Chris Dixon

Here are some things I’ve learned over the years about recruiting programmers* to startups. This is a big topic: many of the points I make briefly here could warrant their own blog posts, and I’m sure I’ve omitted a lot.

- The most important thing to understand is what motivates programmers. This is where having been a programmer yourself can be very helpful. In my experience programmers care about 1) working on interesting technical problems, 2) working with other talented people, 3) working in a friendly, creative environment, 4) working on software that ends up getting used by lots of people. Like everyone, compensation matters, but for programmers it is often a “threshold variable”. They want enough to not have to spend time worrying about money, but once an offer passes their minimum compensation threshold they’ll decide based on other factors.

- Software development is a creative activity and needs to be treated as such. Sometimes a programmer can have an idea on, say, the subway that can save weeks of work or add some great new functionality. Business people who don’t understand this make the mistake of emphasizing mechanistic metrics like the number of hours in the office and the number of bugs fixed per week. This is demoralizing and counterproductive. Of course if you are running a company you need to have deadlines, but you can do so while also being very flexible about how people reach them.

It is sometimes helpful to think of recruiting as 3 phases: finding candidates, screening candidates, and convincing candidates to join you.

- Finding means making contact with good candidates. There are no shortcuts here. You need to show up to schools, hackathons, meetups – wherever great programmers hang out. If your existing employees love their jobs they will refer friends. Try to generate inbound contacts by creating buzz around your company. If you have trouble doing that (it’s hard), try simple things like blogging about topics that are interesting to programmers.

- Screening. Great programmers love to program and will have created lots of software that wasn’t for their jobs or school homework. Have candidates meet and (bidirectionally) interview everyone they’ll potentially be working with. If the candidate has enough free time try to do a trial project. There are also more procedural things that can be useful like code tests (although they need to be done in a respectful way and they are more about getting to know how each side thinks than actually testing whether the candidate knows how to program (hopefully you know that by this stage)).

- Convincing them to join you. This is the hardest part. Great programmers have tons of options, including cofounding their own company. The top thing you need to do is convince them what you hopefully already believe (and have been pitching investors, press etc): that your company is doing something important and impactful. The next thing you need to do is convince them that your company is one that values and takes care of employees. The best way to do this is to have a track record of treating people well and offer those past employees as references.

A few things not to do: you will never beat, say, Google on perks or job security so don’t even bother to pitch those. You’ll never beat Wall Street banks or rich big companies on cash salary so don’t pitch that either. You’ll never beat cofounding a company on the equity grant, but you can make a good case that, with the right equity grant, the risk/reward trade off of less equity with you is worth it.

Finally, I’ve long believed that early-stage, funded startups systematically under-grant equity to employees. Programmers shouldn’t have to choose between owning a fraction of a percent of an early-stage funded company and owning 50% of an unfunded company they’ve cofounded. Naval Ravikant recently wrote a great post about this:

Post-traction companies can use the old numbers – you can’t. Your first two engineers? They’re just late founders. Treat them as such. Expect as much.

Making those first engineers “late cofounders” will dramatically increase your chances of recruiting great people. This is a necessary (but not sufficient) condition for getting the recruiting flywheel spinning where great people beget more great people.

* As someone who personally programmed for 20 years including about 10 years professionally, I preferred to call myself a “programmer.” Some people prefer other words like “hacker” “developer”, “engineer” etc. I think the difference is just uninteresting nomenclature but others seem to disagree.

It was a mixed year for me as a blogger. I didn’t post much as I would have liked – I spent most of the year working with eBay in a process that eventually led to Hunch being acquired. But I also learned a lot and tried to share some of those learnings here. Below are the posts I think were the best and also seemed to get the most pageviews and reader comments.

An internet of people

Making industries “garage ready” for startups

Business development: the goldilocks principle

Some lessons learned

Do you want to sell sugar water or do you want to change the world?

What the NYC startup world needs (and doesn’t need)

Founder/market fit

Best practices for raising a VC round

There are two kinds of people in the world

Apple and the TV industry

Google’s social strategy

MIT is a national treasure

Dropbox and why you should invest in people

SEO is no longer a viable marketing strategy for startups

Selling pickaxes during a gold rush

Predicting the future of the Internet is easy: anything it hasn’t yet dramatically transformed, it will.

For older posts, see the contents page. I haven’t updated this page in a long time but plan to do so soon.

Michael Lewis’ Boomerang is the best book you can read to understand the global credit crisis. Here’s an excerpt from the chapter on Iceland that involves fishing, smelting, banking, and elves. Yes, elves.

Alcoa, the biggest aluminum company in the country, encountered two problems peculiar to Iceland when, in 2004, it set about erecting its giant smelting plant. The first was the so-called hidden people—or, to put it more plainly, elves—in whom some large number of Icelanders, steeped long and thoroughly in their rich folkloric culture, sincerely believe. Before Alcoa could build its smelter it had to defer to a government expert to scour the enclosed plant site and certify that no elves were on or under it. It was a delicate corporate situation, an Alcoa spokesman told me, because they had to pay hard cash to declare the site elf-free, but, as he put it, “we couldn’t as a company be in a position of acknowledging the existence of hidden people.” The other, more serious problem was the Icelandic male: he took more safety risks than aluminum workers in other nations did. “In manufacturing,” says the Alcoa spokesman, “you want people who follow the rules and fall in line. You don’t want them to be heroes. You don’t want them to try to fix something it’s not their job to fix, because they might blow up the place.” The Icelandic male had a propensity to try to fix something it wasn’t his job to fix.

Back away from the Icelandic economy and you can’t help but notice something really strange about it: the people have cultivated themselves to the point where they are unsuited for the work available to them. All these exquisitely schooled, sophisticated people, each and every one of whom feels special, are presented with two mainly horrible ways to earn a living: trawler fishing and aluminum smelting. There are, of course, a few jobs in Iceland that any refined, educated person might like to do. Certifying the nonexistence of elves, for instance. (“This will take at least six months—it can be very tricky.”) But not nearly so many as the place needs, given its talent for turning cod into PhDs. At the dawn of the twenty-first century, Icelanders were still waiting for some task more suited to their filigreed minds to turn up inside their economy so they might do it.

Enter investment banking.

It’s a short book – just 5 chapters covering Iceland, Ireland, Germany, Greece, and California. What’s particular fascinating is how each place had a wildly different reaction to the credit glut.

- Great startup story. Raised a total of $4.2m in venture capital, sold to IAC/Expedia for $210M, and had some interesting adventures and pivots along the way. They started out by trying to aggregate reviews from other websites and white label their product to Expedia and other large travel websites. TripAdvisor.com was just a showcase that accidentally became a destination site. As of today TripAdvisor is an independent public company, trading at a market cap of $3.5B.

- Great for Boston. Fairly or not, Boston is often typecast as an infrastructure, B2B, hardware, and biotech town. Between Tripadvisor and Kayak, Boston now has at least two very important consumer internet companies.

- Big win for the “golden age of SEO”.  By which I’m referring to roughly 2001-2008 when “demand” for content (people typing in search queries) far outpaced supply (good content). Companies like Yelp and TripAdvisor (along with Wikipedia, IMDB, etc) grew huge during this period, almost entirely through SEO. They did this by getting highly defensible flywheels spinning where more content meant more SEO which meant more users which meant more content. It is now far more difficult to grow a startup primarily through SEO. Almost all monetizable search categories have vast excesses of SEOd content. Moreover, Google is creating their own content (e.g. Google Places) which, at least at times, they have favored in their search results.

- The user experience should improve. MG Siegler and others have criticized TripAdvisor for an excess of ads. I don’t disagree with MG, but I also think this is largely the result of the broken online ad attribution system that punishes intent generators and rewards intent harvestors. Travel reviews are for users at the beginning of the travel research process (which on average takes weeks), but all CPA and CPC ad programs pay only for the last click which usually means when users are purchasing tickets or making reservations. Hence review sites are forced to saturate their website real estate with purchasing widgets and display ads. Hopefully as online ad attribution improves this will no longer be necessary.

- It’s weird how little coverage this IPO got and how the financial press missed the interesting stories. TripAdvisor ended the day at ~$3.5B in market cap, making it the second most valuable East Coast consumer internet company (after Priceline). Every story I saw focused on the share price drop over the day. The fact that the price dropped from its opening price simply means the bankers mispriced the stock and therefore insiders didn’t get the sweetheart deal they thought they were getting.

Update: I interviewed the CEO/founder of TripAdvisor on TechCrunch yesterday. Topics include the company’s origins, relationship with Google, SOPA, and advice to fledgling entrepreneurs.

One of Clay Christensen’s favorite concepts is that instead of dividing your customers into segments and asking which features each segment would like, you should think about what “job” the customers are “hiring” you product to perform. Here is an example:

A fast-food restaurant chain wanted to improve its milkshake sales. The company started by segmenting its market both by product (milkshakes) and by demographics (a marketer’s profile of a typical milkshake drinker). Next, the marketing department asked people who fit the demographic to list the characteristics of an ideal milkshake (thick, thin, chunky, smooth, fruity, chocolaty, etc.). The would-be customers answered as honestly as they could, and the company responded to the feedback. But alas, milkshake sales did not improve.

The company then enlisted the help of one of Christensen’s fellow researchers, who approached the situation by trying to deduce the “job” that customers were “hiring” a milkshake to do. First, he spent a full day in one of the chain’s restaurants, carefully documenting who was buying milkshakes, when they bought them, and whether they drank them on the premises. He discovered that 40 percent of the milkshakes were purchased first thing in the morning, by commuters who ordered them to go.

The next morning, he returned to the restaurant and interviewed customers who left with milkshake in hand, asking them what job they had hired the milkshake to do. “Most of them, it turned out, bought [the milkshake] to do a similar job,” he writes. “They faced a long, boring commute and needed something to keep that extra hand busy and to make the commute more interesting. They weren’t yet hungry, but knew that they’d be hungry by 10 a.m.; they wanted to consume something now that would stave off hunger until noon. And they faced constraints: They were in a hurry, they were wearing work clothes, and they had (at most) one free hand.”

The milkshake was hired in lieu of a bagel or doughnut because it was relatively tidy and appetite-quenching, and because trying to suck a thick liquid through a thin straw gave customers something to do with their boring commute. Understanding the job to be done, the company could then respond by creating a morning milkshake that was even thicker (to last through a long commute) and more interesting (with chunks of fruit) than its predecessor. The chain could also respond to a separate job that customers needed milkshakes to do: serve as a special treat for young children—without making the parents wait a half hour as the children tried to work the milkshake through a straw. In that case, a different, thinner milkshake was in order.

There are at least three obvious ways to apply this concept: 1) when searching for startup ideas, think about jobs people want done that they can’t currently get done, 2) when thinking about how to fix or improve your product, understand why existing users are hiring your product (or should be hiring your product) and try to improve those experiences, 3) when analyzing markets, segment companies by the jobs they are hired for. Sometimes products that might appear similar (e.g. two photo sharing apps) are actually hired for very different purposes, and are therefore misclassified as competitors.

In response to my post yesterday about how an internet of people has enabled a new wave of web-based marketplaces, Nick Mango commented:

There’s actually 2 levels of trust here. The first is knowing and trusting the person you’re buying from. And if you don’t know who they are, then you must move on to the second level of trust, which is do you know and trust the platform the person is using.

The ability to have “second order trust” is one of many reasons the internet has made so many institutions obsolete. Take the SEC’s role in policing private companies that market themselves to potential investors. This was sensible consumer protection back when the government was arguably the only organization that had the means and incentives to identify fraudulent investment schemes. But today we have many examples of websites that’ve built mechanisms for reliably tracking the reputations of individuals and organizations. This means the SEC could – in theory – make the unit of regulation platforms instead of investors and startups (something the crowdfunding bill being considered by Congress seems to do at least in part), which in turn could unleash a new wave of innovation among crowdfunding platforms and crowdfunded startups.

Over the past few years, a bunch of web-based marketplaces have gotten popular – Etsy, Kickstarter, AirBnb, to name a few. Many of these business ideas had been tried before but are succeeding only now.

When a trend like this emerges, it’s always interesting to ask “why now?” For example, for almost a decade, entrepreneurs tried to create video sharing services like YouTube, but only succeeded when certain key dependencies – broadband, digital video cameras, a version of Flash that “just worked” – became widespread.

I asked Roelof Botha the “why now” question regarding web-based marketplaces. He said something I thought was really interesting: marketplaces depend on trust, and trust requires knowing the reputation of a prospective counterparty. Today, for the first time, you can get background information on almost any prospective counterparty by searching Google, Facebook etc. Or put more simply: we finally have an internet of people.

I hadn’t looked at cdixon.org site logs in over a year until today.  Here are the numbers according to the Dreamhost panel:

I’ve been blogging more lately which explains why Dec 2011 is tracking to be up near 2M page views (although frankly that number seems high to me- I wonder if somehow they are counting each page view multiple times – maybe due to the way WordPress works?)

As if we needed another reminder of how wrong Compete data is here is their chart:

Not even directionally correct.  Yeah they show UVs and not pageviews but I don’t see any reason those would have gotten decoupled.

(I had cdixon.org tagged with Quantcast for a while but removed it a few weeks ago –  their chart when cdixon.org was tagged makes more sense (as you’d expect)).

I blog just for fun / hobby, so don’t really care about these stats. But it’s interesting to see the (in)correctness of these popular analytics services.

From Marc Andreessen’s “Moby Dick Theory of Big Companies“:

You can count on there being a whole host of impinging forces that will affect the dynamic of decision-making on any issue at a big company.

The consensus building process, trade-offs, quids pro quo, politics, rivalries, arguments, mentorships, revenge for past wrongs, turf-building, engineering groups, product managers, product marketers, sales, corporate marketing, finance, HR, legal, channels, business development, the strategy team, the international divisions, investors, Wall Street analysts, industry analysts, good press, bad press, press articles being written that you don’t know about, customers, prospects, lost sales, prospects on the fence, partners, this quarter’s sales numbers, this quarter’s margins, the bond rating, the planning meeting that happened last week, the planning meeting that got cancelled this week, bonus programs, people joining the company, people leaving the company, people getting fired by the company, people getting promoted, people getting sidelined, people getting demoted, who’s sleeping with whom, which dinner party the CEO went to last night, the guy who prepares the Powerpoint presentation for the staff meeting accidentally putting your startup’s name in too small a font to be read from the back of the conference room…

Man, I wish Marc still blogged.  (ht saul lieberman)

I recently had a number of conversations with CEOs of later-stage startups (generating significant revenue) that went something like this. They want to raise more money, and VCs are offering them money at a high valuation. The CEO is worried that taking money at that valuation will “set the bar too high” and make it difficult to sell the company – if the time comes when he/she thinks it makes sense to sell – at a price that isn’t a significant multiple of that valuation.

These CEOs are worrying too much. VCs know what they are doing and almost always invest with a financial instrument – preferred shares – that protects them even when the valuation is very high. Preferred shares behave like a stock on the upside and a bond on the downside.  The only way investors actually lose money is if the company is sold for less than the amount of money raised (which is generally significantly lower than the valuation).

Here is what the payout function looks like for common stock (for example, what you get when you buy stocks in public markets):

And here is what the payout function looks like for preferred shares:

So, to take a concrete example, Dropbox reportedly raised their last financing at a $4B valuation*. If you think of this as a public market valuation of common stock, you might think this means the VCs are betting $4B is the “fair value” of the company, and will lose money if Dropbox’s exit price ends up being less than $4B.  But in reality, assuming the standard preferred structure, the last round investors’ payout is as follows :

Scenario 1: Dropbox exits for greater than $4B ==> investors get a positive return (specifically, exit price divided by $4B)

Scenario 2: Dropbox exits for between $257M (total money raised) and $4B ==> investors get their money back (possibly more if there is a preferred dividend)

Scenario 3: Dropbox exits for less than $257M ==> investors lose money

If reports are true that Dropbox is profitable and generating >$100M in revenue, then scenario 3 – the money losing scenario – is extremely unlikely.

Will investors be thrilled with scenario 2?  No, but they are pros who understand the risks they are taking.

Going back to the entrepreneur’s perspective, in what sense is a high valuation “setting the bar high”?  In the preferred share payout model, there are two “bars”:  money raised and valuation.  I don’t see any reason why entrepreneurs shouldn’t be as aggressive as possible on valuation, especially if they are confident they won’t end up in scenario 3.

An important point to keep in mind is that, in order to maintain flexibility, entrepreneurs shouldn’t give new investors the ability to block an exit or new financings. Investors can get this block in one of two ways – explicit blocking rights (under the “control provisions” section of a VC term sheet) or by controlling the board of directors. These are negotiable terms and startups with momentum should be very careful about giving them away.

* Note that I have no connection to Dropbox so am just assuming standard deal structure and basing numbers on public reports. I am making various simplifying assumptions such as not distinguishing between pre-money and post-money valuation.