Chris Dixon

People blog for all sorts of reasons. For me, it is mostly about learning. This wasn’t my original intention – it evolved over time. Now I see blogging as part of a continuous learning process:

- Start every morning by skimming through news, blogs, articles, etc. Much of this is tech related. I used to get tech news in the newspaper, then in Google Reader, and now mostly from Twitter. If someone I meet mentions something interesting that was published that I didn’t read, I go back and figure out how I missed it and change who I follow on Twitter so it doesn’t happen again.

- Try to meet with interesting people during the week. The reason being up on tech news is important is so that we can get the most out of the meetings. Often we’ll talk about whatever each of us is working on at the time but it’s also good to have news or blog posts as shared reference points. This makes the meetings more interesting for everyone.

- Try to learn at least one interesting thing each week and then blog about it. Then see how people react in comments, on Twitter etc. I guess some bloggers don’t like comments but for me they are the crucial so that I can get feedback on new hypotheses. Blogging new hypotheses also means a decent portion of your blog posts need to be ignored or ridiculed. Otherwise you are playing it too safe.

Every week a “we are in a tech bubble” article seems to come out in a major newspaper or blog. People who argue we aren’t in a bubble are casually dismissed as promoting their own interests. I’d argue the situation is far more nuanced and that people who engage in this debate should consider the following:

1) Public tech companies: Anyone with a basic understanding of finance would have trouble arguing many large public tech companies are trading at “bubble valuations” – e.g. Apple (14 P/E), Google (18 P/E), eBay (16 P/E), Yahoo (17 P/E). You could certainly debate other public tech stock valuations (there are a number of companies that recently IPOd that many reasonable people think are overvalued), but on a market-cap weighted average the tech sector is trading at a very reasonable 17 P/E.

2) Instagram seems to be the case study du jour for people arguing we are in a bubble. Reasonable people could disagree about Instagram’s exit price but in order to argue the price was too high you need to argue that either: 1) Facebook is overvalued at its expected IPO valuation of roughly $100B, 2) it was irrational for Facebook to spend 1% of its market cap to own what many people considered one of Facebook’s biggest threats (including Mark Zuckerberg – who I tend to think knows what is good for Facebook better than pundits).

3) Certain stages of venture valuations do seem on average over-valued, in particular seed-stage valuations and (less obviously) later-stage “momentum valuations.” The high seed-stage valuations are driven by an influx of angel/seed investors (successful entrepreneurs/tech company employees, VC’s with seed funds, non-tech people who are chasing trends). The momentum-stage valuations are driven by a variety of things, including VC’s who want to be associated with marquee startup names, the desire to catch the next Facebook before it gets too big, and the desire of mega-sized VC funds to “put more money to work”.

4) Certain stages – most notably the Series A – seem under valued. Many good companies are having trouble raising Series As and the valuations I’ve seen for the ones who do have been pretty reasonable. Unfortunately, since the financials and valuations of these companies aren’t disclosed, it is very difficult to have a public debate on this topic. But many investors I know are moving from seed to Series A precisely because they agree with this claim.

5) No one can predict macro trends. The bear case includes: something bad happens to the economy (Euro collapses, US enters double dip recession). The warning sign here will be a drop in profits by marquee tech companies.  The bull case includes: economy is ok or improves, and tech continues to eat into other industries (the “software is eating the world” argument). Anyone who claims to know what will happen over the next 3 years at the macro level is blowing hot air. That’s why smart investors continue investing at a regular pace through ups and downs.

6) The argument that sometimes startups get better valuations without revenue is somewhat true. As Josh Koppelman said “There’s nothing like numbers to screw up a good story.” This is driven by the psychology of venture investors who are sometimes able to justify a higher price to “buy the dream” than the same price to “buy the numbers.” This doesn’t mean the investors think they will invest and then get some greater fool to invest in the company again. For instance, at the seed stage, intelligent investors are quite aware that they are buying the dream but will need to have numbers to raise a Series A.

7) No good venture investors invest in companies with the primary strategy being to flip them. This isn’t because they are altruistic – it is because it is a bad strategy. You are much better off investing in companies that have a good chance to build a big business. This creates many more options including the option to sell the company. Acquisitions depend heavily on the whims of acquirers and no good venture investors bet on that.

Judging from the tech press, you’d think the biggest risk to successful companies is competition. But when you examine the history of technology, incumbents usually decline because the world changes and they lose relevance, or because they lose visionary founders and the organization decays. Some examples:

- Dell thrived when PCs dominated the computer market and Dell was the low cost provider of commodity hardware products. The shift to mobile and tablet computing meant that hardware quality (not price) was once again the primary basis of competition. As a result, Dell’s laser-like focus on cost reduction became a liability.

- The New York Times was, for many decades, one of the few premium channels through which brand and classified advertisers could reach mass consumers. Thus car companies and real estate brokers subsidized foreign reporting and investigative business journalism. The internet provided a vast alternative channel, and the Times became far less relevant. At the same time, the internet provided many new sources for breaking news, editorials etc, hurting the Times on the subscriber side.

- Yahoo didn’t lose because Google out-competed them on search. They lost because they didn’t really care about search – indeed, they outsourced algorithmic search to Alta Vista, Inktomi and then Google itself. The leading portals back in circa 2000 (Yahoo, Excite, Lycos etc) desperately wanted to keep keep users on their site – the buzzword was “stickiness” – but Google knew better and focused on getting users off of Google to other places on the web. Yahoo became just another place to read celebrity gossip and use generic web services.

- Netflix thrived when they could simply ignore the movie companies and rely on the first-sale doctrine to get DVDs. The market shift to streaming video created a new and brutal dependency. They had to go make deals with content companies. Now they are even trying to create their own content to lessen this dependency. They have a brilliant and visionary management team but this is a tough transition to make.

- Sony relied on its Steve-Jobs-like founder, Akio Morita, to repeatedly develop incredibly innovative products (among them: the first transistor radio, the first transistor television, the Walkman, the first video cassette recorder, the compact disc) that seemed to come out of nowhere and create massive new markets. Since he left, the company has floundered and the stock has fallen dramatically.

- Google’s biggest risk isn’t a direct competitor. Startups and incumbents who’ve tried to create better search engines have barely cut into Google’s market share. Google’s primary risk – and they seem to know this – is that they are no longer relevant when people find content through social sites, and where an ever increasing portion of the web is uncrawlable.

Google released their “Dropbox-killer” a few days ago. I don’t know if Dropbox has yet achieved incumbent status, but they certainly seem to be the market leader. They also seem to have a very competent management team. So if history is a guide, Dropbox’s biggest risk isn’t a competitor but irrelevance – if, for example, files become less and less important in a web services world and Dropbox doesn’t adapt accordingly.

With the passage of the JOBS act, it seems that many more Americans will soon be able to buy equity in private companies. I am no expert on the law, but I have been investing in private companies for about a decade, and during that time I’ve seen many cases where large investors used financial engineering to artificially reduce the value of smaller investors’ equity. Here are a few examples.

1) Issuing of senior securities with multiple liquidation preferences. Example:

Series A: Small investor invests in $1m round, getting 1x straight preferred

Series B: Large investor invests $10m, getting 4x senior straight preferred

Company gets sold for $30m. Management gets $3m carveout, Series B investors get $27m, and Series A investors get zero.

2) Issuing of massive option grant to management along with new financing at a below-market valuation. Example:

Series A: Small investor invests in $1m round, getting 1x straight preferred for 10% of the company.

Company is doing well and is offered a Series B at a significantly higher valuation. Instead, large investor invests $5m at below-market valuation, getting 40% of the company, and simultaneously issues options worth 50% of the company to management.

Result: Series A investors are diluted from 10% to 1% of the company, even though the company was doing well and in a normal financing would have only been slightly diluted.

3) The company is actually multiple entities, with the smaller investor investing in the less valuable entity. Example:

Company has entity 1 and 2. Small investors invest in entity 1 that licenses IP from entity 2. Value of IP increases and entity 2 is sold and eventually cancels entity 1′s license, making entity 1 worthless.

4) Pay-to-play or artificially low downrounds. Example:

Series A: Small investor invests in $1m round, getting 1x straight preferred

Series B: Large investor invests $10m in pay-to-play round (meaning any investor that doesn’t participate has their preferred shares converted to common). Smaller investor doesn’t have the cash to re-invest in Series B, but deeper pocketed investors do.

Company sells for $10m. Series B investors get $10m. Series A investors get nothing.

There are ways to protect against these shenanigans. Protections can be written into the Series A financings documents (pro-rata rights, ability to block senior financings, etc). There are also some legal protections all minority investors are granted under, say, Delaware or California law. But usually even when these protections exist (and they exist far less frequently these days than in the past), smaller investors usually can’t, say, invoke blocking rights by themselves (indeed, it’s often not economically viable for smaller investors to hire lawyers to review every financing document for every company they invest in). Another way smaller investors can protect themselves is to set aside capital amounting to, e.g. 30% of every investment made, in case they need it later for defensive purposes (I do this). But in my experience this is all very complicated and difficult to execute in practice, even when the small investors are “professional” investors. I worry it will be even harder for “amateur” investors to protect themselves.

A fundamental principle of business is that you do things in house that you think can give you a competitive advantage and outsource things that you don’t. At an early-stage technology company this means you do in house: product design, software and/or hardware development, PR, recruiting, and customer relations/community management. Ideally, most of these activities are led by founders. You should outsource legal, accounting, website hosting, website analytics etc. (Unless you are starting a company where one of those activities can give you a competitive advantage, e.g. a securities trading startup would need to have in-house legal).

A lot of startups over outsource. A few years ago, you’d sometimes hear tech startups say they were going to outsource software development. Thankfully, founders have gotten smart about this and it rarely ever happens except as a stopgap. It is still common for startups to hire outside PR firms. If you decide to hire an outside PR firm, that means you don’t care about PR. Just because you are willing to spend some of money on it doesn’t mean you think it’s important. You probably shouldn’t hire an investment banker during an acquisition unless your company is later stage. And you might occasionally use an outside recruiter but the core recruiting activity needs be done by founders.

One of the major trends in tech startups what Fred Wilson calls “Mobile first, web second.” Instagram is a great example of mobile first. They barely had a website – it was all about the mobile app.

The excitement over mobile-first apps is justified. Smartphones have unleashed a wave of creativity, resulting in entirely new categories of applications. But to me an even more exciting trend is what people have been calling (for lack of a better phrase) ”offline first, mobile enabled” apps.

For example, Foursquare is primarily about improving your offline experiences (meeting friends and finding new places to go). And it couldn’t exist without smartphones (ok, Dodgeball existed on feature phones but had a fraction of the utility). Similarly, Uber couldn’t exist without smartphones. The Uber apps (one for drivers and one for customers), while essential, are all about enabling for the car service. Square is about making payments more convenient and giving small businesses better analytics. The mobile app is just an enabler.

It seems natural that the first wave of mobile apps would be about improving core smartphone apps (e.g. photo apps) or porting apps from other devices (e.g. games). And there is probably a lot of interesting innovation remaining there. But the really massive opportunity is dreaming up new ways that the little computers loaded with sensors that we carry around with us everywhere can improve our real-world experiences.

There is generally a lot of enthusiasm in the startup world these days. But some observers worry that too many startups are working on “features” instead of world-changing ideas. Founders Fund published a provocative article summed up by the subtitle: “We wanted flying cars, instead we got 140 characters”. Alexis Madrigal writes in The Atlantic that “we need a fresh paradigm for startups”, and dismisses the significance of recent “hot” startups:

What we’ve seen have been evolutionary improvements on the patterns established five years ago. The platforms that have seemed hot in the last couple of years — Tumblr, Instagram, Pinterest — add a bit of design or mobile intelligence to the established ways of thinking.

One thing these critics need to be careful about is that, as Clay Christensen has long argued, many important new inventions start out looking like toys. Twitter (Founder Fund’s headline example of a “trivial” startup) started out looking like a toy but has since transformed the way information is distributed for tens of millions of people. Madrigal dismisses cloud computing as “a rebranding of the Internet” whose only effect has been to make “the lives of some IT managers easier,” overlooking that cloud-based services solve the “third party payer” problem of enterprise sales, thereby completely changing how enterprises adopt new technology.

That said, I generally agree with the sentiment that the startup world is too focused on chasing trends. I don’t think this is the fault of entrepreneurs. I meet entrepreneurs all the time who are working on ideas that seem quite meaningful to me. Some of them are building futuristic new technologies. Some are trying to disintermediate incumbents and thereby restructure large industries. Others are trying to solve stubborn problems in important sectors like education, healthcare, or energy.

The problem I encounter is that many of these “meaningful” startups have trouble raising money from VCs. An entrepreneur working on groundbreaking robot technology recently joked to me that he’d have an easier time raising money if his robots were virtual and existed only on Facebook. He was only partly joking. His startup will require a lot of capital and doesn’t have an obvious near term acquirer. Only a small group of VCs today will even consider such an investment.

I’ve spent the majority of my career building technologies that try to do useful things with large datasets.*

One of the most important lessons I’ve learned is that there are only two ways to make useful products out of large data sets. Algorithms that deal with large data sets tend to be accurate at best 80%-90% of the time (an old “joke” about machine learning is that it’s really good at partially solving any problem). Consequently, you either need to accept you’ll have some errors but deploy the system in a fault-tolerant context, or you need to figure out how to get the remaining accuracy through manual labor.

What do I mean by fault-tolerant context? If a search engine shows the most relevant result as the 2nd or 3rd result, users are still pretty happy. The same goes for recommendation systems that show multiple results (e.g. Netflix). Trading systems that hedge funds use are also often fault tolerant: if you make money 80% of the time and lose it 20% of the time, you can still usually have a profitable system.

For fault-intolerant contexts, you need to figure out how to scalably and cost-effectively produce the remaining accuracy through manual labor. When we were building SiteAdvisor, we knew that any inaccuracies would be a big problem: incorrectly rating a website as unsafe hurts the website, and incorrectly rating a website as safe hurts the user. Because we knew automation would only get us 80-90% accuracy, we built 1) systems to estimate confidence levels in our ratings so we would know what to manually review, and 2) a workflow system so that our staff, an offshore team we hired, and users could flag or fix inaccuracies.

* My first job was as a programmer at a hedge fund, where we built systems that analyzed large data sets to trade stock options. Later, I cofounded SiteAdvisor where the goal was to build a system to assign security safety ratings to tens of millions of websites. Then I cofounded Hunch, which was acquired by eBay – we are now working on new recommendation technologies for ebay.com and other eBay websites.

Two common discussions in the startup world right now are 1) the increasing speed at which new apps/websites can gain mass adoption (Instagram, Pinterest, OMGPOP’s Draw Something, etc), and 2) the rise in seed stage valuations. These two trends are real and related.  An investor with a broad portfolio of companies might rationally invest at an average valuation of, say, 10m (which is historically considered very high for that stage) if they have a chance for one of the investments to become the next Instagram or Pinterest. A billion dollar hit pays for a lot of misses.

The increasing velocity has implications for the valuations of incumbent tech companies. Users have limited time, and while web and app usage are growing, hit startups are growing much faster and therefore gaining adoption, at least in part, at the expense of incumbents. It’s not clear this risk is priced into the valuations of companies like Facebook (P/E expected to be ~100) and Zynga (P/E ~31). In other words, faster velocity should lead to a narrower distribution of valuations from seed to late stages. We’ve seen the seed stage adjust but not the late stage.

The current posture of big VCs seems to be to wait to see what takes off and then chase the winners. Tons of investors tried to invest in Instagram’s A and B rounds, and I’m sure VC interest in Pinterest is intense.

The problem with this model of Series A and B investing is that, in reality, many of the companies with big hits weren’t overnight successes. Pinterest, OMGPOP, Twitter, and Tumblr were around for years before taking off and all benefited greatly from having patient investors. In the current financing environment, a lot of good companies won’t live to get Series As and Bs and big VCs will pay valuations on hits that are priced to perfection.

Increasing velocity is great for users and for the winning companies and investors. But when good companies aren’t getting follow on rounds because they aren’t yet “hockeysticking”, the long term health of the startup ecosystem suffers.

From my March 16 blog post “The myth of the overnight success“:

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.

Fast company on April 3, the opening of “The dirty little secret of overnight success“:

Angry Birds, the incredibly popular game, was software maker Rovio’s 52nd attempt. They spent eight years and nearly 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 it had “catastrophically small numbers” in its first year after launch and that if he had listened to popular startup advice he probably would have quit.

No link or attribution.

Update: Thanks to Fast Company for a fast response and changing it to a quote with citation.