations are at their most vulnerable. For the fund allocator, it ensures that their deployment leads to a permanent shift in the regional market, rather than a temporary pilot that disappears when the budget does.

Closing the gap

We must stop treating non-dilutive funds and grants as free money and start treating them as high rigour funds. The ventures that succeed in Southeast Asia over the next decade will be those that can speak both languages: the language of private sector speed and the language of institutional rigour.

In the institutional world, the best venture does not always win; the most prepared structure does.

After 15-plus years in the regional trenches, I have seen that the scar tissue you build by professionalising for institutional funds is the same asset that makes your venture unignorable to strategic partners in the long run.

Build for rigour, and the capital and the impact will follow.

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The post The hidden reason institutional fund allocators reject otherwise good ventures appeared first on e27.

Imagine this.

You’ve just read a brilliant guide on giving feedback.

You’ve mastered the frameworks: Radical Candour, HHIIPP, GAIN – and you’re ready to build a high-performance culture. You pull a team member aside to deliver a piece of well-intentioned, perfectly structured critical feedback. You’re humble, helpful, and immediate. But instead of a constructive dialogue, you watch the light in their eyes die as the team member retreats into a shell of resentful compliance.

A week later, their LinkedIn status quietly flips to “Open to Work”.

What fresh hell is this? You did everything by the book.

Here, we’ll explore the concept of psychological safety and why this is the most brutally practical predictor of your team’s success. We will dissect what it is, what it isn’t, and how to diagnose its conspicuous absence – especially within the nuanced cultural landscape of an Asian startup.

What psychological safety actually means (and what it doesn’t)

The definition

Let’s be honest. “Psychological Safety” sounds like something you’d discuss at a corporate retreat involving trust falls. Harvard’s Amy C. Edmonson, who put this concept on the map, defines it as a “shared belief that the team is safe for interpersonal risk taking.”

In simple English, it’s the feeling that you can speak up, admit a mistake, ask a “stupid” question, or challenge the status quo without being publicly flogged for it.

This isn’t just a nice-sounding theory. When Google embarked on its Project Aristotle to build the perfect team, they crunched data from hundreds of teams. They found that the single most important dynamic – not individual brilliance, not team size, not even co-location – was psychological safety. It was the secret sauce that allowed talent to translate into results.

The critical misconceptions

Many founders who pride themselves on a high standard or “tough” culture instinctively recoil from the term. They equate safety with softness. They mistake it for a lack of accountability. Let’s dismantle these myths.

• Myth: It means lowering standards. Reality: It means creating an environment where people feel safe to stretch and strive for high standards without fear of blame if they fall short.
• Myth: It’s about being “nice.” Reality: It’s about being direct, candid, and challenging, but with a foundation of respect and a shared commitment to learning. It’s not about avoiding conflict, but about engaging in it productively.
• Myth: It eliminates accountability. Reality: It’s the very thing that enables accountability. When people feel safe, they are more likely to take ownership of their mistakes, making it possible to hold them accountable for learning and improving from them.
• Myth: It’s for weak or fragile teams. Reality: It’s the defining characteristic of the most resilient, innovative, and high-performing teams. Fear-based cultures are the ones that are truly fragile, as they are unable to adapt to change or learn from failure.

Here lies a paradox for all founders to understand: the goal is not to create a comfortable, low-pressure environment. The goal is to pair high psychological safety with high standards. High psychological safety + high standards = The learning zone. This is where innovation, resilience and sustainable high performance live. Without safety, high standards simply create an Anxiety Zone, a toxic pressure cooker of burnout and attrition.

Also Read: Are you a human resource?

Why psychological safety is the #one predictor of team performance

The hard data on performance and retention

Let’s talk numbers. The data shows an alarming outcome about the cost of fear.

• Your best people are leaving: A 2024 BCG study found that employees in low-safety environments are four times more likely to quit within a year (12 per cent, vs three per cent). For diverse talent, the numbers are even more stark: High safety increases retention by 4x for women and BIPOC employees, and 6x for LGBTQ+ employees. In a talent war, you are unilaterally disarming.
• You’re bleeding productivity: Gallup research connects a climate where opinions are valued to a 27 per cent reduction in turnover, a 40 per cent drop in safety incidents, and a 20 per cent boost in productivity. Fear is expensive. It’s a tax on every single action your team takes.

What these numbers represent is the unlocking of human potential. In a safe environment, people stop spending energy on political manoeuvring and self-preservation and start spending it on what you hired them for: solving hard problems. They ask for help, they admit mistakes, they share half-baked ideas that just might be brilliant, and they tell you the truth, even when it’s ugly. For a startup, where learning speed is the only true competitive advantage, this isn’t a luxury; it’s the entire game.

Diagnosing psychological safety — Is your team actually safe?

The Founder is often the last to know about the kingdom’s rotten problems. Forget the obvious – the shouting matches, the public sharings. The real indicators of low psychological safety are far more insidious. The silence in your meetings isn’t consensus, it’s a symptom.

The subtle signs Founders often miss

• The absence of bad ideas: If you’re only hearing well-polished, safe suggestions, it’s not because your team is brilliant. It’s because they are terrified to share the messy, half-formed thoughts where real innovation begins.
• The echo chamber: Your ideas are met with vigorous, uncritical agreement. This isn’t a sign of your genius; it’s a sign that your team has learned it’s easier to agree with you than to engage in debate.
• The proliferation of process: When people are afraid to use their judgment, they cling to process like a life raft. They will follow a bad process to the letter, because the process can’t be fired.
• The backchannel: The real conversations are happening on Slack DMs, in hushed whispers by the coffee machine, and in post-meeting debriefs where everyone says what they really think. The meeting itself is a theatre.
• The solo hero: People would rather struggle alone for days than ask for help and risk looking incompetent. They are optimising for the appearance of competence, not for the speed of execution.

The ultimate litmus test: The flow of bad news

If you want one, brutally simple diagnostic, ask yourself this: When was the last time someone on your team brought you truly bad news, early?

Not after it was already a multi-alarm fire, but when it was just a wisp of smoke. As Amy C. Edmonson warns, “If there’s no bad news, remind yourself: It’s not that it’s not there. It’s that you’re not hearing about it.” The silence is not golden. It’s the sound of your company failing in slow motion.

Also Read: Embracing sustainability: A circular design perspective on e-waste

The Asian startup context — Cultural challenges you must navigate

Now, for our readers in Singapore, Hong Kong, and beyond: if you’ve tried to implement a “speak truth to power” culture and been met with horrified silence, you’re not alone. While the principles of psychological safety are universal, their application is not. For founders in Asia, simply importing Western frameworks without cultural translation is a recipe for failure.

The power distance problem

The hierarchical nature of many Asian societies and different communication norms create unique challenges that must be understood and addressed. In many Asian cultures that score highly on Hofstede’s Power Distance index, the social fabric is woven with threads of hierarchy and deference. Challenging a superior isn’t just a disagreement; it can be perceived as disrespect.

The concept of “saving face” isn’t just a weakness; it’s a fundamental social lubricant.

When a Western-trained founder encourages their team to “challenge everything”, they think they are fostering innovation. But to an employee raised in a high-context, hierarchical culture, they may be asking them to commit a deeply uncomfortable social transgression.

Lost in translation

The very language of psychological safety is a stumbling block. As we’ve noted, “interpersonal risk taking” is a foreign concept. When you ask a team member if it’s “safe” to take a risk, they are likely thinking about financial or project risk, not the risk of disagreeing with you in a meeting. This cognitive mismatch renders most standard surveys and one-size-fits-all approaches useless.

Adapting psychological safety for Asian startups

Building psychological safety in Asia requires you to be a cultural translator, not a doctrinal importer.

• Reframe the mission: Don’t ask people to challenge you. Ask them to honour the company’s mission by stress-testing ideas. Frame dissent not as a challenge to authority, but as a duty to the collective goal.
• Create structured channels: Don’t start with open-floor debates. Begin with structured, safer channels. Use written feedback, 1-on-1 sessions, or even anonymous tools as a bridge. The goal is to build the “muscle” of dissent in a way that feels culturally accessible.
• Lead the face-saving mode: You, the founder, must be the first to “lose face”. Publicly admit your own mistakes. Thank people for correcting you. When you demonstrate that your own ego is secondary to the best outcome, you give your team permission to do the same.

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Innovation is often framed as the domain of executives, R&D teams, or product leaders. Boards are traditionally viewed as monitors of risk, finance, and compliance. But in Asia’s fast-moving markets, innovation is a core governance responsibility. Boards that fail to actively oversee innovation risk stagnation, missed growth opportunities, and competitive irrelevance.

The future-ready board does not replace management in innovation but provides strategic guidance, challenge, and oversight, ensuring that investments in growth initiatives align with long-term value creation.

Why boards must own innovation oversight

Several forces make innovation governance a board priority:

• Rapid digital disruption: AI, cloud platforms, fintech, and platform ecosystems are transforming entire industries.
• Global competitive pressures: Companies in Asia compete with both established multinationals and agile startups.
• Investor expectations: Growth and innovation metrics increasingly influence investor confidence and valuation.
• Complexity of capital allocation: Boards must ensure innovation budgets are optimised, ROI is monitored, and strategic alignment is maintained.

Boards that fail to actively engage risk leaving executives unchallenged, increasing the likelihood of misaligned innovation investments.

A board framework for innovation oversight

Effective boards oversee innovation across strategy, risk, and culture:

Strategic alignment

• Ensure innovation initiatives align with long-term business objectives.
• Evaluate emerging markets, technology trends, and customer needs as part of the strategic agenda.
• Assess portfolio balance: core, adjacent, and transformational initiatives.

Risk-return oversight

• Monitor the innovation pipeline with clearly defined success metrics and stage-gates.
• Encourage scenario planning for high-impact, low-probability innovation failures.
• Understand regulatory, reputational, and operational risks associated with new initiatives.

Talent and culture enablement

• Assess whether the organisation has the right skills, mindset, and leadership to innovate.
• Promote cross-functional collaboration and experimentation while maintaining accountability.
• Monitor incentives and culture to ensure innovation is rewarded and risk-taking is disciplined.

Also Read: Cybersecurity and data governance in the boardroom: A strategic imperative for Asian boards

Key questions boards should ask

Boards should challenge management with questions that drive both oversight and strategic value:

• What are our innovation priorities, and how are they linked to corporate strategy?
• How do we balance short-term performance pressures with long-term experimentation?
• Which emerging technologies or business models could disrupt our market?
• How do we track adoption, impact, and ROI of innovation initiatives?
• Are we building an organisational culture that supports disciplined risk-taking?

The answers allow boards to influence direction without micromanaging execution.

Innovation metrics for boards

Boards can measure innovation through a combination of leading and lagging indicators:

• R&D expenditure relative to revenue
• Time-to-market for new products or services
• Success rate of pilot programs and proof-of-concepts
• Adoption and engagement metrics for digital solutions
• Strategic alignment and contribution to long-term growth

Tracking these metrics ensures that innovation efforts are measurable, monitored, and aligned with enterprise value.

Boards as guardians of responsible innovation

Innovation carries inherent risk — regulatory, reputational, financial, and ethical. Boards must ensure that growth initiatives:

• Comply with laws, regulations, and industry standards
• Incorporate ethical considerations, especially for AI, data, and sustainability initiatives
• Maintain transparency and accountability in decision-making
• Include clear escalation and reporting mechanisms for unexpected outcomes

Boards that integrate these principles create responsible innovation, safeguarding enterprise resilience while enabling growth.

Also Read: Forward-looking governance: Why Asian boards must think like futurists

The independent director’s contribution

Aspiring independent directors bring value by:

• Providing cross-industry insights on emerging technologies and business models
• Challenging assumptions and encouraging robust debate on strategic bets
• Ensuring balance between risk and reward in innovation investments
• Supporting management in building a culture of disciplined experimentation

Their independent perspective enhances governance while empowering executives to innovate boldly yet responsibly.

Conclusion: Growth governance as a board imperative

Innovation is no longer optional; it is a strategic requirement. Boards that integrate innovation oversight into governance:

• Protect against wasted investments and strategic missteps
• Accelerate value creation by guiding strategic experiments
• Strengthen enterprise resilience by balancing risk and reward
• Foster an organisation-wide culture of disciplined innovation

For Asian boards, the challenge is clear: shift from passive approval to active governance of growth initiatives. The boards that do so will lead companies to sustainable, long-term success in increasingly competitive and unpredictable markets.

This article was first published on The Boardroom Edge.

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The story began when I witnessed my mother struggle with a mobile application to monitor her pension salary. What should have been a simple authentication process turned into repeated attempts to scan her face, adjusting angles, moving between rooms, and changing lighting, only to end with the app crashing without explanation. 

When she asked me to contact customer service, I realised something more troubling. There was no clear support channel, no customer service, just an application that failed silently.

Her story was just another dramatic episode. Days later, I tried to extend my vehicle registration after being informed that the process was available online. But the application told another. After following every instruction, I discovered that the “online process” didn’t actually exist. The only option left was to queue offline, again.

These experiences highlight a deeper issue beyond technical glitches. Many public service applications are built to digitise procedures, not to serve citizens. Empathy and user experience are treated as secondary priorities in this case.

Premature digitalisation 

Digital transformation in public services is always branded to build a seamless process. However, it contradicts what the user experiences in real life. I gathered several feedbacks from public service apps users, such as:

Source: Taken from BPJS Google Playstore Review

Source: Taken from Andal by Taspen Google Playstore Review

 Source: Taken from National Digital Samsat Google Playstore Review

User reviews on Google Play Store for applications such as BPJS, Taspen, and the National Digital Samsat reveal a consistent pattern. Despite high star ratings, recent reviews continue to surface unresolved issues, such as failed authentication, unclear instructions, system errors, and a lack of responsive customer support. Even in early 2026, many of these complaints repeatedly happened.

What makes this situation more problematic is the lack of choice. These applications are not optional. For many services, they have become the primary and the only gateway. When digital access fails, users are left without clear alternatives, trapped in a system that offers neither guidance nor accountability.

This approach ignores the diversity of users that public service apps must serve. Platforms like BPJS and Samsat cater to citizens ranging from young adults to elderly citizens, while Taspen primarily serves users above 60 years old or retirees. Designing a single experience without adjusting to different levels of users only creates exclusion. As seen in cases like elderly users struggling with basic authentication flows, the result is not empowerment, but frustration.

Also Read: Building for fragmentation: How ASEAN SaaS leaders architect optionality into a paradox

The intention behind digitising public services is valid. However, launching an app is not the finish line. Digitalisation requires continuous user education, clear instructions, regular improvements, and accessible human support. Without these, “going digital” becomes a one-time project rather than a long-term commitment.

What ultimately emerges is not a lack of technology, but a lack of empathy. Many public service applications are designed to satisfy bureaucratic workflows, while human–computer interaction is treated as a secondary priority.

Next step: Mitigation

Criticising premature digitalisation will not solve the situation. The most important thing to focus on is how these public service apps can accommodate the needs of the users while fulfilling the requirements of being seamless and user-friendly. 

• First, empathy must be treated as the core design principle, not as a secondary concern. This means conducting user research across age groups, regions, and levels of digital literacy. Understand that some Indonesian users are elderly citizens, and these people require closer attention during the research.
• Second, digitalisation is created to cut off long bureaucratic processes. Make sure that the app can shorten the administrative procedure and help users avoid long queues at the offline counter.  
• Third,  public service applications need clear and transparent accountability. Features like step-by-step guidance, error message, customer service button, and dedicated customer service agents are not luxurious features; instead, they are all essential infrastructure. So, when the system fails, users can easily contact the person in charge.
• Lastly, an app must be treated as a living product, not a static prototype. Continuous update, usability testing, and an endless iteration process are necessary to maintain trust from the users.

Digital transformation succeeds not when all processes are moved online, but when a technology reduces anxiety, genuinely helps the lives of people, and builds a supporting ecosystem.  

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Search is no longer just about ranking links. AI systems now quote sources. If you appear in those answers, your brand gets visibility, trust, and most importantly, clicks. If you don’t, you disappear.

A March 2025 SEMrush study found Google’s “AI Overviews” surfaced in 13.14 per cent of all US queries, nearly double the share from January. Brands that appeared in these AI answers saw conversion rates 4.4x higher than traditional organic traffic.

But how do you earn visibility without resorting to shortcuts that could backfire?

What the data shows

SEMrush analysis highlights how fragmented the AI landscape is.

• ChatGPT leans heavily on Wikipedia and Reddit, with tech sites like TechRadar and G2 also surfacing.
• Google AI Mode cites productivity blogs and platforms like Zapier, Medium, and LinkedIn.

In finance, the split is just as stark: ChatGPT draws on Reddit and Wikipedia, while Google AI Mode prefers Bankrate, NerdWallet, and Investopedia.

The lesson: each AI engine has its own source bias. Founders can’t assume one article in a mainstream tech or business outlet guarantees coverage everywhere.

This fragmentation is why PR leaders need to think of AI visibility as a portfolio strategy. Just as financial advisors recommend diversification, content strategists should diversify their evidence assets across formats, publishers, and domains. The more touchpoints an LLM has to draw from, the more resilient your brand’s visibility becomes.

Actionable takeaways

• Know the answer marketplace: AI search is the new SEO, but success depends on verifiable evidence and trusted sources, not keyword stuffing.
• Invest in PR strategy, not just spend: Early-stage founders often push budgets into visibility at any cost. But AI systems reward authority and credibility, not press release blasts.
• Build “evidence assets”: Think beyond brand storytelling. Publish FAQs, explainers, glossaries, and data-backed studies that answer canonical questions clearly. These assets are the ones LLMs like to cite.
• Turn PR from awareness to performance: PR has long been seen as a tool for credibility and brand awareness, but AI search is changing that equation. When your coverage or evidence-rich content is cited in an AI-generated answer, it can drive measurable traffic and conversions. Not just impressions. In this sense, PR now plays directly into performance metrics like clicks, leads, and customer acquisition. The shift is clear: evidence and citations translate into action, not just awareness.
• Understand AI question types: LLMs handle “how,” “what,” and “compare” questions differently. Audit how your industry is being represented, and design assets that map to those question patterns.
• Treat visibility as a flywheel: Once you appear in AI answers, the effect compounds. More citations build more authority, which reinforces discoverability across engines. This is where strategic patience pays off.
• Balance brand and community signals: SEMrush data shows that community-driven platforms like Reddit surface heavily in ChatGPT. Participating ethically in these communities, by providing expertise rather than self-promotion, can help seed organic visibility.

Also Read: When streaming prices ignore how people actually watch

The playbook: PR for AI discoverability

A repeatable framework is emerging:

• Discovery map: Build a query universe that covers your company, its category, competitors, and the key problem statements.
• Authority stack: Anchor your narrative in authoritative explainers, expert quotes, and third-party validation.
• Citable assets: Create pages that LLMs want to reference. Resources like fact sheets, FAQs, and original or proprietary data sets.
• Structure for machines: Use schema.org markup, consistent entity naming, canonical URLs, and alt text. For example, add FAQ schema to common questions, keep your company name consistent across pages, and describe charts/images with meaningful alt text so machines can interpret them.
• Distribution blend: Focus on earned media and credible third-party research citations. Avoid over-relying on sponsored or paid placements.
• Refresh cadence: Update statistics, add new references, and log changes transparently. Recency signals matter for both crawlers and model trainers.

Measurement: A new scorecard

You can’t manage what you can’t measure. Traditional SEO metrics miss the point. Instead, track:

• Share-of-Answer (SoA): Per cent of queries where your brand appears in LLM responses.
• Cross-engine coverage: Presence across ChatGPT, Google AI, Perplexity, and Gemini.
• Citation diversity: Are you showing up via one placement or multiple?
• Answer drift: How stable is your visibility week over week?
• Evidence depth: How many of your assets provide original data or primary sourcing?

Leaders who adopt this scorecard not only understand their brand’s presence but can benchmark competitors and adjust strategy accordingly. Imagine being able to quantify that your rival is cited in 60 per cent of “best AI tools” answers, while you only appear in 20 per cent. That’s actionable intelligence.

AI traffic is overtaking traditional search

Semrush data shows that AI search traffic is rising rapidly and could soon rival or even surpass traditional organic search traffic. This trend is more than a technology shift. It’s a competitive warning. If you don’t begin optimising for LLM visibility now, competitors could establish themselves in AI results and capture the lion’s share of exposure and visits. While the foundations of LLM optimisation overlap with SEO, the two are not identical. The first step is understanding your brand’s visibility within AI-driven results and treating it as a distinct channel.

Also Read: AI at machine speed: What 2026 holds for cybercrime and enterprise security

Ethics: guardrails that matter

The temptation to “game” LLMs is real. How is this done? Through prompt injection, synthetic citations, or manipulating community forums. But the risks are higher than in SEO. A single flagged manipulation can result in removal or worse, reputational damage.

UNESCO’s guidelines on AI ethics stress building trust and accountability. For PR, that means:

• Disclosing conflicts of interest.
• Auditing assets for bias.
• Avoiding misleading statistics or unverifiable claims.
• Differentiating fact from opinion clearly, especially when quoted out of context.

Ethical visibility lasts longer. Tricks don’t.

Checklist: before you publish

• Does this asset answer a clear question in plain language?
• Is it backed by verifiable, citable data?
• Is it structured for both humans and machines?
• Would I be comfortable if this were quoted, without context, in an AI answer?
• Does it align with the principles of transparency and accountability?

If the answer to all five is yes, you’re building for the right kind of visibility.

From a PR perspective, the same checklist applies to press materials and media kits too. Ensure that press releases cite reliable data, founder quotes are attributable and accurate, and fact sheets present details in a clear, structured way. These assets often become the raw material that journalists and AI systems alike draw from.

Closing thought

AI search is shifting PR from link placement to evidence placement. The brands that win won’t be those who find loopholes. They’ll be the ones that publish reference-grade content, earn citations in trusted outlets, and build credibility that machines and people recognise.

The opportunity is clear: treat AI visibility as a long-term reputational asset, not a quick growth hack. Just as SEO rewarded brands that invested in quality over gimmicks, LLM-driven search will favour those who combine ethics, structure, and consistency. For entrepreneurs and leaders, the play is simple: earn your citations.

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

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