The surface-level take: Andrew is simply highlighting an operational inefficiency that smart teams will optimize away.

The deeper read: He's accidentally identified the next phase of startup competitive advantage in the AI era—and most people are going to get it completely wrong.

1. The Speed Trap Everyone's Walking Into

Andrew's math is seductive but incomplete. Yes, prototypes that once required "six engineers three months" can now be weekend projects. But the obsession with prototype velocity misses a more fundamental shift: the economics of validation have changed, not just the mechanics of building.

When your prototype-to-feedback cycle compresses from weeks to days, you don't just need faster product decisions—you need fundamentally different validation strategies. The Valley's current playbook (build → measure → learn → iterate) assumes validation is the scarce resource. But what happens when building becomes nearly free and validation remains expensive?

Singapore's startup ecosystem offers a useful parallel. During our early 2010s acceleration phase, government grants and accelerator programs suddenly made seed capital abundant. Teams that optimized purely for fundraising speed got crushed by those who built systematic approaches to customer validation and market-product fit. Speed without direction is just expensive wandering.

2. The Product Manager Arms Race Nobody Asked For

Andrew floated a fascinating data point: some teams now propose PM-to-engineer ratios of 1:0.5—more product managers than engineers. This sounds like classic Silicon Valley logic: identify the bottleneck, throw resources at it, declare victory.

Except we've seen this movie before. Remember when "growth hacking" was the bottleneck? Or when "data science" was going to unlock everything? The pattern is predictable:

• Phase 1: Shortage creates premium roles

• Phase 2: Market floods with mediocre practitioners

• Phase 3: Only exceptional talent differentiates

• Phase 4: Back to fundamentals

The PM arms race will follow this exact trajectory. By 2027, every AI startup will have hired multiple "customer empathy experts" and "rapid product decision specialists." The real alpha will belong to teams that solved the underlying problem rather than optimizing for the symptom.

3. What Andrew Actually Discovered (But Didn't Say)

The most revealing quote wasn't about PM ratios or weekend prototypes. It was this: teams are "increasingly relying on gut" to make faster decisions.

That's not a bug. That's the feature.

In markets moving at AI speed, systematic data collection often arrives too late to matter. The teams winning in hyper-competitive super-app landscape aren't the ones with the best analytics—they're the ones whose founders have developed the most accurate intuitive models of their markets.

Andrew mentions this obliquely when he talks about PMs needing "deep customer empathy" and the ability to "synthesize lots of signals". But he's underselling the insight. What he's describing isn't product management—it's market sensing as a competitive moat.

Three Quick Provocations for AI-Era Founders

Here is how this trend actually plays out:

Customer Intimacy > Customer Research

The speed differential between building and validating creates systematic pressure to develop intuitive market models. Teams that can rapidly synthesize weak signals (user behavior, competitor moves, ecosystem shifts) will consistently out-maneuver those dependent on formal research cycles.

Tactical shift: Instead of hiring more PMs, invest in customer advisory relationships and systematic founder-market exposure. YCombinator's "get out of the building" remains true—but now it needs to happen at AI speed.

Prototype Portfolio Management > Single Product Iteration

When building costs approach zero, the optimal strategy shifts from perfecting one solution to systematically exploring solution spaces. This requires different metrics, different team structures, and different capital allocation frameworks.

Policy implication: Government innovation programs designed around linear "TRL progression" will systematically miss this shift. Singapore's NRF should consider evergreen exploration grants that reward systematic market sensing rather than just technical milestones.

Network Effects in Validation Speed

The teams with the best customer access networks will compound their advantage as build-validate cycles accelerate. This suggests ecosystem strategies matter more, not less, in the AI era.

Closing Thought

Andrew Ng identified a real problem. The issue isn't that product management needs to speed up to match engineering—it's that traditional product management becomes less relevant when the cost structure of innovation fundamentally changes.

The winning teams won't hire more PMs. They'll develop systematic approaches to market sensing that operate at AI speed. They'll treat customer intimacy as infrastructure, not a departmental function.

The real bottleneck isn't product management. It's developing market judgment that operates at the speed of artificial intelligence.

Just back from a whirlwind of meetings in Singapore, and I couldn't ignore this MIT report that's been blowing up my feed. As someone who's invested in dozens of AI startups through Golden Gate Ventures, I've seen the hype cycle firsthand. But this study? It's a gut check for anyone betting big on AI. Let's break it down, startup-style—because if you're building or funding in this space, these insights could save you millions.

The Headline That Spooked Everyone (And Why It Shouldn't)

The MIT NANDA report dropped like a bomb: 95% of AI pilot projects fail to deliver any real financial uplift. Yeah, you read that right. They looked at 300 projects, chatted with 150 execs, and surveyed 350 employees. The result? Most AI initiatives are burning cash without moving the needle on profits.

Investors freaked out—Nvidia, Microsoft, and others took a hit in the markets. But hold up: this isn't Altman saying public AI stocks are bubbly (though he did call out private startups). And it's not an indictment of the tech itself. As the report points out, the real issue is how companies are using AI, not the AI models themselves.

Digging Into the Data: It's Not the Tech, It's You

NANDA—short for Networked Agents and Decentralized AI—is an MIT project pushing for better AI architectures. Full disclosure: they might have skin in the game, promoting agentic systems as the fix. But their findings align with what I've seen in the field.

Key takeaways from the report:

• Failure Isn't About Capability: Execs blame weak models, but the data shows it's a "learning gap." Organizations don't know how to embed AI into workflows. Wharton prof Ethan Mollick nails it: stop forcing AI into old processes shaped by bureaucracy. Let it redefine how work gets done.

• Startups vs. Corporates: New companies crush it because they lack entrenched systems. If you're a startup founder, this is your edge—build AI-native from day one.

• Buy > Build: Vendor solutions succeed 67% of the time; internal builds? Only 33%. I've advised portfolio companies on this: unless you're in a hyper-regulated space, don't reinvent the wheel. Focus on your core IP.

• Wrong Focus Areas: Too many pour money into marketing/sales AI. The real ROI? Back-end automation that cuts costs. Think ops efficiency over flashy demos.

This echoes other studies—Capgemini saw 88% of pilots flop in 2023, S&P Global noted 42% abandoned this year. It's not new, but it's getting worse as hype outpaces execution.

Lessons from the Trenches: What Winners Are Doing

The pattern? Smart integration and realistic goals. Don't treat AI like a magic wand—it's a tool that needs the right setup.

From the report and my experience:

• Workflow Redesign is Key: Experiment relentlessly. One of our portfolio companies pivoted from generic chatbots to agentic systems that automate entire processes—ROI jumped 3x.

• Data Privacy Isn't an Excuse: Regulated industries hide behind "build internal" for control, but vendors often handle this better. Pick partners wisely.

• Measure What Matters: Track financial savings, not just "AI usage." The report slams vague metrics—get specific on P&L impact.

Oh, and shoutout to Ethan Mollick again: AI shines when you let it bypass office politics. Startups, this is your superpower.

The Bigger Picture: Bubble or Breakthrough?

Look, investor panic is real—shares tanked on headlines alone. But this report isn't doom and gloom. It's a wake-up call that AI's impact is coming, just not how most expect. We're in the trough of disillusionment (Gartner hype cycle, anyone?), but the slope of enlightenment follows.

For founders: Focus on agentic AI that scales autonomously. NANDA's pushing this, and it aligns with what DeepSeek's doing in China—efficient models that compete with OpenAI at a fraction of the cost.

For investors: Don't bail yet. The trillions in data center spend Altman predicts? It's happening, but winners will be those solving real problems, not chasing buzz.

Wrapping It Up: Your AI Playbook

If you're building an AI startup, heed this: 95% failure rate is a feature, not a bug—it's your opportunity to be the 5%. Nail integration, buy smart, automate the boring stuff, and measure ruthlessly.

The AI revolution isn't slowing—it's evolving. China restricting Nvidia sales? That's just accelerating local innovation. Google's Pixel AI features? Table stakes now.

Stay sharp, folks. If you're pitching AI to VCs like me, show how you'll beat these odds.

A timely Stanford study by Prof. Chuck Eesley and co-authors puts fresh data behind this hunch. Their five-year investigation of a major Chinese university’s 1985 credit-system reform shows that when students gain genuine freedom to mix-and-match electives, their likelihood of launching a venture jumps by 159%. A companion Stanford news feature distills two decades of Eesley’s work into a single takeaway: universities evolve into engines of innovation only when three forces converge—flexible curricula, dense mentorship networks, and sustained government capital.

Below are three quick provocations for Singapore, filtered through the lens of our ecosystem’s strengths (capital efficiency, policy agility) and blind spots (risk appetite, uneven social networks).

1. Curriculum Freedom > Classroom Content

Eesley’s data echo what every founder-turned-angel already knows: the spark rarely comes from a standalone “Entrepreneurship 101” module. It’s the serendipity of a chemistry major stumbling into a design-thinking elective, or a CS undergrad hacking on a public-policy capstone, that breeds venture-scale insight.

Yet our local universities still ration electives like C-class shares. NTU’s Renaissance Engineering Programme is a solid start, but most faculties lock freshmen into inflexible tracks by Year 2. The Stanford study suggests a simpler KPI than churning out more incubators: track the percentage of undergrads who cross faculty lines for at least 20 credits. Shift that dial, and you shift the venture funnel.

2. Mentorship Density as Social Infrastructure

Eesley’s earlier work at Stanford’s STVP shows that alumni connections don’t just open doors—they reduce failure rates by steering novice founders toward better decisions. Singapore’s talent BBQ pits (NUS Overseas Colleges, YC-style accelerators) are making progress, but the mentor pool skews toward repeat founders in SaaS and fintech.

We need a broader bench. Imagine pairing deep-tech PhDs with ex-Glaxo scientists who’ve navigated FDA hell, or matching sustainability founders with Keppel veterans fluent in industrial sales cycles. Creating this “mesh network” of expertise is social infrastructure work—slow, unsexy, but catalytic.

3. Government Money Still Matters—But Only If It’s Patient

The Project 985 case in China underlines why targeted, multi-decade funding transforms universities into venture flywheels. Singapore’s NRF investments tick many of those boxes, yet grant timelines often clash with the 8-10-year runway deep-tech firms require.

Two tweaks could unlock outsized returns:

1. Evergreen Proof-of-Concept Funds – rolling grants that follow the researcher, not the calendar year.

2. IP-Light Licensing – allow spin-offs to keep more equity upfront in exchange for revenue share post-exit, reducing the handicap that first-time founders face when negotiating Series A valuations.

Closing Thought

Stanford’s success wasn’t pre-ordained by geography; it was engineered through decades of policy bets that privileged autonomy, mentorship, and patient capital. Singapore’s ecosystem has the hardware—capital, connectivity, credibility. The next leap demands firmware upgrades inside our universities: give students room to roam, surround them with operators, and keep the funding horizon long.

Do that, and the next Grab-scale story might just begin in a seminar room overlooking Clementi rather than Sand Hill Road.

• Based on “University Education Reform and Entrepreneurship,” Eesley et al., SSRN 1884493.ssrn-1884493.pdf
  “The rise of universities as engines of innovation,” Stanford News, 18 Aug 2025.

1. https://ppl-ai-file-upload.s3.amazonaws.com/web/direct-files/attachments/14688030/648c9077-a233-486d-9662-99ced59976ba/ssrn-1884493.pdf
2. https://news.stanford.edu/stories/2025/08/chuck-eesley-insights-entrpreneurship-success-education-government-investment

Here's a number that should keep every policymaker awake at night: Singapore university spin-offs raise an average of $400,000. Stanford's raise $72 million.

That's not a rounding error. It's a 180x gap that exposes everything wrong with how we think about university commercialization.

The Uncomfortable Truth About University Spin-Offs

Don't get me wrong—Singapore's universities aren't broken. NUS and NTU have done exactly what they were asked to do: nurture 400+ teams, create jobs, tick the KPI boxes. 

But here's the brutal reality: when it comes to building companies that Silicon Valley VCs actually want to fund, we're not even playing the same sport.

Looking at the past decade (2015-2025), the numbers tell a stark story:

Spin-outs Created: Stanford (~250), MIT (~260), NUS+NTU (~180)

Total Capital Raised: Stanford (~$180B), MIT (~$42B), NUS+NTU (~$80M)

Notable Exits over $100M: Stanford (~90), MIT (~32), NUS+NTU (4)

This isn't just a Singapore problem. Globally, university spin-offs have raised $158B+ across 8,042 investments over the past decade, but the US dominates with over 40% of all deals³. Most importantly, this isn't about research quality—Singapore's universities produce world-class science. The gap isn't just about money—it's about fundamentally different approaches to what constitutes success.

Why One Size Doesn't Fit All: The Case for Dual Tracks

Here's what Singapore gets right: the National Research Foundation's focus on talent development and job creation has built a solid foundation. Over 400 teams have gone through GRIP, generating meaningful economic contributions through local employment¹. In fact, recognizing this momentum, Singapore just launched the $50M National GRIP program in 2024, combining NUS and NTU efforts to support 300 startups by 2028⁴.

Here's what we're missing: *one size fits nobody*.

Stop trying to make every spin-off fit the same mold. Instead, create two completely different tracks:

Track 1: The Capability Building Track - Keep doing what we're doing. Nurture teams, create employment, satisfy the NRF mandate. Zero changes needed here.

Track 2: The Venture Track - A completely separate pathway for the 5-10% of spin-offs that could actually become global companies. Different rules, different standards, different outcomes.

The Venture Track: Where World-Class Standards Begin

If we're serious about competing with Stanford and MIT, we need to acknowledge an uncomfortable truth: *this will not be easy*. If building venture-backable university spin-offs were straightforward, every university in the world would have cracked the code.

The venture track demands three non-negotiables:

Rigorous Selection Criteria

Not every spin-off belongs here. We need brutal honesty about market size, technological differentiation, and global scalability potential.

World-Class Pitch Development

This is where we separate serious contenders from academic projects. Every venture-track spin-off must develop investor materials that exceed the standards expected in Silicon Valley and London Triangle. No exceptions, no "good enough for Asia" compromises. This means:

- Deep market analysis that rivals what the best investment and consulting firms can produce

- IP strategies crafted by the patent attorneys with global experience

- Go-to-market plans built by people who've identified, invested and scaled billion-dollar global companies

- Businesses and technologies that the best investors in the world would actually fund

Elite Advisory Networks

We cannot build this with good intentions and local expertise alone. We need the best people in the world—Silicon Valley operators, deep-tech investors, successful entrepreneurs who've built billion-dollar companies.

Learning from the Best: What Stanford, MIT, and Berkeley Do Differently

Stanford's StartX doesn't just provide mentorship—it plugs spin-offs directly into Silicon Valley's funding ecosystem. MIT's The Engine combines academic rigor with commercial discipline specifically for tough-tech ventures. UC Berkeley's SkyDeck leverages deep industry partnerships to drive real traction².

The proof is in the results.

UC Berkeley SkyDeck's Advisory Impact: SuperAnnotate, a computer vision startup, went through SkyDeck in 2019. Through the program's 300+ advisor network, they connected with Stanford professors and prominent figures in their field, raising a $14.5M Series A within two years. The founders specifically credited SkyDeck's advisor connections for helping them "crystallize their story and mission."

During COVID, MindfulGarden leveraged SkyDeck's virtual advisory network and achieved remarkable results: $44.8M in venture funding, 5x factory expansion, and 50+ new hires. As their founder noted: "Their knowledge base and connections are unlike anything we've had access to before."

MIT The Engine's Tough-Tech Focus: The Engine specifically targets "tough-tech" ventures requiring patient capital and deep expertise. Commonwealth Fusion Systems, spun out of MIT's Plasma Science and Fusion Center, has raised over $50M from strategic investors like Eni to commercialize fusion energy. Boston Metal, developing zero-emission steel production through molten oxide electrolysis, represents the kind of transformative industrial technology The Engine champions. Quaise Energy, working on geothermal drilling using gyrotron technology, exemplifies how The Engine connects MIT's cutting-edge research with commercial applications.

Stanford's HIT Fund has deployed capital across 100+ portfolio companies spanning life sciences to sustainability⁵.

Singapore must adopt these models wholesale—not adapt them. Being number one in Asia isn't good enough when we're competing with global leaders who attract international capital. NUS's Overseas Colleges program, particularly the Silicon Valley hub, should become mandatory for venture-track teams. If we want world-class results, we need world-class standards from day one, not local variations.

A Call to Arms: Singapore's Ecosystem Must Step Up

Building venture-backable spin-offs requires more than university resources. It demands our entire ecosystem—and that means you.

If you're an investor: We need your deal flow insights and due diligence expertise to help select and prepare venture-track companies.

If you're a successful entrepreneur: Your battle-tested knowledge of what actually works in global markets is invaluable for pitch development and strategy.

If you're a corporate leader: Your understanding of real market needs and partnership opportunities can make the difference between academic curiosity and commercial viability.

If you're a service provider (legal, accounting, consulting): World-class spin-offs need world-class support infrastructure.

The Path Forward: Concrete Next Steps

This isn't a theoretical exercise. Here's how we start:

1. Establish the Venture Track Selection Committee - Form a panel of successful entrepreneurs, VCs, and industry experts to identify genuine global opportunities among current and future spin-offs. Involve them early in the process.

2. Create the Pitch Development Academy - Build a 6-month intensive program where venture-track teams work with world-class advisors to develop investor-ready materials that meet international standards.

3. Launch the Global Immersion Program - Partner with NUS's Silicon Valley NOC (Block71 SV) to provide venture-track teams with direct exposure to successful ecosystems and investors.

4. Build the Advisory Network - Recruit 20-30 world-class advisors willing to commit meaningful time to Singapore spin-offs.

The opportunity is massive, but it's global—not regional. Southeast Asia's fund sizes often outpace returns from our current startup pipeline, but we shouldn't be satisfied dominating a regional market. Singapore's venture-track spin-offs must be built to compete in Silicon Valley, not just Southeast Asia. By building companies that attract top-tier international investors from day one, we can create the power law distribution that transforms Singapore from a regional hub into a global innovation powerhouse.

Want to help fix this? Don't send a LinkedIn message. Take action:

- Investors: Email GRIP/NUS Enterprise/NTU Ventures today. Specify exactly how you'll help select and mentor venture-track companies.

- Successful founders: Offer to be a mentor. Commit real time, not just networking calls.

- Service providers: Propose specific pro-bono packages for venture-track spin-offs.

- Government officials: Ask your team why Singapore's best research creates $400K companies while Stanford's creates $72M ones.

The 180x gap exists because we've been comfortable being #1 in Southeast Asia. 

Time to get uncomfortable. Time to compete globally.

¹ GRIP Annual Report 2024, NUS Enterprise

² Stanford StartX, MIT The Engine, UC Berkeley SkyDeck program data, 2023

³ Global University Venturing, "University Spin-off Statistics 2023" - $158B+ raised globally across 8,042 investments (2013-2022)

⁴ National GRIP Singapore, "$50M National Programme Launch," October 2024

⁵ Stanford HIT Fund Portfolio Data, 2024 - 100+ portfolio companies across life sciences, physical sciences, and sustainability

Singapore's tech startup ecosystem thrives, with over 4,500 ventures securing $2.1 billion in funding last year1. Yet, public listings on the Singapore Exchange (SGX) remain scarce, as unicorns opt for foreign markets. This analysis, informed by Bloomberg, DealStreetAsia, McKinsey, PitchBook, and official data, examines SGX's struggles and draws lessons from Tokyo, Jakarta, and Sydney to chart a path for Southeast Asia's innovation hub.

1. https://wowsglobal.com/resources/blogs-insights/exploring-southeast-asias-vibrant-startup-ecosystem/
2. https://www.linkedin.com/pulse/coffee-break-series-ep-7-southeast-asias-startup-2025-sathiyashiva-ah6dc
3. https://www.xendit.co/en/blog/how-to-get-funded-in-southeast-asia-in-2025-a-guide-for-startup-founders/
4. https://integrapartners.co/integra-perspectives/2024-review-trends-and-predictions-for-2025-in-sea/
5. https://www.linkedin.com/pulse/ep-116-why-2025-year-promise-southeast-asian-startup-ecosystem-77hmc
6. https://jdi.group/southeast-asias-most-active-venture-capital-firms/
7. https://www.telecomreviewasia.com/news/featured-articles/4039-the-rise-of-deep-tech-in-asia/
8. https://asiaconnectmagazine.com/tracking-startup-closures-in-southeast-asia-understanding-the-trends/
9. https://ionanalytics.com/insights/mergermarket/vcs-propose-governance-framework-to-counter-se-asias-start-up-fraud-problem/
10. https://techcollectivesea.com/2025/03/10/the-next-wave-emerging-startup-trends-reshaping-southeast-asias-digital-economy/
11. https://wowsglobal.com/resources/blogs-insights/wows-investment-highlights-march-2025-southeast-asia-powers-up-as-funds-flow-in/
12. https://globalventuring.com/corporate/asia/southeast-asia-startup-struggles/
13. https://ace.org.sg/st-health-tech-2/
14. https://www.ubesg.com/post/singapore-venture-capital-landscape-where-smart-money-flows-now
15. https://group.ntt/en/newsrelease/2025/05/19/250519a.html
16. https://www.cnbc.com/2025/04/25/enture-funds-becoming-pe-funds-venture-capitalists-in-southeast-asia-make-pe-like-deals-into-offline-businesses-.html
17. https://www.dealstreetasia.com/reports/the-state-of-deep-tech-in-se-asia-2025
18. https://global.lne.st/news/2025/07/01/bridging-innovation-and-industry-startup-manufacturing-seminar-at-global-knowledge-hub-forum-august-2025/
19. https://www.jpmorgan.com/content/dam/jpmorgan/documents/cb/insights/banking/cb-insights-q1-2025-southest-asia-venture-capital-q-a.pdf
20. https://www.jpmorgan.com/insights/banking/capital-markets/southeast-asia-venture-capital-update

This analysis is based on quick data scan of market reports and developments from CB Insights, PitchBook, McKinsey, StartupBlink, and other sources throughout 2024-2025, representing the latest trends in AI startup revenue patterns and customer acquisition.

The global IT spending landscape presents both tremendous opportunities and significant challenges for startups seeking to establish themselves in enterprise markets. While the total addressable market appears massive at first glance, the reality for emerging companies is far more nuanced, shaped by regional purchasing behaviors, cultural preferences, and established vendor relationships that can either accelerate or hinder startup growth.

Total IT Market Size Comparison Across US, Europe, and Southeast Asia for 2025

Startup Total Addressable Market comparison showing conservative and optimistic scenarios across three regions

Radar chart comparing startup market attractiveness across multiple factors for US, Europe, and Southeast Asia

The torch of technological rebellion is being passed—and it’s burning brighter than ever. What we’re witnessing today isn’t the corruption of Silicon Valley’s hacker culture, but its magnificent evolution. 

The idealistic builders who gave us Facebook and Twitter have paved the way for a new breed of rebels: entrepreneurs harnessing artificial intelligence to augment human potential, not just connect the world.

The Natural Progression: From Connection to Augmentation

The journey from Mark Zuckerberg’s Harvard dorm room to Roy Lee and Neel Shanmugam’s AI revolution was inevitable. While the social media generation taught us to connect minds across the globe, the AI generation is showing us how to amplify those minds’ power. Facebook, PayPal, and Twitter weren’t just companies—they were the infrastructure that made today’s AI revolution possible. Zuckerberg’s “Hacker Way” of rapid experimentation and boundary-pushing has become the playbook for today’s AI rebels.

Two Generations, One Mission: Breaking Barriers

The data tells a story of unprecedented acceleration. Where social media startups took 18–24 months to reach market, AI-native companies now do it in 6–12 months. Teams have shrunk from 15–25 to just 5–10, thanks to AI’s transformative efficiencies. This isn’t just about moving faster—it’s about fundamentally changing how innovation happens.

Redefining Rebellion: From “Move Fast” to “Think Instantly”

What critics call “cheating,” these visionaries call democratization. When Cluely’s founders say “we want to cheat on everything,” they’re not promoting dishonesty—they’re challenging systems that artificially limit human potential. Lee’s suspension from Columbia for creating Interview Coder wasn’t a setback; it was the catalyst for building a universal platform for AI-augmented performance. This is positive rebellion: breaking the right rules to unlock new possibilities.

The Democratization Revolution

AI is making innovation accessible to more people than ever before—74% of innovators say AI has broadened access to entrepreneurship. Gen Z founders, raised on technology, move fast, experiment freely, and scale globally from their bedrooms. They spot opportunities and create solutions that previous generations might never see.

AI-Powered Entrepreneurship: The Numbers Don’t Lie

Cluely’s meteoric rise illustrates this new paradigm. Within weeks of launch, it attracted 70,000 users and reached $3 million in annual recurring revenue—a pace unimaginable in the social media era. AI-native startups now achieve product-market fit in months, not years, and VC funding is following suit: Cluely secured $15 million in Series A to fuel this rapid growth.

Enterprise Validation and Viral Growth

Cluely isn’t just a consumer phenomenon—it’s already proving itself in enterprise settings, especially in sales, with rapid adoption and real business impact1. The company’s growth team, each with personal audiences over 100,000 followers, exemplifies how the AI generation blends technical prowess with modern marketing.

Positive Disruption: Amplifying, Not Replacing, Human Intelligence

This generation’s rebellion serves a different purpose. While their predecessors connected people and information, the AI generation is focused on amplifying individual human capability. AI isn’t about replacing intelligence—it’s about enabling people to perform at levels never before possible.

A Cultural Shift: Creative Rebels with a Cause

Today’s entrepreneurs are “positive deviants”—rebels with a cause, willing to embrace controversy to advance human potential. Their viral campaigns and user-generated content strategies aren’t just for attention; they’re about demonstrating AI’s real-world impact.

A Utopian Vision: Empowerment at Scale

The future these companies are building isn’t dystopian—it’s utopian. They envision a world where everyone becomes a creator, where technical barriers disappear, and where AI personal assistants are available for every task. Just as Facebook democratized publishing and Twitter democratized broadcasting, the AI generation is democratizing expertise itself.

An Ecosystem of Acceleration

The success of AI-native startups creates a positive feedback loop, inspiring more entrepreneurs and attracting greater investment. The result: an ecosystem where innovation accelerates and barriers to entry continue to fall.

Conclusion: The Spirit of Rebellion Lives On

The entrepreneurs behind Cluely and similar companies aren’t destroying hacker culture—they’re fulfilling its highest aspirations. They represent the evolution from connecting minds to amplifying minds, from breaking things to building intelligence. Their rebellion isn’t about chaos, but about progress: breaking barriers so the rest of us can achieve more than we ever thought possible.

The AI revolution isn’t happening to us—it’s being built by a new generation of audacious entrepreneurs. The rebels are coding, the barriers are falling, and the future is being written in real-time. This is what progress looks like when “impossible” is just the starting line.

This analysis is based on quick data scan of research publications and developments from Stanford University, UC Berkeley, MIT, and Carnegie Mellon University throughout 2024, representing the latest trends in artificial intelligence and machine learning research.

🚀 Like this post? Share it with a founder who’s still hunting for their Delaware C-corp papers and remind them: the next great AI unicorn might be born in San Francisco—but it will grow up everywhere. If they need references on foreign venture funds who can help you access markets, let me know.

For founders based outside the United States, deciding whether to raise capital from American venture capitalists before or after entering the US market is a pivotal and also a costly strategic decision. The optimal approach depends on your industry, market readiness, and operational objectives. Drawing on recent data that we analysed, here’s a comprehensive look at the factors that should guide your choice.

Key Data on Overseas Startups Raising in the US

A study of 153 overseas startups that raised capital in the US reveals several notable trends. On average, these companies secure their first US investment 4.3 years after founding. Nearly 45% obtain US funding within two to five years, while only about 13% do so in their first year. Interestingly, startups founded after 2015 reach US investors more quickly, with those established in 2020 averaging just 1.6 years to their first US investment (due to 2021 bull run).

Patterns: US Hiring vs. Fundraising

The sequence of US hiring and fundraising varies by industry and can influence the scale of capital raised. About 30% of companies hire US employees before securing US funding, a pattern most common in healthcare, biotech, and payments sectors, typically resulting in lower initial capital raised. Roughly 35% raise US funding before making local hires, a trend prevalent in fintech, software, and IT, and these companies tend to secure larger funding rounds. A smaller group, around 14%, hires and raises in the same year, while a niche 3% raise US capital without hiring locally, often in remote-first or specialized sectors. The data suggests that startups raising before hiring in the US are more likely to secure larger investment rounds.

Industry and Geography: What Shapes the Sequence

Industry and geographic origin play a significant role in shaping US entry strategies. Life sciences and regulated sectors, such as healthcare and biotech, often prioritize hiring US talent before fundraising. This approach is driven by the need for local expertise, regulatory navigation, and credibility with investors and customers. In contrast, digital and software startups-especially in fintech and enterprise IT-frequently raise US capital first, leveraging product traction and global relevance to attract investors before building a local team. Startups from Southeast Asia and Australasia are more likely to establish a US presence before fundraising, signaling commitment and reducing perceived execution risk for American investors.

Strategic Implications for Founders

Fundraising Before US Expansion

This approach is best suited for SaaS, fintech, and enterprise software platforms with strong product-market fit and global appeal. Securing US funding before establishing local operations preserves capital for growth, demonstrates capital efficiency, and allows startups to test US market demand before making significant investments. Typically, these companies secure US investment and then set up US operations within two to three years.

Hiring Before Fundraising

For regulated or capital-intensive sectors like healthcare, biotech, or financial services, hiring US talent or leadership before fundraising is often essential. Building a local team enhances credibility, accelerates regulatory approvals, and signals long-term commitment to the market. The typical path involves recruiting key US personnel, setting up local operations, and then approaching US investors.

Simultaneous Approach

Some startups, particularly those with ample resources or operating in highly competitive sectors, pursue parallel strategies-raising funds and hiring in the US simultaneously. While this maximizes speed and market learning, it requires greater capital and operational bandwidth.

Best Practices for US Market Entry

Successful US market entry requires more than just capital or a local presence. Deep market research is essential; founders should avoid assuming that US buyers behave like those in their home markets and must localize their value proposition accordingly. Strategic partnerships with established US players can accelerate credibility and market access, sometimes reducing the need for immediate local hires. Phased rollouts-starting in select regions-allow startups to test and adapt before scaling nationally. For regulated industries, early legal and compliance planning is crucial to avoid costly delays. While many US investors still prefer local teams, especially at early stages, there is a growing acceptance of remote-first models and global teams.

Practical Guidance by Sector

For enterprise software and fintech startups, focus on demonstrating product traction and global relevance. It is often possible to raise from US investors before hiring locally, but you should have a clear plan for US expansion. In healthcare and biotech, prioritize hiring or partnering in the US before fundraising, as local presence is often a prerequisite for regulatory and investor confidence. Regardless of your sector, align your US hiring and fundraising strategies with your operational capacity and market readiness. Investors seek both commitment and capital efficiency.

“There is no universal rule, but your US hiring strategy should align with your market readiness, funding strategy, and operational capacity. Investors want to see commitment, but also capital efficiency and product clarity.”

Bottom Line

There is no one-size-fits-all answer to whether overseas startups should raise capital before or after entering the US market. The optimal sequence depends on your industry, business model, and market strategy. For most SaaS and digital startups, raising capital before building a US team is often preferable-but this usually requires exceptional traction that signals the potential for a 20x or greater return. In regulated or high-touch sectors, establishing a US presence first is crucial to unlocking investor interest and market access. Many successful startups blend both approaches, adapting as they learn from the market and investor feedback. Ultimately, careful planning, deep market understanding, and a tailored strategy are essential for a successful US entry and fundraising journey.

This version 2 research extends our machine learning approach to identify high-potential AI startups from the 2021 vintage, yielding compelling results that further validate our investment methodology.

Project Framework

We applied our established machine learning methodology to identify promising AI ventures in the 2021 cohort, maintaining consistency with our previous analysis.

Our approach continued to incorporate:

- Six predictive variables: Industries, Company Description, Founder Biography, Founder Gender, Location, and Educational Background

- A dual-model ensemble combining Random Forest and XGBoost algorithms

- Advanced text vectorization for unstructured data

Portfolio Performance

Our model selected 19 companies from the 2021 vintage all the way till 2024 (evenly distributed) with the following current performance:

- 9 companies (47.37%) have already achieved valuations exceeding $500M, including:

  - Perplexity AI: Reached $9 billion valuation in December 2024, with over $100 million in annualized revenue as of March 2025

  - Cyera: Secured $300 million in Series D funding, reaching a $3 billion valuation in November 2024

  - Hippocratic AI: Achieved unicorn status with a $1.64 billion valuation in January 2025

  - Anumana: Showcasing leadership in AI-powered cardiovascular solutions

  - World Labs, Protect AI, Mytra, DatologyAI, and Revefi

- 4 companies (21.05%) demonstrate strong growth trajectories

- 4 companies (21.05%) are too early in their development to evaluate conclusively

- 2 companies (10.53%) have not ceased operations but are unlikely to achieve significant success

This 47.37% high-performer rate significantly outperforms the best venture capital unicorn success rates of 5% (Sequoia), with potential to reach 68.42% as companies currently on track continue to develop. The 10.53% failure rate thus far remains substantially lower than industry averages of 75%, not factoring in various constraints of real investing.

Our model continues to demonstrate strong predictive capability while serving as a decision support tool rather than a replacement for comprehensive due diligence.

We will continue to analyze additional vintages across larger geographies and sectors, publishing results as they become available.

Disclaimer: This analysis is for educational purposes only. Past performance does not guarantee future outcomes.

As a Singapore-based VC, I've witnessed how innovation ecosystems evolve naturally when properly supported. Singapore initially emphasised deep tech but allowed market forces to shape developments organically.

Critiques of New Zealand's funding imbalance misses a crucial point: successful startups need significant market power quickly, regardless of their technological depth. Creating numerous small non-deep tech ventures won't deliver the economic impact New Zealand seeks. You need to continue to focus on both.

Three focused recommendations:

1. Establish a national coordination body with a hands-on advisory panel of experienced entrepreneurs and investors who can directly mentor founders to scale globally. This addresses both fragmentation and practical scaling challenges.

2. Develop diverse funding mechanisms prioritizing ventures with global potential rather than simply increasing startup quantities. Government initiatives on grants, fund of funds support should continue with momentum but understand the signs of change and adapt to it.

3. Implement more talent development/retention programs, one example to take note of is Singapore's NUS Overseas College, which immerses students in innovation hubs like Silicon Valley, creating globally-minded entrepreneurs with valuable networks. Net new migration into New Zealand needs to be positive over time, but this is likely to be the toughest challenge yet.

New Zealand should focus more on building globally competitive companies with proper ecosystem support. I know you can do it. You know you can do it. Whāia te iti kahurangi - pursue that which is precious.

Investing in Southeast Asia for many years now, I’ve witnessed cycles of growth, correction, and reinvention. Yet, the current moment feels uniquely challenging—and transformative. The sectors emerging at the forefront for the next few years—B2B and deep tech—are ones where many of us have struggled to find consistent success over the last decade, especially compared to B2C and fintech. At the same time, our fund sizes have grown larger than ever, but the market size we operate in hasn’t proportionally expanded. This mismatch creates a tension that demands we rethink how we approach risk, ambition, and execution.

Since the 2022-2023 crash, large VC funds have increasingly gravitated toward safer, private-equity-like deals while becoming more multi-stage. It’s understandable; preserving capital feels prudent in uncertain times. But playing it safe won’t build the future. We cannot afford to give up on audacious founders—those who dare to think big and aim for transformative impact. These are the people who will unlock new markets, redefine industries, and create outsized returns—not just for investors but for society as a whole.

To thrive in this new era, we must retool and reinvent ourselves as investors. This means recalibrating how we assess risk, developing deeper expertise in emerging sectors, and being smarter and more calculated in our bets (keeping Power Law Distribution firmly in mind). It’s not about reckless optimism; it’s about supporting bold ideas with discipline and clarity.

This is a call to action: let’s not retreat into comfort zones or limit our vision. Let’s figure out what needs to change—within ourselves, our teams, our ecosystems, our founders, and our strategies—and make those changes happen. The future belongs to those willing to take calculated risks on founders with big dreams. Let’s ensure we’re part of building that future.

As we journey through the fascinating world of top venture capitalists, we uncover a treasure trove of insights that shed light on the educational backgrounds, career paths, and the shifts in the demographic landscape of the industry. This data-driven exploration aims to provide a comprehensive view for limited partners, aspiring VCs, and students, as we delve into what makes these venture capitalists stand out.

Key Insights and Importance of Education

Top Undergraduate Universities

- Stanford University (13% of VCs)

- Harvard University (8%)

- MIT (7%)

- University of Pennsylvania (4%)

- Yale University (3%)

Stanford's dominance is unmistakable, emphasizing its pivotal role in the tech VC landscape.

Top Undergraduate Majors

- Engineering, Computer Science, and Related Disciplines (30%)

- Economics (17%)

- Business or Management (11%)

- Public Policy, Political Science, or Government (8%)

- Mathematics & Applied Mathematics (6%)

The importance of a STEM background remains evident, but there's a significant representation of business-related studies, reflecting the need for a multifaceted skill set.

Graduate Education

Graduate Degrees: 67% of VCs hold graduate degrees from esteemed institutions:

  - Stanford GSB (14%)

  - Harvard Business School (12%)

  - Columbia Business School (3%)

  - Wharton School, University of Pennsylvania (3%)

  - MIT Sloan School of Management (2%)

This trend speaks to the value placed on continuous learning and specialization in fields like business, finance, and technology.

Entrée into the Venture Capital Arena

- Direct Entry: 23% of VCs under 45 started their careers directly in VC, compared to only 13% for those over 45. This early specialization trend highlights a demand for nuanced expertise at the outset of one's career.

A New Generation's Rise

- Technical Backgrounds: 38% of VCs under 45 vs. 25% over 45, indicating an industry shift toward tech-savvy investors.

- Investment Banking: Investment banking serves as an initial career path for 45% of young VCs vs. 30% of their older counterparts, showcasing the sector's increasing integration with venture capital.

Experience and Impact in VC

- Founder Experience: 29% of VCs under 45 were founders, in contrast to 37% for those over 45, signifying a slower but still prevalent trend of operational experience.

- Analytical Backgrounds: Both cohorts show high levels of analytical savviness, with older VCs boasting experience in diverse roles like sales, strategic planning, and product management.

Diversifying Demographics

- Female Representation: A gradual increase in the younger cohort to 12% vs. 8% for older VCs, signaling progress in industry diversity.

- International Backgrounds: 36% of top VCs have international roots, underlining the global nature of venture capital, with significant representation from China, India, and Europe.

Key Insights for Stakeholders

For Limited Partners

- Invest in funds with multi-generational VCs to leverage industry trends and seasoned experience.

- Recognize the evolution in career paths, with younger VCs more likely to have an analytical or entrepreneurial background.

For Aspiring VCs and Students

- While technical education is advantageous, business and economic knowledge is equally important for understanding the broader market dynamics.

- Seek internships in investment banking, consulting, sales, or product management for hands-on experience.

Advances in Venture Investment Trends

- Industry Evolution: The venture capital landscape now leans towards sector specialization, with notable increases in tech-focused investments (most recently in AI).

- Diversification: Despite incremental progress in gender diversity, the industry recognizes the need for further internationalization and broader inclusivity.

In conclusion, the profile of top venture capitalists has evolved, adapting to changing industry needs, educational trends, and innovation. The combination of technical knowledge, diverse professional backgrounds, and a nuanced understanding of market dynamics remains key to navigating the entrepreneurial journey and gaining success in venture capital.

The artificial intelligence gold rush isn't just about the tech giants and mega VCs anymore. A new quick and dirty analysis of the top 390 investments in top AI startups reveals a fascinating shift in who's really driving innovation in the AI ecosystem, with individual operators and specialized firms playing an increasingly crucial role.

The Rise of Operator-Investors

Leading the pack is Elad Gil, with investments in 12 cutting-edge AI companies including Perplexity and Character.ai. Gil's investment pattern reveals a keen focus on foundational AI technologies that could reshape entire industries. But what's particularly interesting is how former tech executives are leveraging their operational experience to spot the next big thing in AI.

Take Nat Friedman (former GitHub CEO) and Scott Belsky (Adobe CPO), who have each made strategic bets on three AI startups. Their investments often focus on developer tools and creative AI applications – areas where their deep industry expertise provides unique insight into market needs.

The New Wave of Specialized Firms

While traditional VCs still dominate in terms of dollar amounts, smaller, specialized firms are proving to be remarkably influential in shaping the AI landscape. Firms like Alumni Ventures (18 investments) and HongShan (19 investments) are punching above their weight, particularly in early-stage deals.

What sets these firms apart is their focused approach. Rather than casting a wide net, they're making concentrated bets in specific AI domains:

• Enterprise AI infrastructure
• Developer tools and platforms
• AI in healthcare and biotech
• Generative AI applications

Geographic Diversification

Perhaps most intriguing is the growing geographic diversity of AI investments. While Silicon Valley remains the epicenter, we're seeing increased activity in:

- Toronto (Cohere)

- London (DeepMind spinoffs)

- Berlin (Helsing)

- Beijing (Moonshot AI)

What This Means for the Future

The emergence of these new power players suggests a maturing AI ecosystem where expertise and specialized knowledge are becoming as important as capital. For founders, this means more options for smart money that comes with deep operational expertise and focused support.

The trend also points to a future where AI development might be less centralized than previous tech waves. With individual operators and specialized firms backing startups across the globe, we're likely to see more diverse and innovative applications of AI technology.

For those watching the AI space, keep an eye not just on the big names, but on these emerging kingmakers. They're the ones spotting and nurturing the next generation of AI breakthroughs, often before the bigger players take notice.

The AI investment landscape is rapidly evolving, and while the headlines might focus on the biggest checks, it's these individual operators and specialized firms that are often the first to spot and support the most innovative AI startups. Their growing influence suggests a future where AI development is more distributed, diverse, and potentially more impactful than ever before.

The artificial intelligence (AI) startup ecosystem is a complex network of founders, investors, and companies working together to drive innovation and growth. To better understand the dynamics of this ecosystem, we conducted a quick and dirty network analysis using GAT (Pytorch Geometric) of 601 AI startups (deemed successful since 2013), focusing on the connections between founders and investors. Our findings provide valuable insights for founders seeking to raise funds at different stages of their startup journey.

Our analysis revealed the top 10 most influential investors in the AI startup ecosystem based on their number of connections:

1. Sequoia Capital: 43 connections

2. Insight Partners: 41 connections

3. Tiger Global Management: 40 connections

4. NVIDIA: 35 connections

5. Andreessen Horowitz: 34 connections

6. HongShan: 32 connections

7. Lightspeed Venture Partners: 29 connections

8. Google Ventures: 28 connections

9. BlackRock: 25 connections

10. Intel Capital: 24 connections

These investors play a significant role in shaping the AI startup landscape through their investments and partnerships for the last 10 years.

Series A Investors

1. Sequoia Capital

2. Andreessen Horowitz

3. Lightspeed Venture Partners

4. Google Ventures (tend to invest a little more in Series B in recent years)

5. Intel Capital

Series B and Later-Stage Investors

1. Insight Partners

2. Tiger Global Management

3. NVIDIA

4. HongShan (Focused on China mostly, going international now)

5. BlackRock (More later stages)

Advice for Founders in AI

Based on our analysis, we recommend the following approach for founders seeking to raise funds:

1. Series A: When raising a Series A round, focus on investors like Sequoia Capital, Andreessen Horowitz, and Lightspeed Venture Partners. These investors have a strong track record of backing early-stage startups and can provide valuable support beyond just capital.

2. Series B and Later: As you progress to Series B and later rounds, consider investors like Insight Partners, Tiger Global Management, and NVIDIA. These investors have the resources and expertise to help startups scale rapidly and navigate the challenges of later-stage growth.

3. Build Relationships: Regardless of the stage of your startup, it's important to build relationships with investors early on. Attend industry events, participate in startup accelerators, and leverage your network to get introductions to potential investors.

Disclaimer: Of course, past track record does not mean the future will be the same.

In Southeast Asia's vibrant venture capital and startup landscape, a transformation echoes through the realms of enterprise software and deep technology as they descend upon us. As we embark on this new era, nations, both burgeoning and mature, ponder deeply on nurturing startups that reach for the stars. The venture funding sphere, once a bastion of bold dreams, now yearns for revolutionary creations and teams with global aspirations.

Reflecting upon this, I have discerned three pearls of findings:

First, the Luminance of Product and Technology:

In an age where only the extraordinary captivates, a startup's product or intellectual property (IP)/technology must not just innovate but shine among the global elite. This shift towards top-tier innovation resonates with trends favouring enterprise and deep technology.

Second, The Founding Team's Odyssey Beyond Borders:

The spirit of the founding team is pivotal. In a world without borders, founders must carry belief, confidence, and courage to journey globally. This international vision is the heartbeat of venture capitalists seeking market disruptors.

Third, The Art of Skill and Global Canvas:

The founding team's mastery in global expansion is crucial. Skills in international storytelling, team building, and capital raising form the fabric of their venture. Success is increasingly measured by the global footprint and international business acumen.

As we conclude, remember that in the universe's grand dance, every venture has its place. The pursuit of large venture capital FDI is a journey through an ocean of possibilities.

Echoing Rumi, "You were born with wings, why prefer to crawl through life?" Startups and nations must realize their potential to soar in the global market. Let this understanding uplift them toward horizons of success and innovation.

In recent years, researchers and practitioners alike have been studying the personalities of successful startup founders to understand what makes them tick. By analysing the Big Five personality traits—openness, conscientiousness, extraversion, agreeableness, and neuroticism—we can gain insights into the characteristics that contribute to a founder's success or failure. In this blog post, we will use what a quick Google search list as the best and worst rated founders and take a look at their personality traits. However, I only did a small sample set as a quick experiment. 

Best Rated Founders

The following five founders stand out as examples of those who excel in their roles:

1. Patrick Collison (Stripe)

2. David Vélez (Nubank)

3. Max Levchin (Affirm)

4. Brian Armstrong (Coinbase)

5. Stewart Butterfield (Slack)

These founders generally exhibit high levels of openness and conscientiousness, moderate to high agreeableness, moderate extraversion, and low neuroticism. These traits help them navigate the challenges of building and growing successful startups.

Worst Rated Founders

On the other hand, there are founders whose actions and decisions led to negative consequences for themselves and their companies. Some notable examples include:

1. Travis Kalanick (Uber)

2. Elizabeth Holmes (Theranos)

3. Parker Conrad (Zenefits)

4. Billy McFarland (Fyre Festival)

5. Adam Neumann (WeWork)

Founders here often display high openness and extraversion, but extremely low conscientiousness and agreeableness, along with low neuroticism. Their actions and decision-making processes contributed to the failures of their respective ventures.

Findings

Based on the analysis of these founders, several patterns emerge:

- Successful founders typically exhibit high openness and conscientiousness, moderate to high agreeableness, moderate extraversion, and low neuroticism.

- Unsuccessful founders often show high openness and extraversion, but very low conscientiousness and agreeableness, and low neuroticism.

- Sociopathic founders are characterized by very high extraversion, very low agreeableness, conscientiousness, and neuroticism, with variable openness.

- Founders with Narcissistic Personality Disorder (NPD) tend to have high extraversion, very low agreeableness, moderately low conscientiousness and neuroticism, with no clear pattern in openness.

As Rumi once said, "What you seek is seeking you." Similarly, the qualities that make great founders also attract them to entrepreneurship. 

The Analytical Pathway: A Common Ground

Our findings reveal a striking pattern: a substantial 91.1% of top venture capitalists previously held positions in analytical fields. This statistic underscores the value of an analytical mindset in the world of venture capital. Analytical roles, encompassing areas such as financial analysis, investment management, and data-driven decision-making, equip VCs with the acumen to dissect complex market trends, evaluate business models, and make calculated investment decisions. The high percentage of VCs with this background suggests that an analytical foundation is not just beneficial but perhaps essential in navigating the intricate landscape of venture investment.

Entrepreneurial Experience: Valuable but Less Common

Contrary to the popular belief that most successful VCs are former entrepreneurs, our analysis paints a different picture. Only 21.5% of the top venture capitalists were founders before stepping into their current roles. While this figure highlights the significance of entrepreneurial experience, it also clarifies that it's less common than one might expect. Having been in the founder's shoes does provide unique insights into the challenges and dynamics of starting and scaling a business. However, it appears that having a founder's background, while advantageous, is not a predominant trait among the world's leading VCs.

Diverse Roads to the Top

The journey to becoming a top VC is diverse and multifaceted. While a strong analytical background is prevalent among these successful individuals, it is by no means the only path. The world of venture capital values a variety of experiences, whether it's steering a startup through turbulent waters or navigating the complexities of financial markets. This diversity in backgrounds contributes to a richer, more versatile approach to investment strategies, benefiting both the VCs and the innovative companies they choose to back.

Conclusion: Blending Analytical Acumen with Varied Experiences

The landscape of venture capital is as varied as it is challenging. Our analysis reveals that top venture capitalists often share a common thread of analytical experience, providing them with the skills necessary to assess and manage risk effectively. However, the path to becoming a leading VC is not monolithic. Experiences as diverse as entrepreneurship, financial management, and technology development all play a role in shaping the instincts and insights of these investment leaders. As the venture capital industry continues to evolve, the blend of analytical rigor and diverse experiences will remain pivotal in identifying and nurturing the next generation of groundbreaking companies.