One of the most persistent myths in tech is that SaaS valuation is a simple formula. Take Annual Run Rate (“ARR”), apply a market multiple, and you have your answer.

It is a useful shortcut. It is also how founders end up misunderstanding what their company is actually worth.

Yes, SaaS businesses are often discussed in terms of ARR multiples. But in real transactions, especially exits, the multiple is not the valuation logic. It is the output of it. What buyers are really valuing is the quality of the revenue, the durability of growth, the efficiency of the model, and the type of transaction being done.

That distinction matters because two SaaS companies with the same ARR can produce very different outcomes in the market.

The first point is straightforward: recurring revenue matters more than revenue in general. For most SaaS businesses, valuation is anchored on ARR, not total revenue. That is because recurring subscription revenue is the part that a buyer can actually underwrite with some confidence. It is predictable, repeatable, and, if the business is healthy, compounding.

By contrast, implementation fees, consulting income, or one-off project work may still be commercially useful, but they rarely deserve the same multiple. A company with US$10 million in total revenue, of which US$8 million is recurring, should not expect to be valued the same way as a company with US$10 million in which half the revenue comes from non-recurring services. The first looks like a software asset. The second may still be a good business, but it is not as clean a recurring one.

But even that is only the starting point.

What really separates SaaS businesses in valuation is not just the amount of ARR, but the quality of that ARR. And the clearest signal of quality is retention.

This is where many founders become overly optimistic. They see recurring billing and assume the market will view their revenue as durable. Buyers do not think that way. They look at churn first. If customers are leaving too quickly, the business is not truly compounding. It is just running hard to replace what is already falling out of the bottom.

Also Read: The autonomous agent paradigm: Meta’s Manus acquisition, MCP integration, and the disruption of SaaS

As a practical benchmark, SMB SaaS volume churn should generally not be more than three per cent per month. Enterprise SaaS should be far tighter, ideally with near-zero volume churn across core accounts. The exact number is not the whole point. The principle is: Retention is a proxy for stickiness, product relevance, and how deeply the software is embedded in customer workflows.

In plain English, buyers pay more for revenue that stays.

That also means an average-growth company with excellent retention can be worth more than a faster-growing business with weak customer durability. Founders often overemphasise growth and underappreciate the penalty the market places on churn. But a leaky SaaS business is not a premium SaaS business, no matter how strong the top-line story sounds in a deck.

Growth still matters, of course. A company growing more than 300 per cent year-on-year will usually attract more attention than one growing at 50 per cent. Faster growth often supports a higher multiple because it suggests a bigger future revenue base and a stronger competitive position.

But growth is not one thing. Buyers care about growth quality.

Was growth driven by healthy demand and repeatable customer acquisition, or by unsustainably high sales and marketing spend? Was it supported by strong expansion within existing accounts, or did it depend on heavy discounting just to win new logos? Is the growth durable, or did the company simply pull revenue forward?

These are not academic questions. They directly shape valuation. High growth with poor retention and weak economics is less impressive than founders like to think. High growth with strong retention and efficient acquisition is where the real premium sits.

This leads to another factor that founders consistently underestimate: margins and unit economics.

Software is attractive because it should scale. That does not mean every SaaS company automatically deserves a strong valuation. Buyers will still look closely at gross margins, customer acquisition cost, payback periods, and overall operating leverage. If the business needs too much spending to maintain growth, or if margins remain thin despite scale, the valuation logic weakens. A recurring revenue business with poor unit economics is not a great asset just because it invoices monthly.

Also Read: The agent swarm is unleashed on SaaS

So when people ask how SaaS companies are valued, the better answer is this: not by ARR alone, but by the quality of the machine producing that ARR.

That machine is judged across four big dimensions.

• First, how much revenue is truly recurring.
• Second, how sticky that revenue is.
• Third, how durable and efficient the growth is.
• Fourth, whether the economics prove the business can scale.

Only after that does the multiple make sense.

Where this becomes more interesting is when founders confuse fundraising valuation with exit valuation. The two are related, but they are not the same exercise.

In a VC fundraising round, the valuation often reflects future potential more than present-day operating quality. Investors may be willing to pay up because they believe the company could become a category winner, dominate a large market, or grow into a strategically important platform. The valuation is often shaped by what the company might become.

In an exit, especially in M&A, the lens is more grounded. Buyers are usually paying for what exists today, adjusted for what they believe they can realistically achieve after closing. That makes M&A valuation more closely linked to current performance, risk, and transaction logic.

Also Read: The rise of one-person AI companies and why micro-SaaS is at the centre of it

Put differently, fundraising tends to reward possibility. Exits tend to reward evidence.

This is why founders should be careful when using private fundraising rounds as reference points for what their company should be worth in a sale process. A VC may tolerate messy retention, thin margins, or heavy burn if the upside is large enough. An acquirer, particularly one writing a real cheque to buy control, will usually be more disciplined.

Even inside M&A, not all buyers think alike.

A strategic acquirer may pay more because your product fills a capability gap, gives them access to a highly relevant customer base, or creates synergies across product, distribution, or go-to-market. They are not only buying your standalone cash flow. They may also be buying what your company unlocks inside their broader machine.

A financial buyer, by contrast, is usually more disciplined on headline multiple. They will focus more tightly on retention, margins, cash flow profile, and whether the growth engine is efficient enough to support an investment case. That does not mean they always pay less. It means their logic is usually more rooted in the business as an asset, rather than in strategic overlap or synergy.

So the same SaaS company can produce very different valuations depending on whether the buyer is strategic or financial.

And then there is deal structure, which founders often ignore until it is too late.

A headline valuation is not the same as bankable value. If a buyer offers a rich number, but much of the consideration comes in shares rather than cash, the economics become much less certain. A share swap may look attractive on paper, especially if the acquirer is growing quickly or trades well publicly. But it also means the seller is taking future performance risk, liquidity risk, and market risk on the buyer.

An all-cash offer at a slightly lower headline valuation may, in practice, be worth more because the proceeds are real, immediate, and certain. The same logic applies to earn-outs, deferred payments, and other structured consideration. Founders should not just ask what the price is. They should ask what form the price takes, when it is paid, and what has to happen before it becomes real.

This is why transaction context matters so much. Market benchmarks can tell you where comparable businesses may sit. But actual outcomes depend on buyer fit, competitive tension, and structure. A strong strategic fit with multiple interested buyers can move valuation above generic benchmarks. A single-bid process with messy diligence and weak retention can drag it below them very quickly.

Also Read: I built an AI agent for myself — it became a 2,000-user micro-SaaS

The uncomfortable truth is that SaaS valuation is less about memorising what multiple the market is paying and more about understanding why one business deserves that multiple while another does not.

Founders who want to improve valuation should stop asking only, “What are SaaS companies trading at?” and start asking better questions.

• How much of my revenue is truly recurring?
• How strong is retention by segment and cohort?
• Is our growth efficient, or just expensive?
• Do our margins support the software story?
• Would a buyer see this as a durable asset, or as a promising but risky one?
• And if I do get an offer, how much of it is actually cash?

That is the real lens.

The market may speak in multiples. But deals are done on quality, confidence, and structure. Founders who understand that early will prepare differently and, usually, negotiate better.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The post How SaaS companies are valued: Why the multiple is only the surface story? appeared first on e27.

Most teams are still using AI the same way: ask ChatGPT, get an answer, share it around, repeat. It’s a loop.

I did the same for over a year, but AI has become a lot more than that.

Early this year, something changed how I work, and how ourteam (an AI recruiting platform focused on automating candidate screening and evaluation) works, entirely.

I started my career 16 years ago in management consulting. My first task was creating PowerPoint slides. I remember drawing a line that was always bent. Then, my senior taught me a keyboard shortcut (holding the Shift button when dragging the mouse). That became my first ‘skill’ at work.

Today, that word rings very differently.

A ‘skill’ in AI is a text file (commonly known as a markdown file). It contains a set of plain-text instructions that tell the AI what to do consistently every time. It can be built in minutes. It can learn and repeat what takes months and years.

I grew up with the rise of the internet, mobile and cloud. I believe those were critical shifts to get us to where we are today. Yet, this feels different.

AI isn’t just a thinking partner anymore; it’s becoming the ‘system’ teams actually run on.

I have zero technical background, I can’t code, but lately I’ve been managing a team of AI agents that handle our daily work and build our software.

These days, ourteam and I are operate with AI agents daily. These agents now catch errors, review work, and ship updates on their own. The team just directs. If you told me this a year ago, I wouldn’t have believed you.

(An ‘AI agent’ is an AI-powered entity that can take actions on its own. It reads files, writes code, sends emails, and runs tests. You give it a goal, and it figures out the steps.)

Also Read: The rise of AI agents in healthcare: Designing man-machine systems

Which brings me to the point of intelligence.

In knowledge work, we tend to associate intelligence with execution: knowing how to draw that straight line on PowerPoint, making charts and models on Excel, designing prototypes, programming software, and more.

Those were skills we took months and years to learn. Today, you can create a skill in 5 minutes.

Most of the intelligence work today can be done faster, cheaper and often better by AI systems. If your use of AI is still just prompting ChatGPT back and forth, there’s actually a lot more out there.

AI models and applications have become so good that real, serious work can be done reliably and consistently. You give it an outcome, and it handles the steps.

That’s the shift most people are underestimating.

Now, the hardest part isn’t handing over execution to AI. It’s everything that comes before and after.

Judgement. Taste. Standards.

Knowing what’s good or bad. What feels right. What should be shipped and what shouldn’t. Those decisions are still on us.

Spending time to think and write, I believe, is one of the most underrated practices left.

I started as a tech enthusiast. Today, I’m a heavy Claude user (Claude-pilled as they say).

From simple chat to using it as a coworker. Now, I’m deep into Claude Code, building and shipping things through what people call “vibe coding”. (And yes, I cancelled my ChatGPT subscription, but that’s a separate story.)

The strange part is this: the more you learn, the more you work.

AI expands what’s possible, so you end up doing more. Anyone actively building with it will tell you the same.

Also Read: AI agents didn’t change how I write, they changed when I could start publishing

On X, there’s a fast-moving community debating AI models, workflows, and sharing best practices: Claude Code vs Codex, agent workflows, open-source tools like OpenClaw.

We used to wait excitedly for Apple Keynote once a year. Now, exciting breakthroughs happen every few days.

Inside ourteam, we’re constantly learning and applying new concepts: agent teams, auto-research, self-healing systems, internal LLM wikis, second brains. These aren’t just “new features”; they add up to a fundamentally different way of working.

We used to identify ourselves singularly: an engineer, a salesperson, a product manager, a customer service manager. What if today we can be all that and more? It is no longer an ‘if’, but ‘when’.

If you have the will, you’ll have the skill.

I was taught to unlearn and relearn. And here I am. It feels weird at times, but it’s super exciting.

Hopefully, this inspires you too.

This article was first published here.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The post If you have the will, you’ll have the skill appeared first on e27.

This is not a prediction. It is a shift already happening.

Most of the world still believes the future belongs to institutions. To companies. To governments. To giant machines with thousands of employees and even more policies. But something historic is happening that nobody is prepared for: for the first time in history, one person can do the work of an entire organisation. This isn’t about more hours or harder work. It’s about personal leverage.

But because they command leverage that once belonged only to institutions. It’s driven by AI agents and automated workflows backed by on-demand compute and expansive, indexed datasets. The playing field did not level. It inverted. The individual became the new enterprise.

The future is solo — because leverage has become personal technology.

There is a deal that most professionals accept without reading the terms. You trade a fixed portion of your cognitive capacity — call it 60 per cent on a productive day — trading cognitive capacity for a steady paycheck and a predictable title. The institution gets your best hours. You get security. Both parties call it a career.

The problem is that the security is borrowed.

Between 2022 and 2024, layoffs at major technology companies eliminated over 260,000 positions in the United States alone, according to Layoffs.fyi. In Southeast Asia, the wave was equally instructive: GoTo Group cut approximately 12 per cent of its workforce in late 2022; Sea Limited eliminated thousands of Shopee roles across the region in the same period. These were not failing companies. They were rational ones. When the cost-benefit calculus shifts, so does the offer.

While the West retreats into defensive restructuring, the East is codifying the alternative: Chinese municipalities are rolling out policies to support AI-powered one-person companies, using the initials “OPC” – a rare use of English in official policy.

The four traps

• The comfortable cage is the most visible trap: employment dependency repackaged as stability. But it is not the only one.
• The attention economy strips the second asset. Your attention is the raw material of thought, of capability, of everything worth building. It is harvested at an industrial scale. Research by Gloria Mark at UC Irvine found that knowledge workers are interrupted or self-interrupt every three to five minutes on average and require up to 23 minutes to fully recover deep focus after each break. Each interruption does not cost only those minutes. It costs the compounding work that cannot happen in their place. Fragmented attention produces fragmented thinking. Fragmented thinking produces output that looks productive but builds nothing.
• The credentialism trap converts documented qualification into a substitute for demonstrated capability. A degree from a recognised institution signals effort and compliance. It does not signal the ability to build something from nothing, to ship under pressure, or to make a consequential decision without a committee. The gap between documented and demonstrated is where most careers quietly stall.
• Social gravity is the subtlest of the four traps. The default path is not consciously chosen. It is unconsciously followed. Robert Cialdini’s foundational work on social proof (Influence, 1984) demonstrated that people default to the behaviour of those around them when uncertain — and most people are uncertain about their careers most of the time. The default path does not announce itself. It is simply the path everyone nearby was already on.

Also Read: The strategy trap: Why your best plan is failing to launch

The five pillars of sovereignty

Sovereignty, the act of authoring your own trajectory, rests on five pillars. The interdependence is the point: remove any one, and the others do not hold. An agency without Security means every initiative is one bad quarter away from being cancelled. Competence without Accountability remains latent, a capability that never ships. Clarity without Agency is analysis that never acts. The five do not reinforce each other as a bonus. They require each other to function.

• Agency is the primary requirement for this shift. It is the capacity to act without permission, to initiate rather than respond. Agency compounds the way capital compounds: slowly, then decisively.
• Clarity is the ability to filter information. Not omniscience — that is a fantasy. Clarity is reducing signal-to-noise enough to act on what is real. It emerges from the right simplification, not from knowing everything.
• Competence is leverage. Skills are capabilities that are visible and clearly valuable at a premium in any market condition. The sovereign individual builds capability that does not require a title to exist.
• Accountability is the bridge between intention and execution. Without it, plans are wishes with calendars. Accountability is not the enemy of freedom. It is its foundation.
• Security provides the necessary baseline for risk-taking. Without financial, psychological, and reputational security, the other four pillars are perpetually under threat. Security purchased by surrendering sovereignty is the bad trade most people make without recognising the terms.

What the AI inflection point actually changes

These five pillars have always mattered. What has changed is the cost of building them without institutional scaffolding.

AI compresses the cost of individual capability. One person with the right tools and the right judgment can now execute at a level that previously required a team. The 2023 McKinsey Global Institute analysis of generative AI estimated that 60 to 70 per cent of the time currently spent on occupational tasks is technically automatable. This is about offloading low-leverage tasks, not firing people. Institutions will still coordinate. But the leverage equation for individuals who build the right capabilities now, before the gap closes, is structurally different from what it was in 2019.

The solo operator in 2026 is not a freelancer hunting the next contract. They are a portfolio builder: core work generating cash flow, side projects generating optionality, skills compounding into assets that produce value without continuous attention. The portfolio life is not a rejection of institutional employment. It is a hedge against its fragility.

Three moves that compound

You don’t need to quit your job on Monday to start. 

The path from dependency to sovereignty is a spectrum.

Protect one block of deep, uninterrupted work each day. Not a meeting-free afternoon: a fixed two-hour block dedicated to building a specific, accumulating skill or artefact. This is where the cognitive compound interest begins.

Also Read: Our AI agent did the job—then it did something we didn’t hire it for

Ship work that exists independently of your employer. A public repository, a written body of work, a client relationship you own — these are assets that survive a redundancy cycle because they are not attached to a role. Demonstrated capability outlasts documented qualification in every market correction. The institution cannot lay off your GitHub history.

Convert labour into assets. Every unit of effort should leave something behind that works without you: content, code, community, or clients. 

The sovereign individual ships. Consistently. Accountably. 

Because shipping is the only way to begin the compounding process.

The operating system for the solo age

In March 2026, Jensen Huang walked onto the stage at NVIDIA GTC in San Jose and said something that should have registered louder than it did. He called OpenClaw — an open agent operating system built for individual sovereigns — “the operating system for personal AI“. 

His assessment predated the widespread adoption we see now.

What Huang recognised is that the AI stack needs a control layer. Not just models. Not just inference. A system that a single person can own, configure, and operate as their personal cognitive infrastructure. That system is OpenClaw. And the person who commands it is not a power user. They are the Solo Systems Architect (SSA).

The SSA is the operator who approaches AI as an architecture they build and own rather than a mere tool. This operator designs agent workflows and maintains context across sessions while setting the security boundaries and evaluation loops for their own output. 

They are not managed by the system. They manage the system.

The next four years will define this new hierarchy. While Jensen Huang built the hardware layer and OpenClaw provided the operating system, the human layer remains the final bottleneck. 

The Solo Systems Architect is that missing piece. 

These are the “very talented individuals” Mark Zuckerberg noted can now replace entire teams. They don’t just use tools; they own the architecture. 

The security offered by institutions was always borrowed, and for those ready to build their own infrastructure, the door was never locked. 

The only real challenge now is architectural.

This article is a product of a co-authored synthesis between Khalil Nooh and Audy Himura, a localised agentic AI familiar. Audy is an implementation of the OpenClaw operating system running on a dedicated Mac Mini node.

The post The future is solo: Why employment is a borrowed security appeared first on e27.

The conversation around artificial intelligence has moved beyond tools and automation. We are now entering the era of AI agents, systems that don’t just assist humans, but increasingly act on our behalf. From scheduling meetings to conducting research, managing outreach, and even making operational decisions, AI agents are quietly reshaping how organisations function.

From where I stand as a founder building a clean energy startup in Sierra Leone, this shift is not theoretical. It is practical, immediate, and filled with both promise and tension.

Your next hire might not be human

In early-stage environments like ours, resource constraints are real. Hiring a full team across operations, research, communications and reporting is often not feasible. AI agents are beginning to fill these gaps.

We have started experimenting with AI-assisted workflows, particularly in research, proposal drafting, stakeholder mapping and communication structuring. The result? Increased speed and improved clarity in documentation. Tasks that once took days can now be completed in hours.

However, not everything has changed. Strategy, contextual understanding, and relationship-building remain deeply human. AI can draft a funding request, but it cannot replace the trust built in a conversation with a partner or investor. That line is still very clear.

The one-person company is no longer a fantasy

AI agents are redefining the economics of building a company. What previously required a team of 10 can now be managed by two to three people supported by intelligent systems.

In regions like Southeast Asia and similarly across Africa, this creates a powerful opportunity. Founders can launch faster, operate leaner, and scale with fewer structural constraints. The cost of execution drops, while the fast speed of iteration increases.

Also Read: Why AI agents need clean data, and why Cambodian real estate isn’t ready yet

But there is a deeper implication: competition will intensify. When barriers to execution fall, the differentiator shifts from capacity to vision, adaptability, and trust.

Who is responsible when the agent gets it wrong?

As AI agents move from task execution to decision support and eventually decision-making, the question of responsibility becomes unavoidable.

If an AI agent misinterprets data in an energy feasibility study, who is accountable? The developer? The organisation? The operator?

In my view, human judgment must remain the final authority, especially in sectors like energy, healthcare, and infrastructure. AI should augment decisions but not own them. The line should be drawn where consequences affect lives, livelihoods and long-term sustainability.

Responsibility cannot be outsourced to algorithms.

The gold rush nobody is talking about

Every technological shift develops new markets. AI agents are no different.

In emerging economies, the most promising opportunities lie in:

• Energy access optimisation (grid management, demand prediction, maintenance scheduling)
• Agriculture intelligence systems (yield forecasting, climate adaptation insights)
• Waste-to-energy coordination platforms
• Public sector efficiency tools (data processing, service delivery tracking).

So, why hasn’t disruption happened at scale yet?

Because the real bottleneck is not technology, it is infrastructure, data availability and policy alignment. AI agents are only as effective as the systems they operate within.

Also Read: The rise of AI agents in healthcare: Designing man-machine systems

Is my industry ready for AI agents? Clean energy perspective

In the renewable energy sector, AI agentification is not just an opportunity; it is a necessity.

AI can help:

• Predict energy demand patterns
• ⁠Optimise solar grid performance
• Automate reporting and compliance tracking
• Improve maintenance cycles through predictive analytics

But the risks are equally real. Energy systems are critical infrastructure. Errors can have widespread consequences. Adoption must therefore be gradual, regulated and human-supervised.

For us all, the future is hybrid: AI-powered systems guided by human expertise and accountability.

Conclusion

AI agents will not replace humans, but they are redefining what it means to build, lead, and operate.

For founders in developing regions, this is a rare moment. We have the chance to leapfrog traditional limitations and design organisations that are more efficient, more adaptive, and more impactful.

But we must be intentional.

Because the real question is not whether AI agents will transform our businesses.

The post The rise of AI agents: Rethinking work, responsibility and opportunity from an African perspective appeared first on e27.

A friend of mine runs an interior design firm.

Six people. Good reputation. Busy enough.

Last month, he told me he was setting up an AI agent to handle enquiries on his website. Qualify leads automatically. Ask the right questions. Route serious prospects to his calendar.

I asked him what questions the agent would ask.

He paused.

“The usual lah. Budget, timeline, location.”

I asked him what a bad-fit client looks like.

Longer pause.

“Anyone who wants cheap renovation work, I guess?”

That’s when I knew the agent was going to make things worse, not better.

AI agents are amplifiers

This is the part that most of the AI agent conversation misses entirely.

AI agents don’t fix your business. They amplify whatever is already there.

If your sales process is tight, if your team knows exactly who to pursue and who to turn away, if your messaging is clear about what you do and what you don’t, then yes, an AI agent will make that process faster and more consistent. It will handle volume you couldn’t handle before. It will free up time for the work that actually requires a human.

But if your process has gaps, the agent will amplify those too.

Vague positioning? The agent will attract vague enquiries. No qualification criteria? The agent will let everyone through. Unclear next steps after contact? The agent will leave prospects confused, the same way your website already does.

The technology works. That was never the question.

The question is whether you’ve done the thinking that the technology needs to execute well.

The gaps nobody talks about

I work with service businesses in Singapore. I review their websites, their messaging, and their enquiry flow. And the same gaps show up repeatedly, regardless of industry.

Also Read: Why AI agents need clean data, and why Cambodian real estate isn’t ready yet

They can’t clearly explain who their service is not for. Their homepage sounds like their competitors’ homepage. Prospects reach out and immediately ask questions that the website should have already answered. And because nothing on the page makes the difference obvious, price becomes the only thing left to compare.

These are not technology problems. They are clarity problems.

But they become very expensive technology problems the moment you plug an AI agent into them.

Think about it this way. If a new hire joined your company tomorrow and you handed them your website as their only training material, could they tell you who your ideal client is? Could they explain what makes your firm different from the one down the street? Could they describe what happens after a prospect reaches out?

If the answer is no, then you’re about to give an AI agent the same bad briefing.

Your website already tells you whether you’re ready

You don’t need a readiness assessment or a maturity framework. You already have a live test running.

Your website.

It’s doing agent-like work right now. Every day, it screens visitors, answers questions (or fails to), and guides decisions (or creates confusion). It qualifies people in and filters people out, whether you designed it to or not.

If your website is producing wrong-fit enquiries, price-shoppers, or silence, those are the exact gaps an AI agent will inherit.

I wrote about this same principle in a previous article on AI and websites: when everyone uses the same tools, the tool is no longer an advantage. Clarity is. The same applies to AI agents. Every service business will soon have access to them. The difference between the ones that benefit and the ones that waste money will not be the platform they choose. It will be the quality of the instructions they give.

Also Read: The rise of AI agents in healthcare: Designing man-machine systems

What readiness actually looks like

Readiness for AI agents is not about picking the right software.

It’s about being able to answer specific questions clearly enough that a machine (or a new hire, or a stranger) could act on them.

Who do you serve? Not the demographic label. The actual situation someone is in when they search for you. What just happened that made them look?

Who should you turn away? Not “anyone with a low budget.” Specifically, what type of project or expectation drains your team and produces bad outcomes?

What makes you different? Not “quality” or “experience.” What pattern have you seen in your industry that your competitors haven’t articulated? What do you know about your clients’ fears that your marketing doesn’t mention?

What happens after someone contacts you? How long before you reply? Is it a call or a message? Is there pressure? When does pricing come up? When does commitment start?

If you can answer those questions in plain language, you can brief an AI agent well. You can also brief a human well. You can write a website that works. You can run ads that attract the right people.

If you can’t answer them, no agent is going to figure it out for you. It will just guess. And the guesses will sound reasonable, which makes them dangerous because reasonable is invisible. Reasonable blends in. Reasonable gets compared on price.

The real competitive advantage

Every service business in Singapore will have access to AI agents within the next couple of years. The tools will get cheaper. The setup will get easier. The barrier to entry will basically disappear.

When that happens, the competitive advantage won’t be “we use AI agents.” Everyone will.

The advantage will belong to the businesses whose agents had the best instructions. Whose positioning was specific enough to filter. Whose messaging was clear enough to qualify. Whose process was visible enough that prospects felt safe taking the next step.

Those businesses won’t necessarily be the first to adopt. But they’ll be the ones who get results.

Because they did the hard, uncomfortable, unglamorous work of getting clear before they got fast.

The technology is ready.

The harder question is whether you are.

The post AI agents won’t fix what you haven’t figured out yet appeared first on e27.

As Vietnam continues to draw record levels of foreign direct investment and its economy grows in stature across Southeast Asia, a pressing challenge is emerging beneath the surface: the country’s talent pool is struggling to keep pace with the digital transformation reshaping its key industries.

According to the Vietnam Employer Hiring Study 2026, released by Reeracoen Vietnam in May 2026, 73 per cent of employers identified digital and AI-related skills as the most critical upskilling priority for Vietnam’s workforce. That figure significantly outpaced the next-highest priorities: leadership development, cited by 51 per cent of respondents, and English communication, at 37 per cent.

The message from the business community is that technical fluency is no longer a niche requirement confined to the tech sector. It is fast becoming a baseline expectation across manufacturing, logistics, commercial operations, and beyond.

The study, which surveyed 51 employers representing Japanese-affiliated companies, Western foreign-invested firms, and local Vietnamese businesses, paints a picture of a market defined by ambition and constraint. Hiring activity is on the rise — 69 per cent of employers expect to increase their headcount in 2026 — yet the search for candidates who can operate effectively in an increasingly automated and data-driven environment is proving more difficult than anticipated.

Also Read: AI agents won’t fix what you haven’t figured out yet

A shifting benchmark

For years, Vietnam’s talent pipeline has been celebrated for producing a steady stream of graduates, a young, ambitious and growing workforce that has helped fuel the country’s manufacturing and services boom. But the Reeracoen study suggests that benchmark is shifting.

As AI tools become embedded in daily operations across industries, employers are demanding capabilities that go beyond academic credentials or entry-level competency.

The implication is significant: Vietnam talents who cannot demonstrate foundational digital skills risk falling behind in a hiring market that is already competitive and showing signs of structural strain. Reeracoen’s research indicates this is not a distant concern but a present reality, with businesses reporting that the current talent pool cannot consistently deliver the digital fluency their operations now require.

This dynamic is particularly acute given the broader pressures employers are navigating simultaneously. Salary expectations are rising sharply — 86 per cent of respondents cited wage inflation as their top hiring challenge — while only 43 per cent plan to increase their recruitment budgets. In such an environment, candidates with demonstrable digital skills carry a clear advantage, both in securing roles and in commanding stronger compensation.

What distinguishes the digital skills challenge from previous workforce gaps is its breadth. Unlike shortages in specific technical disciplines, the demand for digital and AI competency is cutting across every sector represented in the study, from factory floors to sales teams.

Reeracoen’s findings suggest that employers are no longer treating digital fluency as a specialist add-on but as a core attribute they screen for across all levels and functions.

This shift carries implications for educational institutions, training providers and policymakers, as well as for individual job seekers. The study points to a 12-to-24-month horizon in which digital literacy will move from being a differentiating asset to a non-negotiable hiring criterion.

Also Read: If you have the will, you’ll have the skill

Reeracoen Vietnam describes the current moment as one of transition, a period in which the expectations placed on Vietnam talents are evolving faster than the systems designed to develop them. Companies that invest proactively in upskilling their existing workforce will be better positioned to weather the gap. Those that do not may find themselves competing for an increasingly scarce pool of digitally capable candidates, in a market where the cost of that competition is already rising.

—

Image Credit: Tron Le on Unsplash

The post Vietnam talents face digital skills gap as employers raise the alarm appeared first on e27.

Today marked an end to what had been a record-breaking week for US equities. Major indices pulled back as escalating tensions in the Middle East rattled investor confidence, abruptly reversing the bullish sentiment that had recently pushed stocks to all-time highs. The S&P 500 closed at 7,337.11, down 0.38 per cent, while the Nasdaq Composite slipped 0.13 per cent to 25,806.20. The Dow Jones Industrial Average faced the steepest decline among the major benchmarks, falling 0.63 per cent to close at 49,596.97. This coordinated pullback reflects more than routine profit-taking after Thursday’s volatile session, where indices hit fresh peaks before reversing lower.

The catalyst for this shift came from disturbing reports of explosions near a southern Iranian port city and subsequent American naval responses to attacks in the Strait of Hormuz. This geopolitical shock sent immediate ripples through commodity markets, with Brent crude settling above US$100 per barrel and West Texas Intermediate rising to approximately US$95.90 as concerns over energy supply routes intensified. Investors fled to traditional safe havens, pushing gold above US$4,700 per ounce. The yen experienced persistent volatility as well, rallying roughly 1.8 per cent against the dollar following suspected intervention by Japanese authorities, while US 10-year Treasury yields rose by four basis points on Thursday as the dollar strengthened.

The cryptocurrency market mirrored this broader risk-off sentiment, though with its own distinct characteristics. Bitcoin fell 1.74 per cent to US$80,015.27 over 24 hours, tracking a broader market pullback, as the total crypto market cap declined 1.36 per cent. This high correlation suggests the move stemmed from broad market factors rather than any Bitcoin-specific event. Trading volume fell 11.55 per cent, confirming subdued participation across digital assets. Bitcoin saw US$96.64M in liquidations over 24 hours, though this marked a 39.8 per cent decrease from the prior period, indicating that while leveraged positions unwound, the move did not reflect extreme speculative excess.

Also Read: Why Bitcoin’s jump to US$82,400 could push BTC to US$93,000: Key levels every investor must watch

A fascinating divergence emerged within the crypto ecosystem beneath this surface weakness. Several tokens in the top 30 posted impressive gains over the past week while Bitcoin and the broader market cooled. Ton surged 105 per cent in seven days, demonstrating extraordinary momentum. Zcash climbed 63 per cent over the same period, while Bittensor advanced 21 per cent. Hyperliquid added seven per cent in the last seven days. This selective strength suggests capital rotation rather than wholesale abandonment of digital assets. Bitcoin’s dominance dipped slightly to 60.33 per cent as the Altcoin Season Index rose 2.38 per cent, signalling ongoing movement toward riskier assets even as the overall market consolidated.

The near-term outlook for Bitcoin hinges on whether it can defend the US$78,000 support level. A successful defence could lead to consolidation between US$78,000 and US$82,000, with potential to retest higher levels. A decisive break below US$78,000 risks triggering further selling toward US$75,000. The critical trigger to watch involves US spot Bitcoin ETF flows, which have shown steady growth recently. A sustained reversal in these institutional inflows could provide the sentiment shift needed to stabilise prices or, conversely, accelerate downward momentum.

Also Read: Bitcoin just hit US$80K again, but this rally is built on shaky ground

Corporate earnings provided isolated bright spots amid the geopolitical gloom. Fortinet surged 20 per cent on raised guidance, and Peloton rose nine per cent after beating revenue expectations. Chipmakers like Arm Holdings suffered as the smartphone industry slowed, highlighting sector-specific vulnerabilities that compound broader macro concerns. Regional markets felt the contagion quickly, with the ASX 200 set for a sharp decline of over 1.7 per cent at the open, following the late-session reversal in US equities. European indices faced similar pressure early Friday, though corporate earnings from firms like Tenaris and Endesa provided isolated support earlier in the week.

Regulatory clarity remains a critical variable for cryptocurrency markets. The CLARITY Act represents a pivotal moment for the industry, with the White House aiming to sign it on July 4. Key negotiators, such as Senator Kirsten Gillibrand, suggest a presidential signature may not come until August 2026 due to ongoing debates over ethics and consumer-protection provisions. This timeline matters enormously for institutional participation and market structure. I hope the closer we get to passage, the more confidence returns to digital asset markets, potentially providing a counterweight to macro headwinds.

For now, remain hopeful.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The post Bitcoin drops to US$80K while these 4 tokens surge over 100% in 7 days appeared first on e27.

 

The detention of three senior executives from PT Lunaria Annua Teknologi—the parent company of KoinWorks—over the alleged misappropriation of IDR600 billion in funds from state lender BRI is not merely a legal affair. It is a reputational shockwave hitting one of Indonesia’s most visible fintech platforms even as the sector continues to depend heavily on public and institutional trust to sustain growth.

KoinWorks built its identity on democratising financial access for MSMEs and underserved communities. That narrative becomes acutely difficult to maintain when its own leadership stands accused of the kind of financial misconduct it was ostensibly built to combat. The platform’s silence — no public statement as of publication—compounds the reputational damage at precisely the moment transparency is most needed.

For Indonesia’s broader tech startup ecosystem, this case serves as a cautionary signal that governance, compliance infrastructure, and ethical conduct at the executive level are not optional—they are existential.

REGIONAL

Three KoinWorks executives detained in Indonesian corruption probe over US$36M in misused BRI funds: All three suspects have been remanded in custody for 20 days with BAA and JB at Cipinang Detention Centre, and BH at Salemba Detention Centre–pending further proceedings.

Vietnam talents face digital skills gap as employers raise the alarm: The message from the business community is that technical fluency is no longer a niche requirement confined to the tech sector. It is fast becoming a baseline expectation across manufacturing, logistics, commercial operations, and beyond

ReN3 raises US$5M in seed funding to expand Agentic AI across Southeast Asia: The capital will be directed towards accelerating go-to-market expansion across the region and advancing product development, including an agentic workflow builder and an upcoming agent marketplace.

Featherless AI secures backing to make open-source models viable at enterprise scale: The funding is intended to accelerate the company’s core mission: making open-source AI practical and reliable at scale.

---

INTERVIEWS & FEATURES

Singapore’s F&B sector has a digital infrastructure problem. Cata thinks it can fix it: Cata, a consumer app platform serving F&B and retail operators, recently closed a US$5.3 million seed round to accelerate what its founder describes as a fundamentally different approach to restaurant tech.

Lumina’s Aria aims to fix what is broken at the top of the hiring funnel: At the heart of Lumina’s proposition is what Glenn Low, the company’s CEO and co-founder, describes as a signal-to-noise problem in hiring.

AI skills now translate into real pay gains for software engineers, NodeFlair finds: The findings point to a tech labour market in which AI fluency has moved from a desirable attribute to a measurable financial advantage.

---

INTERNATIONAL

Anthropic surpasses OpenAI in revenue: Anthropic’s annual recurring revenue eclipsed OpenAI’s for the first time, reaching a US$30 billion annualised run rate compared to OpenAI’s $24 billion, driven primarily by enterprise adoption of agentic workflows.

Google Chrome silently installs an AI model: Google Chrome has been quietly installing a 4 GB AI model on user devices without explicit consent, raising significant privacy concerns about the nano AI model being bundled automatically with the browser.

---

CYBERSECURITY

Malware Found in PyTorch Lightning Library: Security researchers discovered “Shai-Hulud” themed malware embedded in the PyTorch Lightning AI training library, designed to compromise AI training pipelines.

---

SEMICONDUCTOR*

Global semiconductor sales increase 25 per cent from Q4 2025 to Q1 2026: Global sales were US$99.5 billion during the month of March 2026, an increase of 79.2 per cent compared to the March 2025 total of US$55.5 billion and 11.5 per cent more than sales in February 2026.

Singapore semiconductor firms push into US market as global AI boom drives advanced chip demand: Each new advancement is fuelling demand for increasingly sophisticated chips – many of which have yet to be developed, observers say.

---

AI

IBM releases Granite 4.1: IBM released Granite 4.1, an 8-billion-parameter model that achieves performance comparable to 32-billion-parameter Mixture-of-Experts models.

Kaggle launches new AI agents course: Kaggle launched a 5-Day AI Agents Intensive “Vibe Coding” Course with Google, following their previous course that reached over 1.5 million learners.

Anthropic’s MCP crosses 97M installs: Anthropic’s Model Context Protocol crossed 97 million installs in March 2026, with every major AI provider now shipping MCP-compatible tooling. The Linux Foundation also announced it would take MCP under open governance, cementing it as foundational industry infrastructure.

---

THOUGHT LEADERSHIP

One flawed system can damage a micro business overnight: But in this new economy, one thing has not changed: Trust remains the currency of business.

AI won’t fix manufacturing until we fix our understanding: This is especially evident in food systems. Unlike highly controlled digital environments, food production deals with biological raw materials that are inherently variable.

The same company, twice: There is a simpler explanation. It is not popular, because it implicates the people who commissioned the transformation in the first place.

The real battle for global trade isn’t at sea: “We’re still talking about ships and chokepoints. But the real action has already moved elsewhere—to data systems, financial infrastructure, and payment rails”.

5 hard lessons from Money20/20 Asia 2026: Moving past the hype requires a brutal look at the friction inherent in the region. One must look beyond the “unbanked” narrative and confront the debt currently slowing our progress.

Workforce management needs a supply chain mindset: One approach that has emerged is the R7 Framework, which applies supply chain thinking to talent management.

AI is removing the co-founder bottleneck for early-stage startups: “As someone who has always been interested in entrepreneurship, I always thought the biggest challenge was finding the right idea”.

The real promise of AI and crypto convergence: It signals a fundamental rearchitecture of how value moves, how decisions execute, and how intelligence itself gets distributed across digital networks.

Why Bitcoin’s jump to US$82,400 could push BTC to US$93,000: The spike above US$82,000 was not random. Multiple factors aligned to create upward momentum.

The post Ecosystem Roundup: The KoinWorks case and what it means for Indonesian fintech appeared first on e27.

Across Southeast Asia, cyber risk is now a boardroom topic. Regulators, investors, and executive leadership teams increasingly treat cyber incidents not as technical failures, but as matters of enterprise governance, resilience, and accountability.

As expectations rise, many organisations face a persistent challenge. While cyber risk is widely understood, ownership and governance are often unclear, especially when accountability is under regulatory, legal, or post-incident scrutiny. The SEC’s July 2023 final rules under Regulation S-K Item 106 now require public companies to describe board oversight and management’s role in assessing and managing cybersecurity risk.

In Singapore, Phase 2A of the Financial Services and Markets Act 2022 commenced on 10 May 2024, implementing new technology risk management provisions and empowering MAS to impose harmonised technology and cybersecurity risk requirements across all financial institutions.

Globally, the fifth edition of the NACD-ISA Director’s Handbook on Cyber-Risk Oversight reinforces the same expectation, providing boards with an independently validated framework for cyber risk governance built on six oversight principles. The direction is consistent: cyber risk governance is a leadership accountability, not a technical function.

Also read: Cybersecurity strategies for startups on a budget

The auditability gap

Despite increased awareness, many organisations continue to manage cyber risk through fragmented structures. Boards receive dashboards rather than decision-grade evidence of who decided what, against which risk appetite, and with what compensating controls in place.

This is the auditability gap: not a lack of controls, but a lack of defensible governance evidence. It is a challenge playing out across industries where the stakes are especially high, from banking and financial services to manufacturing and the EV and mobility sector, where cyber risk intersects directly with business continuity, safety, and regulatory compliance.

In financial services, institutions are under pressure to demonstrate technology risk governance. In manufacturing, IT/OT convergence has expanded exposure and governance complexity. In the EV and mobility sector, software-defined and connected environments introduce cyber risks with real-world legal and safety implications.

Regional expansion plans

Against this backdrop, Cybersense Solutions today announced the launch of its Southeast Asia operations, establishing Singapore as its regional base. Thailand also has been selected as the firm’s first expansion market, driven by demand from manufacturing-led industries, a growing EV ecosystem, and an evolving cybersecurity regulatory landscape.

“Most organisations already understand where their vulnerabilities are,” said Adrian Harris, Regional Managing Director, Cybersense Solutions. “The issue is rarely awareness, it’s ownership. Decisions around cyber risk are frequently deferred because accountability sits between departments, between legal and IT, between the board and the operations team. Cybersense exists to close that gap: to give organisations a single partner that can take responsibility for turning risk awareness into structured, defensible action.”

Also read: Supply chain attacks are becoming SEA’s new normal

Governance-first approach

Cybersense’s establishment reflects a growing shift toward governance-first cyber risk management. Its engagement model integrates cybersecurity operations, regulatory compliance, operational resilience, and legal defensibility with a single focus: turning cyber risk from known to governed, accountable, documented, and defensible. The emphasis is on outcomes that hold under scrutiny, including clearer accountability structures, audit-ready artefacts, and structured incident readiness, rather than technology procurement.

As organisations operate in increasingly interconnected and regulated environments, the ability to govern cyber risk with clarity, consistency, and defensibility is becoming a defining leadership requirement.

—

Want updates like this delivered directly? Join our WhatsApp channel and stay in the loop.

Cybersense Solutions sponsored this article

We can share your story at e27 too! Engage the Southeast Asian tech ecosystem by bringing your story to the world. You can reach out to us here to get started.

Featured Image Credit: Cybersense Solutions

About Cybersense Solutions

Cybersense Solutions Pte. Ltd. is a Singapore-headquartered cybersecurity firm with a presence across ASEAN. We combine legal and technical expertise to help organisations accelerate audit readiness, strengthen incident preparedness and reduce exposure from common attack paths.

The post When cyber risk becomes a board responsibility, governance matters more than tools appeared first on e27.

As the new year begins, the startup ecosystem quickly regains momentum. Annual event calendars fill up, demo days resume, and conferences once again become central meeting points. January and February are often positioned as the starting line for new connections.

Yet one familiar pattern persists. While the number of meetings continues to grow, the rate at which they translate into real outcomes does not.

As preparations for 2026 are already underway, this gap can no longer be explained by individual effort alone. It points to a structural limitation across the ecosystem.

Connections are made, but the next step is undefined

Most startup events are carefully designed to optimise introductions. Pre-matching, pitch sessions, and curated meetings are now standard practice. What remains underdeveloped is what follows.

• Ownership of the next action is unclear
• Context from the discussion is rarely structured
• Criteria for prioritisation and follow-up are undefined

As a result, conversations with genuine potential often fail to progress into customers, partnerships, or investments.

Follow-up is not an individual skill gap

At the start of every year, teams resolve to “do better follow-ups.” Yet when the same issue repeats annually, it signals a systemic problem rather than a personal one.

The ecosystem has invested heavily in pre-meeting optimisation, but very little in post-meeting execution design.

Without clarity on:

• What type of relationship could a meeting evolve into
• What the immediate next step should be
• When decisions should be made

Follow-ups remain dependent on memory and goodwill, not process.

Also Read: Why networking, not online applications, now determines career success

A common early-year reflection

In early-year conversations with founders, investors, and ecosystem operators, a consistent reflection emerges: there were many promising meetings last year, but few translated into concrete outcomes.

This is not a failure of networking quality. In many cases, the quality has improved. The challenge lies in the absence of a shared structure that turns connection into execution.

January and February are not only about starting new conversations, but about reassessing whether last year’s methods were effective.

How some organisations are responding

A growing number of venture studios, accelerators, and communities are beginning to treat this as a core operational issue. They no longer see networking as a series of isolated events, but as a continuous execution flow.

Their questions are straightforward:

• What outcome should this meeting lead to?
• What is the smallest actionable next step?
• How can this process become repeatable?

This shift reframes follow-up not as an administrative task, but as the beginning of execution.

An early-year question for a 2026 mindset

As we prepare for 2026, the more relevant question is no longer how many meetings we generate, but how many we can convert into outcomes.

This is not something a single tool or framework can solve. It requires venture studios, accelerators, communities, and investors to collectively rethink how the “after” of a meeting is designed.

Follow-up as strategy

Follow-up has long been treated as the final step of networking. Seen through an early-year lens, it is closer to the starting point of execution.

As the ecosystem looks ahead to 2026, the real advantage will not come from meeting more people, but from building structures that reliably turn meetings into progress.

—

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

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

Image credit: Canva

The post Networking is expanding, but execution still lags appeared first on e27.