In the current market, there is perhaps no more heavily invested technology category than search, especially in a market with a dominant player, a dominant business model and an absolutely unbeatable capital advantage. Dozens and dozens of companies are chasing Google for one reason: Tens of millions of people search the Internet and still have a hard time finding what they are looking for.
Indeed, the Top 5 sites in the world are search sites (according to Alexa rankings), if you'll grant that YouTube, currently ranked No. 5, is effectively video search. Each of these businesses is ad supported and each would claim that it targets ads based on the interests -- as expressed in search terms -- of its audience.
It's the same story for nearly every search startup in the market today, and that may well be the Achilles heel. But more on that later.
In defining search as a "market," one needs to separate the technology platform from the expression of that technology to recognize that many "markets" are affected by new search methodologies. We see a concentration of activity in three areas: infrastructure, vertical and social.
We define "infrastructure" to mean fundamental search technology typically commercialized as middleware, a software appliance, or delivered as a specific application. Companies playing in the infrastructure sector might best be thought of as "applied search" technologies, where their search methodologies are applied to specific data sets, typically in large enterprise data centers.
That definition casts a wide net over a set of startups developing core search technologies. The French startup Exalead, for example, provides a uniform search metaphor across data sets from desktop to the Web. Reveal Technology Inc. has just launched a peer-to-peer search application to find documents across workgroup computers. X1 Technologies, an Idealab company, uses fast indexing technology to enable find-as-you-type search of desktop content. Seed-funded System One blends collaboration with search to deliver an enterprise information discovery engine. No doubt that is why we are seeing a resurgence of natural language technologies applied to information search.
Despite decades of fundamental research, the ability for algorithms to handle intention and nuance of meaning remains elusive on a mass scale. The companies playing in the natural language space are doing so in all manner of ways and have one core belief in common: Computers should understand and interact with people at a higher level. Some companies to watch include Powerset, Hakia, HeadCase, Radar Networks, TextDigger and Snap.