Apr 17, 2025, 9:59 AM ETInterview, Fireside Chat, Podcast

a16z's $20BN Fund & Founders Fund's $4.6BN & Why Josh Kushner Has Mastered the Game

Thrive's Core Philosophy: A strategy modeled after Monopoly involves buying the best asset on every block (e.g., OpenAI, Infrastructure, Database) with massive checks to achieve liquidity in a quarter, prioritizing absolute return over internal rate of return (IRR).
Seed vs. Late Stage: Early-stage seed investing is dismissed by this strategy as inefficient for generating massive absolute wealth when "outcomes are north of $1.2 trillion," arguing that writing one big check into a winner is superior to waiting 20 years for an 8x multiple on a small fund.
Correlation Risk: The primary risk of this "buy the best" strategy is not company performance correlation but broad market valuation compression; if equity multiples fall from 30x to 10x, absolute returns on high-priced assets could drop to 0.5x–0.7x despite the assets remaining the "best."

End of Spreadsheet Investing: First-generation SaaS investing based on predictable NRR and growth rates is considered "dead" because the market has saturated; companies like Zoom and DocuSign have captured nearly all necessary users.
Transient Product-Market Fit (PMF): PMF is now volatile, changing every six months rather than every five years, driven by rapid AI model progress and an exploratory phase where features shift constantly, making long-term forecasting impossible.
Intensified Work Cycles: Top startups are now working 100 hours a week (7 days/12 hours) compared to 40-hour weeks previously; failure to match this intensity results in rapid obsolescence due to AI-enhanced competition.

Megafund Justification: Massive funds (e.g., Andreessen's $20B, General Catalyst's $8B) are justified by the ability to deploy capital into later-stage companies that stay private longer, creating a larger opportunity set for capital deployment than the traditional IPO market allowed.
PE Exit Mismatch: Private Equity (PE) firms generally reject standard B2B SaaS companies because they prefer "boring" software with monopoly positions in tiny verticals where they can raise prices, whereas VC-backed companies often operate in broad, competitive horizontal markets lacking pricing power.
Liquidity Crunch: Approximately $2 trillion in mature, slower-growth SaaS private assets faces a liquidity crisis; these companies lack IPO trajectories and are not attractive to PE for consolidation, requiring "grim industrial work" to grind them toward profitability or private-to-private mergers.
Capital Allocation: 70–80% of traditional SaaS investors have pivoted entirely to AI, refusing to invest in "normal" B2B SaaS companies growing at triple-digit rates, creating a funding gap for non-AI but high-performing SaaS firms.

Concentration Strategy: Founders Fund employs a high-conviction strategy of massive concentration (e.g., 30% of a fund in single assets like SpaceX or biotech) and long holding periods (10–15 years) to maximize compounding, accepting that this approach is unsuitable for funds seeking diversification.
B2B Avoidance: Founders Fund intentionally avoids most B2B investing, believing that the "singularity" required for massive returns exists more often in biotech or deep tech than in the crowded B2B software space.
LP Demand: Despite the concentration risk, institutional demand for Founders Fund remains the highest of any single asset, driven by a track record that successfully compounded capital over decades by ignoring traditional diversification rules.

Secondary Pressures: In hot growth rounds, investors are aggressively accepting maximum secondary sales for founders (e.g., 10%–30% liquidity at Series A/B) to win deals, effectively replacing the founders' capital efficiency with immediate cash-out incentives.
Corporate Espionage: The Rippling vs. Deel conflict highlights a trend of "industrial espionage" where aggressive sales tactics and alleged theft of trade secrets become normalized, potentially exposing customers to liability and eroding market trust.
Accounting Manipulation: There is a growing reliance on unreliable ARR (Annual Recurring Revenue) metrics in AI companies, masking the fact that revenue is often not recurring or annual; the shift toward GAAP reporting is urged to expose the true financial health of these entities.
Liquidation Preference Risk: Investors face a rising risk that acquirers (corporate or otherwise) will aggressively structure side deals to bypass VC liquidation preferences, prioritizing the retention of engineering talent over honoring standard cap table structures.

Foundational Model Valuation: Massive valuations for AI model providers (e.g., OpenAI, Anthropic) are driven by the belief that success is binary and dependent on the "secret recipe" of the founding team; funding the original creators is seen as a low-risk bet on technical execution.
Public vs. Private Inefficiency: It is currently a "public policy failure" that companies can access cheaper capital privately (via 2% and 20% funds) than publicly (via low-fee mutual funds), reducing net returns for ordinary investors and removing the incentive for companies to IPO.
Market Cyclicality: The venture ecosystem is expected to overshoot capital allocation due to lagging return metrics; a correction will likely occur only when liquidity (IPOs/M&A) fails to return capital fast enough to satisfy LPs, forcing a withdrawal of funds.
Valuation Benchmarks:
- OpenAI: At a $300B valuation, the investment is rejected as "bad" by the panel, with buy recommendations only emerging if liquidation preferences are guaranteed.
- Cursor: At $10B, the investment is deemed "good" only if the revenue is proven durable and non-AI-replaced, acting as a standard B2B SaaS moat rather than a speculative AI play.