At Echelon Philippines 2025, a dynamic panel of industry leaders gathered to explore how Filipino startup founders can harness the power of spaces and hubs to fuel their growth.

Moderated by BAMentor Online’s Ben Alderson, the discussion brought together Joey Radovan of JLL Philippines, Paul Pajo of Benilde HIFI, Jojo Flores from Plug and Play Tech Center, and Walter Cang of DOHE Philippines. Together, they unpacked how strategic workspace ecosystems — from incubators to innovation hubs — serve as launchpads for emerging startups, offering not just physical infrastructure but critical networks, mentorship, and resources essential to accelerating entrepreneurial success in the Philippine startup landscape.

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K25.ai, an APAC-focused startup attempting to fuse live streaming, creator monetisation, and prediction markets, has secured strategic backing from Nasdaq-listed NewGenIVF Group Limited (NIVF) at a US$100 million pre-Series A valuation.

The deal includes an initial US$2 million investment from NIVF, an option for the public company to increase its stake to up to US$10 million, and an exclusive APAC agency partnership.

Also Read: Streaming the dream: How live streaming technology can increase access to brands

The funding round and partnership mark an important early endorsement for K25.ai, which says it is building what it calls a “watch-to-predict” information market, a platform where audiences can watch live content, engage with creators and make predictions on real-world outcomes.

What K25.ai says it does

K25.ai combines live-streamed content, creator-led channels, and an AI layer intended to automate the lifecycle of a prediction market: event discovery, market generation, live content analysis, data extraction, and outcome resolution. The platform targets a broad set of live moments — sports, e-sports, entertainment, creator-led challenges, and culturally relevant events — and aims to let audiences participate in timely prediction events in markets where such offerings are legal.

Leadership and pedigree

The company is led by Andy Cheung, a veteran operator in digital platforms and digital assets. Cheung’s résumé includes a tenure as chief operating officer at OKEx (now OKX), where he oversaw the scaling of one of the largest digital asset exchanges, and a stint as CEO of Groupon Hong Kong. He has also advised on or held board roles at Nasdaq-listed Prenetics, focused on digital-asset treasury strategy.

In announcing the partnership, Cheung framed K25.ai’s mission in lofty terms: to become “the Google and Meta of this new era” by creating an “information market layer” powered by AI and live streaming to help people “discover truth in real time.” The language is aspirational and highlights a common strategic ambition among startups trying to combine discovery, social signalling and commerce into a single product.

NIVF’s strategic bet

For its part, NIVF, which operates as a Nasdaq-listed public company, gains exposure to an emerging category and, crucially, an avenue to commercialise K25.ai’s product across permitted APAC markets. The strategic relationship reportedly includes commercial support across Thailand, Singapore, Japan, Australia and New Zealand, while explicitly excluding Mainland China, Hong Kong, Macau and other restricted jurisdictions.

Also Read: Livestreaming done right: How brands can turn viewers into loyal customers

NIVF will take board representation from K25.ai’s co-founders, including Cheung, a move the companies say will align strategy and increase governance connectivity as K25.ai readies itself for potential public-market pathways. NIVF’s chairman and CEO, Siu Wing Fung Alfred, described the investment as a “generational opportunity” and highlighted Cheung’s track record as a primary reason for backing the business.

Regulatory and market constraints

K25.ai’s operating model touches on a legal patchwork. Prediction markets can be treated very differently across jurisdictions — from regulated gambling to financial instruments or prohibited activities — and the firm is explicit that it will only operate where permitted and subject to licensing, registration and regulatory approvals. It also notes that the platform is not offered to US persons or users in Mainland China, Hong Kong, Macau, or other restricted territories.

That caution is sensible given the legal risks. Countries in APAC have disparate regulatory stances on betting, derivatives and information products that replicate financial outcome markets. The firm’s success will depend not only on product-market fit but on its ability to navigate those local rules while maintaining user acquisition and monetisation strategies.

Competition and comparison

K25.ai positions itself in a niche that overlaps with prediction-market specialists, live-streaming platforms, and social apps that gamify interaction. The press materials compare K25.ai’s valuation against selected larger global prediction-market companies, stating that the US$100 million valuation represents approximately 0.27 per cent of those selected public valuation references, a contextual note that neither forecasts future performance nor states comparable outcomes.

The market is not empty: established prediction-market protocols and centralised operators exist globally, and large social platforms have repeatedly experimented with live engagement mechanics and real-money integrations. K25.ai’s differentiator, if any, will rest on the quality of its AI-driven event orchestration, creator relationships in APAC and its ability to offer legally compliant, locally relevant content.

Risks and takeaways

Several risks stand out.

• First, regulatory uncertainty across APAC could limit addressable markets and complicate scaling.
• Second, the economics of prediction markets are challenging: liquidity, user retention and fair pricing require substantial user bases and careful product design.
• Third, building a creator ecosystem locked to prediction mechanics assumes creators will trade time and audience attention for a different monetisation mechanism; history shows creators often gravitate to simpler, proven revenue channels unless a new product clearly outperforms incumbents.

That said, the combination of AI to automate market creation and localised content could carve out a defensible niche, particularly in sports and esports, where regional fandoms are fierce and real-time interest is high. K25.ai’s early strategic capital and NIVF’s regional network give the startup initial runway and credibility. Execution will determine whether this is an incremental experiment or the beginning of a scalable, regulated APAC-focused information market.

Also Read: How AI, AR, and live streaming are changing the online shopping experience

Only time will tell whether K25.ai’s vision of an “information market layer” becomes a practical product that draws mainstream users. For now, the company has the ingredients investors like to see — an experienced founder, a clear product thesis, strategic capital and a public-company partner — but it faces the hard work of turning an ambitious idea into a legally compliant, revenue-generating platform across fragmented APAC markets.

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Let’s admit a painful truth about modern business: we are drowning in information, yet starving for wisdom. We are living in the celebrated age of Big Data, but for most companies, that data is less of an asset and more of an enormous, very expensive digital attic. It is filled with boxes labelled “Customer Clicks,” “Server Logs,” and “Unfiltered Sentiment,” all gathering virtual dust.

The chief executive today can recite the mantra: Data is the new oil. Yet, few seem to grasp that oil, in its raw state, is useless. It must be refined, processed, and channelled with immense strategic effort. Most companies have simply struck a gusher and are now happily swimming in crude. They have the capability to collect everything, but the intellectual discipline to understand nothing.

This isn’t a technical failure; it’s a profound failure of curiosity and strategy. We have outsourced the messy, complex work of data collection to machines, but we have become intellectually lazy about the far more critical task: interrogation.

The lie of the metric dashboard

The primary way most organisations deceive themselves is through the elaborate metric dashboard. It is the comfort blanket of the busy manager, where a constantly scrolling set of numbers is designed to feel like insight. It tracks everything, from website bounce rates to quarterly sales figures.

But a number, in isolation, tells you absolutely nothing. It is merely a symptom. The failure lies in confusing measurement with meaning. Knowing that a particular sales figure is up by ten percent is not strategy; it is accounting. Strategy requires the rigorous, uncomfortable question: Why is it up ten percent? And more importantly: What other, unexpected consequence did that surge produce elsewhere in the business?

Also Read: The AI server boom in Southeast Asia: Why data centres are running out of power

Most teams simply react to the red or green indicator. The truly powerful use of data comes from the demanding work of connecting the seemingly unrelated dots. It’s analysing customer service transcripts alongside product adoption curves to reveal the unexpected friction point that is sabotaging growth. That requires deep human curiosity, not just faster processors.

From collection to competitive edge

The vast majority of collected data is entirely generic. It is the digital equivalent of market research everyone else can buy. The true competitive edge comes from the few, rare veins of proprietary data that your company alone collects, and your unique systems for leveraging it.

The real gold is in the unstructured data (the written notes, the voice transcripts, the behavioural sequences, etc) that requires sophisticated thinking to organise. This is where most organisations stop short. They collect the data, but then they lack the institutional patience or skill set to translate that noise into a clear signal. They possess the answer to their greatest strategic challenge, but it is buried beneath petabytes of digital clutter.

The essential shift is understanding that data is not a historical archive; it is a forecasting instrument. It is meant to be used to compel action tomorrow, not just explain failure yesterday.

Also Read: Why global capital keeps flowing into data centres in Singapore despite rising costs

The urgency of the question

This is the great contemporary paradox: we have perfect digital recall of every mistake we’ve ever made, yet we keep repeating the same ones. Why? Because the person responsible for the data is often far removed from the person responsible for the decisions.

The modern company must bridge this gap by prioritising the interrogator over the collector. They must empower teams to hunt for the counterintuitive findings, the insights that explicitly challenge current company dogma. If your data analysis simply confirms what everyone already believes, you haven’t done analysis; you’ve just created expensive validation.

The time for hoarding is over. If your organisation possesses massive datasets but still operates primarily on instinct, anecdote, or the loudest voice in the boardroom, you are not technologically advanced; you are simply a very elaborate digital storage facility. The data isn’t the challenge; the courage to act on what it reveals is.

If your greatest strategic answer is buried somewhere in your existing data, what uncomfortable question are you refusing to ask that would finally unearth it?

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Southeast Asian startups are standing at a critical crossroads. While the region’s startup ecosystem continues to raise billions in 2024, a quiet revolution is reshaping how successful companies operate.

The winners of 2025 and 2026 won’t be those with the biggest teams or deepest pockets; they’ll be the startups that master the “One million+ revenue per person” model through intelligent automation.

This isn’t about replacing humans with machines. It’s about amplifying human potential to unprecedented levels, where each team member becomes a revenue-generating powerhouse capable of driving US$1 million or more in annual business results.

The AI acceleration imperative: Why “next few months” means now

The pace of AI development has reached a tipping point that makes 2019’s digital transformation look mild. According to McKinsey’s State of AI report, 71 per cent of organisations are now regularly using generative AI. For Southeast Asian startups, this creates both an opportunity and an existential threat.

The innovators in your fellow startup industry are going to leapfrog over you if you’re too slow and the window for competitive advantage is shrinking rapidly as AI tools become more accessible and powerful.

While some startups struggle with manual content creation, lead qualification and customer research, their AI-powered competitors are simultaneously running 50 personalised outreach campaigns, analysing competitor strategies in real-time, and nurturing hundreds of leads with human-like precision…all with a team half their size.

Pillars of AI-powered revenue generation

Smart Southeast Asian startups are building their competitive moats across 13 critical automation areas. Each represents a multiplier effect that transforms individual team members into revenue-generating machines.

Research and intelligence: The foundation layer

• Research of social media videos: Tools like Brandwatch and Sprout Social now use AI to analyse millions of video interactions, identifying viral patterns and audience sentiment in real-time. Any company can spot trending topics before competitors and understand what resonates with their target market without watching hours of content manually.
• Research of social media copy: Platforms like BuzzSumo and Socialbakers leverage natural language processing to analyse conversation patterns across social platforms.
  This allows startups to identify customer pain points, monitor brand mentions, and discover collaboration opportunities at scale.
• Outbound research: AI-powered tools like Clay and Apollo automate prospect research by aggregating data from multiple sources: LinkedIn, company websites, news articles, and social media, so as to build comprehensive prospect profiles in seconds rather than hours.

Content creation: Scaling your voice

• Scripting videos: AI scriptwriting tools like Jasper and Copy.ai can generate video scripts tailored to specific audiences and platforms. Combined with data from your research layer, you can create compelling narratives that speak directly to your prospects’ needs.
• Content writing: Beyond basic copywriting, advanced AI tools now understand brand voice, audience psychology, and conversion optimisation.
  They can produce blog posts, email sequences, and social media content that maintains consistency while adapting to different platforms and audiences.
• Video generation: Platforms like Synthesia and Pictory enable startups to create professional video content without expensive production teams.
  From product demos to personalised sales videos, AI handles the heavy lifting while they focus on strategy.
• Image generation: Tools like Midjourney and DALL-E 3 democratise visual content creation. Startups can generate custom graphics, social media visuals, and even product mockups that previously required specialised design teams.

Also Read: How to combat burnout and boost your productivity

Distribution and engagement: Maximising reach

• Distribution and engagement of content: AI scheduling tools like Later and Hootsuite Insights optimise posting times, hashtag selection, and cross-platform distribution.
  More importantly, they engage with audiences through intelligent commenting and response systems that maintain authentic brand voice.
• Outbound distribution: Advanced email and LinkedIn automation platforms like Outreach and Lemlist use AI to personalise messages at scale, optimise send times, and adapt messaging based on recipients’ observed behavior patterns.

Sales and conversion: Closing the loop

• Voice agents: AI voice technology has reached human-level quality. Tools like Dialpad Ai and Gong can handle initial prospect calls, qualify leads, and even conduct basic sales conversations, freeing human team members for high-value relationship building.
• Lead nurturing: Sophisticated marketing automation platforms like HubSpot and Marketo can now use predictive AI to determine the optimal nurturing sequence for each lead, automatically adjusting content and timing based on behavioral signals.
• Sales stack: AI-powered CRM systems like Salesforce Einstein and Pipedrive AI provide real-time coaching, predict deal closure probability and automate routine sales tasks, enabling each salesperson to manage and close significantly more opportunities.
• Automated newsletter: Tools like ConvertKit and Mailchimp use AI to optimise subject lines, personalise content, and determine optimal send frequencies for different subscriber segments, turning newsletters into powerful revenue drivers.

The competitive intelligence advantage

Perhaps the most overlooked opportunity lies in AI-powered competitive intelligence. While most startups sporadically check competitor websites, AI-enabled companies are monitoring:

• Pricing changes: Tools like Price2Spy track competitor pricing in real-time
• Content strategy: AI analyses competitor content performance and identifies gaps
• Job postings: Tracking competitor hiring patterns reveals strategic priorities
• Social media sentiment: Understanding how audiences respond to competitor campaigns
• Product updates: Automated monitoring of competitor product launches and features

This intelligence enables rapid strategic pivots and ensures you’re always one step ahead.

Also Read: Myths vs reality: Remote and hybrid managers report high productivity and trust

The Southeast Asian context: Unique opportunities

Southeast Asian startups have distinct advantages in implementing these AI strategies:

• Mobile-first audience: The region’s mobile-centric user base creates massive datasets for AI optimisation, particularly in social media and messaging platforms.
• Diverse markets: Our multi-language and multi-cultural audiences provide rich testing grounds for AI personalisation at scale.
• Government support: Countries like Singapore and Malaysia are actively promoting AI adoption through an array of grants and support programs, reducing strategy and implementation costs.
• Growing digital infrastructure: Improving internet connectivity and digital payment systems create fertile ground for AI-powered customer experiences.

Implementation strategy: Start small, scale fast

The key to successful AI implementation isn’t trying to automate everything at once. Smart startups follow a strategic sequence:

• Begin with research: Implement social and competitor research tools to understand your market better
• Automate content creation: Scale your content output to increase brand visibility
• Optimise distribution: Ensure your content reaches the right audiences at optimal times
• Enhance sales processes: Use AI to qualify and nurture leads more effectively
• Integrate systems: Connect all tools for seamless data flow and maximum efficiency

The cost of inaction

The startups that hesitate face a compounding disadvantage. According to a October 2024 Boston Consulting Group study, AI leaders over the past three years have achieved 1.5 times higher revenue growth, 1.6 times greater shareholder returns, and 1.4 times higher returns on invested capital.

In Southeast Asia’s competitive startup landscape, where funding is becoming more selective and customer acquisition costs are rising, the efficiency gains from AI aren’t just advantages anymore. They’re now survival necessities.

Conclusion: The time is now

The “One million+ revenue per person” model isn’t a distant possibility for most, it’s happening today among startups that have embraced AI-powered automation. The tools exist, they’re increasingly affordable, and the competitive advantages are clear.

The question isn’t whether AI will transform how startups operate in Southeast Asia. The question is whether your startup will be among those leading the transformation or struggling to catch up.

The next few months will separate the winners from the also-rans. The choice, and the time to act, is now.

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For decades, the safest career path was simple: join a large corporation, climb the ladder, and let scale and brand reputation shield you from risk.

But AI is rewriting that script. Corporates are automating whole job tiers — customer service, data entry, even junior coding roles. Where once thousands of entry- and mid-level roles were the training ground for future managers, now software eats the repetitive layers.

If corporations no longer absorb as much of the workforce, what happens next?

The future may belong less to sprawling headcounts and more to small, high-output teams — SMEs and startups that can combine AI systems with fractional, remote talent to scale globally without scale in payroll.

The generalist dilemma

Generalists thrive on flexibility. They pivot quickly, learn new tools, and can hold multiple hats. In fast-changing industries, this makes them valuable.

But here’s the risk: shallow generalism is where AI strikes first. Email drafting, basic analytics, social media scheduling — the tasks that junior generalists once owned are now automated.

Survival depends on moving beyond “task doer” into connector, strategist, and problem-solver — the human glue between AI systems and business goals.

Also Read: Value creation: When startups die surrounded by capital

The specialist dilemma

The case for specialisation

Specialists possess deep expertise in a specific domain. In fields such as medicine, engineering, or data science, this depth is essential for solving complex problems and driving innovation. AI is accelerating the demand for specialists who can design, implement, and refine advanced technologies.

Advantages of specialisation:

• Mastery of complex, technical subjects
• Higher demand in niche roles
• Ability to command premium compensation
• Recognition as an expert in the field

However, hyper-specialisation can also create vulnerabilities. As AI automates routine and even some advanced tasks, narrowly focused roles may become obsolete or require constant upskilling.

Specialists stand out with deep expertise. They know a field inside out, and their credibility is built on mastery.

But here’s the trap: if your speciality is codable, you risk obsolescence. Junior developers, paralegals, bookkeepers — entire ladders are being shortened as AI handles entry-level work faster and cheaper.

To stay relevant, specialists must climb “up the stack” into roles AI can’t yet replace: system architects, negotiators, leaders of complex, ambiguous projects. Depth remains an asset, but only if paired with the ability to adapt.

The T-shaped answer: The big shift

The workers — and companies — that thrive will be T-shaped:

• Breadth across domains to stay adaptable.
• Depth in one or two areas to stay differentiated.

Example: A business developer with broad skills in outreach, project management, and digital tools — but deep mastery in regional B2B expansion.

For SMEs, this thinking scales up: build teams that are lean, cross-functional, but anchored by specialists who set direction and operators who adapt.

Also Read: Entering post-unicorn phase, Indonesia signals a structural reset in startup investment

Corporates shrink, SMEs rise

If corporates stop hiring armies of juniors, where will talent go?

• SMEs and startups will increasingly act as the “new training ground.” Leaner, hungrier, and willing to take bets on remote workers, they’ll scoop up talent that corporates no longer absorb.
• Remote and fractional work becomes the bridge. Instead of one full-time job, many professionals will build careers across multiple SMEs, platforms, or projects.
• AI + small teams will compete with big enterprises. A five-person SME with automation and offshore operators can now do what used to require a 50-person department.

The real winners will be SMEs that design teams like portfolios — specialists setting standards, generalists driving execution, and AI filling the gaps.

How to prepare

For individuals:

• Build a core speciality that anchors your credibility.
• Layer generalist skills (AI tools, cross-cultural collaboration, digital literacy) to stay versatile.
• Think globally — your next role may not be in your country, or even in one company.

For SMEs:

• Don’t mimic corporates with bloated teams.
• Use AI as the back office, remote operators as the muscle, and local specialists as the brain.
• Build a system where small teams punch above their weight — faster, leaner, and cheaper than corporates can move.

Conclusion

The future worker isn’t choosing between generalist or specialist — they’re choosing to become both.

The future company isn’t competing on headcount — it’s competing on systems, talent mix, and speed of execution.

As corporates shrink, SMEs have the chance to step up as the engines of growth — building global businesses with teams that look small on paper, but deliver like giants.

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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.

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