Chris Dixon

A platform is a technology or product upon which many other technologies or products are built. Some platforms are controlled by a single corporation: e.g. Windows, iOS, and Facebook. Some are controlled by standards committees or groups of companies: e.g. the web (html/http), RSS, and email (smtp).

Platforms succeed when they are 1) financially sustainable, and 2) have a sufficient number of developers that are financially sustainable. Fostering a successful developer community means convincing developers (and, possibly, investors in developers) that the platform is a worthwhile investment of time and money.

Developers who create applications for platforms take on all the usual risks related to launching a new product, but in addition take on platform-specific risks, namely:

1. Platform decline: the platform will decline or go away entirely.
2. Subsumption risk: the platform will subsume the functionality of the developer’s application.

The most successful platforms try to mitigate these risks for developers (not just the appearance of these risks). One way to mitigate platform decline risk is to launch the platform after the platform’s core product is already successful, as Facebook did with its app platform and Apple did with its iOS platform. Platforms that are not yet launched or established can use other methods to reassure developers; for example, when Microsoft launched the first Xbox they very publicly announced they would invest $1B in the platform.

To mitigate subsumption risk, the platform should give developers predictability around the platform’s feature roadmap. Platforms can do this explicitly by divulging their product roadmap but more often do it implicitly by demonstrating predictable patterns of feature development. Developers and investors are willing to invest in the iOS platform because – although Apple will take 30% of the revenue – it is highly unlikely that Apple will, say, create games to compete with Angry Birds or news to compete with The New York Times. Similarly, Facebook has thus far stuck to “utility” features and not competed with game makers, dating apps, etc.

Platforms that are controlled by for-profit businesses that don’t yet have established business models have special challenges. These companies are usually in highly experimental modes and therefore probably themselves don’t know their future core features. The best they can do to mitigate developers’ risks are 1) provide as much guidance as possible on future features, and 2) when developer subsumption is necessary, do so in a way that keeps the developer ecosystem financially healthy – for example, by acquiring the subsumed products.

The least risky platforms to develop on are successful open platforms like the web, email, and Linux. These platforms tend to change slowly and have very public development roadmaps. In the rare case where a technology is subsumed by an open platform, it is usually apparent far in advance. For example, Adobe Flash might be subsumed by the canvas element in HTML5, but Adobe had years to see HTML5 approaching and adjust its strategy accordingly. The predictability of open platforms is the main reason that vast amounts of wealth have been created on top of them and investment around them continues unabated.

Establishing relationships with new users is the hardest part of growing a startup.  For consumer products establishing relationships can mean many things: installs, registrations, purchases, or even just getting users to think of your website as a place to go for certain purposes.  For B2B products, establishing relationships means getting internal users or testers and eventually contracts and payments. For business development partners – for example API/widget partners – establishing relationships usually means getting functionality embedded in partners’ products (e.g. a widget on their website).

One common strategy for establishing this initial relationship is what is sometimes known as the “thin edge of the wedge” strategy (aka the “tip of the spear” strategy).  This strategy is analogous to the bowling pin strategy: both are about attacking a smaller problem first and then expanding out.  The difference is that the wedge strategy is about product tactics while the bowling pin strategy is about marketing tactics.

Sometimes the wedge can be a simple feature that existing companies overlooked or saw as inconsequential. The ability to share photos on social networks was (strangely) missing from the default iPhone camera app (and sharing was missing from many third-party camera apps like Hipstimatic that have popular features like lo-fi camera filters), so Instagram and Picplz filled the void. Presumably, these startups are going to try to use mobile photo sharing as the wedge into larger products (perhaps full-fledged social networks?).

Sometimes the wedge is a “single player mode” – a famous example is early adopters who used Delicious to store browser bookmarks in the cloud and then only later – once the user base hit critical mass – used its social bookmarking features. Other times the wedge lies on one side of a two-sided market, in which case the wedge strategy could be thought of as a variant of the “ladies night” strategy. I’m told that OpenTable initially used the wedge strategy by providing restaurants with terminals that acted like simple, single-player CRM systems. Once they acquired a critical mass of restaurants in key cities (judiciously chosen using the bowling pin strategy), opentable.com had sufficient inventory to become useful as a one-stop shop for consumers.

Critics sometimes confuse wedge features with final products. For example, some argue that mobile photo sharing is “just a feature,” or that game mechanics on geo apps like Foursquare are just faddish “toys.” Some go so far as to argue that the tech startup world as a whole is going through a phase of just building “dinky” features and companies. Perhaps some startups have no plan and really are just building features, likely with the hope of flipping themselves to larger companies. Good startups, however, think about the whole wedge from the start. They build an initial user base with simple features and then quickly iterate to create products that are enduringly useful, thereby creating companies that have stand-alone, defensible value.

In the last decade there have been major advances in storing, analyzing, and acting upon extremely large data sets.  Data sets that were previously left dormant are now being put to (mostly) constructive use. But the vast majority of information in the world isn’t available for analysis because it isn’t being electronically collected.

This is changing rapidly as new data collection mechanisms are implemented – what engineers refer to as instrumentation. Common examples of instrumentation include thermometers, public safety cameras, and heart rate monitors.

Smart phones are one obvious new source of potential instrumentation.  A person’s location, activities, audio and visual environment – and probably many more things that haven’t been thought of yet – can now be monitored.  This of course raises privacy issues.  Hopefully these privacy issues will be solved by requiring explicit user opt-in.  If so, this will require creating incentives for people to do so.

Foursquare instruments location in an opt-in way through the check in. The incentives are social and game-like, but the data produced could be useful for many more “serious” purposes.  Fitbit instruments a person’s health-related activity. The immediate incentive is to measure and improve your own health, but the aggregate data could be analyzed by medical researchers to benefit others.

In manufacturing, there has been a lot of interesting innovation around monitoring machinery, for example by using loosely joined, inexpensive mesh networks.  In homes, protocols like ZigBee allow devices to communicate which allows, for example, automation of tedious tasks and improved energy efficiency.

In the next decade, there will be a massive amount of innovation and opportunity around the big data stack. Instrumentation will be the foundational layer of that stack.

My Hunch cofounders and I frequently ask ourselves: “If we were to start over today, would we build our product the same way we had so far?” This exercise is meant to counter a number of common cognitive biases, such as:

1. The sunk costs trap.  People tend to overvalue past investments when making forward-looking investment decisions. From the rumors I’ve heard, Joost was a company that fell into the sunk costs trap. In the beginning, their p2p architecture was their main differentiator. Thus they invested a lot in building p2p infrastructure and required users to download a software client. When browser-based web video companies like Hulu and YouTube surpassed them, Joost switched to a browser-based client but still required a special plugin so they could maintain their p2p architecture. In fact, the problem the p2p architecture was solving – reducing bandwidth costs – had, in the meantime, become a secondary basis of competition.  By the time Joost finally discarded the p2p model, it was too late.

2. The Bridge on the River Kwai syndrome.  This is when entrepreneurs fall so in love with their engineering project qua engineering project that they lose site of the larger mission.  Former engineers (like me) are particularly susceptible to this as we often get excited about technology for its own sake. Many products can be built much more quickly and cheaply by settling for good technology plus a bunch of hacks – human editing, partnerships, using 3rd party software – versus creating a perfect technology from scratch. At my last company, SiteAdvisor, we made the decision up front to build a non-perfect system that did 99% of what a much more expensive, “perfect” technological solution would have done.  The software wasn’t always pretty – to the annoyance of some of our engineers – but it worked.

3. Solving the wrong problem. Location-based social networks have been around for years. Foursquare came along just a year ago and has seemingly surpassed its predecessors. The other companies built elaborate infrastructures: e.g they partnered with wireless carriers so that users’ locations could be tracked in the background without having to “check-in”.  Foursquare built a relatively simple app that added some entertaining features like badges and mayorships. It turned out that requiring users to manually check in was not only easier to build but also appealing as users got more control over their privacy. Foursquare’s competitors were solving the wrong problem.

Ask yourself: if you started over today, would you build the same product?  If not, consider significant changes to what you are building. The popular word for this today is “pivoting” and I think it is apropos. You aren’t throwing away what you’ve learned or the good things you’ve built. You are keeping your strong leg grounded and adjusting your weak leg to move in a new direction.

The first million people who bought VCRs bought them before there were any movies available to watch on them. They just wanted to “time shift” TV shows – what we use DVRs for today. Once there were millions of VCR owners it became worthwhile for Hollywood to start selling and renting movies to watch on them. Eventually watching rented movies became the dominant use of VCRs, and time shifting a relatively niche use. Thus, a product that eventually had very strong network effects* got its initial traction from a “standalone use” – where no other VCR owners or complementary products needed to exist.

I was talking to my friend Zach Klein recently who referred to products as having single player and multiplayer modes. I like Zach’s terminology because: 1) it is borrowed from video games where a lot of thought has gone into making these modes compelling in distinct ways, 2) the word “mode” reminds us that people can switch from moment to moment – that even when a product is primarily social or networked and has reached critical mass it might still be useful to offer a single player mode.

Many products that we think of as strictly multiplayer also have single player modes. In many cases this single player mode helped adoption in the early stages when the network effects were not yet strong. For example, you could use Flickr just to store photos privately if you wanted to. I thought of Foursquare as strictly multiplayer until my Hunch cofounder Tom Pinckney told me he uses it solely to keep track of restaurants he’s gone to so he’ll remember which ones to go back to. For some products it’s really hard to imagine single player modes. This is true of pure communication products like Skype and perhaps also social networks like Facebook (although apps like games seem to have provided single player modes for Facebook).

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* Products with so-called networks effects get more valuable when more people use them.  Famous examples are telephones and social networks.  Network effects can be your friend or your enemy depending on whether your product has reached critical mass.  Getting to critical mass in network effect markets is sometimes called overcoming the “chicken and egg problem.”  More here.

If you want to start a company and are working on new ideas, here’s how I’ve always done it and how I recommend you do it.  Be the opposite of secretive.  Create a Google spreadsheet where you list every idea you can think, even really half-baked ones.  Include ideas you hear about (make sure you keep track of who had which idea so you can credit them/include them later).

Then take the spreadsheet and show it to every smart person you can get a meeting with and walk through each idea.  Talk to VCs, entrepreneurs, potential customers, and people working at big companies in relevant industries. You’ll be surprised how much you’ll learn.  The odds that someone will hear an idea and go start a competitor are close to zero.  The odds you’ll learn which ideas are good and bad and how to improve them are very high.

Every conversation will contain some signal and some noise. Separating the two is tricky. Here are some broad rules of thumb I’ve developed for how to filter feedback based to the profession of the person giving it to you.

1) Employees at relevant big companies. These people are great at providing facts (“Google has 100 people working on that problem”) but their judgment about the quality of startup ideas is generally bad. They tend to have goggles on that makes them think every good idea in their industry is already being built within their company.  For example, every security industry person I talked to thought SiteAdvisor was a bad idea.  (If it wasn’t, they think, someone at McAfee or Symantec company would have already built it!)

2) VCs. VCs are good at telling you about similar companies in the past and present and critiquing your idea in an “MBA-like” way:  will it scale? what are the economics? what is the best marketing strategy?  I would listen to them on these topics but pretty much ignore whether they think your idea is good or bad.

3) Potential customers.  If your product is B2B, remember you’ll be selling to that person 2-3 years from now and by then the world and their priorities will likely have radically changed.  If your product is B2C, it’s interesting to hear how regular consumers think about your product but often they really need to use it fully built and in the proper context to really judge it.

4) Entrepreneurs. This is the one group I listen to without a filter.

Even though I have no intention of starting a new company for a long time (if ever), I still keep my idea spreadsheet and update it periodically.  Some of the ideas I wrote down a few years ago are now companies started by other people (some successful, some not).  A few I had the chance to invest in. It’s interesting to compare my notes and ratings of each idea with how those companies have actually performed. I also keep a list of “on the beach” ideas in case I have time in between startups. These are mostly non-profit ideas.  I don’t know if I’ll ever get to those but they are particularly fun to think about.

* Thanks to James Cham for inspiring & contributing ideas to this post!

There are techies (if you are reading this blog you are almost certainly one of them) and there are mainstream users – some people call them “normals” (@caterina suggested “muggles”). A lot of people call techies “early adopters” but I think this is a mistake: techies are only occasionally good predictors of which tech products normals will like.

Techies are enthusiastic evangelists and can therefore give you lots of free marketing. Normals, on the other hand, are what you need to create a large company. There are three main ways that techies and normals can combine to embrace (or ignore) a startup.

1. If you are loved first by techies and then by normals you get free marketing and also scale.  Google, Skype and YouTube all followed this chronology.  It is startup nirvana.

2. The next best scenario is to be loved by normals but not by the techies. The vast majority of successful consumer businesses fall into this category. Usually the first time they get a lot of attention from the tech community is when they announce revenues or close a big financing. Some recent companies that fall in this category are Groupon, Zynga, and Gilt Group. Since these companies don’t start out with lots of free techie evangelizing they often acquire customers through paid marketing.

(My last company – SiteAdvisor – was a product tech bloggers mostly dismissed even as normals embraced it.  When I left the company we had over 150 million downloads, yet the first time the word “SiteAdvisor” appeared on TechCrunch was a year after we were acquired when they referred to another product as “SiteAdvisor 2.0″.)

3. There are lots of products that are loved just by techies but not by normals. When something is getting hyped by techies, one of the hardest things to figure out is whether it will cross over to normals. The normals I know don’t want to vote on news, tag bookmarks, or annotate web pages.  I have no idea whether they want to “check in” to locations.  A year ago, I would have said they didn’t want to Twitter but obviously I was wrong. Knowing when something is techie-only versus techie-plus-normals is one of the hardest things to predict.

One of the amazing things about the internet economy is how different the list of top internet properties today looks from the list ten years ago.  It wasn’t as if those former top companies were complacent – most of them acquired and built products like crazy to avoid being displaced.

The reason big new things sneak by incumbents is that the next big thing always starts out being dismissed as a “toy.”  This is one of the main insights of Clay Christensen’s “disruptive technology” theory. This theory starts with the observation that technologies tend to get better at a faster rate than users’ needs increase. From this simple insight follows all kinds of interesting conclusions about how markets and products change over time.

Disruptive technologies are dismissed as toys because when they are first launched they “undershoot” user needs. The first telephone could only carry voices a mile or two. The leading telco of the time, Western Union, passed on acquiring the phone because they didn’t see how it could possibly be useful to businesses and railroads – their primary customers. What they failed to anticipate was how rapidly telephone technology and infrastructure would improve (technology adoption is usually non-linear due to so-called complementary network effects). The same was true of how mainframe companies viewed the PC (microcomputer), and how modern telecom companies viewed Skype. (Christensen has many more examples in his books).

This does not mean every product that looks like a toy will turn out to be the next big thing. To distinguish toys that are disruptive from toys that will remain just toys, you need to look at products as processes. Obviously, products get better inasmuch as the designer adds features, but this is a relatively weak force. Much more powerful are external forces: microchips getting cheaper, bandwidth becoming ubiquitous, mobile devices getting smarter, etc. For a product to be disruptive it needs to be designed to ride these changes up the utility curve.

Social software is an interesting special case where the strongest forces of improvement are users’ actions. As Clay Shirky explains in his latest book, Wikipedia is literally a process – every day it is edited by spammers, vandals, wackos etc., yet every day the good guys make it better at a faster rate. If you had gone back to 2001 and analyzed Wikipedia as a static product it would have looked very much like a toy. The reason Wikipedia works so brilliantly are subtle design features that sculpt the torrent of user edits such that they yield a net improvement over time. Since users’ needs for encyclopedic information remains relatively steady, as long as Wikipedia got steadily better, it would eventually meet and surpass user needs.

A product doesn’t have to be disruptive to be valuable. There are plenty of products that are useful from day one and continue being useful long term. These are what Christensen calls sustaining technologies. When startups build useful sustaining technologies, they are often quickly acquired or copied by incumbents. If your timing and execution is right, you can create a very successful business on the back of a sustaining technology.

But startups with sustaining technologies are very unlikely to be the new ones we see on top lists in 2020. Those will be disruptive technologies – the ones that sneak by because people dismiss them as toys.

Microsoft used to be considered the king of feature creep.  Here was Microsoft Word when it was most cluttered:

I don’t use any of Microsoft’s software anymore, but from what I hear they’ve toned down the feature creep a lot in recent versions of Windows and Word.

Google has been adding so many new features to its results page, they are starting to feel like the new Microsoft.  Here’s an approximation of what Google used to look like (I couldn’t find an image of actual Google 1998 SRPs — anyone have one?)

And here is Google today:

Options on the left, ads on top and on the right, news results up top, images, and buttons to vote results up/down and annotate them.  But worst of all are the new scrolling “real time” results.  The static image I’ve embedded doesn’t do justice to how annoying this is. Random, out-of-context, and mostly asinine fragments of conversations scrolling by.  I think it might be Google’s Clippy.

“SEO” (==”Search Engine Optimization”) is a term widely used to mean “getting users to your site via organic search traffic.”  I don’t like the term at all.  For one thing, it’s been frequently associated with illicit techniques like link trading and search engine spamming.  It is also associated with consultants who don’t do much beyond very basic stuff your own developers should be able to do.   But the most pernicious aspect to the phrase is that the word “optimization” suggests that SEO is a finishing touch, something you bolt on, instead of central to the design and development of your site. Unfortunately, I think the term is so widespread that we are stuck with it.

SEO is extremely important because normal users – those who don’t live and breath technology – only type a few of their favorite websites directly into the URL bar and for everything else go to search engines, most likely Google*.  In the 90s, people talked a lot about “home pages” and “site flow.” This matters if you are getting most of your traffic from people typing in your URL directly.  For most startups, however, this isn’t the case, at least for the first few years. Instead, the flow you should be thinking about is users going to Google, typing in a keyphrase and landing on one of your internal pages.

The biggest choice you have to make when approaching SEO is whether you want to be a Google optimist or a Google pessimist**. Being an optimist means trusting that the smart people in the core algorithm team in Mountain View are doing their job well – that, in general, good content rises to the top.

The best way to be a Google optimist is to think of search engines as information marketplaces – matchmakers between users “demanding” information and websites “supplying” it. This means thinking hard about what users are looking for today, what they will be looking for in the future, how they express those intentions through keyphrases, where there are gaps in the supply of that information, and how you can create content and an experience to fill those gaps.

All this said, there does remain a technical, “optimization” side to SEO. Internal URL structure, text on your landing pages, and all those other things discussed by SEO consultants do matter.  Luckily, most good SEO practices are also good UI/UX practices.  Personally I like to do all of these things in house by asking our programmers and designers to include search sites like SEOMoz, Search Engine Land, and Matt Cutts in their daily reading list

* I’m just going to drop the illusion here that most people optimize for anything besides Google.  ComScore says Google has ~70% market share but everyone I know gets >90% of their search traffic from Google.  At any rate, in my experience, if you optimize for Google, Bing/Yahoo will give you SEO love about a 1-6 months later.

** Even if you choose to be a pessimist, I strongly recommend you stay far away from so-called black hat techniques, especially schemes like link trading and paid text ads that are meant to trick crawlers.  Among other things, this can get your site banned for life from Google.