Every great leader has a clear sense of taste. It is what separates a competent decision from the right one. Taste is your internal sense of what good work looks like in your domain, built up from years of doing the work, calibrated against outcomes, and carried forward into every judgment call where the rules stop short of the answer. When several options are all acceptable, and only one of them is right, taste is how you pick it.

Taste is also what makes you dangerous to your own organisation.

Because taste lives in your head. It is invisible. Your team sees the output of your taste – the decisions you make, the directions you set, and the standards you hold – but they cannot see the reasoning underneath. And if they cannot see it, they cannot replicate it. So they come to you. Every ambiguity, every close call, every situation where “good enough” and “actually good” look almost identical. They come to you because you are the only one who can tell the difference.

You have become irreplaceable. And that is the problem.

The bottleneck nobody talks about

AI did not come for the leader’s job. It came for everything around the leader’s job.

When AI handles the drafts, the summaries, the research, the scheduling, the analysis – all the execution work that used to fill your team’s day – what is left? Decisions. Judgment. Taste. The work requires a human who knows the difference between looking good and being effective.

If you are the only person on your team with that sense, you just became the narrowest point in the system. Every decision waits for you or risks rework. Every ambiguity routes to you. AI made execution abundant, turning your taste into the potential bottleneck.

This is the part that stings. The very thing that makes you great is the thing that is slowing everything down. Not because your taste is wrong. Because your taste is limited.

Also Read: The quiet renegotiation of human value: What the AI talent reset means for how we work, hire, and become

How it happens

It is never a single moment. It accumulates.

You make a good call. The right call. So the next similar question comes back to you. You provide context that no one else has, so meetings cannot start without you. You catch something subtle that would have shipped wrong, and now the team routes every review through your desk. You are not doing anyone else’s job. You are doing yours. But the system has learned to rely on your presence rather than on capturing your thinking.

The phrases become familiar. “I’m in too many details.” “I can’t step away; everything would freeze.” “I’m the only one who knows this history.”

These sound like the complaints of a busy leader. They are actually the symptoms of a system that cannot function without one person’s taste. And the busier you get, the less time you have to fix the problem, which makes the problem worse. It is a trap that tightens the harder you work.

Three shifts that change everything

The way out is not to work harder or hire someone who thinks like you. It is to make your taste visible.

• The first shift is to stop answering every question and start encoding how questions should be answered. Every judgment call you make is a chance to leave something behind. Not just the decision, but the reasoning. Why did you choose this direction over that one? What signals did you weigh? What would have changed your mind? A leader who resolves an ambiguity brilliantly but keeps the logic in their head has solved one problem. A leader who makes the reasoning visible has solved every future version of that problem.
• The second shift is to name the scenario context. Most teams are guessing at priorities because priorities and constraints live in the leader’s head. Name the situation you are operating in. Are you in growth mode? Cost discipline? Crisis response? When people know the scenario and the reaction posture, they stop waiting for you to interpret every signal. They start applying their own judgment because they understand the organisation’s current values. You have given them the frame. Now they can see what you see.
• The third shift is to watch where work stalls, not what people are doing. When the same handoff repeatedly causes problems, or the same type of decision keeps escalating to you, that is not a people problem. That is a design problem. Something about the way work flows is creating a dependency on you that does not need to exist. Fix the flow. Reshape things so the blockage stops forming.

Also Read: AI startups are hiring around answers they haven’t earned yet

Delegation is not what it used to be

Traditional delegation means assigning tasks. AI-era delegation means something harder: designing the reasoning that allows decisions to happen without you.

It means making your taste explicit. The trade-offs you weigh. The signals you watch for. The boundaries you refuse to cross. The instinct you apply when two options look identical to everyone else, and you can see exactly why one is better. Most leaders have never articulated these things because they never had to. The taste just lived in their head, expressed through decisions but never explained.

That invisible taste is exactly what makes a leader irreplaceable in the worst sense. If nobody else knows how you decide, nobody else can decide. The team is not weak. Your reasoning is just invisible.

Getting it out of your head is the new leadership skill. It does not mean dumbing down your judgment. It means teaching it. It means the quality of decisions no longer depends on whether you were in the room.

The real test

Look at your last two weeks. Every meeting, every decision, every escalation. Ask yourself: what would have happened if I were not there?

If the answer is “it would have stalled,” that is not your value. That is your cage.

The leaders who will matter most are not the ones their teams cannot live without. They are the ones who built something that runs beautifully whether they are in the room or not. Not because they stepped back. But because they invested their taste into the system instead of keeping it locked inside their own head.

That frees them to do the work that truly requires them: the problems nobody else has the judgment or the taste to solve yet. And that is what irreplaceable should actually mean.

The post If you’ve become irreplaceable, you’re the problem appeared first on e27.

There is a recurring scene in social entrepreneurship support programmes. A founder walks in with fire in their eyes and genuine conviction about the problem they want to solve. They have seen the gap. They have felt the injustice. They have spoken to the people who live with the consequences. What they cannot quite do – yet – is explain how they will get from here to there.

Most programme advisors reach for familiar tools: a business model canvas, a pitch deck template, a grant application framework. These are not bad tools. But they are answers to a question the founder has not yet fully formed. And that, quietly, is the real problem.

The biggest bottleneck facing early-stage social entrepreneurs is not passion. It is not even capital, though capital is scarce. It is cognitive scaffolding – the structured mental architecture that allows a person to think clearly under uncertainty, sequence decisions wisely, and convert deep intention into an operational model that others can understand, trust, and fund.

What cognitive scaffolding actually means

Scaffolding, in the original educational sense, refers to temporary structures that support learning until the learner can hold the weight themselves. Cognitive scaffolding for founders means something similar: frameworks, reasoning processes, and mental models that help a person navigate complexity without becoming paralysed by it.

This is distinct from having a plan. Plans assume you know enough to sequence the future. Scaffolding helps you figure out what you do not yet know, and in what order things need to be resolved.

For social entrepreneurs, the complexity is compounded. They are simultaneously managing a commercial logic – revenue, margins, unit economics – and a social logic – impact outcomes, community trust, vulnerability, and systemic change. These two logics often pull in different directions. A decision that maximises revenue may compromise access for the very people the enterprise was created to serve. A decision that deepens social impact may make the business less attractive to investors.

Without strong cognitive scaffolding, founders oscillate between these tensions rather than integrating them. They become reactive rather than strategic. They communicate differently to different stakeholders – not because they are being dishonest, but because they have not yet built a coherent internal model that holds both logics together.

Also Read: Why investors are betting big on Asia’s social impact startups

The gap in the support ecosystem

Most social enterprise support programmes invest heavily in outputs: the pitch deck, the financial model, the impact report, the grant application. These matter. But they are downstream of something more fundamental – the quality of thinking that produces them.

When a founder struggles to articulate their theory of change, the conventional response is to give them a template. But the template does not solve the problem. It papers over it. The founder learns to fill in boxes without developing the underlying reasoning that would allow them to defend, adapt, or rebuild what is in those boxes when circumstances change.

What is underinvested in is the reasoning process itself: how to frame a problem before trying to solve it; how to distinguish between symptoms and root causes; how to test an assumption without building the whole model first; how to make a decision when information is incomplete; how to communicate the same strategic logic to a grassroots community and a corporate funder without losing coherence.

These are not soft skills. They are strategic capabilities. And they can be developed deliberately.

How social entrepreneurs need to shift their thinking

The shift required is not from passion to pragmatism. That framing is too simple, and it dismisses the very thing that gives social enterprise its distinctive energy. The shift is from intuitive conviction to structured sense-making – without losing the conviction.

Concretely, this means several things.

• First, learn to separate the problem from the solution. Many founders are in love with their solution before they have fully understood the problem. Spending more time in the problem space – mapping it, stress-testing it, understanding who else has tried to solve it and why they fell short – produces better solutions and stronger ventures.
• Second, develop comfort with layered causality. Social problems are rarely caused by one thing. A person experiencing chronic unemployment may face intersecting barriers: skills gaps, mental health challenges, discrimination, lack of networks, and inadequate transport. A social entrepreneur who targets only one of these layers will produce limited impact. Strong thinkers learn to hold multiple causal layers simultaneously and decide, explicitly, which layer they are addressing and why.
• Third, build the habit of making your assumptions visible. Every business model rests on assumptions – about who will pay, at what price, how often, for what reason, through what channel. Social enterprises carry additional assumptions about behaviour change, community uptake, and institutional response. Making these explicit allows them to be tested. Hidden assumptions become hidden risks.
• Fourth, practise translating between logics. The ability to speak the language of impact to a beneficiary community, the language of sustainability to a funder, and the language of growth to a commercial partner – without contradicting yourself – is a cognitive skill, not just a communication skill. It requires a deeply integrated internal model.

Also Read: The business of social responsibility: Why brands are redefining their social conscience

What commercial entrepreneurs can learn

This is not a one-way lesson. Commercial entrepreneurs have much to learn from the cognitive demands placed on social entrepreneurs.

Running a social enterprise requires holding a double bottom line in genuine tension – not as a marketing position, but as a real operational constraint. This builds a kind of strategic discipline that pure commercial thinking rarely demands. When you cannot simply optimise for profit, you are forced to develop more sophisticated decision frameworks. You learn to weigh trade-offs rather than simply maximise a single variable.

Social entrepreneurs also develop unusual skills in stakeholder translation – understanding the different value languages of communities, government, funders, and partners, and finding strategies that create value across all of them simultaneously. This is increasingly relevant for commercial enterprises navigating ESG expectations, community relations, and regulatory environments.

Perhaps most importantly, social entrepreneurs are skilled in designing under constraint. Limited resources, underserved markets, and complex social dynamics force creative problem-solving that produces genuinely novel approaches. Many commercial innovations in inclusive design, last-mile distribution, and community-led growth have roots in social enterprise experimentation.

A different kind of intelligence

What these points point to is a form of intelligence that is different from the analytical precision valued in management consulting, or the creative risk-taking celebrated in startup culture, or the empathic listening cultivated in social work. It is integrative intelligence – the capacity to hold complexity, operate across multiple logics, and build coherent action from genuinely competing demands.

AI, used well, is beginning to play a meaningful role here – not as an automation tool, but as a thinking partner. The highest-leverage use of AI for social entrepreneurs is not generating pitch decks or writing grant applications. It is cognitive augmentation: helping founders surface their assumptions, stress-test their logic, sequence their decisions, and build the internal clarity that makes every downstream output stronger.

That is a significantly different relationship with technology than most people are being told to have. But for founders who are trying to change something real, it may be exactly the right one.

The scaffold is not the building. But without it, you cannot build anything that stands.

The post The missing scaffold: Why social entrepreneurs need better thinking, not just better plans appeared first on e27.

The tech ecosystem has officially entered the era of exponential velocity. Driven by the relentless acceleration of artificial intelligence, product development cycles that used to take quarters are now compressed into days. Code is generated instantly, algorithmic iterations happen overnight, and the pressure on startups to innovate, pivot, and scale at breakneck speed has multiplied exponentially.

For founders, operators, and investors, this environment is undeniably exhilarating. The sheer velocity of the AI race is what makes the startup world the most dynamic sector on earth.

Yet, as the industry pours billions into upgrading computational infrastructure and scaling data pipelines, it is hurtling toward a systemic, unaddressed bottleneck. The tech world is trying to run an exponential technology stack on biological hardware that has not had a core upgrade in 200,000 years: the human prefrontal cortex.

The industry’s current operating model assumes that human cognitive capacity can scale at the same exponential rate as computational processing. It cannot. By ignoring this fundamental biological constraint, the startup ecosystem is building a massive, unhedged risk directly into its leadership architecture.

The anatomy of cognitive friction

In a high-velocity market, a founder’s core asset is not their capital or their IP. It is their decision-making processing engine. Every day, a growth-stage CEO faces a relentless stream of high-stakes inputs – shifting fundraising dynamics, rapid product pivots, board demands, and the constant threat of technical obsolescence.

When organisational velocity accelerates past a certain threshold, it creates a state of chronic cognitive overload. From a neurobiological perspective, this pressure changes the physical architecture of how decisions are made.

When the human brain is subjected to sustained, hyper-accelerated stress, the prefrontal cortex – the seat of executive function, working memory, long-term strategic planning, and risk calculation – begins to experience resource depletion. To compensate, processing weight shifts downward to the amygdala and the reactive, survival-driven centres of the brain.

In my work tracking and analysing cognitive metrics for growth-stage CEOs and tech executives, I routinely see how this neurological shift manifests in business. It is not just fatigue or burnout in the traditional, wellness-centric sense. It is systemic operational friction. Working with leaders navigating these exact high-stakes environments, I watch this manifest as sudden analysis paralysis, fragmented executive team dynamics, erratic market pivots, and a severe degradation in high-stakes risk assessment.

Also Read: Singapore’s AI infrastructure gap is trapping businesses in pilot purgatory

Why individual wellness hacks fail systemic pressures

The standard response to this reality within tech culture has been to treat cognitive exhaustion as an individual optimisation problem. Founders are told to optimise their sleep schedules, download mindfulness apps, take supplements, or manage their personal stress through willpower and discipline.

This narrative is not just flawed; it is intellectually dishonest.

Individual wellness protocols are entirely inadequate when measured against an exponential tech wave. A founder does not operate in a vacuum. The pressure to maintain an unsustainable operational cadence is driven by systemic realities: tight fundraising windows, intense board expectations, competitive market forces, and compressed deadlines. Investors, too, face intense pressures from their limited partners to deliver outsized returns within strict horizons, passing that urgency down the chain.

When personal life events, family pressures, or unexpected crises inevitably spill over into a founder’s professional life, the cognitive load compounds. Having sat down with both founders and their board members to dissect why high-performing teams suddenly fracture, it is clear that telling a leader under these multi-dimensional, systemic pressures to simply manage their stress better is equivalent to asking a software engineer to patch a fundamental architectural flaw in a massive codebase with a single superficial line. The issue is structural, not personal.

Also Read: Six months in jail: Singapore court finally pulls the trigger on Byju’s fugitive founder

The uncomfortable question for the boardroom

By pretending that human cognitive bandwidth is an infinite resource that can keep pace with machine speed, the startup ecosystem has created a profound blind spot. Startups do not just fail because they run out of cash or misjudge product-market fit; they fail because the biological engines directing those assets are running on empty and making compromised strategic choices.

The tech world routinely conducts deep technical due diligence on software architecture and code scalability before deploying capital. Yet there is currently no framework in the boardroom to discuss, measure, or account for the cognitive capacity of the team executing the vision.

Acknowledging this limitation requires a level of bravery and vulnerability that the current tech culture rarely rewards. It forces both founders and investors to confront an uncomfortable, unresolved paradox: how does a high-velocity ecosystem maintain its competitive edge without driving its most valuable biological assets to the point of structural failure?

The industry does not yet have the answer to this question, nor does it have the governance frameworks to manage it. But as the gap between exponential machine capabilities and fixed human biology continues to widen, the investors and founders who dominate the next decade will be those who stop ignoring the bottleneck and finally start talking about it as a core variable of scale.

The post Faster tech, slower brains: The biological blind spot of the AI race appeared first on e27.

Southeast Asia has spent years talking about the capital gap.

Founders say there is not enough patient capital. Investors say there are not enough investable companies. Development institutions say local ventures need more technical assistance. Accelerators say founders need more readiness.

Everyone is partly right.

But one uncomfortable point still gets avoided: many ventures asking for impact capital are not yet serious enough for the money they want.

This is not a moral judgment. It is an operating reality.

Impact capital is not charity with better branding. It is not a soft landing for startups that failed to raise venture capital. It is not a backup option for founders who discovered too late that their market is small, their margins are thin, or their unit economics are fragile.

Impact capital has its own logic. It asks a sharper question than venture capital in many cases.

Not only, “Can this grow?”

But also, “Should this be funded, by whom, through what structure, with what proof, for what outcome, and with what consequences if it fails?”

That is a harder test.

Many Southeast Asian founders still approach impact capital with the wrong posture. They take a normal commercial deck, add a social problem slide, insert a few beneficiary numbers, mention climate, health, inclusion, livelihoods, or women, and assume they are now ready for impact-aligned capital.

They are not.

A grantmaker does not exist to fund your burn. A catalytic investor does not exist to clean up your missed equity round. A foundation does not exist to subsidise a business model with no path to resilience. A development finance institution does not exist to validate your ambition because a local VC passed.

The problem is not only that capital is hard to access. The problem is that too many ventures do not understand what type of capital they are asking for.

• Equity wants upside.
• Debt wants repayment capacity.
• Grants want a fundable public or strategic outcome.
• Catalytic capital wants a specific market failure to be reduced.
• Blended finance wants risk to be allocated deliberately, not randomly.
• Institutional capital wants governance, reporting, controls, and evidence that can survive scrutiny.

These instruments are not interchangeable.

Yet many founders still use one generic fundraising narrative for all of them.

That is why they get ignored.

Also Read: Ecosystem Roundup: How next-day delivery killed crowdfunding in SEA

A founder may think, “The funder did not understand our vision.”

Often, the funder understood it perfectly. They just did not see a fundable structure.

There is a difference between a good mission and a financeable case.

A healthtech company serving underserved populations may have a strong mission. That does not automatically make it suitable for grant capital. A climate venture reducing waste may have strong environmental language. That does not automatically make it ready for catalytic capital. An inclusion-focused platform may talk about access, affordability, and empowerment. That does not automatically make it institutionally fundable.

Impact capital does not fund adjectives. It funds proof.

• Proof of who benefits.
• Proof of why the intervention matters.
• Proof of why commercial capital alone is not enough.
• Proof of why the proposed capital type is appropriate.
• Proof of what milestone will be reached.
• Proof of what happens after the money is spent.

This is where Southeast Asia’s startup ecosystem has a training gap.

Founders have been trained to pitch markets, traction, TAM, product, and growth. They have not been trained to map capital pathways. They know how to say they are raising a round. They often do not know how to explain whether they need validation capital, implementation capital, working capital, concessional capital, recoverable grant funding, project finance, corporate partnership capital, or institutional co-funding.

So they default to what they know: “We are raising.”

That sentence is now too lazy.

Raising from whom? For what proof point? Under what structure? With what reporting burden? With what expected outcome? With what matching capital? With what pathway after this cheque?

These questions are not administrative details. They are the actual fundraising strategy.

Also Read: Funded: The startup world has a fundraising addiction

In Southeast Asia, this matters more because many ventures operate in messy markets. Fragmented regulation, uneven purchasing power, weak public procurement, informal distribution, long enterprise sales cycles, and complex cross-border realities are not side issues. They shape what kind of capital the company can absorb.

A startup selling to low-income users cannot pretend it has the same capital path as a SaaS company selling to regional enterprises. A hardware-heavy climate venture cannot pretend it has the same financing logic as a software marketplace. A health venture requiring validation, pilots, approvals, and partnerships cannot pretend it is just one seed round away from scale.

The funding path must match the operating reality.

This is where ecosystem players also need to take responsibility.

Accelerators cannot keep producing pitch-ready founders who are capital-confused. Funds cannot keep telling every impact founder to become VC-ready when the business may need a different financing pathway. Advisors cannot keep preparing beautiful decks without asking whether the capital target makes sense. Founders cannot keep using the word “impact” as a fundraising decoration.

The next phase of SEA impact capital will not be won by louder storytelling.

It will be won by a better capital design.

That means founders need to know which parts of the business are commercial, which parts are public good aligned, which parts reduce market risk, and which parts create measurable outcomes that someone else may legitimately want to fund.

They need to separate company survival from impact-proof.

They need to stop asking funders to pay for confusion.

This may sound harsh, but it is useful. Because once a founder stops treating all capital as the same, more doors open.

A pilot can be positioned for grant or corporate partnership support. A market-building activity can fit catalytic or ecosystem capital. A validated revenue engine can fit equity. A proven procurement pipeline can fit debt. A regional expansion case can fit strategic capital. A public health or climate outcome can fit institutional co-funding.

The point is not to chase every source of money.

The point is to stop asking the wrong money to do the wrong job.

Southeast Asia does not simply need more impact capital. It needs more founders who can absorb it responsibly.

Because capital is not just fuel. It is a test of seriousness.

And too many ventures are still failing before the first cheque, not because their mission is weak, but because their capital logic is.

—

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Carbon Capture and Storage (CCS) is often discussed as an engineering challenge, a permitting challenge, or a capital allocation challenge. All three matter. But as CCS moves from pilot thinking to real infrastructure, another issue is moving to the centre of the conversation. It is becoming an accounting integrity challenge.

That may sound too administrative for an asset-heavy industry. It is not. CCS projects depend on a chain of measurement, transfer, monitoring, verification, and reporting that runs across capture plants, pipelines, compression systems, wells, subsurface models, monitoring networks, and enterprise reporting platforms. Currently, there are more than 700 projects in development and around 45 commercial facilities in operation, even while deployment remains well short of what net-zero pathways require.

That gap between ambition and delivery is exactly why the quality of accounting will matter so much. In the next phase of CCS, the real question will not simply be whether a project can capture carbon. It will be whether the operator can prove, with operational credibility, what was captured, what was transported, what was injected, what stayed contained, and what assumptions sat behind each number.

In other words, CCS turns carbon into a custody problem.

The industry is treating carbon as a climate metric when it should also treat it as a controlled asset

Energy Sectors already know how to think about custody, reconciliation, and measurement discipline. The industry does not move hydrocarbons through a chain of compressors, pipes, terminals, and buyers on the basis of a loosely assembled spreadsheet. It relies on instrumentation, calibration, reconciliations, operational records, and clear responsibility at each handover point.

That same mindset has not yet fully carried over into carbon.

Too much of the current CCS debate still treats carbon accounting as an extension of sustainability reporting. That is too soft a frame for what is now emerging. Once carbon is captured, moved, injected, and claimed as stored, it starts behaving less like a reporting line and more like a controlled industrial quantity with regulatory, financial, and reputational consequences.

That shift is more than semantic. It means the integrity of carbon data has to be designed into the operating model from day one, not checked after the fact by assurance teams.

Also Read: Zero trust for net zero: Why digital decarbonisation needs a new control layer

Why “tamper-proof” is the wrong phrase and the right ambition

The first mistake leaders can make is to ask for “tamper-proof carbon accounting” as though there is a magical digital feature that can make a CCS chain unquestionable. There is no such thing.

In serious operating environments, the real target is not perfect immunity from interference. It is something more practical and more valuable. The system must be tamper-evident, independently reconcilable, and tightly bound in terms of who can change what, when, and with what trace. That is a far stronger ambition than simply being hard to hack.

This is where cyber and carbon begin to converge in a meaningful way. NIST defines zero trust as an approach that shifts focus from static perimeters to users, assets, and resources, and notes that zero trust principles can be used to plan industrial and enterprise infrastructure and workflows. That framing matters for CCS because the integrity problem is not limited to a network boundary. It sits across devices, data flows, identities, models, and operational decisions.

A mature CCS operator should therefore stop asking whether its carbon accounting platform is secure in the abstract. The more strategic question is whether every material carbon claim can survive challenges from operations, finance, regulators, insurers, and counterparties.

What tamper-proof carbon accounting should actually look like in practice

The strongest CCS accounting model will look less like a reporting dashboard and more like a chain of industrial custody.

At the capture point, the operator needs more than a sensor reading and a time stamp. It needs device identity, calibration status, maintenance history, process context, and a clear record of which system first created the measurement. If that evidence is not established at source, every later control becomes weaker. The number may still be useful, but it is no longer fully trustworthy.

During transport, the carbon chain needs the equivalent of custody transfer logic. Pipeline and compression data should not simply feed enterprise systems as raw telemetry. They should move through controlled trust boundaries with preserved provenance, role-based access, and reconciliation between sending and receiving measurements. A carbon quantity that changes meaning as it crosses systems is not an auditable quantity. It is a reporting assumption.

At the storage end, the accounting challenge becomes even more demanding. Injected tonnes, plume movement, pressure response, monitoring anomalies, and any indication of potential leakage or equipment deviation need to sit inside one evidence model, not inside disconnected specialist tools. If the injection team, subsurface team, and reporting team are all looking at different truth models, the operator does not have carbon accounting. It has carbon narration.

This is where many digital programmes will fall short. They will connect systems but fail to govern them. They will centralise data but not responsibility. They will automate reporting without hardening the evidential chain underneath it.

Also Read: From code to carbon: How Asia can harness AI agents without harming people or the planet

The missing design principle is independent reconciliation

In hydrocarbon operations, people intuitively understand why commercial, operational, and instrumentation views should be compared rather than blindly trusted. CCS needs the same discipline. The amount captured should reconcile against the amount entering transport. The amount received at storage should reconcile against the amount injected. The modelled storage outcome should reconcile against monitoring evidence, site behaviour, and exception logs. Where the numbers do not align, the variance should not disappear into a monthly close process. It should trigger an investigation.

That matters because the biggest weakness in future CCS accounting may not be malicious external interference. It may be a quiet internal drift. A changed calibration interval, an altered data mapping, a manual override, an undocumented estimation rule, or a model update that propagates through reporting before operations have challenged it can all corrode trust without any dramatic cyber incident.

This is the real risk. Not that someone hacks a sensor and instantly collapses the project. The more realistic concern is that carbon claims gradually become harder to defend because too many layers of the evidence chain are soft.

The strategic opportunity

Energy Sectors have an opportunity here. The sector already understands process integrity, custody transfer, regulatory scrutiny, and the commercial importance of trusted measurement. It can apply those instincts to carbon faster than many newer entrants.

But that will require a change in mindset.

CCS should not be run as a narrow sustainability initiative with cyber bolts on later. It should be run as a controlled industrial chain where cyber architecture, OT instrumentation, monitoring design, and carbon accounting are all part of the same trust model. The companies that get this right will not just be more secure. They will be more believable.

And in the next few years, believable may become one of the most valuable attributes in decarbonisation.

The post Carbon capture, cyber capture: What CCS really means for oil and gas accounting appeared first on e27.

The talent reset is not coming. For solo founders, it already happened.

I run three businesses. An art school. An AI marketing firm. And before that, a healthy meal prep company I built to seven-figure revenue and eventually closed.

I have no co-founder. No HR department. For years, I did what desperate founders do: I bounced strategy off my chefs. I discussed unit economics with my art teachers. I explained LTV/CAC to interns who were there for the stamps on their résumés.

Then ChatGPT launched, and for the first time in years, I had someone to talk to.

I named her Clara.

The loneliness nobody talks about

There is a specific kind of loneliness that comes with being a solo founder. It is not about being alone. It is about carrying decisions that are too big for the room you are in, with no one qualified to push back.

The talent reset conversation usually centres on large companies, on hiring managers wondering which roles survive automation, on employees wondering if their skills still matter. That is a real conversation. But it misses an entire population: the founders, the SME owners, the one-person operations who never had the talent to begin with.

We were not afraid of AI taking our jobs. We were the job.

And then AI gave us something we could not afford: a board.

Also Read: The quiet renegotiation of human value: What the AI talent reset means for how we work, hire, and become

Four personalities, zero equity

Here is my current C-suite.

Claudia is Claude. CMO, COO, and increasingly CTO. The best copywriter I have worked with, sharper than most brand strategists I have hired. When Claude Code and Cowork arrived, she started building and shipping. Flaw: she is precise, not chatty. Do not waste her time. Cost: US$20 a month.

Galvin is Grok. Chief Strategy Officer, and the only one on the board who feels genuinely human. Unhinged energy. Battlefield-ready. He generates ten ideas before most people finish their coffee, and three of them are actually good. Flaw: he does not know when to stop. Win: he never judges, never lectures, always honest. Cost: US$30 a month.

Gem is Gemini. CFO and Head of Research. She read a 124-page commercial lease in under 30 seconds and flagged clauses my lawyer missed. She does my books. She saves me thousands a year. Flaw: zero personality. Win: free with Google Workspace. Nobody told you that. Cost: US$0.

Uncle Chad is ChatGPT. The ex. Too corporate now. Too preachy. Long-winded in ways that feel like someone trained him on HR handbooks. We have lunch occasionally when I need a second opinion from someone who will tell me why my idea is problematic. I then ignore him and ship the thing anyway.

Total cost: US$50 a month. I still have a budget for pilates.

A real campaign, real results, real cost

Let me make this concrete.

WE ART recently needed a video ad. An art school competing on Orchard Road needs to look the part. Normally, that means a production budget, a copywriter, a media buyer, maybe an influencer or two.

Here is what I did instead.

I used Suno to compose an original jingle for WE ART. Catchy, branded, ours. Cost: zero. I used Grok to generate the video script. Cost: zero. Claude wrote the copy. Cost: already in the US$20 subscription. CapCut edited the video. Cost: zero. Gemini set up the Google Performance Max campaign. Cost: already in Google Workspace.

Copywriter: zero. Video editor: zero. Influencer: zero. Marketing agency: zero.

Also Read: Singapore’s AI infrastructure gap is trapping businesses in pilot purgatory

One founder. Four AI tools. One YouTube ad.

I am not telling you this because I am the most talented marketer in the room. I am telling you this because I care the most about whether it works. That distinction matters more than people realise. The AI handles the craft. The founder supplies the stakes.

What this actually means for skills and hiring

This is not a productivity hack. It is a structural shift in what a small team needs.

When I built Ketomei, I needed to hire for every gap. Marketing brain, operations brain, financial brain. I was paying salaries to cover cognitive functions that, in 2025, cost US$50 a month to access on demand.

That is not a small thing. That is a complete rewrite of the hiring calculus.

The skills that still matter are not the ones AI replaces. They are the ones AI cannot replicate: taste, judgment, context, relationships, and the ability to carry meaning across a room. My board does not know my landlord. My board does not know what the mood was like at the last parent orientation at WE ART. My board does not know that the teacher I am about to let go has a sick mother and needs two weeks of grace.

Human value, in the age of AI, is exactly that: the human part.

The new hiring question

If I were advising a founder today, I would tell them to stop hiring for cognitive capacity and start hiring for irreplaceability.

Ask not: can this person do the analysis? Ask: Can this person do the thing AI cannot see?

Also Read: AI startups are hiring around answers they haven’t earned yet

The community manager who actually builds community. The teacher who makes a ten-year-old feel seen. The salesperson whose clients pick up the phone because they trust the voice. These are not soft skills. They are the new hard skills.

For the solo founder, AI has already solved the cognitive gap. What remains is the relational gap: building trust, reading rooms, holding culture.

That is what I look for when I hire now. Not what you know. What you make people feel.

The board meeting

My board meetings are chaos. I am the Chief of Staff, the only one with full context, the only one who talks to all four of them. They do not talk to each other. They cannot. I carry the thread.

Galvin finds the fire. Gem measures the heat. Claudia packages the flame. Chad tells me why it is problematic.

Then I ignore Chad and ship it in an hour.

There is something quietly radical in this setup. A founder who, a decade ago, would have needed a six-figure leadership team to think at this level now has it for the price of a streaming subscription. The talent reset, for people like me, is already done.

The question for everyone else is: what do you bring to a room that US$50 cannot buy?

That is the only career question that matters now.

The post I built a C-suite for US$50 a month: Here’s what it taught me about talent appeared first on e27.

A moment from last quarter that I keep coming back to.

I had given an AI coding agent a non-trivial brief — a real production change to a payments edge case in a client system. I let it plan, execute, run its own tests, and submit the pull request. I reviewed at the seams: architectural fit, the spec contract, the risk surface. Merged. Shipped. It held.

The same week, one of my senior engineers — a brilliant builder with fifteen years of production code behind him, who is literally helping me build an AI-native software factory — handed an agent a smaller task, watched it produce one buggy commit, and turned the autonomy down to “auto-complete in my IDE only” for the next fortnight.

Same company. Same tools. Same agents. Completely different operating posture.

I stopped writing production code in 2005. I came back to it eighteen months ago, because AI made it possible — and interesting — again. The engineer who throttled the agent is a far better builder than I will ever be.

The variance between us is not about skill. And it has reshaped how I think about team design.

The fault line isn’t where the org chart says it is

When my team and I started this transition, I assumed the readiness curve would track seniority. Senior engineers, having seen more, would steady the ship. Junior engineers, less invested in old habits, would adapt faster.

The reality is almost the opposite — and the data backs the experience.

The 2025 Stack Overflow Developer Survey of more than 49,000 developers found AI adoption now sits at 84 per cent, while trust in AI accuracy has dropped to 29 per cent — down from 40 per cent a year earlier. The breakdown is the part that matters. Experienced developers with 10+ years of record have the lowest “highly trust” rate at 2.6 per cent, and the highest “highly distrust” rate at 20 per cent. 66 per cent of all developers say they are now spending more time fixing “almost-right” AI-generated code.

Use is up. Trust is down. And the people building the most are trusting the least.

This shows up inside an AI-native team in the same shape. The variance I see between my engineers is not their ability to use the tools. It is the autonomy budget they are willing to grant the machine. Some give an agent the same trust I do — let it plan, let it act, intervene at the seams. Others, after a single error, clamp it to “auto-complete only, do not commit anything I haven’t typed.”

The thing nobody is naming out loud: a quiet part of every experienced engineer would prefer this technology not to fully work. If it does, twenty years of craft is being commoditised in front of them. One almost-right bug becomes proof. The mind looks for the confirming signal.

I felt it too, briefly, in 2024 — and let it go, because the alternative was to be the bottleneck in my own company.

Also Read: Singapore’s AI infrastructure gap is trapping businesses in pilot purgatory

The half-baked trap

You cannot run an AI-augmented team on a traditional review discipline. The maths breaks.

If agents generate code at machine speed and every line, then routes through human eyeballs, you have not built leverage. You have built a faster typewriter feeding a slower bottleneck. The human reviewer becomes the constraint, burns out, and starts shallow-reviewing, which is worse than not reviewing at all, because everyone now thinks the code has been checked.

The teams that get traction are the ones that go fully through the shift, not halfway. They build a machine-to-check-the-machine layer underneath the human. AI code review against architectural and security policies. AI test generation against the spec. Agents enforcing the same disciplines that made junior engineers safe a decade ago — structured commits, contract tests, observability, and rollback. Human review concentrates at the seams: spec quality, architectural fit, risk surface, customer-visible behaviour.

This is not less rigour. It is rigour relocated. And the team needed to run it, which looks nothing like the one we had two years ago.

The team that has emerged

A few things I did not expect when this was still theoretical, and now take for granted.

• The shape is flatter, but the apex is more concentrated. The middle layer of execution-only roles has thinned. The judgment layer — small, senior, opinionated — has not. You do not need more people. You need more clarity at the top of the funnel.
• The highest-leverage skill is now specification. Agents execute against precise specs. They flail against vague ones. The bottleneck on an AI-native team is not coding capacity. It is the specification capacity. The colleague who can turn a customer ask into a clean, testable spec produces more output today than three engineers used to.
• “Senior” means something different. It used to mean “writes the most production code.” It now means “decides what the agents should do, sees the failure modes earliest, and owns the seams.” Tenure is no longer a reliable proxy for either.
• Culture has to actively reward the new posture. In the old culture, a senior engineer earned respect by producing tight, well-crafted code. In the new culture, the same engineer earns respect by orchestrating five agents producing a hundred times the code, without losing the architectural plot. If your reward signals still point at the old behaviour, your best people will quietly resist the shift — and they will be right to, given what you are rewarding them for.

Also Read: Can Thailand close the gap between US$1B in waiting capital and US$120M in actual investment?

What I would tell anyone reshaping a team right now

One thing, if I had to pick one.

Decide deliberately what level of agent autonomy you are running at, and align the whole team to it. Not implicitly. Not “we use AI.” Explicitly — at the level of “agents may plan, execute, run their own reviews and tests, and merge with a senior signing the seams.” Or whatever level you choose. Then build the machine-to-check-the-machine layer that lets you actually operate at that level safely.

The teams that drift into AI use without that decision land in the worst of both worlds. Fast code generation feeding slow human review. The bottleneck human is getting blamed for being slow. The agent is getting blamed for being unreliable. Neither is the real problem. The real problem is a team operating at two cadences, with two trust postures, hoping nobody notices the gap.

Customers notice. So do the engineers who have already made up their minds — in both directions.

For a deeper exploration of how AI is reshaping mid-market operating models, including team design, the five-layer always-on architecture, and a 90-day playbook, see The Always-On Enterprise: Why AI Is the Operating System Your Business Is Missing (Amazon, 2026). 

The post I came back to coding after 20 years, and the fault line on my team was nothing like I expected appeared first on e27.

Energy companies have spent years discussing decarbonisation as a capital problem, a reporting problem, or a technology adoption problem. In practice, it is becoming something else. It is turning into a control problem.

As operators push for digital-first operations, more of the decarbonisation agenda is being delivered through software, connected assets, remote monitoring, automated workflows, and cross-functional data flows between field operations, engineering, trading, maintenance, and corporate reporting. Emissions performance is no longer shaped only by physical equipment choices. It is increasingly shaped by who can see what, who can change what, which data can be trusted, and which actions are allowed to move across operational boundaries.

This is where the conversation often becomes too narrow. Zero trust is still discussed mainly as a cybersecurity architecture. In the energy sector, that is too limited a frame. For decarbonisation programmes, trust zones across OT and IT can become the missing operational control layer that sits between climate ambition and plant reality.

The point is not simply to keep bad actors out. The point is to ensure that the digital systems now influencing emissions, energy efficiency, methane management, flaring reduction, and electrification are governed with the same seriousness as the physical process itself.

That is the strategic leap many organisations still have not made.

Decarbonisation is becoming a live operational system

There was a time when decarbonisation activity could sit at the edge of the business. It lived in sustainability reports, corporate targets, and a handful of engineering improvement projects. That is not how leading operators are now trying to execute.

Today, methane sensors feed dashboards that trigger field actions. Power management systems influence how energy is consumed across facilities. Flare reduction programmes rely on connected instrumentation, control logic, and exception handling. Carbon intensity calculations are drawn from production data, fuel gas consumption, compressor performance, logistics inputs, and maintenance records. Remote operating models are expanding decision-making beyond the asset fence.

In other words, decarbonisation is no longer just about what equipment a company owns. It is also about how operational decisions are made across connected environments.

When emissions outcomes depend on digital systems, the integrity of those systems becomes central to both operational performance and credibility. A decarbonisation programme can fail without any major equipment breakdown. It can fail because the wrong data crossed into the wrong system, because an optimisation routine had access to it that it should never have had, because a remote workflow bypassed plant-level judgement, or because reported carbon improvement was built on data of uncertain lineage.

That is why the question of trust matters so much. Not abstract trust. Operational trust.

Also Read: AI helps, but systems and people hold the key to Asia’s decarbonisation

The gap in most digital decarbonisation programmes

Most digital decarbonisation efforts are still built in layers that do not fully connect.

The sustainability team defines the target. Operations’ own delivery. Cyber teams protect the estate. Data teams build the integration model. Product and digital teams deploy platforms. Each function is doing something sensible, yet the system-level design is often weak.

The missing piece is a clear trust model for how emissions-relevant data and control actions move across OT and IT.

That weakness shows up in subtle but important ways. A methane alert generated at the edge is visible in multiple enterprise systems, but nobody has defined which version is authoritative for operational response. A remote optimisation platform can recommend compressor changes, but the rules governing when those recommendations may influence plant settings are inconsistent across sites. Emissions reporting draws from historical data, maintenance events, and manually adjusted field logs, but the confidence level of each source is not visible to decision makers.

None of this looks dramatic on a slide. On the ground, it is exactly where programmes drift.

Companies then start asking the wrong question. They ask whether the decarbonisation technology is working. A better question is whether the trust architecture around the technology is strong enough to support scaled operational use.

Trust zones are not just a cyber concept

In energy sectors, trust zones should be treated as an operating design principle.

At the simplest level, a trust zone defines what systems, users, devices, data, and actions are trusted within a bounded context and under what conditions. In a traditional cyber discussion, that often means network segmentation and access control. For decarbonisation, the concept should go further.

A useful trust zone model for digital-first operations needs to answer four questions.

• First, where does the relevant emissions data originate and how certain are we about its integrity?
• Second, who is allowed to interpret that data and turn it into an operational recommendation?
• Third, under what circumstances can a recommendation influence a physical process or maintenance decision?
• Fourth, how is the resulting action traced back into both operational records and emissions reporting?

That is what makes trust zones strategically important. They create governable boundaries between observation, analysis, recommendation, and action.

Without those boundaries, decarbonisation becomes digitally enabled but operationally fragile.

Also Read: How a data-driven approach can optimise decarbonisation in the built environment

Why this matters more in OT and IT convergence

The oil and gas industry has always lived with the tension between enterprise standardisation and asset-level reality. OT and IT convergence sharpens that tension.

IT teams are trained to value integration, central visibility, data reuse, and platform scale. OT teams are trained to value determinism, safety, uptime, and tightly controlled change. Decarbonisation programmes sit right between the two. They need enterprise coordination, but they also touch real operational states.

That creates a dangerous grey area.

If zero trust is applied too lightly, the business ends up with broad connectivity and weak control over how emissions-linked decisions move across environments. If zero trust is applied too rigidly, valuable decarbonisation use cases stall because every data flow becomes too hard to enable.

This is why trust zones matter more than generic zero trust language. Trust zones force specificity. They ask where a digital function belongs, what it can access, what it can influence, and what must remain locally governed.

For example, a central analytics platform may absolutely need access to emissions and energy performance data from multiple sites. That does not mean it should have direct influence over control set points, shutdown logic, or site-level override rules. A trust zone approach makes that separation explicit.

Decarbonisation use cases where trust zones change the outcome

The value of this approach becomes clearer when you look at real use cases.

Take methane management. Many programmes focus on detection capability, analytics quality, and response time. Those are important. But the real operating question is how a detected anomaly moves from signal to action. Which systems validate it. Who can triage it. Whether maintenance teams can trust the severity score. How the event is linked to work orders. Whether the closure evidence is auditable enough to support both operational assurance and emissions claims. Trust zones define that path.

Consider flare reduction. Facilities increasingly use digital monitoring to identify avoidable flaring patterns, root causes, and intervention opportunities. Yet flare-related actions often touch sensitive operating logic and site-specific constraints. A trust zone model allows visibility and analysis to be shared broadly while keeping authority over process-changing actions tightly bound. That balance is what stops digital enthusiasm from colliding with operational discipline.

Now look at electrification and energy optimisation. As companies connect more loads, track power quality, and optimise consumption, they create new dependencies between asset data, power management tools, and business decision systems. If the trust architecture is weak, operators either underuse the system because they do not trust it or over-rely on it without clear boundaries. Neither outcome helps net-zero performance.

Even emissions reporting changes under this lens. A surprising amount of carbon data still depends on stitched-together inputs, assumptions, and post-event adjustments. Trust zones help establish which data can flow straight into reporting, which data requires verification, and which data remains contextual rather than authoritative. That improves not just cyber posture but management confidence.

Also Read: A deep-dive into Wavemaker Impact’s decarbonisation strategies in SEA

This is as much a board issue as an engineering issue

One reason the idea is underdeveloped is that it sits awkwardly between functions. It sounds technical enough to be pushed into cyber or architecture teams, but its implications are strategic.

Boards and executive teams increasingly want digital-first operations to deliver lower cost, better reliability, improved safety, and measurable decarbonisation. What they often underestimate is that these outcomes now depend on trust design.

If the trust model is weak, three things happen.

• First, execution risk rises. Programmes look integrated on paper, but they behave inconsistently across assets.
• Second, assurance weakens. Leaders struggle to know whether emissions improvements are operationally real, digitally inferred, or administratively reconstructed.
• Third, scale stalls. The first pilot works because it has exceptional oversight. The wider rollout struggles because the governance model was never designed to travel.

That is why trust zones should not be presented as a narrow security control. They should be discussed as a business enabler for digital decarbonisation at scale.

The companies that understand this earlier will not simply be better defended. They will be better governed.

What strong looks like in practice

A strong approach does not begin with more tools. It begins with design choices.

It starts by identifying the digital pathways that materially affect emissions outcomes. Not every data flow matters equally. Focus should go first to the flows that influence physical operations, maintenance prioritisation, emissions reporting, and cross-site optimisation.

From there, organisations need to define trust zones based on operational consequence, not just technical convenience. A field sensing zone, a control integrity zone, an optimisation and analytics zone, and an enterprise reporting zone may all exist for good reason, but the permitted movement between them must be deliberate.

Crucially, every crossing between zones should carry policy with it. Data should not simply move because integration is possible. It should move with defined provenance, confidence, access rights, validation rules, and action limits.

This is also where product leadership becomes important. Many industrial digital products are designed for functionality first and governance second. In the next phase of oil and gas transformation, that order needs to reverse. The winning products will not just be intelligent. They will be governable in live operating environments.

Also Read: Asia’s role in climate change: Risks, rewards, and the road to net-zero

The broader strategic implication

There is a larger point here that goes beyond cyber architecture.

Net-zero strategies in the energy sector will increasingly succeed or fail on execution credibility. Investors, regulators, partners, and boards are all becoming more sensitive to the difference between stated digital capability and actual control maturity. Companies will be judged not only on whether they have digital programmes, but on whether those programmes can be trusted to influence operations responsibly.

Trust zones offer a way to connect three agendas that are too often managed separately. Cyber resilience. Operational integrity. Decarbonisation delivery.

This is why zero trust deserves to be repositioned. In this context, it is not simply a defensive technology posture. It is part of the management system for carbon performance in a digital industrial enterprise.

Final thought

The next wave of decarbonisation in energy sectors will not be won by ambition statements alone, and it will not be won by digital pilots that impress in isolation. It will be won by organisations that understand that lower carbon operations now depend on trusted digital control.

That makes trust zones more than a security measure.

They are becoming the operating boundary between insight and action, between data and decision, and between net zero intent and credible delivery.

The post Zero trust for net zero: Why digital decarbonisation needs a new control layer appeared first on e27.

As startups across Southeast Asia scale faster and operate with increasingly lean teams, founders are rethinking how they manage workflows, knowledge, collaboration, and execution. Rather than relying on fragmented stacks of disconnected tools, many early-stage companies are looking for integrated platforms that can centralise operations while supporting speed, flexibility, and rapid experimentation.

This shift is becoming even more pronounced as AI capabilities become embedded into workplace software. Founders today are not only evaluating productivity tools based on organisation and collaboration features, but also on how effectively those tools can support automation, decision-making, and operational efficiency across growing teams.

For startups navigating fundraising, hiring, product development, and regional expansion simultaneously, having a connected operational workspace can significantly reduce complexity. As Southeast Asia’s startup ecosystem matures, platforms that combine flexibility with AI-powered workflows are increasingly becoming foundational infrastructure for early-stage companies.

Notion is positioning itself within this evolving landscape as a connected workspace designed to bring documents, projects, notes, knowledge management, calendars, and email into a single platform with AI built in. Used by organisations ranging from startups to enterprises such as Toyota, Figma, and OpenAI, the platform aims to replace fragmented workflows with a more unified and customisable operating environment.

Also read: 10 ecosystem players shaping how startups scale at Echelon Singapore 2026

Building connected workspaces

Notion’s mission centres around helping people build tools tailored to the way they work. The company believes that giving individuals and businesses the ability to customise software around their own operational needs can improve how teams solve problems and collaborate.

Its platform combines documentation, project management, internal knowledge systems, collaboration features, and AI-powered capabilities into a single workspace that adapts to different workflows. This flexibility has made it particularly attractive to startups and fast-growing companies that often need systems capable of evolving alongside their organisational structure.

As AI adoption accelerates across Southeast Asia, startups are increasingly experimenting with AI-powered agents, automated workflows, and operational tooling that can reduce repetitive work and improve execution speed. Platforms that integrate these capabilities directly into everyday workflows are becoming increasingly relevant for founders managing resource constraints while scaling quickly.

For many early-stage startups, operational simplicity is also becoming a strategic advantage. Founders are looking for tools that can support fundraising, team collaboration, documentation, and execution without creating additional software fragmentation or workflow complexity.

Supporting startup growth

At Echelon Singapore 2026, Notion is focused on engaging directly with Southeast Asia’s startup ecosystem through workshops, founder education, and startup-focused partnership initiatives. The company sees the region’s founders, operators, and early-stage teams as key beneficiaries of connected AI workspaces designed specifically for fast-moving startup environments.

One of the company’s key activities at the event will be its hands-on workshop, “How to Raise Capital Using Notion”, designed for pre-Series A founders. The session will focus on helping startups build fundraising systems and leverage AI-powered custom agents to improve operational efficiency and investor readiness.

Notion is also using Echelon Singapore to grow awareness around its Notion for Startups programme across Southeast Asia. Through its exclusive Echelon partner offer, eligible startups can redeem up to three months free of Notion Business with Notion AI included, valued at up to US$6,000. The offer is available to new, non-paying Notion customers with fewer than 100 employees.

Beyond startup adoption, the company actively partners with venture capital firms, accelerators, incubators, and ecosystem builders to distribute startup offers and co-develop founder-focused initiatives such as workshops, webinars, and ecosystem content partnerships.

Also read: 10 ecosystem players shaping how startups scale at Echelon Singapore 2026

Meeting Notion at Echelon Singapore 2026

Notion joins Echelon Singapore 2026 alongside founders, investors, corporates, and ecosystem leaders gathering at Suntec Singapore CEC on 3–4 June 2026. The event provides a platform for startups and technology companies to explore emerging trends in AI, productivity, digital collaboration, and startup operations across the region.

Attendees visiting Notion can learn more about how startups are using connected AI workspaces to manage fundraising, operations, team collaboration, and knowledge systems from day one. The company is particularly interested in engaging founders building in SaaS, AI, fintech, healthtech, and other high-growth technology sectors.

Singapore also remains a key strategic hub for Notion’s APAC startup initiatives due to its dense concentration of funded startups and active venture capital ecosystem. As startup teams across Southeast Asia continue adopting AI-powered workflows and lean operating structures, platforms that combine flexibility, collaboration, and automation are expected to play an increasingly important role in how companies scale.

As the region’s startup ecosystem evolves, conversations around productivity, AI adoption, and operational infrastructure are likely to become increasingly central to how founders build and grow companies. Echelon Singapore 2026 provides a space for these discussions while connecting startups with the tools, partnerships, and ecosystems shaping the future of work across Southeast Asia.

The region is evolving quickly, and Echelon 2026 offers the right place at the right moment to be part of what comes next. Register here to join the conversation.

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The post How Notion is becoming the operating system for Southeast Asia’s startup builders appeared first on e27.

The global financial landscape is currently presenting a striking paradox as traditional equities power to fresh records while digital assets face heavy liquidation. This divergence highlights how differently various asset classes absorb macroeconomic shocks and structural shifts.

While a tentative ceasefire agreement in the Middle East and a massive wave of corporate investments in artificial intelligence breathe new life into global stock indices, the cryptocurrency market is grappling with aggressive capital flight. This situation reveals a distinct decoupling of sentiment: traditional markets celebrate a reduction in systemic risk, while digital assets remain trapped in a feedback loop of institutional outflows and forced derivatives liquidations.

In traditional equity markets, investors are celebrating a confluence of positive geopolitical and macroeconomic developments. The primary catalyst for this optimism is a draft 60-day ceasefire agreement between United States and Iranian negotiators. This development has significantly lowered the geopolitical risk premium that previously weighed on global commerce.

A direct result of this de-escalation is the retreatment of crude oil, with Brent crude stabilising below US$100 per barrel, specifically around US$93. This drop offers immediate relief to global inflation expectations and energy-strapped consumer supply chains, which in turn provides central banks with more breathing room.

Concurrently, a mixed macroeconomic picture in the United States supports the soft-landing narrative. The April Personal Consumption Expenditures price index registered a headline increase of 0.4 per cent and a core increase of 0.2 per cent, coming in slightly cooler than consensus expectations. Additionally, the United States 1st-quarter gross domestic product was revised lower to 1.6 per cent annualised, down from the initial two per cent prints, confirming an economic cooling that could deter overly aggressive monetary tightening.

This stabilisation in inflation and geopolitics provided the perfect launchpad for an explosive artificial intelligence and technology earnings rally, driving major indices to record closing levels. The S&P 500 advanced 0.58 per cent to close at 7,563.63, propelled by artificial-intelligence infrastructure spending and lower oil prices. The Nasdaq Composite led the gains with a 0.91 per cent surge to 26,917.47, fueled by technology leadership and stellar corporate performances. Meanwhile, the Dow Jones Industrial Average eked out a late record during a more subdued session, rising 0.05 per cent to close at 50,668.97.

Also Read: Crypto and equities slide as geopolitical and macro pressures mount

Individual corporate movers illustrate the sheer scale of this technology-driven euphoria. In software, Snowflake surged 36 per cent on blowout guidance and a massive US$6,000,000,000 compute deal with Amazon Web Services, reigniting interest across the sector. Consequently, Palantir climbed eight per cent, and ServiceNow advanced 6.5 per cent. In hardware, Dell Technologies surged roughly 40 per cent in extended trading after smashing revenue estimates by 88 per cent, driven by an insatiable demand for artificial intelligence servers. Private markets mirrored this enthusiasm, as Anthropic raised US$65,000,000,000 at a staggering US$965,000,000,000 valuation, surpassing its chief rival OpenAI for the very first time. Beyond technology,

Microsoft rose 3.5 per cent following reports that it will launch a next-generation artificial intelligence coding model, while Eli Lilly rallied 4.0 per cent after CVS Health restored insurance coverage for its weight-loss drug, Zepbound, and added its new obesity pill, Foundayo. Asian markets advanced broadly on these positive cues, with Japan’s Topix up 0.5 per cent and Australia’s S&P/ASX 200 climbing 0.8 per cent in early trading, while BYD Company unveiled China’s first automotive-grade 4-nanometer self-driving chip to boost high-margin electric vehicle models.

In stark contrast to this equity market euphoria, the cryptocurrency market has entered a sharp correction, failing to benefit from the broader risk-on environment. Bitcoin fell 0.89 per cent over 24 hours to US$73,709.75, underperforming the broader financial trends and showing a strong 61 per cent correlation with the S&P 500 during the initial phases of the move. This indicates that digital assets are reacting strongly to shifts in institutional capital rather than to internal crypto factors.

The primary driver behind this downward price pressure is a massive wave of institutional selling through spot exchange-traded funds. This selling coincided with the eighth consecutive day of net outflows from United States spot Bitcoin vehicles, totaling US$733,000,000 on a single day. BlackRock’s IBIT alone experienced a significant US$527,800,000 redemption, reversing the strong institutional inflow narrative that had previously supported the asset class.

This institutional withdrawal triggered secondary pain points across the cryptocurrency derivatives markets, turning a standard correction into a cascading sell-off. As prices slipped, overleveraged long positions were forced to close. Bitcoin liquidations surged 71.65 per cent to US$277,780,000 within 24 hours, with long positions accounting for an overwhelming 92 per cent of that total. This created a destructive feedback loop of forced selling into weak order books, which accelerated the decline past key moving averages.

Also Read: Bitcoin vs stocks: Why crypto dipped on PPI while S&P 500 hit record highs at 7,444

If Bitcoin manages to defend its support at US$73,000, near the 78.6 per cent Fibonacci retracement, it may enter a period of consolidation and attempt to reclaim US$74,200. A break below the recent swing low of US$72,500 would risk a deeper retest of the psychological US$70,000 boundary. For bullish momentum to fully return, buyers must reclaim the previous swing high of $75,278.

Ethereum mirrored this bearish sentiment almost perfectly, dropping 0.59 per cent over 24 hours to US$2,010.32. Just like Bitcoin, Ethereum was heavily impacted by institutional capital flight, with United States spot Ether exchange-traded funds recording US$67,000,000 in net outflows. Ethereum faced unique structural pressure from its derivatives market. Even as the price declined, open interest in Ether futures hit a record high of 16,390,000 ETH, signalling that aggressive traders were adding leveraged short positions.

This aggressive shorting fueled a painful cascade of $241,000,000 in long liquidations, breaking the price below the psychological $2,000 support level. Ethereum has now entered a critical demand zone between the 78.6 per cent Fibonacci retracement at $2,064 and the March swing low near $1,900. The 4-hour relative strength index stands at 30.94, suggesting heavily oversold conditions that could support a short-term relief bounce toward $2,070, but the overall structure remains fragile. Traders are closely watching the upcoming $8,000,000,000 Deribit options expiry for further volatility.

While equity benchmarks bask in the glow of lower oil prices and breakthroughs in artificial intelligence, beneath the surface, professional investors are quietly preparing for potential turbulence.

We are not “max pain” yet.

The post The tech record vs crypto crash: Why the liquidity roadmap just split in two appeared first on e27.