Stanford’s Big AI Bet: What if It Busts?

Stanford has gone all-in on artificial intelligence. The Stanford Institute for Human-Centered AI (HAI) spans all seven schools, positioning AI not as a tool for one discipline but as infrastructure for all. We have a campus-wide AI Playground open to the Stanford community, a new GPU supercomputer, Marlowe and a founder ecosystem that reliably spins up companies that shape the industry — from Google to today’s wave of startups. If AI is the next internet, Stanford is the on-ramp.

However, the financial establishment is raising flags. The Bank of England warned about a potential “sharp correction” if sentiment sours on AI, citing high valuations and rapid debt accumulation. 

Some also question the economics of frontier models like ChatGPT. One Epoch AI analysis estimates that a model’s “shelf life” may be too short to earn back R&D costs. Reuters’ Breakingviews argued that AI economics are fundamentally different because inference is expensive and the industry must keep funding new model generations, dragging margins below classic software. 

Prominent voices questioning the long-term prospects of AI prompt a question Stanford has to confront: What would an AI bust mean for campus?

Stanford isn’t just exposed to an AI bust through market correlation. We are closely connected with the companies at risk. Open AI CFO Sarah Friar recently joined Stanford’s Board of Trustees. The board also includes partners from Sequoia Capital, Index Ventures and AME Cloud Ventures. While their expertise is an asset, it highlights why the university must plan for a downside scenario.

Our financial dependence on the success of technology is deeply embedded. Stanford’s investment base is heavily weighted toward private markets: as of Aug. 31, 2025, private equity alone (primarily venture capital and growth equity) was about $21.6 billion of roughly $60.1 billion in consolidated investments. Historically, this has proven fruitful. The university’s early Google license alone produced a $336 million windfall. But if AI’s economics look more like costly infrastructure than high-margin software, the returns Stanford can capture may be thinner and less predictable.

Stanford has served as both a launchpad for generative AI talent and a hub that supports ongoing work in the field. Fei-Fei Li, the godmother of AI and co-director of HAI, served as Chief Scientist of AI/ML at Google Cloud and has since co-founded World Labs. HAI channels research grants across campus. Stanford has boosted its computing power through its Sherlock and Marlowe research supercomputers. Yet we remain dependent on industry resources sustained by venture funding. Stanford’s GPU clusters are small compared to hyperscalers. 

This creates a cascade scenario: if AI corrects, our endowment faces mark-downs in venture portfolios; donor wealth linked to tech pauses; research cloud credits are withdrawn or rationed; faculty recruiting and retention become harder as startup options cool; and job placements soften in tandem with hiring freezes. None of this is certain, but it’s plausible enough to plan for.

Start with the budget. Investment income is the backbone of financial aid and labs; in FY2024, endowment payout funded over 21% of operating expenses, which is vital to areas like students’ financial aid. The new federal law has raised the endowment excise tax: Congress replaced the 1.4% flat rate with a tiered structure that can rise to 8% for the wealthiest institutions. Stanford’s payout-smoothing rule delays the impact of market losses, but if public and private markets slide together, pressure stacks on top of last year’s $140M in budget cuts. 

The job industry would also feel it. A cooler AI cycle typically slows campus recruiting, fellowship dollars and startup valuations; startups reliant on rich terms — or terms favorable to investors — see rounds compress, and research dependent on subsidized compute loses steam as credits tighten. None of this argues for backing away from AI; rather, it argues for planning for volatility with the same seriousness we applied to expansion.

A public AI-downturn scenario — modeling, say, a 30% valuation reset — would help the community see how we would buffer financial aid, research and student services. We should publish a simple stress-test that states what we will protect (for example, financial aid and essential student services), what we will preserve with active management (research continuity and teaching compute) and what we would pause first (nonessential expansions). 

Compute strategy should be flexible, with open models that can run locally as a backup. As open models get closer in performance and cheaper to use, resilience is becoming more affordable. Stanford can also shift large trainings to off-peak hours, protect compute for teaching, and require transparency in industry deals to avoid lock-in. And we should keep strong firewalls between corporate interests and academic decisions. 

While maintaining AI leadership, we should diversify research bets that thrive in any macro: quantum, biotech, climate, and fundamental mathematics. Those fields are durable engines of discovery.

It’s also worth saying that the boom in AI may persist. AWS recently reported 20% year-over-year growth, and hyperscalers continue to bankroll generational infrastructure, with investment still making up a smaller share of the economy than in past electricity and internet booms. Even in this bull market, hedges make our wins more durable: they keep computing affordable when chips are tight, they protect financial aid if markets get choppy and they keep our intellectual life from narrowing to whatever is hottest on a given earnings call.

So the ask is simple. Disclose how much endowment value traces to AI-related exposure, stress-test scenarios where major tech donors can’t fulfill pledges, set concentration limits for any single technology theme, formalize governance walls where roles overlap and build compute that does not depend on corporate charity. 

Stanford has always thrived where ambition meets adaptation. We’ve built the ambition side of the AI equation impressively. Now we need our adaptation to catch up. With transparent risk management and resilient compute, Stanford can remain the on-ramp to AI’s future while protecting against its uncertainties. If the boom keeps rolling, these safeguards save money. If it busts, they might save momentum. That’s how you build a university that thrives through the next California gold rush.

Utsav Gupta is pursuing a master’s of liberal arts at Stanford. A former patent litigator turned Silicon Valley entrepreneur, he now works on AI and spatial computing.

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