Bill Maris: How Google Could Crush AI Competitors, Why Small Funds Win, and AI's Atari Stage

Bill Mares and Section 32 Overview

• Bill Mares, founding CEO of Google Ventures (GV), is returning to the investment world with a new fund, Section 32.
• Section 32 has raised $150 million as its initial fund size, a departure from Mares's previous experience managing multi-billion dollar funds.
• Mares states a core investment mandate: "We're going to invest for a financial return. Any other metric is impossible to measure and therefore won't succeed."
• Mares previously founded Google's data center business, incubated Waymo, Google X, and Calico, and served as Google's VP of Special Projects.
• Mares describes the current technological landscape as a century-long shift where the next 100 years will see change "orders of magnitude" larger than the previous century due to AI.

Four Key Lessons on Investing

• Lesson 1 (Seeing the Future): Mares compares his 1997 experience of spotting a server in a Wall Street closet to glimpsing the future of the internet, leading him to quit his job to build a data center business.
• Lesson 1 (Entrepreneurial Risk): Mares illustrates the necessity of "insanity" in entrepreneurship by recounting tarring a leaking apartment roof during a thunderstorm in Vermont to protect servers, choosing to risk personal injury over data loss.
• Lesson 2 (Predicting Adoption): Mares uses photos of inaugurations (1989 vs. 2005) to show how quickly technology adoption (cameras, live-streaming) becomes ubiquitous, emphasizing that successful entrepreneurs see "secret" futures others ignore.
• Lesson 3 (Computer Science): Mares argues one should never "bet against computer science," citing his use of AI/machine learning (when internally labeled "machine learning" to avoid "AI" stigma) to design Google Ventures' portfolio construction.
• Lesson 3 (GV Performance): Mares reports that Google Ventures (2009–2018) achieved estimated returns of 4.1x, performing within the top quartile of VC returns despite early internal skepticism.
• Lesson 4 (Fund Size vs. Performance): Mares presents data indicating small funds outperform large funds, asserting "small funds outperform large funds" is a mathematical reality.
• Lesson 4 (Performance Metrics): Data cited shows funds smaller than $750 million averaged 4.76x DPI returns, while funds larger than $1 billion averaged 2.42x.
• Lesson 4 (Portfolio Math): Mares calculates that to achieve a 3x return, a $500 million fund needs $15 billion in exits, whereas a $7 billion fund requires $210 billion in exits, a figure that exceeds most years' total venture-backed IPO/M&A value.
• Section 32 Track Record: Section 32 has deployed six funds averaging $400 million each, all performing in the top decile, with early investments in CrowdStrike, Cohere, and Coinbase.

Market Dynamics and Future Trends

• AI and Google's Leverage: Mares predicts Google will eventually cut token costs by 80%, using its capital as a weapon to undercut competitors like OpenAI and Anthropic, potentially threatening their business models.
• Valuation Risks: Mares warns that companies staying private longer to avoid public scrutiny are forcing overpriced assets onto 401(k) holders and retail investors once they eventually go public, creating "bag holders."
• Bimodal Venture Returns: Despite some fund's "excessive" returns, Mares notes venture capital remains bimodal, with 75% of funds losing money and a small handful of winners generating massive returns.
• AI Investment Strategy: Mares likens current AI to the Atari era, projecting a shift to the "PlayStation 10" stage within five years; he focuses on infrastructure (controllers, physics engines, GPUs) rather than large model training.
• Life Sciences Shift: Mares notes a flight of capital to India and China for biotech due to U.S. regulatory hurdles, NIH/CDC funding cuts, and an "anti-science vibe," while U.S. investors focus on computational biology.
• Deep Tech Viability: Mares suggests deep tech is becoming more tractable for investment as AI enablement and physics engines accelerate timelines for long-cycle capital-intensive projects.

Panel Discussion on Fund Structures

• Andreessen Horowitz Comparison: Panelist Sachs questions if Section 32's model contradicts the need to scale to firms like Andreessen Horowitz (a16z), citing the "late-stage" preference of larger funds.
• Incentive Misalignment: Sachs argues that large funds (e.g., $5 billion) can return 1.01x and still raise the next fund, whereas a small fund (e.g., $500 million) must return 3x to survive, yet the larger fund manager earns more absolute carry on the lower multiple.
• Founder Incentives: Sachs notes founders often accept inflated valuations from massive funds (e.g., $250M for 1% at a $4B valuation) rather than strategic advice from smaller funds, distorting market prices.
• Market Correction: Sachs predicts the current preference for late-stage investing and giant capital bases is unsustainable and that the "pendulum will swing back" toward earlier-stage, smaller funds.
• Check Size Determination: Sachs emphasizes that fund size dictates strategy, noting that a $500 million fund can diversify across 20–25 names, whereas larger funds face pressure to write $50M+ checks that push valuations out of proportion.