Standing On The Shoulders Of Giants #1
The idea maze, bad pattern recognition, and reading decks in advance
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It’s been a while since I’ve written anything, and I’m feeling the effects. Even this introduction is strained. The paralysis I’m feeling at the start of every sentence worries me as a sign of atrophy.
I didn’t want to stop writing. It’s just not easy to contribute original thinking and novel viewpoints to a discourse on any topic, but definitely in technology. Perhaps I set the bar too high. After all, there’s lots of value in simply synthesising ideas from different disciplines and thinkers, especially when used to present a more evocative perspective on the same subject. That’s really all I was doing with Socratic VC: read widely, synthesise, and draw connections. It’s a very gratifying feeling to demonstrate how the ideas of different thinkers are interlocked, unbeknownst to the original thinkers themselves.
One of the best things about working in technology is the common thirst for knowledge shared by my peers. There’s an infinite amount of knowledge available to us at our fingertips. Our only challenge is to curate this.
I’ve personally grappled with the problem of curation for a long time. Should I read tangentially relevant things? Should I read the same newsletters everyone else is reading? Or should I try to find thinkers that no one has found yet (for better or worse) so that I can pawn off their ideas as my own without the risk of being called out?
As Matt Ridley argues in How Innovation Works and as Shane Parrish has so brilliantly articulated:
Innovative ideas have to come from somewhere. No matter how unique or unprecedented a work seems, dig a little deeper and you will always find that the creator stood on someone else’s shoulders. They mastered the best of what other people had already figured out, then made that expertise their own. With each iteration, they could see a little further, and they were content in the knowledge that future generations would, in turn, stand on their shoulders.
When it comes to being a great technology investor, we’re spoiled with almost 20 years of investors sharing the secrets of the craft through blogs, podcasts, YouTube videos and other rich content worth perusing. That’s just from investors. Great innovators have been more gracious in sharing the ingredients of their success for far longer. It would be a waste not to consume as much of this rich wisdom and knowledge as possible.
Don’t take it from me. Take it from Bill Gurley.
Every week I’ll be sending you a curated list of ideas and writings from the archives, with a brief summary and my commentary added. In time I hope to resume long-form writing, but this will do for now. I hope you find at least one element each week that resonates with you and, if so, that you reach out to spark a discussion. With that, I’ll end this soliloquy.
The Idea Maze by Chris Dixon, 2013.
A16z has a lot of Partners. That said, I was quite disappointed to only learn of Chris Dixon late last year, coinciding with crypto’s capture of mainstream attention. As General Partner of a16z’s crypto funds, Chris has been a stalwart of Web3 since 2013. More importantly, he’s one of the most lucid thinkers I’ve ever listened to. If I had to name one podcast episode to offer to the most ardent cynic of crypto, it would be Patrick O’Shaughnessy’s interview of Chris Dixon on Invest Like The Best. Hearing Chris lay out the potential of the blockchain is riveting; only those of us around in the ‘90s would have had the privilege of witnessing a new technology effect profound transformation on all aspects of our lives.
We’ll visit his thinking on Web3 in the future, but for today I’ll bring your attention to his views on the Idea Maze, a concept posited by Balaji Srinivasan.
A good founder is thus capable of anticipating which turns lead to treasure and which lead to certain death. A bad founder is just running to the entrance of (say) the “movies/music/filesharing/P2P” maze or the “photosharing” maze without any sense for the history of the industry, the players in the maze, the casualties of the past, and the technologies that are likely to move walls and change assumptions
In other words: a good idea means a bird’s eye view of the idea maze, understanding all the permutations of the idea and the branching of the decision tree, gaming things out to the end of each scenario. Anyone can point out the entrance to the maze, but few can think through all the branches. If you can verbally and then graphically diagram a complex decision tree with many alternatives, explaining why your particular plan to navigate the maze is superior to the ten past companies that fell into pits and twenty current competitors lost in the maze, you’ll have gone a long way towards proving that you actually have a good idea that others did not and do not have. This is where the historical perspective and market research is key; a strong new plan for navigating the idea maze usually requires an obsession with the market, a unique insight from deep thought that others did not see, a hidden door.
Chris gives the oft-cited example of Netflix and whether it would have been possible in 1997 - were the conditions in place for online streaming to usurp DVD-by-mail? Or what insights did Dropbox latch onto to not fall prey to the same challenges that felled many prior attempts at cloud storage? It’s worth plugging Mike Maples’ writing on inflections here.
Chris offers four sources to increasing the resolution of your idea maze, one of which is the history of your field. To that end, Chris laments the idea of being in ‘stealth’ mode. Setting your LinkedIn status as ‘Building in stealth’ as a founder is quite common nowadays. Does one deprive themselves of rich learning opportunities from fellow practitioners? I’m not sure. Networks are rich and thriving outside of online communities (having been built on online communities but then taken offline), so the lost connections from staying in stealth mode ought to be marginal…
Anyway, the idea maze is a nice analogy worth using with founders. What have you learned from the graveyard in that corner of the maze, or why is that door the right one to the exit and not this other one?
Making Uncommon Knowledge Common by Kevin Kwok, 2019.
Kevin Kwok is a former investor at Greylock Partners. I stumbled onto his blog when reading about Sutter Hill Ventures, the oldest and one of the most successful venture capital firms in Silicon Valley. Kevin’s piece on Sutter Hill is a must-read, but we’ll visit it here in the future. For now, we’ll focus on his piece on Rich Barton.
Kevin describes Rich Barton as possibly the best consumer tech founder of all time.
He’s founded three consumer companies each worth over a billion dollars with Expedia ($18.6B), Zillow ($8.8B), and Glassdoor (Said to have been acquired for $1.6B).
Repeatable success is key, especially in Consumer tech which is one of the hardest areas to succeed in. Companies that sell to large Enterprise customers are relatively well understood now, and even our understanding of SaaS metrics and business model decisions has matured a lot over the last decade. The Consumer tech sector, however, remains dark magic. The playbooks are far less developed—and no one’s playbook has demonstrated the repeatability of Rich Barton’s.
There are a few consumer investors who have multiple multi-billion dollar wins. But it’s hard to name people who have founded three consumer companies worth over a billion dollars each.
The common thread running through Rich’s successes, Kevin argues, is Data Content Loops to dominate Search. As prosaic as that sounds, Search remains one of the most powerful moats incumbents possess.
How did Rich achieve this?
Expedia: Prices for flights and hotels that before you’d have to get from travel agent
Zillow: Zestimate of what your house is likely worth that before you’d have to get from broker
Glassdoor: Reviews from employees about what a company is like that before you’d have to get from a recruiter or the company itself
Information that was once guarded by gatekeepers (uncommon knowledge) was made common. This content allowed each of these companies to dominate Search, which became their main acquisition channel. Moreover, these brands accrued credibility from customers.
Creating common knowledge creates a network effect. All companies in Silicon Valley want to build network effects, but few have followed Barton’s path despite its effectiveness. The more people use and trust Glassdoor, the more companies must take it seriously. And as users see more people contributing to Glassdoor, they can be more confident they’ll stay anonymous when they add their review. There are virtuous loops in common knowledge.
There are few playbooks that have shown repeated success and data content loops seem to be one of them.
Bad Pattern Recognition Gone Wild by Frank Rotman, 2015.
Like Chris Dixon, Frank (Partner at QED, a globally renowned fintech VC firm) has a gift for elucidating the most esoteric of topics. I first became a follower of Frank’s through his Twitter threads. For all fintech junkies out there, Frank is well worth a follow - I’ve often signposted friends to his threads on non-dilutive financing, producing a 3x VC fund and more.
Frank is characteristically humble in this post on bad pattern recognition, as he starts out by reciting the advice he got from seasoned investors when he commenced his career as an investor.
It shouldn’t come as a surprise that much of the advice was generic and of the “no duh” variety. After a handful of conversations the wisdom being shared with me about how to spot a great business started to sound alike. Only back great teams. Serial Entrepreneurs succeed more than first time Founders. Back businesses with low burn rates. Make sure that the business is serving a large addressable market and solving a real problem. Answer the question “is the market ready now”? And on and on and on the genericized list went.
This is a topic for another day, but one strand certainly worth exploring is just how much of a skill the job of an investor is. Before I ruffle too many feathers, I’ll quickly move on.
My conclusion at the time was that making good investment decisions collapsed to an exercise in Pattern Recognition and was even told such by many very successful investors who prided themselves on their skills in the space.
If we’re honest with ourselves, that’s all it really is. In a Bayesian way, over time we’re able to get closer and closer to an understanding of the archetype of a good business - this is definitely the case for later-stage investing, but could even be said of early stage investing (to a lesser extent).
That said, bad pattern recognition is rife. As Frank says:
The key is to know when to trust previous patterns and when to ignore them — not a simple task. Sometimes art needs to override science and intuition needs to override history. This is one of the keys to being a great investor.
This manifests itself in various forms as a technology investor.
European venture is only two decades old. We’ve yet to see the volume and scale of exits that the US ecosystem has. Naturally, investors are sensitive about pricing and acquiring the degree of ownership necessary to deliver satisfactory returns. Sometimes I wonder, though, if we’re underestimating the potential value creation that is to come in this decade, as Packy McCormick’s brilliant piece Compounding Crazy argues.
But markets are not static. Based on data from the World Bank and Sibil Research, the combined market caps of all of the world’s publicly listed companies has grown from $1.138 trillion in 1975 to $117 trillion today, a roughly 100x increase, or a 10.6% CAGR over the 46 year period.
Based on that projection, $203 trillion in public equity market cap will be created in the next decade, nearly double today’s combined market cap in new value alone.
Why is this relevant? Well, our mental models of exit valuations are primarily influenced by the past, and rightly so. My only rejoinder is that there may be a discounting of the future value creation that is to come, which makes the topic of valuations a lot more challenging.
Frank points our some dubious pattern recognition relating to the company’s location, the founder’s gender, who the company is selling to, the presence of competitors, and more. I’d posit one more: existing investors. Incoming investors often obsess over companies with ‘tier-1’ investors already behind them. The halo of being backed by Sequoia Capital is undeniable, but this is an imperfect heuristic and almost certainly comes at the cost of giving enough time and attention to great businesses without such logos on their cap table.
Frank closes with a salient reminder of bad pattern recognition gone wrong:
And for what it’s worth, one of the best companies we ever funded was a result of the QED Partnership seeing something that others didn’t. We’re extremely happy to have led the Series A for Credit Karma and it wouldn’t be in our portfolio if we didn’t use our own judgment and ignore the patterns that everyone else thought they saw. Use your brains people….they’re there for a reason!
False Positives, False Negatives, and Reading Decks in Advance by Charles Hudson, 2014.
Charles Hudson is the Managing Partner of Precursor Ventures and former Partner at Unqork Capital. If you polled junior VCs in the US on what investors they look up to, Charles would be a fairly common answer. I’d encourage you to listen to his podcast appearance on Next Play Perspectives to get an insight into Charles’ philosophy on building relationships in venture capital.
Reading decks (in detail) before taking meetings with founders is generally seen as a logical thing to do, if only to respect the founder’s time and have a deeper conversation. Charles suggests that reading decks in advance leads to taking fewer meetings with potentially promising founders and sets up more meetings with underwhelming founders. Or, as he puts it much more eloquently:
There is the risk of false negatives, which I define as declining meetings you should in fact take. The alternative, false positives, are meetings that you feel like you want to take but end up being less valuable or interesting than you anticipated going into the meeting.
Charles argues that reading decks in advance has the following downsides:
Decks do not communicate personal energy
Decks nullify in-person questioning by answering everything in advance
Not all founders disclose the secret sauce in their deck
Some founders are better at pitching in person than creating decks
Those seem like pretty fair grounds, yet I’m convinced this is one of the more divisive topics among venture capitalists. I used to retort to the argument that the ability to communicate your pitch effectively through a deck is a good proxy for many of the other skills needed to successfully build a company, but maybe that is bad pattern recognition (admittedly based on a sample of zero).
I suppose it comes down to time. If decks didn’t act as a filtering mechanism, one would be taking far too many meetings.. right? Right?
That’s all for today. That ended up being much longer than I anticipated. Ultimately this is as much a personal exercise of flexing certain muscles as it is a service to you, the reader. I hope you enjoyed this format, but I would be even more thrilled to hear your feedback.
In the meantime, have a great rest of your Sunday.