Mike Ryan (ex Harvard Endowment) on why AI gives polished answers to the wrong questions

Mike Ryan is the founder of Bullet Point Network. Before that he spent two decades at Goldman Sachs, where he was a partner and co-head of Global Equities, and then sat on the investment committee at the Harvard Endowment, allocating $ 12B to outside funds and $ 6B directly into companies. The episode opens with a comparison the host can't reconcile: one of the best-known venture firms published its performance, and measured against a Nasdaq-100 index fund the average return came out slightly below the index — with much worse liquidity. That pattern isn't unique to one firm. So how do a hundred very smart analysts produce index-matching returns? Ryan's answer has two parts. The first is that public technology stocks have had an unusually strong run on a risk-adjusted basis over the last five, ten and seventeen years. The second is about what goes wrong inside investment firms, and it isn't what most people assume: the largest funds see nearly every deal, employ excellent people, and produce enormous amounts of analysis. What that analysis often contains, he argues, is a repackaging of information the company itself supplied. A firm can hire very smart people, build a beautiful process, and still deliver index-like returns or worse. He then pushes back on the premise. The last five years had a specific problem — funds haven't been able to exit and realise profits — and meanwhile the value creation has moved into private markets. Companies are now going from nothing to a hundred billion, and in a few cases close to a trillion, before they ever go public. He points out that Microsoft, one of the best companies of its era, did almost all of its growing as a public company, and says the new pattern is something he has not seen before in his lifetime. The host raises Ryan's undergraduate thesis, written at Yale under David Swensen, who created the endowment model that pushed universities toward illiquid assets, and asks whether he ever suspected the model had stopped fitting. Ryan treats the 2008 financial crisis as the real test of that conviction: institutions forced to sell into the liquidity crunch suffered badly, while those who held were rewarded over the following two decades. But he expects the next ten to twenty years to be weaker than the last, largely because interest rates are returning to normal levels, and he grants directly that venture returns over the past decade have not objectively beaten the Nasdaq. The first segment ends on what actually lets an investor hold through a downturn: the temperament to stay invested, and a structure with no capital calls to meet. From there the conversation moves to where fund size and fee income pull a manager's incentives away from the people whose money they manage, and why the current buildout may nonetheless require very large funds. Ryan is asked whether making a firm more rigorous can make it worse — whether a fund can pressure-test its way out of the one bet that would have returned it — and where he draws the line between the work he hands to AI systems and the work he insists a human keep. The last third turns to the audience's own problem: how a founder with no revenue and no product should present a real probability of failure to an investor, and what the best of them have stopped hiding.