Network effect business models Network effects were used as justification for some of the dot-com business models in the late 1990s. These firms operated under the belief that when a new market comes into being which contains strong network effects, firms should care more about growing their market share than about becoming profitable. This was believed because market share will determine which firm can set technical and marketing standards and thus determine the basis of future competition. A good example of this strategy was that deployed by Mirabilis, the Israeli start-up which pioneered instant messaging (IM) and was bought by America Online. By giving away their ICQ product for free and preventing interoperability between their client software and other products, they were able to corner the market for instant messaging. Because of the network effect, new IM users gained much more value by choosing to use the Mirabilis system (and join its large network of users) than they would using a competing system. As was typical for that era, the company never made any attempt to generate profits from their dominant position before selling the company. Network effects become significant after a certain subscription percentage has been achieved, called critical mass. At the critical mass point, the value obtained from the good or service is greater than or equal to the price paid for the good or service. As the value of the good is determined by the user base, this implies that after a certain number of people have subscribed to the service or purchased the good, additional people will subscribe to the service or purchase the good due to the positive utility:price ratio. A key business concern must then be how to attract users prior to reaching critical mass. One way is to rely on extrinsic motivation, such as a payment, a fee waiver, or a request for friends to sign up. A more natural strategy is to build a system that has enough value without network effects, at least to early adopters. Then, as the number of users increases, the system becomes even more valuable and is able to attract a wider user base. Joshua Schachter has explained that he built Del.icio.us along these lines -he built an online system where he could keep bookmarks for himself, such that even if no other user joined, it would still be valuable to him. It was relatively easy to build up a user base from zero because early adopters found enough value in the system outside of the network aspects. The same could be said for many other successful websites which derive value from network effects, e.g. Flickr, MySpace. Beyond critical mass, the increasing number of subscribers generally cannot continue indefinitely. After a certain point, most networks become either congested or saturated, stopping future uptake. Congestion occurs due to overuse. The applicable analogy is that of a telephone network. While the number of users is below the congestion point, each additional user adds additional value to every other customer. However, at some point the addition of an extra user exceeds the capacity of the existing system. After this point, each additional user decreases the value obtained by every other user. In practical terms, each additional user increases the total system load, leading to busy signals, the inability to get a dial tone, and poor customer support. The next critical point is where the value obtained again equals the price paid. The network will cease to grow at this point, and the system must be enlarged. The congestion point may be larger than the market size. New Peer-to-peer technological models may always defy congestion. Peer-to-Peer systems, or "P2P," are networks designed to distribute load among their user pool. This theoretically allows true P2P networks to scale indefinitely. But market saturation will still occur. Network effects are commonly mistaken for economies of scale, which result from business size rather than interoperability (see also natural monopoly). To help clarify the distinction, people speak of demand side vs. supply side economies of scale. Classical economies of scale are on the production side, while network effects arise on the demand side. Network effects are also mistaken for economies of scope. Examples Web sites Many web sites also feature a network effect. One example is web marketplaces and exchanges, in that the value of the marketplace to a new user is proportional to the number of other users in the market. For example, eBay would not be a particularly useful site if auctions were not competitive. However, as the number of users grows on eBay, auctions grow more competitive, pushing up the prices of bids on items. This makes it more worthwhile to sell on eBay and brings more sellers onto eBay, which drives prices down again as this increases supply, while bringing more people onto eBay because there are more things being sold that people want. Essentially, as the number of users of eBay grows, prices fall and supply increases, and more and more people find the site to be useful. The collaborative encyclopedia Wikipedia also benefits from a network effect. The theory goes that as the number of editors grows, the quality of information on the website improves, encouraging more users to turn to it as a source of information; some of the new users in turn become editors, continuing the process. Social networking websites are also good examples. The more people register onto a social networking website, the more useful the website is to its registrants. By contrast, the value of a news site is primarily proportional to the quality of the articles, not to the number of other people using the site. Similarly, the first generation of search sites experienced little network effect, as the value of the site was based on the value of the search results. This allowed Google to win users away from Yahoo! without much trouble, once users believed that Google's search results were superior. Some commentators mistook the value of the Yahoo! brand (which does increase as more people know of it) for a network effect protecting its advertising business. Alexa Internet uses a technology that tracks users' surfing patterns; thus Alexa's Related Sites results improve as more users use the technology. As theory would predict, no competing technology has emerged to compete successfully with Alexa, but this may be because of other factors. Alexa's network relies heavily on a small number of browser software relationships, which makes the network more vulnerable to competition. Google has also attempted to create a network effect in its advertising business with its Google AdSense service. Google AdSense places ads on many small sites, such as blogs, using Google technology to determine which ads are relevant to which blogs. Thus, the service appears to aim to serve as an exchange (or ad network) for matching many advertisers with many small sites (such as blogs). In general, the more blogs Google AdSense can reach, the more advertisers it will attract, making it the most attractive option for more blogs, and so on, making the network more valuable for all participants. Network effects and technology lifecycle If some existing technology or company whose benefits are largely based on network effects starts to lose market share against a challenger such as a disruptive technology or open standards based competition, the benefits of network effects will reduce for the incumbent, and increase for the challenger. In this model, a tipping point is eventually reached at which the network effects of the challenger dominate those of the former incumbent, and the incumbent is forced into an accelerating decline, whilst the challenger takes over the incumbent's former position. Network Effects and Lock-in Not surprisingly network economics became a hot topic after the diffusion of the Internet across academia. Most people know only of Metcalfe's law as part of network effects. Network effects are notorious for causing vendor lock-in with the most-cited examples being Microsoft products and the qwerty keyboard. Network effects are a source of, but distinct from, lock-in. Lock-in can result from network effects, and network effects generate increasing returns that are associated with lock-in. However, the presence of a network effect does not guarantee that lockii will result. For example, if the network is open there is no issue of lock-in. Types of Network effects There are two kinds of economic value to be concerned about when thinking of network effects: Inherent -my value from my using the product Network -my value from your using the product Network value itself can be direct or indirect. Direct network value is an immediate result of other users adopting the same system. Some examples of this are fax machines and email. Indirect is a secondary result of many people using the same system. For example, complementary goods are cheaper or more available when many people adopt a standard. Toner may be cheaper for widely used printers. An example of this is that Windows and Linux can be seen as competing not for users, but for software developers, as shown by Nicholas Economides and Evangelos Katsamakas. Negative and Positive Network Effects Positive networks effects are obvious. More people means more interaction. Wikipedia itself depends on positive network effects. Negative network effects beyond lock-in also exist. Negative network effects result from resource limits. Consider the connection that overloads the freeway --or the competition for bandwidth. In fact, the automobile and ethernet congestion examples illustrate that there can be threshold limits. In this case, the n+1 person begins to decrease the value of a network if additional resources are not provided. The result is that in some networks there is an exclusion value. This is clear to anyone who has considered problems of authentication or trust on the modern internet. Another negative network effect is provider complacency. The absence of viable competitors in a successful network can cause a provider to restrict resources, consider fees increases, or otherwise create an environment contrary to the users' benefit. These situations are typically accompanied by vocal complaints from the users. (In a competitive environment the users would simply change vendors rather than complain.) Classic examples are the US Postal Service or telephone companies during the 1960s and 1970s. More recent examples include Microsoft's operating system and Ebay's auction site. Metcalfe's law From Wikipedia, the free encyclopedia Metcalfe's law states that the value of a telecommunications network is proportional to the square of the number of users of the system (n2). First formulated by Robert Metcalfe in regard to Ethernet, Metcalfe's law explains many of the network effects of communication technologies and networks such as the Internet and World Wide Web. The law has often been illustrated using the example of fax machines: A single fax machine is useless, but the value of every fax machine increases with the total number of fax machines in the network, because the total number of people with whom each user may send and receive documents increases. Revisions There have been subsequent revisions, however, to the estimated value added; for example, that Metcalfe's law may exaggerate that benefit. Since a user cannot connect to itself, the reasoning goes, the actual calculation is the number of diagonals and sides in an n-gon (see also the triangular numbers): In March 2006, Andrew Odlyzko and Benjamin Tilly published a preliminary paper which concluded Metcalfe's law significantly overestimates the value of adding connections. The rule of thumb becomes: "the value of a network with n members is not n squared, but rather n times the logarithm of n." Their primary justification for this is the idea that not all potential connections in a network are equally valuable. For example, most people call their families a great deal more often than they call strangers in other countries, and so do not derive the full value n from the phone service. In the July 2006 IEEE Spectrum, Bob Briscoe, Odlyzko and Tilly state more succinctly: "Metcalfe's Law is Wrong". Robert Metcalfe responded to the IEEE article, defending his namesake law, on a partner's blog. In contrast, Reed's law asserts that Metcalfe's law understates the value of adding connections. Not only is a member connected to the entire network as a whole, but also to many significant subsets of the whole. These subsets add value independent of either the individual or the network as a whole. Including subsets into the calculation of the value of the network increases the value faster than just including individual nodes. Applications of Metcalfe's law Metcalfe's Law can be applied to more than just telecommunications devices. Metcalfe's Law can be applied to almost any computer systems that exchange data. Examples of devices include: • Telephones • FAXs • Operating systems • Applications • Social networking websites Metcalfe's Law frequently predicts whether a single vendor or interface standard will tend to dominate a marketplace. This has implications for whether an innovative solution can enter a marketplace that requires different interfaces. The same law can be applied to other technological systems, such as personal genome sequencing. As more human genomes are sequenced and tied to personal health information in an interconnected system, the value of the information that a personal genome can contribute to personal health grows. Metcalfe’s Law: More misunderstood than wrong The industry is at it again–trying to figure out what to make of Metcalfe’s Law. This time it’s IEEE Spectrum with a controversially titled “Metcalfe’s Law is Wrong”. The main thrust of the argument is that the value of a network grows O(nlogn) as opposed to O(n2). Unfortunately, the authors’ O(nlogn) suggeston is no more accurate or insightful than the original proposal. There are three issues to consider: • The difference between what Bob Metcalfe claimed and what ended up becoming Metcalfe’s Law • The units of measurement • What happens with large networks The typical statement of the law is “the value of a network increases proportionately with the square of the number of its users.” That’s what you’ll find at the Wikipedia link above. It happens to not be what Bob Metcalfe claimed in the first place. These days I work with Bob at Polaris Venture Partners. I have seen a copy of the original (circa 1980) transparency that Bob created to communicate his idea. IEEE Spectrum has a good reproduction, shown here. The unit of measurement along the X-axis is “compatibly communicating devices”, not users. The credit for the “users” formulation goes to George Gilder who wrote about Metcalfe’s Law in Forbes ASAP on September 13, 1993. However, Gilder’s article talks about machines and not users. Anyway, both the “users” and “machines” formulations miss the subtlety imposed by the “compatibly communicating” qualifier, which is the key to understanding the concept. Bob, who invented Ethernet, was addressing small LANs where machines are visible to one another and share services such as discovery, email, etc. He recalls that his goal was to have companies install networks with at least three nodes. Now, that’s a far cry from the Internet, which is huge, where most machines cannot see one another and/or have nothing to communicate about… So, if you’re talking about a smallish network where indeed nodes are “compatibly communicating”, I’d argue that the original suggestion holds pretty well. The authors of the IEEE article take the “users” formulation and suggest that the value of a network should grow on the order of O(nlogn) as opposed to O(n2). Are they correct? It depends. Is their proposal a meaningful improvement on the original idea? No. To justify the logn factor, the authors apply Zipf’s Law to large networks. Again, the issue I have is with the unit of measurement. Zipf’s Law applies to homogeneous populations (the original research was on natural language). You can apply it to books, movies and songs. It’s meaningless to apply it to the population of books, movies and songs put together or, for that matter, to the Internet, which is perhaps the most heterogeneous collection of nodes, people, communities, interests, etc. one can point to. For the same reason, you cannot apply it to MySpace, which is a group of sub-communities hosted on the same online community infrastructure (OCI), or to the Cingular /AT&T Wireless merger. The main point of Metcalfe’s Law is that the value of networks exhibits super-linear growth. If you measure the size of networks in users, the value definitely does not grow O(n2) but I’m not sure O(nlogn) is a significantly better approximation, especially for large networks. A better approximation of value would be something along the lines of O(SumC(O(mclogmc))), where C is the set of homogeneous subnetwworkscommunities and mc is the size of the particular sub-community/network. Since the same user can be a member of multiple social networks, and since |C| is a function of N (there are more communities in larger networks), it’s not clear what the total value will end up being. That’s a Long Tail argument if you want one… Very large networks pose a further problem. Size introduces friction and complicates connectivity, discovery, identity management, trust provisioning, etc. Does this mean that at some point the value of a network starts going down (as another good illustration from the IEEE article shows)? It depends on infrastructure. Clients and servers play different roles in networks. (For more on this in the context of Metcalfe’s Law, see Integration is the Killer App, an article I wrote for XML Journal in 2003, having spent less time thinking about the problem ;-)). P2P sharing, search engines and portals, anti-spam tools and federated identity management schemes are just but a few examples of the myriad of technologies that have all come about to address scaling problems on the Internet. MySpace and LinkedIn have very different rules of engagement and policing schemes. These communities will grow and increase in value very differently. That’s another argument for the value of a network aggregating across a myriad of sub-networks. Bottom line, the article attacks Metcalfe’s Law but fails to propose a meaningful alternative. Critical mass (sociodynamics) From Wikipedia, the free encyclopedia Critical mass is a sociodynamic term to describe the existence of sufficient momentum in a social system such that the momentum becomes self-sustaining and fuels further growth. Critical mass is a concept used in a variety of contexts, including physics, group dynamics, politics, public opinion, and technology. Reed's law From Wikipedia, the free encyclopedia Reed's law is the assertion of David P. Reed that the utility of large networks, particularly social networks, can scale exponentially with the size of the network. The reason for this is that the number of possible sub-groups of network participants is , where N is the number of participants. This grows much more rapidly than either • the number of participants, N, or • the number of possible pair connections, (which follows Metcalfe's law) so that even if the utility of groups available to be joined is very small on a per-group basis, eventually the network effect of potential group membership can dominate the overall economics of the system. Derivation Given a set A of N people, it has 2N possible subsets. This is not difficult to see, since we can form each possible subset by simply choosing for each element of A one of two possibilities: whether to include that element, or not. However, this includes the (one) empty set, and N singletons, which are not properly subgroups. So 2N − N − 1 subsets remain, which is exponential, like 2N. Quote From David P. Reed's, "The Law of the Pack": "[E]ven Metcalfe's Law understates the value created by a group-forming network as it grows. Let's say you have a GFN with n members. If you add up all the potential two-person groups, three-person groups, and so on that those members could form, the number of possible groups equals 2n. So the value of a GFN increases exponentially, in proportion to 2n. I call that Reed's Law. And its implications are profound." That Sneaky Exponential—Beyond Metcalfe's Law to the Power of Community Building by David P. Reed [Note: an earlier version of this essay was prepared as an online supplement to an article in Context magazine published in Spring 1999] Bob Metcalfe, inventor of the Ethernet, is known for pointing out that the total value of a communications network grows with the square of the number of devices or people it connects. This scaling law, along with Moore's Law, is widely credited as the stimulus that has driven the stunning growth of Internet connectivity. Because Metcalfe's law implies value grows faster than does the (linear) number of a network's access points, merely interconnecting two independent networks creates value that substantially exceeds the original value of the unconnected networks. Thus the growth of Internet connectivity, and the openness of the Internet, are driven by an inexorable economic logic, just as the interconnection of the telephone network was forced by AT&T's long distance strategy. This strategy created huge and increasing value to AT&T customers, based on the same (then unnamed) law of increasing returns to scale at the beginning of the 20th century. In the same way, the global interconnection of networks we call the Internet has created huge and increasing value to all its participants. But many kinds of value are created within networks. While many kinds of value grow proportionally to network size and some grow proportionally to the square of network size, I've discovered that some network structures create total value that can scale even faster than that. Networks that support the construction of communicating groups create value that scales exponentially with network size, i.e. much more rapidly than Metcalfe's square law. I will call such networks Group-Forming Networks, or GFNs. Even if it's not your business to supply communications services, your business participates in many networks—perhaps the most important are supply networks that allows access to and bidding among suppliers and distribution networks that allows access to and competition among customers. The structure of these networks or market spaces, especially the value of the connectivity and relationships produced in these networks, can play a crucial role in defining the value of your business. If you can manage or influence the networks that connect you to suppliers and customers to create more value for all concerned, that extra value can be used as a competitive weapon. So paying attention to network value is a crucial strategic issue, especially as businesses move their customer and supplier relationships into the 'net. What kind of value are we talking about, when we say the value of a network scales as some function of size? The answer is the value of potential connectivity for transactions. That is, for any particular access point (user), what is the number of different access points (users) that can be connected or reached for a transaction when the need arises. As a simple illustration, consider a phone that can call only 911. A customer for such a phone buys it because of a low probability future need to call for emergency help; in fact, the customer probably takes other steps never to need to use the phone. But the existence of a lucrative market for such phones indicates that customers can value potential connectivity to a single point, even though the connection is never used. Potential connectivity to many points should have value proportionally larger, since it is not necessary to use the connection to find value in its availability. The value of potential connectivity is the value of the set of optional transactions that are afforded by the system or network. Economically, the value of each optional transaction is like a financial option (e.g., the value of an option to buy a share of stock at a particular price). To simplify the model and focus on scaling, I'll assume that the value of any particular optional transaction in a network comes from a distribution that does not depend strongly on the number of participants in the network. Metcalfe's law, simply derived, says that if you build a network so that any customer can choose to transact with any other customer, the number of potential connections each of the N customers can make is (N-1), giving a total number of potential connections as N(N-1) or N²-N. Assuming each potential connection is worth as much as any other, the value to each user depends on the total size of the network, and the total value of potential connectivity scales much faster than the size of the network, proportional to N². At this point a skeptic once said to me, that's too simplistic, that's not the most important source of value in the network like the phone network. For example, there's a weather service that everyone calls once a day, and the 911 service, and a couple of other services like that. And each user typically has a fixed, small set of friends and family that they call all of the time. Since the value of these services to a particular user does not depend on the number of other users of the network, the total value of these services grows more slowly—proportional to N, not N². The skeptic was right that some important services scale only linearly, but in drawing his conclusion missed a crucial, very important point about scale and growth. To illustrate this, let's assume that we can lump all of the kinds of value that grow proportional to N in one term, which we can write by the formula aN, where a is a constant that represents the value per customer. We can also represent the value of potential connectivity by bN², where b is the constant value of a potential connection between a pair of customers. Let's assume that b is much smaller than a, so that for a modest size network, the total network value aN + bN² is for all intents and purposes the same as aN. But still, if we allow N to get large enough, the total network value will get closer and closer to bN². Thus, the "square" value of potential connectivity dominates all linear sources of value once N gets sufficiently large. See figure 1, which shows how, when N is small, the total value is approximately linear, but as N gets larger, the total value begins to follow bN². This dominance of peer connection value suggests that it is foolish for phone companies to lust after the video rental business in an attempt to compete with cable. As networks get larger, the value of peer connectivity is likely to dominate the combined capability of phone and cable networks. Only when the potential consumers of peer connectivity have been saturated does it seem sensible to go after businesses where value grows linearly with customer base. Of course, the same scaling dominance suggests that cable TV networks ought to do whatever is necessary to enable their systems to support telephony and other transactional services if possible! But there's an additional, new wrinkle to network scaling. I recently discovered a new value-creation effect that dominates even the remarkable effects of Metcalfe's Law, when a network supports it. In networks like the Internet, Group Forming Networks (GFNs) are an important additional kind of network capability. A GFN has functionality that directly enables and supports affiliations (such as interest groups, clubs, meetings, communities) among subsets of its customers. Group tools and technologies (also called community tools) such as user-defined mailing lists, chat rooms, discussion groups, buddy lists, team rooms, trading rooms, user groups, market makers, and auction hosts, all have a common theme—they allow small or large groups of network users to coalesce and to organize their communications around a common interest, issue, or goal. Sadly, the traditional telephone and broadcast/cable network frameworks provide no support for groups. What I found that's surprising and important is that GFNs create a new kind of connectivity value that scales exponentially with N. Briefly, the number of non-trivial subsets that can be formed from a set of N members is 2N-N-1, which grows as 2N. Thus, a network that supports easy group communication has a potential number of groups that can form that grows exponentially with N. The exponential, 2N, is a sneaky function. Though it may be very small initially, it grows much faster than N², N³ or any other power law. So if there is any portion of the total network value that grows exponentially, scale effects will eventually bring that value to the fore, where it will dominate any other source of value. (To put it simply, if a network's value consists of components that scale proportional to N, N², and 2N, we can write the total value as aN + bN² + c2N where a, b, and c are constants. As long as a, b, and c are positive, there will be some M such that the total value is dominated by the term c2N for all N>M. Even if c is quite small, the exponential will eventually dominate.) See figure 2. Law: Sarnoff Metcalfe GFN (Reed) Optional Transactions Tune In Broadcast Connect Peers Join/Create Groups Examples OnSale, Remote Access Yahoo! Classifieds, EMail eBay, Chat Rooms Value of N member net N N2 2N Combined Value of N, M member nets N + M N2 + M2 + 2NM 2N x 2M This exponential law of GFNs, like Metcalfe's Law, creates increasing returns as scale increases, which has surprising economic results. Both laws give a powerful bonus to interconnection; mergers and partnerships of networked companies should be able to extract a premium resulting from these laws. When we combine two networks together so that users of one network can connect seamlessly to users of the other, Metcalfe's Law tells us already that substantial new value is created: (M+N)² = M² +N²+2MN. This bonus term, 2MN, is substantial-up to 100% of the value in the original unconnected networks. Thus there is an enormous incentive to find ways to interconnect networks, since the members of each network can access a much larger set of potential transaction partners. With the GFN law, interconnection is even more powerful, creating many new potential groups that span the two networks: 2M+N = 2M2N. The GFN interconnection bonus percentage itself grows exponentially with the size of the smaller network. What we see, then, is that there are really at least three categories of value that networks can provide: the linear value of services that are aimed at individual users, the "square" value from facilitating transactions, and exponential value from facilitating group affiliations. What's important is that the dominant value in a typical network tends to shift from one category to another as the scale of the network increases. Whether the growth is by incremental customer additions, or by transparent interconnection, scale growth tends to support new categories of killer apps, and thus new competitive games. See figure 3. We can see this scale-driven value shift in the history of the Internet. The earliest usage of the Internet was dominated by its role as a terminal network, allowing many terminals to selectively access a small number of costly timesharing hosts. As the Internet grew, much more of the usage and value of the Internet became focused on pairwise exchanges of email messages, files, etc. following Metcalfe's Law. And as the Internet started to take off in the early ' 90's, traffic started to be dominated by "newsgroups" (Internet discussion groups), user created mailing lists, special interest websites, etc., following the exponential GFN law. Though the previously dominant functions did not lose value or decline as the scale of the Internet grew, the value and usage of services that scaled by newly dominant scaling laws grew faster. Thus many kinds of transactions and collaboration that had been conducted outside the Internet became absorbed into the growth of the Internet's functions, and these become the new competitive playing field. What's important in a network changes as the network scale shifts. In a network dominated by linear connectivity value growth, "content is king." That is, in such networks, there is a small number of sources (publishers or makers) of content that every user selects from. The sources compete for users based on the value of their content (published stories, published images, standardized consumer goods). Where Metcalfe's Law dominates, transactions become central. The stuff that is traded in transactions (be it email or voice mail, money, securities, contracted services, or whatnot) are king. And where the GFN law dominates, the central role is filled by jointly constructed value (such as specialized newsgroups, joint responses to RFPs, gossip, etc.). In "real" networks, it is important to note that although the total value of optional transactions that involve pairs and groups grows faster than linearly, the total price that can be paid cannot grow that fast. Typically, the consumers of the value have money and attention resources that scale linearly with N. So the law of supply and demand will kick in, lowering prices until the available resources (dollars and attention) are saturated. What's interesting is that this saturation process affects all types of optional transactions-so GFN value, peer transaction value, and broadcast content value all compete for the same resources. Once N grows sufficiently large, GFN transactions create more value per unit of network investment than peer transactions, and peer transactions create more value per unit of network investment than do broadcast transactions. So what tends to happen is that as networks grow, peer transactions out-compete broadcast content in the arena of attention and return on investment. And remarkably, once N gets sufficiently large, GFN transactions will out-compete both of the other categories. The chart in Figure 4 is based on a simple model of saturation and competition for dollars and attention. As N grows, first peer transactions start to gain "market share" at the cost of broadcast, and the GFN transactions gain share. Scale driven value shifts will powerfully shape electronic commerce. The Internet auction pioneer, OnSale, is based on a business model that scales linearly, proportionally to the reach of its network. OnSale buys closeout products and auctions them to anyone who can access its website to present a bid. In return, OnSale gets margin on every sale. Online classifieds, which connect buyers to sellers on a peer-to-peer basis, would seem to create a market space whose value follows Metcalfe's Law. We'd expect to see a shift of dominance from OnSale to online classifieds once the network tools for safe peer transactions can be made to work. But the newly public company eBay seems destined to capture a scale shift to the exponential logic of GFNs in creating value for its members. EBay's concept is to help its members to set up specialized auction communities on its website as buyers and sellers of many kinds of collectibles, art, and other easily traded special interest goods. Though eBay's share of the transactions it facilitates is much smaller than OnSale's share, eBay's returns can increase much more rapidly with scale. This growth in returns will be driven by an exponential growth in value of the eBay GFN as its membership increases (and new kinds of jointly constructed value become important-e.g. eBay's origins in creating a market for Pez candy dispenser collectors). Scale driven value shifts have already caused IBM subsidiary Lotus, the pioneer of enterprise groupware, to incorporate features into Notes/Domino to interconnect in a limited way with the fastergroowin Internet. But Notes' enterprise focus makes it difficult to support ad hoc groups that live outside large enterprise boundaries or span multiple enterprises. Though the email capabilities of Notes can easily interconnect with other Internet email systems across boundaries to capture a fair share of Metcalfe's Law value as the Internet grows, Lotus has chosen an enterprise-oriented model for sales and an enterprise focused security model. That choice effectively limits GFN reach to a few islands scattered throughout a vast Internet. Thus scale driven value seems likely to shift dominance to new groupware products and business models that can capture exponential value growth by enabling all Internet users to affiliate flexibly. By facilitating easy ad hoc creation of "teamrooms", Instinctive's eRoom and Excite's Excite Communities, among others, seem likely to capture big shares of a scale driven value shift from email to ad hoc project team collaboration. As digital networking brings scale and global reach to all aspects of our lives and activities, there will be many more ways that we'll see scale driven value shifts that threaten established business networking patterns. For example, health care networks may move from treatment transactions to collaborations around disease management. Risk management in the financial services sector may move to group-forming structures that facilitate management of "micro-risks". How will it impact your business and your industry? I'd like to close with a speculative thought. As Francis Fukuyama argues in his book Trust, there is a strong correlation between the prosperity of national economies and social capital, which he defines culturally as the ease with which people in a particular culture can form new associations. There is a clear synergy between the sociability that Fukuyama discusses and the technology and tools that support GFNs-both are structural supports for association. As the scale of interaction grows more global via the Internet, isn't it possible that a combination of social capital and GFN capital will drive prosperity to those who recognize the value of network structures that support free and responsible association for common purposes?