The unique lessons of Oculus

The news broke today that Oculus is joining Facebook for $2 billion, and we couldn’t be happier for Palmer, Brendan, Nate, Laird and the amazing crew over at Oculus. Working with a team like this on a mission like this is why you work in, invest in, and love startups. As Santo wrote about, we fell for this company hard from the first moment we saw them.

There will be lots of stories about what this means for Facebook, Oculus, and the world of virtual reality but I think mostly about how this team has executed so incredibly well while carving a very unique path every step of the way. 

These guys are the epitome of a missionary company trying to bring a truly amazing product to the world. That missionary nature has allowed them to ignore much of the standard startup ethos and follow their hearts in several ways that defy convention. 

1. Don’t be afraid to be small - Oculus started as an ambitious hobby project, and they have fought hard to not lose those roots. A lot of startups try to look bigger than they are, but Oculus has taken a different track by just being transparent about their position.

For instance they don’t have a launch date because, simply, they don’t know when the consumer product will be good enough. They have been remarkably open and blunt about the technical hurdles needed to bring the product to market. And they initially started raising on Kickstarter because that was the stage of the business at that time even though some said it would send the wrong message.

If you spend all your time trying to be as polished as Apple or Sony then you are also setting expectations that you are going to execute your first product like them. And that, unfortunately, leaves absolutely no room for error.

The folks at Oculus were mission driven from the core, which let them have the courage to say you should believe in them not because they were the most polished company but because they cared more than anyone else and they were your best shot. That meant that even when an order page went down, or first developer units made some people nauseous, instead of complaining everyone was on their side. For all the press attention on Oculus every week, they have spent most of their life actually looking smaller than they really are.

2. The best marketing is a mission - There is no Chief Marketing Officer at Oculus, there is not even a VP of Marketing. Despite the tremendous amount of Oculus press that happens every week there is basically no playbook. That isn’t to say they aren’t amazing at communicating, but there is no inbound content marketing strategy or master plan at work.

The simple fact is that they built an undeniably amazing product, so they shared it with others and talked about it as much as they could. When you have a mission, and an amazing product, you can dominate the market not by tactics but by heart. In many ways when you in the rare situation of creating a market versus competing in a market, this can be the best strategy.

3. Hire your CEO - I have been a founder/CEO twice, and I personally believe it is the best situation when the founder can be the CEO long term. But Palmer Lucky had the intuitive sense, and tremendous humility, to realize he might be an exception. Palmer brought in Brendan Iribe as CEO not to try and raise money, or because some board of directors said so. In fact he brought in Brendan before there was any outside money or a board of directors.

He brought in Brendan because he knew he wanted a person to do that job and he didn’t put too much ego on that title. That bit of information should tell you a lot about who Palmer is, and about who Brendan is in getting so heavily involved that early. I don’t expect this to be the right plan of action for a lot of startups, but it is a textbook case of ignoring convention and doing what is right for your company.

4. Be an outsider - Like a lot of hardware startups the early team at Oculus is mostly software engineers learning hardware. Palmer and others are hardware hackers for sure, but this is not the story of an amazing hardware visionary like Tony Fadell, the “father of the iPod,” spinning out of Apple to start Nest.

This is a group of folks building something because they believe in it, adding world class expertise like John Carmack along the way, but always keeping an outsider mentality. It made the learning curve much steeper, but it also allowed them to question a lot of common assumptions.

Of course no startup should follow these steps and assume they will work for them. They just remind me that these guys have been successful because they have been true to themselves. And that will likely continue for the team with Facebook.

They will have deep pockets from a partner that is focused on the platform of the future. But also, in Mark they have an entrepreneurial partner that is also an outsider to the machinations of the hardware business. I expect that much like Instagram and Whatsapp they will be left to their own devices to flourish, and I expect they will. The future for them is exciting.

The excitement about seeing that future is of course a little bittersweet. Our involvement with Oculus was all too short and I will miss those day flights down to Irvine. But all of us at Spark will continue to help them in any way we can because, just as before we invested, we believe in the vision. We’re rooting for them.

bijan

Hallway Chat #18 with Nabeel Hyatt (@nabeel) and Bijan Sabet (@bijan)

Today’s show: “What does Sony’s entry to VR mean for Oculus? Plus Calendars, Secret, and Reed Hastings latest moves.”

We recorded this show Friday afternoon but finally had a chance to post it today. Please send us your feedback and suggestions on topics  you would like us to cover on our next episode!

I started out as an actor, where you seek to understand yourself using the words of great writers and collaborating with other creative people. Then I slid into show business, where you seek only an audience’s approval, whether you deserve it or not. I think I want to go back to being an actor now.
Alec Baldwin on ending his public life as an actor, although it could be said about many a startup life. It’s easy to get distracted from what matters when the light is so bright.

Postmates

I was sitting at The Creamery coffee shop a couple months ago with a friend, who without much segueway started raving about a local company called The Juice Shop. “Have you tried their A+ Deep Green juice? Life changing!” I was of course open to try it but their nearest location was in Cow Hollow, and so I resigned myself to likely forgetting this recommendation by the next time I was in the neighborhood.

Not to be deterred, my friend whipped out his phone and 10 minutes later a Postmate walked into the coffee shop where we sat and delivered our juices. I’ve been a regular of The Juice Shop since then.

When people talk effusively about Postmates it’s often stories like this. From the point of view of the customer this is another example of your phone as a “remote control to the physical world,” much like Uber or HotelTonight. Postmates is also often described as Kozmo, probably the most beloved of the late 90s flameout startups, only with a business model.

For a firm like Spark where we guide ourselves by the product, the strength of the experiences Postmates generates is incredibly compelling. We’ve seen the type of businesses those reactions can build. But the long term effects on the supplier side are actually just as interesting.

The Juice Shop gained a new loyal customer that day, leaning on a local logistics infrastructure for growth even though they have no formal relationship with Postmates. They did not have to hire a van and a driver, open a website or buy Facebook ads, and it did not disrupt their business, it just augmented it.

Commerce on the Internet has always had two stories to tell. There have been marketplaces that enable smaller companies (Ebay, Etsy, Storify, Storenvy) and those that run centralized services (Amazon, Walmart). This isn’t a value judgement, I love my Amazon Prime and I’m a regular buyer at Etsy as well.

In a new world of mobile local commerce there are also two models emerging. After all Postmates is hardly the only company trying to deliver goods to your home, and companies like Amazon Fresh, FreshDirect, Instacart, and Google Shopping Express are all in various early states of success. In attempting to compare them you could talk about who is growing the fastest and has the largest fleet, in this case that would be Postmates. Or you could simply say they have a different selection or target audience. But more importantly they have taken fundamentally different approaches to the supply side of their business.

While Amazon and others are using a centralized resource and distribution model, much like their offline approach, Postmates delivers from local businesses. As a user, Postmates feels to me more like a window into the local businesses of the city. The service is necessarily delivering very different things in Seattle, San Francisco, or New York, as it’s a reflection of the local culture. When you are in San Francisco you don’t get pizza, you get Delfina’s Pizza. When you are in New York you get Joe’s.

When I first met Postmates co-founder and CEO Bastian Lehmann almost a year ago this was the topic he first dug deep on. He talked about the specific ways, and traits, of how local businesses compete well with national brands. We talked about how to showcase the uniqueness of what a local store has to offer (ideas you’ll see start to come to fruition over the next few months). And of course he talked about his vision for the consumer side of the product.

That day wasn’t the opportunity to invest, it was just a product and strategy session. But it was an illuminating one and led to many more where we got to know his team and what they are accomplishing. Today we are happy to be able to count ourselves as investors and believers in Postmates.

bijan

This week on our “Hallway Chat” podcast Bijan and I talk about the 30th anniversary of the Mac, Facebook Paper, Android, and Tivo. 

We also had a lively discussion about the tension in growing a venture firm, but unfortunately due to audio issues I had to cut it out. Hopefully we will revisit the topic next time.

Click to play, or you can always subscribe to Hallway Chat via iTunes here as Apple still hasn’t kicked us off. 

The VC - CEO relationship

I’m in the process of closing a new investment right now and getting to know this CEO has been a particular joy. There is a long road ahead, but it’s a great sign when we can start off just being honest with each other.

As an entrepreneur I got terrible advice on how to relate to investors from fellow founders. One CEO described his “attachment method” of overly communicating to create the sense of ownership that would make a VC keep giving them money. Then another would tell me to never give the slightest detail of the business because it just gave them the ammo to think they can control you. The problem with these approaches is that they assume there is some formula for how to win at managing an investor.

A CEOs relationship with an investor is exactly that, a relationship. And just like there is no “paint by numbers” template on how to interact in a marriage there is not a single right way for the CEO - VC relationship.

Many VCs are pretty terrible, but that doesn’t mean the goal should be to cynically “manage them.” Some of the best companies happen to have extremely strong partnerships that form with a board member. There is a reason that so many CEOs and investors reacted so strongly to the photo of David and Bijan after the Tumblr sale. They know how good those relationships can be when they happen. 

I’ve had overly nervous VCs that were regularly disruptive, and I’ve had the benefit of true loyalty from real partners. I’ve had largely passive investors that were there when I needed them, and very involved investors that were a huge help. As a founder I tried to not get cynical about investors, and I fight to have that same approach on the other side now. 

As my friend and entrepreneur turned investor Keith Rabois says, it’s easier to get divorced than get rid of an investor, so you might as well treat this as a relationship worth getting right. There’s a person across the table, they aren’t perfect, but they are family now. 

I’m excited about this new company, but also about the people I’m going to be partnering with. Speaking from experience, when it is right the CEO - investor relationship can be one of the most rewarding relationships in business.

bijan

bijan:

On Friday, 12/13, @nabeel and I recorded our latest edition of Hallway Chat.

Show notes:

  • Review of the new Nest Protect, Xmas drones & our surveillance state
  • Fred Wilson’s post & the future of capitalism
  • Twitter’s #1 ranking on Glassdoor & the nature of building a world class company culture
  • And a question for everyone on a company communication tools we should try

As always, thanks for listening and feedback welcome!

The problem with analyzing Unicorns

Aileen Lee wrote a very interesting piece on Techcrunch where she lays bare some of the analysis she has done on “Unicorns” - or startups that have entered the $1b club.

It’s a rarified club, to be sure. In fact, it’s enough of a rarified club that I would call into question any conclusions one would assume by Aileen’s analysis. The long version of why this is not a good idea was the subject of my last post, but the short version is this: 39.

The starting data set is 39 companies, and perhaps it’s a bit more than that as people expand the list, but it’s a pretty small number no matter what. A number that small is just hard to reach any definitive conclusions with.

Anyone who has done a controlled study has seen many situations where the first 10 results you get point in the exact opposite direction of the later conclusion. For instance, right now the conclusion is that nearly half of the co-founders in “Unicorn” companies have worked together in school. But, if the four unicorns from this year happen to have met after college, then that number could drop to 40%.

What would a theoretical 20% drop in a single year tell us? If we follow the original line of thinking that produced the analysis then we would say that there is a new trend to follow! Don’t invest in folks that have worked together in school it’s on the downswing! But of course the likely reality is that we were making assumptions based on a statistical anomaly.

The other issue is one of selection bias. This becomes most obvious when you see that non-whites and women are under-represented on the list, or that the Top 10 Universities are over-represented. It may actually be that a CEO from an “underdog” University has a higher percentage chance of becoming a billion dollar company, but simply that fewer of them get venture funding in the first place.

As we talk about all this data, let’s just keep it in the proper context. No one should be investing based on these stats. I don’t think that’s what Aileen is suggesting, but I worry that other investors are going to start taking these as markers for where they should be putting their money to work. And if you are a founder I wouldn’t suggest take any steps at all based on this data.

As an example, at the time that the first several rounds of investment in Tumblr were made by Spark and USV, David Karp was a solo founder, from a non-elite school, that was drastically below the average age for a “Unicorn CEO.” He would have not passed any test put in place by this kind of thinking.

Instead of trying to draw conclusions based on this data, I would treat this type of research the way you should be treating a Malcolm Gladwell book (and to be clear I think he’s one of the best storytellers of our time). These are valuable in the way that a biography of Jeff Bezos or Steve Jobs is fascinating. They give us a window into a state of the world, but they are largely anecdotes. Speaking in percentages doesn’t change that fact.

Where “Moneyball VC” won’t work

Most early stage Venture Capitalists use very little data when investing. It is largely a world of intuition, relying on mutual relationships, and in some cases sector knowledge or thesis development.

But that tide is shifting. Today there was an article on how Steve Blank now thinks accelerators should go the “Moneyball” route. As “big data” gets more popular, the idea of using a quantitative approach to help make better early stage investment decisions is becoming more frequent. As Rob Go recently wrote, there is a strong rise in the number of VCs employing data scientists, and a couple have even made a firm-wide bet on being entirely data driven. 

Unfortunately most of the press coverage of this trend generally falls into a very surface level narrative about “quantitative vs qualitative” investing. That is, early stage investing is about gut belief and vision, and you better not try to rely on numbers to make a decision. This is a false conflict, and blinds us to the fact that data, properly applied, doesn’t deter our intuitive senses it informs them. 

"Quants vs the Scouts"

I think the best illustration of how the “qualitative vs quantitative” argument is false is to examine how Moneyball in baseball actually worked. The story depicted in the book was, partly for dramatic purposes, presented as a “quants vs intuition” situation. But that’s really an oversimplification.

There is no question that Moneyball has transformed baseball. While the book depicted the transformation of the Oakland A’s, it was famously the Boston Red Sox that used quant approaches to baseball scouting in order to bring Bambino’s curse to an end. I was living in Boston at the time, and being intellectually curious about their approach I actually spent quite a bit of time talking to some of the quants involved in the Red Sox to really understand what they had done. I think it can help illustrate the way forward for VCs investing in the startup ecosystem in ways that will help startups and VCs.

The truth is that both for the grizzled old school scouts, and for the new school finance nerds, statistics are used heavily in their evaluation of players. Scouts were using numbers like ERA, pitch speed, and the “impact toolbox” of observational stats like hitting for power, combined with their own judgement. Meanwhile, quants were looking at new sets of data like on-base percentage. Incredible storytelling aside, Moneyball was really just about the evaluation of two things:

a) whether the use of a host of new statistics like on-base percentage would be a helpful addition to the more traditional stats in evaluating talent.

b) whether a “statistics only” approach could bear better fruit than the mixture of qualitative and quantitative analysis that was the standard.

The results on A turned out to be true, new ways of looking at stats allowed formerly overlooked players to be unearthed. Those that adopted these new metrics faster had a serious upper hand, until inevitably most teams caught up with the tactic.

And in the case of B, whether these new quant methods would perform well as the only signal, the results are equally clear. As Nate Silver details in his book “The Signal and the Noise” using only these new metrics as a way of evaluating talent was a disaster. These models consistently underperformed the hybrid quant/qual approach of the best scouts.

Pure “moneyball” doesn’t even work in baseball!

The reason qualitative evaluation is still so important is because in baseball, venture investing, product management and in fact with any activity, there is always a gap in what we know we can measure, and what is actually happening. And while we constantly strive to find more truth objectively, we must use qualitative measurements to best approximate the areas we have not yet found a way to measure.

The fact that we still find this hybrid approach to be the most performant method in baseball, which is possibly the most measured activity we have outside of pure finance, should give us some indication of how well a purely quantitative approach would perform in early stage venture investing.

Where Moneyball VC likely won’t work

Niels Bohr famously quipped, “prediction is very difficult, especially if it’s about the future.” That said, our ability to predict is not evenly distributed. To state the obvious, the big question here is whether startups are an environment where the conditions exist for us to model. Whether any area is ripe for forecasting depends on three criteria, and I’ll extend the baseball metaphor just a little bit longer for comparison.

1) What are our inputs? In baseball, the data piece is fairly straightforward: there are hundreds (even thousands) of players, with thousands of pieces of data each, and their stats are completely public. [1]

In startups the inputs are relatively sparse and often not accurate. While there is preliminary research indicating that models like Multiple Criteria Decision Analysis (MCDA) might be helpful in deciding which startups will succeed, that’s only if we have correlative data. Suffice to say, Alexa ratings and Karma scores don’t get you there. We have no easy means of even starting with the kind of consistent data that baseball scouts, economic forecasters, and weathermen start with.

2) What are our outputs? The second thing we need to do is to have enough positive outcomes (ie exits) where we have the relevant data to draw causations. In consumer technology, for instance, there have been 10 companies in the last 5 years with over $1b in M&A or IPO value. [2] Compare this to the 750 players who are succeeding in the majors each year and you begin to see why, as Mike Greenfield says, "big data beats small data."

So far we have a dearth of information on both of these axis. You have much less data, and much less reliable data, than baseball. And you have fewer results because there are so few billion dollar companies being created.

Since the ultimate goal is a large scale exit, this puts the challenge at a significantly higher bar than picking who is going to be a great major league player next year. A more comparable statistical challenge in baseball would be trying to pick who is going to be in the hall of fame, 5 years from now, by staring at somewhat spotty data of the thousands of minor league players. Not particularly ripe territory.

And that is before we take our third criteria into account.

3) The math is constantly changing. One of the most common mistakes people make in statistical forecasting is to assume that all statistics are like physics, a fundamentally unchanging set of rules that might be unknown but are fixed.

In areas where this is true, such as weather prediction (which is based on actual physics of course), we have improved our forecasting accuracy immensely over the last 20 years. But in areas where relationships between the numbers change we have made little progress.

One area that has such a problem is the assessment of young founders. Many investors try to take a read on a founders leadership skills, persistence, determination and understanding their own strengths and weaknesses. But, as Arthur Jensen discusses in a brilliant book on brain development called G Factor [3] a persons mental ability are actually things that don’t start to settle until a person is in their mid to late 20s.

And sure enough, baseball scouts have developed similar theories that the mental part of the game is not worth evaluating deeply before the age of 25. This is another reason why evaluating minor league talent vs major league talent is so difficult. You just have no idea if that person is going to develop into a great on-field leader because their brain is still changing too aggressively and unpredictably. With 23 year old founders, you’ve got that same problem.

A second example is when externalities change all the data, best represented in black swan events. These are the rare events that by their very definition could not be included in any data set. An earlier startup of mine, Ambient Devices, was started just prior to 9/11 — a categorical black swan event that drastically changed our ability to raise money, sell product, and partner with some of the financial institutions we were talking to at the time. The opening of the Facebook platform was a similar event that had an effect on a whole generation of startups but could not have been predicted only a couple years beforehand.

My friend Rob Coneybeer, an investor at Shasta Ventures, used this analogy, “Moneyball VC is like trying to predict the next great player when every time you play the game you are changing the number of outs, innings, and number of players on the field.”

So let’s step back a moment. Imagine we were an awesome quant forecaster, looking to make our mark on the world and revolutionize some industry the way baseball forecasting was changed. If we made a list of hundreds of potential markets, then rated them for their ripeness based on these key criteria, it is likely that early stage Venture Capital would be in the bottom third of that list. 

That said I still believe there are some places where quantitative analysis could work to help investors, and entrepreneurs. And I’ll try and detail some early thinking on that tomorrow in Where “Moneyball VC” may work.

——

(Thanks to Keith R, Andrew P and Mike G for reading drafts of this and helping form my thinking)

[1] What’s most important to both quantitative and qualitative assessment is the caliber of the inputs. The St. Louis Cardinals, which had one of the most legendary scout programs in baseball, have transformed their program into a quant-based approach over the last decade. However, they have not replaced their scouts out in the field with armies of wonks pivoting tables in Excel all day. In fact, they have increased the number of scouts on the team to compliment the addition of quants. They understood that the rise deeper statistical analysis is not mutually exclusive with qualitative analysis.  Also see http://www.baseballprospectus.com/article.php?articleid=20928

[2] From Jacob Mullins analysis: http://techcrunch.com/2013/03/02/how-do-you-build-a-1b-consumer-company/ — Except I added in Tumblr which occurred after this piece was published. 

[3] The G Factor - http://www.amazon.com/The-Factor-Evolution-Behavior-Intelligence/dp/0275961036