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.

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.

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.

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.

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 e27 team produced this article.

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