Sports are focused on a score over a discrete period of time to make the outcomes fun and simple to talk about. Emerging technologies don’t follow this pattern, but we often apply the same model to our discussion of them.
Modern sports reduce complex events to a simplistic measure–points over a discrete period–and then declare a winner. Encoding the outcome of a few hours of physical struggle into a score is socially efficient. Everyone can talk about a score in simple, absolute, transmittable terms. You can have a “who won the big game” conversation with any stranger. For this reason ESPN executives describe their value as a data clearinghouse–not a sports analysis talent farm. I call this kind of information reduction and formatting “social encoding.” Emerging technologies resist simplistic social encoding because:
- Emerging technologies rarely pit competitors head to head in a fixed market space. We love sports because of this head to head absolute containment.
- Emerging technologies don’t have clear rules of winning and losing in the same way sports do. This especially applies to time, where the beginning and end are unknown.
- Emerging technologies companies are suspect to false positives and negatives throughout their life. Strong growth in one segment may happen simultaneously with missing opportunity in a larger space.
Social Encoding is prevalent outside of sports despite the lossy compression
The social encoding instinct extends itself to another data clearinghouse centric ecosystem–Wall Street, where stocks are often described as winners and losers based on their last month’s or even daily performance. As in sports– these descriptions have the virtue of being literally true and highly time bound. Netflix was a winner, then a loser, then a winner again–moment in time ”scoring” in emerging tech often means very little!
Social encoding is the plague of tech conference panels. Highly trained executives and technologists often exchange little more than a few words of anecdotal color around key buzz words (buzzword is winning! buzzword is not ready yet).
Venture capitalist gossip is another social “win/loss” centric ecosystem. Many invested in small public PaaS startups after the Heroku “win!” but just before the onset of viable and well funded open source projects. Now many VCs have soured on “PaaS” as a category because their investments have done poorly. Meanwhile AWS has greatly enhanced their platform level features, and Cloud Foundry, only recently for sale, has started winning lucrative enterprise customers. Otherwise smart people are mistaking the failure of a few early and poorly positioned investments for the failure of an emerging technology category. Its like betting against the internet in late 2001.
The problem with lossy compression and emerging technologies
Emerging technologies are especially hard to keep a time-bound discreet score of. Paul Graham’s startup curve is more analog than most hype cycle reductions for a reason. If the curve for a single hypothetical startup is so noisy imagine the complexity of the ten year journey I’ve experienced since I started out to help define the “IT as a service” strategy for Sun Microsystems in the early 2000’s. Thousands of bets have been made on this trend, and many failed–but the trend has held strong and grown despite countless failures along the way.
Punctuated equilibrium scenarios such as the migration of enterprise buying patterns to “IT as a service” can be pretty boring to watch in any given month, but this lack of episodic entertainment value has little bearing on their eventual outcome and importance.
Bad actors hack the social encoding system with false positives
The youthfully excited blog from Boris Renski of Mirantis about their “win!” at eBay, predicting they would assume the management of all 70k eBay servers from VMware was later retracted–but not before several major investment and business publications picked up the story as a “score” to broadcast widely.
To Boris’ credit this is exactly the kind of discrete, time bound, winner and loser narrative social encoding wants most. Misinformation is as much a demand side problem as a supply side one. The only catch–his statements weren’t remotely true. Paypal itself was forced to reluctantly wade into the fray to explain this. While this was an effective PR stunt, its unclear if it helped their position with real customers who view the two technologies as complementary and suitable for different types of workloads. Highly successful and brand rich companies never define themselves through a simplistic confrontation with others. Try to find one quote from Google ever mentioning Facebook.
Analog, application by application technology data also has a habit of avoiding simple vendor generalizations. Even the extreme case of micro startups inside of Y Combinator show heterogeneity in their infrastructure choices. If lean startups show this diversity, imagine the complexity in the larger market.
Emerging technologies are not like time bound sports events, and the competition between players is rarely as direct as two opposing teams on the same field. Despite our desire to abstract the results into a socially consumable format, the analog facts on the ground rarely support this compression. Silicon valley is not an episode of Iron Chef, or Monday night football.
Social encoding is neither a good nor a bad thing, but just a fact of information flows through a human system.
As Nietzsche observed:
“The irrationality of a thing is no argument against its existence, rather a condition of it.”