Seth Goldstein

Self Operating Machines

The most exciting new Internet companies are focused on lead generation, behavioral targeting, co-registration paths (aka coreg) and domain name brokerage.

I seem to stumble every day across some new firm propping itself up on the shoulders of Google, Yahoo! or others to take advantage of a current wrinkle in an otherwise perfectly efficient landscape.  The fact is, however, that these wrinkles never seem to disappear.  These advertising mechanisms emerged alongside of the pure media companies, starting with Doubleclick back in 1996, and followed by other advertising networks such as Flycast, Overture and Advertising.com, each firm iterating on the prior model to gain some head room from the imminent internal advertising service offerings of the pure media companies.

Although these advertising technologies have focused on targeting the behavior of consumers, they have also tended to ignore the role of consumers in the production process of the media that they consume.  This is why these networks tend to dynamically balloon in terms of sales growth.  They capitalize on a behavioral blindspot, where the supply of inventory versus the demands of advertising value are disjointed.  As consumers become smart about these artificial mechanisms (banners, keywords, freeipods) their effectiveness drops and they look to get acquired by larger media entities.

The elusive goal of internet media (and the advertising that drives its value) has been to keep up with changing consumer preferences as the technologies of communication continue to evolve.  The adoption of a new means of using the Internet (whether it be ecommerce, webmail, search engines, or shortly blog readers) creates enormous economic value for the donors incredibly quickly. 

The latest shift in online consumption has been the institutionalization of amateur publishing tools.  The unique attribute of RSS is how simply it enables individual publishers of data to connect with individual subscribers to such data.  RSS tools like typepad, flickr, adsense and del.icio.us have made it easy for individuals to syndicate their preferences, memories and desires.  RSS tracking tools like feedburner make it easy for individuals to track who is paying attention.  At the site you are on, I am streaming my Flickr photos, am recommending books as an Amazon associate and am promoting a Google ad-sense ad.

Not unlike dial-up authentication protocols (remember the classic AOL logon "hand-shake"),  the interaction between RSS feeds and their readers is a structured negotiation:  do you, John Q. Public, agree to take the whole feed, nothing but the feed, until you delete it?  I do. Click.   

When you aggregate all of these individual reading and writing agents, it looks more like a landscape of cellular automata than a tradition publishing model.  This would seem to be the essence of social media (props to my wife and guide Tina Sharkey for coining this years ago and registering the domain) and social computing, two memes that seem to be growing in influence.  When individual decisions such as applying certain tags to pages or photos achieve a broad social consensus, then it as if these tags begin to self replicate which is the essence of automatic behavior.

There is a good word to describe this, which comes out of physics, namely Excitable Media.  as per Wikipedia:

Cellular automata provide a simple model to aid the understanding of excitable media. Each cell of the automaton is made to represent some section of the medium (for example, a patch of trees in a forest, or stress in heart tissue). Each cell can be in one of the three following states:

Quiescent or excitable — the cell is unexcited, and can be excited. In the forest fire example, this corresponds to the trees being unburnt.

Excited — the cell is excited. The trees are on fire.

Refractory — the cell has recently been excited and has not yet been through the refractory period. A patch of land where the trees have burnt and the vegetation has yet to regrow.

The concept of cellular automata is useful as a metaphor for next generation Internet content, which is similarly dynamic, member-generated, and excitable.  In the next post, I will focus on algorithms, as they transform the automatic social media into business rules and procedures. 

Last month I held a workshop with the elegant, imaginative programmer Steve Steinberg of GGH at Web 2.0.  The conference was at times a best-hits convention of the SF Internet scene circa 1999, but in other important ways a series of discussions about the evolution of the Web as a platform.  Instead of large sites connected by single links, as the web may have looked 10 years ago, now we have niche pages and targeted offers connected through elaborate search algorithms, RSS feeds, and affiliate marketing networks. 

The conversation with Steve started as follows:

It is easy to confuse the Internet as a medium of distribution with the Internet as a computational platform. Nowhere is this confusion more pronounced than in the use of the Internet by investors.

Usually, investors think of Yahoo! Finance or Ameritrade. No doubt the Internet has emerged as a useful source of information and a convenient brokerage interface, but do these uses qualify as platform applications?

Beneath the user functionality of the web hums a constant drone of data processing. This is the under-web of click-streams, item listings, user queries, and other flora and fauna of network behavior.  This is the web tone for investors: filled with important data insights usually lost in the noise of random traffic.

The challenge is how to interpret the Internet platform for financial gain. In so far as there is no “Bloomberg” for analyzing Internet and other electronic transaction patterns, investors today are faced with myriad tools, techniques and sources of information that require significant manual effort to correlate.

The typical search box is a limited tool for investors, since understanding what metrics one is looking for is almost as important as the metrics themselves. Most publicly-traded companies (for instance the majority of those in health care, technology, retail and financial services) serve different customers differently and so understanding such companies require monitors across the full length of the supply and demand chain.

We proceeded to lay out a number of examples of how the Web could be searched, scraped, bot-ted, mined, and grokked for unique information on publicly traded equities.  We touched on the dangerous temptation to trust data simply because it’s data (when in fact as we all know data carries with it many of the same assumptions and biases of anecdotal water cooler observations).  We also focused on the gray area separating legitimate data discovery from the act of stumbling across (or better yet, browsing into) material non public information in an otherwise public network such as the Web.

The combination of insider information restrictions and Regulation Fair Disclosure has become the impetus for equity research to continually reinvent itself.  As the SEC has written in the case of Reg FD:

"Whenever an issuer, or any person acting on its behalf, discloses any material nonpublic information regarding that issuer or its securities to [certain enumerated persons], the issuer shall make public disclosure of that information… simultaneously, in the case of an intentional disclosure; and… promptly, in the case of a non-intentional disclosure."

The Emergence of a Packet Switched Research Model?

Insofar as such information is disclosed by a person (ie the CFO) to another person (ie an analyst at a Brokerage or at a fund) then the steps required are simple enough:  issue a press release immediately to everybody via the web.  It is decidedly more complex when such information is disclosed in bits and pieces, asynchronously through a “network of experts” composed of employees and consultants inside and outside of the company in question, issued to a buzzing audience of analysts and portfolio managers pushing and prodding at various aspects of the company’s performance.  If the CFO’s wink to the Sell Side analyst at the 17^th hole was the equivalent of dedicated circuit switch, then the current instance of buy-side analyst communities polling networks of industry and sell-side experts suggests the emergence of a packet switched research model.

Perhaps the full obsolescence of Reg FD as we know it emerges when research shifts its register from people to data entirely—when companies become fully transparent (cf. Bill Gross’s latest invention Snap), or else their transparency is enabled by others (cf. Majestic Research’s analysis of Ebay as well as other examples of Internet-based economic research).  At such moment, the entire notion of a privileged perspective dissolves—there is no longer an edge in trying to "game" the quarter– and the investor is forced to develop a new strategy for evaluating the equity. 

Otherwise, the index funds may integrate the public data themselves and force a number of highly paid fund managers to leave for the summer house in January.