For decades, we’ve treated education as the ultimate equaliser.

Study hard. Get certified. Climb the ladder.

That formula powered the industrial economy and then the early knowledge economy. Degrees signalled competence. Credentials signalled readiness. Access to elite institutions signalled advantage.

Then GenAI arrived.

And quietly, without protest, it collapsed the scarcity model that education was built upon.

Today, anyone with a prompt can access legal reasoning, financial modelling, medical summaries, code scaffolding, strategic frameworks, and global research. The gates are no longer guarded.

This raises a difficult question: If AI has access to almost all codified knowledge — and most people now do too — what exactly is the education system optimising for?

The original purpose of education

Modern education systems were designed for three primary objectives:

• Standardisation of knowledge
• Industrial workforce readiness
• Credential-based sorting

It rewarded:

• Memorisation
• Compliance
• Accuracy within structured evaluation
• Linear problem-solving

In the industrial era, this worked. Fact recall was valuable. Access to information was limited. Standardisation ensured predictable output.

But in the GenAI era, memorisation is automated. Information retrieval is instant.

Structured reasoning can be generated in seconds. If the value of knowledge used to lie in having it, today the value lies in knowing what to do with it.

And this distinction exposes the cracks in the current system.

When AI has all the answers

GenAI does not experience impostor syndrome. It doesn’t doubt its competence. It doesn’t gatekeep information. It doesn’t fear being “found out.”

It simply accesses and synthesises.

Ironically, humans — who built the education system — are now the ones experiencing inadequacy. Because we were trained in scarcity.

Scarcity of:

• Access
• Tools
• Elite networks
• Research
• Mentorship

AI operates in abundance.

So the question shifts from: “Can you recall the answer?” to “Can you ask the better question?”

And this is where the current education model shows its limits.

Also Read: Gender gap in GenAI skills is narrowing, but progress remains uneven, Coursera finds

The hidden limitation: Education rewards convergence

Most education systems reward convergence thinking:

• Find the correct answer
• Follow the expected method
• Produce the accepted framework

But GenAI excels at convergence.

What it struggles with — and where human advantage lies — is divergence:

• Challenging premises
• Identifying unseen patterns
• Questioning assumptions
• Connecting disciplines in novel ways
• Acting with contextual judgment

Our education systems largely assess answers. The future economy will reward judgment. Those are not the same.

Education as a signalling mechanism is weakening

Degrees once signalled:

• Rigor
• Persistence
• Domain expertise
• Access to curated knowledge

But when AI can:

• Summarise an MBA textbook
• Draft a legal memo
• Generate a financial model
• Write production-ready code

Then the credential alone becomes insufficient. Not irrelevant — but insufficient.

What differentiates tomorrow’s knowledge worker is no longer: “How much you know.”

It becomes:

“How deeply you understand.”

“How effectively you apply.”

“How clearly you decide.”

Education, in its current form, does not consistently measure these dimensions.

The new divide: Curiosity vs compliance

GenAI does something profound. It removes knowledge access as a structural advantage.

But it introduces a new differentiator: curiosity.

Two individuals can access the same AI.

Only one chooses to:

• Probe deeper
• Refine prompts
• Challenge outputs
• Cross-check assumptions
• Explore adjacent domains

Education traditionally rewarded compliance:

• Follow curriculum.
• Pass exam.
• Meet benchmark.

The new economy rewards inquiry:

• What else?
• Why not?
• What’s missing?
• What’s next?

This is not a minor adjustment. It’s a systemic shift.

Also Read: GenAI in lending: Faster approvals, smarter risks, and personalised credit

What education must evolve into

If we are serious about preparing a generation of true knowledge workers, education must shift across five structural dimensions.

• From memorisation → Meta-learning

Teach students:

• How to learn
• How to unlearn
• How to validate AI outputs
• How to interrogate sources

AI can retrieve answers. Humans must validate relevance.

• From siloed disciplines → Interdisciplinary synthesis

Real-world problems do not come neatly packaged:

• Climate intersects with finance.
• Healthcare intersects with data ethics.
• Supply chains intersect with geopolitics.

True knowledge workers will be synthesisers, not specialists confined within narrow lanes.

• From fixed curriculum → Dynamic learning models

Curricula often lag the industry by years.

In a world where AI models update in months, static syllabi become outdated quickly.

Education must become:

• Modular
• Continuous
• Adaptive
• Stackable

Learning cannot end at graduation.

• From exams → Applied judgment

Assessment should increasingly measure:

• Scenario reasoning
• Ethical trade-offs
• Decision framing
• Risk calibration

The world does not grade people on multiple-choice questions. It rewards decision quality under uncertainty.

• From credential prestige → Portfolio evidence

Future differentiation will likely come from:

• Projects
• Problem-solving artifacts
• Real-world experimentation
• Public thinking

What you build may matter more than where you studied. It implies application.

The knowledge worker of the new age

Peter Drucker popularised the term “knowledge worker” decades ago.

But GenAI forces us to redefine it.

A true knowledge worker in the AI era:

• Does not compete on access
• Does not compete on recall
• Does not compete on surface frameworks

Instead, they compete on:

• Depth
• Context
• Original framing
• Decision velocity
• Ethical clarity
• Strategic foresight

Education systems must therefore cultivate:

• Systems thinking
• Probabilistic reasoning
• Bias awareness
• Creativity under constraint
• Communication clarity
• Cross-domain fluency

These are not exam-friendly traits. But they are future-critical capabilities.

Talent vs experience in an AI-accelerated world

AI compresses learning curves.

A junior analyst can produce outputs once reserved for senior professionals.

An executive can independently generate strategy drafts without layers of support.

So, where does experience fit?

Experience now becomes:

• Pattern recognition under ambiguity
• Judgment calibrated by lived consequence
• Crisis-tested decision making
• Ethical discernment

Talent becomes:

• Speed of synthesis
• Intellectual curiosity
• Cross-domain integration
• Learning agility

Education should nurture both.

But today, it often privileges standardised performance over adaptive capability.

Also Read: The use of GenAI is turning innocent employees into insider threats: Here’s how to fix it

The structural recalibration we need

If we continue educating for yesterday’s scarcity economy, we will produce graduates optimised for irrelevance.

If instead we redesign education for:

• Abundance of information
• AI-augmented productivity
• Continuous reinvention
• Portfolio-based credibility
• Judgment-based differentiation

Then we create a generation that does not fear AI — but compounds with it.

The real imposter is not the human.

It is the outdated system that measures humans by metrics AI can outperform.

In conclusion

GenAI is not replacing education. It is exposing what education was truly built to optimise.

The future knowledge worker will not win by competing with AI on answers.

They will win by:

• Asking sharper questions
• Integrating broader perspectives
• Exercising wiser judgment
• Pursuing depth relentlessly
• Exploring “what’s next” before it becomes obvious

Education must therefore evolve from a delivery system of knowledge into a training ground for discernment.

In a world where AI knows almost everything, the true advantage belongs to those who know what matters.

And that begins with rethinking how we educate — not just what we teach.

This article is Part 4 of a four-part series on “Redefining Knowledge Work: AI, Ownership, and the Future of Value.” Explore the rest of the series: Part 1, Part 2, Part 3.

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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.

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Your pitch isn’t too long because you care too much. It’s too long because you don’t yet understand.

January 9, 2007 — Macworld, San Francisco. Steve Jobs takes the stage.

“Today, we’re introducing three revolutionary products. A widescreen iPod with touch controls. A revolutionary mobile phone. And a breakthrough internet communications device.”

He pauses. Repeats it. Then pauses again.

“These are not three separate devices. This is one device.”

Most people remember the reveal. Almost nobody studies the structure. Jobs did not explain — he engineered expectation, then collapsed it. He let the audience close the distance themselves, then handed them the reward of inevitability.

That is compression.

Meanwhile, your forty-seven-slide deck is sitting quietly in someone’s inbox. Unread. A polite pass is already half-drafted.

These two facts are not coincidental.

The battlefield you chose

Daniel Kahneman divided the mind into two systems: fast, intuitive System 1 and slow, deliberate System 2. Founders tend to assume investors operate in the second. They don’t.

Early-stage decisions are made in System 1. System 2 exists mostly to justify them afterwards.

Here is what that means in practice: The moment your pitch becomes dense — over-explained, over-hedged —you force System 2 online. And when System 2 activates, the investor is no longer listening. They are auditing.

Auditing is adversarial by design. It looks for gaps, inconsistencies, overreach — and it finds them, because every business has them. You have chosen to fight on the only terrain where you are guaranteed to lose.

The deck didn’t just fail to convince. It selected the wrong game.

Also Read: Cambodia startups move from pitch to payoff

The founder who speaks less — but lands precisely. Who answers without rushing toward silence. Who leaves space unguarded? That restraint carries a signal no slide deck can manufacture: I don’t need to persuade you. This already stands.

Over-explanation is not passion. It is fear, wearing the costume of diligence.

What Shannon knew

Claude Shannon defined information as entropy — the degree of surprise in a message. What you cannot predict carries information. What you already expect carries none.

The average pitch deck is 90 per cent predictable. TAM/SAM/SOM.  A competitive matrix. Five-year projections no one believes. Entropy: zero. Signal: zero. Noise, presented with the production value of rigour.

Now consider this: Fei-Fei Li raised US$230 million anchored on two words — Spatial Intelligence. No slides. No deck. A concept so compressed it reshaped the room. Inside those two words was the complete answer to every investor’s three-part question: Why now? Why her. Why does it change everything?

That is density. That is what a pitch is supposed to be — not a document, but a gravitational event.

A black hole compresses vast mass into finite space — not by removing meaning, but by eliminating everything that isn’t load-bearing. A great pitch obeys the same physics. 120 seconds that hold the market’s contradiction, the team’s irreversible proof, and the investor’s fear of missing it — all at once, without remainder.

If you cannot compress, you have not yet reached the centre of your own idea. The pitch is not the failure. The understanding is.

The asset called silence

Japanese aesthetics has a concept: ma — the charged space between notes, between gestures, between words. Not absence. Potential. The silence doesn’t mean the music has stopped; it means something is about to land.

In the best pitches, silence is not a gap in delivery. It is where the investor’s imagination enters. And once they begin to co-create the narrative — once they are supplying the ending — you no longer need to sell it.

Also Read: Your agency’s pitch deck is a clone: Here’s why Meta’s new rules and AI will force you to evolve or collapse

Mike Moritz once reflected on his first meeting with Google’s founders. “Larry and Sergey said very little,” he recalled. “But their silence said everything.”

That is not charisma. That is structure — trust, rendered in its most economical form.

Three tests

Don’t audit your pitch by adding. Audit it by removing.

What length actually reveals

Da Vinci wrote: “Simplicity is the ultimate sophistication.” In venture, this is not a sentiment. It is filtration. Investors use it as a screen before they finish their second slide.

A 120-second pitch is not a format. It is a measurement device. It reveals — with a precision no due diligence process can match — whether you have reached the centre of your own idea, or are still orbiting it.

And investors can read that signal before they open the deck.

“Your pitch isn’t getting longer because you care more. It’s getting longer because you’re not done thinking.”

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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.

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In the digital asset ecosystem, custody is not just a feature. It is the foundation upon which everything else rests. Cryptocurrency operates as a bearer asset. This means whoever holds the private keys effectively owns the funds. This simple truth carries profound implications. Unlike traditional banking, where a forgotten password triggers a straightforward reset process, losing or compromising a private key in the crypto world often results in permanent and irreversible loss. There is no customer service hotline to call, and no administrator exists to undo a transaction. No safety net catches you when you fall.

As we navigate 2026, the importance of proper custody has evolved from a technical consideration to an existential necessity. Blockchain immutability means that transactions cannot be undone. If assets are stolen via a compromised key, there is simply no recourse to recover them. The numbers tell a sobering story. Approximately 20 per cent of all Bitcoin, or roughly 4 million BTC, is estimated to be permanently inaccessible due to lost keys or poor personal custody practices. That is billions of dollars worth of value vanished into the digital ether.

The threat landscape has grown increasingly sophisticated. Phishing attacks were responsible for approximately 83 per cent of stolen funds in 2025. High-profile exchange breaches like the devastating US$1.5 billion Bybit hack in early 2025 sent shockwaves through the industry. These incidents underscore a harsh reality. Basic storage methods are no longer sufficient to protect against modern threats. Meanwhile, institutional adoption has reached a tipping point. As of 2026, 74 per cent of family offices are actively engaged in cryptocurrency. They come with stringent requirements. These sophisticated investors demand qualified custodians who can meet fiduciary duties, ensure proper asset segregation, and provide comprehensive insurance coverage. The message is clear. Custody has matured from a DIY experiment into a professional service industry.

Also Read: Bitcoin holds US$71K as Ethereum surges 15%: What’s driving the US$2.44T crypto rally

For those entering the crypto space, the question is not whether to use custody solutions. The question is which model best fits their needs. The industry has converged around three primary approaches. Each comes with distinct advantages and trade-offs.

Self-custody remains the purist choice. It offers total autonomy and privacy. This model appeals to tech-savvy individuals who value sovereignty above all else. When you hold your own keys, you answer to no one. No platform can freeze your assets. No intermediary can deny your transactions. No third party can surveil your holdings. This freedom comes with a sobering responsibility because there is no forgot password button. User error is the primary risk, and mistakes are unforgiving. A lost seed phrase, a compromised device, or a simple typo can result in permanent loss. Self-custody demands technical competence, meticulous attention to detail, and an acceptance of absolute personal responsibility.

Third-party custody offers professional security and insurance coverage. This makes it ideal for institutions and beginners alike. These platforms employ teams of security experts. They maintain robust infrastructure and often carry insurance policies to protect against losses. The trade-off is counterparty risk since you are trusting another entity with your assets. Platform insolvency, regulatory action, or internal malfeasance can all threaten your holdings. Recent history has shown that even the most reputable exchanges can fall. They can take customer funds with them. Third-party custody simplifies the user experience. It requires careful due diligence in selecting a trustworthy provider.

Emerging as the goldilocks solution for many is the hybrid model utilising Multi-Party Computation technology. This approach offers distributed control and flexibility. It is particularly attractive to enterprises and exchanges. MPC splits private keys into encrypted shares distributed across different parties. This ensures the complete key never exists in one place. This occurs even during transaction signing. This eliminates single points of failure while maintaining operational efficiency. This sophistication comes at a cost. Operational complexity is the primary risk. Implementing and managing MPC solutions requires technical expertise and careful coordination among multiple parties.

Also Read: Crypto falls 1.29% to US$2.34T as geopolitical fear triggers risk-asset selloff

Modern custody solutions have evolved far beyond simple password protection. Today, the security arsenal includes multiple layers of defence. These are designed to eliminate vulnerabilities and protect against increasingly sophisticated threats. Cold storage remains the bedrock of secure custody. It keeps private keys entirely offline in air-gapped hardware that cannot be accessed remotely. This physical separation from the internet provides robust protection against hacking attempts. It makes cold storage ideal for long-term holdings. For those who choose this path, hardware wallets have become increasingly user-friendly while maintaining military-grade security.

Multi-Party Computation represents the cutting edge of custody technology. By splitting private keys into encrypted shares distributed across different locations or devices, MPC ensures that no single point of failure exists. Even during the critical moment of transaction signing, the complete key never materialises in one place. This mathematical elegance provides security that is greater than the sum of its parts. Multi-signature technology adds another layer of protection. It requires multiple independent keys to authorise transactions. A typical setup might require three out of five designated keys to approve a transfer. This ensures that a single compromised device cannot move funds. This distributed authorisation creates a system of checks and balances. It mirrors traditional financial controls. Hardware Security Modules provide tamper-resistant physical protection for key generation and storage. These specialised devices automatically wipe their contents if physical interference is detected. This provides a final line of defence against determined attackers.

So how should you approach custody? The answer depends on your technical comfort, risk tolerance, and usage patterns. For long-term holdings that you do not need to access frequently, cold storage via hardware wallets remains the gold standard. The inconvenience of physical access is a small price to pay for the security of keeping your keys completely offline. For active trading or frequently accessed funds, reputable exchanges offer convenience. They should be used judiciously. A prudent approach is to keep only a small portion of your portfolio, perhaps less than 20 per cent, on exchanges. Treat them as transactional tools rather than storage solutions. Move profits to cold storage regularly. Never leave more on an exchange than you can afford to lose. For those managing significant assets or operating businesses, the hybrid MPC model offers an attractive balance of security and functionality. It requires careful implementation and ongoing management.

Also Read: Why crypto market cap falls to US$2.53T despite regulatory clarity win and 6-day ETF streak?

The crypto custody landscape reflects the maturation of the entire ecosystem. What began as a libertarian experiment in self-sovereignty has evolved into a sophisticated industry. It offers solutions for every type of user. This ranges from the casual investor to the institutional giant. The technology is more robust. The options are more diverse. The stakes are higher than ever. Your private keys are more than just strings of code. They are the keys to your financial kingdom.

Choose your custody solution wisely. Understand the trade-offs. Never forget that in the world of cryptocurrency, you are ultimately your own bank. With great power comes great responsibility. In 2026, the tools to exercise that responsibility have never been more advanced. The question is not whether you can afford to take custody seriously. It is whether you can afford not to.

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Cambodia’s digital tourism sector takes a major step forward as Tovtrip, the country’s first comprehensive travel super app, showcases its platform at Echelon Singapore 2026, one of Asia’s leading technology and startup events.

Built to transform the way travelers explore Cambodia, Tovtrip offers a seamless booking experience across the entire travel journey. The platform integrates flights, hotel bookings, transportation rentals, spa and massage services, tours, activities, and local travel experiences — all within a single application.

To date, more than 600 Cambodian merchants including hotels, tour operators, transportation providers, and local experience vendors have joined the platform, making Tovtrip one of the fastest-growing local travel marketplaces in Cambodia.

Why Tovtrip Was Created

Tovtrip was developed to solve key challenges within Cambodia’s tourism ecosystem and empower local businesses through digital technology.

Enhancing Productivity and Visibility

Tovtrip directly connects travelers with local vendors, giving small and medium tourism businesses greater visibility in the digital marketplace. By optimizing how travelers discover and book services, the platform helps local merchants increase productivity while delivering better service to visitors.

Driving the Transition from Offline to Online Tourism

Cambodia’s tourism sector has historically been dominated by offline bookings. Tovtrip aims to accelerate the transition to digital travel services, enabling local communities and businesses to participate in the growing online travel economy while promoting destinations across the country.

Data-Driven Tourism Development

Tovtrip also generates valuable insights into traveler behavior and preferences. These analytics provide stakeholders — including businesses and tourism authorities — with actionable data to make informed decisions about tourism strategies, resource allocation, and destination development.

Also read: Meet the companies taking the floor at Echelon Singapore 2026

Cambodia’s growing travel market

The opportunity for digital travel services in Cambodia continues to grow rapidly.

The Total Addressable Market (TAM) for Cambodia’s internal travel market reached approximately $1.9 billion in 2024 and is expected to grow at 6.9% annually, reaching around $2.78 billion by 2025. The number of internal travelers is projected to increase from 22.5 million travelers in 2024 to approximately 33 million travelers by 2025.

Within this ecosystem, the Serviceable Available Market (SAM) — representing the online travel booking segment — is expected to reach around $139 million, equivalent to approximately 1.65 million travelers booking travel services online.

With the growth of local digital platforms, around 10% of these travelers (165,000 users) are expected to use local platforms to plan and book their trips.

Tovtrip aims to capture 0.5% of this market by 2026, representing approximately 8,500 active users. With an estimated average booking value of $84 per transaction, this could generate roughly $700,000 in revenue.

Empowering Cambodia’s tourism ecosystem

What makes Tovtrip particularly meaningful is the strong support from Cambodia’s local users and businesses.

Unlike global platforms, Tovtrip is designed specifically for Cambodia’s tourism ecosystem — supporting local language, local payment preferences, and direct connections with verified local merchants.

The platform also ensures:

• Authentic listings from real Cambodian businesses
• Direct engagement between travelers and local service providers
• Better economic opportunities for local communities
• Increased exposure for emerging destinations across Cambodia

This local-first approach has helped Tovtrip build trust with both merchants and travelers while strengthening Cambodia’s digital tourism infrastructure.

Also read: Builders wanted: Close the AI execution gap for SMEs

Representing Cambodia on the regional stage

By participating in Echelon Singapore 2026, Tovtrip aims to showcase Cambodia’s growing innovation ecosystem and demonstrate how local startups can play a vital role in shaping the future of tourism in Southeast Asia.

As Cambodia’s first travel super app, Tovtrip is committed to building a connected, data-driven, and inclusive tourism ecosystem — empowering local businesses while making travel across Cambodia easier than ever.

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I am a product marketer. My world is go-to-market strategy, user journeys, and positioning. For most of my career, if I had an idea for a product flow or wanted to test a concept with users, I had to brief a developer, wait, iterate through feedback loops, and hope the final output matched what I had in my head. That process could take weeks. Now with AI, I can build the prototype myself without coding. Not a rough sketch, an actual working prototype, sometimes in the same afternoon I had the idea.

And what used to be separate workstreams, building the product, documenting it, creating the marketing materials, crafting the use cases, now feed into each other. I can build a prototype, screenshot the flow, write the copy around it, and have a landing page up before the end of the day. That kind of speed used to require a team.

I am not alone in this. Developers I know are writing their own documentation in minutes instead of hours. The walls between disciplines that used to define what each of us could and could not do are coming down fast. And with them, the barriers around who can access jobs, capital, and the tools to build something from scratch.

But what made that possible for me is part of a much bigger shift happening right now, and not everyone is going to benefit from it equally.

The stakes are bigger than most people realise

In mid-March, Jensen Huang stood on stage at GTC 2026 and told the world he could see at least US$1 trillion in AI infrastructure spending through 2027. Anthropic CEO Dario Amodei, speaking recently in Bangalore, said AI adoption in India has doubled in just three to four months. Andrej Karpathy, one of the people who helped build the foundations of modern AI, admitted he has not typed a line of code himself since December.

Also Read: Ethical implications of using AI in hiring

These are not just impressive numbers; they are signals that the rules of the next economy are being written right now, mostly by a small number of players with very large infrastructure budgets. In simple terms, tokens are the units AI runs on and gets paid for. Every response, every piece of analysis, every bit of generated content is measured in tokens, and a gigawatt data centre costs around US$40 billion before a single chip goes in. The countries and companies that can build those factories will define the cost of intelligence for everyone else. In this new economy, your token budget is becoming as critical as your cloud spend, and if that cost gets set by a handful of players in one part of the world, everyone else ends up buying at a price they had no say in.

This is what infrastructure inequality looks like in practice, and it is worth understanding what game you are actually playing.

The part that gets missed

Dario made a point in Bangalore worth paying attention to. He said Anthropic does not come to markets like India looking for consumers; they want to work with local builders who actually understand their own market. Every two or three months, a new model release opens up something that was not possible before.

OpenClaw, the open source agentic AI framework that Jensen described as the most downloaded open source project in history, surpassing Linux in weeks, makes this even more concrete. Karpathy called it the operating system for agentic computers, the same role Windows played for the personal computer. A developer anywhere in the world can now build on the same foundation as one in San Francisco.

The infrastructure layer requires billions to build, which means it is dominated by players with the deepest pockets, but the application layer, meaning the tools and products built on top of AI, is still wide open. That is where the real opportunity sits for founders and builders in this region. That window is real, but only if you know it is there.

The real barrier is the on-ramp

Most people assume the barrier to AI adoption is access, that if you just had the right tools, you would be fine. But that is not what the data shows. 64 per cent of Southeast Asian sellers cite high costs and time as major obstacles, and while 41 per cent of SMEs say they are adopting AI, only five per cent are actually using it in a meaningful way. The barrier is not access. It is the on-ramp.

A small logistics company in Southeast Asia, with five people and no dedicated tech team, recently started using AI to handle customer communications, route queries, and generate weekly ops summaries. What used to need a part-time coordinator now runs largely on its own, and the founder ended up not hiring the person she had budgeted for. That is what the tool working as promised actually looks like.

But getting there took three weeks of trial and error, a developer friend who helped with setup, and the willingness to push through a lot of frustration. Three weeks and a developer friend are not things everyone has.

Also Read: A new era of automation: Establishing best practices for intelligent automation and generative AI

This is the gap that does not show up in press releases or keynote slides. The tools exist, but most people still cannot figure out how to get started. Karpathy talks about spending sixteen hours a day in what he jokingly calls AI psychosis, basically an obsessive state of directing multiple AI agents at the same time, each working on different tasks, while he reviews, adjusts, and keeps them all moving. That is what mastery looks like right now, and that gap does not close on its own.

What actually needs to change

So what does closing it actually look like? Some of it is already happening. Google’s Stitch update in March 2026 means a founder who cannot afford a designer can now generate a full UI, interactive prototype, and design system in under an hour, for free, with no design skills required. Figma’s stock dropped 8.8 per cent the day the update was announced. The market saw the shift before most people did, and this is exactly the direction things need to keep moving: tools that start from what you want to achieve rather than assuming you already know how to build it.

That is why I think this category of operator-first tools matters, including what we are building with Fuseful Workflow Studio at Morpheus Labs. Most automation tools still assume you know how to build the system. Fuseful starts from the business outcome instead, built with operators in mind, not engineers.

But tools alone are not enough. The average enterprise now runs more than ten AI applications, yet 76 per cent report negative outcomes because the tools do not connect, and nobody was trained to use them together. Anthropic has a team they internally call the Ministry of Education, and that is not a trivial signal. Companies serious about equity need to treat capability-building in their users the same way they treat feature development, not as an afterthought but as the actual product.

And the last piece sits with local builders. Dario is right that Anthropic cannot and should not build for every vertical. The real opportunity for domain-specific, market-specific, culturally-grounded applications sits with the people who actually know those markets. Funding those builders and not cannibalising them when they find success is what building equity by design actually looks like in practice.

The big labs will keep building, and the infrastructure will keep scaling. That part is not really up for debate anymore. What is still up for debate is what gets built on top of all of it, who gets trained to use it, and who gets funded to try.

Those decisions do not belong to Jensen or Dario. They belong to every founder, operator, and builder in ecosystems like this one. And we are still early enough to get them right.

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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.

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