Why My Startup Isn’t in Stockton: How to Further the Culture and Network Effects of Silicon Valley

TL;DR

1. Tech innovation is generally a good thing and should be encouraged.
2. Tech companies are better positioned to succeed if they’re in Silicon Valley, because of culture and network effects.
3. It follows from (1) and (2) that it’s good to facilitate the growth of tech companies in Silicon Valley.
4. There are three things slowing the growth of tech companies in Silicon Valley: education, immigration policy, and lack of housing.
5. The most immediately addressable impediment to innovation and growth is the lack of housing.
6. The tech community should be pushing cities in the Bay Area to do more to allow and encourage new housing, as doing so would foster innovation.

Starting Up Somewhere Else

Can you please build your startup company in Detroit or Stockton? They need the job growth there. We in Palo Alto do not!

This is a fairly common attitude among those who are opposed to any growth (residential or commercial) in Palo Alto, the city where I live and run my startup.

I know this because I’ve recently been working with my wife Elaine and several others on a new organization called Palo Alto Forward. I’ve spent some time reading about Palo Alto’s policies and plans and — warily — reading a handful of the online comments. Reading mostly anonymous online comments is at turns extraordinarily painful and surprisingly insightful, and in my generous moods I place the pseudo-quote above in the “surprisingly insightful” category. Several people asserted that startups should locate in depressed areas rather than Palo Alto, in order to help those areas grow.

I was surprised to see that sentiment, because it is completely at odds with the prevailing wisdom of Silicon Valley. My professional peers — those who live in the Bay Area and work with tech startups — are almost all here because they believe that Silicon Valley is the best place to start, grow, and work for a tech company. And nowhere else comes close.

Culture and Network Effects

In a recent blog post, HelloSign founder Joseph Walla did a good job explaining why he’s here. He cites eight reasons to locate startup is in the Bay Area and not in his home state of Minnesota. Those eight can fall into two simple categories: culture and network effects.

Silicon Valley has a great culture for building startups: we embrace risk and uncertainty, we hold entrepreneurs, engineers and product designers in high esteem, and we have a trust-based, pay-it-forward mentality that fosters good, productive behavior.

And Silicon Valley has strong network effects from its history as the center of the world of tech entrepreneurship: capital, knowledge, talent, and easier access to great people and companies.

As Joseph says in his piece, none of those things mean it’s impossible to build a great company elsewhere. It’s just a lot harder.

Moreover, because of those cultural and network advantages, founders and employees tend to learn more quickly in Silicon Valley than anywhere else in the world. If a childhood friend from my home town of Philadelphia came to me and said he wanted to spend six months learning how he could build a startup, I would very strongly recommend that he drop what he’s doing, move to Palo Alto or San Francisco, and surround himself with people who know how to build great companies.

Starting a company in Detroit or Stockton or even Philadelphia sounds like a nice idea: I could help the surrounding community and hire people from that community. But the reality isn’t so nice: I’d have to embrace a culture that is far less risk-seeking than Silicon Valley and convince investors to invest in a company they’d have much less interaction with. I’d have little surrounding talent to hire and learn from, and I’d have to convince engineers to come to a small pond with far fewer experienced colleagues and mentors.

In short, because of culture and network effects, my startup is likely to grow far more quickly in Palo Alto than it would in any of those other cities. Starting a company here is the equivalent of putting a child in a nurturing, education-focused home: it creates the best conditions for success.

Growing the Startup Ecosystem

For the reasons I laid out, most tech entrepreneurs believe that this is the best place to start a company.

If you believe that, and if you also believe the growth of new products and technologies is a generally good thing, it follows that you (like me) would seek to bring more skilled and talented people — entrepreneurs and employees — to build the next batch of great companies in Silicon Valley.

When it comes to top people, quantity is very important. As every founder will tell you, truly skilled and talented people are in short supply, even in the Bay Area. Almost all of them are trying to hire great engineers, for instance, and there simply aren’t enough to go around. That supply can be increased in three ways:

1) Education. The more educated the population is, the more skilled people can contribute to innovation and growth in Silicon Valley and elsewhere. No one refutes this as an important goal, even if its benefits won’t be seen for many years.

2) Immigration. There are many incredible entrepreneurs, engineers, and others who have been forced to leave Silicon Valley because of extremely strict immigration laws. This is a complex issue at the federal level, but there is broad consensus in Silicon Valley that immigration laws should be loosened to allow in highly skilled people.

3) Housing Supply. Silicon Valley is the best place for innovation to happen. But for all of the improvements that facilitate collaboration, nothing beats having people close to one another. And to have people working side by side, there need to be nearby places for them to live. Yet the tech community’s leaders have been surprisingly quiet about the need to increase housing supply. As a result, anti-development voices have had the upper hand, which has led to a scarcity of housing units and prices that are unaffordable even for professionals making six-figure salaries.

This is an issue we need to pay more attention to, in large part because techie/startup types can have a greater impact on the housing supply than on immigration or education. It is easier to influence local zoning/building/housing policies than it is to influence state educational policies or federal immigration policies.

Room for Growth

Palo Alto is the birthplace of Silicon Valley and has been its center for decades. It has spawned many amazing companies, from HP to Google to Facebook. But its growth as a place for people to live hasn’t kept up with its increasing economic importance: Palo Alto has just 25% more residents than it did in 1960, even as California’s population has grown by 150%. The result of that housing shortage has been astounding: the median home price has climbed above two million dollars.

Because houses cost two million dollars and supply hasn’t increased, even well-paid professionals working in Palo Alto and making six figures can’t afford to live in the city. Palo Alto’s draconian zoning policies enforce suburban-style development, ignoring the substantial demand for more dense townhouses, condos, and apartments within walking distance of services. Those policies make Palo Alto’s density just a quarter that of other top university towns located near big cities — Evanston, Berkeley, and Cambridge.

To be sure, a more dense Palo Alto would require infrastructure investments. But in a place where companies are spending billions of dollars to put together the big technologies for the next fifty years, better bus and train lines are surely not too much to ask.

Palo Alto, Ahead of the Game?

I live and work in Palo Alto largely because it’s the best place to start and grow important innovative companies. That’s great for me and my wife, and it’s a good example for our young daughters to see.

As the world changes, Palo Alto is going to change with it.

By being forward-thinking and growing intelligently, I believe Palo Alto and Silicon Valley can stay ahead of the game and continue to foster innovation.

P.S. If this resonates and you live in or near Palo Alto, please join me at Palo Alto Forward.

How the Heck Would I Know What I Should Do For YOUR Company?

This morning, shortly after I got into the office, I got an IM from a friend I hadn’t heard from in a while.

Mike, can I complain to you for ~15 seconds about something completely unrelated to whatever it is you’re doing right now?

This sounded promising! I said yes please.

So, [the company where this person has worked for several years] is a tough act to follow.
But startup CEOs are really annoying.
Whenever I ask them what they want me to do, they reply by asking, “what do you want to do?”
which is like, duh, if I knew the answer to that, I would go do it
this is why I should have learned a marketable skill- like typing, or how to run an engineering organization.

He sent that string of thoughts to me pretty quickly, so I replied that I thought he might actually go for the typing route after all. Then I said something a bit more serious:

So it’s actually funny you mention that… when I was planning to leave PayPal, I was already working part-time at LinkedIn and was pretty sure I was going to go there.
I did interview with one company, though — just met with the CEO and he kept asking me “what do you want to do”, and I kept saying “what do you need.”
And basically we went back and forth in that form for close to an hour… at the end he told me has was interested in hiring me, but I’d have to tell him what job I wanted. And I felt like I didn’t really understand the company, so there was no way I could tell him what I wanted to do for them.

In other words, I could relate to my friend. But, I continued, I could relate to the other side as well.

Now I’m on the other side, and I can see the crazy CEO perspective. You don’t entirely know what you’re doing or what you need… especially within a realm (e.g., [my friend's area of expertise]) that you may not personally understand.
And you feel like your best bet is to hire people who will figure it out themselves.
Obviously, that takes a lot of trust and belief on both sides.

My friend was intrigued, and told me about how he’d joined his current company:

i sort of wonder if this is [founder of his current company]‘s sort of black art genius gift
b/c he made it very easy for me to leave [big company where he'd worked before] for an ill-defined job at [current company, which was small at the time]
like, there was a broad goal/desire, but not much in the way of details about how it should be accomplished
and i remember feeling, on my first day, that i was basically useless
it really wasn’t obvious exactly what I should do
and I sort of panicked and set to work trying to figure it out

That made sense. Some people rush toward crazy ambiguity and thrive with it. Others hate it and will be crushed by it. I’m in the first camp; my friend is somewhere in the middle. As I explained, what he described was similar to my experiences at PayPal and LinkedIn:

yeah, my guess is that that’s reasonably typical… was pretty much my experience at PayPal and LinkedIn
at PayPal I at least knew what problem I was supposed to solve (find fraudsters); at LinkedIn I didn’t really even have that

Our conversation lasted longer than fifteen seconds, but covered some interesting pieces of startup psychology.

How to Bring Innovative, Not-Insanely-Wealthy People Back to Palo Alto

The other night, I found myself reading my almost-three-year-old daughter a children’s book from 1950. The book focused on family life, and I had to explain some of the things she didn’t recognize. The father was smoking a pipe, and sitting in front of a fire. There were no TVs, phones, or laptops anywhere in sight. Explaining to her that I’d had neither a laptop nor a cellphone growing up unleashed a series of “why”s I could only answer meekly.

The world has changed immensely — not only since 1950, but since 1996, when I arrived at Stanford as an 18-year-old freshman from Philadelphia. There are things I like about those changes, and things I don’t like, but one thing is clear: there’s no going back.

In 2006, ten years after first moving to the Bay Area, my wife and I bought a small house in downtown Palo Alto. We chose Palo Alto largely because of its innovative culture. Palo Alto was a place for crazy ideas that just might change the world, a place that wasn’t afraid to reinvent itself, a place where brilliant twenty-somethings could create something amazing.

In 2006, Palo Alto was the place for crazy ideas, the place where the smartest (if not the most acclaimed) would go to do a startup. My company, LinkedIn, had around 60 employees on one side of Palo Alto. On the other side, Palantir and Facebook were setting up small offices downtown.

Eight years later, Palo Alto is still great, but it has lost a little bit of its optimistic, youthful craziness. The city’s population has aged a bit, and a twenty-five year-old grad student with a brilliant idea — like Sergey Brin or Larry Page in 1998 — couldn’t afford to live in Palo Alto, nor could he afford to set up his office here. At nearly $100/square foot/year, office space in Palo Alto costs two to three times as much as office space in San Francisco. That’s a big part of the reason that most of the Bay Area’s top startups of the past five years — Twitter, Dropbox, Uber, Airbnb, Pinterest — are based in San Francisco.

When I started my own company last year, we decided to be in Palo Alto, but we’ve paid the price in multiple ways. It took us six months to find a 1000-foot sublease downtown, and we pay over $6000/month for a space that isn’t likely to grace the cover of an architecture magazine. Why is office space so expensive and hard to find? The answer is very simple: there is virtually no available inventory, as demand far exceeds supply. The same dynamic exists in the residential market. That’s a relatively good problem to have, but it’s still a problem: if it continues, Palo Alto’s population will continue to get older, and its offices will be populated not by startups but by venture capitalists, law firms, and a few big companies like Palantir.

So how can Palo Alto sustain its vibrant, innovative spirit? The answer is simple on the surface: the city needs to increase its supply of both housing and office space, so that costs will decrease and more not-insanely-wealthy people can be accommodated. There’s a very good reason that developers want to build houses, apartment buildings, and offices in Palo Alto: people want to live and work here. If it relaxed some of its extraordinarily stringent zoning policies, Palo Alto could see substantial residential and commercial inventory growth that would benefit many potential new residents and workers.

This is easier said than done, however. The first concern is political: the loudest local voices on this issue are saying Palo Alto needs less development. Those voices generally come from people who aren’t suffering from the supply-demand imbalance. They are mostly longtime residents who frequently travel by car, and are frustrated by traffic and parking troubles. Their reaction, and the city’s default options in creating a plan for the future, would seem to solve the parking and traffic issues: either make it harder for individuals and developers to build, or at least keep it as hard as it is now. However, this approach looks at two of the symptoms of the problem (traffic and parking), and deals with those symptoms rather than the deeper problems of imbalance, vibrancy, and infrastructure. Moreover, this “stop any development” would exacerbate the supply-demand imbalance, and turn Palo Alto into a town of 40-, 50-, and 60-something executives and VCs. I have nothing against 50-something VCs, but I’d also love to see more 20-something entrepreneurs — not to mention the engineers, teachers, designers, and city employees who can’t afford to live here now.

How, then, can the city put in place something that improves Palo Alto’s vibrancy while dealing with the challenges of those loudest voices? Palo Alto needs to simultaneously address two challenges: the supply-demand mismatch (easy: ease regulation), and the city’s transportation and infrastructure challenges (harder).

Solving the transportation issue is hard, because the obvious approach — more parking spaces — exacerbates the traffic problem. With free or very cheap parking and few non-driving options, there is currently little reason for most people not to drive to work, so traffic increases. To reduce traffic and parking problems, the city should charge an appropriate amount for parking. 200 square feet of office space downtown costs approximately $17,000 per year. In contrast a 200-square foot parking space in a lot costs a few hundred dollars a year, and that same parking space on the street is free. Increasing parking costs overnight would be a huge shock; instead the city could make the change gradually and charge $10/month to park on the street next month, $20 the month after that, then $30, $40, etc., up to a fair market rate, giving drivers time to adjust.

Also needed are alternatives to driving. Many thousands of people work in downtown Palo Alto, which means the area can be a great laboratory for new transportation solutions. Are there places in Redwood City, San Jose, and San Francisco that could support Google bus-style shuttles? Could Palo Alto encourage commuting by means other than cars with moderate density buildings that would have fast and regular transportation to downtown, with far fewer parking spots per unit? Could a dedicated fast bus or train between downtown Palo Alto and downtown Mountain View be a reasonable alternative to VTA buses (too slow), trains (too infrequent), and driving (doesn’t scale)? I’d love to see the city test programs like these, much as a startup would test a new product: no fancy studies, no commissions to talk about talking about something — just try it, see if it works, and scale it if it does. A transportation demand management program could be well-suited for this.

In short, Palo Alto needs to embrace a changing world. Let’s not defensively try to return to the “good old days” of decades ago, let’s be bold and decisive. Some day, we or our kids can look back at 1996 and 2014 and smile at all of the crazy backwards things people were doing. The best way to do that is to go on offense: allow the growth that both businesses and potential residents are clamoring for, and put in place intelligent infrastructure and transportation worthy of one of the world’s most innovative cities.

Care about these issues? Palo Alto’s City Council will soon be deciding on a comprehensive plan for future development. Send them a quick email with your thoughts: city.council@cityofpaloalto.org

Best of Numerate Choir

Here are my best posts from the past few years:

Founder Stories

Silicon Valley

Data

Data + Product + Growth

Crazy Ideas

Design vs. Darwinism. Data vs. Darkness.

The data geek, it’s said, wants to make every decision based only on the numbers. Test this shade of blue against that shade. Pick the winner. Test something else.

The designer is a creative artist, creating something beautiful, something people love. The antithesis of the data geek.

I’ve been thinking about this because I’m a data geek, I just started a new company, and I know that a skilled UX designer could help our product immensely. Are my data-loving values in conflict with the values of those who are UX-focused?

No. As someone who spent lots of time painting in college, I assert that the artist vs. data geek model is an overly simplistic view of the world.

That dichotomy assumes that the data geek cares only about superficial numbers, and lacks the thoughtfulness and creativity to understand things that are hard (or impossible) to measure. It also assumes that the designer cares only about beauty and creativity, and not about whether they’re building actually works in the real world.

It’s easier to understand ourselves with these two questions:

1) Do you want to scientifically understand the way people are using your product, and use that understanding as part of your decision making process?

2) Is your business an automatically shifting, evolutionary machine that moves itself purely based on numbers, or is someone guiding it in a specific direction?

My answer to the first question is a very strong yes: I want my company to deeply understand how people are using its products.

The second question is a bit tougher for me. I like evolution, and I understand that natural selection can yield great outcomes. On the other hand, guidance and clear direction can be a far more efficient way to get to the best outcomes.

My first blog post, The Visionary and The Pivoter, discussed my experience building a company that wound up being more evolutionary than directed, and the challenges of that.

Here’s how I see things now:

Sites focused purely on viral content — Buzzfeed, Upworthy, et al — are in the top left: impressive (to me) for their ability to iterate based on data, but far more reactive than visionary.

My last startup, Circle of Moms, was focused on improving the lives of a specific audience (moms!), but we too were more evolutionary than visionary.

Amazon is a very data-centered company, but one with clear visions on where their product and business will move the world. Apple, on the other hand, possesses clarity of vision and an intent to push the world in a certain direction, but is seemingly less data-focused. Clearly, both of those models can yield tremendous successes.

Being reactive/evolutionary and in the dark with respect to data is the worst combination: you don’t know where you want to go, but you can’t see anything around you to help you find a good path. I’ve seen a few startups like that — they change their strategy every month based not on data but on a (bad) blog post someone writes — and it’s ugly.

Many companies move up on the scientific scale over time. There’s a real cost to collecting and analyzing data, and it’s easier to invest in doing it correctly with 100 employees than with ten.

I’d like to be in the brown box that has my picture: deeply scientific, but more directed than evolutionary.

Long term, I suspect that most great user experience people won’t be too far from me. They’ll use data to help them design things that more people like. But they’ll be thoughtful in the application of that data, so they won’t feel forced into a massive, evolutionary pinball game that throws them around randomly.

Your Metrics Are Bad and Why “Data Driven” Isn’t Enough

Being “data driven” is all the rage these days.

We all — businesses, government entities, sensor-equipped individuals — have more and more data that can help with decisions. The era of Big Data is here, yada yada yada: you know the annoying cliches as well as I do.

There are more and better tools. Dozens of startups are working on better ways to collect data, process it, query it, visualize it.

I recently talked with an entrepreneur who, fresh off of raising a big round of funding, was told by his investors that he needed to make his company more data driven. He wasn’t sure what “more data driven” actually meant, and he told me he wasn’t sure his investors did either.

It sure sounds nice, though — doesn’t it?

Honestly, I don’t know how I’d define “data driven”, and I’m not sure I care enough about the term to really think it through. But I’m pretty sure I know what’s missing.

Very, very few companies know what questions to ask of their data. They have metrics that are beautifully plotted on their real-time data dashboards. They’re calculated in technologically scalable ways, using something that’s much simpler than SQL, and they’re accessible by everyone inside the company.

But more often than not, the metrics are superficial and poorly thought through. They’re not reflective of the health of the product or the business.

I’ve certainly been guilty of this: for months if not years at my last startup, anything other than new user registrations barely mattered to me. For Circle of Moms, getting new users was extremely important, but at times distracted us from more important long-term goals.

And I see this again and again with tech companies. There’s a focus on one or two superficial metrics, rather than a deep understanding of what it will take to build out the broader ecosystem necessary to make the company successful.

I don’t want to be too negative: the understanding of these ecosystems has significantly improved in the decade-plus I’ve been in Silicon Valley. Ten years ago, entrepreneurs building consumer startups barely thought about distribution (if we build a great product, people will come to us!). Five years ago, entrepreneurs (myself included) started to realize that distribution mattered, but rarely took the next step (Facebook is the notable exception). Today, more and more entrepreneurs understand that both distribution and engagement matter, even if they can’t get at all of the underlying components.

Today, a few of the strongest consumer companies — Facebook, LinkedIn, Twitter — have built out growth and data teams that collectively measure and understand the key dynamics.

But there are still huge areas of our society — small non-tech businesses, government at all levels, medicine, academic studies, many startups — where there are lots of data, but not much understanding of what the data actually mean.

And that’s a big problem: I’ve long felt that having bad metrics is often worse than having no metrics at all.

If I were trying to gauge a basketball player’s skill level, my top preference would be to use a well-structured metric incorporating an entire season’s worth of extremely detailed, second-by-second data, looking at his impact on all aspects of the game. My second choice would be a good coach’s purely qualitative assessment of his skill. And my last choice would be a simple stat — say points per game — that was state of the art in 1950.

Today, most businesses are using the equivalents of the coach’s qualitative assessment and points per game to make their decisions. And quite frequently, “data driven” effectively means “we’re using points per game.”

Most of the new “Big Data” companies are focused on the relatively simple stuff: speed of processing data, ease of accessing data, beauty of data presentation. Those are all valuable, but they aren’t enough.

So how will the “bad metric” problem be solved? Certainly with some mix of better data training for everyone, plus tools that automatically discover and surface the important metrics. Both are important and I’m not sure whether training trump technology or technology trumps training.

Either way, if we want these new data to improve our collective decision-making, the good metric-bad metric problem badly needs to be solved.

To Avoid the Perils of Get-Rich-Quick, Work With People Who’ll Play the Game Again (and Again)

Why is it so irritating to be nickel and dimed?

There is of course a financial aspect (you’re trying to take more of my money) and one of expectations (I might have paid $15 if you’d quoted me that, but you told me it was $10 and now you’re asking for $15).

But perhaps more important is the message you’re sending: a few extra bucks on this transaction is more important to you than our relationship.

In an extreme case, that behavior is completely rational (even if dishonest). If you weren’t well off and had limited future opportunities, you’d probably opt to “nickel and dime” Bill Gates for a million bucks.

In most real world cases, it’s a little more dubious.

Prisoner’s Dilemma, Over and Over

Everyone knows about the Prisoner’s Dilemma, in which the rational strategy for both arrested parties is to testify against their partner. This results in a situation where both men wind up worse than if they’d colluded and remained silent.

In this situation, the players only play the game one time, and almost inevitably wind up screwing one another over.

However, the optimal strategy in other variations of the game gets a lot more complex. If rather than just playing once, players play an unknown number of repeated games, a “tit for tat” strategy can be a very good one.

In a tit for tat strategy, a player starts off playing nice. He plays nice in subsequent rounds if his opponent just played nice with him, but he plays mean if his opponent just played mean with him. If both players adopt this strategy, they’ll wind up colluding and they will consistently help one another out.

Playing the Business Game Many Times

Business is usually both more enjoyable and more successful when your co-players expect to play the game repeatedly.

I can try to extract as much value out of you from you as possible for the thing I’m working on right now. That may mean taking your money, driving you to overwork yourself on my behalf, or getting you to do me favors. I’ll probably be better off tomorrow than I would have been, but you won’t trust me and I won’t be as well positioned the next time I play the game.

Or I can work with the expectation that we’ll play the game again: by doing a little more for you and asking a little less, you’ll treat me well in the future and we’ll both wind up better than we otherwise would have.

Oddly, those who likely have the most games in front of them — new professionals just out of school — are generally more likely to act like they’re only playing the game once. When I first started working, I didn’t have the experience of working with the same people at different companies.

Sure, I might have thought, I’m working with that guy now, but could I really have imagined that I would be part of his company or he of mine ten to twenty years down the road? Probably not: I didn’t fully internalize the importance of investing in relationships.

When, later in your career, you’ve had the experience of working with one person two or three times, you see the pattern. You realize that for any of your colleagues, this may not be the last time you work together.

LinkedIn is, at its core, an embodiment of the importance of ongoing professional relationships. Keith Rabois recently mentioned that an amazing five of LinkedIn’s first twenty-seven people (Keith, Lee Hower, Reid Hoffman, Josh Elman, and Matt Cohler) are partners at VC firms; in large part, that’s because each plays the game collaboratively, with the expectation that there will be many future iterations. Keith, Lee, Reid, Josh, and Matt are all in it for the long term.

It’s very easy to adopt a mentality that prioritizes the thing that’s in front of you. In business, that often manifests itself as a get rich quick scheme.

If your goal is success right now (screw the future), you can spend time with the nickel-and-dime, get-rich-quick types. If you’re interested in the longer term, I suggest working with people like Keith, Lee, Reid, Josh, and Matt.

How to Build a Quant Venture Fund

We’re Moneyball for carpet cleaning!

It’s easy to mock the mindless business metaphors of the day, and many of the “Moneyball for _____” ideas deserve that mockery.

But in the field of venture capital, there’s a huge opportunity for data — used correctly — to help investors make better decisions. Last month TechCrunch published a story suggesting that it’s already happening.

My somewhat informed guess is that most of what’s in the article is unsophisticated and/or vaporware. The “deep data mines” Leena Rao mentions are probably more like spreadsheets filled in by interns and/or Python scripts. Such spreadsheets — complete with stats from app stores, Alexa, and AngelList — would certainly be useful, but hardly qualify as analytically brilliant.

So what could an innovative VC do instead? I will walk through a model, mainly tailored for an early stage fund.

Data Collection

Probably the most important piece of building a predictive model is the collection of a great set of features that are likely to be predictive.

In the context of a venture fund, you’d likely want to collect a bunch of data at the time of an investment. You’d then have to wait a while (probably a couple of years) to see how those data predicted winners and losers.

For the data to be predictive, you need (1) underlying data structure that doesn’t change much and (2) a world where the same factors are likely to predict success.

That’s relatively easy if you’re trying to figure out whether a restaurant’s second location will be successful: you can look at a bunch of common metrics like customers, revenue per customer, employees per customer, demographics of the old location and the new one, etc.

It’s a lot tougher in the startup world, where the rules of the game are constantly changing. A million users today is very different from a million users in 2003; most of today’s important platforms didn’t even exist ten years ago.

That then begs the question: what are the “metrics” that are most likely to be predictive in the world of 2018 or 2023? To create a model that survives for more than a year or two, one would have to look at variables that will look similar in five or ten years.

Perhaps surprisingly, that means ignoring (or at least de-emphasizing) the variables of 2013 being tracked in that Python spreadsheet.

Instead, the model would use scores generated by (hopefully insightful) VCs themselves. Those VCs — and possibly other reviewers — would score each startup on traits like the following:

– how charismatic is the best founder? (1-5)
– how business-smart is the best founder?
– how tech-smart is the best founder?
– how well do the founders seem to complement one another?
– do you think the best founder could be “world class” at something some day (even if not today)?
– what about the worst founder?
– how well do the founders understand the market?
– how scrappy and hard-working do the founders seem?
– how would you rate the potential size of the business (independent of founders)?
– how would you rate the company’s user traction?
– how would you rate the company’s revenue traction?

This is just a set of example questions.

If you got the same (say) 5-6 people to rate all of those companies, you’d have 50+ data points for each startup. It might be the case that averaging scores (e.g., the average founder charisma score) would wind up being key. Or it might turn out that one VC’s “how scrappy” score is incredibly predictive while another’s was not at all.

Ideally, you’d do the actual reviews in a very standardized way: reviewers would either always talk to people in person for a certain amount of time, always talk to them on Skype, etc.

You’d also compile the answers to more straightforward questions: how many founders were there, how much revenue did they have, how many months had they been working together, had they started companies before, what colleges did they attend, etc. Some of those are probably predictive.

You’d have to do this for a bunch of companies (over 100, ideally a lot more), but not actually do anything with the data. i.e., if a firm invests in a startup and VC #1 rates the founder’s charisma a 4, business smarts a 3, etc., you just stick that in a database and let it sit.

The Model

When you build a predictive model, you use those inputs to predict something. Sometimes, what you’re trying to predict — who will win a basketball game, whether a customer will pay for your service — is fairly straightforward. In this case, it’s not.

The obvious value to be predicted is the long-term valuation of the company (or the fraction thereof that an investment would capture): the money it returns to investors when it folds, its acquisition price, or its valuation at IPO. This would, after accounting for details like liquidation preferences, dilution, and taxes, reflect the return for a potential investor.

It’s not necessarily clear, however, that this would yield the best predictive model. As any VC knows, returns are shaped in large part by one or two exceptional wins: many funds have one very successful investment that provides the majority of returns. If there are only a few such companies a year, even an all-knowing VC is unlikely to have the data scale to make a good predictive model.

Instead, an investor might look at a few different scores:

1) How good did we think this investment would be when they first pitched us?
2) How good did we think they’d be 2 months after we invested? In two months working together, the VC should hopefully learn a lot to tell her if this was a sound investment. This is probably only a useful data point for actual investments, as opposed to rejected deals.
3) How good did we think they’d be 1 year after we invested?

etc., etc.

It may be the case that VCs (et al) are pretty good at #2 but not so good at #1. To gauge that, they could build a predictive model to predict how they’ll feel a few months in, given the dozens of measurements they took pre-investment. Within a couple of months, they may be able to more effectively forecast the likelihood of intermediate success potential. This intermediate gauge is subjective but still more thorough than what’s being done: what predicts how I’ll assess this opportunity when I really understand it more deeply?

Because returns are driven by a few outliers, asserting an exact expected long term valuation for early stage companies is difficult. If a promising but young company has a 0.1% chance of some day having a $100 billion valuation, they’re ostensibly worth at least $100 million. If, however, those odds are only 0.001%, they may be worth as little as $1 million. And a venture firm has virtually no chance of having enough data to distinguish between 0.1% odds and 0.001% odds.

However, a venture firm should have enough data to distinguish between larger tiers of companies: companies at the 98th percentile or higher, companies between the 95th and 98th, companies between 90-95, companies between 75 and 90, and everyone else. Some of the companies in the top tier — defined by competence and potential more than ultimate outcome — will fail; others will be incredibly successful and go public.

The goal of this model would be to predict the tier, rather than the ultimate outcome.

Implementation

Step one of this process is about recording data, not about changing decision making. And step two would be to take months or years of data and build a model.

Only after months or years, when the firm has actual outcome data and a predictive model, would it actually use the model to help with decisions.

An early stage fund that makes dozens of investments a year is best positioned to execute on this strategy. There are two reasons for this:

1) The metrics defined are more about people and potential, and less about business fundamentals; except in rare cases it would be irresponsible for a late-stage fund to give a company a $100 million valuation based only on those scores.
2) They have a lot more data.

All of the “human” factors that make an angel fund successful today — dealflow, partners’ skill at evaluating founders, helpfulness to entrepreneurs — would be equally important in this sort of fund. However, a formalized predictive decision-making process could improve returns significantly.

See also: Data Scale: Why Big Data Trumps Small Data

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!