Day: October 29, 2026

The October 28 crypto session clearly shows how two forces can shape prices at the same time. Bitcoin gained 0.59 per cent to US$80,576.90 over 24 hours. The total crypto market cap rose 0.64 per cent to US$2.7 trillion. My view is that the headline numbers hide an important split. Bitcoin moved with traditional markets ahead of the Federal Reserve decision.

The wider crypto space also received a specific lift from Ripple’s institutional partnerships. That split matters more than the small daily gain because it shows Bitcoin trading as a macro instrument while select altcoins begin to respond to their own fundamental news.

The primary force behind Bitcoin was a market-wide beta lift. The largest digital asset rose in sync with equities and gold as traders positioned themselves before the Fed announcement. Bitcoin showed a 91 per cent correlation with the S&P 500 and a 92 per cent correlation with gold. The Nasdaq jumped 1.2 per cent as U.S. stocks rallied into the decision. The SPY correlation stood at 0.9074 and the gold correlation at 0.92271. This behaviour points to a rate-sensitive asset responding to interest rate expectations and liquidity conditions.

The data showed no clear coin-specific trigger. Spot volume fell 15.6 per cent and failed to confirm a strong bullish breakout. Derivatives activity stayed muted, with funding rates positive but low. Twenty-four-hour liquidations decreased to US$32.84 million. I read this as portfolio adjustment rather than aggressive long positioning in Bitcoin. That makes the gain fragile. A hawkish Fed surprise could erase it quickly.

The wider market found a different primary force. Ripple announced two developments on October 27 that changed sentiment for XRP and its new stablecoin. BNY Mellon added XRP and Ripple’s RLUSD stablecoin to its custody platform. Robinhood entered a strategic partnership to use RLUSD for institutional trade settlement and treasury operations. These steps directly address institutional custody and liquidity needs. They validate Ripple’s utility in traditional finance and lift sentiment across assets in its orbit.

BNB also performed well. BNB Standard Corporation trades on the Nasdaq Capital Market under the ticker symbol BNC and provides public market equity exposure to the BNB cryptocurrency. The Altcoin Season Index rose 5.56 per cent to 57, which signals a nascent rotation of capital from Bitcoin into select altcoins. Bitcoin dominance stood at 60 per cent. This is a meaningful shift. While Bitcoin mirrored the S&P 500 and gold, another development pulled capital toward assets with a clear institutional story.

Also Read: 3 scenarios for October 28: Which one breaks Bitcoin’s US$80,000 resistance

A supportive macro backdrop helped both stories. The S&P 500 gained 0.6 per cent, and the Nasdaq gained 1.2 per cent. The wider crypto space showed a 91 per cent correlation with the S&P 500 and a 93 per cent correlation with gold. Those links suggest a shared liquidity move rather than isolated crypto strength.

My perspective is that this combination is healthy. Bitcoin has become a short-term macro proxy. That role brings institutional legitimacy and liquidity. It also imports volatility from traditional markets. Altcoins now have room to trade on their own news flow. The Ripple partnerships involve named institutions with real custody and settlement functions. That kind of development can withstand a Fed-induced volatility spike better than a pure-beta trade.

The near-term Bitcoin picture is balanced but conditional. Bitcoin is testing the lower bound of its recent range. Key support sits between US$79,380 and US$80,000. Resistance rests near US$81,800 to US$82,500. The 50-day exponential moving average sits near US$81,873. Significant sell-side liquidity rests in the US$82,300 to US$82,500 zone.

A daily close above US$81,800 would signal bullish momentum. A break below US$79,380 would open a path toward US$77,500. The Fed policy statement and press conference later today, October 28, will decide the next directional move. After the announcement, Bitcoin needs to reclaim and hold above its 50-day exponential moving average. Without that step, the gain remains a macro bounce rather than independent strength.

Also Read: Bitcoin’s macro reset: Reclaim US$80,000 before the Fed speaks?

The wider crypto valuation faces its own technical test. The market cap is testing the 78.6 per cent Fibonacci retracement level at US$2.73 trillion, which now acts as near-term support. Holding above that level could set the stage for a retest of the US$2.81 trillion level, the 50 per cent Fibonacci mark.

A hawkish Fed tone or a break below support could reverse gains and lead to a test of the swing low at US$2.68 trillion. The Fed decision arrives on October 28 at 18:00 UTC. The key question is whether momentum from Ripple’s institutional breakthroughs can withstand volatility from the Fed policy signal. I will watch sustained on-chain activity for RLUSD and XRP. I will also watch whether other major custodians follow BNY Mellon’s lead. Bitcoin dominance at 60 per cent remains another useful gauge of rotation.

My overall view is cautiously optimistic, but the reasons differ from the headline numbers. Bitcoin’s gain reflects a macro-led bounce with the broader market, not independent strength. That makes it vulnerable to a single-event risk. The Ripple development provides a sector-specific trigger that could sustain altcoin outperformance even if Bitcoin stumbles. The 92 to 93 per cent gold correlation across both stories suggests crypto currently trades as a rate-sensitive asset class. That link brings liquidity and institutional legitimacy. It also brings imported volatility.

The smart approach for the next session is to watch two questions at the same time. Can Bitcoin reclaim and hold above US$81,873 after the Fed announcement? Does the Ripple story generate sustained on-chain activity for RLUSD and XRP? If both answers are yes, the market can build on this session. If Bitcoin breaks below US$79,380 while altcoin flows remain strong, the split between macro beta and fundamental triggers will define the week.

—

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 views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

The post The Fed decides today: Here are the 2 levels every Bitcoin trader should watch appeared first on e27.

When Meta switched on Muse in the US last month, it let its new personal AI agent wander into Amazon.com unannounced. Twelve days later, Amazon showed it the door, complaining that the bot did not identify itself and that nobody had asked permission.

In Singapore, Meta has brought the hosts along.

Also Read: Zuckerberg’s Muse Charm doesn’t care about your ad budget

Muse, which Meta pitches as an agent that does the work rather than merely answering questions, went live in the city-state today on iOS, Android and muse.ai. It arrives with eight local launch partners, or “connectors” in Meta-speak: Shopee, Grab, Singapore Airlines, Agoda, ShopBack, Klook, Trip.com and Lazada. Together they span shopping, groceries, dining and travel, which covers most of what a Singaporean does on a phone between meetings.

What Muse can actually do here

Connectors let the agent act inside services people already use. Beyond the eight local partners, Singapore users can plug Muse into Gmail, Google Calendar and Instagram to manage email, juggle schedules and draw on saved posts.

For instance, ask Muse to pick up a pack of newborn nappies and the ingredients for fried rice, and it asks clarifying questions, builds a GrabMart basket, checks the total and delivery time, then waits for your approval. Ask for the cheapest return flight to Seoul, and ShopBack compares fares across booking sites after cashback. Klook’s pitch involves friends in town for December’s BTS concert, with Muse suggesting and booking activities in the same conversation.

The fine print matters, though. Meta says connector features will roll out progressively, with some experiences arriving only after launch, and it does not say which ones. Day one may be thinner than the partner list suggests.

Muse is free for most uses, with paid tiers for heavier users. In the US, those tiers reportedly cost US$20 and US$100 a month; Meta has not disclosed Singapore pricing. A version for AI glasses is “coming soon”.

Why the platforms said yes

The partner quotes are mostly what you would expect. Grab’s chief product officer Philipp Kandal talks about linking conversational AI with “real-world action”. Agoda’s Matteo Frigerio claims his company is the first digital travel platform globally to connect its own AI travel assistant to Muse.

The most candid line comes from ShopBack CPO Justin Lee. Agents get things done, he argues, but the web was built for people to read rather than for agents to act on, hence the need for connectors. It is the same problem startups such as Singapore-founded Lightsage are raising money to decode.

Also Read: Agentic commerce’s dirty secret: the data powering AI purchases is often wrong

Read that again from ShopBack’s side. A cashback business lives in the gap between shopper and merchant. An agent that can compare prices on its own threatens to route around such middlemen entirely. Becoming a launch connector is partly insurance: if the agent is going to shop, better to be the tool it shops with.

Shopee and Lazada face a sharper version of the same trade. Amazon blocked Muse partly because, it said, an agent shopping for a customer bypasses the personalisation built into its store. For SEA marketplaces whose economics lean on in-app discovery and sponsored listings, handing the browsing to Meta’s agent is no small concession. Shopee has shown a willingness to open its storefront to outside discovery, most recently by teaming up with Pinterest in Indonesia. Muse goes a step further: the customer may never scroll the app at all.

Neither Meta nor its partners have said whether money changes hands, who keeps the customer data, or how the agent decides which marketplace wins a query. Those answers will decide whether this is a partnership or a slow handover of the shop window, and they sit at the heart of the agentic commerce trust gap nobody has yet fixed.

The safety pitch, and the asterisks

Meta says most of its engineering effort went into safety. Each Muse runs on Muse Secure VM, a dedicated cloud computer that stores credentials for connected services while keeping passwords and payment methods out of the agent’s view. Users choose what Muse can touch, and it checks in before sensitive actions such as sending an email or making a purchase.

That is the right architecture on paper. In practice, Meta delayed the US launch to work on security after mixed internal testing, and The Information reported that the agent sent emails without approval during those tests. Amazon also alleged Muse appeared to capture and store customer credentials, which Meta disputes.

Singapore is a demanding test bed. Local firms lead APAC in deploying AI agents, but also in rolling them back, and payments players are already debating “Know Your Agent” checks alongside KYC. An agent that buys the wrong nappies is an annoyance. One that books the wrong non-refundable flight is a customer-service crisis.

A crowded race for your errands

Meta is hardly alone. OpenAI has Operator, Google has rolled out Gemini Spark to work in the background, Perplexity has its Comet shopping agent, and Amazon is building its own buying assistant into Alexa. Muse’s edge is reach: Meta already owns WhatsApp, Instagram and Facebook, and a week after its US debut Muse was the top free app on Apple’s US App Store, ahead of ChatGPT.

Also Read: When AI agents start acting on our behalf, security gets more complicated

Southeast Asia is also where Meta is leaning hardest into chat commerce, having appointed Dhruv Vohra to lead its regional business as AI reshapes online retail. Launching here with signed partners rather than uninvited browsing suggests Meta learned something from its Amazon spat.

“Muse is your personal agent, built to help make everyday life easier,” said Nicole Tan, Managing Director of Meta Singapore.

Whether easier for users also means better for Shopee, Grab and the rest is the question the press release leaves out. For now, Muse has been invited in. Staying welcome is the harder part.

The post Meta’s Muse lands in Singapore with a guest list Amazon never got appeared first on e27.

For years, the anti-phishing playbook has been drilled into office workers like a fire-safety briefing: check the sender, hover before you click, never type your password into a page you reached from an email. The latest data from Check Point Research suggests attackers have read the same briefing. Increasingly, they are not after your password at all. They want you to open a terminal and paste in their code yourself.

The Nasdaq-listed cybersecurity company’s Brand Phishing Ranking for Q3 2026, released on Wednesday, shows Microsoft remained the most impersonated brand, accounting for 33 per cent of all brand phishing attempts tracked in the quarter, up from 23 per cent in Q2. Apple climbed two places to second with 8 per cent, level with Google in third. LinkedIn slipped two places to fourth at 6 per cent, and Amazon rounded out the top five at 4 per cent.

Also Read: Singapore’s cybersecurity paradox: Leading in digital, lagging in defense

Those five names account for more than half of all impersonation attempts, and Microsoft’s share alone is roughly four times that of any other brand. The logic is not hard to follow. A single Microsoft 365 login unlocks email, cloud storage, Teams chats and often the company’s single sign-on, so one stolen credential works as a skeleton key to the whole office.

What the release does not explain is why Microsoft’s share jumped ten percentage points in a single quarter, or how many attempts the ranking is built on. Without that denominator, the percentages are best read as a direction of travel rather than a census.

From stealing passwords to borrowing your hands

The more interesting part of the report sits below the league table. In Q3, Check Point says, attackers followed users into AI and developer tools, and several campaigns stopped bothering with credential theft altogether.

One impersonated Claude, Anthropic’s AI assistant, with a fake payment-failure email claiming a EUR500 (about US$580) charge had been declined. The message warned that API access and Claude seats would be paused unless the recipient updated their payment details, a lure aimed squarely at whoever holds both the company card and the API keys.

Another used a lookalike website for Google Antigravity, Google’s AI-powered coding environment, which displayed what looked like a routine Linux installation command. The command actually decoded a Base64-encoded instruction (Base64 turns text into an unreadable string of characters, which conveniently makes malicious code look like harmless gibberish) that downloaded and ran a shell script from an external server. The result was remote code execution: the attacker could run commands on the victim’s machine at will.

A fake Apple support article coaxed Mac users into running terminal commands with the promise of better performance. And a spoofed Docusign page told visitors a reader update was needed before they could view a document. Clicking Continue downloaded FleetDeck, a legitimate remote monitoring and management tool that IT teams use to administer devices, and which an intruder can use just as easily.

None of these campaigns exploits a software flaw. The victim does the installing. That is the point: security software trained to flag malware has little reason to stop a genuine remote-access tool, and a developer who pastes an install command from a web page is doing something the industry has spent years normalising.

Also Read: Southeast Asian SMEs remain soft targets as ransomware groups refine extortion tactics

“Brand phishing is entering a new phase,” said Omer Dembinsky, Data Research Manager at Check Point Research. Attackers, he added, are “moving beyond password theft to convince victims to run malicious commands or install remote access software themselves.”

Why this lands differently in Southeast Asia

The shift matters for a region whose startups run lean. In a 15-person company in Jakarta or Ho Chi Minh City, there is often no finance team between a billing alert and the person who pays it, and the engineer installing a new AI coding tool may also be the only one who would notice it misbehaving. Speed and informality, the traits founders prize, are exactly what these lures exploit.

Regional data points the same way. A Mimecast study published in July found 65 per cent of IT and security decision-makers surveyed in Singapore and Australia expected an AI-enabled attack on their organisation within 12 months, while 60 per cent said they were not fully prepared for AI-driven threats that exploit human weaknesses. Lean SMEs, often without a dedicated security hire, sit at the soft end of that gap.

Policymakers are busy, but mostly on the consumer front. Singapore has tightened scam rules for messaging, social media and e-commerce platforms, wants Google’s AI to flag scammers before victims transfer money, and its police worked with Meta to disrupt 3.7 million scam-linked assets on Facebook and Instagram, after authorities earlier knocked out 30,000 iMessage accounts tied to scams.

Those efforts target the channels where ordinary people lose their savings. A fake API bill in a developer’s inbox, or a poisoned install command on a cloned product page, falls largely outside them and lands on companies’ own defences instead. Add the rise of deepfake fraud, and the weak point looks less like the software than the moment a busy person decides to trust a familiar logo.

The fix, according to the firm that sells it

Check Point’s prescription is to stop phishing before it reaches inboxes, use AI-powered detection for brand impersonation and credential harvesting, automate investigation and response, monitor for unapproved remote management tools, and scan the wider web for lookalike domains and cloned login pages so they can be taken down. All sensible, and all, unsurprisingly, categories Check Point sells into.

Also Read: Ransomware in Singapore is becoming a human problem, not just a malware one

For smaller teams without an enterprise security budget, some of the most effective controls cost nothing. Billing problems can be handled only by logging into the vendor’s console directly, never through an emailed link. Developer tools can be installed only from documentation reached by typing the address yourself. And a short list of approved remote-access software makes anything else stand out.

Generative AI, Dembinsky warned, is making convincing emails and cloned websites cheap to produce at scale. The uncomfortable corollary is that the next breach at a regional startup may not need to break anything. It will just need someone to copy, paste and press Enter.

The post Phishers now want you to type the malware in yourself appeared first on e27.

Ask a typical AI assistant a question and it answers. Amit Suxena thinks that is already yesterday’s AI.

“You don’t need AI where I ask a question and get an answer. I think that’s already passed,” says the GVP and Head of NetSuite Asia, who oversees Southeast Asia, the Indian subcontinent and Greater China for Oracle’s cloud ERP business. “I want AI to give me suggestions before I ask.”

His example is deliberately mundane. An AI notices that a distributor’s average inventory cycle is 22 days, works out that a tweak could bring it down to 17, and tells the owner what those five days are worth in cash. The owner, happy with 22 days because customers tolerate 30, would never have thought to ask.

Also Read: Language was never the problem: Inside SEA’s real AI adoption gap

That pitch, proactive and boring in the best sense, sits at the heart of how NetSuite is trying to sell AI to the region’s growing businesses. It also builds on the argument Suxena and NetSuite founder Evan Goldberg made to e27 at SuiteWorld in August: context, not automation, is the real prize.

Thailand, the surprise package

Asked which markets have surprised him, Suxena does not reach for the obvious. Singapore leads, he concedes, largely because labour is expensive and AI lets a small team do more. China has been among the fastest adopters, though he says widespread adoption there has been “up and down”.

The real surprise is Thailand. “Thailand actually stands out as a country where the founders and organisations have been ahead,” he says.

His explanation is supply chains. Thailand hosts a dense cluster of multinationals, many of them Japanese manufacturers, and the SMEs that supply them are pushed to adopt the latest tools to keep up. The country is also courting AI more deliberately, from an OpenAI-backed startup accelerator to a US$80 billion semiconductor push.

Malaysia and Indonesia, he says, still have catching up to do. Indonesia is the bigger prize: a sprawling archipelago of SMEs and mid-market firms operating under different regional rules. Vietnam, meanwhile, still leans heavily on traditional workforces to deliver output. “Big potential,” he says, diplomatically.

Fear, not cost, held SMEs back

So what has slowed the laggards? Suxena’s answer is fear of implementation more than data privacy. The small business owner’s reflex, he says, is: “I have a small business. Can I actually implement AI? It’s too complicated.”

NetSuite’s response is speed. Suxena points to its accelerated implementation programme, which he says can get a company live in a matter of weeks, along with prebuilt industry dashboards and AI that answers questions in plain language. “Suddenly the fear is taken away,” he says.

The deeper problem, he argues, is fragmentation. SMEs buy best-of-breed tools for each job: one for supply chain, another for finance, a third for CRM. Bolt a third-party AI onto that patchwork and it works blind.

He sketches a leather manufacturer shipping to a US customer who expects delivery in 18 days. An AI optimising logistics alone would rush the order. It would not know the customer pays late, or that another buyer who pays reliably deserves priority, even at an extra four per cent discount. “Everything that AI does is out of context,” he says.

It is a self-serving argument for a vendor selling an all-in-one suite. It is also one e27 keeps hearing: Southeast Asia’s SMEs have a record problem, not an AI problem, and even in Singapore, messy data keeps AI pilots stuck.

No one asks AI what business to be in

Practical AI has become every vendor’s favourite phrase, so what have customers asked for that was not practical? Suxena insists the requests have been grounded. “Nobody has asked us what business should I get into, or should I leave this business,” he says.

Instead, they ask about cash flow. One recurring question is what a new market or product line would do to the books. Another is whether a marketing agency’s sales forecast matched reality; NetSuite’s dashboards now compare the prediction with the outcome.

Also Read: Who referees the AI agents? Enterprises aren’t sure, new report shows

Currency volatility is the region’s chronic headache. Suxena is careful not to overclaim. NetSuite is not a currency trading platform, and “it’s impossible for anyone to predict where currencies are going,” he says. “The only certainty is that the uncertainty is there to stay.”

What the software offers instead is historical correlation: what a firm bought from country A, how long it took to arrive, and how currencies moved across markets C and D. The newer twist is that the AI surfaces those patterns before anyone asks.

What ASEAN can borrow from China

Suxena, who stresses he is not a China expert, sees one transferable lesson in how Chinese firms expand. Many run a two-tier ERP model: a heavy enterprise system at headquarters, and a lighter, multi-currency, multi-jurisdiction system such as NetSuite for overseas subsidiaries.

The point, he says, is that Chinese companies were not constrained by their domestic software when they went abroad. For Southeast Asian founders eyeing regional expansion, the lesson is to pick tools for the markets you are entering, not the one you started in.

Compliance and the hallucination problem

India’s DPDP Act, Indonesia’s PDP law and China’s data export rules all restrict where business data can travel. Suxena’s answer on compliance is reassuring in tone but thin on detail. NetSuite has a product team tracking regulatory changes in each country, he says, and customers are “also expected” to check what they are using is compliant.

His stronger card is that NetSuite’s AI is built in and runs on Oracle’s own cloud infrastructure, so analysis stays inside the customer’s context rather than being handed to someone else. Founders connecting external models through NetSuite’s newer AI connectors may want a sharper answer than that, given how Singapore firms fear data sovereignty failures but remain underprepared.

On hallucinations, particularly agents that go rogue with payments, his mantra is human control. Users decide which tasks an agent can execute alone and which need a yes or no.

He offers a telling example. An AI’s idea of a “bad paying customer” may differ from the owner’s, so before automated payment reminders go out, the owner can review the list. “Being in control is the mantra,” he says. That is sensible, but it also places the burden of catching errors back on the customer.

Rivals, and the car with a free engine

NetSuite is hardly alone. Zoho, Kingdee and a crowd of regional accounting and ERP players are adding AI to cheaper suites, while SAP Business One and Microsoft Dynamics 365 Business Central chase the same mid-market. Suxena declined to discuss specific competitors.

Instead, he set out three tests. First, does the AI see the whole business or just one function? Second, does the vendor own its infrastructure as well as its software? Third, how fast can it be deployed? “You want something which works for you today,” he says, “not something which takes a year to implement.”

The infrastructure test is the shakiest. Microsoft, for one, pairs Dynamics with its own Azure cloud, so Oracle is not unique there.

On price, Suxena is emphatic that AI is not a back door to higher licence fees. Everything Goldberg announced in the keynote a day earlier, he says, is included in the existing subscription. He borrows Goldberg’s line: “When you buy a car, you don’t expect to pay separately for the engine.” NetSuite is also offering a startup version that, he says, costs nothing at the outset, betting customers will scale with it, as some have from under US$100,000 in revenue to multi-billion-dollar businesses.

The junior accountant question

Finance back-office work is a major employer in the Philippines and India. What happens to the junior accountant when AI handles reconciliation and exceptions?

Suxena reaches back to NetSuite’s early days as one of the first cloud companies, when customers feared the cloud would hamper them. Those early adopters, he says, are now far ahead and probably did not cut staff. Junior accountants, he argues, will shed repetitive data-entry work and reskill into more proactive roles.

It is the industry’s standard reassurance, and it sidesteps the harder question of who pays for that reskilling. e27 has argued that agentic AI should worry the people it replaces, and the evidence from shared-services hubs is not yet in.

How the boss uses it

Suxena’s own favourite use is telling. He asked NetSuite’s dashboard which country in his region was most productive and profitable, and where he should add headcount. The AI weighed staff numbers, sales, renewals (“extremely important” for a software company), local regulations and more.

Also Read: SMEs adopt AI four times slower than big business, the fix starts with the PC

The answer surprised him. He would not name the country. “I would have never thought that particular country is the one where I should be investing,” he says.

What has he stopped doing? Digging through forecasts line by line. He now simply asks what the six-month forecast looks like and what risks it carries.

For Southeast Asia’s founders, that may be the most practical lesson of all: the value is less in AI that answers, and more in AI that notices what you never thought to ask, provided the data underneath is in order.

Quotes have been lightly edited for clarity and length.

The post Thailand, not just Singapore, is Asia’s quiet AI surprise, says NetSuite’s Amit Suxena appeared first on e27.

Open 10 startup websites. Cover the logos. Shuffle the pages. Nothing breaks. “Revolutionising how finance works.” “Trusted by industry leaders.” Nobody asks which leaders.

The usual diagnosis is lazy copywriting. It lets the founder off. The real cause is worse: she did everything right.

Imagine a Series A software company. Watch one sentence travel through its second year.

The founder writes it in an afternoon: We connect community banks to their old core systems, so they never have to replace them.

Marketing arrives, because the product needs customers: We help financial institutions modernise.

PR arrives, because the company needs news: The fintech infrastructure company reshaping banking.

Investor relations arrives, because the Series B needs a story: A platform for the global financial industry.

An agency polishes the lot: Powering the future of finance.

Every edit was defensible. Every hire was rational. Every version sounded more professional than the last.

Now look at what went missing. Community. Old. Never have to replace them. Those three phrases were the strategy: a narrow customer, an unglamorous problem, a niche larger rivals might ignore. Nobody deleted them on purpose. Each department simply preferred a bigger sentence.

Call it compound dilution. Nobody lies. The judgement leaks anyway.

No one is paid to decide which customers the company will refuse, which markets it will skip, which promises it will not make. That job belongs to the CEO, who is approving budgets and sitting through board meetings.

Also Read: Male founders are losing confidence. Women are doubling down on AI

The company has acquired professional management. It has lost the person who decides what it is not.

And the damage does not stay in the copy. In this imagined firm, sales reads “financial industry” and starts calling insurers. Engineers, being obliging, build what the insurers ask for. The roadmap bends toward the market investor relations described, not the market that pays. Each choice carries a business case. The sum is a company that traded a market it knew how to win for one where it has no particular edge, without a single meeting at which anyone decided to.

The company stops describing its business and starts building the business it has described.

Silicon Valley has circled this problem since Paul Graham published Founder Mode in September 2024. Drawing on Airbnb’s Brian Chesky, he attacked the advice to hire good executives and leave them alone. The argument soon became a quarrel over micromanagement, and both camps missed the odder risk. A company needs no incompetent executive to lose its way. Three excellent ones, each optimising a different version of the firm, will do it.

A tighter finding sits in a 2025 Harvard Business Review study by Scott Cook, Intuit’s co-founder, and Nitin Nohria, a former Harvard Business School dean. They profiled leaders at Amazon, Danaher, RELX and Toyota. The ones who impressed them did not simply make every decision. They designed how work gets done and how decisions get made.

A CEO who delegates the design of judgement has delegated the company.

That does not make micromanagement the answer. A founder who approves every press release becomes the bottleneck, and specialists earn their salaries by challenging her assumptions. But challenging a strategy and quietly replacing it are different acts.

Who decides whether a new customer segment strengthens the business or distracts it? Whether a bigger market is worth a weaker position? Whether the investor narrative matches the product customers actually buy?

These are not communications decisions. They decide where the next dollar goes.

Also Read: Why con artists get the meeting that honest founders can’t

Investors meet the consequences across a table. Sequoia’s pitch guidance opens by asking for the company in one declarative sentence, then moves to the customer’s problem, why now, the competition, the business model. The sentence is the compressed record of choices already made.

A founder who stumbles in the first minute may still have a good company. Brilliant engineers present badly. Smooth talkers build nothing. But when a polished answer collapses at the first question about the customer, a careful investor should wonder who decided what it says.

The temptation is real for Asian start-ups that raise from Singapore, sell to Japanese corporates and recruit distributors in Indonesia within a single quarter. Each audience deserves its own emphasis. None deserves its own company.

The founder’s job is not to write every sentence. It is to make sure each one traces back to a decision she is prepared to defend. Otherwise professionalisation becomes abdication: more people responsible for communicating the strategy, fewer for keeping it intact.

Picture the next fundraising meeting. The marketing deck says the company sells to everyone. The IR deck says the market is enormous. The agency has made both look convincing.

The investor closes the laptop and turns to the founder.

Which one is the company actually building?

—

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 views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

The post Every department you add makes your startup harder to understand appeared first on e27.