How LPs Allocate to Venture in 2026: What They Want, What They Don’t | Baylor University CIO

Institutional Context & Strategic Imperative

• Baylor University's endowment stands at approximately $2.6 billion, with a primary strategic goal of maximizing distributions to support tuition costs amidst a declining domestic high school student population and reduced international enrollment.
• The fund views the next 10–15 years as a period where endowment distributions will become increasingly critical due to financial pressure on higher education.
• Historically, the office prioritized downside protection (often flat or outperforming when the S&P 500 dropped) and has spent the last five years restructuring to improve upside capture.

Asset Allocation & Portfolio Construction

• Current Allocation: Approximately 45% private markets and 55% public markets.
• Target Liquidity Range: The fund targets 35–55% in private assets, scaling to 55% during market downturns (denominator issues) to avoid forced selling of public assets.
• Private Market Strategy: The fund has exited real assets and is focusing exclusively on Venture Capital (VC), expansion/growth equity, and buyouts to maximize returns; "anything that doesn't generate excess returns is being wound down."
• Growth Equity Preference: Significant allocation to growth equity due to a "fewer zeros" profile (lower probability of total loss) and annualized returns of approximately 30%, which comfortably exceeds the 8–9% bogey.
• Venture Capital Specifics: The fund holds approximately 2.5% of the endowment in Anthropic via external managers, with no exposure to SpaceX or OpenAI.

Critique of Private Equity Mechanics & Incentives

• Fund Duration: Moorhead critiques the extension of fund lifecycles from 10–12 years to 15–18 years, arguing this misaligns GP incentives with LP compounding math.
• Velocity of Capital: The fund prioritizes capital velocity over raw returns; the math suggests that redeploying 3x returns every 6 years yields a 27x multiple over 18 years, versus a single fund yielding 15x over the same period.
• GP-LP Alignment: The fund explicitly rejects the GP incentive to maximize fund size and marketing (e.g., holding winners longer to show 6x vs. 3x) in favor of maximizing the absolute dollar pile for the university.
• Direct Access to GPs: To optimize risk/return, Baylor negotiates direct deals with GPs to customize allocations (e.g., avoiding over-concentration in a single stock like NVIDIA if already held, or demanding larger tranches if the asset is missing).

Investment Philosophy & Decision Framework

• Risk Management: A core office rule mandates that "you are not allowed to talk about returns without also talking about time"; returns are meaningless without the duration context.
• Valuation Discipline: The fund requires conservative valuation marks from managers to prevent psychological bias; their marks typically lag public market gains by 60–90% prior to liquidity events, compared to 30–50% market averages.
• Deployment Methodology: The fund employs a mechanistic, dollar-cost averaging approach into declining markets, allocating capital in 10% increments (e.g., "down 20% = 20% in; down 30% = another 20% in") rather than drawing hard lines in the sand.
• Position Sizing: Sizing is based on the absolute dollar impact to the fund (targeting $2.5–$3 million in each underlying company) rather than percentage fund allocation, ensuring a 5x return on a $3 million position is material to the portfolio.

Manager Selection & Personnel Strategy

• Hiring Model: The team hires almost exclusively from undergraduate ranks to ensure long-term retention in Waco, Texas, trading high initial training costs for team stability (e.g., a member with 16 years tenure).
• Manager Constraints: The fund fires managers who deviate from their mandate (e.g., a third baseman moving to second base), even if returns are high, prioritizing strict adherence to the agreed asset class and strategy.
• Respected Peers: Brown University is cited as the peer the fund respects most for their courage and long-term investment focus, despite Baylor's recent outperformance.

Macro Outlook & Thematic Bets

• Software & AI: The fund took a contrarian long position in software in early 2026, betting that human behavior and trust in legacy systems (SaaS) will prevent software replacement by AI, viewing the market sell-off as a buying opportunity.
• Data Centers: The fund identifies "permits and power" as the primary bottlenecks for data center development in the US and UK; they hold positions in permitted sites which have appreciated 50% in six months due to scarcity.
• Biotech: This sector is a primary growth area for the next 10 years, viewed as less correlated with market cycles and driven by science solving diseases rather than treating symptoms.
• Private Credit: The fund is not invested in private credit, viewing it as an "overhyped" asset class where the risk-reward profile is skewed (equity-like downside without equity upside).

Challenges & Future Outlook

• Scaling Constraints: Moorhead notes that investing at a $20B+ scale (e.g., Harvard, Notre Dame) fundamentally changes the impact of returns, where even a 50x return on a $20M check is negligible compared to the overhead of managing such a massive balance sheet.
• Regulatory Risks: AI's impact on education is a concern regarding "human brain atrophy" from over-reliance on AI tools, though the fund believes the educational system will adapt.
• Growth Trajectory: With the endowment growing from $1.4B to $2.6B in recent years, the office is currently navigating the "one to five billion" inflection point, focusing on systematization while retaining creative flexibility.