Facebook is dead. Long live Facebook.

 Posted by on January 17, 2011 at 22:02  internet, startups  1 Response »
Jan 172011
 

The inflection point at which large companies start to flat line is very close in time to the point where their revenue goals drive their roadmaps more than their users drive their roadmaps.  Facebook is there.

FB is now a huge company. To grow even small percentages annually will require massive absolute growth.  For FB, massive growth means mainly enabling more third parties to have better and deeper ways to monetize more Facebook users.  That grows revenue but won’t always provide more value to Facebook users.

  • When talented FB engineers work on a revenue-driven initiative, what’s the opportunity cost?  Many features that provide users with great value don’t start that way – they iterate.  If the grandfather of feature x isn’t released now then the killer feature grandson will be birthed elsewhere.
  • When FB makes changes or adds feature for revenue, how many of those will annoy me from user perspective? How muddy does the FB water become over time? We’ve seen FB do this already but so far they’ve survived each storm of user protest – but very few, no matter how strong, can weather too many of these storms.
  • How many times will dirty words like self-cannibalization and competing interests enter design discussions where they don’t belong?  Big companies like Amazon have brilliantly avoided this for the most part – but that will be harder for FB with their revenue reliance on third parties and advertising.
  • On a micro-level, would FB have done an Instagram two years ago?  On a macro level, should FB have a mobile platform that’s in the ballpark with Android and iOS?
  • How many things would FB have done without a second thought as a small startup that they wouldn’t be willing to do as a large company with more hierarchy, bureaucracy and legal considerations? 

FB won our social graph by being the best for basic online communication, social discovery and media sharing. FB hit those use cases within a simple, powerful, engaging, interactive, social user experience.  Not easy and FB crushed it, and as a result now own our social graph.  But the world changes:

  • How many good choices are there now for those use cases?  How many of them are in your pocket on your phone?
  • How much of what used to be done on Facebook is now done on Twitter, Instagram and mobile IM?  How much will be done on GroupMe, Beluga and The Next Big Thing? 
  • How different is what you would have done on Facebook four years ago to what you’d do now with the amount of eyeballs and APIs exposing that data to third parties?  How will FB competitors take advantage of that?

Facebook does own our social graph, but even that is under pressure as we really have social graphs, plural, and will demand control of each.  FB could potentially innovate in key adjacent areas such as mobile, virtual goods, gaming, analytics and location.  They obviously have very talented employees, good processes and an excellent foundation.  They should have some funds to buy external innovation. But I think Facebook – the original Facebook juggernaut – is dead.  If Facebook is to survive, they’ll evolve to a different company than the one we know today. Not many big companies pull that off.

Disclaimer: I pronounced Facebook dead a few years ago too, and being wrong then was one reason why my startup failed (more startup learnings here), so maybe I should move on to being wrong about something new. 

    car stereo + internet

     Posted by on December 16, 2010 at 15:55  internet  1 Response »
    Dec 162010
     

    car-stereoI want a car stereo with a mobile broadband connection, receiver and a separate interface to customize my car audio experience from the web – customized algorithmically but with a layer of individual curation to bound the algorithms.

    Traffic and weather “channels” will push content (broadcast signal or web streaming) to me based on my location (mobile triangulation is precise enough or GPS can provide) and future location (partially based on the Google Maps query I did last night). Algorithms will push “station one” to me if it is 8:00 and that station does traffic at the top of the hour, or push station two if their time better matches, or push the most recent cached update if none matches, or push me the station with the highest ratings for traffic. When Twitter and similar data is better curated, I can listen to tweets about an accident that just happened at an intersection that I’m due to hit in ten minutes.

    My favorite music will be pulled in from Internet radio services like Pandora. If I want to surf randomly, but based on my preferences, other stations will pull in local broadcast stations that meet my criteria. Programmed, smart “seek”. Same for sports, news, weather etc. Similar model for all other content in the cloud – podcasts, audio books, MP3s etc. Ditto for other content that I may want in audio form – voicemails, texts converted to voice, etc.

    The algorithms that build my channels will incorporate social networking and social graph, recommendation engines and crowdsourcing, along with my preferences and curation. Channels that feature content that is most in play in my social network. Or least in play if that’s my persuasion. Brand new content that I might not know about but is recommended based on my preferences, listening history and social graph. Etc. When my wife drives my car, she switches to her profile – as long as I’m not a passenger ; ). And that’s just from a non-professional driver’s perspective – there are more interesting use cases for professional drivers, from trucking to FedEx to taxis.

    I think we’ll see this model of individual customized pushes of slices of content in many other areas too, e.g. TV, as a specific type of signal to noise solution via web services, but think specific use cases like car audio experience could be the first to develop with the least barriers in the way and the most opportunity for the various players.

      signal to noise

       Posted by on December 2, 2010 at 15:08  internet  No Responses »
      Dec 022010
       

      bat-signalShare this. Tweet that. Like everything. Noise screaming from every site, page, app and applet. Signal to noise ratio approaching zero.

      Everyone is now a publisher. Blogs, tweets, videos, podcasts, wall updates, broadcasts, forums, magazines, movies, ebooks, reviews, comments, aggregations. Long-form to 140-character form. All goodness but lots of Noise.

      Signal? Google. Google can get the first page of results “right” most of the time. PageRank has been brilliant but even today falls short and in the future it too will be a dinosaur.

      David Segal did a nice job showing one example of a kink in the armor in his Bully Finds a Pulpit on the Web article. Google showed that they can act quickly like a small company while leveraging long-term signal to noise R&D efforts that their large company resources funds by adding some signal to noise algorithms for this specific use case as described by Amit Singhal in this blog post.

      This is just one use case. The more general signal to noise development will be fascinating. More on that another time except to list few variables that need to be better developed in the signal to noise algorithms:

      + individual-level, granular, portable reputation
      + the interesection of algorithmic curation, human curation and crowdsourcing
      + social graph intelligence
      + use of presence and location to add metadata automatically
      + feedback loops amongst these