Practical Operations of a Micro VC Focused on YC Projects: Fundraising, Screening, and Decacorn Return Models by Lobster Capital Founder

1. Background and Fund Positioning: Lobster Capital 1. Founder Background and Transition Path • Serial Entrepreneur Gene: Founder Gabriel Jarrosson is from France and has previously founded multiple companies, viewing the operation of Lobster Capital as another entrepreneurial venture. • From Europe’s Largest Angel Syndicate to Silicon Valley Micro VC: Initially built the largest angel investment syndicate in Europe through a YouTube channel, investing over $36 million in startups since 2020. 2. Fundraising Journey of the First Fund • Difficult 18-Month Fundraising: The initial target for the first fund was $8 million, taking 18 months to raise, facing significant psychological pressure and numerous rejections. • Oversubscribed at $12 million: The first fund ultimately closed at $12 million, and preparations for a larger second fund are now fully underway. 3. Extremely Focused Investment Strategy • Only Invest in Y Combinator (YC) Graduates: The fund has a strict rule—projects not produced by YC are never touched. • Solo GP Breakthrough Logic: In the competitive Demo Day environment filled with large funds in Silicon Valley, the fund relies on extreme focus, rapid decision-making, and empathy with founders to secure seed and Series A shares. 2. Mathematical Logic Underlying Investments and YC Power Law 1. Why Limit the Pool to YC? • Significant Advantages in Probability: • Unicorn Hit Rate: The unicorn output rate (valued over $1 billion) for YC incubated projects is about 4.5%, while the rate for other seed-stage startups is only about 2.5%. • Series A Advancement Rate: The proportion of YC projects successfully obtaining Series A funding is as high as 45%, significantly above the industry average of about 33%. • Historical Performance: Since its establishment in 2005, YC has funded over 5,000 companies, nurturing over 90 unicorns, with an overall portfolio valuation exceeding $600 billion. 2. Fund Economics (Unit Economics) and the "Decacorn" Law • Capital Allocation Model: • Total scale of $12 million, with approximately $10 million available for deployment after management fees. • Expected to diversify investments in about 30 startups, with an average check size of about $300,000 per company. • Fund Performance Standard: Must deliver at least 3x returns to LPs (i.e., $36 million net profit). • Ordinary unicorns alone cannot support a micro fund: • If entering at a $20 million valuation, a $300,000 initial stake represents about 1.5%; after subsequent rounds of dilution, the exit holding is often diluted to about 0.5%. • If the project exits at $1 billion (1x unicorn), the single return would only be $5 million (only about one-seventh of the total fund). • Must hit a Decacorn valued over $10 billion: • If the project grows into a $10 billion valuation giant, 0.5% equity at exit can be realized at $50 million, covering over four times the entire fund size. • Among the unicorns that emerged from YC, about a quarter eventually grow into giants valued over $10 billion; thus, the core competitive point is to lower the entry valuation (double equity for the same amount) and focus on extraordinary projects that can become decacorns. 3. Insights from Venture Capital Practice: Signals in Financing Negotiations and Relationship Rules 1. Identifying Investors' "True Interest" Signals • Moving Away from "PPT Reports" to "Two-Way Deep Dialogue": • Encourage founders to abandon rigid Slide Deck scripts and engage directly in equal, natural conversations about the essence of the business. • Key Advancement Signals: • False Politeness: "Send me the business plan (Deck), and we’ll follow up next week" is usually dismissive. • Genuine Interest: At the end of discussions, actively clarifying next steps for due diligence, discussing current round dynamics, asking "how much time is left to get in," or even requesting founders to cancel meetings with other institutions on the spot. 2. Differences in Conversion Rates for VC Fundraising and Startup Financing • Startup Financing: Conversion rates typically around 5% to 10%. • Early-stage VC funds fundraising from LPs: Extremely low conversion rates, usually only 1% to 2% (even top-performing fund managers only achieve 3% to 5%), requiring hundreds of rejections and finding steadfast supporters. 3. Long-term Relationships Matter More Than One-time Transactions • Investment is a long-cycle business lasting 10 to 12 years; even if not investing in a founder at the moment, establishing a deep supportive relationship can secure opportunities when the founder embarks on a second venture or experiences business growth. 4. Practical Case Studies in Investment Portfolio: AI-Native Reconstruction of Traditional Industries (Example: Harper) 1. Transitioning from "Selling AI Tools" to "Becoming a Business Entity" • Major Trend in YC Over the Past Two Years: Early-stage entrepreneurs attempted to sell AI efficiency plugins to traditional insurance brokers but faced high resistance in sales and adaptation. • Directly Disrupting Traditional Intermediaries: The Harper team (a second entrepreneurial venture with the same core team) decided to use self-developed tools to directly replace traditional intermediaries, transforming into an AI-native commercial insurance company, rapidly expanding from 7-8 people to 55 people, with cumulative financing of $47 million. 2. The "Utility" Nature of Commercial Insurance • Capturing Essential and Trillion-Level Demand: All 36 million businesses in the U.S. are required to have commercial insurance, but traditional intermediaries often complicate comparisons, frequently ghost clients, and take weeks to process. • Transparency and Trust Barriers: By using algorithms to compare optimal prices among numerous underwriting institutions in seconds, insurance is transformed into a highly transparent infrastructure that is as stable and instantly usable as electricity. 5. Core Mindset for Early Founders • Asymmetric Advantage of AI-Native Teams Against Giants: Traditional industry giants, even with 100,000 employees, can be easily outmaneuvered by agile small teams lacking AI-native structures; currently, no traditional business is immune to the AI wave. • Paranoia and Extreme Urgency: Early-stage entrepreneurship is filled with uncertainty; only by maintaining high-frequency execution, being extremely sensitive to details, and maintaining absolute resilience in adversity can one navigate through the survival fog of the early stages. Video Source: https://www.youtube.com/watch?v=uFlc1MwYLkg