After the Thrill Is Gone

A growing startup can be bliss.

When Circle of Friends was growing rapidly, I’d wake up suddenly at three AM, my heart jumping with a mix of excitement and nervousness. Because our technology was brittle, I’d walk out to the kitchen and look at my laptop to make sure the site hadn’t crashed. And I’d notice that, amazingly, our traffic for the period between midnight and three AM was our highest ever.

Our highest ever? Our highest ever!?!? How could I go back to sleep now? It was three AM and my day had started: I boiled some water to make a pot of tea, and I got to work.

That was the startup honeymoon phase, and it was exhilarating.


Circle of Friends (which became Circle of Moms) had its first office in our little house in downtown Palo Alto. We’d bought the house in 2006, knowing it needed work. Elaine, being an architect, saw this as a positive: it was a house we could truly make our own.

After a few months where I was both living in the Palo Alto house (with Elaine) and working in its kitchen (with Ephraim during the day — but alone at 3 AM), the center of my world moved to San Francisco. Elaine and I decided we’d rent out our house and find an apartment in the city, and Ephraim and I would get an office in SoMa.

That basic arrangement — living near Dolores Park in SF, working South of Market) — was my life for nearly four years.

When, in early 2011, we found out Elaine was pregnant, renovations on our house in Palo Alto were nearly finished. At that point, we hadn’t decided what would happen after the house was ready to live in: we were open to selling it, renting it, or living in it ourselves.

The fact that we’d soon have a little daughter made living in it — and a move to Palo Alto — more appealing. Our own new place, with a nice room for baby, lots of natural light, a beautiful kitchen (nice work, Elaine!), and an edible garden seemed like a great option.

But I also knew myself and my work patterns. It was hard to imagine having a baby, a startup, and an hour-plus commute. Dealing with two of those three would be challenging; dealing with all of them would just be stupid.

The more I thought about it, the more I realized what a move to Palo Alto would mean: the beginning of the end of my time at Circle of Moms.

Elaine and I soon decided we’d move back to the house when our daughter was born, and that meant my time at Circle of Moms would be limited. Certainly not “I’m giving two weeks notice” limited, but also not another five years.

I told Ephraim of my intentions, assuring him that my goal was to get Circle of Moms to a place where we’d both feel comfortable telling the team that my role would change. We agreed that it would not yet make sense to share those plans with the rest of the Circle of Moms team.

That marked the beginning of a very different phase from the one where I’d wake up at three AM and excitedly sit down to work.


On a Monday morning in July 2011, Elaine was now six months pregnant, and we were at the doctor’s office. She had gone to the bathroom, and my phone buzzed with an IM from a colleague.

hey mike – will you be in the office today?

“Yes”, I replied, “am at a doctors appt now, will be in around 10″. A moment later, I got my colleague’s response:

oh okay – can i grab you for a chat sometime before 11?

Before 11? Crap. She’s going to tell me she’s leaving the company.

I was right, it turned out. And though my co-founder and I did what we could that week to try to convince her to stick around, we knew our odds were slim. We were ultimately unsuccessful, and she left Circle of Moms a few weeks later.

The Responsible Founder

At the time, there was a part of me that wanted to check out and move to the next thing, but as a responsible founder, that wasn’t a viable option.

In each of my three jobs before becoming a founder, I’d decided at a certain point that I’d had enough. Within weeks of coming to that conclusion, I’d told my boss I was leaving; by a few weeks after that, I’d formally moved on.

On that July Monday in 2011, I’d just heard my employee’s plans to leave and join another company, and I knew she’d soon be out the door. Meanwhile, my writing was on the wall and I was ready for something new — but acting on it now was simply not an option. I had to pull myself together, convince the team that everything would be fine, and get back to work.

Fortunately, the process went about as smoothly as it could. The team took things well, another employee immediately stepped into the shoes of the woman who departed, and we continued to make progress as a company.

But that summer was still a challenge for me personally. Weekends, which Elaine and I spent in Palo Alto, were awesome. We were in our beautiful new home, getting everything ready for ourselves and for the baby, cooking in our spacious kitchen, and enjoying the warm sun. During the week, we’d stay in cold, foggy San Francisco, and slowly pack up everything from our small dark kitchen.

Several other people left the company over the next few months. Our traffic was growing and we were executing well on the product, but we certainly didn’t have rocketship growth and our talented employees had plenty of other options. Again, I was tempted to follow my employees and also move on to the next thing; again it was not an option.

With all of that going on, I’d become more and more open to being acquired. That was a big change from a year or two prior, when the acquisition offers that came our way held little appeal for me: earlier, I would have been disappointed if another entity had ultimate control over the product and the company. But now, I’d been through the startup building process, realized I was unlikely to be the visionary of this business, and was open to a “strategic partnership.”

I spoke with another founder who had recently been through the acquisition of a similar-sized company. At a certain point, he told me, he’d realized that his company wasn’t likely to be a huge success, and that an acquisition was the way to go. He spent an entire year getting the company to that stage, in the process building relationships with his eventual acquirer, and said it was easily the toughest year of his life.


Our experience was much the same: it was just under a year from the time I knew I was ready to move on until February 2012, when Sugar acquired Circle of Moms. It wasn’t the most fun year, but it was a productive one in many ways.

And I was fortunate. With a combination of luck and skill, we’d already built a real business, a strong team, a solid product. We wound up making money for our investors — even if it wasn’t a billion dollar sale — but the process still took us a year. Many founders spend a year or more on the less fun parts of running a company, without seeing a positive exit.

Christine Tsai, with whom I now work at 500 Startups, wrote a great essay comparing parenthood to building a startup. I agree with everything she says: in both domains, you work hard, it’s hard to figure out who to listen to, and it’s terrible… and fantastic.

There’s one other parallel I’ll add. As both a founder and a parent, you’re responsible, and you can’t just pick up and leave when you feel like it.

A mother’s work is never done. A founder’s work most likely will eventually be done, but it certainly won’t be today at 5 PM, and he can’t know in advance exactly when it will be.

And that means a founder can’t just give his two week notice and disappear. Even after the thrill is gone.

Why You Should Play Games In Inefficient Markets

Team Rankings, a business I started in 2000, takes a quantitative approach to understanding and predicting sports events. Over the years, we’ve built products targeted at a number of different groups within the world of sports enthusiasts.

Two products we’ve built — one to help people beat the Las Vegas line, another to help them win their NCAA Tournament office pools — are both data-driven tools to predict outcomes of sporting events. However, they reflect two very different sides of the world of quantitative decision-making.

The Skilled Opponent

When I was working at PayPal in the early 2000s, I was spending most of time building predictive models to detect fraud. At home one night, I decided to see if I could apply a similar methodology to sporting events to use on Team Rankings. My system incorporated a large number of inputs — how each team had been playing, what their respective strengths were, how far each had travelled — and used them to find a blackbox (i.e., really complicated) predictive model to assess the likelihood of a bunch of events related to a game. Among these: how likely is each team to win, how likely is each team to cover the point spread, and what is the expected final score.

It turns out that this approach works well enough to consistently beat Las Vegas. For the current models — which use essentially the same methodology — here are the results for college basketball since 2008-2009:

For these two bet types — against the spread and over/under — Team Rankings’ picks have been correct 5214 times and incorrect 4452 times. The odds of this level of success by a dart-throwing monkey or TV commentator — who would expect to be correct 50% of the time — is less than one in a billion.

However, while 50-50 constitutes “break-even” from a statistical perspective, the break-even point for a Las Vegas gambler is higher. The house takes an extra cut: in most cases, a gambler bets $110 on a game to win $100. That means that you need to be correct at least 52.4% of the time just to break even.

Team Rankings’ results beat that threshold too: 5214 wins and 4452 losses constitutes a 54% winning percentage. And that means that someone who gambles strictly based on Team Rankings picks will make a profit over the long term. Bet $100 on each game, and you’ll make an average of $7,000 over the course of a season; bet $1000 per game, and your expected return will be $70,000.

That’s a terrific return, and I’m proud of it.

But still, it begs a question. Team Rankings has a great team (I’m a tiny part), some cool technology, and lots of data, and the best we can do is 54%? That’s barely better than a coin flip! It means an expected return of about 3% on each bet — a good investment, but hardly earth-shattering and a far cry you get from the “100% GUARANTEED PICKS!!!” you see on sleazy gambling picks sites.

[Disclosures: Team Rankings' picks are for entertainment purposes only. Of course. And while I'm capable of thinking like a scientist (constantly skeptical), I'm also capable of thinking like a writer (cherry picking to prove my point). These are among the better-performing of our models, but overall our results are likewise very strong and profitable.

At some point, I'll take Anthony from Kaggle up on his offer to run a contest to predict games, so people smarter than I can have a crack at it. If we did that, we might improve the number to 55-56%.]

The Unskilled Opponent

Some of Team Rankings’ recent product innovations tell a very different story.

For many years, we’ve provided odds and analyses related to the NCAA Tournament. Our products have included tools to match up two teams, along with probabilities for each team to make each round of the tournament. So, for instance, you can see the odds of Gonzaga making the Sweet 16, the Crazy Eight, the Final Four, etc.

A few years ago, I realized that those features could help people win their NCAA Tournament pools, but only if they used our numbers in the correct context. Say, for instance, that Kansas is the team most likely to win the tournament, with 20% odds, but Kentucky is just behind at 19%. Kansas would be a better pick to win than Kentucky, right?

Not necessarily. Let’s say Kansas is a really popular pick, but Kentucky is not. 50% of people are picking Kansas to win and only 10% are picking Kentucky. If you picked Kansas, you’d have a slightly better chance of being right on the winner, but because you’d be competing head-on with many more people, you’d almost certainly have a smaller chance of winning your pool. In entrepreneur-speak, picking Kansas would be like trying to build a mobile photo sharing app in 2011, a group buying site in 2010, or a Facebook game in 2009: good business or not, you’re setting yourself up for a lot of competition.

So, with some help from Brad, we built a tool that allowed us to answer the question of what picks would maximize someone’s odds of winning their pool. To do that, we combined teams’ win probabilities (which many others do) with data on how many people picked each team (which no one else does). We simulated millions of tournaments, randomizing both the outcome of the games and the picks made by people in your pool.

The end results are pretty astounding. Our top brackets — based on a moderately conservative set of assumptions — had an expected return on investment (ROI) of around 800%. This bracket, for instance, would have had about a 0.9% chance of winning a 1000-person pool, nine times higher than the average participant. By picking as our champion Ohio State — a very strong team not given enough credit by the public — our odds would be much better than if we picked Kentucky, the best team but also one strongly favored by the general public.

Our 10-person bracket looked quite different: with a smaller number of people to beat, our simulations indicated it was a stronger strategy to pick mostly favorites, with Kentucky as the eventual champion. This bracket had a less ridiculous but still quite impressive 186% expected ROI.

Kentucky wound up winning the national championship. Most of our users in small pools did quite well and won their pool; our users in large pools did not. We won’t be successful every year, but over time our results have been very strong. And this approach works: I feel comfortable with the assertion that the average yearly return for our strategy will be at least 200%.

Comparing Markets

How do we synthesize all of this and bring it back to the non-sports world?

A bet on a game at a Las Vegas sportsbook and an entry in your colleague’s NCAA tourney pool both constitute a wager on sports. However, the underlying market dynamics could not be much more different.

Las Vegas is more or less efficient: if lots of people bet on one side of a game, they’ll update the odds. In contrast, your friends and colleagues in the NCAA pool are likely making impulsive decisions that are economically irrational.

Hence there’s one world (Vegas) where 3% returns are celebrated as amazing wizardry, and another (friends’ pools) where you can have expected returns of 900% without anyone really paying attention.

If you’re looking for a financial return, though, there’s a catch. There’s only one NCAA Tournament per year, so your opportunity to make money is limited. In theory, you could enter lots of pools with distinct but complementary undervalued picks, perhaps giving yourself a 50% chance of winning with only 15-20% of the pot. But you’d be putting your marbles in one basket.

By contrast, each year there are thousands of regular games on which you can bet against Las Vegas. Adding those together can yield a solid expected ROI over the course of a year. Quantitative hedge funds generally take this against-Vegas approach: they find short-term inefficiencies, and bet on them again and again.

Though difficult, Team Rankings and hedge funds show that betting in Vegas-style almost efficient markets can be extremely profitable. From the actor’s perspective, it’s a bunch of bets in established markets with positive ROI. Yet from the world’s perspective, it’s a bunch of minor market efficiency improvements, but a world that hasn’t really improved in any meaningful way. In other words, something that’s more Wall Street than Silicon Valley.

By contrast, the pool of your buddies — while itself not a world-saving problem — represents a far larger and more profound inefficiency. The large-scale decision-making of Las Vegas and Wall Street is close to being economically efficient, but one-off decision making by individuals and businesses is not. Most choices — companies deciding whom to hire or where to put resources, government choices on how to run cities and schools, individual choices on where to invest, and which teams to pick for your NCAA pool — are made haphazardly and could be improved a lot.

It’s tougher to build a business to address these massive inefficiencies: to build something large, you need to find important, quantifiable decisions that have associated data and haven’t already been examined properly. That type of problem is more interesting, has more upside (+800% vs. +3%), and can be much more impactful than its Vegas-style alternative. And it’s why, without hesitation, I choose Silicon Valley over Wall Street.

A Founder’s Constant State of Rejection

When recruiters ping me about open positions at hot companies, I tell them “thanks, but the next company I work for will be (another) one I start myself.”

It’s not clear whether I’m masochistic or just dumb; life was a lot easier before I got started on this whole founder thing.

An Easier Existence

The first seven years of my career were pretty straightforward. I was either an individual contributor or leading a small team inside a larger company. Within a year, I’d figure out a few things I could do to be successful, and I was able to cruise along easily.

PayPal hired 22-year-old me in 2000 to help solve the company’s massive fraud problem. For a few months, I didn’t really know what to do and flailed around a bit. But I soon created a template for predicting fraud, and used it repeatedly to apply a few techniques to solve many fraud problems. I was an individual contributor and making a comfortable salary; though I was working hard enough, my job lacked major challenges and I had little stress.

From there I went to LinkedIn, where I spent two and a half years leading the data analytics team. I faced more stress at LinkedIn than I had at PayPal: I had to hire people, I had to meet regularly with LinkedIn’s executives, and I was a lot closer to the company’s decision-making. Moreover, while at PayPal I had a known problem (detecting fraud) with an unknown solution, at LinkedIn I had an unknown problem (lots of data; what to do with it?) and an unknown solution.

Still, while I was at LinkedIn, my work-related stress was almost nil. I was occasionally exasperated by my colleagues’ decisions, but what could I do? LinkedIn’s successes were nice but hardly life-affirming; its failures made me roll my eyes but not search my soul.

In these larger companies, I found myself in positions where I was almost assured of success: I was skilled and solving problems I knew how to solve. I’d soon learn that life as a founder is completely different.

Founder Changes

When I co-founded the company that became Circle of Moms, I fount that my day-to-day responsibilities changed greatly. Instead of working in a cubicle in a huge office, I sat across from my co-founder at my kitchen table. Instead of asking IT to set up a new database for me, I figured out how to do it myself. Instead of asking a marketing person to write copy for the emails I wanted to send to users, I wrote the emails. Instead of being the crazy analytics guy the engineering team would never want writing production code, I coded the whole darned site myself.

And those are the unimportant changes. Here’s the important one:

A founder must continually put himself and his company out on the line for others to judge.

For an asocial geeky dude, that was an enormous shift. At LinkedIn and PayPal, I rarely took big risks and didn’t have to put myself out on the line. As a founder at Circle of Moms, I did it every single day.

When you’re a founder, your company defines you. That means that your company’s daily ups and downs become your personal ups and downs; that’s a big adjustment.

I’m a fairly even-keeled person: when my co-founder would jump up and down with excitement after seeing good feedback on a new feature, I’d describe it as “encouraging”. I maintained a healthy lifestyle over those 4.5 years: I exercised almost every day, I ate a home-cooked dinner with my wife most nights, and usually maintained a good balance between working hard and living the rest of my life. Nevertheless, I’d still leave the office on many a Friday night completely despondent about the week I’d had, worried about the company and its prospects.

Five Ways to Fail

A consumer Internet company must do well in five areas: product metrics, revenue metrics, hiring, team culture/productivity, and fundraising. In the four and a half years I spent as CTO of Circle of Moms, we never had a time when all five were on a great path.

Just after we launched the site, our product metrics were excellent, but thanks to the financial crisis investors weren’t eager to invest in anything. In 2010, our revenue numbers were excellent, but our traffic stats were dipping. In early 2011, our traffic recovered strongly, but we had more trouble selling ad inventory.

Team culture may be the area where founders take success and failure most personally. If I showed up at 7 AM and left at 8 PM, made honest appraisals of company strengths and weaknesses, and took full responsibility for my failures, shouldn’t my colleagues do the same? And if they didn’t, was it a personal rebuke of me?

Good founders feel strongly about establishing the right environment for a happy and productive team; that’s surprisingly hard to do. A challenging but not unusual week might feature one employee taking an extra day off after a vacation, another one calling in sick with an important deadline the next day, and two others playing big-company-style political games against one another.

Those three ordeals were independent of one another and seem small in retrospect. But at the time, I felt like the roof was caving in: our employees were rejecting my leadership and they were getting lazy, political, and unproductive. The end was surely near.

Likewise, hiring is vitally important and requires thick skin. At Circle of Moms, we’d reach out to dozens of top candidates and usually hear nothing in response. I’d spend a full day at Stanford pitching our company to CS undergrads — far more tiring than any day I’d ever spent coding. After several months, we’d finally find one good candidate and make him an offer. When he’d instead choose to work for another startup — whose name was well-known to TechCrunch readers but whose vision we didn’t quite get — it was hard to avoid getting flustered.

Raising capital almost invariably features many rejections from investors, even with companies that become very successful. We experienced periods where our traction was good and fundraising was almost too easy (we turned down money from one VC because we saw he hadn’t even bothered to sign up for our product), but we also failed in several attempts to close a larger venture round. It’s easy to see a lack of fundraising progress as a company (and personal) failure: if you can’t raise a lot of capital, there must be something wrong with you.

As a techie individual contributor in a larger company, I could go to work everyday and execute 99% predictably. As a founder, I had to find ways to plead your case over and over — to employees, investors, candidates, advertisers, users — and I got rejected a lot. For an introvert, the amount of pleading and subsequent rejection came as quite a shock.

As a founder, you need to be prepared for this sort of rejection. It should affect you: if it doesn’t, it means you don’t care enough and should be doing something else. But a rejection of your company is a (hopefully) rational move by someone else, and it’s not a reflection on you as a founder or an individual. Don’t take it personally.

Of course, the founder/non-founder divide I describe doesn’t need to be binary: non-founders can and do sometimes work like the founder I describe above. A number of the top people at Circle of Moms took ownership and were truly wrapped up in the company’s success, and that helped us immensely. And those are the best people to have on your team.

Founder or not, taking ownership and repeatedly putting yourself in front of the world to be judged is difficult. But ultimately, it’s a tremendous way to learn, grow, and succeed.

Why Campaigns Are Smart and Policymakers Are Dumb

There are lots of things to hate about political campaigns.

From the never-ending attack ads to the vague but inevitable pleas for “change” on both sides, from the repeated half-true talking points from candidates to the baseless grand claims of pundits, it’s easy to get irritated. And no doubt many Americans — especially those in swing states — look forward to getting on with their lives post-Election Day.

Though it may be surrounded by triviality, however, presidential campaigns are showing an increasing and admirable level of sophistication in their execution plans. Each election, the campaigns gain a better and better understanding of the electorate, and use that information to improve their decision-making.

How exactly do they do that? First, they ask, how likely is this guy to vote for me? The answer to that informs their tactical decisions. If there’s a 99% chance he votes for the other guy, the best bet is probably to just write him off. If there’s a 99% chance he’ll vote for me, on the other hand, I don’t need to spend much effort persuading him — but I want to do everything I can to make sure he votes. And if there’s a 50% chance he votes for me? In that case, I’m going to try to find out what he likes better about me, and accentuate it.

This, in a nutshell, is what “big data” — silly buzzword or not — is about: used well, data improve decision-making. The more of it a campaign has, the better and more targeted its decisions can be.

In some ways, it’s exciting that the campaigns are using these techniques. With everyone on the team rowing together toward the same goal, friction is minimal; a well-executed operation can significantly improve their chances of success.

Still, these improvements likely don’t help the average citizen. Each campaign gets better at crafting messages and allocating resources, but the political game is still a zero-sum battle. The sides get better at marketing themselves, but not at governing. It’s similar with political contributions: if I give $100 to Obama and you give $100 to Romney, each candidate gets a little more to spend on commercials, and the only party that really wins is the TV network that gets their ad dollars.

But, you ask, couldn’t these same politicians apply these methodologies to the decisions they make when they’re actually in office?

They certainly could, but it’s not happening very much today.

In a campaign, everyone’s moving toward the same clear goal. They want to beat the other guy, and they’re going to pull out all of the stops to get there. That means using data — which obviously help — with decision-making.

The world in which the next president will govern is a very different one. Unlike campaigns, many government outposts aren’t even collecting data, let alone using it wisely.

Both parties are to blame. On the left, for instance, there’s often an aversion to collecting data that might be used to fire ineffective employees. Unions still wield tremendous power in Washington and in states, and their guiding principle is job security (and higher wages) rather than workplace effectiveness. There are too many ineffective teachers in our schools. But because their unions are primarily focused on teacher job security, it’s tremendously difficult to find and replace those who aren’t doing a good job.

On the right, there’s often a gut response against “big government,” even for tasks where government might be able to make more effective decisions than anyone else. President Obama was attacked mercilessly during the healthcare debate for the notion that the government would “ration” healthcare. But rationing is simply another word for resource allocation, something every individual and every business does. Health care already comprises over 15% of GDP; if we want that number to stop growing, we need to understand effectiveness and costs and ration intelligently.

There’s certainly some room for optimism. Inane campaign statements aside, Romney and Obama have both shown themselves to be smart and pragmatic. Romney bucked many of the orthodoxies of his party when he worked on healthcare reform in Massachusetts. Obama has strayed from the traditional Democratic alliance with teachers’ unions, taking steps a toward accountable, data-informed education policy.

Still, the collection and use of data by governments leaves much to be desired. Where campaigns are able to move swiftly, policy is often driven less by effectiveness and more by the goal of winning points with the electorate. As a result, there are many bad reasons data aren’t collected: bureaucracy, vague fears of privacy, abstract concerns around “big government”. Without good data, decision-making suffers.

Today is Election Day. We’ve now seen months of data-led sophistication from our political leaders. May the winner bring that same sophistication to the policies of his administration.

FiveThirtyEight, Slow News, and Fast News

There’s been much discussion of late on the accuracy and legitimacy of FiveThirtyEight. I’ve been a fan of FiveThirtyEight since just after it got started, but I find myself having some mixed feelings about the topic.

Here are three articles that are central to many of the arguments being made:

1) David Brooks, who works with FiveThirtyEight founder Nate Silver at the NY Times, writes of the futility of watching and using polls. His implications are twofold: that as an individual it’s a waste of time to track polls, and that it’s impossible to predict what will happen in a presidential election because it’s too complex (and “even experts with fancy computer models are terrible at predicting human behavior”).

2) Dylan Byers at Politico argues that Silver is a biased hack who throws around numbers that don’t make sense.

3) Ezra Klein at the Washington Post rebuts Byers, arguing that the betting markets clearly favor Obama, and that a bettor could use a better system to beat them… but only if a better system existed. Moreover, he makes the point that, unlike Silver, most pundits aren’t accountable, and that their main goal is traffic and/or attention, not accuracy. Hence he gives Silver a tentative endorsement.

I like elements of what Brooks, Klein, and Silver are saying and doing. On the other hand, I think Byers doesn’t understand probability and is completely full of crap.

My feelings can be summed up as follows:

1) As both Klein and Brooks point out, predicting elections is hard. As Klein notes, Silver doesn’t publish his formula (in his shoes, I wouldn’t either), so it’s tough to say definitively how good it is. There are a limited number of historical examples of presidential elections, so Silver must cleverly combine the results of different types of elections (governor, Senator) in a way that effectively multiplies the sample size by a lot. Does this work? Probably to some extent, but since there aren’t many past elections to test his methodology against, it’s tough to know for sure. And the results of this election will neither confirm nor refute his methodology (even though some will claim otherwise). As I’ve written in the past, big data trumps small data, and elections are small data.

2) The world is becoming more quantifiable and accountability-driven. At some point, that means that pundits like Byers are likely to get left behind.

3) Brooks’ comment about computer models doing a poor job at predicting human behavior suggests that he doesn’t really understand the difference between different types of models. There are certain situations where behavior can with near certainty be very well predicted: the same kind of kids who eat the marshmallow today will eat it tomorrow, and a predictive model can be quite accurate. On the other hand, a model that has implicit assumptions about the surrounding world — who is going to repay their mortgage — can be a very different story. It’s debatable where on this gradient an election falls.

4) I share some of Brooks’ concern about people (like me!) wasting time reading about polls. Silver brings a much needed rigor to the poll-watching, making it more intelligent. But ultimately, reading FiveThirtyEight is still mostly about “fast news” and addictive news consumption. I can read a few hundred words every day, and all I get out of it is a slightly better understanding of the horse race (there’s a great chance that Ohio is the deciding state!)

As both a data guy and someone with a vested interest in good journalism, that seems like a bit of a waste. Nate Silver’s a talented and resourceful data guy/journalist, and he’s almost exclusively focused on fast news-update me right now-horse race journalism.

Thanks to a few twists of fate, I sit on a panel that awards an investigative journalism prize (the Goldsmith Prize). Being on that panel, I read over a hundred “slow news” investigative stories from around the U.S. Every year, I see more and more data-centered reporting. That happens because additional public data sets become available and journalists (slowly) become more data-savvy. The end result is more good stories like this and this.

These big investigative reports — slow news — can be very valuable to our democracy. But historically, many such reports have been anecdotal and lacking in rigor. That’s changing: the Las Vegas Sun story on health care was wonderful in its depiction of individuals’ tales, but also painted a solid quantitative picture.

Each year, in spite of newsroom cuts, the public seems to get more and more stories combining slow news with data-driven rigor. That’s great for society, but it’s happening more slowly than it could be.

Hence my reaction to Silver is two-fold. On one hand, I applaud him for his statistical rigor towards a topic that’s too long been the domain of those who can speak loudest. I hope it serves as a model for others working in a variety of fields.

On the other hand, I question whether spending time on fast news like election coverage is the best use of his talent. I hope to see more and more folks like Nate Silver applying themselves to the deeper but slower news stories of our day.

Yes, Developers and Investors Are Really Different

In my recent post on what developers want, I wrote about which industries appeal to software developers.

The three areas at the top of developers’ lists are education, productivity, and human resources. The three areas least interesting to developers are entertainment, payments, and games.

After posting that, my friend Jeremy sent me an email suggesting there might be differences between companies that are desired by engineers and companies that are successful. (Belated credit: Jeremy was the person who complained to Keith about my job suggestions in this blog post.)

It was a question I found interesting. After four-plus years as a developer-founder (aka CTO), I’ve seen a bit of the other side via 500 Startups (where I help companies with data/growth), AngelList (great for understanding the venture ecosystem), and as an occasional individual angel investor. My gut instinct is straightforward: developers and investors don’t seem to have much in common.

Hence I thought Jeremy was likely right, but I wasn’t sure I’d be able to show it definitively. AngelList is a terrific data source, but it hasn’t been around for very long. As a result, the number of companies who have used it to both fundraise and hire is limited.

Fortunately, it’s comparatively easy to look at the fundraising success of companies within each industry to uncover trends. Breaking things down into industry categories, I could look at companies’ average level of fundraising success.

Because I didn’t want to overweight the importance of a few companies who raised a ton of money, I defined a set of fundraising steps a company could achieve. The lowest step consists only of raising non-zero capital; the highest step is raising a round of $5 million or more. I didn’t go higher than $5 million, because the percentage of companies raising that amount of money is tiny.

I only looked at investments that have taken place since 2010.

The X-axis denotes the mean number of fundraising steps taken by companies in the given industry (more steps means more money).

The top three categories favored by investors are Payments, Tech Infrastructure, and Education. Popular as it may be among investors, Payments is among the worst industries for recruiting engineers. Education and Tech Infrastructure, on the other hand, have it all: both do well at attracting developers.

The bottom three categories for investment are Productivity, Local Infrastructure, and Local Business. Productivity is among the most popular industries with developers, but does a lot less well with VCs. Local Business and Local Infrastructure are both in the middle of the pack with developers.

In other words, there’s not a huge amount of overlap between what investors want and what developers want. Here’s a graph mapping developer interest against VC interest:

There are three major outliers: Productivity (developers love, investors hate), Education (both love), and Payments (developers hate, investors love). There are no categories hated by both developers and entrepreneurs.

In my last post, I said that

Developers want to build something useful and societally valuable.

All things being equal, VCs may feel the same way. However, money usually chases returns, and that means that collectively, VCs are going to as well. Thus it stands to reason that VCs would likely behave in a more economically rational manner than the world of (depending on your perspective) naive and/or altruistic developers.

What about developers relative to other potential employees?

Let’s look at the same type of graph, with developer interest on the X axis and non-developer interest on the Y axis.

There’s an extremely high correlation (0.59) between a developer’s excitement about an industry and that of a non-developer. Productivity is the favorite industry of non-developers; it’s the second favorite industry of developers. Education is the favorite industry of developers; it’s well above average among non-developers. Games, Advertising, and Entertainment all fare poorly among both groups.

There’s only one major difference in this set: Payments. Developers generally dislike Payments companies, while Payments companies rate highly among non-developers.

So what to make of all this? Here are my top takeaways:

  • Investors and employees tend to be driven by different factors.
  • At least in startup-land, developers and non-developers are far more similar than they are different.
  • Everyone loves Education (but it’s really hard to build an Education company).
  • Non-devs and investors like Payments; developers don’t.
  • Employees have soured on Games, Entertainment, and Advertising, even while investors have put a lot of money into those industries.
  • Non-developers and VCs are more excited about data and analytics than developers are, but all three are positive about the field.
  • Non-developers love Productivity companies and developers like them; VCs are much less excited about the industry.
  • Way back when Facebook was a private company, they raised an insane sum of money. They (and a few others) have raised so much that trying to compute any sort of “average” for an industry winds up being an absurd exercise. Thank you, power law!
  • And once again: investors and developers are really, really different.

Why Developers Aren’t Interested In Your Startup

A few months after we launched Circle of Moms, I hired my first superstar senior back-end developer. Brian had it all: his coding practices balanced speed and scalability, he’d been around the startup block as a founder and employee, and he had a quiet but kickass work ethic that would set the right tone for the team. Over the years, he’d re-architect our (messy) core technology, build many of the features most responsible for our growth, and patiently mentor younger employees. Hiring Brian may have been the best move I made at Circle of Moms.

I spent the next three years trying to find someone else like Brian. It never happened, for a variety of reasons: some people weren’t the right fit for us, some weren’t interested in building a product for moms, others wanted big-company salaries we couldn’t afford.

The experience underscored the importance of a strong team on a company’s success. On one hand, we were lucky to have someone like Brian; without him, I’m sure we would have been less successful. On the other hand — speaking as a Stanford Basketball fan spoiled by the dominance of both the Collins twins and the Lopez twins — I wish he’d come with a twin brother.

In the current developer-constrained world, hiring becomes a product marketing problem. There’s a limited supply of top people, and the startups that aren’t able to market their “product” (the career opportunities they offer) will have trouble hiring and growing.

To get big, today’s technology companies need to market themselves to three groups:

1) Customers/users
2) Investors
3) Developers

Many a book and blog post has been written on satisfying customers and raising capital. They’re both important and interesting topics. I’ve written about them in the past and will write more about them in the future.

Attracting Developers

Almost nothing has been written about attracting developers, and I wanted to figure out why it was so difficult for me at Circle of Moms.

Over the past few months, AngelList Talent has emerged as a strong marketplace for jobs, attracting both top companies and top developers (as well as non-developers).

(Disclosure: I’m an adviser to AngelList.)

Notably, the AngelList product doesn’t work like a traditional job site. Potential candidates don’t apply to work at a company, and companies don’t send messages to potential candidates. Instead, candidates see a summary of a company and its positions, and indicate — one-click, HotorNot style — whether they’re interested in the company. Companies do the same, viewing candidates one-by-one, and saying yes or no to each. Matches are then introduced.

This approach yields a large, clean data set with explicit information on which company profiles appeal to developers (and vice versa). Some people get off on fancy cars, fancy clothes, or fancy wines; for a data geek like me, it doesn’t get much better than large, clean, explicit indications of individual preference.

Top Companies

So who are the companies I should hate for hiring the people I wanted? At the time — 2010-2011 — the companies who “beat” us were Zynga, Google, BranchOut, and Federated Media (some of them probably aren’t having as much success now). Today, these are the companies that developers on AngelList like best:

1) Quora
2) Pocket
3) Path
4) Pulse
5) Instameet
6) ClassDojo
7) Ark
8) Rally
9) Locu
10) Clever

You’ve probably heard of most of these, but there still are some surprises. Pocket (disclosure: I’m an investor) has done very well as a business, but I still wouldn’t have expected them to be a notch above Path. I wasn’t familiar with Instameet, so seeing them in the top ten was unexpected. Meanwhile, some other more prominent companies — 42Floors, Kaggle, OUYA, Skillshare, wikiHow — find themselves just outside the top ten.

At the bottom of the list are five companies I’d never heard of (sorry, I’m not going to tell you who they are). Two have products aimed at improving people’s nightlife experience; one is a management tool for a very specialized audience; one is a mobile company focused on saving memories; one tries to improve experiences with physical devices. Like the top companies, most are in the Bay Area.

The difference between top companies and bottom ones is large. When someone expresses a preference between a top-five company and a bottom-five company (which has happened almost 500 times) they favor the more highly ranked company over 95% of the time.

(Quick note on methodology: I only included self-described developers in my analysis, I only looked companies with complete AngelList profiles, and I excluded votes from developers who either always or never express interest in startups. I also weighted all companies evenly, irrespective of the amount of interest they’ve received, and dampened the scores of outliers with only a small number of votes.)

Now for the tough part: what separates the top companies from the bottom ones?

Several factors don’t make a difference.

Having a technical co-founder, which one might think would make a company more attractive to developers, does not affect a company’s desirability. And somewhat surprisingly, the equity stake listed does not have any impact, though my analysis here does not account for company size.

Four factors matter a lot:

1) Industry. A company’s industry has a large impact on its ability to attract top developers. Nearly 40% of self-identified education startups show up in the top tier of companies most able to recruit, while under 10% of companies in games, entertainment, or advertising are in the top tier.

A quarter of games, payments, and entertainment companies find themselves at the back in competing for developers. By comparison, very few companies — under 10% — trying to improve personal finance, human resources/hiring, or personal productivity find themselves in that bucket.

(More blue and less red is good.)

Overall, personal finance, HR, personal productivity, and education stand out as the industries most attractive to developers, while entertainment, games, payments, and advertising stand out as the worst.

2) Investor Quality. Top investors certainly aren’t a guarantee of success, but they help a lot.

Roughly a third of companies that have the most highly rated investors (based on AngelList’s proprietary models) are among the very best at attracting talent. Interestingly, however, these companies aren’t immune to being among the worst at attracting talent. On the flip side, companies with the least prestigious investors are only slightly more likely to be among the bottom tier at attracting talent, but they are far less likely to find themselves among the top tier of companies like Quora, Pocket, and Path.

3) Founder Quality. Founder quality, using the same AngelList proprietary models as investor quality, can be a very good predictor of recruiting success. Companies with highly rated founders — like Path and Quora, both founded by early Facebook employees — are more likely to be highly attractive to developers than anyone else. However, such companies are relatively rare, as most companies are founded by first-time founders who don’t personally have strong track records.

As a result, with founders we see a large “middle”: there are lots of companies started by founders who, on paper, are hard to distinguish as especially good or especially bad. These companies, not surprisingly, are about average at hiring.

4) Salary. High salaries can make a substantial difference in a company’s hiring fortunes. Companies with the most attractive salary offerings — roughly corresponding to a high range of $125,000 — are more than five times as likely to be in the top tier at hiring as companies with the least attractive offerings. And companies offering high salaries are very unlikely (around 7%) to find themselves in the bottom tier.

On the opposite side, we see that it’s very difficult to be great at hiring — at least on AngelList — when salaries are on the low end. These days, good developers have lots of choices.

Other Factors

Salary, founder and investor quality, and industry all matter, but they certainly don’t explain away all of the differences in developer interest. Looking only at a statistical model that incorporates those four factors, one would expect that Quora would be the sixth most effective company at attracting developers. In fact, they’re a clear #1, and four of the other five companies the model likes most aren’t in the actual top ten (Clever is the exception).

So what’s missing from the model? My guess (which is completely non-quantitative) is that there are four things:

1) Company Reputation. This is an area where the top companies shine, even beyond the fact that most of them have successful founders and/or investors. Quora doesn’t really need to explain itself, since many top developers are using it all the time.

2) Well-Written Profile. One positive outlier less famous than Quora is LaunchRock, whose tagline is “We *get* users” and whose hiring page starts with “We like to party.” They then confidently describe their business and culture, leaving enough out to keep the reader intrigued. Developers show a lot of interest in working for them, placing them just outside the top ten.

Clarity, a big vision, and intrigue all help a lot. Empty buzzwords and corporate BS, on the other hand, aren’t terribly effective at attracting developers. Some of the worst-performing job pages have phrases that set off my BS filter and likely those of other developers as well: “virtual currency”, “revolutionizing”, “siloed”, “* as a service.”

3) Big and Important Problem for Society. I don’t think it’s a coincidence that personal finance, productivity, hiring, and education are at the top of the list, while games and entertainment are at the bottom. Developers want to build something useful and societally valuable. Industry correlates with “useful and societally valuable” but it’s not an perfect relationship. Many of the companies that are more successful attracting developers than the model would predict have a real chance to positively impact the world, while those that are less successful are often playing zero sum arbitrage games.

4) The Rest of the Hiring Process. We aren’t yet in a world where clicking on a button in AngelList commits a developer to a company: there are many additional stages. Initial intrigue is one thing, but as I learned at Circle of Moms, there’s a lot more to hiring than one “yes, I’m interested” click. Some companies may have a low “interested” rate, but a high rate of closing candidates; as a former (and likely future) hiring manager, I’d take that tradeoff.

Product and Marketing

The best companies have great products, well-marketed, at an attractive price for consumers.

The same applies to startups trying to appeal to developers.

The product — the existing team and its culture, the business’ goals and prospects, and the opportunity for the individual — is central. But the marketing — in this case, a clear articulation of the company’s vision — is also crucial, and the right price (salary) helps as well.

All that said, I’ll still offer a nice referral bonus to anyone who can clone a twin for Brian…

How AngelList Quantitatively Changes the Investing Game

Late in 2007, Circle of Friends was adding hundreds of thousands of users a day, and Ephraim and I knew the time was right to expand our office beyond my kitchen table and raise some money.

We each reached out to a few of our friends, and quickly got a number of introductions to angel investors and venture capitalists. My friend Jared introduced us to Mike Maples. Friends of Ephraim introduced us to Jeff Clavier and Naval Ravikant.

Mike, Jeff, and Naval all wound up investing in our company. Immediately after signing on, and then for years after, each of them introduced us to a number of other well-regarded investors (thanks, guys!).

That’s been a pretty typical experience in Silicon Valley over the past couple of decades. When someone wants to raise money, they reach investors through trusted contacts. When one investor signs on, she then introduces the entrepreneur to other investors she knows well. These introductions can have a strong weight, especially when the initial investor is a trusted source.

This often works pretty well, but it means that companies often raise money from just a small number of highly connected cliques. That has positive implications — those involved likely have more trust in one another — but it also leads to a process that’s relatively closed. Investors often form clusters and invest together. So if, for instance, Mike sits in one cluster and Naval and Jeff are in another, it’s likely that many of our future investors will also hail from one of those two clusters.

To understand this story quantitatively, I looked at angels’ co-investment patterns, using AngelList’s investment data to group the most prolific investors into clusters. I put the top 870 investors into 25 distinct clusters. Each cluster represents a group of people from whom co-investments are more common, for any of a number of reasons — geographic, industry-based, philosophical, or reputational.

(Methodology note: when companies listed multiple investors from a venture firm like 500 Startups, I only counted an investment from one of the partners.)

So do the clusters reflect real investor patterns? To answer this, we can compare to a “random” world, where investors find startups and make decisions on their own, without any social input.

In this random world, the second investor in a startup would sit in a different cluster from the first investor 82% of the time. But the real world is very different from that. In fact, when a second investor comes on board, there’s a 57% chance that he’ll fall into a different cluster from the first investor. In other words, in the real world, investor #2 is almost 2.5x as likely to fall in the same cluster (18% vs. 43%) as in random world.

This trend continues as the number of investment grows. When the existing pool of capital comes from two, three, or four distinct clusters, the odds that the next investor comes from one a new cluster is 47%. If it comes from five to eight of the 25 clusters, the odds go to 43%. All of these are considerably lower than one would expect in a random world: the universe of traditional angel investment is well-networked and influential upon itself (perjoratively: an old boys club).

Enter AngelList, which is increasingly important in the startup ecosystem (disclosure: I’m an adviser). AngelList functions much more like an open marketplace for startups than the traditional model. In most cases, startups make public to all investors that they are seeking investment, effectively widening their pool of potential investors. So does this actually change the makeup of a startup’s investors?

In a word, yes.

There’s a simple way to test whether AngelList is truly opening up funding (and investing) opportunities. If it is, founder-investor connections that come via AngelList should lead to more cluster diversity than those that come through other channels.

Recall that 57% of second investments come from someone in a different cluster from the first investment. If, however, the second investor is someone the founder met via an AngelList introduction, the odds rise to 63%. For startups raising money the “old-fashioned” way with 2-4 clusters represented, the odds of staying in a known cluster are 47%. If the investor comes via an AngelList intro, the odds are 59%. And with 5-9 clusters represented and only 43% chance a new traditional investor will create additional cluster diversity, the odds for an AngelList intro are 55%. Here’s the graphical comparison:

When two or more clusters are already represented, investments via AngelList introductions are about 30% more likely to yield a relationship with a new cluster than their non-AngelList equivalents.

The numbers for AngelList intro investors are still a long way from the “random” equivalents — location, reputation of other investors, sector, and other factors matter on AngelList too — but they’re clearly indicative of a big shift.

Many technology advances over the past decade or so can be put in one of two categories:

1) They make the world more social, allowing us to see what our friends do and like. Basically, Facebook.
2) They make the world more efficient, by giving people access to information and markets. Basically, Google.

AngelList sits in between those extremes. On the friends side, its follower model means that much of what people see is from the people they already know or at least know of. But on the information side, it opens up something that was almost entirely governed by word of mouth, and creates something that at least takes a big step toward being a marketplace.

I don’t pretend to know enough about the macro dynamics of investor management to predict how this will affect company operations. But the effect on the investor pool is clear. As AngelList and crowdsourcing grow, the impact of the old boys’ clubs will shrink. For companies, the pool of investors is growing.

The Four Things That Motivate Me

There I was, a medium-sized fish in a pretty big pond. There was nothing wrong with that, but I found myself disinterested in fish size or pond size: I wanted to create a new pond. It was 2007, and I left my job at LinkedIn because I wanted to start a company.

At the time, my goals focused on the pond creation above all others. The company I’d start wouldn’t need to be anything specific; building a business was an end in itself. Of course, there were some restrictions: I had no desire to build office chairs or games, and I wanted to use at least some of my more prominent skills in social network data, ranking systems, and predictive modeling.

A friend of mine — who’d already built a large company — told me he was only interested in starting something that could be huge and world-changing, and couldn’t conceive of doing anything less than that. By contrast, I just wanted to achieve some success as an entrepreneur working on an interesting problem. I was excited to venture out on my own and test my skills as a founder.

After I left LinkedIn, I co-founded a company that allowed me to achieve much of what I set out to do. Ephraim (my co-founder) and I led a team that built out one of the world’s top few mom-focused websites, Circle of Moms. Our list of accomplishments is substantial: we built a product that helps millions of moms, a profitable business, a positive team culture, and a bunch of cool technology. And all of those things were attractive to Sugar, Inc., which acquired Circle of Moms this past February after 4.5 years as an independent company.

I left when the Circle of Moms acquisition closed, and have since been (among other things) thinking about my next big move. One option would be to spread my time across companies, as VC’s and a handful of others do.

This is what I’ve largely done over the past six months, albeit in a more scattered form. I’ve spent time working with many founders: some in my role with 500 Startups, others who started companies I individually invested in or advise. That’s been fun and educational. It’s highlighted areas where I feel investor/adviser types can add real value, by allocating resources effectively and then spreading expertise across a number of companies.

But that experience also reinforced my initial leanings: I want to start another company.

My second time through, I’m approaching things differently. I’m less excited about just starting a company; I’m being more deliberate about what it is that I start. I also recognize that many of the first decisions you make — from business model to company vacation policy — can have long ranging implications. And perhaps more than anything else, I know myself a little better, understanding both what really gets me excited and where my strengths and weaknesses are.

As part of that, I’m asking myself to think through and answer a handful of questions fundamental to the founder’s existence. Though these questions are hugely important, they’re difficult to answer when you’re actually running a startup: preparing that investor pitch, pushing out that next feature, and wooing that candidate all seem like better uses of time today.

Though it may be more individual detail than some want to see, I’ve decided to write up my answers as blog posts. There are a few reasons for this.

First, it makes me accountable: writing up my thoughts for an audience will force me to be crisper than notes I jot down for myself only.

Second, I feel this is a process all founders should go through in some form: a weekend spent thinking about these questions can lead to good decisions that will pay off for years to come. I’m currently in the fortunate position where I don’t have any competitors, so I’m happy to share things that can hopefully “raise all ships” without fear of helping the competition.

Finally, this will clearly frame my view of the world for potential collaborators; perhaps through this blog, I’ll connect with one or two readers who see the world similarly.

Here are some of those questions. I’ll answer the first in this post, and others in posts over the next few weeks.

1) What matters most as you evaluate a startup idea?

2) What are you good at, and who complements you well?

3) What kind of company culture do you want to encourage?

4) What are some areas that have room for significant innovation?

What matters most as you evaluate a startup idea?

Several experienced entrepreneurs I respect have asked me this question.

To do this properly, a founder must choose between priorities. “I want to save the world and make billions of dollars and design the most beautiful product ever and build the coolest technology ever and become famous and …” may sound ideal, but it’s not realistic and it doesn’t inform choices. Do you take the quick, safe route because you want to sell your company to Facebook in a year, or do you take a bigger chance to make more of an impact?

Here are some common traits a founder might value in a potential company:

  • Is disruptive: makes a market more efficient, by stamping out longstanding un-innovative types (US Postal Service, realtors, the taxi medallion system, etc.)
  • Affects the lives of many people
  • Significantly improves the world
  • Builds a product people love
  • Gives the founder a shot at a huge financial payout
  • Sets the founder up for an acquihire-level financial payout
  • Could lead to the respect of ______ (peers, mentors, parents, etc.)
  • Allows the founder to do better (in terms of fame, finances, etc.) than a personal rival
  • Has a natural strategy for growth/distribution
  • Has a natural strategy for profitability
  • Is technology-focused
  • Is design-focused
  • Is sales-focused
  • Is brand-focused
  • Is timely with respect to available technologies
  • Fits the skills of the founder
  • Is intellectually interesting to the founder
  • Fits in with the founder’s world view

As you can tell, this list encompasses a wide range of characteristics. Some are in opposition to one another: I’d run away from a startup that told the world it wanted to be technology-focused AND design-focused AND sales-focused AND brand-focused. Others are independent: it’s easy to imagine design-focused startups that have natural strategies for distribution and/or profitability, and others that don’t have such strategies.

There’s of course no right answer to which traits the global set of startups should prioritize, but there may well be a right answer to which traits YOUR startup should prioritize. If you’re going to spend three, five, ten, fifteen years building a company, it should be something that’s going to get you excited every day.

Having a list this long forces one to choose. Here’s how I think of each of the above; everyone will be different.

Is disruptive: makes a market more efficient, by stamping out longstanding un-innovative types (US Postal Service, realtors, the taxi medallion system, etc.)
I like the idea of building a disruptive startup, but it’s hard to imagine the idea of disruption being the central one that gets me out of bed in the morning. For other people, beating the crap out of a privileged, ossified sector of the economy would be a laudable goal. For me, it might be fun, but is not something to which I aspire.

Affects the lives of many people
Affecting people’s lives — without regard to whether the effect is good or bad — doesn’t rate for me as a criterion. I’d say that Facebook and YouTube have both clearly had big effects on the world, by allowing people to find and share videos and photos online. But I’m not completely convinced that either makes the world a better place, and it would be difficult to make a strong argument either way.

Significantly improves the world
On the other hand, a startup that has a positive impact on the world — and for me, the magnitude is key — is one of the central drivers of what I choose to work on. Being an introverted nerdy type, I’m happy and comfortable to abstract this out a couple of levels: I don’t need to physically see that I’m solving someone’s hunger problems. I’d give LinkedIn high marks on this for its role in facilitating professional relationship building, which allows the economy to grow more quickly. Likewise, Wikipedia propagates free, generally high-quality information, which is useful in many respects for everyone who’s online. This was also one of the appeals of building a product to help moms.

Builds a product people love
Building a product people love — and getting positive feedback — is nice, but it’s not ultimately what drives me. I’m just as happy to do something that helps people’s lives without them directly realizing it.

Gives the founder a shot at a huge financial payout / Sets the founder up for an acquihire-level financial payout
I wouldn’t turn down a large financial payout, but it’s not why I’m playing the startup game. Having been at least a small part of three financial successes (PayPal, LinkedIn, Circle of Moms), I’m financially comfortable, if not super wealthy. So an acquihire-type startup outcome wouldn’t be financially life-changing for me. And though it would be great to build a very valuable company, I’d much, much rather go to my deathbed having built Wikipedia than Zynga, even if Zynga would be much more lucrative.

Could lead to the respect of ______ (peers, mentors, parents, etc.) / Allows the founder to do better (in terms of fame, finances, etc.) than a personal rival
Respect from others and competition with rivals are emotions that drive me on short-term projects, but don’t underlie my long term motivations. At times, I’ve worked harder and smarter to impress someone I look up to; at others I worked my tail off to do better than someone I didn’t want to beat me. But both were over the course of a month or three: year to year, I’m not really driven by the mentorship of others, the desire to have someone’s approval, or long term competition. It’s hard to imagine consistently waking up in the morning and jumping out of bed to get to work because I want to beat or impress someone: it’s just not who I am.

Has a natural strategy for growth/distribution
As is likely obvious from my background, I think a lot about distribution and the creation of strong and sustainable online ecosystems. Thus while I wouldn’t place distribution and ecosystem at the very top of my “must have for my next startup” list, it’s one of the top things I think about. If I’m considering a consumer product, and don’t believe there’s a cost effective way to scale it, I’ll probably pass.

Has a natural strategy for profitability
The same isn’t true for profitability: I’m comfortable with short-term ambiguity around the monetization of a product, as long as my intuition tells me there’s a way to bring in revenue. Circle of Moms and LinkedIn both fell into this bucket, as neither had a clear early revenue model. Others gravitate more toward ideas with clarity around business model.

Is technology-focused /
Is design-focused / Is sales-focused / Is brand-focused

I’m less particular about whether a company is technology-, design-, brand-, or sales-focused than many are. I like technology hurdles and intellectual challenges (more on that soon), but was excited to work for a company like LinkedIn which (in the early days at least) never really felt like a pure technology company.

Is timely with respect to available technologies
Timeliness is, in my opinion, a very valuable tool in finding large businesses. Most of the largest technology businesses around today couldn’t have been formed two years earlier, because something — technology, infrastructure, culture shift — hadn’t existed. To that end, it’s an important part of brainstorming, and something I consider in evaluating a business’ viability, but it’s not a core part of my checklist telling me what I’d be happy working on.

Fits the skills of the founder
Matching a company with the skills I have is something that’s high on my list. I’m not a top notch developer, I’m certainly not a sales person, I’m not going to be a talking head on TV, and I doubt I’d be strong as a dealmaker. But I’m skilled with data, am not completely full of crap (I hope!), can understand product ecosystems better than most, and can pull together marketing, technology, and product skills in ways many others cannot. If you’ve gotten this far, it’s perhaps an indication that I can write competently. Since I get A’s on parts of my self-evaluation and D’s and F’s on others (more on this in a future post), I place a high priority on making sure that the good stuff comes out. That doesn’t mean that I don’t want to push myself — I do — but I want my company to use the unfair advantages that I have.

Is intellectually interesting to the founder
One of my not-so-good traits is a tendency to get bored. Without challenges, particularly intellectual ones, I get antsy. When I’m bored, I tend to search for difficult solutions to simple problems, because they keep me entertained. That’s not a great characteristic, but it’s who I am. So it’s better for me to work on problems I find intellectually captivating. That way, I won’t get bored and can focused on the best, simplest solutions rather than the most interesting ones.

Fits in with the founder’s world view
Fitting in with the founder’s world view is valuable to founders who have a very specific view of where the world is going. At PayPal, Peter Thiel would speak at company meeting about PayPal supplanting government-controlled currencies; that fit into his libertarian view of the world. I am passionate about moving toward a world where better decisions are made with the help of data and I’m passionate about the notion that the standard rhetoric of both the left and the right oversimplify in unfortunate ways. However, though these help inform my direction, they won’t drive it.

As you can tell, there’s a lot involved in going through that exercise.

My Top Four

Ordering my selections above, I get the following as my foremost concerns as I think about my next startup:

1) Intellectually interesting to me
2) Significantly improves the world
3) Fits my skillset
4) Can create a strong and sustainable ecosystem

This is a reflection of a good balance for me: what would keep me engaged, what would I look back upon with pride, what’s a good use of my skills, and what can really work.

When evaluating a startup idea, I measure the concept against each of my top four characteristics to gauge its appeal; I expect my next (TBD) startup to rate highly on at least three of the four. Of course, the people I might work with on a new project factor in considerably; I’ll address that in a future post.

As you’ll likely see, going through this exercise is both fun and insightful. What’s most important to you?

Job Creation Stats Under Republicans and Democrats

Former President Clinton mentioned last night in his speech that there had been 42 million jobs created in the last 24 years with Democratic presidents, compared to only 24 million jobs created in the last 28 years with Republican presidents.

That kind of statistic can be misleading in lots of ways; the two most obvious are that he’d cherry picked a time period or that a few very good or very bad years would sway the results.

So I took a look at the BLS data on nonfarm employees. They’re a little different from the numbers Clinton cited, so I imagine he’s using a slightly different definition of jobs. Nevertheless, the trends are the same, so I’m comfortable using the numbers for a comparison.

I looked, year by year, at the net change in jobs, for every year since 1953 (Eisenhower’s first year in office). This was defined as January 31 to January 31, to best coincide with the presidential term. Overall, nearly 48 million jobs were created under 23 years of Democratic presidents (over 2 million per year; I excluded 2012) and nearly 35 million under 36 years of Republican presidents (just under 1 million per year).

I sorted the years by the net percentage change in jobs, to look at whether good and bad years are more likely under presidents of one party.

The twenty best years of the last 59 were:

1955 (Eisenhower, R)
1978 (Carter, D)
1965 (Johnson, D)
1977 (Carter, D)
1966 (Johnson, D)
1972 (Nixon, R)
1983 (Reagan, R)
1984 (Reagan, R)
1968 (Johnson, D)
1964 (Johnson, D)
1994 (Clinton, D)
1959 (Eisenhower, R)
1973 (Nixon, R)
1988 (Reagan, R)
1987 (Reagan, R)
1997 (Clinton, D)
1976 (Ford, R)
1999 (Clinton, D)
1996 (Clinton, D)
1993 (Clinton, D)

1955 had the best jobs numbers with a 5% growth rate; 1993 saw a 2.5% growth rate.

In eleven of those twenty, the president was a Democrat. The years are relatively evenly spread across decades other than the 2000s: two in the 1950s (out of seven years), four in the 1960s, four in the 1970s, four in the 1980s, and five in the 1990s.

If we take out the first year after a party change in the White House, we remove two years that cast Democrats in a favorable light (1993 and 1977) and none that cast Republicans in a favorable light. That means nine out of eighteen “good” years had each party in office.

The worst twenty years tell a different story:

2008 (GW Bush, R)
2009 (Obama, D)
1982 (Reagan, R)
1957 (Eisenhower, R)
2001 (Bush, R)
1953 (Eisenhower, R)
1960 (Eisenhower, R)
1974 (Nixon/Ford, R)
1991 (GHW Bush, R)
1981 (Reagan, R)
1970 (Nixon, R)
2002 (GW Bush, R)
1990 (GHW Bush, R)
1954 (Eisenhower, R)
2003 (GW Bush, R)
1980 (Carter, D)
2007 (GW Bush, R)
1958 (Eisenhower, R)
2010 (Obama, D)
2000 (Clinton, D)

The worst year for job growth was 2008, with a net loss of 3.2%; 2000 saw a slight gain of 1.2%.

You’ll notice that on this list, we see a lot of R’s: fourteen of the fifteen worst years, and sixteen of the twenty worst years were when a Republican was in office. Taking out the transition years of 2009, 2001, 1953, and 1981, we still see the same trend: the eleven worst years, and thirteen of the worst sixteen happened during a Republican presidency.

The middle tier of nineteen years of roughly average job growth shows a Democrat-Republican split in between those of good and bad: eight years with Democrats in office and eleven years with Republicans in office. Excluding transition years, the numbers are seven and ten.

Clearly, these results indicate stronger jobs numbers under Democratic presidents. That begs the question of whether the trend is just random variation, or something that’s statistically meaningful.

So I ran some statistical tests: what’s the likelihood that — in a completely random world — of the top twenty years, at least eleven would be under a Democrat, versus four or fewer of twenty bad years under a Democrat? About 2.5% of the time. What if you only looked at comparable numbers for non-transition years? It’s higher: around 6%. What if you defined good and bad as only the top fifteen? 4% and 1.7% for all and non-transition years, respectively. All of these are one-sided tests, meaning they answer the question “what is the likelihood of Democrats doing as well or better than ___ by chance?”

Statistical significance is often at the 5% (95%) threshold, meaning that an event in the middle 95% of a probability distribution is judged not different, while an event outside that threshold is significant. That means a significant event should fall at or under 2.5%.

The numbers above suggest borderline statistical significance: with some parameters, the differences between Republicans and Democrats look significant; by tweaking them slightly, they appear insignificant.

Based on all that, it’s certainly plausible that there’s a real difference between job creation under Democratic presidents and Republican presidents. After this fairly cursory analysis, I wouldn’t feel comfortable defending such a relationship, but the claim is closer to reality than much of what was stated at the recent conventions.

However, comparing Obama and Romney — who seemingly both fit within the historical mainstream of their parties on economic policy — it’s a different story. If my goal was strictly to generate more job growth, there’s no question that I’d pick Obama. While the trend is borderline from a scientific perspective, it’s strong enough to use as a significant weighting in a real world gut-based decision.