Why ‘A’ Players Should Collude

They should have been paying me more.

That was my thinking, at least. I was building predictive models for PayPal to detect fraud, and my models were effective at saving the company money. From 2002 through 2004, that work was likely saving PayPal at least ten million dollars a year.

Knowing how much I was helping the company, I figured I’d have some pretty sweet leverage if I ever wanted to try to negotiate a better pay package. My work was easily quantified, and it was making a big difference.

If my models were capturing an extra ten million bucks, surely the company could reward me with half that, or at least a quarter of it, right? Especially since PayPal was being valued relative to earnings; at a multiplier of 30x earnings, those models had created an extra $300 million in shareholder value. Even a few percent of $300 million would make my day!

(At the time, I was not the type to “lean in” and negotiate harder, so that never came to fruition — but that’s a separate story.)

The Non-Zero Baseline

Sadly, my thinking was flawed. My assumption was that the baseline — the business equivalent to the “replacement” player in baseball was a business that was exactly break-even. I could add $10 million in profits, four others could do the same, and the company could effectively divvy up those $50 million in profits among the five of us.

The problem is that the baseline for PayPal — what would have happened with average players at every position — wasn’t zero. The baseline was a business that was losing hundreds of millions of dollars a year.

So I was saving the company $10 million per year, so were perhaps nineteen of my colleagues. My statistical models were ‘A’ work and a huge improvement over the baseline, but so were our fraud policies, our viral growth channels, our eBay integration, our legal maneuverings, and many other areas.

Without all of those accomplishments, PayPal would have gone on losing hundreds of millions of dollars until we went out of business. If the baseline of mediocrity was a business that lost $180 million per year, adding twenty strong people (or teams) whose “above replacement” value was each $10 million per year could collectively improve our bottom line by $200 million — but that would still only lead to a business that made $20 million in profits.

In that sort of business — which is roughly what the pre-acquisition PayPal of 2002 looked like — my original reasoning made no sense.

Collusion Is Good

Recently, I’ve been evaluating a new startup idea. It’s something that’s as ambitious as PayPal, but also just as fraught with challenges: there are a lot of ways that it could lose money. And that’s encouraged me to revisit some of my PayPal memories.

One of my biggest lessons is the importance of team quality for such a difficult and ambitious company. Looking back, it’s as if a bunch of smart and hard-working people colluded and decided to work together on a business that would have failed if we hadn’t all worked on it. Without that collusion, PayPal wouldn’t even have a Wikipedia page, let alone a huge business or a mafia.

2002 was the ideal time for that sort of collusion, because there weren’t a whole lot of options in Silicon Valley. No one was starting their own company, and the list of hot private companies I was aware of had exactly two entries: PayPal and Google.

To solve the toughest problems — and building a slightly more elegant social networks doesn’t qualify — one still needs that type of collusion from ‘A’ players. The good news for founders is that Silicon Valley is attracting more talent than it was in 2002; the bad news is that starting a company has become cool again (I’m guilty too!) and the hot company list has grown from two to dozens.

Collusion generally has a negative connotation, but in this context it can be a very good thing. If, rather than spread themselves among ten mediocre companies, ten all-stars can be like LeBron (and Dwyane Wade and Chris Bosh) and collude, they can see better results and solve bigger problems. And unlike LeBron, they don’t have to do it in zero-sum games.

Happy 10th Birthday, LinkedIn!

Ten years ago today, LinkedIn was born. It’s radically changed my life over the past decade.

I had no knowledge of LinkedIn’s existence on the day it launched in 2003. But a few days later, I got an invitation from my former PayPal colleague Keith Rabois to join this new site, LinkedIn. I signed up (as user number 1400 or so), saw a few familiar names on it, and was immediately intrigued.

In the months to come, I used LinkedIn a handful of times, connecting to colleagues and meeting a couple of entrepreneurs who reached out to me for advice on fraud prevention.

In late 2003 or early 2004, my former PayPal colleague Lee Hower reached out to me to see if I “knew anyone” who might be interested in working on data-type problems for this company LinkedIn. Lee and I had lunch, and I learned that LinkedIn had built its network out to be a couple of hundred thousand users. This was pretty cool, and something I could certainly see working on.

A week or so later, I was sitting in a Mountain View conference room with Reid Hoffman — with whom I’d spoken exactly once when he was a PayPal exec — and Jean-Luc Vaillant, brainstorming cool stuff we could do with data.

Not long after that, in February 2004, Jean-Luc set me up with a Mac laptop, and I started delving into the data. For the next few months, I was essentially moonlighting at LinkedIn, coming into the office one or two days a week, while mostly still at PayPal.

When I found that I was consistently more excited to get out of bed on the LinkedIn days than on the PayPal days, I decided I would (after biking around France for a month!) join LinkedIn full-time.

Thus began my LinkedIn employment odyssey. I worked full-time at LinkedIn from October 2004 until January 2007, leading a small analytics team with two awesome hires, Jonathan Goldman and Shirley Xu.

I learned a ton at LinkedIn, worked on some interesting and important products, and got to collaborate with lots of great people I now consider friends (and of course, LinkedIn connections).

After I left, my team became the large, influential and highly-regarded (kudos to Jonathan and DJ Patil) Data Science team.

Though I was no longer employed at LinkedIn from 2007 on, the company has continued to play a crucial role in my life. The first two engineers we hired at Circle of Moms, Brian Leung and Louise Magno, came in through the same LinkedIn job post in 2008.

Looking back at the Inmails I’ve sent, I see a number of people I reached out to try to hire and now know well; in many cases we didn’t wind up working together, but they became friends and valuable connections.

LinkedIn has prepared me for meetings with hundreds and hundreds of people. I wish I still had database access so I could run the query to figure out just how many.

When I first started poking around LinkedIn in 2003, I had a couple dozen connections. I looked at the profiles of people like Lee and Reid, seeing well over 100 connections. I figured I was simply not the kind of person who’d ever amass that number of professional contacts.

Today, I have over 900 connections on LinkedIn; the vast majority of those are people I’d feel comfortable reaching out to for an important professional purpose. Part of that increase is a reflection of my evolution, but a lot of it is thanks to LinkedIn.

To Reid, Jean-Luc, Lee, Allen, Chris, Sarah, Matt, and the millions of others who have helped to build LinkedIn, thanks and happy birthday!

Defending the Brash Arrogance of Silicon Valley

The best thing about being a statistician is that you get to play in everyone’s backyard.

I read this quote in a New York Times obituary in 2000, and it’s stuck with me ever since. As a data guy (I’m too much of a hack to be called a statistician), I love the idea of playing in lots of backyards.

If statisticians are playing in everyone’s backyard, the best Silicon Valley entrepreneurs are knocking down all of the houses and businesses in the neighborhood and putting in place something completely different.

Oh, and by the way, this is their first construction project, and it’s all going to be finished next month.

It’s pretty arrogant to attempt that sort of thing, isn’t it? It’s arrogant even if you’re not being forced to reinvent your neighborhood — or get the latest smartphone and move half of your communications to Facebook/Twitter/LinkedIn.

Early on at PayPal, the founders brashly spoke of reinventing the way people paid one another, even describing PayPal as a new world currency. Who were these founders who wanted to reinvent payments?

Peter Thiel was a thirty-something former lawyer and hedge fund manager who had little experience with either payments or tech companies. Max Levchin was a recent college grad with coding skills and no special knowledge of payments.

So did they hire other “experts” to do all of the detailed work? Not really. To solve the fraud problems that were draining the company, they hired people like me: a recent Stanford grad who’d never thought about, let alone worked on, understanding fraud patterns.

They did the same thing in other areas central to the company’s success. Peter brashly spoke of PayPal’s lack of “adult supervision”; clearly he reveled in being the cocky first-timer, destroying the experts at their own game.

Today, there are at least two Silicon Valleys. One is the Silicon Valley of yesteryear, building faster and smaller processors, bigger and clearer screens, and lighter and longer lasting batteries. The other is the Silicon Valley I got to know at PayPal. That Silicon Valley arrogantly tries to reinvent industries with a mix of deep technology, persuasive marketing, appealing products, and data-driven insights.

In the past few months, I’ve listened to entrepreneurs’ pitches for shaking up a vast array of industries, everything from diabetes care to home-buying, from photography to car insurance, from restaurant payments to government budgeting. Most of these entrepreneurs — like Elon Musk with SpaceX and Tesla, and Max and Peter fifteen years ago — have little or no experience in the industries they’re trying to upend.

In most of the world, people wouldn’t have the guts to do that. But Silicon Valley encourages a special type of arrogance, a type that claims a few smart “kids” can solve problems that have been vexing experts for generations.

Inevitably, that arrogance can be off-putting: is the slightly awkward 22-year-old computer science major who was spending half his free time at frat parties two months ago really the right person to reinvent health care?

Most likely, he isn’t the right person. But like science — a set of theories which are sometimes individually wrong but collectively get closer and closer to truth over time — Silicon Valley is a system.

By going after the big stuff — sometimes unjustifiably or arrogantly — the system collectively increases the probability of big breakthroughs. For that system to work well, you need a culture that encourages smart people who don’t know everything to brashly assert that they can do better.

I’ve seen that pattern again and again: like many in Silicon Valley, I’ve worked in a range of areas (payments, social networking, parenting) where most of my colleagues and I started with no expertise whatsoever. And the results — both for me and for many others — have been astounding, reinventing industry after industry.

Every now and then, someone like Chamath Palihapitiya bemoans the lack of big innovation in today’s startups.

I suspect that this claim is quantitatively wrong: though there are many frivolous me-too startups, there are probably more ambitious (arrogant) work-on-a-big-problem startups than ever.

Nevertheless, I completely agree with Chamath on where we need to go. Silicon Valley at its best is both arrogant and thoughtful: brashly trying to conquer problems others couldn’t solve, while thinking seriously about the societal ramifications.

Let’s go knock down some neighborhoods and build them up to be a whole lot better. Metaphorically… right?