AGI Will Replace Average VCs. The Best Ones? Different Game.

The performance gap between tier-1 human VCs and current AI on startup selection isn't what you think. VCBench: a new standardized benchmark where both humans and LLMs evaluate 9,000 anonymized founder profiles, shows top VCs achieving 5.6% precision. GPT-4o hit 29.1%. DeepSeek-V3 reached 59.1% (though with brutal 3% recall, meaning it almost never said "yes").[1]​

That's not a rounding error. It's a 5-10x gap in precision, the metric that matters most in VC, where false positives (bad investments) are far costlier than false negatives (missed deals).[1]​

But here's what the paper doesn't solve: VCBench inflated the success rate from real-world 1.9% to 9% for statistical stability, and precision doesn't scale linearly when you drop the base rate back down. The benchmark also can't test sourcing, founder relationships, or board-level value-add, all critical to real fund performance. And there's a subtle time-travel problem: models might be exploiting macro trend knowledge (e.g., "crypto founder 2020-2022 = likely exit") rather than true founder quality signals.[2]​

Still, the directional message is clear: there is measurable, extractable signal in structured founder data that LLMs capture better than human intuition. The narrative that "AI will augment but never replace VCs" is comforting and wrong. The question isn't if AGI venture capitalists will exist—it's when they cross 15-20% unicorn hit rates in live portfolios (double the best human benchmark) and what that phase transition does to the rest of us.​

The math is brutal for average funds

Firebolt Ventures has been cited as leading the pack at a 10.1% unicorn hit rate—13 unicorns from 129 investments since 2020. (Stanford GSB VCI-backed analysis, as shared publicly) Andreessen Horowitz sits at 5.5% on that same "since 2020" hit-rate framing, albeit at far larger volume. And importantly: Sequoia fell just below the 5% cutoff on that ranking—less because of a lack of wins and more because high volume dilutes hit rate.[3]​

The 2017 vintage—now mature enough to score—shows top-decile funds hitting 4.22x TVPI. Median? 1.72x. Most venture outcomes are random noise dressed up as strategy.​

Here's the punchline: PitchBook's 20-year LP study has been summarized as finding that even highly skilled manager selectors (those with 40%+ hit rates at picking top-quartile funds) generate only ~0.61% additional annual returns, and that skilled selection beats random portfolios ~98.1% of the time in VC (vs. ~99.9% in buyouts). (PitchBook analysis, as summarized).​

If the best fund pickers in the world can barely separate signal from noise, what does that say about VC selection itself?​

AGI VCs won't need warm intros

Current ML research suggests models can identify systematic misallocation even within the set of companies VCs already fund. In "Venture Capital (Mis)Allocation in the Age of AI," the median VC-backed company ranks at the 83rd percentile of model-predicted exit probability—meaning VCs are directionally good, but still leave money on the table. (Lyonnet & Stern, 2022). Within the same industries and locations, the authors estimate that reallocating toward the model's top picks would increase VCs' imputed MOIC by ~50%.​

That alpha exists because human VCs are bottlenecked by:

Information processing limits. Partners evaluate ~200-500 companies/year. An AGI system can scan orders of magnitude more continuously.​

Network constraints. You can't invest in founders you never meet. AGI doesn't need warm intros—it can surface weak signals from GitHub velocity, hiring patterns, or web/social-traffic deltas before the traditional network even sees the deck.​

Cognitive biases. We over-index on storytelling, pedigree, and pattern-matching to our last winner. Algorithms don't care if the founder went to Stanford or speaks confidently. They care about predictors of tail outcomes.​

Bessemer's famous Anti-Portfolio—the deals they passed on Google, PayPal, eBay, Coinbase is proof that even elite judgment systematically misfires. If the misses are predictable in hindsight, they're predictable in foresight given the right model.​

The five gaps closing faster than expected

AGI isn't here yet because five bottlenecks remain:

Continual learning. Current models largely freeze after training. A real VC learns from every pitch, every exit, every pivot. Research directions like "Nested Learning" have been proposed as pathways toward continual learning, but it's still not a solved, production-default capability.​

Visual perception. Evaluating pitch decks, product demos, team dynamics from video requires true multimodal understanding. Progress is real, but "human-level" is not the default baseline yet.​

Hallucination reduction. For VC diligence—where one wrong fact about IP or founder background kills the deal—today's hallucination profile is still too risky. Instead of claiming a universal "96% reduction," the defensible claim is that retrieval-augmented generation plus verification/guardrails can sharply reduce hallucinations in practice, with the magnitude depending on corpus quality and evaluation method. ​

Complex planning. Apple's research suggests reasoning models can collapse beyond certain complexity thresholds; venture investing is a 7-10 year planning problem through pivots, rounds, and market shifts.​

Causal reasoning. Correlation doesn't answer "If we invest $2M vs. $1M, what happens?" Causal forests and double ML estimate treatment effects while controlling for confounders. The infrastructure exists; it's not yet integrated into frontier LLMs. Give it 18 months.​

Unlike the theoretical barriers to general AGI (which may require paradigm shifts), the barriers to an AGI VC are engineering problems with known solutions.​

The phase transition nobody's pricing in

Hugo Duminil-Copin won the Fields Medal for proving how percolation works: below a critical threshold, clusters stay small. Above it, a giant component suddenly dominates. That's not a metaphor—it's a rigorous model of network effects.​

Hypothesis (not settled fact): once AGI-allocated capital crosses something like 15-25% of total VC AUM, network effects could create nonlinear disadvantage for human-only VCs in deal flow access and selection quality. Why? Because:​

Algorithmic funds identify high-signal companies before they hit the traditional fundraising circuit. If you're a founder and a fund can produce a high-conviction term sheet on a dramatically shorter clock—with clear, inspectable reasoning—you take the meeting.​

Network effects compound. The AGI with the best proprietary outcome data (rejected deals, partner notes, failed pivots) trains better models. That attracts better founders. Which generates better data. Repeat.​

LPs will demand quantitative benchmarks. "Show me your out-of-sample precision vs. the AGI baseline" becomes table stakes. Funds that can't answer get cut.​

The first AGI VC to hit 15% unicorn rates and 6-8x TVPI will trigger the cascade. My estimate: 2028-2029 for narrow domains (B2B SaaS seed deals), 2030-2032 for generalist funds. That's not decades—it's one fund cycle.​

What survives: relationship alpha and judgment at the edge

The AGI VC will systematically crush humans on sourcing, diligence, and statistical selection. What it won't replace—at least initially:

Founder trust and warm intros. Reputation still opens doors. An algorithm can't build years of relationship capital overnight.​

Strategic support and crisis management. Board-level judgment calls, operational firefighting, ego management in founder conflicts—those require human nuance.​

Novel situations outside the training distribution. Unprecedented technologies, regulatory black swans, geopolitical shocks. When there's no historical pattern to learn from, you need human synthesis.​

VCs will bifurcate: algorithmic funds competing on data/modeling edge and speed, versus relationship boutiques offering founder services and accepting lower returns. The middle—firms that do neither exceptionally—will get squeezed out.​

Operating system for the transition

If you're building or managing a fund today, three moves matter:

1. Build proprietary outcome data now. The best training set isn't Crunchbase—it's your rejected deal flow with notes, your portfolio pivots, your failed companies' post-mortems. That's the moat external models can't replicate. Track every pitch, every IC decision, every update. Structure it for ML ingestion.​

2. Instrument your decision process. Precommit to hypotheses ("We think founder X will succeed because Y"). Log the reasoning. Compare predicted vs. actual outcomes quarterly. This builds the feedback loop that lets you detect when your mental model is miscalibrated—and when an algorithm beats you.​

3. Segment where you add unique value vs. where you're replaceable. If your edge is "I know this space and can move fast," you're exposed. If it's "founders trust me in a crisis and I've navigated three pivots with them," you're defensible. Be honest about which deals came from relationship alpha versus statistical pattern-matching. Double down on the former; automate the latter.​

The real test

In three years, when an AGI fund publishes live performance data showing 12-15% unicorn rates and 5-6x TVPI, the LP conversation changes overnight. Not because the technology is elegant—because the returns are real and the process is transparent.​

That's the moment VCs have to answer: What alpha do we generate that a model can't? For many funds, the answer will be uncomfortable. For the best ones—the ones who've always known that determination, speed, and earned insight compound faster than credentials—it'll be clarifying.​

The AGI VC era doesn't kill venture capital. It kills the pretense that average judgment plus a warm network equals outperformance. What's left is a smaller, sharper game where human edge has to be provable, not performative.​

And if you can't articulate your edge in a sentence—quantifiably, with evidence—you're not competing with other humans anymore. You're competing with an algorithm that already sees your blind spots better than you do.​

  1. https://arxiv.org/pdf/2509.14448.pdf
  2. https://www.reddit.com/r/learnmachinelearning/comments/1no8xji/vcbench_new_benchmark_shows_llms_can_predict/
  3. https://www.linkedin.com/posts/ilyavcandpe_top-unicorn-investors-by-hit-rate-since-2020-activity-7362200145880367104-7zTv


The LeCun Pivot: Why the Smartest Researcher in AI Just Changed His Mind—Publicly

Yann LeCun, the Turing Award winner who helped build the GPU-fueled LLM machine, just walked away from it. He didn't retire. He didn't fade. He started a new company and said out loud: we've been optimizing the wrong problem.

That's not ego protection. That's credibility.

What changed

For three years, while Meta poured hundreds of billions into scaling language models, LeCun watched the returns flatten. Llama 4 was supposed to be the inflection point. Instead, the benchmarks were manipulated and the real-world performance was middling. Not because he lacked conviction—because he paid attention to what the data was actually saying.

His diagnosis: predicting the next token in language space isn't how intelligence works. A four-year-old processes more visual data in four years than all of GPT-4's training combined. Yet that child learns to navigate the physical world. Our LLMs can pass the bar exam but can't figure out if a ball will clear a fence.

The implication: we've been solving the wrong problem at massive scale.

The funder's dilemma

Here's what makes this important for founders and investors: LeCun isn't alone. Ilya Sutskever left OpenAI making the same call. Gary Marcus has been saying it for years. The question isn't whether they're right—it's how to position when the entire industry is collectively getting less wrong, but slowly.

LeCun's answer is world models—systems that learn to predict and simulate physical reality, not language. Instead of tokens, predict future world states. Instead of chatbots, build systems that understand causality, physics, consequence.

Theoretically sound. Practically? Still fuzzy.

His JEPA architecture learns correlations in representation space, not causal relationships. Marcus, his longtime critic, correctly notes this: prediction of patterns is not understanding of causes. A system trained only on balls going up would learn that "up" is the natural law. It wouldn't understand gravity. Same correlation problem, new wrapper.

What founders should actually watch

The real lesson isn't which architecture wins. It's that capital allocation is broken and about to correct.

Hundreds of billions flowed into scaling LLMs because the returns were obvious and fast—chips, cloud, closed APIs. The infrastructure calcified. Investors became trapped in the installed base. When the problem shifted from "scale faster" to "solve different," the entire system had inertia.

Now LeCun, with €500 million and Meta's partnership, is betting that world models will see traction faster than skeptics expect. Maybe he's right. Maybe the robotics industry, tired of neural networks that fail on novel environments, will actually deploy these systems. Maybe autonomous vehicles finally move because prediction of physical futures beats reactive pattern-matching.

Or maybe it takes a decade and world models remain research while LLMs compound their current dominance.

For founders: this is the opening. When paradigm-level uncertainty exists, the cost of hedging drops. Build toward physical understanding, not linguistic sophistication. Robotics, manufacturing, autonomous systems—these verticals benefit immediately from world models and can't be solved by bigger LLMs. That's your wedge.

The adaptability play

What separates LeCun's move from ego-driven pivots: he didn't blame market conditions or bad luck. He said: "I was wrong about where to allocate effort, and here's why."

That transparency that public course-correction without shame changes how people bet on him.

The founders who win in 2026-2027 won't be the ones married to LLM scaling or world model purity. They'll be the ones who notice when reality diverges from the plan and move—fast, openly, without defensiveness.

LeCun just did that at scale.

The question isn't whether he's right about world models. It's whether his willingness to change publicly, with evidence, keeps him first-mover on whatever intelligence actually looks like next.

Climate Tech in 2026: The Founder's Playbook

The climate tech sector is correcting. After the hype peak of 2021—$51 billion in funding—reality hit hard. Funding dropped 75% by 2024. AI vacuumed up the oxygen. Generalist VCs fled. What's left is brutal clarity: only capital-efficient, economically-defensible businesses survive. This is actually good news for the right founders.

The pattern is familiar. In Cleantech 1.0, founders built for virtue. They believed the mission transcended unit economics. Investors believed it too. Then gravity reasserted. Now, in 2026, the founders winning aren't the most ideologically pure—they're the fastest learners operating in spaces where climate solutions and profit align naturally. That convergence is real. AI's power hunger is creating $100+ billion in infrastructure demand. Industrial customers are electrifying and saving money. Adaptation is no longer abstract; it's quantifiable risk reduction. The founders who see this clearly, move fast, and adjust when reality diverges from assumptions will outcompete the ones still waiting for policy to validate their dreams.

What Separates Fundable Climate Founders

First: ruthless unit economics focus. Your climate impact is a feature, not your business model. Lead with cost reduction, reliability improvement, or regulatory compliance value. Sustainability is the bonus. Companies solving problems that generate immediate economic returns—regardless of climate benefit—are three years ahead of those betting on green premiums or subsidies. Test this: can your customer buy your product if the Inflation Reduction Act evaporates tomorrow? If not, you're not building a durable business.

Second: capital efficiency like your life depends on it. Hardware climate startups raised massive rounds in 2021 at inflated valuations. They're now burning cash with no path to Series B. The founders winning in 2026 are raising half as much, hitting milestones with it, and extending runway to 24+ months between rounds. This isn't conservatism; it's survival instinct. Assume 18-24 months to next capital, not 12-15. Build backward from that reality.

Third: strategic positioning over technical perfection. Breakthrough innovation is necessary but not sufficient. You need corporate acquirers to see themselves in your business. Identify 3-5 likely buyers (heat pump OEMs, industrial conglomerates, utilities, hyperscalers) before closing your seed round. Pilot with them. Build product around their workflows. The M&A market is open and accelerating—doubling in 2025. Your exit isn't IPO; it's acquisition. Optimize for that.

Fourth: cognitive flexibility meets determined execution. The best climate founders hold convictions lightly. They commit fully to current hypotheses, measure relentlessly, and pivot when data demands it—without ego. Industrial decarbonization looked like broad-market play two years ago; now the wins are vertical-specific (cement, steel, chemicals). Adaptation was niche; now it's 28% of deals. Grid software was boring; now it's critical infrastructure. The founders shipping fast, gathering customer evidence, and adjusting course are outpacing those white-knuckling outdated strategies.

The Operating Tactic That Works

Install a monthly red-team session. Invite your most skeptical advisor, your closest customer, and your CFO. The question: what would kill this business in six months? Force specificity. Pre-commit to metrics that trigger a pivot or sunset. Don't wait for funding to force the reckoning; design it in.

The Move

Climate tech rewards founders who are shameless about changing their mind. Not wishy-washy—decisive. You see new evidence (customer feedback, policy shift, competitive move), you recalibrate, you ship the update. You tell investors exactly why you changed course. That's not weakness; that's intelligence. That's fundability.

The climate problem is still 30 years away from solved. But the winners solving it in 2026 aren't the ones with the best intentions. They're the ones learning fastest and adapting hardest. Ship, test, refactor. That's your operating system.

Meta’s $2B Manus Deal: A Practical Playbook for Ambitious Founders

Founders often ask: “Will more US tech giants buy Asian startups?”

The sharper question is: if only a small fraction of companies generate most of the returns, can you afford to build anything that isn’t capable of becoming a global outlier?​

Meta’s US$2+ billion acquisition of Manus—a company founded in Beijing, redomiciled in Singapore, and integrated into Meta’s AI stack in under a year—is not just a China‑US‑Singapore story. It’s a concrete example of how to design a company that can scale across borders, survive geopolitics, and be acquirable at speed.​

What Manus Actually Did

Manus launched publicly around March 2025 with an AI agent that could autonomously research, code, and execute multi‑step workflows. Within roughly eight months it reportedly crossed US$100 million in ARR, reaching a US$125 million revenue run rate before Meta signed the deal.​

Operationally, it:

  • Processed over 147 trillion tokens and supported tens of millions of “virtual computers” spun up by users, which only makes sense at global internet scale.​

  • Ran as an orchestration and agent layer on top of multiple foundation models (including Anthropic and Alibaba’s Qwen), avoiding dependence on a single model provider.​

On the corporate side, Manus:

  • Started in Wuhan and Beijing under Beijing Butterfly Effect Technology, with a mostly China‑based team.​

  • Shifted its headquarters to Singapore in mid‑2025, moving leadership and critical operations out of Beijing.​

  • Restructured so that, by the time Meta announced the acquisition, Chinese ownership and on‑the‑ground China operations would be fully unwound; the company committed to ceasing services in China.​

Meta bought a product already scaled, a revenue engine compounding at nine‑figure ARR, and a structure that could clear US regulatory and political review.​

Geopolitics as a Design Constraint

Manus scaled in the wake of DeepSeek’s R1 moment, when a Chinese lab demonstrated frontier‑class performance at a fraction of Western compute budgets and shook confidence in US AI dominance. That moment accelerated a narrative where AI is treated as strategic infrastructure: tighter export controls, outbound investment restrictions on Chinese AI, and public scrutiny of anyone funding Chinese‑linked AI companies.​

Benchmark’s US$75 million Series B in Manus was investigated under Washington’s new outbound regime and criticized as “funding the adversary.” Two details mattered:​

  • Manus did not train its own foundation models; it built agents on top of existing ones, placing it in a less‑restricted category.​

  • It was structured via Cayman and Singapore, with a stated pivot away from China.​

Meta then finished the derisking: buying out Chinese shareholders, committing to end China operations, and framing Manus as a Singapore‑based AI business joining Meta.​

For founders, the lesson is blunt: jurisdiction, ownership and market footprint now sit beside product and traction as first‑order design choices. They can’t be an afterthought if you want a strategic buyer.

What This Implies for How You Build

The Manus story turns a vague ambition (“go global”) into specific requirements:

1. Infrastructure built for real scale

Handling 147 trillion tokens and millions of ephemeral environments was possible only because Manus was architected from day one to operate like a web‑scale SaaS, not a regional tool. As a founder, that means:​

  • Cloud‑native design with serious observability and reliability.

  • Data and compliance posture that won’t collapse under US or EU due diligence.

2. A team that isn’t anchored to one country

Manus began in China but rapidly built a presence across Singapore, Tokyo and San Francisco, aligning product, sales and hiring with global customers and capital pools. Practically:​

  • At least one founder or senior leader who has operated in major tech hubs.

  • Early design partners or users outside your home market.

3. Legal and cap table flexibility

Manus showed that unlocking a large exit might require:

  • Redomiciling to a neutral or “trusted” jurisdiction like Singapore.

  • Reworking the shareholder base to remove politically sensitive investors.

  • Exiting a big home market entirely, if that market blocks strategic buyers.​

If your current structure makes those moves impossible or prohibitively expensive, your future options are already constrained.

4. Revenue ambition that assumes a global customer

Crossing US$100M ARR in under a year is only achievable if:

  • The problem you solve is universal.

  • Your pricing and packaging make sense for large customers in New York, Berlin or Tokyo, not just in your home market.​

You can start with regional customers, but you should be honest about whether the 100th customer could be a global enterprise rather than just a better‑known local logo.

Three Questions to Ask Yourself Now

If you’re a founder in an emerging market post‑Manus, a simple self‑audit goes a long way:

  1. If a Meta‑scale acquirer appeared in 12 months, what would break first—structure, regulation, or infra?
    Make that list explicit. Those are not “later” issues anymore.​

  2. Could your current architecture handle a 100x increase in usage without a total rebuild?
    If not, you’re placing an invisible ceiling on your own upside before power‑law dynamics can ever help you.​

  3. Do your first 10 hires and first 10 customers make expansion easier or harder?
    Manus’ user base and team footprint made going beyond its origin market feel like scaling, not reinventing.​

The Manus deal doesn’t suggest everyone will be bought for billions. It does show that markets are now rewarding teams that design for scale across borders, anticipate geopolitical friction, and stay acquirable.

If you’re serious about building something that matters, that’s the bar.