Ideas

What Bill Gurley Saw

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Something really interesting happens when you consume a boatload of information: your brain begins to pick out patterns from the mass of stories in your head. This is true regardless of whether it’s books that you’re reading, or podcasts, or even just research programs that you’re executing (I know friends who — on becoming a parent — go deep into child development research; the same thing probably happens for them once they cross a certain threshold of information consumption.)

I read 53 books last year, most of it as an alternative to therapy for pandemic-related burnout. If a stranger were to look at my list of books, it would look like a random grab bag of topics, authors, and ideas. But when I look at my list, I see books falling into clusters, each cluster representing a set of questions I want answered, and in some cases have been investigating for a number of years now.

I thought I’d start 2021 with one of the questions that leapt out at me over the course of 2020, along with the books (and external sources!) that I consumed in order to investigate it.

Uber: How Do You See Into The Future?

I read Mike Isaac’s Super Pumped and Brad Stone’s The Upstarts fairly late in the year last year, shortly before the Airbnb IPO, and two years after the Uber one.

The books are ostensibly about Uber’s rise to greatness (and in Super Pumped, Uber’s fall from greatness). Both authors spend some time on the structural and technological changes that led to the rise of the sharing economy, but the bulk of the book is focused on the narrative arc of the main actors. I sometimes read such business biographies and struggle to find useful, generalisable things that I can learn from them — you would think that in Uber’s case, the lessons might be “winning at any cost is often not worth it” or “VCs might stab you in the back if you represent too large a size of their portfolio, because this turns things into a finite game (vs an infinite one)” and “you really really really need to get lucky if you want to take advantage of a structural shift; nobody recognised the underlying reasons for the sharing economy until after the fact”. As with many business biographies, the lessons are often too specific to the story to matter; you read the narrative simply to add an extra business pattern to your head.

But that last lesson — on the recognition of structural shifts — isn’t exactly true. While nobody could say that they understood the full opportunity in ride-hailing, I think there’s a plausible case that Uber board member and venture capitalist Bill Gurley saw more than most. I spent three books and a couple of podcast episodes looking into it last year — mostly because I was interested in the kind of thinking that allowed Gurley to bet big on Uber. In other words: what did Bill Gurley see? What did he focus on in the fog of uncertainty around early ride-hailing? And how did he pick the right horse?

It’s easy to forget that in the early days of ride-hailing, it wasn’t at all clear that Uber would emerge as the winner. One of the dangers of reading business history is that everything seems pre-ordained — of course Uber would win; of course they would prevail against government regulation; of course Kalanick was a ridiculously effective combatant in the ride-hailing wars. But for a taste of what it must’ve felt like circa 2011, recall how the fog of uncertainty felt in the early days of the pandemic last year, or even the confusion one feels about the multitude of crypto projects today.

To hear Isaac and Stone tell it, Gurley met with every Uber-like startup in the 2010-2011 period, long before ride-hailing was on most peoples’s radars. He eventually backed Travis Kalanick and Uber. The story isn’t that straightforward, however: before he found Kalanick, he offered $8 million to Taxi Magic at a $32 million valuation (he was turned down), and tried to get into an early Cabulous financing round (the founder, John Wolpert, told him the round was ‘full’).

How did Gurley win? One — trite — answer is that Gurley got lucky. He had been searching for taxi-related marketplace startups for many months, and found Uber only after every other competitor had turned him down. It was thus a stroke of luck that the first — and last! — deal he made in the market turned out to be the winning one.

But that trite answer obscures another fact: when it comes to marketplace startups, Gurley has had a hell of a track record. He invested in GrubHub, Nextdoor, LiveOps, OpenTable, Stitch Fix, and Zillow, amongst others. And indeed, whenever I listen to Gurley talk about marketplaces or network effects on a podcast or at an interview, his comments tend to be a lot more nuanced when compared to his contemporaries. You get the feeling that he’s skimming from the surface of a very deep pond.

Take, for instance, this interview with Patrick O’Shaughnessy:

Gurley: And I've come up with this phrase I use internally that I made up. So one day I'll have to write a definition of it, but I call it liquidity quality. And I tell entrepreneurs, “I care way more about that than I do how broad you are. We can use venture dollars and growth playbooks to go broad if the fires burning bright. And so how do you get this liquidity quality high?” And Jeremy (the Yelp founder), being at those nightclubs in San Francisco and people being super passionate and their review frequency being high, that caused the quality of the experience, even though it was in a very small area.

And so I very frequently run into entrepreneurs who think they need to expand to 10 cities really quickly to raise their A or their B or whatever. And I'm like, “No, if you have like incredible unit economics and growth metrics in a single city where it's obvious that your playbook's working and things are spinning and things are getting better and you're basically having network effects, that's way more interesting.”

O’Shaughnessy: Are there interesting markers of whether it's liquidity quality or the health of the network itself in a contained area that you find especially interesting?

Gurley: I mean, I think that's exactly what you have to look for. The reason I can't say measure A, B and C is you have to know the system that you're looking at and then define what that is. But I think every company that's trying to build a marketplace, UGC (user generated content) and network effect, should have some definition of what quality looks like or liquidity threshold that matters and track it like crazy.

Gurley’s conception of ‘liquidity quality’ made me sit up. If you spend enough time sniffing for tacit mental models, you would learn to recognise signs of expert pattern recognition. Gurley’s inability to articulate ‘liquidity quality’ was likely due to the tacit nature of his recognition: that is, the LQ metric was different for every marketplace startup that Gurley had seen; he could — to paraphrase Supreme Court Justice Potter Stewart — “know it when he saw it”. Later on in the podcast, for instance, Gurley explains:

Gurley: … Tinder, ironically did this with parties, not really a UGC, but kind of a UGC company in each city that they would launch in because you want to get the quality up to a certain high level. And Nextdoor, we did the craziest thing in the world. We said, "Until you get to 10 members, you can't open your neighbourhood." So intentionally restrictive because we didn't want ghost town experiences and we knew quality of the experience was a function of how many neighbours were in your system.

So we thought the anticipation of getting in was a better user experience than getting in and finding it poor. And so we'd encourage you to go find the other so you could unlock your neighbourhood, is almost like a game heuristic. And so every one of these you'll find there's somebody that understands that nuance and about how this is going to tip and have some type of growth playbook is what people would call it. But I find the number of entrepreneurs that are capable of successfully launching a UGC play is a very small fraction, maybe less than 1% of all entrepreneurs. And they just tend to have a nuanced feel for what it takes to make something come alive and the feature sets that matter and how much they care about quality, I keep going back to that word, but it's what makes the kind of fire burn and then makes it possible to grow into ever bigger circles.

Think about the implication of that insight: Gurley and company restricted the growth of Nextdoor in order to optimise for ‘liquidity quality’ — that ineffable mix of user value and density; they only allowed it to spread to neighbourhoods that were adjacent to existing ones with high LQ. That way, when you signed up for Nextdoor, you would be able to search for reviews of local businesses and services, because it would have already been filled in by users in the adjacent neighbourhoods.

My point: Gurley appears to have thought really deeply about the nature of networked marketplaces, at a level above many of his peers. One reason for this might be that he started much earlier compared to everyone else:

Gurley: So between '93 and '96 I was working on Wall Street at Credit Suisse First Boston in the research department. And literally the first or second day fell into a friendship with Mike Mauboussin, who you've had on many times and who you know well. And Mike and I, even though he was the food analyst and I was the tech analyst, we started sharing ideas and books and whatnot and I actually don't remember which one of us, he might remember, had a book called Complexity by Mitchell Waldrop which was about the rise of the Santa Fe Institute. And in that book, Brian Arthur is one of the heroes, early professor at Santa Fe Institute and someone that had a lot of different radical ideas. And so we had read that book and I think visited Santa Fe and met Brian, but this notion of increasing returns that some company that got to a big level would find it even easier to get to the next level.

To replicate what Gurley experienced, I went and read Complexity, followed by Brian Arthur’s original paper. And I quickly realised that Arthur’s articulation of ‘increasing returns’ was likely the very first explanation of network effects in business; Gurley read both the book and the paper a few years before he became lead analyst on the Amazon IPO.

What Gurley Saw

So, again: what did Bill Gurley see? I think the right answer is this: Gurley was one of the few people to have deeply internalised the implications of Arthur’s ‘increasing returns’ theory; he understood more about network effects and networked marketplaces than just about anyone in tech at the time. (In the podcast with O’Shaughnessy, Gurley bemoans the overuse of the phrase ‘network effects’, saying “everyone’s heard it and repeats it so it’s been polluted […] having looked at so many different businesses over the years now, there are (actually) different levels of it.”)

So when the ride-hailing opportunity turned up, Gurley was well prepared for it. He must have gone after Taxi Magic/Cabulous/Uber the same way he went after any startup that looked like it could take advantage of ‘increasing returns at scale’. And because he had sat with the problem long enough, and because he had more experience with more networked companies than others, he could recognise when an operating team had figured out ‘liquidity quality’, and then go all in on them.

Of course, Gurley did get lucky — everyone who is wildly successful gets lucky at some point. But it’s not too much of a stretch to say that Brian Arthur’s paper helped Gurley see the future; it does seem like his investing career has been defined by the nuances of a single, wonderful, idea. And it probably explains why — now that he’s squeezed all the returns out of that one idea — Gurley announced, early last year, that he was finally going to stop. Arthur’s idea helped Gurley see the future, but now the future he saw had finally arrived.


Content Sources

Here’s everything I consumed when I was trying to investigate this question:

  • Complexity by M. Mitchell Waldrop
  • The Upstarts by Brad Stone
  • Super Pumped by Mike Isaac
  • Invest Like The Best podcast — Bill Gurley - All Things Business and Investing, where he explains liquidity quality, as well as the influence of Arthur’s ideas. There is no single point where he explicitly defines LQ; if you listen carefully you can hear him weave the idea into his descriptions of all the marketplace companies he’s been involved with over the years.
  • On Competing Technologies and Historical Small Events: The Dynamics of Choice under Increasing Returns by Brian Arthur; linked article.
  • Invest Like The Best on the Acquired podcast — O’Shaughnessy: “I sent a message to Gurley, one of the smartest guys on so many things, but marketplaces are one of them. I remember messaging him and saying, what do you think? And he just drew this little chart conceptually, which was on one axis, the producer penetration. What percent of obituary writers do you have signed on? On the other axis was the benefit to consumers. Basically, conceptually, think about it as does the service keep getting better as you penetrate deeper into the supplier pool? That line should look straight. I think he said, once you penetrate a certain amount, the marginal supplier is not going to make the service better, and that's going to happen pretty early. So you're going to get this little bump and then a flatline, and that's not a good marketplace business.”
  • It's also worth reading Gurley's blog, to try and get at the worldview from which he writes.

If there's a generalisable question from this investigation, it might be this: if Gurley really did build his career around a single, secret idea, how might you copy that? Unfortunately, I don't have any prescriptions. The world is weird, and sometimes people stumble onto insights that turn out to be ridiculously valuable. (For an equally compelling story with the same shape, go read Gregory Zuckerman's The Man Who Solved the Market). The conservative lesson that you might take away from Gurley's story is simply “huh, this is something that can happen”; you file it away in your head and then carry on.

Originally published , last updated .
  

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