Episode Summary
Executive Summary: Patrick O’Shaughnessy hosts Bill Gurley and Michael Mauboussin for a wide-ranging talk on AI, increasing returns, regulation, capital allocation, venture dynamics, and genius. The conversation frames how technology waves create value, why incumbents are often advantaged, and where investors should look for durable edge.
Main Topics: AI: hype, limits, and incumbent advantage: They separate LLMs from broader AI, warning against inflated expectations while noting incumbents moved fast. Increasing returns and network effects: They explain why rare feedback loops can compound value for customers and firms. Intangibles, recombination, and innovation: They argue modern growth is driven by scalable digital assets and recombining existing ideas. Regulatory capture and blocked progress: They say entrenched regulation helps incumbents and slows innovation in healthcare, energy, finance, and telecom. Venture capital structure and cyclicality: They criticize VC’s low barriers to entry, high barriers to exit, and too much capital chasing too few great deals. Capital allocation under low rates: They show companies did not behave as theory predicted during ZIRP, with buybacks, cash, and leverage diverging from expectations. Physical-world technology and hard-tech: They see opportunity in energy, nuclear, robotics, and healthcare, but note regulation and capital intensity raise the bar.
Key Arguments: AI is profound, but LLMs are narrower than AI and won’t quickly deliver fantasy outcomes. Incumbents have an edge because AI adoption is highly choreographed and API-based. Foundational model companies face startup-like valuations without normal startup-market discipline. True increasing returns are rare but can create exponential value by raising willingness to pay. Intangibles scale fast, but they are easy to copy and can become obsolete quickly. Regulation often becomes the friend of the incumbent and reduces competition after major laws pass. Venture has low barriers to entry and high barriers to exit, so too much capital destroys returns. Public companies shrank 46%, yet private-market expansion is not a simple fix. Companies used a ~15% hurdle rate even when the cost of capital changed materially. The biggest market winners matter most: 2% of companies created $50T of $55T of wealth.
Data Points: Date of recording: April 15th in 2024 - Patrick timestamps the discussion for AI-era context Magnificent Seven economic profit share: about 45% - Michael says the group generated this share of U.S. stock market economic profit Market cap share of Magnificent Seven: 25 or 30% - Michael contrasts valuation share with profit contribution Return on capital trend: starting around the year 2000, that flipped - Michael says large-company returns overtook smaller-company returns around then Tech CapEx vs energy CapEx: 2x - Michael says top five tech companies spend twice top five energy companies’ CapEx U.S. public-company count decline: down 46% - Michael cites the shrinkage in public listings Venture market size: a little over a trillion, maybe trillion and a half - Michael estimates U.S. venture AUM Benchmark fund size: 450 or 500 million dollars - Bill references his firm’s funds in contrast to mega-funds Cost of capital hurdle rate used by CFOs: roughly 15% - Michael cites John Graham’s CFO survey Treasury-bill underperformance share: just under 60% - Michael summarizes Bessembinder’s public-market study Aggregate wealth destroyed by underperformers: $9 trillion - Michael cites Bessembinder’s estimate Aggregate wealth created by winners: $64 trillion - Michael cites Bessembinder’s estimate Net U.S. market wealth creation: $55 trillion - Michael gives the net result from 1926 through 2022 Share of companies creating most wealth: 2% - Michael says this tiny slice created $50T of the $55T Value created by the top 2%: $50 trillion - Michael cites Bessembinder's concentration result Probability of platform success: less than one in seven - Michael references an academic study on firms seeking to become platforms Cost decline from Wright’s Law: 20% - Michael says cost per unit falls for each doubling of cumulative output Historical auto diffusion example: within a decade - Bill says automatic transmissions spread across manufacturers in about 10 years Boeing/bridge comparison: 12 days - Bill cites the I-95 bridge being rebuilt in 12 days as an exception to bureaucratic delay
Pivotal Quotes: "What could go right?" — Bill Gurley: He describes the best mindset for partner meetings and evaluating new ventures "The patterns have a half-life and they decay." — Bill Gurley: He warns against overusing pattern recognition in venture investing "If you want to be an optimist, that's certainly one way you could argue for that." — Michael Mauboussin: He frames recombination and digital tools as a source of future progress
Implications: Investors should separate real compounding mechanisms from hype, and watch where AI, energy, and healthcare can genuinely rewire workflows despite regulation.
From the Episode
Different things. One is just to embrace that attitude. I think Bruce came back from reading Wanna Ridley's book and used the phrase what could go right at a partner meeting. And so it's very easy, especially with a big group, with a group bigger than about five, it's very easy to fall into cynicism as a sport to start taking shots at stuff. And so having this what could go right attitude, in other words, make the Primary part of the discussion: how big could this be? Rather than trying to nitpick whether or not it might fail. And so that's one. Two, I think it requires just exhaustive behavior. You can't stop looking. How would you know that you looked under every rock? There's no way to know that other than to be exhaustive about it. And so creating a culture where everyone feels that responsibility is important.
Or is it a sufficiently changing world, as you point out, that you can't rely on those patterns? I think the patterns have a half-life and they decay. Or I saw Toby from ShockFire was talking about the VCs passing on them. I fear we were one of those. And one of the rule sets that caused that kind of mindset was in the software world, Intuit was the only company that succeeded with small business and everyone else failed. And so you develop this anti-small business mindset, which keeps you out of HubSpot and Shopify and Twilio. You then miss massive amounts. And so you need a framework that allows you to benefit from the group dynamics of pattern recognition, but to not be holistically tied to any pattern or not let one pattern break. This is a new subject for this discussion, but we always talk in venture like there's two error types.
And because we have digital technologies, it allows us to do things, search the space much faster than we could before. And hence we can come up with solutions faster than we could before. If you want to be an optimist, that's certainly one way you could argue for that. What does this look like in the wild, Bill? When you're engaged with people that are the recombiners of the ideas, trying to build something novel and new, how do you talk about this with them? Do they even think about it as such? Or are they just saying, look, now there's mobile and there's GPS, so I'm going to build Uber. How's your experience with this in the real world? Some of it might fall under the phrase best practice. And so there are discussions, there are more discussions than ever because of podcasts and whatnot. And somebody is talking about their viral growth loop and how that works. And they come up with a framework and a model and they share that. They put it in a PowerPoint. And then other people are using that kind of thing. In a more concrete form, this is open source writ large. And I think open source is one of.
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