Chris Dixon

Like many in tech, I believe all software patents should be abolished. That said, I think Facebook made the right move by filing a lawsuit against Yahoo’s patent attack.

As I see it, Facebook had 4 choices:

- Settle. Given their pending IPO, this would have been the easiest route. But, by rewarding Yahoo, settling would have encouraged more frivolous patent lawsuits.

- Defend without countersuing. On the surface this would have been the “principled” stance, but it would have severely weakened their legal position, and therefore would have made it more likely that Yahoo profited from the lawsuit.

- Countersue without signaling any aversion to patent lawsuits.

- Countersue and signal that they are averse to patent lawsuits, which in turn signals that they will drop the lawsuit if Yahoo does. This seems to be what Facebook has done:

“From the outset, we said we would defend ourselves vigorously against Yahoo’s lawsuit,” Ted Ullyot, Facebook’s general counsel, said in a statement. “While we are asserting patent claims of our own, we do so in response to Yahoo’s short-sighted decision to attack one of its partners and prioritize litigation over innovation.” [emphasis added] – NYTimes

Countersuing gives Facebook the best chance of fending off Yahoo’s lawsuit – and therefore not rewarding patent lawsuits. And signaling they are only doing so in response to Yahoo (hence might drop the suit if Yahoo does) keeps them on the right side of innovation.

A few years ago, the trend of companies raising smaller seed rounds combined with the emergence of new seed funds caused many big VCs to create seed investment programs. This triggered a debate among entrepreneurs and investors about whether it was risky for seed-stage companies to take small investments from large VCs. (I blogged about the issue here, here, here).

Since then, enough founders have directly experienced the downside of taking seed money from big VCs that I think it’s safe to say there is no more room for debate. I can think of about 15 founders I’ve spoken to recently who tried or are trying to raise Series As but are seriously hampered by the fact that a big VC invested in the seed round but isn’t participating in the Series A. (I’d love to mention specific companies and firms but it wouldn’t be appropriate for me to do so – I guess I’ll just have to cite Jay Rosen’s “I’m there, let me tell you what I see” principle of reporting).

There are two important nuances to point out here. First, there are big VCs who invest in seed rounds the right way – with the genuine expectation to follow on and the intention to help out during the seed stage (some that I’ve invested with include USV, True, and Spark). One important sign of this is how much they want to invest. If a $300M fund wants to invest $100K, they are buying an option. If they want to invest $500K, they are more likely making an investment.

The second nuance can be counterintuitive: the danger of taking seed money is positively correlated with the reputation of the firm. If a top VC invests in the seed round and then passes on the A, other VCs will have difficulty overlooking that the smartest money that knows the company the best isn’t following on. If the VC isn’t well respected, it is easier for other VCs to second guess them.

I’m not revisiting this issue to criticize big VCs. A healthy startup environment requires smart, ethical investors at all stages. But I don’t think these big VC seed programs benefit anyone. And there are enough angry entrepreneurs out there that I expect the message will get through.

Companies that employ the “freemium” business model give away a product or service for free and then charge for additional features. The freemium model has gotten more popular as the cost to deliver free services has dropped but the cost of employing sales and marketing people hasn’t. One of the hardest questions around freemium models is deciding how to divide free from paid features.

One particularly effective version of freemium is: “give away the diagnostic, sell the remedy.” The best known example of this is anti-virus companies that give away free virus scans but charge for virus removers. In fact, this tactic works so well for anti-virus that it almost seems coercive (and has indeed been abused, for example, by “anti-spyware” software that deliberately conflates cookies and viruses). But, in general, giving away a diagnostic seems like a reasonable way to demonstrate the effectiveness of a product while still being able to sell valuable additional features.

Selling the remedy has become increasingly popular with B2B companies. For example, a friend recently wanted to ensure that his company’s (non-spam) e-mails weren’t getting blocked by spam filters, so he contacted an “email delivery optimization” company. They ran a free test and reported that his emails weren’t getting filtered. Two months later they called back and said “uh oh, your emails are getting filtered.” Sure enough his open rates had dropped and his anecdotal tests confirmed that his emails were being inaccurately labelled as spam. Because of the free diagnostic, he had confidence in the company’s technology, and was willing to pay them to fix his problem. And the email optimization company had spent almost nothing to acquire a new customer.

Angry Birds was Rovio’s 52nd game. They spent eight years and almost went bankrupt before finally creating their massive hit. Pinterest is one of the fastest growing websites in history, but struggled for a long time. Pinterest’s CEO recently said that they had “catastrophically small numbers” in their first year after launch, and that if he had listened to popular startup advice he probably would have quit.

You tend to hear about startups when they are successful but not when they are struggling. This creates a systematically distorted perception that companies succeed overnight. Almost always, when you learn the backstory, you find that behind every “overnight success” is a story of entrepreneurs toiling away for years, with very few people except themselves and perhaps a few friends, users, and investors supporting them.

Startups are hard, but they can also go from difficult to great incredibly quickly. You just need to survive long enough and keep going so you can create your 52nd game.

A famous story in artificial intelligence is how the US military developed algorithms to determine whether an image had a tank in it. They used a standard machine learning method: feed the computer a “training set” of photos, some of which had tanks in them and some of which didn’t, and let algorithms identify which features in the photos correlated to tanks being shown.

This method worked for a while but then mysteriously stopped working. Since the features the computer identified were embedded in complicated mathematical equations, no one could figure out what it was really doing and therefore why it stopped working. Eventually someone realized that in the training set, all of the images with tanks were taken on a cloudy day, and all the images without tanks were taken on a sunny day. The algorithms had fixated on the most obvious pattern – the color of the sky. When the algorithm was tested on new photos where the weather varied, it was completely flummoxed.

It is commonly said that good startup investors develop “pattern recognition” that allows them to identify great entrepreneurs and companies. If you look at the hugely successful startups of the last decade, the founders have many similarities that are easy to observe. When they started, many were male, young, unmarried, computer programmers, dropouts of elite universities, etc. As a result, a lot of investors look for founders with these characteristics. But without an understanding of the deeper reasons these founders succeeded, these observable characteristics could just as well be the color of the sky and not the tanks.

At the level of individual investors, pattern recognition can lead to bad investments and missed opportunities. In the context of markets, it can cause companies and sectors with the “right patterns” to be overvalued, and ones with the “wrong patterns” to be undervalued. In the broader cultural context, it can cause large groups of talented entrepreneurs to be denied access to capital.

The classic scientific method provides a better model for investing. Scientists observe data, notice patterns, develop hypotheses, and then test those hypotheses. Pattern recognition is only a step along the way to developing hypotheses about the underlying cause.

Perhaps dropping out of college shows a strong level of commitment. Knowing computer science was probably a necessary condition for starting a tech company in the past, but no longer is. Being young could mean you are inexperienced enough to pursue bold ideas that more experienced people would consider crazy. I am just speculating – I don’t know why these characteristics are common among past successful founders. But the mere repetition of patterns shouldn’t be satisfactory to anyone who wants to understand and predict the success of startups.

Here are a few things I’ve learned over the years about the best ways for entrepreneurs to interact with the press (by press I mean blogs as well as traditional media).

- Don’t be afraid to ask what the rules are. Is this on or off the record? If they are writing an article about your company, do they require exclusivity? What is the angle of the story?

- Don’t use a PR firm unless you are so successful that you need someone to help you manage inbound press interest. Most journalists, when talking candidly, will tell you they’d vastly prefer getting an email from the founder of a startup than a PR firm. If you’re Bill Gates, it is understandable that you have someone reaching out for you. If you are a small startup, having a PR rep contact a journalist says “I’m not competent enough to reach you” or “I don’t respect your time enough to reach out directly.”

- Treat journalists with respect. Tech/business journalists often interact with rich and powerful people, some of whom treat them disrespectfully. Like entrepreneurs, journalists are usually interesting people with diverse interests. You’ll probably like them if you talk to them and might even become friends.

- Unless you’re a super hot startup, the existence of your company is not a news story. Exclusives of launches, financings and acquisitions are usually news stories. Trend stories that you are part of could be a news story. Relating your startup or data your startup generates to something already newsworthy (journalists call this “pegging”) can dramatically increase your chances of getting covered.

- Whether you like it or not, the press will put your company into a category, and might run “horserace” stories comparing how the companies in your category are doing. The best you can do here is to try to choose which category you’ll be put into. Arguing that you have no competitors or are creating a new category is pretty much impossible.

- Try to put yourself in the mindset of the journalist. How will this story get them on Techmeme or featured by their editors? What were their most successful recent stories? Do background research on any reporter before talking and read a bunch his/her articles.

- Don’t just contact reporters when you need them: try to be helpful even when you don’t. Sometimes, I get calls to talk about, say, the state of the venture market or asking for some background on a tech sector that is new to the journalist. My guess is they appreciate this and are more responsive when I contact them about a possible story.

From Innovation and the Bell Labs Miracle in today NYTimes:

Innovation is an important new product or process, deployed on a large scale and having a significant impact on society and the economy, that can do a job “better, or cheaper, or both.” Regrettably, we now use the term to describe almost anything. It can describe a smartphone app or a social media tool; or it can describe the transistor or the blueprint for a cellphone system. The differences are immense. One type of innovation creates a handful of jobs and modest revenues; another, the type Mr. Kelly and his colleagues at Bell Labs repeatedly sought, creates millions of jobs and a long-lasting platform for society’s wealth and well-being.

The conflation of these different kinds of innovations seems to be leading us toward a belief that small groups of profit-seeking entrepreneurs turning out innovative consumer products are as effective as our innovative forebears. History does not support this belief. The teams at Bell Labs that invented the laser, transistor and solar cell were not seeking profits. They were seeking understanding. Yet in the process they created not only new products but entirely new — and lucrative — industries.

Putting aside the obvious rebuttal that large companies like Intel, Microsoft, Apple and even AT&T were once startups, the author seems to confuse “jobs” with “income streams”. For example, it would be easy to dismiss a website like Craigslist as a “social media tool” that has only created a few dozen jobs for its employees. But in fact it has created billions of dollars of income streams for people buying and selling things on its platform. The internet is increasingly reshaping our economy from one of huge corporations with lots of jobs to huge platforms with lots of income streams.

One of the interesting things about having been investing in startups for a number of years is that at any moment you get an inside peek at startups at a variety of different stages. In the course of a few weeks, I might talk to people who are ideating around new business ideas, people raising seed rounds, people raising later (VC) rounds, people whose products are blowing up, people whose product are struggling, people getting acquired, people leaving acquirers to start new companies, etc. Sadly, there are also usually a few companies that are struggling and facing the serious possibility of running out of money and being forced to shut down.

One side-by-side comparison struck me recently.  Company A is just now raising a seed round. The money they raised will last 12 months (personally, I strongly recommend raising 18 months of runway – if you have the option to do so). Company A was also, in my opinion, not ready to raise money (they needed to work on their plan and team more). Company B raised a seed round about 10 months ago and is now struggling to raise more. Company B had the option to raise more money back then but chose to only raise 12 months runway in order to minimize dilution. Company B also made the mistake of having a large VC invest $100K in the round (a meaningless amount to a large VC). The large VC has since said they won’t support the company (despite the fact that the company made pretty good progress on the business) creating a massive signaling problem.

In the current “frothy” environment, where seed investors are aggressively offering money to entrepreneurs, it is easy for an entrepreneur to think “well, if I’m getting offered money this easily at the seed stage, I’ll get offered money easily later.” In fact, once you take professional investor money, the attitude of investors (both insiders and outsiders) changes dramatically: you’ve gone from planning mode to operations mode. When you do planning, research, experimenting etc. without having raised money, investors think you are prudent (I recently interviewed the Warby Parker founders for TechCrunch and they said they spent 1.5 years planning/researching before they raised money). When you do it with other people’s money, and don’t make what they perceive to be enough progress, the investors can quickly lose faith.

The obvious lesson is well known by experienced entrepreneurs. Don’t raise money until you are ready, and when you do, raise enough to have a good shot at reaching “accretive milestones” so you can raise more money, become profitable, or whatever your goals might be.

“Big timing” is a phrase I’ve heard a lot lately which refers to people who are “higher ranking” acting disrespectfully toward people who are “lower ranking”. Example usage: “so and so VC partner big timed my associate,” meaning they talked down to him/her or didn’t meet with him/her or whatever.

Big timing is a huge mistake. Why? 1) big timers vastly underestimate the degree to which senior people trust their junior people, 2) most non-jerks (no matter how successful) interpret big timing to be an insult to their firm and therefore to their senior people, 3) junior people are often far more active and informed than senior people and therefore great people to spend time with.

It would be great to think that in the startup industry, people would realize that today’s junior person could become “big time” tomorrow, and that you should therefore be meritocratic and respectful to everyone. But that’s not my experience.

One of the great unsolved questions in computer science is the P vs NP problem. It is one of the seven Millennium Prize Problems - if you solve one of them, you get $1 million and become really famous among mathematicians and computer scientists.

Here’s my non-technical interpretation of the essence of the P vs NP problem:

Can every answer that can be feasibly verified also be feasibly calculated?

What I am calling “feasible” is what computer scientists call algorithms that can run “polynomial” as opposed to “exponential” time.

There are at least four possible outcomes to the attempts to solve this problem: 1) the current situation continues – no proof of anything is found, 2) P=NP is proved true, 3) P=NP is proved false, 4) it is proved that it’s impossible to prove P=NP to be true or false.

If P=NP were proved true, there would be many serious real-world consequences. All known encryption schemes rely on the fact that prime factors of large numbers are something that can be feasibly verified but not calculated. If P=NP, that means there would also be feasible ways to calculate prime factors, and hence decrypt codes without their private keys. So if someone does prove P=NP, he or she should probably inform authorities before publishing the proof and all hell breaks loose (thanks Matt for this observation – you could also imagine a lot of conspiracy theories about what happens to scientists who try to prove P=NP..!)

Most computer scientists seem to suspect P does not equal NP. MIT computer scientist Scott Aaronson gives informal arguments against P=NP in this entertaining blog post, including this philosophical argument:

If P=NP, then the world would be a profoundly different place than we usually assume it to be. There would be no special value in “creative leaps,” no fundamental gap between solving a problem and recognizing the solution once it’s found. Everyone who could appreciate a symphony would be Mozart; everyone who could follow a step-by-step argument would be Gauss; everyone who could recognize a good investment strategy would be Warren Buffett. It’s possible to put the point in Darwinian terms: if this is the sort of universe we inhabited, why wouldn’t we already have evolved to take advantage of it?

He follows up with a much longer essay (which I found really interesting but ultimately unconvincing) on the philosophical implications of computational complexity (the field of computer science that studies questions like P vs NP).