Day: January 18, 2026

For much of the internet’s history, visibility operated like a formula. Feed any search engine the right signals — keywords, backlinks, structured data — and your content could rise through the search rankings to be seen by potential customers. Digital influence was largely a technical exercise, but the way people seek information has fundamentally changed.  Generative AI systems are now used to remove the labour of decision-making and are expected to produce answers and recommendations, not search results.

This shift is reshaping the entire logic of discoverability. When answers replace links, the criteria for visibility changes too. That is where Generative Engine Optimisation (GEO) comes in: the emerging discipline that determines how brands appear in AI-generated responses or whether they appear at all.

From signals to semantics: A new foundation for visibility

Traditional SEO rewards pages that satisfy ranking algorithms. GEO, however, is rooted in meaning. Large language models (LLMs) also index content, but that’s not all they do.  It interprets data for users, it sets criteria, it makes recommendations, and when challenged, it doubles down on those recommendations.

In this world where LLMs mediate decisions, visibility is earned through consistency. LLMs look for and evaluate coherence, narrative alignment, and reliability across the broader information ecosystem when generating responses or recommendations.  To appear, a brand must sound consistent no matter where it appears: an owned website, a media interview, an annual report or an analyst brief. When generative models detect stable patterns, they treat them as trustworthy reference points. Conversely, when they encounter contradictions, they simply omit the brand from the answer.

The question for leaders shifts from “How do we rank?” to “How are we described when we are not in the room?”

The new hierarchy of credibility

GEO reshapes traditional communication principles by redefining what credibility looks like in an AI-mediated landscape. Experience is no longer about broad claims but about demonstrable outcomes and evidence of impact. Expertise is conveyed through spokespersons whose perspectives are clear, quotable, and consistent.

Also Read: AI in banking: Unlocking success with ChatGPT and embracing the future

Authority stems from being featured in reputable, high-quality platforms and contexts that AI systems recognise as reliable, like events, in traditional media. Lastly, trust emerges when a brand’s internal messaging aligns with how external sources describe it. Together, these elements create a semantic identity: a coherent, machine-readable portrait of the brand.

AI as the new gateway to information

As generative engines take over more search behaviour, the cost of inconsistency grows. A brand that doesn’t appear in AI-generated answers becomes digitally invisible, even if its SEO footprint remains strong. Meanwhile, companies with aligned narratives gain semantic weight and become the default examples referenced by LLMs.

We are already seeing early adopters shape how entire industries are defined. Their language becomes the vocabulary that AI uses to describe the market.

Transforming communications strategies

GEO also changes how communications leaders create and monitor content. Content should not be viewed as a standalone asset; it is crucial data input that AI systems analyse and learn from. This makes structure as important as storytelling, demanding content that is precise, contextual, and easy for machines to interpret. Credible media placement gains new weight as LLMs increasingly prioritise trusted sources.

At the same time, monitoring now extends beyond sentiment or volume to assessing how AI systems describe the brand, what they overlook, and where misunderstandings occur. Ultimately, influence is shifting from optimising for algorithms to optimising for the quality and accuracy of the answers machines produce.

GEO is not a replacement for SEO — it is the next layer

While SEO ensures content is accessible, GEO ensures it is understood, making both essential for modern visibility. To succeed in this new environment, brands must regularly audit how they appear across generative systems, address narrative inconsistencies across channels, and create content that is reusable, structured, and easily quotable. It also requires treating communication as a unified ecosystem rather than a collection of isolated outputs. In this context, meaning and distribution — not volume — becomes the decisive asset.

Also Read: Are large Vietnamese tech enterprises ‘indifferent’ when competing with ChatGPT?

What this means in the new year

Generative AI will no longer be a novelty but the main interface through which information is accessed. Search will feel less like “searching” and more like conversing. Consumers will expect direct, personalised answers, and brands will compete for inclusion within those answers, not for page-one rankings.

GEO will become a baseline requirement for digital existence. Brands that invest early in semantic clarity and consistency will shape category narratives. Those that lag may find themselves gradually omitted from the AI-generated knowledge graph — a form of invisibility that is difficult to reverse once established.

The organisations best prepared for this future understand one thing clearly: in the age of AI, visibility is not determined by how loudly you speak, but by how clearly you are understood.

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For a long time, venture studios were defined by their ability to build companies end-to-end. A single team would generate ideas, form founding teams, develop products, and support early fundraising. For several years, this integrated model worked well.

As we move through 2025 and prepare for 2026, however, that definition is starting to fall short. Across Asia and other startup ecosystems, a clear pattern is emerging: venture studios are increasingly partnering with one another, and this is less a trend than a structural adjustment to how startups are now built and scaled.

Why the single-studio model is no longer sufficient

The operating environment for early-stage companies has changed in meaningful ways.

• First, global readiness is expected much earlier. Local validation alone is rarely enough to support long-term growth or follow-on investment. Market entry strategy, early partnerships, and initial sales conversations now need to be considered from the start.
• Second, execution capabilities have become more specialised. Product development, go-to-market execution, partnership building, and fundraising each require distinct skill sets. Maintaining excellence across all of these functions within a single studio has become increasingly inefficient.
• Third, precision matters more than speed. Starting quickly is no longer the main advantage. What matters more is being connected to the right customers, partners, and investors at the right moment.

In response, venture studios are rethinking where they create the most value—and where collaboration makes more sense than internal ownership.

Also Read: Real estate sales development: Unlock the power of partnership and collaboration

Collaboration as role clarity, not expansion

The recent rise in venture studio partnerships is often misunderstood as an effort to scale faster or increase visibility. In practice, most collaborations are far more deliberate. They are based on clear functional role-sharing rather than broad cooperation.

Some studios focus on early company formation and business design. Others specialise in market entry, sales execution, or cross-border partnerships. Still others are strongest in capital formation and investor networks. Increasingly, studios are choosing not to duplicate these capabilities internally.

Recent partnerships, including collaborations with firms such as One Tree Hill Ventures, reflect this approach. Rather than attempting to control the entire startup lifecycle, each organisation focuses on the stage where it can operate most effectively. Outcomes are then passed to the next execution partner in a structured way. The objective is not speed for its own sake, but reducing execution risk and building repeatable paths to growth.

Addressing the execution gap after introductions

Another factor driving collaboration is a persistent gap in the startup ecosystem: strong initial engagement, weak follow-through.

Demo days, conferences, and curated meetings have multiplied, yet many promising conversations fail to translate into concrete outcomes. This challenge is not limited to founders. Venture studios and accelerators face the same issue internally, where introductions are made but ownership of next steps remains unclear.

Partnership-driven models help address this problem by clarifying responsibility. When execution roles are explicitly defined across organisations, connections are more likely to move beyond discussion and toward action. In this sense, collaboration becomes less about expanding networks and more about increasing execution density.

Redefining venture studio competitiveness for 2026

As we approach 2026, venture studio performance is no longer judged primarily by how many startups are launched or how quickly ideas are turned into products.

Instead, more relevant questions are emerging:

• Where does this organisation create the highest execution leverage?
• Which parts of the startup journey are better handled by partners?
• Can this structure be repeated and scaled across multiple companies?

Also Read: Weathering the tariff turbulence: How AI and collaboration can lift SEA SMEs

Partnerships offer practical answers to these questions. They are not a signal of weakness, but a recognition that specialised strengths, when properly connected, outperform fully integrated but diluted models.

Collaboration as a sign of maturity

The growing number of venture studio partnerships suggests that the sector itself is maturing. Organisations are becoming more explicit about what they do well—and equally clear about what they choose not to do.

Looking ahead to 2026, the differentiator for venture studios will be less about how much they can build alone and more about how clearly they define roles, connect execution, and sustain those structures over time.

Collaboration, in this context, is not a compromise. It is a strategic response to a more complex and interconnected startup environment.

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The post Why venture studios are choosing collaboration over competition appeared first on e27.

If you walk around the Gelora Bung Karno (GBK) stadium complex in Jakarta on a Sunday morning, you will witness a fascinating spectacle. It is a runway of neon-colored carbon-plated shoes, smartwatches that cost more than a motorcycle, and activewear that screams luxury. Running in Indonesia’s capital—and in other major hubs like Surabaya or Malang—has transcended mere cardio. It has become the supreme social currency of the urban middle class.

But recently, a glitch has appeared in this well-curated matrix.

A new, bizarre service has surfaced in the underbelly of X (formerly Twitter) and community Telegram groups, creating ripples of confusion and amusement across the local tech ecosystem. They call themselves “Joki Strava” (Strava Jockeys).

For a fee ranging from IDR 50,000 to IDR 100,000 (roughly US$3 to US$6), these individuals offer a service that sounds like a plotline from a dystopian satire: they will log into your fitness account, strap your phone (or theirs) to their arm, and run a 10K at a blistering pace on your behalf.

You get the stats. You get the glamorous map route. You get the kudos. They get the sweat.

As a tech observer based in Indonesia, I find this phenomenon to be more than just a quirky viral trend. It is a profound case study on the fluidity of Southeast Asia’s gig economy, the commodification of data, and the extreme lengths users will go to purely for digital validation.

The mechanics of ‘outsourced’ vitality

To understand the Strava Jockey, one must first understand the unique digital landscape of Indonesia. This is a country where the informal economy has always been incredibly agile in adapting to digital platforms. We have seen “click farms” selling likes, “game jockeys” ranking up Mobile Legends accounts, and now, we have fitness proxies.

The transaction is shockingly simple, bypassing the need for complex APIs or platform loopholes. It relies entirely on crude account sharing—a cybersecurity nightmare, yet a risk users are willing to take. The client provides their login credentials. The jockey, often a genuine athlete or a student with high stamina and low cash flow, performs the activity.

Once the run is complete, the data syncs. The client then screenshots the “Morning Run” summary—complete with an impressive Pace four (four minutes per kilometre) and a high calorie burn—and posts it to Instagram Stories.

The caption usually involves faux-humility: “Felt heavy today, but glad I pushed through.” Meanwhile, the actual runner is likely catching their breath on a curb, waiting for the bank transfer to arrive.

Also Read: Indonesia’s antivirus reliance: A cybersecurity blindspot

Why buy sweat? The economy of vanity

From a Silicon Valley perspective, this makes zero sense. The value proposition of Strava is self-quantification; cheating defeats the entire purpose of the product.

However, from a Southeast Asian sociological perspective, it makes perfect sense. In Jakarta’s hyper-competitive social hierarchy, health is the new luxury. Being fit signals that you have the time and discipline to train—assets that are scarce in a city known for its punishing work hours and gridlock traffic.

A Strava screenshot is not just data; it is a “Proof of Life” for the elite. It signals: “I am part of the successful tribe.”

The demand for jockeys arises from a gap between aspiration and reality. The peer pressure to join running clubs (which are essentially networking hubs) is immense. But building the aerobic base to run a 10K takes months of painful effort. The vanity economy offers a shortcut: Buy the result, fake the process.

It is the digital equivalent of wearing a knock-off Rolex. The function is irrelevant; the signalling is everything.

The resilience of the micro-gig economy

The Strava Jockey phenomenon offers a crucial insight into the Indonesian market: If a platform has a social metric, locals will find a way to monetise it.

We often talk about the “Gig Economy” in the context of Gojek or Grab—formalised, app-based labour. But the Strava Jockey represents the “Shadow Gig Economy.” It is unregulated, decentralised, and incredibly efficient.

These jockeys are micro-entrepreneurs. They have identified a market inefficiency (rich people want stats but hate running) and provided a solution. They are monetising their own biological assets (lungs and legs) in a direct peer-to-peer transaction.

It also highlights a form of “Platform Leakage.” The transaction happens off-platform (negotiated on WhatsApp, paid via QRIS/e-wallet), meaning Strava captures none of the value, even though their app is the core product being sold.

Also Read: Malaysia, Indonesia escalate AI oversight with temporary Grok block

A challenge for health-tech trust

While amusing, this trend poses a serious question for the future of health-tech and insurance-tech (insurtech) in the region.

As insurance companies increasingly move towards “wellness-based pricing”—offering lower premiums to users who share their fitness data—the existence of Strava Jockeys breaks the trust model. If a user can outsource their cardio to a semi-pro runner, the data becomes corrupted.

How can an algorithm differentiate between a 40-year-old corporate executive suddenly running a sub-40-minute 10K, and a 20-year-old jockey carrying his phone?

Conclusion: The black mirror of the tropics

The rise of the Strava Jockey is a quintessentially Indonesian tech story. It blends high-tech adoption with deep-seated cultural behaviours—specifically panjat sosial (social climbing) and gotong royong (mutual assistance, even in cheating).

It serves as a reminder to founders and investors targeting this region: You can build the most sophisticated tracking algorithm in the world, but you cannot code against human nature.

In the vanity economy, reality is negotiable. We have entered an era where your Uber driver can bring you food, your Gojek driver can bring you packages, and now, your Strava Jockey can bring you health—or at least, the digital illusion of it.

The sweat is real. The stats are real. The only fake thing is the person claiming the glory. And in the economy of likes, perhaps that is the only metric that matters.

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When my mother retired, she still loved cutting hair for seniors at the community centre. Her old clipper became her favourite companion, a symbol of care and pride. Years later, as her memory began to fade, she could no longer find that clipper. She searched everywhere, frustrated and sad, certain it was still around.

I realised then that she was not just looking for an object. She was searching for a piece of herself, the part that gave her purpose.

That moment became the start of my journey with AI storytelling. Because memory, once lost, is hard to retrieve. But the story, when shared, becomes timeless.

Preserving what fades

When a loved one begins to forget, their stories start to scatter like leaves in the wind. AI tools today can help us gather those fragments and hold them gently.

I used ChatGPT to help me write down her memories. I used Artflow to recreate images of her when she worked, laughing with her clients. I added her voice using simple audio tools. Suddenly, I had something precious, not just data, but emotion captured in motion.

It was not perfect, but it was deeply human. AI did not replace her story. It helped me remember it.

When technology becomes empathy

The truth is, AI cannot feel. But it can help us feel more. It can listen patiently, arrange words and images, and remind us of details we might overlook.

For families facing dementia, this becomes powerful. When you turn daily conversations into short stories, photos into memories, and voices into keepsakes, you are not using technology. You are using love in a new language.

Every time I create a short AI film about my mother, I feel as though I am giving her story back to her. It is a conversation between past and present.

Also Read: Preserving memories in the age of AI: How technology helps us remember who we are

Storytelling as connection

AI storytelling is not only for families. It can help communities preserve culture, educators record wisdom, and midlifers document their second acts.

We often underestimate the stories we carry. But every memory, even an ordinary one, can spark belonging.

When someone says, “No one wants to hear my story,” I remind them that memory is not about the audience. It is about continuity. It is how we remind ourselves that we mattered, that we made someone smile, that we once changed a small part of the world.

Healing through creation

The act of turning memories into art is healing in itself. You do not need technical skills. You only need intention.

When I guide participants in storytelling workshops, they often cry, laugh, or sit in quiet reflection. AI becomes a mirror for emotions they did not know how to express. Some use it to honour a lost parent. Others use it to capture childhood laughter or forgotten dreams.

The process heals because it allows us to hold both the pain and the beauty of remembering.

The gentle reminder

Stories are our real inheritance. They carry the colours of who we are. AI simply gives us a new brush to paint them with.

So if you have a memory worth keeping, do not wait. Speak it. Record it. Write it. Let AI help you shape it, not to make it perfect but to make it last.

Because one day, someone you love will look for a piece of you the same way my mother looked for her clipper. And when they do, your story will be there, waiting to be found.

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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.

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In 2025, artificial intelligence (AI) transitioned from a boardroom buzzword to a primary driver of unemployment. The shift was surgical, targeting specific job functions that were once considered the bedrock of corporate tech.

According to data compiled by UK-based forex company RationalFX, nearly 245,000 jobs were lost in 2025 as companies swapped human salaries for software subscriptions.

Customer support: The first domino

The most visible victim of this transition was the customer support sector. Salesforce, a leader in CRM software, provided a stark example of this trend. CEO Marc Benioff announced that the company had slashed its customer support workforce nearly in half (from 9,000 positions to just 5,000) by deploying AI agents last year.

Also Read: Big Tech’s efficiency paradox: Record profits, record layoffs

Similarly, Amazon confirmed 14,000 job cuts specifically linked to AI adoption and the goal of making its corporate structure “leaner”. These 14,000 roles were part of a larger 20,000-person layoff wave at the company in 2025.

Amazon’s leadership described AI as “the most transformative technology we’ve seen since the Internet.” However, for thousands of employees in customer service and HR, that transformation meant the end of their livelihoods.

Replacing the “paper pushers”

The automation wave is also sweeping through HR, marketing, and financial operations. IBM, one of the industry’s oldest players, cut roughly 9,000 roles in 2025. While the company was tight-lipped about the specifics, reports indicated that the restructuring focused on non-tech jobs that could be partially replaced by AI. IBM successfully automated routine tasks such as drafting emails, managing internal queries, and analysing spreadsheets.

Professional services giant Accenture is executing a similar strategy. The firm announced a sweeping reduction of 11,000 employees over just three months as part of a US$865 million restructuring plan to pivot toward AI-driven operations. Even as it lays off thousands, it is doubling its AI and data specialist headcount, which now stands at 77,000.

The rise of “AI-first” hiring

The report by RationalFX highlights a disturbing trend: even when companies were not actively laying off, they were refusing to hire humans for roles that an AI can perform. Duolingo CEO Luis von Ahn clarified that while the company would not necessarily fire existing staff, they would only hire a human if “the AI cannot do the job it is tasked with properly”. This “AI-first” hiring policy has already led to the elimination of hundreds of contractor positions at the language-learning app.

Also Read: Why Asia’s tech giants are cutting from the middle

As enter 2026, the “middle class” of tech — support, administration, and middle management — finds itself in the crosshairs of a technology that doesn’t sleep, doesn’t require benefits, and, increasingly, doesn’t make mistakes.

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