WashU CIO Reveals $20B Endowment Strategy
Scott Wilson of WashU details how he grew the endowment from $8B to $20B by eliminating hedge funds, focusing on direct private investments, and maintaining strict valuation discipline. The analysis covers the shift from fund-of-funds to direct co-investments, the risks of mega-fund scaling, and the current valuation disconnect in AI markets.
Strategic Pivot to Direct Exposure
Scott Wilson, CIO of the Washington University in St. Louis endowment, has transformed the portfolio from a traditional fund-of-funds model to a concentrated, direct-investment strategy. By eliminating 36-37 hedge funds and reallocating capital, Wilson grew the endowment from $8 billion to nearly $20 billion. The core of this strategy is a fundamental shift in time allocation: 80-90% of the team’s effort is now spent meeting individual companies rather than external managers. This direct engagement allows the team to independently verify theses and co-invest in high-conviction opportunities, a tactic Wilson credits for moving the portfolio from peer-group average to top-tier performance.
The Case for Concentration
Wilson argues that diversification offers diminishing returns beyond 20-30 underlying exposures. Consequently, the portfolio is structured so that 50% of assets are in the top 50 look-through positions. This concentration strategy relies on identifying unique theses early and compounding them over long periods. A prime example is the SpaceX position, which at one point represented over 20% of the portfolio. Wilson advocates for holding such positions through continuation vehicles to let them compound, rather than taking liquidity prematurely. This approach contrasts sharply with the typical LP model, which often spreads capital too thin across numerous managers.
Navigating the AI Valuation Bubble
The current market environment presents a significant challenge: private market valuations for AI companies are decoupling from public market realities. Wilson notes that companies like Anthropic and OpenAI could raise capital at valuations north of $2 trillion in private markets, but public markets may not support such premiums. This disconnect creates a risk of poor distribution performance (DPI) as companies delay IPOs to maintain high valuations. Wilson warns that the commoditization of AI models threatens the profit pools of frontier providers, as customers quickly switch to cheaper alternatives. He advises maintaining discipline around valuation, even in hot markets, and warns against the "stupidity" of paying exorbitant premiums for companies with unclear long-term defensibility.
Operational Discipline and Future Outlook
Wilson emphasizes that the biggest mistake in his tenure was not adding to winners due to frame-of-reference bias. He also highlights the importance of valuation discipline when selecting fund partners, often churning commitments if entry prices are too high. Looking ahead, Wilson believes the optimal allocation for an endowment without payout constraints would be 70-80% private, but liquidity needs currently cap this at 50%. He remains bullish on private markets for their inefficiency and alpha potential but cautions that the current AI cycle will likely see a shakeout as valuations normalize. The strategy remains focused on finding high-quality, under-the-radar opportunities where price and value diverge significantly.
Key insights
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Direct co-investments are the primary driver of alpha in endowment portfolios. Without direct exposure, the WashU portfolio would have only matched peer group averages, despite strong manager selection.
Impact: Institutional investors should reallocate team resources from manager due diligence to direct company engagement to capture outsized returns.
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Quantitative benefits of diversification diminish rapidly beyond 20-30 underlying exposures. Concentrating 50% of the portfolio in top 50 positions maximizes the impact of high-conviction winners.
Impact: Over-diversification dilutes returns; investors should focus on a smaller number of high-impact positions rather than broad exposure.
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Mega-funds (over $10B) face structural headwinds in generating multiple money. Doubling fund size requires higher valuations or more investments, both of which reduce returns.
Impact: LPs should avoid mega-funds and prioritize smaller, early-stage funds where a single check can significantly impact fund returns.
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Private market valuations for AI companies are significantly higher than public market equivalents, creating a risk of poor DPI. The commoditization of AI models threatens the profit pools of frontier providers.
Impact: Investors should be cautious about paying private market premiums for AI companies, as public markets may not support these valuations at IPO.
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Valuation discipline is the primary reason for churning fund commitments. Investors should prioritize entry price over deployment speed to ensure long-term risk-adjusted returns.
Impact: LPs should maintain strict valuation criteria when committing to funds, even if it means missing out on fast-deploying managers.
Action items
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Reallocate 80-90% of team time to direct company meetings rather than manager calls. Build internal skills to independently verify theses and underwrite companies.
Impact: This shift enables direct co-investments, which are the primary driver of alpha in endowment portfolios.
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Target 50% of the portfolio in top 50 look-through positions. Reduce the number of underlying exposures to 20-30 to maximize the impact of high-conviction winners.
Impact: Concentration strategy allows for compounding of high-impact positions, moving the portfolio from peer-group average to top-tier performance.
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Avoid funds over $10B in size. Prioritize smaller, early-stage funds where a single check can significantly impact fund returns.
Impact: Mega-funds face structural headwinds in generating multiple money; smaller funds offer better access to early-stage, high-growth opportunities.
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Maintain strict valuation discipline when committing to funds. Churn commitments if entry prices are too high, even if the manager is deploying capital quickly.
Impact: Valuation discipline ensures long-term risk-adjusted returns, even if it means missing out on fast-deploying managers.
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Monitor the valuation disconnect between private and public markets for AI companies. Be cautious about paying private market premiums, as public markets may not support these valuations at IPO.
Impact: Avoiding overvalued AI positions reduces the risk of poor DPI and protects the portfolio from potential shakeouts in the AI cycle.
Quotes
“We hardly ever talk about, like, fund performance. We only really talk about the underlying exposures in the portfolio.”
“Price is what you pay, value is what you get.”
“I think if you look at the relative performance, you know, over the last decade, we've probably compounded our privates by in the low to mid 20s.”