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The Narrative Around VC Lately: The Broken VC Framework. Is Venture Capital Stuck in a Pattern-Matching Loop? 🟣

Venture capital prides itself on identifying the next big thing before anyone else. Yet, the data keeps revealing a consistent flaw: VCs systematically overlook high-potential startups while backing companies that are statistically likely to underperform.

Two recent discussions on LinkedIn, backed by machine learning studies, highlight just how broken the VC decision-making framework is, and why the industry might be its own worst enemy.

The Problem: VCs Are Misallocating Capital at Scale

Harry Stebbings recently pointed out five massively successful startups that were initially rejected by over 50 European VCs; UiPath, Wolt, Bolt, Glovo, and Flo Health. These aren’t minor misses; these are multi-billion-dollar outcomes that funds completely passed on.

Dan G. expanded on this issue, citing a machine learning study that analyzed VC decision-making at scale. The study found that:

VCs consistently overweight subjective founder attributes:
Male founders, elite school backgrounds, and headquarters in major tech hubs all increased the likelihood of receiving funding, despite little correlation to long-term success.

Pattern-matching bias leads to predictable errors:
Rather than objectively analyzing startup fundamentals, investors rely on heuristics and familiar traits, causing them to miss exceptional outliers.

Machine learning could help, but only if designed correctly:
AI models can surface overlooked startups based on performance signals, but if trained on historically biased VC decisions, they’ll reinforce existing blind spots rather than fix them.

Why Does This Happen?

The research by Victor Lyonnet and Léa Stern breaks down exactly why VCs keep making the same mistakes:

1. Overreliance on human capital signals.

   Founders from top-tier universities are 3x more likely to receive VC funding, and founders in major tech hubs are 2x more likely, even though these factors don’t predict success.

2. Cognitive shortcuts in high-choice environments.

   When investors have too many options, they default to subjective criteria rather than rigorously assessing each opportunity.

3. Lack of structured evaluation processes.

   Many investment decisions are made based on narrative, gut instinct, and social proof—rather than measurable traction.

How Can VC Fix Its Selection Bias?

To combat these issues, venture firms need to rethink how they assess startups. That means:

Flipping the evaluation order. 
Start with traction, market signals, and business fundamentals before considering the founding team’s background.

Redesigning internal decision-making structures. 
The best investors challenge biases at the sourcing stage, before subjective criteria distort the process.

Using AI thoughtfully. 
Machine learning can highlight hidden opportunities, but only if it’s designed to counteract, not reinforce, historical biases.

Recognizing that outliers don’t fit molds. 
The best investments are always unconventional. Relying on pattern-matching to past successes is a losing strategy.

The Future of Venture: Is It Time to Rethink the Playbook?

VCs have built an industry around gut instinct repackaged as experience. But if data-driven analysis keeps proving that these instincts are flawed, how long before the most successful investors ditch the old playbook?

Is venture capital ready to evolve, or will it keep making the same mistakes?

 
New Funds Raised in February 2025 🟣

We’ve added a new section to EverythingStartups with newly announced funds, which will be updated weekly!

↳Conviction Partners - $230M fund

↳Cherry Ventures - $500M fund

↳Ulu Ventures - $208M fund

↳Antler - $100M fund

↳Swizzle Ventures - $6.6M fund

↳Cherryrock Capital - $172M fund

↳Slow Ventures Creator Fund - $63M fund

↳Hitachi Ventures - $400M fund

↳Emblem - $85M fund

↳Nina Capital - $50M fund

↳Afore Capital - $185M fund

↳Greenfield Partners - $400M fund

↳Coatue Management - $248.8M fund

↳Angular Ventures - $125M fund

↳GTMfund - $54M fund

↳Footwork - $225M fund

↳Perplexity - $50M fund

↳Thomson Reuters - $150M fund

↳Prebys Foundation - $50M fund

 
Linkedin Post Analysis: Why They Went Viral 🟣

Insider thoughts from our team on why certain posts go viral. Use these tips for your own posts!

This week’s post.

Here’s our expert breakdown:

Why It Works:

👍 It challenges conventional wisdom from the start.
The first line, "Big teams are no longer a sign of a successful company," immediately disrupts a common belief. People expect large, successful companies to require massive teams, so this flips expectations and hooks the reader.

👍 It plays on the “future vs. reality” contrast.
The post sets up an exciting tension: "How close are we to the '$1B one-person startup'?" This creates curiosity and makes people want to read further.

👍 It mixes macro trends with specific, surprising data.
The takeaways about revenue per employee, AI startup efficiency, and Midjourney’s ARR without funding all reinforce the argument with hard numbers. Readers love posts that balance strong opinions with compelling data.

👍 It taps into startup and AI hype.
AI is one of the most discussed topics on LinkedIn, and the idea of extreme efficiency resonates with both founders and investors. By highlighting new AI startups and their financials, the post naturally aligns with trending conversations.

👍 It uses digestible, well-structured takeaways.
Bullet points make key insights easy to skim, while bolded phrases highlight the most important data. Each point provides a clear takeaway that reinforces the main theme.

The Template: How to Write a Post Like This

Start with a thought-provoking statement.
("X is no longer true. But for now, we still need Y.") → Creates intrigue by challenging assumptions.

Introduce a timely, relevant question.
("I analyzed [X number] of [industry] companies to see how close we are to [future vision].") → Builds anticipation and makes the reader curious.

Deliver insights backed by real data.
("14 out of 30 AI startups have revenue per employee above $200K.") → People trust numbers, and unique data drives engagement.

Break down key takeaways with clear formatting.
Bolded insights → Help skimmers absorb the information quickly.
Bullet points → Keep the post structured and readable.

End with a simple, compelling CTA.
("Full breakdown in the comments - don’t miss it!") → Drives engagement and link clicks.

Now try crafting your own post using this template 😉 

VC Internships Galore 🟣

🚨 We’ll soon be adding VC jobs in the USA & Europe.

Summer MBA Intern - Energize Capital
Location: Chicago
Apply here

Venture Studio Intern - Launch Factory
Location: San Diego
Apply here

Temasek Associate Internship Programme - Temasek
Location: New York
Apply here

MBA Summer Intern - Cherryrock Capital
Location: San Francisco Bay
Apply here

Intern, NY Ventures - Empire State Development
Location: San Mateo
Apply here

For the Founders: 10 VC Terms to Know 🟣

VCs speak a different language.

If you don’t know these 10 terms as a founder, you’re walking into negotiations blind.

1. Anti-dilution

   Protect investors from losing ownership when new shares are issued. You could lose control.
   

2. Articles of Association

   The company’s rulebook. Investors might change it to lock in their rights.
   

3. Cap Table

   Who owns what in your company. Getting it wrong could cost you in an exit.
   

4. Down Round

   Raising money at a lower valuation. A necessary evil, but it hurts your equity.
   

5. Drag Along Rights

   Majority can force minority shareholders to sell. No escape if they want out.
   

6. Good Leaver/Bad Leaver

   Defines who keeps their shares when they leave. It’s all about the exit conditions.

7. Lock-In

   Restricts key players from selling shares. A smooth exit might not be so smooth after all.
   

8. ROFR (Right of First Refusal)

   Investors get the first shot at buying new shares. You could lose control.
   

9. Term Sheet

   A non-binding outline of investment terms. What’s agreed here sets the tone for everything.
   

10. Waterfall

    Who gets paid first in a sale? It’s usually the investors, not you.

Read the full breakdown of each term in this carousel.

 
VC & Startup Favorites + News 🟣

Latest tidbits, resources, and social gems from the ecosystem.

The ultimate list of the 131 most prestigious venture capital firms in the world

6 common vanity metrics (and what to track instead)

The top 5 reasons to be a jack of all trades

Why I think AI take-off is relatively slow

A visual introduction to VC financing

9 lessons I learned failing to scale my business to $1M per month

The startup founder’s secret weapon: The periodic table of acronyms

Our favorite investable areas in brain health

Top 5 reasons why 75% of new VC fund managers fail to launch their first fund

You think your fund is differentiated...LPs don’t

Early-stage venture is about evaluating people, not just companies

Claude 3.7 Sonnet: A breakthrough in AI reasoning and software development

Perplexity AI is launching $50 million venture fund to back early-stage startups

Balyasny Asset Management raising $350M for firm’s 1st VC fund

a16z hires former Republican congressman Patrick McHenry as advisor

Top Pre-Seed to Series A funding rounds of the week 🟣

Top 10 pre-seed to Series A funding rounds of the week:

⚫ AI ⚫

1. Bridgetown Research, a startup that develops AI-powered agents, raised a $19 million Series A round.

→ Investors: Lightspeed, Accel
→ Founded by: Harsh Sahai

2. Henry AI, a startup that provides an AI-powered platform to help commercial real estate brokers, raised a $4.3 million seed round.

→ Investors: Susa Ventures, 1Sharpe Ventures, RXR, and others
→ Founded by: Sammy Greenwall and Adam Pratt

3. Edera, a startup that enhances security for Kubernetes and AI workloads, raised a $15 million Series A round.

→ Investors: M12, Mantis VC, In-Q-Tel, and others
→ Founded by: Emily Long, Alex Zenla, and Ariadne Conill

4. Behavix, a startup that collects data to provide businesses with insights into consumer behavior, raised a $2.5 million seed round.

→ Investors: Vendep Capital, Superhero Capital
→ Founded by: Hannu Verkasalo and Surath Chatterji

5. Lovable, a startup whose AI-powered platform enables users to create web applications without writing code, raised a $15 million pre-Series A round.

→ Investors: Creandum
→ Founded by: Anton Osika 

⚫ Insurance ⚫

6. Adaptive Insurance, a startup that offers parametric insurance products to help U.S. businesses financially prepare for weather-related power outages, raised a $5 million seed round.

→ Investors: Congruent Ventures, Montauk Climate, Generation Space
→ Founded by: Mike Gulla and Arik Yelovitch

⚫ Software ⚫

7. Vayu, a startup that offers a no-code revenue management platform, raised a $7 million seed round.

→ Investors: Flint Capital, The Garage, Fresh.Fund, and others
→ Founded by: Erez Agmon, Shenhav Avidar, and Shai Gross

8. LangWatch, a startup that provides tools for tracking performance, evaluating quality, and optimizing costs of AI applications, raised a $1.1 million pre-seed round.

→ Investors: Passion Capital, Volta Ventures, Antler
→ Founded by: Manouk Draisma and Rogerio Chaves

⚫ Electric Vehicle ⚫

9. Vidyut, a startup that allows customers to rent EV batteries, raised a $2.5 million round.

→ Investors: Flourish Ventures
→ Founded by: Xitij Kothi and Gaurav Srivastava

⚫ Femtech ⚫

10. Millie, a maternity care provider, raised $12 million in Series A funding.

→ Investors: TMV, Foreground Capital, Pivotal Ventures
→ Founded by: Anu Sharma

Huge congrats to all these startups on their successful funding rounds!