Andrew Ng Just Solved the Wrong Problem (And Why That's Actually Perfect)

A fresh conversation on the No Priors podcast has Andrew Ng declaring product management the new bottleneck in AI startups. His core thesis: while engineering teams can now build prototypes "10x faster" thanks to AI-assisted coding, product teams are still operating with pre-AI workflows, creating a dangerous mismatch that's "really painful" for founders.

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.

Why Venture Studios Rarely Deliver as Fund Returners – Lessons from the VC Trenches

Lately, I've been fielding questions about venture studios – those ambitious setups that aim to manufacture startups at scale. On paper, they look revolutionary: higher success rates, juicier IRRs, and a systematic approach to innovation. But as someone who's evaluated countless models for our portfolio, I can tell you the reality often falls short, especially when viewing them as an asset class for serious fund returns. Today, let's unpack the evidence why most don't work, and the rare formula that makes some succeed. This isn't armchair theory; it's grounded in data, case studies, and insights from my own deal flow.

The Seductive Promise – And Why It Often Crumbles

Venture studios burst onto the scene promising to fix VC's biggest pains: inconsistent dealflow, high failure rates, and inefficient capital deployment. The stats are eye-catching – studio-backed companies reach Series A 72% of the time versus 42% for traditional startups, with IRRs hitting 53% compared to 21.3% norms. They shave timelines too, getting to Series A in about 25 months instead of 56. I've seen pitches where studios position themselves as "startup factories," and honestly, who wouldn't want that in their portfolio?

But here's the rub: most flame out within 24 months. That's not random bad luck; it's baked into the model. In my experience reviewing studio proposals, the hype masks deep structural flaws that make them a risky bet for LPs seeking scalable, reliable returns. Let's break it down with the evidence.

The Structural Cracks That Sink Most Studios

From stakeholder wars to capital headaches, these issues aren't edge cases – they're systemic.

The Impossible Balancing Act of Four Stakeholders

Traditional VCs have it simple: LPs provide capital, GPs pick winners, founders execute. Studios? They must satisfy entrepreneurs (who want autonomy and upside), studio staff (needing comp and growth), follow-on investors (demanding clean terms), and LPs (chasing returns). It's a recipe for conflict. I've passed on studios where entrepreneurs felt like cogs in a machine, leading to talent flight and diluted innovation. Data shows this tension torpedoes governance and decision-making.

Resource Dilution: Great in Theory, Impossible at Scale

Studios tout "hands-on support" as their secret sauce, but the numbers don't add up. Launch one company a week with 100 staff? That's barely two full-timers per venture – hardly the deep operational help promised. Scale up, and quality tanks; stay small, and you can't deploy enough capital for fund-level returns. I've seen this play out in SEA studios that overextend, ending up as glorified consultancies rather than return generators.

Fundraising Nightmares and Wonky Capital Structures

Evidence from 2020-2024 reveals traditional VCs are 1.6x more likely to close funds than studios. Why? Complex valuations create the "valuation trap," higher fees scare LPs, and perceptions of "dead equity" deter later rounds. Add longer timelines and regulatory ambiguities (fund or operating company?), and you're burning cash before deploying it. In my due diligence, this often signals execution risk – markets shift, and the studio's left holding the bag.

Why Institutions Steer Clear

For big allocators like pensions or endowments, studios are a tough fit. Most cap at sub-$200M funds, too small for meaningful commitments, and their sector-specific focus creates concentration risks. Metrics suffer from survivorship bias too – we only hear about winners, not the ideation failures. Corporate studios? Even worse, often becoming "innovation theater" bogged down by parent company bureaucracy and mismatched incentives.

The Winning Formula: What It Takes to Make Studios Work

Not every studio crashes and burns. From my vantage point, the successes – think specialized operators in niches like fintech or healthtech – share a rigorous playbook. Here's the evidence-backed essentials, scored by their impact based on research and my observations:

  • Strategic Focus & Specialization (9.2/10): Ditch the generalist approach; vertical-agnostic studios succeed only 19% of the time. Winners leverage proprietary edges like data access or industry networks – crucial in emerging markets like SEA.

  • Operational Excellence & Proven Playbooks (9.0/10): Codified processes for everything from ideation to scaling. This includes stage-gates that validate ideas early, reducing waste and accelerating paths to revenue.

  • Significant Ownership Stakes (8.8/10): Holding 30-50% equity justifies the ops investment and captures value from solid exits, not just unicorns.

  • Experienced Team & Leadership (8.7/10): A mix of serial entrepreneurs, functional experts, and investors. Weak teams are a red flag in my evals – you need proven builders to navigate the chaos.

  • Quality Control Systems (8.6/10): Strict validation thresholds for market fit, tech viability, and business models. This kills duds fast and preserves capital.

  • Proper Governance & Alignment (8.5/10): Dual structures separating ops from investing, with clear equity rules to align all stakeholders.

  • Adequate Capitalization (8.3/10): Patient, reserved funding – balance sheet style, not skimping on ops budgets.

  • Market-First Approach (7.9/10): Prioritize customer validation over building; it's the best defense against vanity projects.

Implement all eight? You've got a shot at outsized returns. Skimp on any, and you're statistically doomed.

Wrapping Up: A VC's Practical Advice

Venture studios aren't the VC extinction event some predict – they're a niche tool, better for hands-on operators than broad institutional plays. The evidence shows their flaws often outweigh the upsides for fund returners, but in the right hands, they can be game-changers, especially in underserved regions.

If you're building or backing one, stress-test against these factors. 



    MIT's AI Reality Check: Why 95% of Pilots Are Failing (And What It Means for Startups)

    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.

    Education, Autonomy & Ambition: Lessons from Stanford for Singapore’s Next Generation of Founders

    If you hang around Block 71 long enough, you’ll hear two recurring complaints from investors and founders: “Talent is thin,” and “We need better deal-flow from the universities.” Both issues share a common root—how our education system shapes (or stifles) entrepreneurial ambition.

    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